EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Potential Drivers and Methodological Considerations in Urbanisation Trends in the Iberian Peninsula (1992–2022)
---
generated from a multi-sensor surface reflectance time series derived from several satellite missions. As sensor technology evolved, different instruments were used to produce consistent global composites.**

| Surface Reflectance (SR) Input | Reference Period | Satellite Sensor / Mission | Main Characteristics |
|---|---|---|---|
| AVHRR SR composites | 1992–1999 | AVHRR-2 (NOAA-11, NOAA-14) | ~1 km spatial resolution, visible and near-infrared observations |
| SPOT-VGT SR composites | 1999–2013 | SPOT-4 / SPOT-5 VEGETATION | 1 km spatial resolution, 4 spectral bands (blue, red, NIR, SWIR) |
| MERIS SR composites | 2003–2012 | Envisat MERIS | ~300 m spatial resolution, 15 spectral bands in visible and near-infrared |
| PROBA-V SR composites | 2013–2019 | PROBA-V | ~300 m spatial resolution, 4 spectral bands (blue, red, NIR, SWIR) |
| Sentinel-3 SR composites | 2020 | Sentinel-3 OLCI | ~300 m spatial resolution, multispectral observations |
| Sentinel-3 SR composites | 2021–2022 | Sentinel-3 OLCI + SLSTR | Optical and thermal observations supporting land-cover mapping |

Source: See Figure 1-3 in C3S, LC - Product User Guide and Specification (2024)

- Due to the limited spatial resolution of early sensors (AVHRR and SPOT-VGT), reliable detection of annual urban change was challenging prior to 2003. During this period, the representation of urban areas is guided by reference urban footprints derived from the [**Global Human Settlement Layer (GHSL)**](https://doi.org/10.2788/656115) datasets for **1990** and **2000**. These datasets provide baseline constraints for the urban class within the **1992–1999** and **2000–2003** epochs, helping to stabilise the detection of urban areas while still allowing minor variations arising from classification adjustments and mixed-pixel effects. From **2003 onwards**, urban areas are derived from higher-resolution observations. To ensure temporal consistency, **urban areas are only allowed to expand over time**, and the resulting footprint is constrained between a **minimum extent defined by GHSL 2000** and a **maximum extent defined by GHSL 2014** [(C3S - LC Algorithm Theoretical Basis Document, 2024)](https://dast.copernicus-climate.eu/documents/satellite-land-cover/WP2-FDDP-LC-2021-2022-SENTINEL3-300m-v2.1.1_ATBD_v1.2_final.pdf).

- In addition, the [**Global Urban Footprint (GUF)**](https://geoservice.dlr.de/web/datasets/guf) dataset is combined with **GHSL 2014** to reduce potential omissions of built-up areas [(C3S - LC Algorithm Theoretical Basis Document, 2024)](https://dast.copernicus-climate.eu/documents/satellite-land-cover/WP2-FDDP-LC-2021-2022-SENTINEL3-300m-v2.1.1_ATBD_v1.2_final.pdf).

- In addition to optical surface reflectance observations, auxiliary datasets are used to improve land-cover mapping. In particular, **Envisat ASAR Wide Swath Mode (WSM)** radar observations (2005–2012) are incorporated as ancillary information [(C3S - LC Target Requirements and Gap Analysis Document, 2024)](https://dast.copernicus-climate.eu/documents/satellite-land-cover/WP3-TR-GAD-2023_LC_v1.1_final.pdf).

