Datasets:

---

LLMDH

/

OpenScience

like 0

Follow

LLM - Digital Humanities 15

ERROR: type should be string, got "https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Sensitivities of subgrid-scale physics schemes, meteorological forcing, \nand \ntopographic \nradiation \nin \natmosphere-through-bedrock \nintegrated process models: A case study in the Upper Colorado River \nBasin Zexuan Xu1, Erica R. Siirila-Woodburn1, Alan M. Rhoades1, Daniel Feldman1 \n5 \n1 Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory \nCorrespondence to: Zexuan Xu (zexuanxu@lbl.gov) Abstract. Mountain hydrology is controlled by interacting processes extending from the atmosphere through the b Abstract. Mountain hydrology is controlled by interacting processes extending from Abstract. Mountain hydrology is controlled by interacting processes extending from the atmosphere through the bedrock. Integrated process models (IPM), one of the main tools needed to interpret observations and refine conceptual models of the \n10 \nmountainous water cycle, require meteorological forcing that simulates the atmospheric process to predict hydroclimate then \nsubsequently impacts surface-subsurface hydrology. Complex terrain and extreme spatial heterogeneity in mountainous \nenvironments drive uncertainty in several key considerations in IPM configurations, and require further quantification and \nsensitivity analyses. Here, we present an IPM using the Weather Research and Forecasting (WRF) model coupled with an \nintegrated hydrologic model, ParFlow-CLM, implemented over a domain centered over the East River Watershed (ERW), \n15 \nlocated in the Upper Colorado River Basin (UCRB). The ERW is a heavily-instrumented 300 km2 region in the headwaters \nof the UCRB near Crested Butte, CO, with a growing atmosphere-through-bedrock observation network. Through a series of \nexperiments in water year 2019 (WY19), we use four meteorological forcings derived from commonly used reanalysis \ndatasets, three subgrid-scale physics scheme configurations, and two terrain shading options within WRF to test the relative \nimportance of these experimental design choices on key hydrometeorological metrics including precipitation, snowpack, as \n20 \nwell as evapotranspiration, groundwater storage, and discharge simulated by the ParFlow-CLM. Results reveal that sub-grid \nscale physics configuration contributes to larger spatiotemporal variance in simulated hydrometeorological conditions, \nwhereas variance across meteorological forcing with common sub-grid scale physics configurations is more spatiotemporally \nconstrained. For example, simulated discharge shows greater variance in response to the WRF simulations across subgrid-\nscale physics schemes (26%) rather than meteorological forcing (6%). Topographic radiation option has minor effects on the \n25 \nwatershed-average hydrometeorological processes, but adds profound spatial heterogeneity to local energy budgets (+/-30 \nW/m2 in shortwave radiation and 1 K air temperature differences in late summer). The findings from this study provide \nguidance on an IPM setup that most accurately represents atmospheric-through-bedrock hydrometeorological processes and \ncan be used to guide future modeling and fieldwork in mountainous watersheds. Abstract. Mountain hydrology is controlled by interacting processes extending from the atmosphere through the bedrock. Sensitivities of subgrid-scale physics schemes, meteorological forcing, \nand \ntopographic \nradiation \nin \natmosphere-through-bedrock \nintegrated process models: A case study in the Upper Colorado River \nBasin Integrated process models (IPM), one of the main tools needed to interpret observations and refine conceptual models of the \n10 \nmountainous water cycle, require meteorological forcing that simulates the atmospheric process to predict hydroclimate then \nsubsequently impacts surface-subsurface hydrology. Complex terrain and extreme spatial heterogeneity in mountainous \nenvironments drive uncertainty in several key considerations in IPM configurations, and require further quantification and \nsensitivity analyses. Here, we present an IPM using the Weather Research and Forecasting (WRF) model coupled with an y\ny\n,\np\ng\ng (\n)\np\nintegrated hydrologic model, ParFlow-CLM, implemented over a domain centered over the East River Watershed (ERW), \n15 \nlocated in the Upper Colorado River Basin (UCRB). The ERW is a heavily-instrumented 300 km2 region in the headwaters \nof the UCRB near Crested Butte, CO, with a growing atmosphere-through-bedrock observation network. Through a series of \nexperiments in water year 2019 (WY19), we use four meteorological forcings derived from commonly used reanalysis \ndatasets, three subgrid-scale physics scheme configurations, and two terrain shading options within WRF to test the relative integrated hydrologic model, ParFlow-CLM, implemented over a domain centered over the East River Watershed (ERW), \n15 \nlocated in the Upper Colorado River Basin (UCRB). The ERW is a heavily-instrumented 300 km2 region in the headwaters \nof the UCRB near Crested Butte, CO, with a growing atmosphere-through-bedrock observation network. Through a series of \nexperiments in water year 2019 (WY19), we use four meteorological forcings derived from commonly used reanalysis \ndatasets, three subgrid-scale physics scheme configurations, and two terrain shading options within WRF to test the relative importance of these experimental design choices on key hydrometeorological metrics including precipitation, snowpack, as \n20 \nwell as evapotranspiration, groundwater storage, and discharge simulated by the ParFlow-CLM. Results reveal that sub-grid \nscale physics configuration contributes to larger spatiotemporal variance in simulated hydrometeorological conditions, \nwhereas variance across meteorological forcing with common sub-grid scale physics configurations is more spatiotemporally \nconstrained. For example, simulated discharge shows greater variance in response to the WRF simulations across subgrid- importance of these experimental design choices on key hydrometeorological metrics including precipitation, snowpack, as \n20 \nwell as evapotranspiration, groundwater storage, and discharge simulated by the ParFlow-CLM. Results reveal that sub-grid \nscale physics configuration contributes to larger spatiotemporal variance in simulated hydrometeorological conditions, \nwhereas variance across meteorological forcing with common sub-grid scale physics configurations is more spatiotemporally \nconstrained. 1. Introduction \n30 An improved predictive understanding of watershed dynamics and response to perturbations is particularly important for \nmountainous watersheds due to the multitude of natural services they provide even while those services are highly \nvulnerable to anthropogenic and natural environmental change (Hubbard et al., 2018; Siirila-Woodburn et al., 2021). The \nUpper Colorado River Basin (UCRB), which supports 40 million people and ecosystems that has experienced major \nhydrological change in recent decades (James et al., 2014). Discharge may have decreased by ~9.3% per degree Celsius of \n35 \nwarming, due to processes extending from the atmosphere through the subsurface (Milly and Dunne, 2020). Drought is \ncommon to the region, however, the current multi-decade drought is unprecedented in at least the last 1200 years (Williams \net al., 2022). To better estimate how aridification of the UCRB might continue, processes that shape the water cycle in this \nregion must be considered holistically, including atmospheric processes such as large-scale vapor transport, precipitation and \nradiation, land surface processes such as evapotranspiration and snowpack metamorphosis, and surface-through-subsurface \n40 \nhydrological processes. Atmospheric and land surface processes all interact and influence river discharge through riverine \nprocesses, infiltration, and subsurface flow and storage, but their impact varies depending on the temporal and spatial scales \nof analysis (Siirila-Woodburn et al., 2021). Unfortunately, there is a dearth of observational data to constrain these processes \nat their relevant scales, leading to persistent biases in the predictive understanding of the mountainous hydrologic cycle with \ndirect implications for water resource management (Sturm et al., 2017; Rhoades et al., 2018a,b,c; Xu et al., 2019). A recent \n45 \nstudy by Lundquist et al. (2019) highlighted that calibrated models, which themselves have numerous deficiencies, have \nlikely outpaced the skill of observationally-based gridded products in advancing the understanding of the integrated \nmountainous hydrologic cycle. Fortunately, observational campaigns, combined with coordinated modeling activities, \nrepresent a potential path forward towards enhancing our predictive understanding of the hydrologic cycle in complex terrain \n(Feldman et al., 2021). 50 An improved predictive understanding of watershed dynamics and response to perturbations is particularly important for \nmountainous watersheds due to the multitude of natural services they provide even while those services are highly \nvulnerable to anthropogenic and natural environmental change (Hubbard et al., 2018; Siirila-Woodburn et al., 2021). https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Sensitivities of subgrid-scale physics schemes, meteorological forcing, \nand \ntopographic \nradiation \nin \natmosphere-through-bedrock \nintegrated process models: A case study in the Upper Colorado River \nBasin For example, simulated discharge shows greater variance in response to the WRF simulations across subgrid- scale physics schemes (26%) rather than meteorological forcing (6%). Topographic radiation option has minor effects on the \n25 \nwatershed-average hydrometeorological processes, but adds profound spatial heterogeneity to local energy budgets (+/-30 \nW/m2 in shortwave radiation and 1 K air temperature differences in late summer). The findings from this study provide \nguidance on an IPM setup that most accurately represents atmospheric-through-bedrock hydrometeorological processes and \ncan be used to guide future modeling and fieldwork in mountainous watersheds. scale physics schemes (26%) rather than meteorological forcing (6%). Topographic radiation option has minor effects on the \n25 \nwatershed-average hydrometeorological processes, but adds profound spatial heterogeneity to local energy budgets (+/-30 \nW/m2 in shortwave radiation and 1 K air temperature differences in late summer). The findings from this study provide \nguidance on an IPM setup that most accurately represents atmospheric-through-bedrock hydrometeorological processes and \ncan be used to guide future modeling and fieldwork in mountainous watersheds. 1 1 1. Introduction \n30 Unfortunately, there is a dearth of observational data to constrain these processes \nat their relevant scales, leading to persistent biases in the predictive understanding of the mountainous hydrologic cycle with radiation, land surface processes such as evapotranspiration and snowpack metamorphosis, and surface-through-subsurface \n40 \nhydrological processes. Atmospheric and land surface processes all interact and influence river discharge through riverine \nprocesses, infiltration, and subsurface flow and storage, but their impact varies depending on the temporal and spatial scales \nof analysis (Siirila-Woodburn et al., 2021). Unfortunately, there is a dearth of observational data to constrain these processes \nat their relevant scales, leading to persistent biases in the predictive understanding of the mountainous hydrologic cycle with direct implications for water resource management (Sturm et al., 2017; Rhoades et al., 2018a,b,c; Xu et al., 2019). A recent \n45 \nstudy by Lundquist et al. (2019) highlighted that calibrated models, which themselves have numerous deficiencies, have \nlikely outpaced the skill of observationally-based gridded products in advancing the understanding of the integrated \nmountainous hydrologic cycle. Fortunately, observational campaigns, combined with coordinated modeling activities, \nrepresent a potential path forward towards enhancing our predictive understanding of the hydrologic cycle in complex terrain Here, we explore how modeling activities can best support that path forward. Process models provide an essential tool for \nquantifying linear and non-linear interacting processes across spatiotemporal scales that arise in mountains and can help to \nfill observational gaps. However, the processes that are represented in these process models are a mixture of fundamental Here, we explore how modeling activities can best support that path forward. Process models provide an essential tool for \nquantifying linear and non-linear interacting processes across spatiotemporal scales that arise in mountains and can help to \nfill observational gaps. However, the processes that are represented in these process models are a mixture of fundamental fill observational gaps. However, the processes that are represented in these process models are a mixture of fundamental \nphysics and subgrid-scale parameterizations, many of which were not developed with mountainous hydrologic cycle \n55 \nprocesses in theory, and/or are based on decades-old field and laboratory data. To further compound those issues, cross-scale \ninteractions in complex terrain are challenging to resolve at their native scales with currently available advanced computing \nresources (Siirila-Woodburn et al., 2021). 1. Introduction \n30 The \nUpper Colorado River Basin (UCRB), which supports 40 million people and ecosystems that has experienced major An improved predictive understanding of watershed dynamics and response to perturbations is particularly important for \nmountainous watersheds due to the multitude of natural services they provide even while those services are highly \nvulnerable to anthropogenic and natural environmental change (Hubbard et al., 2018; Siirila-Woodburn et al., 2021). The \nUpper Colorado River Basin (UCRB), which supports 40 million people and ecosystems that has experienced major hydrological change in recent decades (James et al., 2014). Discharge may have decreased by ~9.3% per degree Celsius of \n35 \nwarming, due to processes extending from the atmosphere through the subsurface (Milly and Dunne, 2020). Drought is \ncommon to the region, however, the current multi-decade drought is unprecedented in at least the last 1200 years (Williams \net al., 2022). To better estimate how aridification of the UCRB might continue, processes that shape the water cycle in this \nregion must be considered holistically, including atmospheric processes such as large-scale vapor transport, precipitation and hydrological change in recent decades (James et al., 2014). Discharge may have decreased by ~9.3% per degree Celsius of \n35 \nwarming, due to processes extending from the atmosphere through the subsurface (Milly and Dunne, 2020). Drought is \ncommon to the region, however, the current multi-decade drought is unprecedented in at least the last 1200 years (Williams \net al., 2022). To better estimate how aridification of the UCRB might continue, processes that shape the water cycle in this \nregion must be considered holistically, including atmospheric processes such as large-scale vapor transport, precipitation and radiation, land surface processes such as evapotranspiration and snowpack metamorphosis, and surface-through-subsurface \n40 \nhydrological processes. Atmospheric and land surface processes all interact and influence river discharge through riverine \nprocesses, infiltration, and subsurface flow and storage, but their impact varies depending on the temporal and spatial scales \nof analysis (Siirila-Woodburn et al., 2021). Unfortunately, there is a dearth of observational data to constrain these processes \nat their relevant scales, leading to persistent biases in the predictive understanding of the mountainous hydrologic cycle with radiation, land surface processes such as evapotranspiration and snowpack metamorphosis, and surface-through-subsurface \n40 \nhydrological processes. Atmospheric and land surface processes all interact and influence river discharge through riverine \nprocesses, infiltration, and subsurface flow and storage, but their impact varies depending on the temporal and spatial scales \nof analysis (Siirila-Woodburn et al., 2021). 1. Introduction \n30 While discipline-specific process models, such as those used to explore and \npredict atmospheric or subsurface processes have advanced scientific understanding in a myriad of ways through sustained \nengagement with extensive user communities (Gutowski et al., 2020), Integrated Process Models (IPMs), in which these \n60 \ndiscipline-specific process models are coupled, are relatively novel and are still being vetted for various scientific \napplications in complex terrain. For example, Maina et al. (2020) explored how the horizontal resolution of atmospheric physics and subgrid-scale parameterizations, many of which were not developed with mountainous hydrologic cycle \n55 \nprocesses in theory, and/or are based on decades-old field and laboratory data. To further compound those issues, cross-scale \ninteractions in complex terrain are challenging to resolve at their native scales with currently available advanced computing \nresources (Siirila-Woodburn et al., 2021). While discipline-specific process models, such as those used to explore and \npredict atmospheric or subsurface processes have advanced scientific understanding in a myriad of ways through sustained 60 engagement with extensive user communities (Gutowski et al., 2020), Integrated Process Models (IPMs), in which these \n60 \ndiscipline-specific process models are coupled, are relatively novel and are still being vetted for various scientific \napplications in complex terrain. For example, Maina et al. (2020) explored how the horizontal resolution of atmospheric 2 2 Additionally, Mallard et al. (2017) evaluated that the sensitivity of near-surface \n80 \ntemperatures and precipitation to changes in land use representation is smaller than the model error for those fields, while \nRudisill et al., (2021) found that the details of snow cover in the initial conditions of a WRF simulation in complex terrain \nare key to ensuring the skill of that simulation, not just in 2-meter air temperature but also in the surface energy budget. Meanwhile, Rahimi et al (2022) found minimal sensitivity of SWE in WRF simulations across the entire western United \nStates to microphysics schemes, but found large effects due to model resolution. On the other hand, the effects of \n85 \nmeteorological forcing as the lateral boundary conditions of WRF simulations have also been recognized. For instance, Xu et \nal. (2018) identified that the simulations of hydroclimate in California using WRF are largely driven by large-scale forcing \ndatasets. Taken together, the published literature suggests a one-size-fits-all WRF model configuration for hydrological \nstudies in complex terrain may not be possible. In other words, the WRF configuration is likely case- and region-specific, \nand could depend either on the representation of processes within the WRF simulation domain or the boundary conditions of \n90 \nWRF forced by the large-scale meteorological forcing. The options of subgrid-scale physics schemes and large-scale \nmeteorological forcing datasets need to be fully tested to understand their sensitivities to atmospheric and hydrological \nprocesses in the ERW. physics configuration on precipitation and snowpack processes in the UCRB (Rasmussen et al., 2011; Liu et al., 2011; Liu et \nal., 2017; Rasmussen et al, 2020). Outside of the UCRB, Orr et al. (2017) found cloud microphysics schemes have \nsignificant impacts on monsoon precipitation simulation in the complex-terrain Himalayan regions, with the Morrison \n75 \nmicrophysics scheme producing the best agreement with observations. Conversely, Comin et al. (2018) found that the \nMorrison microphysics scheme produced excessive snowfall and exhibited poor performance when evaluated in the Andes, \nwhile the Goddard (WDM6) scheme exhibited the best performance with respect to observed snowfall. In terms of land \nsurface process, Jin et al. (2010) explored that land surface model complexity improves temperature simulation, but has a significant impacts on monsoon precipitation simulation in the complex-terrain Himalayan regions, with the Morrison \n75 \nmicrophysics scheme producing the best agreement with observations. Conversely, Comin et al. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. forcings (40km to 0.5 km) in the Cosumnes River watershed, California, simulated by a widely-used regional climate model \n(Weather Research and Forecasting (WRF; Powers et al., 2017), result in differences in surface and subsurface hydrologic \nmetrics when used to force the integrated hydrologic model (ParFlow-CLM; Maxwell et al., 2015). We expand that \n65 \nsensitivity analysis in this study, including model meteorological forcing and subgrid-scale physics configuration choice, and \ntheir influence on the surface-through-subsurface response of the integrated hydrologic model. The goal of this work is to \nprovide the mountain hydrology research community with a properly-configured IPM that can inform ongoing and future \nfield campaigns and their process-modeling needs in the UCRB. forcings (40km to 0.5 km) in the Cosumnes River watershed, California, simulated by a widely-used regional climate model \n(Weather Research and Forecasting (WRF; Powers et al., 2017), result in differences in surface and subsurface hydrologic \nmetrics when used to force the integrated hydrologic model (ParFlow-CLM; Maxwell et al., 2015). We expand that \n65 \nsensitivity analysis in this study, including model meteorological forcing and subgrid-scale physics configuration choice, and \ntheir influence on the surface-through-subsurface response of the integrated hydrologic model. The goal of this work is to \nprovide the mountain hydrology research community with a properly-configured IPM that can inform ongoing and future \nfield campaigns and their process-modeling needs in the UCRB. 65 70 Standalone WRF simulations have been widely investigated in complex terrain, and provide context for the unfilled gaps in \nIPM investigation and development in complex terrain. For example, several papers detailed the role of subgrid-scale \nphysics configuration on precipitation and snowpack processes in the UCRB (Rasmussen et al., 2011; Liu et al., 2011; Liu et \nal., 2017; Rasmussen et al, 2020). Outside of the UCRB, Orr et al. (2017) found cloud microphysics schemes have \nsignificant impacts on monsoon precipitation simulation in the complex-terrain Himalayan regions, with the Morrison \n75 \nmicrophysics scheme producing the best agreement with observations. Conversely, Comin et al. (2018) found that the \nMorrison microphysics scheme produced excessive snowfall and exhibited poor performance when evaluated in the Andes, \nwhile the Goddard (WDM6) scheme exhibited the best performance with respect to observed snowfall. In terms of land \nsurface process, Jin et al. (2010) explored that land surface model complexity improves temperature simulation, but has a \nminimal impact on simulated precipitation. (2018) found that the \nMorrison microphysics scheme produced excessive snowfall and exhibited poor performance when evaluated in the Andes, \nwhile the Goddard (WDM6) scheme exhibited the best performance with respect to observed snowfall. In terms of land \nsurface process, Jin et al. (2010) explored that land surface model complexity improves temperature simulation, but has a significant impacts on monsoon precipitation simulation in the complex-terrain Himalayan regions, with the Morrison \n75 \nmicrophysics scheme producing the best agreement with observations. Conversely, Comin et al. (2018) found that the \nMorrison microphysics scheme produced excessive snowfall and exhibited poor performance when evaluated in the Andes, \nwhile the Goddard (WDM6) scheme exhibited the best performance with respect to observed snowfall. In terms of land \nsurface process, Jin et al. (2010) explored that land surface model complexity improves temperature simulation, but has a minimal impact on simulated precipitation. Additionally, Mallard et al. (2017) evaluated that the sensitivity of near-surface \n80 \ntemperatures and precipitation to changes in land use representation is smaller than the model error for those fields, while \nRudisill et al., (2021) found that the details of snow cover in the initial conditions of a WRF simulation in complex terrain \nare key to ensuring the skill of that simulation, not just in 2-meter air temperature but also in the surface energy budget. Meanwhile, Rahimi et al (2022) found minimal sensitivity of SWE in WRF simulations across the entire western United minimal impact on simulated precipitation. Additionally, Mallard et al. (2017) evaluated that the sensitivity of near-surface \n80 \ntemperatures and precipitation to changes in land use representation is smaller than the model error for those fields, while \nRudisill et al., (2021) found that the details of snow cover in the initial conditions of a WRF simulation in complex terrain \nare key to ensuring the skill of that simulation, not just in 2-meter air temperature but also in the surface energy budget. Meanwhile, Rahimi et al (2022) found minimal sensitivity of SWE in WRF simulations across the entire western United minimal impact on simulated precipitation. Additionally, Mallard et al. (2017) evaluated that the sensitivity of near-surface \n80 \ntemperatures and precipitation to changes in land use representation is smaller than the model error for those fields, while \nRudisill et al., (2021) found that the details of snow cover in the initial conditions of a WRF simulation in complex terrain \nare key to ensuring the skill of that simulation, not just in 2-meter air temperature but also in the surface energy budget. Meanwhile, Rahimi et al (2022) found minimal sensitivity of SWE in WRF simulations across the entire western United States to microphysics schemes, but found large effects due to model resolution. On the other hand, the effects of \n85 \nmeteorological forcing as the lateral boundary conditions of WRF simulations have also been recognized. For instance, Xu et \nal. (2018) identified that the simulations of hydroclimate in California using WRF are largely driven by large-scale forcing \ndatasets. Taken together, the published literature suggests a one-size-fits-all WRF model configuration for hydrological \nstudies in complex terrain may not be possible. In other words, the WRF configuration is likely case- and region-specific, and could depend either on the representation of processes within the WRF simulation domain or the boundary conditions of \n90 \nWRF forced by the large-scale meteorological forcing. The options of subgrid-scale physics schemes and large-scale \nmeteorological forcing datasets need to be fully tested to understand their sensitivities to atmospheric and hydrological \nprocesses in the ERW. and could depend either on the representation of processes within the WRF simulation domain or the boundary conditions of \n90 \nWRF forced by the large-scale meteorological forcing. The options of subgrid-scale physics schemes and large-scale \nmeteorological forcing datasets need to be fully tested to understand their sensitivities to atmospheric and hydrological \nprocesses in the ERW. Furthermore, few studies have assessed how these choices impact the subsequent simulation of surface-through-subsurface \n95 \nhydrologic processes. These types of analysis are needed because the WRF model can be configured in myriad ways for a Furthermore, few studies have assessed how these choices impact the subsequent simulation of surface-through-subsurface \n95 \nhydrologic processes. These types of analysis are needed because the WRF model can be configured in myriad ways for a 3 Using an IPM, we address an outstanding question: does synoptic-scale meteorological forcing or meso-to-micro scale atmospheric processes have a more direct effect on surface and subsurface hydrologic \n110 \nprocesses in a mountainous watershed? forcing or meso-to-micro scale atmospheric processes have a more direct effect on surface and subsurface hydrologic \n110 \nprocesses in a mountainous watershed? In order to answer this question, we undertake a series of experiments with different synoptic-scale meteorological forcings, \nand different, plausible choices for meso-to-micro scale parameterizations in the IPM. This is informed by prior standalone \nWRF studies that have utilized different shortwave and longwave radiation, microphysical, and surface and planetary \n115 \nboundary layer schemes (Skamarock et al., 2019). Additionally, topographical shortwave shading effects are tested to \nunderstand how spatial heterogeneity in the surface radiation budget influences evapotranspiration and snowpack \naccumulation and ablation processes (Arthur et al., 2018). Then we explore how the surface and subsurface hydrology fields \nrespond to these various experimental setup choices, especially discharge in the ERW of the UCRB (described below). With \na discrete set of simulations, we establish the relative importance of these choices. We can also establish the relative \n120 \nimportance of subgrid-scale parameterizations that affect water and energy budgets. Our hypothesis is that if synoptic-scale \nforcings produce a much larger spread in surface and subsurface hydrology fields than subgrid-scale physics scheme choice, \nthen predictive hydrology in the UCRB should prioritize improving large-scale weather products and analyses. Conversely, \nif model subgrid-scale physics scheme choice produces more variability in hydrologic response, then smaller scale \natmospheric and hydrological processes affected by surface heterogeneity in the ERW should be prioritized for model \n125 \ndevelopment. Finally, we can establish if there are spatial and/or temporal patterns to differences between models and \nobservations that point to model configuration choices and thereby motivate further, directed model development and \nsensitivity studies. In order to answer this question, we undertake a series of experiments with different synoptic-scale meteorological forcings, \nand different, plausible choices for meso-to-micro scale parameterizations in the IPM. This is informed by prior standalone \nWRF studies that have utilized different shortwave and longwave radiation, microphysical, and surface and planetary \n115 \nboundary layer schemes (Skamarock et al., 2019). Additionally, topographical shortwave shading effects are tested to \nunderstand how spatial heterogeneity in the surface radiation budget influences evapotranspiration and snowpack \naccumulation and ablation processes (Arthur et al., 2018). https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. given domain, and feedbacks to the surface and subsurface hydrology can yield a potentially large range of results. The \naforementioned IPM study by Maina et al. (2020) showed that biases of 5-10% in basin-average surface water storage can \nresult from forcing resolution differences in WRF alone, with localized differences in groundwater head by several meters. given domain, and feedbacks to the surface and subsurface hydrology can yield a potentially large range of results. The \naforementioned IPM study by Maina et al. (2020) showed that biases of 5-10% in basin-average surface water storage can \nresult from forcing resolution differences in WRF alone, with localized differences in groundwater head by several meters. Schreiner-McGraw and Ajami (2020) show that water partitioning across four commonly used meteorological forcings \n100 \ndiffers substantially within a Sierra Nevada watershed, and that the combination of precipitation uncertainty, soil \nparameterization, and topographic position all impact the severity to which these differences in forcing exert on the \nhydrology. Schreiner-McGraw and Ajami (2020) show that water partitioning across four commonly used meteorological forcings \n100 \ndiffers substantially within a Sierra Nevada watershed, and that the combination of precipitation uncertainty, soil \nparameterization, and topographic position all impact the severity to which these differences in forcing exert on the \nhydrology. 100 In spite of the range of WRF sensitivity investigations, the connections between uncertainty in a WRF configuration and its \n105 \ninfluence on surface-through-subsurface hydrology is underexplored and therefore the focus of this work. It should be noted \nthat our investigation is not to explore general principles behind IPM uncertainty quantification and error propagation, but \nrather to present a concrete use-case to guide the advancement of atmosphere-through-bedrock modeling and its connections \nto mountainous hydrological science. Using an IPM, we address an outstanding question: does synoptic-scale meteorological In spite of the range of WRF sensitivity investigations, the connections between uncertainty in a WRF configuration and its \n105 \ninfluence on surface-through-subsurface hydrology is underexplored and therefore the focus of this work. It should be noted \nthat our investigation is not to explore general principles behind IPM uncertainty quantification and error propagation, but \nrather to present a concrete use-case to guide the advancement of atmosphere-through-bedrock modeling and its connections \nto mountainous hydrological science. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Therefore, this article is organized as follows: first, we present details of study site and hydroclimate in the water year, as \n130 \nwell as the IPM including the coupling between WRF and ParFlow-CLM and the justifications for using WRF and ParFlow-\nCLM as the atmospheric and surface-through-subsurface process models in the IPM, respectively. Then, we describe the \nWRF experiments that we performed to test the relative importance of synoptic-scale boundary forcing and meso-to-micro \nscale model subgrid-scale physics schemes for driving ERW integrated hydrological simulations. Next, we present the Therefore, this article is organized as follows: first, we present details of study site and hydroclimate in the water year, as \n130 \nwell as the IPM including the coupling between WRF and ParFlow-CLM and the justifications for using WRF and ParFlow-\nCLM as the atmospheric and surface-through-subsurface process models in the IPM, respectively. Then, we describe the \nWRF experiments that we performed to test the relative importance of synoptic-scale boundary forcing and meso-to-micro \nscale model subgrid-scale physics schemes for driving ERW integrated hydrological simulations. Next, we present the simulated discharge, evapotranspiration and groundwater storage using ParFlow-CLM, to quantify the responses to changing \n135 \nWRF configurations. We conclude by contextualizing these results in light of the ongoing field campaign activities in the \nERW. simulated discharge, evapotranspiration and groundwater storage using ParFlow-CLM, to quantify the responses to changing \n135 \nWRF configurations. We conclude by contextualizing these results in light of the ongoing field campaign activities in the \nERW. simulated discharge, evapotranspiration and groundwater storage using ParFlow-CLM, to quantify the responses to changing \n135 \nWRF configurations. We conclude by contextualizing these results in light of the ongoing field campaign activities in the \nERW. Then we explore how the surface and subsurface hydrology fields \nrespond to these various experimental setup choices, especially discharge in the ERW of the UCRB (described below). With WRF studies that have utilized different shortwave and longwave radiation, microphysical, and surface and planetary \n115 \nboundary layer schemes (Skamarock et al., 2019). Additionally, topographical shortwave shading effects are tested to \nunderstand how spatial heterogeneity in the surface radiation budget influences evapotranspiration and snowpack \naccumulation and ablation processes (Arthur et al., 2018). Then we explore how the surface and subsurface hydrology fields \nrespond to these various experimental setup choices, especially discharge in the ERW of the UCRB (described below). With a discrete set of simulations, we establish the relative importance of these choices. We can also establish the relative \n120 \nimportance of subgrid-scale parameterizations that affect water and energy budgets. Our hypothesis is that if synoptic-scale \nforcings produce a much larger spread in surface and subsurface hydrology fields than subgrid-scale physics scheme choice, \nthen predictive hydrology in the UCRB should prioritize improving large-scale weather products and analyses. Conversely, \nif model subgrid-scale physics scheme choice produces more variability in hydrologic response, then smaller scale a discrete set of simulations, we establish the relative importance of these choices. We can also establish the relative \n120 \nimportance of subgrid-scale parameterizations that affect water and energy budgets. Our hypothesis is that if synoptic-scale \nforcings produce a much larger spread in surface and subsurface hydrology fields than subgrid-scale physics scheme choice, \nthen predictive hydrology in the UCRB should prioritize improving large-scale weather products and analyses. Conversely, \nif model subgrid-scale physics scheme choice produces more variability in hydrologic response, then smaller scale atmospheric and hydrological processes affected by surface heterogeneity in the ERW should be prioritized for model \n125 \ndevelopment. Finally, we can establish if there are spatial and/or temporal patterns to differences between models and \nobservations that point to model configuration choices and thereby motivate further, directed model development and \nsensitivity studies. atmospheric and hydrological processes affected by surface heterogeneity in the ERW should be prioritized for model \n125 \ndevelopment. Finally, we can establish if there are spatial and/or temporal patterns to differences between models and \nobservations that point to model configuration choices and thereby motivate further, directed model development and \nsensitivity studies. 