- While the dataset relies on multiple satellite sensors, it is not generated through simple sensor-to-sensor transitions. Instead, the global land-cover time series is built around a baseline land-cover map derived from MERIS observations (2003–2012). Earlier periods (1992–2003) are reconstructed through back-dating using AVHRR and SPOT-VGT data, while later years are produced through incremental updates using SPOT-VGT, PROBA-V, and Sentinel-3 observations. During several periods, multiple sensors are used simultaneously, with different roles (e.g., temporal consistency versus spatial refinement). As a result, potential discontinuities in the time series are more likely to arise from changes in processing strategy, data fusion, and constraints, rather than from simple sensor replacements.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Potential Drivers and Methodological Considerations in Urbanisation Trends in the Iberian Peninsula (1992–2022)
---
with different roles (e.g., temporal consistency versus spatial refinement). As a result, potential discontinuities in the time series are more likely to arise from changes in processing strategy, data fusion, and constraints, rather than from simple sensor replacements.

| Global LC database | Reference period | Satellite data source |
|---|---|---|
| Baseline 10-year global LC map | 2003–2012 | • MERIS FR/RR global SR composites between 2003 and 2012 |
| Global annual LC maps | 1992–1999 | • Baseline 10-year global LC map
 • AVHRR global SR composites between 1992 and 1999 for back-dating the baseline |
|  | 1999–2013 | • Baseline 10-year global LC map
 • SPOT-VGT global SR composites between 1999 and 2013 for up- and back-dating the baseline
 • MERIS FR global SR composites between 2003 and 2012 to delineate the identified changes at 300 m spatial resolution
 • PROBA-V global SR composites at 300 m for year 2013 to delineate the identified changes at 300 m spatial resolution |
|  | 2014–2019 | • PROBA-V global SR composites at 1 km for years 2014 to 2019 for updating the baseline
 • PROBA-V time series at 300 m for 2014 to 2019 to delineate the identified changes at the LC map spatial resolution |
|  | 2020–2022 | • S3 global SR composites at 1 km for years 2020 to 2022 for updating the baseline
 • S3 time series at 300 m for 2020 to 2022 to delineate the identified changes at the LC map spatial resolution |

Source: See Table 1-3 in C3S, LC - Product User Guide and Specification (2024)

(code-section-3)=


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 3. Breakpoint Detection
---
A first step in assessing temporal behaviour is to analyse the **rate of change** of the urban-area time series. This provides an intuitive view of how the pace of urban expansion evolves over time and helps highlight periods where the trajectory deviates from its typical pattern.

However, while such exploratory diagnostics identify points of interest, they do not by themselves confirm whether the observed changes are structurally meaningful. Year-to-year variations may reflect noise, smoothing effects, or gradual adjustments in the underlying product. To formally assess structural changes, a **statistical breakpoint detection method** is applied.

The analysis therefore combines **rate-of-change diagnostics** with **statistical segmentation**, with particular attention to the monotonic nature of the data and the need to avoid over-interpreting gradual trends as discrete breakpoints.

---

**Rate of Change Calculation**

- The **first derivative** of the urban-area time series is computed to quantify the **year-to-year change in urban extent**.
- This highlights **periods where the pace of urban expansion deviates from its typical behaviour**, providing an initial indication of potential anomalies or shifts in the series.

---

**Statistical Thresholding of Derivative Spikes**

- To identify unusually strong deviations, a dynamic threshold is defined as:

**Threshold = mean(|rate of change|) + 1.5 × standard deviation**

- Years where the **absolute rate of change exceeds this threshold** are flagged as **derivative spikes**.
- These spikes represent periods where the magnitude of change is unusually large relative to the typical variability of the series.
- This step serves as a **diagnostic indicator of anomalous growth behaviour**, but does not in itself define structural breakpoints.

---

**Breakpoint Detection Using the PELT Algorithm**

- The **Pruned Exact Linear Time (PELT)** algorithm is used to detect **changes in the statistical behaviour of the growth rate**, rather than in the cumulative urban-area series itself.
- This is important because the urban-area time series is **monotonic and non-decreasing**, meaning that applying segmentation directly to the cumulative values can lead to artificial breakpoints driven by gradual growth.

- The algorithm is therefore applied to the **first derivative (growth rate)**, allowing detection of:
  - transitions between periods with different **rates of urban expansion**, rather than absolute levels.

- The implementation uses:
  - the **L2 cost function** (`model="l2"`) to minimise variance within segments,
  - a **fixed penalty parameter** (`pen = 2.5`) applied to a standardised growth-rate series,
  - and a **minimum segment length (`min_size = 3`)** to avoid short, unstable segments.