4 4 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. The Weather Research & Forecasting (WRF) model version 4.0 is used in this study (Powers et al., 2017). WRF was chosen \nbecause of its widespread use in the investigation of atmospheric and land processes, and contextualizing observations in \ncomplex terrain (Rasmussen et al., 2011; Rasmussen et al., 2014). The WRF model is comprised of a fully coupled \n160 \natmospheric and land surface model with a range of user-specific options for subgrid-scale physics schemes. WRF is a \nregional climate model that requires boundary and initial conditions provided by either global climate model (GCM) outputs \nor atmospheric reanalyses datasets. Our configuration of the WRF model is designed with three nested domains, with an \nouter, middle and inner domains at grid resolution of 4.5 km, 1.5 km and 0.5 km, respectively, centered around Crested \nB tt\nC l\nd\nh\nth E\nt Ri\nt\nh d i l\nt d (Fi\n1)\n165 While the stand-alone WRF model has been used extensively to advance the understanding of atmospheric processes, it has \nlower fidelity and applicability to investigate surface-through-subsurface hydrologic processes, and therefore is limited as an \nassessment tool for understanding integrated mountainous hydrologic cycle. Therefore, to provide an estimate of the entire \nhydrologic budget, we use a one-way coupling between WRF and an integrated hydrologic model, ParFlow-CLM (Maxwell \n170 While the stand-alone WRF model has been used extensively to advance the understanding of atmospheric processes, it has \nlower fidelity and applicability to investigate surface-through-subsurface hydrologic processes, and therefore is limited as an \nassessment tool for understanding integrated mountainous hydrologic cycle. Therefore, to provide an estimate of the entire \nhydrologic budget, we use a one-way coupling between WRF and an integrated hydrologic model, ParFlow-CLM (Maxwell \n170 \net al., 2015, described in further detail below), in order to simulate the hydrological response of key variables not otherwise \nquantifiable in standalone WRF, such as discharge and groundwater storage. While the stand-alone WRF model has been used extensively to advance the understanding of atmospheric processes, it has \nlower fidelity and applicability to investigate surface-through-subsurface hydrologic processes, and therefore is limited as an \nassessment tool for understanding integrated mountainous hydrologic cycle. Therefore, to provide an estimate of the entire assessment tool for understanding integrated mountainous hydrologic cycle. 2. Study Site This investigation focused principally on modeling and analysis of the ERW, a representative mountainous headwater \ncatchment of the UCRB near Gothic, Colorado (Hubbard et al., 2018). This 300 km2 watershed of the Upper Colorado River \n140 \nBasin is at a high-level, representative of the UCRB that it has very large gradients in precipitation (e.g., a factor of 2 range \nin precipitation between the northern and southern boundary of the ERW) and surface-through-subsurface hydrology. The \nERW has a continental, subarctic climate with long, cold winters and short, cool summers. At an average elevation of 3266 \nmeters above sea level, the watershed has a mean annual temperature of 0℃, and distinct winter and growing seasons that influence hydrologic and biogeochemical cycles. River discharges are driven primarily by snowmelt in late spring to early \n145 \nsummer, with mid- to late-summer monsoonal rainfall inducing rapid but punctuated increases in streamflow. The ERW \nreceives ~1200 mm/yr of precipitation and we focus here on Water Year 2019 (Oct 1, 2018 - Sep 30, 2019). The ERW has become a mountainous community testbed for improving predictive understanding of multi-scale atmosphere-\nthrough-bedrock system dynamics and is the centerpiece of such focused activities because it is one of two major tributaries \n150 \nthat form the Gunnison River, which in turn accounts for near half of the Colorado River’s discharge at the Colorado–Utah \nborder. In the past decade, several synthesis research efforts have been established in this region, including a wide range of \nfieldwork and modeling activities (Hubbard et al., 2018). The ERW has become one of the most heavily-instrumented \nmountainous watersheds in the world, which makes it an ideal focus for this research given the potentially large number of \nobservational constraints available for the IPM efforts presented here. 155 3. Methods \n3.1. WRF models 3. Methods \n3.1. WRF models 5 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. cells or 100 by 100 km extent) and their associated elevations (left). The Global Multi-resolution Terrain Elevation Data \n2010 (GMTED2010) elevation data in meters above mean sea-level is used in the WRF simulation. Right: the innermost \nParFlow-CLM domain and spatial extent of the East River Watershed (white line) and associated land cover type derived \nfrom the National Land Cover Dataset (NLCD) (Homer et al, 2020) and upscaled to 100 m (right). 180 180 A major experimental design decision when simulating the integrated mountainous hydrologic cycle is computational cost \nassociated with the throughput of the simulations (e.g., simulated years per actual day) that are determined by model \nhorizontal, vertical, and timestep resolutions and subgrid scale physics parameterization complexity. Computational \nexpenses for exploring the sensitivities of WRF configuration choice in this study were significant: one simulated year \n185 \nrequires approximately 100,000 CPU hours on LBNL’s Lawrencium lr6 supercomputing system. As such, it was highly \nimpractical to simulate the entire configuration space of meteorological forcing and subgrid-scale parameterization choice. A \ndiscrete sub-sample of configurations, as presented here, is used to isolate and systematically determine which combination \nof subgrid scale parameterization choice is superior for a given domain such as the ERW. We therefore adopted a \nparsimonious approach to explore the space of possible WRF configurations, described below. 190 Therefore, to provide an estimate of the entire \nhydrologic budget, we use a one-way coupling between WRF and an integrated hydrologic model, ParFlow-CLM (Maxwell \n170 \net al., 2015, described in further detail below), in order to simulate the hydrological response of key variables not otherwise \nquantifiable in standalone WRF, such as discharge and groundwater storage. hydrologic budget, we use a one-way coupling between WRF and an integrated hydrologic model, ParFlow-CLM (Maxwell \n170 \net al., 2015, described in further detail below), in order to simulate the hydrological response of key variables not otherwise \nquantifiable in standalone WRF, such as discharge and groundwater storage. Figure 1. Left: Three nested WRF domains D01 (4.5 km grid resolution, 201 by 201 grid cells or 900 by 900 km extent), D01 \n175 \n(1.5 km grid resolution, 201 by 201 grid cells or 300 by 300 km extent), and D03 (0.5 km grid resolution, 201 by 201 grid Figure 1. Left: Three nested WRF domains D01 (4.5 km grid resolution, 201 by 201 grid cells or 900 by 900 km extent), D01 \n175 \n(1.5 km grid resolution, 201 by 201 grid cells or 300 by 300 km extent), and D03 (0.5 km grid resolution, 201 by 201 grid 6 3.1.1. Subgrid-scale physics schemes In addition, the \nNCEP FNL (NCEP, 2000) operational global analysis and forecast data are on a 0.25-degree grid resolution from the Global \nData Assimilation System (GDAS) (Kleist et al, 2009). All meteorological forcing datasets are processed at 6-hourly by the \nWRF Preprocessing System (WPS)\n220 atmosphere-land-ocean-sea ice coupling, assimilates satellite radiances. MERRA2 is another atmospheric reanalysis based \n215 \non data assimilation (Gelaro et al., 2017), which is the first long-term global reanalysis to assimilate space-based \nobservations of aerosols and represent their interactions with other physical processes in the climate system. In addition, the \nNCEP FNL (NCEP, 2000) operational global analysis and forecast data are on a 0.25-degree grid resolution from the Global \nData Assimilation System (GDAS) (Kleist et al, 2009). All meteorological forcing datasets are processed at 6-hourly by the 3.1.1. Subgrid-scale physics schemes Three well-established suites of subgrid scale physics schemes for WRF are evaluated in this study (Table 1). One scheme \nwas developed by NCAR and is used for a wide range of simulations over domains extending across the entire Conterminous \nUnited States (CONUS) (Liu et al., 2017). Another scheme that we consider here has been used for decadal-length \nhydroclimate simulation over California (Huang et al., 2017; Xu et al., 2018; Ullrich et al., 2018), and since it was initially \n195 \ndeveloped by researchers at the University of California, Davis, it is denoted as UCD here. More recently, Flores et al. (2016) and Rudisill et al. (2021) implemented a WRF configuration that focused on exploring land-atmosphere interactions \nin complex terrain. This configuration was developed by researchers at Boise State University, and is referred to as BSU \nhere. 200 \n \nTable 1: Microphysics, radiation, land surface model, surface layer, and planetary boundary layer schemes used for the \nthree different WRF configurations of the IPM tested here. Subgrid-scale \nphysics \nschemes \nNCAR (CONUS) \nBSU \nUCD \nMicrophysics \nThompson \nThompson \nWSM6 \nShortwave radiation \nRRTMG \nRRTM \nRRTMG Three well-established suites of subgrid scale physics schemes for WRF are evaluated in this study (Table 1). One scheme \nwas developed by NCAR and is used for a wide range of simulations over domains extending across the entire Conterminous \nUnited States (CONUS) (Liu et al., 2017). Another scheme that we consider here has been used for decadal-length \nhydroclimate simulation over California (Huang et al., 2017; Xu et al., 2018; Ullrich et al., 2018), and since it was initially \n195 \ndeveloped by researchers at the University of California, Davis, it is denoted as UCD here. More recently, Flores et al. (2016) and Rudisill et al. (2021) implemented a WRF configuration that focused on exploring land-atmosphere interactions \nin complex terrain. This configuration was developed by researchers at Boise State University, and is referred to as BSU \nhere. 200 Table 1: Microphysics, radiation, land surface model, surface layer, and planetary boundary layer schemes used for the \nthree different WRF configurations of the IPM tested here. Subgrid-scale \nphysics \nschemes \nNCAR (CONUS) \nBSU \nUCD \nMicrophysics \nThompson \nThompson \nWSM6 \nShortwave radiation \nRRTMG \nRRTM \nRRTMG 7 7 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Longwave radiation \nRRTMG \nRRTM \nRRTMG \nLand surface model \nNoah \nNoah-MP \nNoah \nSurface layer \nEta similarity \nMonin-Obukhov \nRevised MM5 \nPlanetary \nBoundary \nlayer \nMellor-Yamada-Janjic \nscheme \nMellor-Yamada-Janjic \nscheme \nUW (Brethert\nPark) \n3.1.2. 3.1.1. Subgrid-scale physics schemes Meteorological forcing RRTMG \nNoah \nRevised MM5 RRTMG \nNoah \nRevised MM5 \nUW (Bretherton and \nPark) RRTMG \nNoah \nRevised MM5 UW (Bretherton and \nPark) Mellor-Yamada-Janjic \nscheme Each of these WRF configurations must specify a set of initial and lateral boundary conditions at the synoptic scale and, at \n205 \nleast in the outer domain, are typically derived from high-resolution atmospheric reanalyses. The reanalysis from the \nNational Centers for Environmental Prediction (NCEP), Climate Forecast System Reanalysis version 2 (CFSR2), The \nModern-Era Retrospective analysis for Research and Applications - Version 2 (MERRA2), European Centre for Medium-\nRange Weather Forecasting Reanalysis version 5 (ERA5) were used in this study. 210 \nERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate on a 30 km grid resolution (Hersbach et \nal, 2020), and combines model data with observations from across the world into a globally complete and consistent dataset. The CFSR2 is also global and is designed to provide an operational product for forecasting and analysis purposes at 0.3 \ndegree grid resolution (Saha et al, 2010). The CFSR2 data were generated by an advanced assimilation schemes, 210 \nERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate on a 30 km grid resolution (Hersbach et \nal, 2020), and combines model data with observations from across the world into a globally complete and consistent dataset. The CFSR2 is also global and is designed to provide an operational product for forecasting and analysis purposes at 0.3 \ndegree grid resolution (Saha et al, 2010). The CFSR2 data were generated by an advanced assimilation schemes, ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate on a 30 km grid resolution (Hersbach et \nal, 2020), and combines model data with observations from across the world into a globally complete and consistent dataset. The CFSR2 is also global and is designed to provide an operational product for forecasting and analysis purposes at 0.3 \ndegree grid resolution (Saha et al, 2010). The CFSR2 data were generated by an advanced assimilation schemes, atmosphere-land-ocean-sea ice coupling, assimilates satellite radiances. MERRA2 is another atmospheric reanalysis based \n215 \non data assimilation (Gelaro et al., 2017), which is the first long-term global reanalysis to assimilate space-based \nobservations of aerosols and represent their interactions with other physical processes in the climate system. 3.1.3 Topographic radiation Topographic effects for shortwave radiation flux calculations in complex terrain are evaluated (Arthur et al., 2018). One is \nthe “slope_rad” namelist option, which modifies surface solar radiation flux according to terrain slope by correcting it based \non the solar zenith angle relative to the local surface normal vector. This adjustment ensures that the solar radiation received \nat the surface in WRF is consistent with the geometric projection of incoming sunlight onto local, non-flat surfaces. The \n225 \nother namelist option, “topo_shading”, allows for shadowing of neighboring grid cells. When “topo_shading” is active, WRF \ndetermines if any topography intersects a line drawn between a given grid point and the location of the sun at the time step Topographic effects for shortwave radiation flux calculations in complex terrain are evaluated (Arthur et al., 2018). One is \nthe “slope_rad” namelist option, which modifies surface solar radiation flux according to terrain slope by correcting it based \non the solar zenith angle relative to the local surface normal vector. This adjustment ensures that the solar radiation received the “slope_rad” namelist option, which modifies surface solar radiation flux according to terrain slope by correcting it based \non the solar zenith angle relative to the local surface normal vector. This adjustment ensures that the solar radiation received \nat the surface in WRF is consistent with the geometric projection of incoming sunlight onto local, non-flat surfaces. The \n225 \nother namelist option, “topo_shading”, allows for shadowing of neighboring grid cells. When “topo_shading” is active, WRF \ndetermines if any topography intersects a line drawn between a given grid point and the location of the sun at the time-step \nof the WRF run. If so, a topographic shadow is cast on that grid point and the direct component of the incoming solar at the surface in WRF is consistent with the geometric projection of incoming sunlight onto local, non-flat surfaces. The \n225 \nother namelist option, “topo_shading”, allows for shadowing of neighboring grid cells. When “topo_shading” is active, WRF \ndetermines if any topography intersects a line drawn between a given grid point and the location of the sun at the time-step \nof the WRF run. If so, a topographic shadow is cast on that grid point and the direct component of the incoming solar 225 8 ParFlow is coupled to the land \nsurface model, the Common Land Model (CLM), which calculates a coupled water energy balance at every surface cell of surface model, the Common Land Model (CLM), which calculates a coupled water energy balance at every surface cell of \nthe domain (Dai et al., 2003) and incorporates spatially distributed vegetative processes by including specified land use types \n245 \nparameterized by the International Geosphere-Biosphere Program standard database. Hourly meteorological forcing derived \nfrom WRF drives ParFlow-CLM, and includes the following eight variables: precipitation, two-meter surface air \ntemperature, longwave radiation, shortwave radiation, 10-meter east-west and south-north wind speeds, atmospheric \npressure, and specific humidity. Computational expenses for ParFlow-CLM are also less substantial than that of WRF for \nthis model configuration, but still require high performance computing. Excluding the time for a multi-year initial condition \n250 \ni\ni\nl\nt\nf th P Fl\nCLM i\nl ti\n64\nth NERSC’ C\ni\nti\nt\ni the domain (Dai et al., 2003) and incorporates spatially distributed vegetative processes by including specified land use types \n245 \nparameterized by the International Geosphere-Biosphere Program standard database. Hourly meteorological forcing derived \nfrom WRF drives ParFlow-CLM, and includes the following eight variables: precipitation, two-meter surface air \ntemperature, longwave radiation, shortwave radiation, 10-meter east-west and south-north wind speeds, atmospheric \npressure, and specific humidity. Computational expenses for ParFlow-CLM are also less substantial than that of WRF for this model configuration, but still require high performance computing. Excluding the time for a multi-year initial condition \n250 \nspinup, a single water year of the ParFlow-CLM simulations on 64 cores on the NERSC’s Cori supercomputing system is \napproximately 1,000 CPU hours. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. radiation is set to 0. In this study, simulations in which “slope_rad” and “topo_shading” are jointly enabled are termed \n“3DRad” and when jointly disabled are termed “no3DRad”, in the inner domain of the WRF simulation. 230 230 Figure 2. Conceptual framework for developing a set of different WRF configurations of the IPM to evaluate the sensitivities \nof subgrid-scale physics parameterization choice, meteorological forcing, and radiation scheme in the representation of \nmountain water and energy budgets. 235 Figure 2. Conceptual framework for developing a set of different WRF configurations of the IPM to evaluate the sensitivities \nof subgrid-scale physics parameterization choice, meteorological forcing, and radiation scheme in the representation of \nmountain water and energy budgets. 235 Figure 2. Conceptual framework for developing a set of different WRF configurations of the IPM to evaluate the sensitivities \nof subgrid-scale physics parameterization choice, meteorological forcing, and radiation scheme in the representation of \nmountain water and energy budgets. 235 235 Table 2: East River Watershed WRF experiment configurations. Three subgrid-scale physics schemes, four meteorological \nforcings, and the topographic radiation options were assessed. Subgrid-scale physics \nschemes \nMeteorological \nforcing \nTopographic \nradiation \nBSU \nCFSR2 \n3DRad_inner \n \n \nno3DRad_inner \n \nERA5 \n3DRad_inner \n \n \nno3DRad_inner \n \nMERRA2 \n3DRad_inner \n \nNCEP \n3DRad_inner able 2: East River Watershed WRF experiment configurations. Three subgrid-scale physics schem\nrcings, and the topographic radiation options were assessed. 9 UCD \nCFSR2 \n3DRad_inner \n \nERA5 \n3DRad_inner \nNCAR \nCFSR2 \n3DRad_inner \n \nERA5 \n3DRad_inner \n3.2 ParFlow-CLM description \nhttps://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. UCD \nCFSR2 \n3DRad_inner \n \nERA5 \n3DRad_inner \nNCAR \nCFSR2 \n3DRad_inner \n \nERA5 \n3DRad_inner ParFlow is a physically based surface-subsurface hydrologic model that solves the coupled flow of saturated and variability-\n240 \nsaturated groundwater and overland surface water (Ashby and Falgout, 1996; Jones and Woodward, 2001; Maxwell, 2013). The three-dimensional form of Richards equation is used to solve for lateral and vertical groundwater flow in the subsurface \nand the kinematic wave approximation is used to solve two-dimensional overland flow. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. the year. On the other hand, the Schofield Pass station is located upstream of the ERW and, on average, receives 1.2 m of \n265 \nprecipitation and reaches 0.9 m in maximum snow water equivalent. In addition, we use the snow water equivalent product \nof the Airborne Snow Observatory (ASO; Painter et al, 2016) on 04/07/2019 to evaluate the spatial pattern skill of the \nsnowpack simulation across WRF configurations (Figure S-6). Notably, ASO SWE estimates are lower than SNOTEL SWE \nmeasurements (ASO: 389 mm at Butte, 938 mm at Schofield Pass; SNOTEL: 490 mm at Butte, 1260 mm at Schofield Pass). In addition to SNOTEL station data, stream gauges measurement of discharge at the pumphouse, the outlet of the ERW, is \n270 \nused to evaluate the ParFlow-CLM simulation results. the year. On the other hand, the Schofield Pass station is located upstream of the ERW and, on average, receives 1.2 m of \n265 \nprecipitation and reaches 0.9 m in maximum snow water equivalent. In addition, we use the snow water equivalent product \nof the Airborne Snow Observatory (ASO; Painter et al, 2016) on 04/07/2019 to evaluate the spatial pattern skill of the \nsnowpack simulation across WRF configurations (Figure S-6). Notably, ASO SWE estimates are lower than SNOTEL SWE \nmeasurements (ASO: 389 mm at Butte, 938 mm at Schofield Pass; SNOTEL: 490 mm at Butte, 1260 mm at Schofield Pass). In addition to SNOTEL station data, stream gauges measurement of discharge at the pumphouse, the outlet of the ERW, is \n270 \nused to evaluate the ParFlow-CLM simulation results. In addition to SNOTEL station data, stream gauges measurement of discharge at the pumphouse, the outlet of the ERW, is \n270 \nused to evaluate the ParFlow-CLM simulation results. 3.3 Reference Datasets The Parameter-elevation Relationships on Independent Slopes Model (PRISM) dataset (Daly et al., 2008) was used as the \nreference dataset to assess model performance of precipitation and temperature in this study. PRISM uses observations from \n255 \nquality-controlled meteorological stations along with a topographic correction method against elevation based on empirical \nregression to create daily gridded 800-meter total precipitation, and daily average, minimum and maximum two-meter \nsurface temperature. Snowpack Telemetry (SNOTEL) data have been widely used in snowpack assessment (Serreze et al, 1999; Fassnacht et al, \n260 \n2003), and we use three SNOTEL stations (Butte, Schofield Pass, Upper Taylor) within the WRF inner domain to assess the \nsnowpack simulation skill of each IPM configuration. Significant heterogeneity is sampled by the three SNOTEL stations \n(within or near the ERW) due to the complex topography. For example, the Butte station is located downstream of the ERW \nand, on average, receives approximately 0.8 m of precipitation, and reaches 0.4 m in maximum snow water equivalent over 10 4. Results We start by presenting a number of time-series of spatial averages over the ERW for WY19. They indicate the gross \nperformance of the IPM across the water year, and whether a configuration produces generally reasonable estimates relative \nto observational products. Figure 3 shows cumulative precipitation, two-meter surface air temperature, and snow water \n275 \nequivalent (SWE). For cumulative precipitation, each configuration produces amounts higher than PRISM (cumulative \nprecipitation of 1201 mm), and the UCD simulates the highest cumulative precipitation. For surface air temperature, the \nseasonal cycle and daily variability are captured by all configurations, however, exhibit systematic cold biases relative to \nPRISM (annual average two-meter surface air temperature of 0.6 degrees Celsius). In terms of SWE, all model \nconfigurations concur in their representation of the snowpack accumulation season and melt season in late spring and into \n280 \nsummer, except UCD which simulates an earlier peak timing of SWE. configurations concur in their representation of the snowpack accumulation season and melt season in late spring and into \n280 \nsummer, except UCD which simulates an earlier peak timing of SWE. The spread in cumulative precipitation when comparing across different meteorological forcing dataset is apparent (Figure \n3). Although UCD and NCAR configurations show greater difference in precipitation forced by ERA5 and CFSR2, the 3). Although UCD and NCAR configurations show greater difference in precipitation forced by ERA5 and CFSR2, the \nconsistency across BSU configurations is notable, which also shows the closest agreement with PRISM. When comparing \n285 \nthe relative roles of subgrid-scale physics scheme choice to meteorological forcing, the percent difference of cumulative \nprecipitation, calculated with (maximum - minimum)/minimum*100, across BSU-CSFR2, UCD-CSFR2 and NCAR-CSFR2 \nschemes is nearly 34% of the mininum cumulative precipitation simulated by BSU-CFSR2, compared to the 4.6% of the \nsimulations across BSU configurations with different meteorological forcing (CSFR2, ERA5, MERRA2 and NCEP). consistency across BSU configurations is notable, which also shows the closest agreement with PRISM. When comparing \n285 \nthe relative roles of subgrid-scale physics scheme choice to meteorological forcing, the percent difference of cumulative \nprecipitation, calculated with (maximum - minimum)/minimum*100, across BSU-CSFR2, UCD-CSFR2 and NCAR-CSFR2 \nschemes is nearly 34% of the mininum cumulative precipitation simulated by BSU-CFSR2, compared to the 4.6% of the \nsimulations across BSU configurations with different meteorological forcing (CSFR2, ERA5, MERRA2 and NCEP). consistency across BSU configurations is notable, which also shows the closest agreement with PRISM. 4. Results When comparing \n285 \nthe relative roles of subgrid-scale physics scheme choice to meteorological forcing, the percent difference of cumulative \nprecipitation, calculated with (maximum - minimum)/minimum*100, across BSU-CSFR2, UCD-CSFR2 and NCAR-CSFR2 \nschemes is nearly 34% of the mininum cumulative precipitation simulated by BSU-CFSR2, compared to the 4.6% of the \nsimulations across BSU configurations with different meteorological forcing (CSFR2, ERA5, MERRA2 and NCEP). 290 \nBSU simulations are generally in agreement with PRISM. However, the UCD simulations are outliers relative to the other \nsimulations, with cumulative precipitation of 1706 mm, or 42% higher at the end of the water year, with the most notable \ndifferences occurring in March through September. NCAR simulations show general agreement with PRISM and BSU \nthroughout the water year, save for June through September. The two-meter surface air temperature time-series reveals that \nthe UCD simulation is systematically colder throughout the winter and spring regardless of which meteorological forcing\n295 290 \nBSU simulations are generally in agreement with PRISM. However, the UCD simulations are outliers relative to the other \nsimulations, with cumulative precipitation of 1706 mm, or 42% higher at the end of the water year, with the most notable \ndifferences occurring in March through September. NCAR simulations show general agreement with PRISM and BSU \nthroughout the water year, save for June through September. The two-meter surface air temperature time-series reveals that \nthe UCD simulation is systematically colder throughout the winter and spring, regardless of which meteorological forcing \n295 the UCD simulation is systematically colder throughout the winter and spring, regardless of which meteorological forcing \n295 \ndataset is used. The persistent cold bias simulated by the UCD, NCAR and BSU schemes has been found in previous WRF 11 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. In addition to the domain-averages, spatial heterogeneity due to land-surface cover and topographic effects are shown in \n310 \nFigure 4. The systematic cold bias simulated throughout the water year appears to be an elevation-dependent phenomena \nwith higher-elevations exhibiting an enhanced cold bias compared with PRISM. However, the river valley and relatively \nlower-elevation areas at the southern edge of the ERW, which includes Crested Butte Mountain, stands out as these regions \nare warmer than the PRISM dataset. Figure 4b shows precipitation in BSU-CFSR2 is wetter in the western regions, and drier In addition to the domain-averages, spatial heterogeneity due to land-surface cover and topographic effects are shown in \n310 \nFigure 4. The systematic cold bias simulated throughout the water year appears to be an elevation-dependent phenomena \nwith higher-elevations exhibiting an enhanced cold bias compared with PRISM. However, the river valley and relatively \nlower-elevation areas at the southern edge of the ERW, which includes Crested Butte Mountain, stands out as these regions \nare warmer than the PRISM dataset. Figure 4b shows precipitation in BSU-CFSR2 is wetter in the western regions, and drier in the eastern, of the ERW in comparison against PRISM. Figure S-3 and S-4 show comparisons between PRISM and the \n315 \nIPM configurations and indicates no biases that are persistent across seasons. During summer, the BSU-CFSR2 simulation \nconsistently produces more precipitation than PRISM. in the eastern, of the ERW in comparison against PRISM. Figure S-3 and S-4 show comparisons between PRISM and the \n315 \nIPM configurations and indicates no biases that are persistent across seasons. During summer, the BSU-CFSR2 simulation \nconsistently produces more precipitation than PRISM. in the eastern, of the ERW in comparison against PRISM. Figure S-3 and S-4 show comparisons between PRISM and the \n315 \nIPM configurations and indicates no biases that are persistent across seasons. During summer, the BSU-CFSR2 simulation \nconsistently produces more precipitation than PRISM. Although the two-meter surface air temperature bias is evident, it doesn't vary significantly across either subgrid-scale \nphysics scheme or meteorological forcing, subsequent exploration will be predominantly focused on precipitation. The \n320 \nbottom row in Figure 4 shows the grid-cell standard deviation of monthly precipitation across subgrid-scale physics schemes \n(i.e., UCD, NCAR and BSU simulations with CFSR2 meteorological forcing – bottom left) and BSU simulation driven by \ndifferent meteorological forcing datasets (ERA5, CFSR2, MERR2 and NCEP – bottom right). https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. studies within western US mountain regions (Xu et al., 2018, Rudisill et al. 2021). The SWE time-series again shows a \nsimilar relationship with precipitation, with the outlier being UCD-ERA5, in terms of the seasonal timing of when snowpack \npeaks and melts (Figure S-4). 300 Figure 3. Cumulative precipitation, two-meter surface air temperature and snow water equivalent (SWE) simulated within \nthe ERW using an IPM with different subgrid-scale physics schemes and meteorological forcings. The cumulative \nprecipitation and temperature results are compared relative to PRISM. 10-day moving averages of daily temperature are \n5 \nshown in b). The percent difference in cumulative precipitation across subgrid-scale physics schemes (black brackets) and \nmeteorological forcing (green brackets), calculated by (maximum - minimum)/minimum*100, are provided on the right y-\naxis. Figure 3. Cumulative precipitation, two-meter surface air temperature and snow water e Figure 3. Cumulative precipitation, two-meter surface air temperature and snow water equivalent (SWE) simulated within \nthe ERW using an IPM with different subgrid-scale physics schemes and meteorological forcings. The cumulative \nprecipitation and temperature results are compared relative to PRISM. 10-day moving averages of daily temperature are \n5 \nshown in b). The percent difference in cumulative precipitation across subgrid-scale physics schemes (black brackets) and \nmeteorological forcing (green brackets), calculated by (maximum - minimum)/minimum*100, are provided on the right y-\naxis. Figure 3. Cumulative precipitation, two-meter surface air temperature and snow water equivalent (SWE) simulated within \nthe ERW using an IPM with different subgrid-scale physics schemes and meteorological forcings. The cumulative \nprecipitation and temperature results are compared relative to PRISM. 10-day moving averages of daily temperature are \n05 \nshown in b). The percent difference in cumulative precipitation across subgrid-scale physics schemes (black brackets) and \nmeteorological forcing (green brackets), calculated by (maximum - minimum)/minimum*100, are provided on the right y-\naxis. 305 12 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. standard deviation of annual cumulative precipitation is plotted for subgrid-scale physics schemes (c) and meteorological \nforcings (d). The values in the parentheses are the domain average differences over the water year. standard deviation of annual cumulative precipitation is plotted for subgrid-scale physics schemes (c) and meteorological \nforcings (d). The values in the parentheses are the domain average differences over the water year. standard deviation of annual cumulative precipitation is plotted for subgrid-scale physics schemes (c) and meteorological \nforcings (d). The values in the parentheses are the domain average differences over the water year. Based on the assessment of simulated precipitation and two-meter surface air temperature compared with PRISM, the BSU-\n335 \nCFSR2 configuration is selected as a baseline to further explore the influence of topographic radiation scheme effects. Figure \n5 shows daily ERW spatial average time series over the water year for the major mountainous water and energy budget \nvariables. By isolating the impacts of subgrid-scale physics schemes and meteorological forcings across IPM simulations, it \nis easier to to systematically intercompare cause-and-effect across different topographic radiation options. Consistent with \ni\nfi di\nll\nfi\nti\ntill\nti\nt\nl ti\ni it ti\nd\nt\nld\nl ti\nt PRISM\n340 Based on the assessment of simulated precipitation and two-meter surface air temperature compared with PRISM, the BSU-\n335 \nCFSR2 configuration is selected as a baseline to further explore the influence of topographic radiation scheme effects. Figure \n5 shows daily ERW spatial average time series over the water year for the major mountainous water and energy budget \nvariables. By isolating the impacts of subgrid-scale physics schemes and meteorological forcings across IPM simulations, it \nis easier to to systematically intercompare cause-and-effect across different topographic radiation options. Consistent with \nprevious findings, all configurations still overestimate cumulative precipitation and are too cold relative to PRISM. 340 previous findings, all configurations still overestimate cumulative precipitation and are too cold relative to PRISM. 340 Figure 5. Spatial-average cumulative precipitation, two-meter surface air temperature and snow water equivalent (SWE) \n(first row), shortwave and longwave radiation, and latent and sensible heat (second row) over the ERW as simulated by the \nIPM configurations with and without realistic topographic radiation effects, along with, where available, estimates from \n345 \nPRISM. 