- Prior to segmentation, the growth-rate series may be **standardised** to ensure comparability across regions with different magnitudes of urban change.

---

**Breakpoint Selection**

- Breakpoints are identified directly from the segmentation of the growth-rate series.
- Unlike earlier approaches, **no explicit filtering based on derivative spikes is applied**, as this was found to introduce additional assumptions and potentially remove meaningful structural changes.
- Instead, derivative spikes and breakpoints are analysed **in parallel**, allowing independent but complementary perspectives on the temporal behaviour of the series.

---

**Methodological Considerations**

- The urban-area time series is **strictly monotonic and cumulative by construction**, reflecting constraints in the land-cover product that prevent decreases in urban extent.
- As a result:
  - segmentation applied to the raw series tends to produce **regularly spaced breakpoints**, even in the absence of true structural changes.
  - applying breakpoint detection to the **growth rate** mitigates this issue by focusing on changes in dynamics rather than accumulated values.

- Even when applied to growth rates, breakpoint detection should be interpreted cautiously:
  - some detected changes may reflect **gradual adjustments in processing or classification**, rather than abrupt real-world events.
  - others may correspond to **genuine shifts in the pace of urban expansion**.

- For this reason, breakpoint detection is treated as a **diagnostic tool**, not as definitive evidence of discrete events.

---

**Interpreting Breakpoint Timing**

Breakpoint positions should be interpreted as **approximate indicators of when a change in growth dynamics begins**, rather than exact points of maximum change.

Because segmentation identifies **changes in statistical behaviour**, the detected breakpoint may:

- precede the most visible increase in growth rate, or
- occur within a broader transition period.

As a result, breakpoint timing should be considered **indicative rather than exact**, and interpreted in conjunction with derivative-based diagnostics.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 3. Breakpoint Detection
---
precede the most visible increase in growth rate, or
- occur within a broader transition period.

As a result, breakpoint timing should be considered **indicative rather than exact**, and interpreted in conjunction with derivative-based diagnostics.

Calculate breakpoints and derivative spikes for each region
GHSL change years shown as hard-line transitions
Midpoint used for before/after GHSL switch classification
Number of subplot columns in the regional figure layout
Threshold multiplier used to flag unusually large derivative spikes:
spike if |growth rate| > mean(|growth rate|) + spike_std_factor * std(growth rate)
Minimum number of derivative observations allowed in each PELT segment.
Penalty term for PELT breakpoint detection.
Standardize the growth-rate series before applying PELT so that breakpoint
detection is comparable across regions with different magnitudes of urban change.
remove invalid and terminal interval
==================================================
==================================================
==================================================
==================================================

Plot Breakpoints Series
Store spikes
Store breakpoints
Background phases
GHSL change years as hard lines
Remove empty axes
Processing phases legend
Line/signal legend
Compact spacing between legends and plots

To assess whether detected discontinuities in the urban-area time series may be influenced by methodological factors, a targeted alignment analysis is performed. The aim is to evaluate whether **growth-rate breakpoints** and **derivative spikes** occur shortly after known **processing-chain changes**.

For each detected event, the time difference relative to each processing-change year is computed. An event is considered potentially related to a processing change only if it occurs **within the two years following that transition**. This forward-looking window reflects the temporal characteristics of the dataset, where land-cover changes are only confirmed if they persist over two consecutive years.

In addition to temporal alignment, the analysis also considers the **spatial consistency of detected signals**, quantified as the number of regions affected within a given interval. Methodological effects are expected to influence the dataset in a systematic way, and therefore to appear consistently across multiple regions. In contrast, signals affecting only a small number of regions are more likely to reflect **local dynamics** or region-specific variations rather than processing-related artefacts.

The results are summarised in **four complementary tables**, distinguishing between:

- **Event type**: breakpoints vs derivative spikes
- **Analytical scope**: aligned vs full set of identified breakpoints and derivative spikes

The first two tables report only events that occur **within two years after a processing transition**, while the two additional tables report **all detected events**.