3DRad indicates a simulation with topo_shading and slope_rad turned on in the WRF inner domain but not the \nouter WRF domains, no3DRad indicates a simulation with top_shading and slope_rad turned off in both the inner and outer \nWRF domains. 10-day moving averages are shown in b) temperature, and radiation variables (d, e, f, g). Figure 5. Spatial-average cumulative precipitation, two-meter surface air temperature and snow water equivalent (SWE) \n(first row), shortwave and longwave radiation, and latent and sensible heat (second row) over the ERW as simulated by the Figure 5. Spatial-average cumulative precipitation, two-meter surface air temperature a\n(first row), shortwave and longwave radiation, and latent and sensible heat (second row) Figure 5. Spatial-average cumulative precipitation, two-meter surface air temperature and snow water equivalent (SWE) \n(first row), shortwave and longwave radiation, and latent and sensible heat (second row) over the ERW as simulated by the \nIPM configurations with and without realistic topographic radiation effects, along with, where available, estimates from \n345 \nPRISM. 3DRad indicates a simulation with topo_shading and slope_rad turned on in the WRF inner domain but not the \nouter WRF domains, no3DRad indicates a simulation with top_shading and slope_rad turned off in both the inner and outer \nWRF domains. 10-day moving averages are shown in b) temperature, and radiation variables (d, e, f, g). Figure 5. Spatial-average cumulative precipitation, two-meter surface air temperature and snow water equivalent (SWE) \n(first row), shortwave and longwave radiation, and latent and sensible heat (second row) over the ERW as simulated by the \nIPM configurations with and without realistic topographic radiation effects, along with, where available, estimates from \n345 \nPRISM. 3DRad indicates a simulation with topo_shading and slope_rad turned on in the WRF inner domain but not the \nouter WRF domains, no3DRad indicates a simulation with top_shading and slope_rad turned off in both the inner and outer \nWRF domains. 10-day moving averages are shown in b) temperature, and radiation variables (d, e, f, g). IPM configurations with and without realistic topographic radiation effects, along with, where available, estimates from \n345 \nPRISM. 3DRad indicates a simulation with topo_shading and slope_rad turned on in the WRF inner domain but not the \nouter WRF domains, no3DRad indicates a simulation with top_shading and slope_rad turned off in both the inner and outer \nWRF domains. 10-day moving averages are shown in b) temperature, and radiation variables (d, e, f, g). Similar to the conclusions \ndrawn from Figure 3, Supplementary Figure S-4 also shows the monthly spatial standard deviations across subgrid-scale physics schemes are generally greater than meteorological forcing, particularly in regions of higher-elevation during the \n325 \nwinter season. 13 13 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Figure 4. Upper row: Differences in spatial distributions of annual average two-meter surface air temperature (a\n330 \ncumulative precipitation (b) between the BSU-CFSR2 WRF configuration and PRISM. Lower row: For all scheme Figure 4. Upper row: Differences in spatial distributions of annual average two-meter surface air temperature (a) and \n330 \ncumulative precipitation (b) between the BSU-CFSR2 WRF configuration and PRISM. Lower row: For all schemes, the Figure 4. Upper row: Differences in spatial distributions of annual average two-meter surface air temperature (a) and \n330 \ncumulative precipitation (b) between the BSU-CFSR2 WRF configuration and PRISM. Lower row: For all schemes, the 14 Figure 6 shows the seasonally-resolved shortwave radiation, two-meter surface air temperature, latent heat flux and SWE for \n350 \ndifferent configurations of shortwave radiation in the simulation with and without topo_shaing and slope_rad options in the \ninner domain. While no3DRad does not adjust the SWdown, 3DRad simulation recalculates the SWdown based on the Figure 6 shows the seasonally-resolved shortwave radiation, two-meter surface air temperature, latent heat flux and SWE for \n350 \ndifferent configurations of shortwave radiation in the simulation with and without topo_shaing and slope_rad options in the \ninner domain. While no3DRad does not adjust the SWdown, 3DRad simulation recalculates the SWdown based on the Figure 6 shows the seasonally-resolved shortwave radiation, two-meter surface air temperature, latent heat flux and SWE for \n350 \ndifferent configurations of shortwave radiation in the simulation with and without topo_shaing and slope_rad options in the \ninner domain. While no3DRad does not adjust the SWdown, 3DRad simulation recalculates the SWdown based on the 15 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. shadows cast by nearby topography. Spatial differences in IPM-simulated shortwave radiation (Figure 6b) are seen in the \nnortheast and western portions of the ERW, when topographic effect of shortwave radiation is included. As a result, a \ncorresponding change in the spatial pattern of simulated two-meter surface air temperature and latent heat flux are seen, \n355 \nwhich are driven by the change in downwelling shortwave radiation with topographic shading (Figures 6a and 6d). The \nresulting pattern change in SWE (Figure 6c) shows that the northern and northeastern sections of the ERW, where snowpack \nare concentrated, are sensitive to shortwave radiation. This is expected and consistent with previous findings that included \ntopographic effects in shortwave radiation and found distinct spatial patterns of hydrologic variable sensitivity due to both \nshadows and surface reflection that produce time-varying effects on net surface radiation (Lee et al., 2015; Palazzi et al., \n360 \n2019; Gu et al., 2020; Hao et al., 2021). 355 shadows and surface reflection that produce time-varying effects on net surface radiation (Lee et al., 2015; Palazzi et al., \n360 \n2019; Gu et al., 2020; Hao et al., 2021). Although Figure 5 shows that realistic shortwave radiation produces small effects on the seasonal cycle of the surface energy \nand mass budgets when averaged over the entire watershed, including annual average SWE (Figure 5c), Figure 6c shows that Although Figure 5 shows that realistic shortwave radiation produces small effects on the seasonal cycle of the surface energy \nand mass budgets when averaged over the entire watershed, including annual average SWE (Figure 5c), Figure 6c shows that \nmountains and valleys have different amounts of SWE. Furthermore, seasonal patterns show simulated latent heat is \n365 \ndiminished at lower elevations from March to May, when snowmelt occurs in the valley, and the remaining snowpack in the \nmountains and late snowmelt in 3DRad simulation causes lower latent heat flux shown in July (Figure S-5). The 3DRad \nsimulation has less SWE in the valleys during the accumulation season but more SWE at higher elevations during the melt \nseason, which is a direct result of the differences in shortwave radiation redistribution. Figure S-5 also shows that the latent mountains and valleys have different amounts of SWE. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. To understand how the aforementioned WRF configurations and forcings impact the integrated water budget, Figure 7 shows \n380 \nthe simulated hydrologic output from the ParFlow-CLM model for watershed outlet discharge (top row) and watershed-\naverage groundwater storage (bottom row). Discharge at the watershed outlet (see exact location on Figure 1) shows general \nagreement across the different WRF subgrid-scale physics scheme configurations and meteorological forcings, where the \ndaily averaged time series (left) shows only minor differences through time. However, cumulative discharge by year-end \nreveals substantial differences (right), especially after peak snowmelt where estimates of cumulative discharge begin to \n385 \ndiverge. Differences across the WRF configurations are especially large; the difference across the three subgrid-scale physics \nscheme configurations with ERA5 (UCD, NCAR, and BSU) varies by 26% by year-end. Differences across meteorological \nforcing (using the BSU physics configuration as a control, shown in green) are also noteworthy, although smaller, \napproximately 6%. These results are consistent with variation of simulated precipitation in WRF described earlier, \nconfirming that for this basin, meteorological forcing drives less variance on hydrologic response than subgrid-scale physics \n390 \nscheme \nconfiguration. To understand how the aforementioned WRF configurations and forcings impact the integrated water budget, Figure 7 shows \n380 \nthe simulated hydrologic output from the ParFlow-CLM model for watershed outlet discharge (top row) and watershed-\naverage groundwater storage (bottom row). Discharge at the watershed outlet (see exact location on Figure 1) shows general \nagreement across the different WRF subgrid-scale physics scheme configurations and meteorological forcings, where the \ndaily averaged time series (left) shows only minor differences through time. However, cumulative discharge by year-end \nreveals substantial differences (right), especially after peak snowmelt where estimates of cumulative discharge begin to \n385 \ndiverge. Differences across the WRF configurations are especially large; the difference across the three subgrid-scale physics \nscheme configurations with ERA5 (UCD, NCAR, and BSU) varies by 26% by year-end. Differences across meteorological To understand how the aforementioned WRF configurations and forcings impact the integrated water budget, Figure 7 shows \n380 \nthe simulated hydrologic output from the ParFlow-CLM model for watershed outlet discharge (top row) and watershed-\naverage groundwater storage (bottom row). Furthermore, seasonal patterns show simulated latent heat is \n365 \ndiminished at lower elevations from March to May, when snowmelt occurs in the valley, and the remaining snowpack in the \nmountains and late snowmelt in 3DRad simulation causes lower latent heat flux shown in July (Figure S-5). The 3DRad \nsimulation has less SWE in the valleys during the accumulation season but more SWE at higher elevations during the melt \nseason, which is a direct result of the differences in shortwave radiation redistribution. Figure S-5 also shows that the latent heat differences in north-facing and south-facing sides are most apparent in the snowmelt and warm seasons. This is \n370 \nconsistent with previous findings (Lee et al., 2015; Palazzi et al., 2019; Gu et al., 2020; Hao et al., 2021), that a more \nrealistic treatment of shortwave radiation, which includes shadows and projected insolation on sloped surfaces, results in \nlower shortwave insolation on the surface at this time of year. The lower shortwave radiation should, in turn, decrease the \nenergy available for the IPM to produce snowmelt. 16 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 5 \nFigure 6. Topographic radiation differences (3dRad minus no3dRad) annual average two-meter surface air tempera\nshortwave (SW) and latent heat flux, and snow water equivalent (SWE) over the ERW. The values in the parentheses ar\nERW average differences over the water year, which are small and consistent with Figure 5. 5 375 Figure 6. Topographic radiation differences (3dRad minus no3dRad) annual average two-meter surface air temperature, \nshortwave (SW) and latent heat flux, and snow water equivalent (SWE) over the ERW. The values in the parentheses are the \nERW average differences over the water year, which are small and consistent with Figure 5. 17 Discharge at the watershed outlet (see exact location on Figure 1) shows general \nagreement across the different WRF subgrid-scale physics scheme configurations and meteorological forcings, where the \ndaily averaged time series (left) shows only minor differences through time. However, cumulative discharge by year-end \nl\nb t\nti l diff\n( i ht)\ni ll\nft\nk\nlt\nh\nti\nt\nf\nl ti\ndi\nh\nb\ni\nt\n385 reveals substantial differences (right), especially after peak snowmelt where estimates of cumulative discharge begin to \n385 \ndiverge. Differences across the WRF configurations are especially large; the difference across the three subgrid-scale physics \nscheme configurations with ERA5 (UCD, NCAR, and BSU) varies by 26% by year-end. Differences across meteorological \nforcing (using the BSU physics configuration as a control, shown in green) are also noteworthy, although smaller, \napproximately 6%. These results are consistent with variation of simulated precipitation in WRF described earlier, \nconfirming that for this basin, meteorological forcing drives less variance on hydrologic response than subgrid-scale physics \n390 \nscheme \nconfiguration. 18 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Figure 7. Time-series of ParFlow-CLM simulations of discharge rate (a), cumulative discharge (b), groundwater storage \nper unit area of the watershed (c), and cumulative average unsaturated groundwater storage per area of the watershed (d) \nfor the IPM configurations described in Table 2. The brackets on the far-right indicate the percent difference of cumulative \ndischarge and unsaturated groundwater storage per area (b and d, respectively) for WRF simulations across different \nmeteorological forcings (green) and subgrid-scale physics schemes (black). Figure 7. Time-series of ParFlow-CLM simulations of discharge rate (a), cumulative di Figure 7. Time-series of ParFlow-CLM simulations of discharge rate (a), cumulative discharge (b), groundwater storage \nper unit area of the watershed (c), and cumulative average unsaturated groundwater storage per area of the watershed (d) \n395 \nfor the IPM configurations described in Table 2. The brackets on the far-right indicate the percent difference of cumulative \ndischarge and unsaturated groundwater storage per area (b and d, respectively) for WRF simulations across different \nmeteorological forcings (green) and subgrid-scale physics schemes (black). Figure 7. Time-series of ParFlow-CLM simulations of discharge rate (a), cumulative discharge (b), groundwater storage \nper unit area of the watershed (c), and cumulative average unsaturated groundwater storage per area of the watershed (d) \n395 \nfor the IPM configurations described in Table 2. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. ParFlow-CLM which is consistent with the discussion surrounding Figure 3 in relationship to PRISM. An early-fall peak in \nsimulated discharge is also seen in all WRF simulations, and not in observed discharge, although a significant increase in simulated discharge is also seen in all WRF simulations, and not in observed discharge, although a significant increase in \nSNOTEL precipitation was measured in October of that year (see Figure S-1, S-2). This further supports a temperature bias, \n405 \nalbeit opposite that of the cold-bias discussed previously, where precipitation around that October storm-event falling as rain \n(as opposed to snow) leads to a sharp increase in discharge. A sensitivity analysis of the BSU-ERA5 model run for a lower \nprecipitation year (water year 2018, which was nearly half the precipitation of 2019), showed better agreement with \nobserved discharge, which suggests the bias in timing may be a function of accumulated precipitation and/or snowmelt, and \nis reserved for future studies (not shown). 410 SNOTEL precipitation was measured in October of that year (see Figure S-1, S-2). This further supports a temperature bias, \n405 \nalbeit opposite that of the cold-bias discussed previously, where precipitation around that October storm-event falling as rain \n(as opposed to snow) leads to a sharp increase in discharge. A sensitivity analysis of the BSU-ERA5 model run for a lower \nprecipitation year (water year 2018, which was nearly half the precipitation of 2019), showed better agreement with \nobserved discharge, which suggests the bias in timing may be a function of accumulated precipitation and/or snowmelt, and \ni\nd f\nf\ndi\n(\nh\n)\n410 SNOTEL precipitation was measured in October of that year (see Figure S-1, S-2). This further supports a temperature bias, \n405 \nalbeit opposite that of the cold-bias discussed previously, where precipitation around that October storm-event falling as rain \n(as opposed to snow) leads to a sharp increase in discharge. The brackets on the far-right indicate the percent difference of cumulative \ndischarge and unsaturated groundwater storage per area (b and d, respectively) for WRF simulations across different \nmeteorological forcings (green) and subgrid-scale physics schemes (black). A comparison to observed discharge is also shown on Figure 7, which for all scenarios suggest a delayed snowmelt response \n400 \nin the IPM. While the objective of this study is not to replicate the observations, but rather determine sensitivity across IPM \nconfiguration choice, the mismatch in streamflow response suggests a systematic cold-bias from the WRF input into A comparison to observed discharge is also shown on Figure 7, which for all scenarios suggest a delayed snowmelt response \n400 \nin the IPM. While the objective of this study is not to replicate the observations, but rather determine sensitivity across IPM \nconfiguration choice, the mismatch in streamflow response suggests a systematic cold-bias from the WRF input into 19 A sensitivity analysis of the BSU-ERA5 model run for a lower \nprecipitation year (water year 2018, which was nearly half the precipitation of 2019), showed better agreement with \nobserved discharge, which suggests the bias in timing may be a function of accumulated precipitation and/or snowmelt, and \nis reserved for future studies (not shown)\n410 Basin-average groundwater storage, shown in Figure 7c in area-normalized units, shows a strong annual signal for all WRF \nconfigurations with notable, but minimal differences across IPM configurations. Here all groundwater, inclusive of saturated \nor unsaturated storage, is considered. The cumulative, area-normalized annual groundwater storage, when accounting for Basin-average groundwater storage, shown in Figure 7c in area-normalized units, shows a strong annual signal for all WRF \nconfigurations with notable, but