Plot the aligned breakpoints and derivative spikes with known methodological changes present in more than half of the regions
Build tables

```text
### Breakpoint alignment table ###
```

```text
### Derivative spike alignment table ###
No derivative spike signals affect at least 10 regions within the known methodological changes transition windows
```

Plot all breakpoints and derivative spikes
Rename Interval column
Add relation to processing transition
Drop helper column
Sort by number of regions, then by interval
Build tables
Display nicely

```text
### Breakpoint Table ###
```

```text
intervals  number of regions  \
0  2016-2017                 13
1  2001-2002                 12
2  2006-2007                  6
3  1996-1997                  3
4  2011-2012                  2

regions name  \
0  Cantabria, Castilla y León, Castilla-La Mancha, Cataluña, Centro (PT), Comunidad Foral de Navarra, Comunitat Valenciana, Galicia, Norte, País Vasco, Principado de Asturias, Región de Murcia, Área Metropolitana de Lisboa
1                                                            Alentejo, Algarve, Aragón, Cantabria, Castilla y León, Castilla-La Mancha, Centro (PT), Comunidad Foral de Navarra, Extremadura, Galicia, Illes Balears, La Rioja
2                                                                                Alentejo, Algarve, Andalucía, Cataluña, Illes Balears, País Vasco
3                                                                                Cataluña, Comunitat Valenciana, País Vasco
4                                                                                Castilla y León, Región de Murcia

nearest processing changes
0        None within 2 years
1         GHSL_switch (2000)
2        None within 2 years
3        None within 2 years
4        None within 2 years
```

```text
### Derivative Spike Table ###
```


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 3. Breakpoint Detection
---
GHSL_switch (2000)
2        None within 2 years
3        None within 2 years
4        None within 2 years
```

```text
### Derivative Spike Table ###
```

```text
intervals  number of regions  \
0   2016-2017                  8
1   2013-2014                  7
2   2012-2013                  5
3   2001-2002                  4
4   2002-2003                  4
5   2017-2018                  4
6   2000-2001                  2
7   2003-2004                  2
8   2004-2005                  2
9   2006-2007                  2
10  2014-2015                  2
11  2005-2006                  1

regions name  \
0   Andalucía, Castilla y León, Castilla-La Mancha, Comunidad Foral de Navarra, Comunidad de Madrid, Comunitat Valenciana, La Rioja, Región de Murcia
1                                      Andalucía, Castilla y León, Castilla-La Mancha, Centro (PT), Galicia, Principado de Asturias, Región de Murcia
2                                                                      Andalucía, Aragón, Centro (PT), Región de Murcia, Área Metropolitana de Lisboa
3                                                                                Algarve, Cataluña, Norte, Área Metropolitana de Lisboa
4                                                                                Alentejo, Algarve, Cataluña, Illes Balears
5                                                                                Andalucía, Aragón, Extremadura, La Rioja
6                                                                                Cataluña, Área Metropolitana de Lisboa
7                                                                                Algarve, Illes Balears
8                                                                                Norte, País Vasco
9                                                                                Comunidad Foral de Navarra, Extremadura
10                                                                                Cantabria, Galicia
11                                                                                País Vasco

nearest processing changes
0                                                         None within 2 years
1   SPOT-VGT_finish (2013); MERIS_baseline_finish (2012); PROBAV_start (2013)
2                                                MERIS_baseline_finish (2012)
3                                                          GHSL_switch (2000)
4                                                         None within 2 years
5                                                         None within 2 years
6              GHSL_switch (2000); AVHRR_finish (1999); SPOT-VGT_start (1999)
7                             GHSL_switch (2003); MERIS_baseline_start (2003)
8                             GHSL_switch (2003); MERIS_baseline_start (2003)
9                                                         None within 2 years
10                                SPOT-VGT_finish (2013); PROBAV_start (2013)
11                                                        None within 2 years
```


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 3. Breakpoint Detection > Breakpoint Analysis
---
Breakpoint detection and derivative analysis capture two complementary aspects of temporal dynamics in land cover time series, a distinction widely recognised in change detection literature ([Chelali et al., 2019](https://doi.org/10.1109/JURSE.2019.8808967); [Chang et al., 2018](https://doi.org/10.1088/1755-1315/113/1/012087)):

- **Breakpoints** identify the onset of structural changes in the time series
- **Derivative spikes** highlight years of maximum year-to-year variation

Because of this, spikes may occur slightly after breakpoints, reflecting the difference between **initiation** and **peak expression** of change ([Chelali et al., 2019](https://doi.org/10.1109/JURSE.2019.8808967)).

The interpretation is guided by:

- **temporal alignment with processing-chain transitions**, and
- **spatial consistency across regions**

This is particularly important in satellite-derived land cover products, where methodological updates (e.g. sensor transitions, classification schemes, or reference layers) can introduce artificial discontinuities in time series.

A signal affecting many regions simultaneously is more likely to reflect a **systematic driver** (either methodological or large-scale real dynamics), whereas signals affecting few regions are more likely **local effects** ([Chang et al., 2018](https://doi.org/10.1088/1755-1315/113/1/012087)).

---

**Breakpoint ~2016–2017**

This period exhibits the **highest spatial consistency**, with breakpoints detected across a large majority of regions. The signal is also supported by concurrent derivative spikes, indicating both **structural change** and **increased short-term variability** in the time series.

Importantly, this breakpoint cluster shows **no temporal alignment with any identified processing-chain transition**, ruling out a methodological origin.

**Interpretation**
This pattern provides strong evidence of a **genuine structural shift in urban dynamics**. The combination of high spatial coherence and lack of methodological alignment suggests a system-wide driver, consistent with the **post-2008 recovery phase**, during which urban growth resumed after a period of contraction ([Martínez & García, 2015](https://doi.org/10.1016/j.habitatint.2015.01.010); [Palmero-Iniesta et al., 2021](https://api.semanticscholar.org/CorpusID:238829360)).

More broadly, renewed urban expansion in the Iberian Peninsula during the 2010s has been linked to economic recovery, infrastructure investment, and renewed real estate activity ([González & Leal, 2018](https://doi.org/10.1016/j.landusepol.2018.05.023); [López et al., 2017](https://doi.org/10.1016/j.jtrangeo.2017.02.005)). The breakpoint reflects a sustained change in growth trajectory, while the associated spikes indicate short-term adjustments during this transition.

---

**Breakpoint ~2001–2002**

A prominent breakpoint cluster is observed in this period, affecting a large number of regions and indicating a **highly coherent spatial signal**. This interval falls within the **transition window associated with the GHSL reference update (1999–2000)**.

**Interpretation**
This signal is best understood as a **composite effect**, arising from the interaction of:

- **Methodological influences**, particularly the constraint imposed by changes in reference datasets such as GSHL, which can introduce step-like adjustments in land cover products
- **Underlying urban expansion dynamics**, corresponding to the early phase of the Iberian housing boom, characterised by rapid land consumption and development pressure ([González & Leal, 2018](https://doi.org/10.1016/j.landusepol.2018.05.023))

The persistence of the breakpoint signal across regions suggests a structural component; however, its temporal proximity to a major processing transition prevents a clear separation between **artefact and real change**.