minimal differences across IPM configurations. Here all groundwater, inclusive of saturated \nor unsaturated storage, is considered. The cumulative, area-normalized annual groundwater storage, when accounting for \nonly vadose zone storage (Figure 7d), which is most responsive to sub-annual differences in precipitation inputs, is \n415 \nmeaningful in this context because it relates a cumulative impact on near-surface groundwater storage due to IPM \nconfiguration. Similar to year-end cumulative discharge, year-end departures in vadose zone groundwater storage across the \ndifferent simulations are evident. Differences across the IPM configurations of subgrid-scale physics schemes are slightly \nlarger than the difference across the forcing simulations (4% versus 2%, respectively). While the differences in groundwater \nsignals are not as pronounced as the discharge signals, the slower, more muted-nature of infiltration and impacts on deeper \n420 \naquifer reserves relative to discharge would likely be more notable for multi-year simulations and/or in more water-limited only vadose zone storage (Figure 7d), which is most responsive to sub-annual differences in precipitation inputs, is \n415 \nmeaningful in this context because it relates a cumulative impact on near-surface groundwater storage due to IPM \nconfiguration. Similar to year-end cumulative discharge, year-end departures in vadose zone groundwater storage across the \ndifferent simulations are evident. Differences across the IPM configurations of subgrid-scale physics schemes are slightly \nlarger than the difference across the forcing simulations (4% versus 2%, respectively). While the differences in groundwater signals are not as pronounced as the discharge signals, the slower, more muted-nature of infiltration and impacts on deeper \n420 \naquifer reserves relative to discharge would likely be more notable for multi-year simulations and/or in more water-limited \nenvironments (or water years) where plant-water use demands are higher. signals are not as pronounced as the discharge signals, the slower, more muted-nature of infiltration and impacts on deeper \n420 \naquifer reserves relative to discharge would likely be more notable for multi-year simulations and/or in more water-limited \nenvironments (or water years) where plant-water use demands are higher. signals are not as pronounced as the discharge signals, the slower, more muted-nature of infiltration and impacts on deeper \n420 \naquifer reserves relative to discharge would likely be more notable for multi-year simulations and/or in more water-limited \nenvironments (or water years) where plant-water use demands are higher. Figure 8 shows maps of standard deviations in annual total evapotranspiration (ET) simulated by ParFlow-CLM across IPM \nconfigurations (top row), as well as the cell-binned relationship of those standard deviations of annual ET with land use and \n425 \ncover type, as well as elevation (bottom). Consistent with variations shown in the simulated discharge and groundwater \nstorage, Parflow-CLM simulates greater variations of ET under WRF configurations driven by different subgrid-scale \nphysics schemes (Figure 8a), compared to the simulations conducted with different meteorological forcings (Figure 8b). These results suggest that locations populated by high-water demanding vegetation (namely evergreen and deciduous forests) at mid-elevations result in the highest ET variability across IPM configurations. Conversely, low-water demanding \n430 \nvegetation (barren/sparsely vegetated land and grasses), which reside across a range of elevations in the study domain, result \nin the lowest variability in annual ET across IPM configurations. These differences in water demand essentially magnify any \ndifferences in atmospheric conditions. forests) at mid-elevations result in the highest ET variability across IPM configurations. Conversely, low-water demanding \n430 \nvegetation (barren/sparsely vegetated land and grasses), which reside across a range of elevations in the study domain, result \nin the lowest variability in annual ET across IPM configurations. These differences in water demand essentially magnify any \ndifferences in atmospheric conditions. forests) at mid-elevations result in the highest ET variability across IPM configurations. Conversely, low-water demanding \n430 \nvegetation (barren/sparsely vegetated land and grasses), which reside across a range of elevations in the study domain, result \nin the lowest variability in annual ET across IPM configurations. These differences in water demand essentially magnify any \ndifferences in atmospheric conditions. 20 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Figure 8. Pixel-level standard deviation in annual total evapotranspiration (ET) over the ParFlow-CLM domain from WR\nwith different subgrid-scale physics schemes (a,c) or meteorological forcing (b,d). https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 5. Discussion and Conclusions \n440 In spite of previous efforts to characterize the sensitivity of WRF simulations to model configuration choices, the mountain \nclimate and hydrology scientific community has not sufficiently explored the implications of those choices for surface and \nsubsurface hydrology in high-altitude complex terrain. Here, we used an IPM produced by coupling WRF and ParFlow-\nCLM to assess the hydrometeorology of the ERW which is characterized by strong hydrological gradients indicative of \nmountain environments of the UCRB. 445 In this paper, we present a number of numerical experiment results that are informative for the scientific community to better \nunderstand atmosphere-through-bedrock process interactions, with an eye towards how to represent those interactions in \nclimate and hydrological models. First, the uncertainties associated with meteorological forcing choice are less important In this paper, we present a number of numerical experiment results that are informative for the scientific community to better \nunderstand atmosphere-through-bedrock process interactions, with an eye towards how to represent those interactions in \nclimate and hydrological models. First, the uncertainties associated with meteorological forcing choice are less important \nthan subgrid-scale physics scheme choice, at least in the ERW. This finding has important implications for IPM in complex \n450 \nterrain, since it reveals that the differences in reanalysis products are less consequential for initializing and forcing IPMs than \natmospheric configurations, and that efforts to advance IPMs such as collecting observations and using them to evaluate \nphysical process parameterizations at the sub-HUC-8 scale could help to better constrain model performance. This result also \nshows that the boundary conditions of the IPM simulation are less important in driving the magnitude and spatial variability \nf k\nh d\nl\ni\nl\ni bl\nh\nh d\nil i\nh\ni\nd\ni i i\nh i\nb\nid\nl\nh\ni\nh In this paper, we present a number of numerical experiment results that are informative for the scientific community to better \nunderstand atmosphere-through-bedrock process interactions, with an eye towards how to represent those interactions in \nclimate and hydrological models. First, the uncertainties associated with meteorological forcing choice are less important \nthan subgrid-scale physics scheme choice, at least in the ERW. 5. Discussion and Conclusions \n440 This finding has important implications for IPM in complex \n450 \nterrain, since it reveals that the differences in reanalysis products are less consequential for initializing and forcing IPMs than \natmospheric configurations, and that efforts to advance IPMs such as collecting observations and using them to evaluate \nphysical process parameterizations at the sub-HUC-8 scale could help to better constrain model performance. This result also \nshows that the boundary conditions of the IPM simulation are less important in driving the magnitude and spatial variability \nof key hydrometeorological variables than the details in choosing and optimizing atmospheric subgrid-scale physics schemes \n455 \n(e.g., microphysics or boundary layer turbulence). Ultimately, we found that the BSU-CFSR2 configuration produced the \nmost accurate recreation of WY2019 in the ERW which allows researchers, in this case, to prioritize process studies and the \ndevelopment of associated observational constraints within the ERW. However, further investigation is need to evaluate the \nsystemic cold bias across IPM configurations, particularly at higher elevations, and the consequence of delayed snowmelt climate and hydrological models. First, the uncertainties associated with meteorological forcing choice are less important \nthan subgrid-scale physics scheme choice, at least in the ERW. This finding has important implications for IPM in complex \n450 \nterrain, since it reveals that the differences in reanalysis products are less consequential for initializing and forcing IPMs than \natmospheric configurations, and that efforts to advance IPMs such as collecting observations and using them to evaluate \nphysical process parameterizations at the sub-HUC-8 scale could help to better constrain model performance. This result also \nshows that the boundary conditions of the IPM simulation are less important in driving the magnitude and spatial variability than subgrid-scale physics scheme choice, at least in the ERW. This finding has important implications for IPM in complex \n450 \nterrain, since it reveals that the differences in reanalysis products are less consequential for initializing and forcing IPMs than \natmospheric configurations, and that efforts to advance IPMs such as collecting observations and using them to evaluate \nphysical process parameterizations at the sub-HUC-8 scale could help to better constrain model performance. This result also \nshows that the boundary conditions of the IPM simulation are less important in driving the magnitude and spatial variability of key hydrometeorological variables than the details in choosing and optimizing atmospheric subgrid-scale physics schemes \n455 \n(e.g., microphysics or boundary layer turbulence). 5. Discussion and Conclusions \n440 This is because the spatial redistribution of shortwave radiation leads to In the investigation of topographical and slope gradient effects on shortwave radiation, our study shows those considerations \nin WRF are essential in redistributing radiation flux over regions of complex terrain, even though the differences in spatial-\naverage performance over ERW is minimal. This is because the spatial redistribution of shortwave radiation leads to in WRF are essential in redistributing radiation flux over regions of complex terrain, even though the differences in spatial-\naverage performance over ERW is minimal. This is because the spatial redistribution of shortwave radiation leads to \napproximately +/- 30 W/m2 difference in the east/west facing slopes that lead to +/- 1 K difference in two-meter surface air \n465 \ntemperature in August and September (when snowpack is nonexistent). Throughout most of the water year when snowpack \nexists, the spatial heterogeneity of temperature differences are less apparent than for shortwave radiation. Latent heat is \nposited to buffer differences in the shortwave radiation contribution to the radiation budget, and causes early snowmelt in the \nhigh elevation mountains in those simulations with topographical and slope gradient shortwave radiation effects turned on. approximately +/- 30 W/m2 difference in the east/west facing slopes that lead to +/- 1 K difference in two-meter surface air \n465 \ntemperature in August and September (when snowpack is nonexistent). Throughout most of the water year when snowpack \nexists, the spatial heterogeneity of temperature differences are less apparent than for shortwave radiation. Latent heat is \nposited to buffer differences in the shortwave radiation contribution to the radiation budget, and causes early snowmelt in the \nhigh elevation mountains in those simulations with topographical and slope gradient shortwave radiation effects turned on. At the same time, this finding is potentially indicative of challenges in extrapolating findings from one mountainous \n470 \nwatershed to another. If atmospheric process details are significant for surface and subsurface hydrological modeling and if \nthe findings regarding atmospheric processes in one study area are marginally or completely irrelevant to other mountainous At the same time, this finding is potentially indicative of challenges in extrapolating findings from one mountainous \n470 \nwatershed to another. The ERW outline is overlain in black \nthe upper row, a-b. The relationship between annual ET, elevation, and land cover type are shown as scatter plots on t\nbottom row, c-d. See Figure 1 for maps of land cover types. 435 Figure 8. Pixel-level standard deviation in annual total evapotranspiration (ET) over the ParFlow-CLM domain from WRF \nwith different subgrid-scale physics schemes (a,c) or meteorological forcing (b,d). The ERW outline is overlain in black in \nthe upper row, a-b. The relationship between annual ET, elevation, and land cover type are shown as scatter plots on the \nbottom row, c-d. See Figure 1 for maps of land cover types. 21 5. Discussion and Conclusions \n440 Ultimately, we found that the BSU-CFSR2 configuration produced the \nmost accurate recreation of WY2019 in the ERW which allows researchers, in this case, to prioritize process studies and the \ndevelopment of associated observational constraints within the ERW. However, further investigation is need to evaluate the \nsystemic cold bias across IPM configurations, particularly at higher elevations, and the consequence of delayed snowmelt \nand timing of discharge peaks. 460 of key hydrometeorological variables than the details in choosing and optimizing atmospheric subgrid-scale physics schemes \n455 \n(e.g., microphysics or boundary layer turbulence). Ultimately, we found that the BSU-CFSR2 configuration produced the \nmost accurate recreation of WY2019 in the ERW which allows researchers, in this case, to prioritize process studies and the \ndevelopment of associated observational constraints within the ERW. However, further investigation is need to evaluate the \nsystemic cold bias across IPM configurations, particularly at higher elevations, and the consequence of delayed snowmelt \nand timing of discharge peaks. 460 of key hydrometeorological variables than the details in choosing and optimizing atmospheric subgrid-scale physics schemes \n455 \n(e.g., microphysics or boundary layer turbulence). Ultimately, we found that the BSU-CFSR2 configuration produced the \nmost accurate recreation of WY2019 in the ERW which allows researchers, in this case, to prioritize process studies and the \ndevelopment of associated observational constraints within the ERW. However, further investigation is need to evaluate the \nsystemic cold bias across IPM configurations, particularly at higher elevations, and the consequence of delayed snowmelt \nand timing of discharge peaks. 460 In the investigation of topographical and slope gradient effects on shortwave radiation, our study shows those considerations \nin WRF are essential in redistributing radiation flux over regions of complex terrain, even though the differences in spatial-\naverage performance over ERW is minimal. This is because the spatial redistribution of shortwave radiation leads to \napproximately +/- 30 W/m2 difference in the east/west facing slopes that lead to +/- 1 K difference in two-meter surface air \n465 \ntemperature in August and September (when snowpack is nonexistent). Throughout most of the water year when snowpack \nexists, the spatial heterogeneity of temperature differences are less apparent than for shortwave radiation. Latent heat is In the investigation of topographical and slope gradient effects on shortwave radiation, our study shows those considerations \nin WRF are essential in redistributing radiation flux over regions of complex terrain, even though the differences in spatial-\naverage performance over ERW is minimal. Another methodological constraint is that our WRF and Parflow-CLM \n480 \nexperiments were only one-way coupled instead of two-way coupled, which ignores potentially important feedbacks from \nthe subsurface hydrology to the atmosphere via ET and the radiation budget. For example, Givati et al. (2016) reported that \nsimulated precipitation was improved with two-way coupling in WRF-Hydro compared to WRF-only and Forrester et al. (2018) showed that boundary layer dynamics were impacted in IPM simulations in regions where shallow water tables exist. 485 \nFuture work will include integration of data, either indirectly through IPM benchmarking or directly through data \nassimilation into the IPM, from a recently deployed atmospheric observatory in the ERW as part of the Surface Atmosphere \nIntegrated Field Laboratory (SAIL) Campaign, which will run from September, 2021 to June, 2023. SAIL is collecting a \nwide-array of observations with the intent to advance understanding of precipitation, snow, aerosol, aerosol-cloud Future work will include integration of data, either indirectly through IPM benchmarking or directly through data \nassimilation into the IPM, from a recently deployed atmospheric observatory in the ERW as part of the Surface Atmosphere \nIntegrated Field Laboratory (SAIL) Campaign, which will run from September, 2021 to June, 2023. SAIL is collecting a \nwide-array of observations with the intent to advance understanding of precipitation, snow, aerosol, aerosol-cloud \ninteraction, and radiation processes in complex terrain and establish the minimum-but-sufficient level of process \n490 \nunderstanding to develop a robust predictive understanding of seasonal surface water and energy budgets in the ERW \n(Feldman et al., 2021). SAIL is working in conjunction with the Watershed Function Scientific Focus Area (WF-SFA) and \npartners including the National Oceanic and Atmospheric Administration (NOAA)’s Study for Precipitation, the Lower \nAtmosphere, and Surface for Hydrometeorology (SPLASH), the United States Geological Survey’s Next Generation Water \nObserving System (NGWOS), the National Science Foundation’s Sublimation of Snow (SOS) project, and numerous state \n495 \nand local agencies and organizations, including the Rocky Mountain Biological Laboratory, to develop a wide range of \nhydrometeorological datasets to constrain atmosphere, surface, and subsurface processes simultaneously. Together, these \nresources are contributing to the establishment of a highly-instrumented and studied UCRB watershed. Our study highlights \nthat the benchmarking provided by these data collections will be critical in addressing the systemic IPM cold bias by \nproviding a more constrained estimate of radiation budgets in complex terrain that ultimately shape snowmelt and discharge. 