---

**Breakpoint ~2006–2007**

This breakpoint cluster affects a **moderate number of regions** and is not accompanied by strong or synchronised derivative spikes, indicating the absence of abrupt year-to-year changes.

No temporal association is found with processing-chain transitions.

**Interpretation**
This pattern reflects a **progressive structural adjustment** rather than a sudden shift. The breakpoint likely captures a **change in the underlying growth regime**, consistent with the late phase of the Iberian housing expansion cycle ([González & Leal, 2018](https://doi.org/10.1016/j.landusepol.2018.05.023)).


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 3. Breakpoint Detection > Breakpoint Analysis
---
underlying growth regime**, consistent with the late phase of the Iberian housing expansion cycle ([González & Leal, 2018](https://doi.org/10.1016/j.landusepol.2018.05.023)).

The absence of strong derivative spikes suggests that the transition occurred through **gradual changes in growth rate**, rather than discrete jumps, supporting its interpretation as a **real, process-driven signal**.

---

**Minor breakpoint clusters**

Additional breakpoint detections are observed, each affecting a **limited number of regions** and lacking both strong spatial coherence and consistent alignment with processing transitions.

**Interpretation**
These signals are interpreted as **localised or context-specific dynamics**, rather than manifestations of system-wide processes. Their limited spatial extent and lack of temporal consistency indicate that they do not reflect either:

- large-scale methodological artefacts
- coherent regional-scale urban transitions

Instead, they likely correspond to **region-specific developments**, such as:

- tourism-driven coastal urbanisation ([Rullan, 2014](https://doi.org/10.1016/j.landusepol.2014.02.009))
- peri-urban expansion processes ([Silva et al., 2017](https://doi.org/10.1016/j.cities.2017.04.002))
- planning policies and land-market dynamics ([Silva & Fernandes, 2019](https://doi.org/10.1016/j.landusepol.2019.04.012))
- demographic shifts, including rural depopulation and urban migration ([Sánchez et al., 2019](https://doi.org/10.1016/j.geoforum.2019.06.015); [Gutiérrez et al., 2016](https://doi.org/10.1016/j.jrurstud.2016.03.008))

(code-section-4)=


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 4.Trend Assessment
---
The next and final step is to determine whether the trend should be computed for the entire period (total trend) or divided into segments based on detected breakpoints.

The decision between using the total trend or segmented trends is based on Sen’s Slope (Trend Magnitude) and Mann-Kendall p-values (Trend Significance). Sen’s Slope is a non-parametric estimator that calculates the median rate of change over time, making it robust to outliers and suitable for detecting monotonic trends. First, Sen’s Slope is computed for the total trend, capturing the overall rate of urban change. Then, Sen’s Slope is calculated for each segmented trend to assess whether breakpoints introduce significant shifts in trend magnitude. The Mann-Kendall test is a non-parametric statistical test used to assess the presence of a monotonic upward or downward trend in a time series without requiring the data to follow any particular distribution. In parallel, the Mann-Kendall p-value is evaluated for both the total and segmented trends to measure trend significance - lower p-values indicate stronger evidence of a significant trend.

The final decision is based on the following approach:

- If segmented trends show substantially different Sen’s Slopes compared to the total trend and exhibit stronger statistical significance (lower p-values), segmentation is preferred.
- If segmented trends closely resemble the total trend or do not provide a clear statistical advantage, the total trend is used to maintain a simpler and more robust interpretation.

Prepare data for trend calculation
Keep only valid rows

Calculate trends
Compute total + segmented trends
Total Sen slope
Convert to % area per decade, as requested by reviewer
Mann-Kendall
Breakpoints and segmented trends

The following summary table reports:

- **Growth rate (%/decade)** → long-term rate of urban expansion
- **Significance** → statistical robustness of the full trend
    - `***` → highly significant (p < 0.001)
    - `**` → significant (p < 0.01)
    - `*` → moderately significant (p < 0.05)
    - no symbol → not statistically significant
- **Breakpoints** → number of structural changes detected
- **Segmented trend needed?** → whether segmented trends change the interpretation
- **Confidence** → degree of agreement between full and segmented trend significance

Build summary table
Urban area for each region in the latest year
Sort by growth rate
Round for display
Normalize text values
HTML table display