5. Discussion and Conclusions \n440 If atmospheric process details are significant for surface and subsurface hydrological modeling and if \nthe findings regarding atmospheric processes in one study area are marginally or completely irrelevant to other mountainous At the same time, this finding is potentially indicative of challenges in extrapolating findings from one mountainous \n470 \nwatershed to another. If atmospheric process details are significant for surface and subsurface hydrological modeling and if \nthe findings regarding atmospheric processes in one study area are marginally or completely irrelevant to other mountainous 22 500 wide-array of observations with the intent to advance understanding of precipitation, snow, aerosol, aerosol-cloud \ninteraction, and radiation processes in complex terrain and establish the minimum-but-sufficient level of process \n490 \nunderstanding to develop a robust predictive understanding of seasonal surface water and energy budgets in the ERW \n(Feldman et al., 2021). SAIL is working in conjunction with the Watershed Function Scientific Focus Area (WF-SFA) and \npartners including the National Oceanic and Atmospheric Administration (NOAA)’s Study for Precipitation, the Lower \nAtmosphere, and Surface for Hydrometeorology (SPLASH), the United States Geological Survey’s Next Generation Water interaction, and radiation processes in complex terrain and establish the minimum-but-sufficient level of process \n490 \nunderstanding to develop a robust predictive understanding of seasonal surface water and energy budgets in the ERW \n(Feldman et al., 2021). SAIL is working in conjunction with the Watershed Function Scientific Focus Area (WF-SFA) and \npartners including the National Oceanic and Atmospheric Administration (NOAA)’s Study for Precipitation, the Lower \nAtmosphere, and Surface for Hydrometeorology (SPLASH), the United States Geological Survey’s Next Generation Water Observing System (NGWOS), the National Science Foundation’s Sublimation of Snow (SOS) project, and numerous state \n495 \nand local agencies and organizations, including the Rocky Mountain Biological Laboratory, to develop a wide range of \nhydrometeorological datasets to constrain atmosphere, surface, and subsurface processes simultaneously. Together, these \nresources are contributing to the establishment of a highly-instrumented and studied UCRB watershed. Our study highlights \nthat the benchmarking provided by these data collections will be critical in addressing the systemic IPM cold bias by Observing System (NGWOS), the National Science Foundation’s Sublimation of Snow (SOS) project, and numerous state \n495 \nand local agencies and organizations, including the Rocky Mountain Biological Laboratory, to develop a wide range of \nhydrometeorological datasets to constrain atmosphere, surface, and subsurface processes simultaneously. Together, these \nresources are contributing to the establishment of a highly-instrumented and studied UCRB watershed. Our study highlights \nthat the benchmarking provided by these data collections will be critical in addressing the systemic IPM cold bias by providing a more constrained estimate of radiation budgets in complex terrain that ultimately shape snowmelt and discharge. 500 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. watersheds, then additional field work would be needed in mountainous hydrology research to address this issue, given that \nthe extrapolation of fieldwork results remains a central challenge for field-based research and modeling activities. 475 A limitation of our study, given the computational constraints of running IPMs, is that it was infeasible to explore the full \nparameter spaces of WRF and ParFlow-CLM exhaustively; thus, our conclusions are limited to the selected subgrid-scale \nphysics schemes and meteorological forcing datasets analyzed. Additional work is needed to improve the systemic cold bias \nin two-meter surface air temperature throughout all experiments as this may have been the major driver in the delayed \nsnowmelt and peak discharge simulated by the IPM. Another methodological constraint is that our WRF and Parflow-CLM \n480 A limitation of our study, given the computational constraints of running IPMs, is that it was infeasible to explore the full \nparameter spaces of WRF and ParFlow-CLM exhaustively; thus, our conclusions are limited to the selected subgrid-scale \nphysics schemes and meteorological forcing datasets analyzed. Additional work is needed to improve the systemic cold bias \nin two-meter surface air temperature throughout all experiments as this may have been the major driver in the delayed \nsnowmelt and peak discharge simulated by the IPM. Another methodological constraint is that our WRF and Parflow-CLM \n480 \nexperiments were only one-way coupled instead of two-way coupled, which ignores potentially important feedbacks from \nthe subsurface hydrology to the atmosphere via ET and the radiation budget. For example, Givati et al. (2016) reported that \nsimulated precipitation was improved with two-way coupling in WRF-Hydro compared to WRF-only and Forrester et al. (2018) showed that boundary layer dynamics were impacted in IPM simulations in regions where shallow water tables exist. snowmelt and peak discharge simulated by the IPM. Another methodological constraint is that our WRF and Parflow-CLM \n480 \nexperiments were only one-way coupled instead of two-way coupled, which ignores potentially important feedbacks from \nthe subsurface hydrology to the atmosphere via ET and the radiation budget. For example, Givati et al. (2016) reported that \nsimulated precipitation was improved with two-way coupling in WRF-Hydro compared to WRF-only and Forrester et al. (2018) showed that boundary layer dynamics were impacted in IPM simulations in regions where shallow water tables exist. snowmelt and peak discharge simulated by the IPM. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Author contributions: ZX, ESW, AMR and DF designed the study together. ZX performed the WRF simulations and \nanalyzed the results. ESW performed the ParFlow-CLM simulations. All authors contributed to the writing and approve of \nthis manuscript. Author contributions: ZX, ESW, AMR and DF designed the study together. ZX performed the WRF simulations and \nanalyzed the results. ESW performed the ParFlow-CLM simulations. All authors contributed to the writing and approve of \nthis manuscript. Competing interests: The contact author has declared that neither they nor their co-authors have any competing interests. Competing interests: The contact author has declared that neither they nor their co-authors have any competing interests. 0 Competing interests: The contact author has declared that neither they nor their co-authors have any competing interests. Competing interests: The contact author has declared that neither they nor their co-authors have any competing interests. 510 \n \nAcknowledgements: This work was supported by the Laboratory Directed Research and Development Program of Lawrence \nBerkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231. This research used \nresources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported Acknowledgements: This work was supported by the Laboratory Directed Research and Development Program of Lawrence \nBerkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231. This research used \nresources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported Acknowledgements: This work was supported by the Laboratory Directed Research and Development Program of Lawrence \nBerkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231. This research used \nresources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported Acknowledgements: This work was supported by the Laboratory Directed Research and Development Program of Lawrence \nBerkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231. This research used \nresources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under that same contract. This research also used the Lawrencium \n515 \ncomputational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (supported by the \nDirector, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under contract no. DE-\nAC02-05CH11231). Data Availability: All WRF model output files can be found at 505 23 Ashby, S. F. and Falgout, R. D.: A parallel multigrid preconditioned conjugate gradient algorithm for groundwater flow \nsimulations, Nuclear science and engineering, 124, 145–159, https://doi.org/10.13182/NSE96-A24230, 1996. Department of Energy Regional \nand Global Climate Modeling (RGCM) Program through the Calibrated and Systematic Characterization, Attribution and \nD\ni\nf E\n(CASCADE) S i\nF\nA\nd h\nI\nd E\nl\ni\nf h\nSi\nl\nd H d\nli\n530 Detection of Extremes (CASCADE) Science Focus Area and the Integrated Evaluation of the Simulated Hydroclimate \n530 \nSystem of the Continental US project also under the same contract. Arthur, R. S., Lundquist, K. A., Mirocha, J. D., and Chow, F. K.: Topographic effects on radiation in the WRF Model with \nthe immersed boundary method: Implementation, validation, and application to complex terrain, Monthly Weather \nR\ni\n146 3277 3292 htt\n//d i\n/10 1175/MWR D 18 0108 1 2018\n35 The authors acknowledge the helpful guidance provided by Professor Lejo Flores and Dr. Will Rudisill \nof Boise State University regarding the WRF configurations conducted in this study over the ERW. by the Office of Science of the U.S. Department of Energy under that same contract. This research also used the Lawrencium \n515 \ncomputational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (supported by the \nDirector, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under contract no. DE-\nAC02-05CH11231). The authors acknowledge the helpful guidance provided by Professor Lejo Flores and Dr. Will Rudisill \nof Boise State University regarding the WRF configurations conducted in this study over the ERW. y\ng\ng\ng\ny\n \n520 \nFinancial support: First author Xu's material pertaining to WRF simulations was supported by the Laboratory Directed \nResearch and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract \nNo. DE-AC02-05CH11231, and his material pertaining to the analysis using reference and observation datasets was \nsupported by the Lawrence Berkeley National Laboratory’s Watershed Function Science Focus Area of the U.S. Department Financial support: First author Xu's material pertaining to WRF simulations was supported by the Laboratory Directed \nResearch and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract \nNo. DE-AC02-05CH11231, and his material pertaining to the analysis using reference and observation datasets was \nsupported by the Lawrence Berkeley National Laboratory’s Watershed Function Science Focus Area of the U.S. Department \nof Energy’s Environmental System Science (ESS) Program also under the same contract\n525 of Energy’s Environmental System Science (ESS) Program, also under the same contract. 525 \nCo-authors Feldman and Siirila-Woodburn's material was supported by the Laboratory Directed Research and Development \nProgram of Lawrence Berkeley National Laboratory under the same contract. Co-author Rhoades was funded by the \nDirector, Office of Science, Office of Biological and Environmental Research of the U.S. Department of Energy Regional \nand Global Climate Modeling (RGCM) Program through the Calibrated and Systematic Characterization, Attribution and of Energy’s Environmental System Science (ESS) Program, also under the same contract. 525 \nCo-authors Feldman and Siirila-Woodburn's material was supported by the Laboratory Directed Research and Development \nProgram of Lawrence Berkeley National Laboratory under the same contract. Co-author Rhoades was funded by the \nDirector, Office of Science, Office of Biological and Environmental Research of the U.S. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Comin, A. N., Schumacher, V., Justino, F., and Fernandez, A.: Impact of different microphysical parameterizations on \nextreme \nsnowfall \nevents \nin \nthe \nSouthern \nAndes, \nWeather \nand \nclimate \nextremes, \n21, \n65–75, \nhttps://doi.org/10.1016/j.wace.2018.07.001, 2018. 540 Dai, Y., Zeng, X., Dickinson, R. E., Baker, I., Bonan, G. B., Bosilovich, M. G., Denning, A. S., Dirmeyer, P. A., Houser, P. R., Niu, G., Oleson, K. W., Schlosser, C. A., & Yang, Z.: The Common Land Model, Bulletin of the American \nMeteorological Society, 84(8), 1013-1024, doi:10.1175/BAMS-84-8-1013, 2003. Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United \n545 \nStates, International Journal of Climatology: a Journal of the Royal Meteorological Society, 28, 2031–2064, \nhttps://doi.org/10.1002/joc.1688, 2008. Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow depth: a review, Journal of Glaciology, 59, \n467–479, https://doi.org/10.3189/2013JoG12J154, 2013. ,\ngy\ny\ng\ny,\n,\n,\nhttps://doi.org/10.1002/joc.1688, 2008. Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow depth: a review, Journal of Glaciology, 59, \n467–479, https://doi.org/10.3189/2013JoG12J154, 2013. Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow depth: a review, Journal of Glaciology, 59, \n467–479, https://doi.org/10.3189/2013JoG12J154, 2013. Fassnacht, S., Dressler, K., and Bales, R.: Snow water equivalent interpolation for the Colorado River Basin from snow \n550 \ntelemetry (SNOTEL) data, Water Resources Research, 39, https://doi.org/10.1029/2002WR001512, 2003. Feldman, D.R. et al.: Surface Atmosphere Integrated Field Laboratory (SAIL)
 Science Plan, Ed. by Robert Stafford, ARM \nuser facility. DOE/SC-ARM-21-004, 2021. Forrester, M. M., Maxwell, R. M., Bearup, L. A., and Gochis, D. J.: Forest disturbance feedbacks from bedrock to \natmosphere using coupled hydrometeorological simulations over the Rocky Mountain headwaters, Journal of \n555 \nGeophysical Research: Atmospheres, 123, 9026–9046, https://doi.org/10.1029/2018JD028380, 2018. Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., et al.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), \nJournal of climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. References: Review, 146, 3277–3292, https://doi.org/10.1175/MWR-D-18-0108.1, 2018. Ashby, S. F. and Falgout, R. D.: A parallel multigrid preconditioned conjugate gradient algorithm for groundwater flow \nsimulations, Nuclear science and engineering, 124, 145–159, https://doi.org/10.13182/NSE96-A24230, 1996. 24 Fassnacht, S., Dressler, K., and Bales, R.: Snow water equivalent interpolation for the Colorado River Basin from snow \n550 \ntelemetry (SNOTEL) data, Water Resources Research, 39, https://doi.org/10.1029/2002WR001512, 2003. Feldman, D.R. et al.: Surface Atmosphere Integrated Field Laboratory (SAIL)
 Science Plan, Ed. by Robert Stafford, ARM \nuser facility. DOE/SC-ARM-21-004, 2021. Forrester, M. M., Maxwell, R. M., Bearup, L. A., and Gochis, D. J.: Forest disturbance feedbacks from bedrock to \natmosphere using coupled hydrometeorological simulations over the Rocky Mountain headwaters, Journal of \n555 \nGeophysical Research: Atmospheres, 123, 9026–9046, https://doi.org/10.1029/2018JD028380, 2018. Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., et al.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), \nJournal of climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. Forrester, M. M., Maxwell, R. M., Bearup, L. A., and Gochis, D. J.: Forest disturbance feedbacks from bedrock to \natmosphere using coupled hydrometeorological simulations over the Rocky Mountain headwaters, Journal of \n555 \nGeophysical Research: Atmospheres, 123, 9026–9046, https://doi.org/10.1029/2018JD028380, 2018. Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., et al.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), \nJournal of climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. Givati, A., Gochis, D., Rummler, T., and Kunstmann, H.: Comparing one-way and two-way coupled hydrometeorological \n560 \nforecasting \nsystems \nfor \nflood \nforecasting \nin \nthe \nMediterranean \nregion, \nHydrology, \n3, \n19, \nhttps://doi.org/10.3390/hydrology3020019, 2016. G\nC\nH\nA\nW\nY\nY\nB\nM\nX\nZh\nX\nd C i S\nEff\nt\nf\nb\nid t\ni\ndi ti\nf\ni\nth Givati, A., Gochis, D., Rummler, T., and Kunstmann, H.: Comparing one-way and two-way coupled hydrometeorological \n560 \nforecasting \nsystems \nfor \nflood \nforecasting \nin \nthe \nMediterranean \nregion, \nHydrology, \n3, \n19, \nhttps://doi.org/10.3390/hydrology3020019, 2016. Gu, C., Huang, A., Wu, Y., Yang, B., Mu, X., Zhang, X., and Cai, S.: Effects of subgrid terrain radiative forcing on the \nability of RegCM4. 1 in the simulation of summer precipitation over China, Journal of Geophysical Research: Gu, C., Huang, A., Wu, Y., Yang, B., Mu, X., Zhang, X., and Cai, S.: Effects of subgrid terrain radiative forcing on the \nability of RegCM4. 1 in the simulation of summer precipitation over China, Journal of Geophysical Research: Gu, C., Huang, A., Wu, Y., Yang, B., Mu, X., Zhang, X., and Cai, S.: Effects of subgrid terrain radiative forcing on the \nability of RegCM4. 1 in the simulation of summer precipitation over China, Journal of Geophysical Research: \nAtmospheres, 125, e2019JD032 215, https://doi.org/10.1029/2019JD032215, 2020. 565 Atmospheres, 125, e2019JD032 215, https://doi.org/10.1029/2019JD032215, 2020. 565 \nGutowski, W. J., Jr, Ullrich, P. A., Hall, A., Leung, L. R., O’Brien, T. A., Patricola, C. M., Arritt, R. W., Bukovsky, M. S., \nCalvin, K. V., Feng, Z., Jones, A. D., Kooperman, G. J., Monier, E., Pritchard, M. S., Pryor, S. C., Qian, Y., Rhoades, \nA. M., Roberts, A. F., Sakaguchi, K., Urban, N., and Zarzycki, C.: The Ongoing Need for High-Resolution Regional \nClimate Models: Process Understanding and Stakeholder Information, Bulletin of the American Meteorological Society, 570 25 Lee, W.-L., Gu, Y., Liou, K., Leung, L., and Hsu, H.