```text
21 regions were assessed. Segmented trends are not required in 21 of 21 regions. Confidence is High in 21 regions, Medium in 0, and Low in 0.
```


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 4.Trend Assessment > Trend Analysis
---
The results demonstrates that, although breakpoints were identified, they do not substantially alter the underlying trend estimates. In all cases, the total trend was preferred, as segmented alternatives did not exhibit significantly stronger slopes or lower p-values.

This suggests that the dataset provides a coherent temporal structure for monitoring urban land cover change. While localised variations exist, the broader trend of urban expansion remains largely continuous and statistically stable.

Furthermore, the reliability of the total trend is evident even in regions with marked breakpoint clusters, such as Galicia, Andalucía, and Región de Murcia. Despite temporary changes in slope magnitude, the segmented trends did not offer a statistically significant improvement over the total series, suggesting that short-term accelerations or decelerations are well-integrated into the full-period estimate.

Geographical characteristics modulate this effect. Larger metropolitan areas or regions with long-standing urban infrastructure exhibit smoother temporal evolution, while smaller or emerging urban zones may reflect sharper shifts in land cover classification. Nevertheless, these fluctuations did not systematically translate into divergent trend recommendations.

In summary, the analysis confirms that the dataset is suitable for deriving consistent urbanisation trends across multi-decadal periods. Segmentation remains a valuable diagnostic tool for identifying local deviations, but for most spatial planning and land management applications, the total trend offers a statistically supported and methodologically stable representation of urban growth dynamics.

(code-section-5)=


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 5. Discussion
---
The results of this analysis demonstrate the suitability of the dataset for assessing long-term urbanisation trends. Despite the presence of breakpoints the overall consistency of the dataset supports its use for reliable trend estimation in urban land cover.

For spatial planning and land management applications, this temporal continuity is critical. The ability to trace urban expansion over a multi-decadal period enables stakeholders to evaluate the spatial footprint of urban growth, assess policy impacts, and inform future development strategies. The dataset captures the general trajectory of urbanisation effectively, even in the presence of short-term fluctuations or classification refinements.

Nonetheless, the analysis highlights the importance of methodological awareness. Some of the breakpoints occurre around years with known changes in the processing chain. These may introduce artificial discontinuities in urban extent, particularly in regions undergoing rapid development or where classification thresholds are more sensitive to spectral or spatial resolution. Such effects must be considered when interpreting apparent changes in urban dynamics.

Geographic and demographic context also influence data interpretation. Larger metropolitan regions, such as Madrid or Lisbon, tend to exhibit smoother trends, as broader patterns of growth absorb minor classification inconsistencies. In contrast, smaller or fast-developing regions may show more pronounced breaks, either due to real shifts in urbanisation or amplified sensitivity to methodological updates.

From a practical standpoint, the dataset supports robust urban trend detection, but effective application requires a cautious and informed approach. Users should:

- Consider the potential influence of sensor transitions and processing updates on detected breakpoints
- Prioritise total trends for general assessments, while using segmentation to highlight context-specific deviations
- Interpret statistical outputs alongside relevant contextual knowledge, such as infrastructure investments, economic cycles, or policy milestones.

In conclusion, the dataset provides a strong foundation for monitoring urbanisation trends. Its temporal stability and spatial resolution are appropriate for regional-scale planning.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > ℹ If you want to know more > Key Resources
---
* The CDS catalogue entry for the data used was [Land cover classification gridded maps from 1992 to present derived from satellite observations](https://cds.climate.copernicus.eu/datasets/satellite-land-cover?tab=overview)