-H.: A global model simulation for 3-D radiative transfer impact on \n600 \nsurface hydrology over the Sierra Nevada and Rocky Mountains, Atmospheric Chemistry and Physics, 15, 5405–5413, \nhttps://doi.org/10.5194/acp-15-5405-2015, 2015. Orr, A., Listowski, C., Couttet, M., Collier, E., Immerzeel, W., Deb, P., and Bannister, D.: Sensitivity of simulated summer \nmonsoonal precipitation in Langtang Valley, Himalaya, to cloud microphysics schemes in WRF, Journal of Geophysical \n625 \nResearch: Atmospheres, 122, 6298–6318, 2017. Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, \nR., Marks, D., et al.: The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-\nbased modeling for mapping snow water equivalent and snow albedo, Remote Sensing of Environment, 184, 139–152, \nhttps://doi.org/10.1016/j.rse.2016.06.018, 2016. 630 \nPalazzi, E., Mortarini, L., Terzago, S., and Von Hardenberg, J.: Elevation-dependent warming in global climate model \nsimulations at high spatial resolution, Climate Dynamics, 52, 2685–2702, https://doi.org/10.1007/s00382-018-4287-z, \n2019. Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., \nPeckham, S. E., et al.: The weather research and forecasting model: Overview, system efforts, and future directions, \n635 \nBulletin of the American Meteorological Society, 98, 1717–1737, https://doi.org/10.1175/BAMS-D-15-00308.1., 2017. Milly, P. C. and Dunne, K. A.: Colorado River flow dwindles as warming-driven loss of reflective snow energizes \nevaporation, Science, 367, 1252–1255, https://doi.org/10.1126/science.aay9187, 2020. Orr, A., Listowski, C., Couttet, M., Collier, E., Immerzeel, W., Deb, P., and Bannister, D.: Sensitivity of simulated summer \nmonsoonal precipitation in Langtang Valley, Himalaya, to cloud microphysics schemes in WRF, Journal of Geophysical \n625 \nResearch: Atmospheres, 122, 6298–6318, 2017. 625 Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, \nR., Marks, D., et al.: The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-\nbased modeling for mapping snow water equivalent and snow albedo, Remote Sensing of Environment, 184, 139–152, \nhttps://doi.org/10.1016/j.rse.2016.06.018, 2016. 30 630 Palazzi, E., Mortarini, L., Terzago, S., and Von Hardenberg, J.: Elevation-dependent warming in global climate model \nsimulations at high spatial resolution, Climate Dynamics, 52, 2685–2702, https://doi.org/10.1007/s00382-018-4287-z, \n2019. Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Hao, D., Bisht, G., Gu, Y., Lee, W.-L., Liou, K.-N., and Leung, L. R.: A parameterization of sub-grid topographical effects \non solar radiation in the E3SM Land Model (version 1.0): implementation and evaluation over the Tibetan Plateau, \nGeoscientific Model Development, 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, 2021. Hersbach, H, Bell, B, Berrisford, P, et al.: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological \nSociety, 146, 1999– 2049, doi:10.1002/qj.3803, 2020. 575 Homer, C., et al.: Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover \nDatabase, ISPRS Journal of Photogrammetry and Remote Sensing, 162, 184-199, doi:10.1016/j.isprsjprs.2020.02.019, \n2020. Huang, X., Rhoades, A. M., Ullrich, P. A., and Zarzycki, C. M.: An evaluation of the variable-resolution CESM for \nmodeling \nCalifornia’s \nclimate, \nJournal \nof \nAdvances \nin \nModeling \nEarth \nSystems, \n8, \n345–369, \n0 \nhttps://doi.org/10.1002/2015MS000559, 2016. 580 Hubbard, S. S., Williams, K. H., Agarwal, D., Banfield, J., Beller, H., Bouskill, N., Brodie, E., Carroll, R., Dafflon, B., \nDwivedi, D., et al.: The East River, Colorado, Watershed: A mountainous community testbed for improving predictive \nunderstanding \nof \nmultiscale \nhydrological–biogeochemical \ndynamics, \nVadose \nZone \nJournal, \n17, \n1–25, \nhttps://doi.org/10.2136/vzj2018.03.0061, 2018. 585 585 Ikeda, K., Rasmussen, R., Liu, C., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Miller, K., et al.: \nSimulation \nof \n60 \nseasonal \nsnowfall \nover \nColorado, \nAtmospheric \nResearch, \n97, \n462–477, \nhttps://doi.org/10.1016/j.atmosres.2010.04.010, 2010. James, T, et al.: The economic importance of the Colorado River to the basin region. Final Rep., L. William Seidman \nResearch Institute, Arizona State University, 54, https://businessforwater.org/wp-content/uploads/2016/12/PTF-Final-\n590 \n121814.pdf, 2014. Jin, J., Miller, N. L., and Schlegel, N.: Sensitivity study of four land surface schemes in the WRF model, Advances in \nMeteorology, 2010, https://doi.org/10.1155/2010/167436, 2010. Lee, W.-L., Gu, Y., Liou, K., Leung, L., and Hsu, H.-H.: A global model simulation for 3-D radiative transfer impact on \n600 \nsurface hydrology over the Sierra Nevada and Rocky Mountains, Atmospheric Chemistry and Physics, 15, 5405–5413, \nhttps://doi.org/10.5194/acp-15-5405-2015, 2015. 26 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Liu, C., Ikeda, K., Thompson, G., Rasmussen, R., and Dudhia, J.: High-resolution simulations of wintertime precipitation in \nthe Colorado Headwaters region: Sensitivity to physics parameterizations, Monthly Weather Review, 139, 3533–3553, \nhttps://doi.org/10.1175/MWR-D-11-00009.1, 2011. 605 Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F., Chen, F., Chen, L., Clark, M., Dai, A., et al.: \nContinental-scale convection-permitting modeling of the current and future climate of North America, Climate \nDynamics, 49, 71–95, https://doi.org/10.1007/s00382-016-3327-9, 2017. Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S.: Our skill in modeling mountain rain and snow is bypassing the \nskill of our observational networks, Bulletin of the American Meteorological Society, 100, 2473–2490, \nhttps://doi.org/10.1175/BAMS-D-19-0001.1, 2019. 610 Maina, F. Z., Siirila-Woodburn, E. R., and Vahmani, P.: Sensitivity of meteorological-forcing resolution on hydrologic \nvariables, Hydrology and Earth System Sciences, 24, 3451–3474, https://doi.org/10.5194/hess-24-3451-2020, 2020. Mallard, M. S., Spero, T. L., and Taylor, S. M.: Examining WRF’s sensitivity to contemporary Mallard, M. S., Spero, T. L., and Taylor, S. M.: Examining WRF’s sensitivity to contemporary land-use datasets across the \ncontiguous United States Using Dynamical Downscaling, Journal of applied meteorology and climatology, 57, 2561–\n615 \n2583, https://doi.org/10.1175/JAMC-D-17-0328.1, 2018. Mallard, M. S., Spero, T. L., and Taylor, S. M.: Examining WRF’s sensitivity to contemporary land-use datasets across the 615 contiguous United States Using Dynamical Downscaling, Journal of applied meteorology and climatology, 57, 2561–\n615 \n2583, https://doi.org/10.1175/JAMC-D-17-0328.1, 2018. Maxwell, R., Condon, L., and Kollet, S.: A high-resolution simulation of groundwater and surface water over most of the\ncontinental US with the integrated hydrologic model ParFlow v3, Geoscientific model development, 8, 923–937, contiguous United States Using Dynamical Downscaling, Journal of applied meteorology and climatology, 57, 2561–\n615 \n2583, https://doi.org/10.1175/JAMC-D-17-0328.1, 2018. Maxwell, R., Condon, L., and Kollet, S.: A high-resolution simulation of groundwater and surface water over most of the \ncontinental US with the integrated hydrologic model ParFlow v3, Geoscientific model development, 8, 923–937, \nhttps://doi.org/10.5194/gmd-8-923-2015, 2015. Maxwell, R. M.: A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic \n620 \nmodeling, Advances in Water Resources, 53, 109–117, https://doi.org/10.1016/j.advwatres.2012.10.001, 2013. Maxwell, R. M.: A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic \n620 \nmodeling, Advances in Water Resources, 53, 109–117, https://doi.org/10.1016/j.advwatres.2012.10.001, 2013. Milly, P. C. and Dunne, K. A.: Colorado River flow dwindles as warming-driven loss of reflective snow energizes \nevaporation, Science, 367, 1252–1255, https://doi.org/10.1126/science.aay9187, 2020. J., Ahmadov, R., \nPeckham, S. E., et al.: The weather research and forecasting model: Overview, system efforts, and future directions, \n5 \nBulletin of the American Meteorological Society, 98, 1717–1737, https://doi.org/10.1175/BAMS-D-15-00308.1., 2017. 635 Peckham, S. E., et al.: The weather research and forecasting model: Overview, system efforts, and future directions, \n635 \nBulletin of the American Meteorological Society, 98, 1717–1737, https://doi.org/10.1175/BAMS-D-15-00308.1., 2017. 27 645 \nRasmussen, R., Ikeda, K., Liu, C., Gochis, D., Clark, M., Dai, A., Gutmann, E., Dudhia, J., Chen, F., Barlage, M., et al.: \nClimate change impacts on the water balance of the Colorado headwaters: high-resolution regional climate model \nsimulations, Journal of Hydrometeorology, 15, 1091–1116, https://doi.org/10.1175/JHM-D-13-0118.1, 2014. 645 warmer climate, Journal of Climate, 24, 3015 3048, https://doi.org/10.1175/2010JCLI3985.1, 2011. 645 \nRasmussen, R., Ikeda, K., Liu, C., Gochis, D., Clark, M., Dai, A., Gutmann, E., Dudhia, J., Chen, F., Barlage, M., et al.: \nClimate change impacts on the water balance of the Colorado headwaters: high-resolution regional climate model \nsimulations, Journal of Hydrometeorology, 15, 1091–1116, https://doi.org/10.1175/JHM-D-13-0118.1, 2014. Rhoades, A. M., Ullrich, P. A., and Zarzycki, C. M.: Projecting 21st century snowpack trends in western USA mountains \nusing variable-resolution CESM, Climate Dynamics, 50, 261–288, https://doi.org/10.1007/s00382-017-3606-0, 2018a. 50 650 Rhoades, A. M., Ullrich, P. A., Zarzycki, C. M., Johansen, H., Margulis, S. A., Morrison, H., Xu, Z., and Collins, W. D.: \nSensitivity of mountain hydroclimate simulations in variable-resolution CESM to microphysics and horizontal \nresolution, Journal of Advances in Modeling Earth Systems, 10, 1357–1380, https://doi.org/10.1029/2018MS001326, \n2018b. Rhoades, A. M., Jones, A. D., and Ullrich, P. A.: Assessing mountains as natural reservoirs with a multimetric framework. 655 \nEarth's Future, 6, 1221– 1241. https://doi.org/10.1002/2017EF000789, 2018c. Rudisill, W., Flores, A., and McNamara, J.: The Impact of Initial Snow Conditions on the Numerical Weather Simulation of \na Northern Rockies Atmospheric River, Journal of Hydrometeorology, 22, 155–167, 2021. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Liu, Z., Berner, J., Wang, W., Powers, J.G., Duda, M.G., Barker, Rhoades, A. M., Jones, A. D., and Ullrich, P. A.: Assessing mountains as natural reservoirs with a multimetric framework. 655 \nEarth's Future, 6, 1221– 1241. https://doi.org/10.1002/2017EF000789, 2018c. Earth's Future, 6, 1221– 1241. https://doi.org/10.1002/2017EF000789, 2018c. Rudisill, W., Flores, A., and McNamara, J.: The Impact of Initial Snow Conditions on the Numerical Weather Simulation of \na Northern Rockies Atmospheric River, Journal of Hydrometeorology, 22, 155–167, 2021. Rudisill, W., Flores, A., and McNamara, J.: The Impact of Initial Snow Conditions on the Numerical Weather Simulation of \na Northern Rockies Atmospheric River, Journal of Hydrometeorology, 22, 155–167, 2021. a Northern Rockies Atmospheric River, Journal of Hydrometeorology, 22, 155–167, 2021. https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Rahimi, S., Krantz, W., Lin, Y.-H., Bass, B., Goldenson, N., Hall, A., Lebo, Z. J., and Norris, J.: Evaluation of a Reanalysis-\nDriven Configuration of WRF4 Over the Western United States From 1980 to 2020, Journal of Geophysical Research: \nAtmospheres, 127, e2021JD035 699, https://doi.org/10.1029/2021JD035699, 2022. Rasmussen, K. L., Prein, A. F., Rasmussen, R. M., Ikeda, K., and Liu, C.: Changes in the convective population and \n40 \nthermodynamic environments in convection-permitting regional climate simulations over the United States, Climate \nDynamics, 55, 383–408, https://doi.org/10.1007/s00382-017-4000-7, 2020. Rasmussen, K. L., Prein, A. F., Rasmussen, R. M., Ikeda, K., and Liu, C.: Changes in the convective population and \n640 \nthermodynamic environments in convection-permitting regional climate simulations over the United States, Climate \nDynamics, 55, 383–408, https://doi.org/10.1007/s00382-017-4000-7, 2020. Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Yu, W., et al.: High-\nresolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and \nwarmer climate, Journal of Climate, 24, 3015–3048, https://doi.org/10.1175/2010JCLI3985.1, 2011. 645 \nRasmussen, R., Ikeda, K., Liu, C., Gochis, D., Clark, M., Dai, A., Gutmann, E., Dudhia, J., Chen, F., Barlage, M., et al.: \nClimate change impacts on the water balance of the Colorado headwaters: high-resolution regional climate model \nsimulations, Journal of Hydrometeorology, 15, 1091–1116, https://doi.org/10.1175/JHM-D-13-0118.1, 2014. Rhoades, A. M., Ullrich, P. A., and Zarzycki, C. M.: Projecting 21st century snowpack trends in western USA mountains \nusing variable-resolution CESM, Climate Dynamics, 50, 261–288, https://doi.org/10.1007/s00382-017-3606-0, 2018a. 650 \nRhoades, A. M., Ullrich, P. A., Zarzycki, C. M., Johansen, H., Margulis, S. A., Morrison, H., Xu, Z., and Collins, W. D.: \nSensitivity of mountain hydroclimate simulations in variable-resolution CESM to microphysics and horizontal \nresolution, Journal of Advances in Modeling Earth Systems, 10, 1357–1380, https://doi.org/10.1029/2018MS001326, \n2018b. Dynamics, 55, 383–408, https://doi.org/10.1007/s00382-017-4000-7, 2020. Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Yu, W., et al.: High-\nresolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and \nwarmer climate, Journal of Climate, 24, 3015–3048, https://doi.org/10.1175/2010JCLI3985.1, 2011. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Liu, Z., Berner, J., Wang, W., Powers, J.G., Duda, M.G., Barker, Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Liu, Z., Berner, J., Wang, W., Powers Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Liu, Z., Berner, J., Wang, W., Powers, J.G., Duda, M.G., Barker, \nD.M. and Huang, X.Y.: A description of the advanced research WRF model version 4. National Center for Atmospheric \n660 \nResearch: Boulder, CO, USA, 145, p.145, 2019. D.M. and Huang, X.Y.: A description of the advanced research WRF model version 4. National Center for Atmospheric \n660 \nResearch: Boulder, CO, USA, 145, p.145, 2019. Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., et al.: The \nNCEP climate forecast system reanalysis, Bulletin of the American Meteorological Society, 91, 1015–1058, \nhttps://doi.org/10.1175/2010BAMS3001.1, 2010. Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., et al.: The \nNCEP climate forecast system reanalysis, Bulletin of the American Meteorological Society, 91, 1015–1058, \nhttps://doi.org/10.1175/2010BAMS3001.1, 2010. Schreiner-McGraw, A. P. and Ajami, H.: Impact of uncertainty in precipitation forcing data sets on the hydrologic budget of \n665 \nan integrated hydrologic model in mountainous terrain, Water Resources Research, 56, e2020WR027 639, \nhttps://doi.org/10.1029/2020WR027639, 2020. Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United \nStates snowpack from snowpack telemetry (SNOTEL) data, Water Resources Research, 35, 2145–2160, \nhttps://doi.org/10.1029/1999WR900090, 1999. 670 g\ny\ng\n,\n,\n,\n,\nhttps://doi.org/10.1029/2020WR027639, 2020. Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United \nStates snowpack from snowpack telemetry (SNOTEL) data, Water Resources Research, 35, 2145–2160, \nhttps://doi.org/10.1029/1999WR900090, 1999. 670 Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United \nStates snowpack from snowpack telemetry (SNOTEL) data, Water Resources Research, 35, 2145–2160, \nhttps://doi.org/10.1029/1999WR900090, 1999. 670 670 28 https://doi.org/10.5194/egusphere-2022-437\nPreprint. Discussion started: 27 June 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Siirila-Woodburn, E. R., Rhoades, A. M., Hatchett, B. J., Huning, L. S., Szinai, J., Tague, C., Nico, P. S., Feldman, D. R., Siirila-Woodburn, E. R., Rhoades, A. M., Hatchett, B. J., Huning, L. S., Szinai, J., Tague, C., Nico, P. S., Feldman, D. R., \nJones, A. D., Collins, W. D., et al.: A low-to-no snow future and its impacts on water resources in the western United \nStates, Nature Reviews Earth & Environment, 2, 800–819, https://doi.org/10.1038/s43017-021-00219-y, 2021. Rhoades, A. M., Hatchett, B. J., Huning, L. S., Szinai, J., Tague, C., Nico, P. S., Feldman, D. R Siirila-Woodburn, E. R., Rhoades, A. M., Hatchett, B. J., Huning, L. S., Szinai, J., Tague, C., Nico, P. S., Feldman, D. R., \nJones, A. D., Collins, W. D., et al.: A low-to-no snow future and its impacts on water resources in the western United \nStates, Nature Reviews Earth & Environment, 2, 800–819, https://doi.org/10.1038/s43017-021-00219-y, 2021. Sturm, M., Goldstein, M. A., and Parr, C.: Water and life from snow: A trillion dollar science question, Water Resources \nh\n3 3 34 3 44 h\n//d i\n/0 1002/201\n020840 201 Jones, A. D., Collins, W. D., et al.: A low-to-no snow future and its impacts on water resources in the western United \nStates, Nature Reviews Earth & Environment, 2, 800–819, https://doi.org/10.1038/s43017-021-00219-y, 2021. Sturm, M., Goldstein, M. A., and Parr, C.: Water and life from snow: A trillion dollar science question, Water Resources \nResearch, 53, 3534–3544, https://doi.org/0.1002/2017WR020840, 2017. 675 Ullrich, P., Xu, Z., Rhoades, A., Dettinger, M., Mount, J., Jones, A., and Vahmani, P.: California’s drought of the future: A \nmidcentury \nrecreation \nof \nthe \nexceptional \nconditions \nof \n2012–2017, \nEarth’s \nfuture, \n6, \n1568–1587, \nhttps://doi.org/10.1029/2018EF001007, 2018 Williams, A. P., Cook, B. I., and Smerdon, J. E.: Rapid intensification of the emerging southwestern North American \nmegadrought in 2020–2021, Nature Climate Change, 12, 232–234, https://doi.org/10.1038/s41558-022-01290-z, 2022. 680 Xu, Y., Jones, A. and Rhoades, A.: A quantitative method to decompose SWE differences between regional climate models \nand reanalysis datasets. Sci Rep 9, 16520. https://doi.org/10.1038/s41598-019-52880-5, 2019. Xu, Z., Rhoades, A. M., Johansen, H., Ullrich, P. A., and Collins, W. D.: An intercomparison of GCM and RCM dynamical \ndownscaling for characterizing the hydroclimatology of California and Nevada, Journal of Hydrometeorology, 19, \n1485–1506, https://doi.org/10.1175/JHM-D-17-0181.1, 2018. 685 Xu, Z., Rhoades, A. M., Johansen, H., Ullrich, P. A., and Collins, W. D.: An intercomparison of GCM and RCM dynamical \ndownscaling for characterizing the hydroclimatology of California and Nevada, Journal of Hydrometeorology, 19, 1485–1506, https://doi.org/10.1175/JHM-D-17-0181.1, 2018. 685 1485–1506, https://doi.org/10.1175/JHM-D-17-0181.1, 2018. 685 685 29 29"