* Product User Guide and Specification of the dataset [version 2.1](https://dast.copernicus-climate.eu/documents/satellite-land-cover/D5.3.1_PUGS_ICDR_LC_v2.1.x_PRODUCTS_v1.1.pdf) and [version 2.0](https://dast.copernicus-climate.eu/documents/satellite-land-cover/D3.3.11-v1.0_PUGS_CDR_LC-CCI_v2.0.7cds_Products_v1.0.1_APPROVED_Ver1.pdf)

* [Eurostat NUTS](https://ec.europa.eu/eurostat/web/nuts) (Nomenclature of territorial units for statistics)

* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), c3s_eqc_automatic_quality_control, prepared by [B-Open](https://www.bopen.eu/)


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > ℹ If you want to know more > References
---
Vargo, J., Habeeb, D. M., & Stone, B. (2013). The importance of land cover change across urban-rural typologies for climate modeling. Journal of Environmental Management, 114, 243-252. https://doi.org/10.1016/j.jenvman.2012.10.007

Chang, Y., Hou, K., Li, X., Zhang, Y., & Chen, P. (2018). Review of land use and land cover change research progress. IOP Conference Series: Earth and Environmental Science, 113, 012087. https://doi.org/10.1088/1755-1315/113/1/012087

Chelali, A., Benediktsson, J. A., Chanussot, J., & Akbari, V. (2019). A review of artificial intelligence techniques for land cover change detection using remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(10), 7649–7668. https://doi.org/10.1109/JURSE.2019.8808967

Delgado, L., & Romero, J. (2021). Evaluating the impact of Madrid Central on urban mobility: A step toward sustainable cities? Cities, 113, 103120. https://doi.org/10.1016/j.cities.2021.103120

González, M. J., & Leal, J. (2018). Urban growth and real estate development in the Iberian Peninsula: The boom of 1996–2007. Land Use Policy, 76, 648–657. https://doi.org/10.1016/j.landusepol.2018.05.023

Gutiérrez, F., Rodríguez, V., & Prieto, C. (2016). Aging population and urbanization: Implications for Spain’s demographic landscape. Journal of Rural Studies, 45, 85–98. https://doi.org/10.1016/j.jrurstud.2016.03.008

López, F. A., Martín, J. C., & Albalate, D. (2017). High-speed rail and urban expansion in the Iberian Peninsula: The cases of AVE and Alfa Pendular. Journal of Transport Geography, 60, 57–68. https://doi.org/10.1016/j.jtrangeo.2017.02.005

Martínez, P., & García, R. (2015). The impact of the 2008 financial crisis on urban expansion in southern Spain. Habitat International, 47, 42–50. https://doi.org/10.1016/j.habitatint.2015.01.010

Palmero-Iniesta, H., Fernández, J. A., & Olcina, J. (2021). Land-use changes and natural land recovery after the 2008 crisis: Evidence from Spain. Environmental Science & Policy, 116, 72–83. https://api.semanticscholar.org/CorpusID:238829360

Rullan, O. (2014). The impact of tourism-driven urbanisation in Spain and Portugal: Coastal transformations and land use. Land Use Policy, 38, 139–150. https://doi.org/10.1016/j.landusepol.2014.02.009

Sánchez, M., Pérez, L., & Torres, D. (2019). Rural depopulation and urban migration trends in Spain and Portugal. Geoforum, 104, 78–91. https://doi.org/10.1016/j.geoforum.2019.06.015

Silva, E., & Fernandes, J. (2019). The influence of urban planning policies on land prices and informal settlements in Iberian cities. Land Use Policy, 89, 104200. https://doi.org/10.1016/j.landusepol.2019.04.012

Silva, V., Santos, R., & Moreira, J. (2017). The expansion of peri-urban areas in Lisbon and Porto: Drivers and consequences. Cities, 66, 101–112. https://doi.org/10.1016/j.cities.2017.04.002


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Quality assessment question
---
* **Can the C3S Lake Water Levels dataset be used to conduct extreme value analyses to detect and quantify flood events?**

In 2020, Lake Victoria experienced an extreme flood event with significant regional impacts. Accurate definition and characterization of such events are essential for understanding their causes and informing adaptation strategies. This notebook explores whether the [Lake water levels from 1992 to present derived from satellite observations](https://cds.climate.copernicus.eu/datasets/satellite-lake-water-level?tab=overview) (C3S-LWL v5.0) dataset, a freely available and harmonized satellite-derived product, is suitable for defining high-impact flood events through statistical analysis.

Inspired by methodologies such as those applied by Pietroiusti et al. (2024) [[1]](https://esd.copernicus.org/articles/15/225/2024/) using the DAHITI dataset, this analysis applies extreme value analysis (EVA) to assess the 2020 flood event. The goal is to evaluate whether C3S-LWL v5.0 can serve as a reliable resource for event definition in impact attribution workflows, particularly in data-scarce regions like the Lake Victoria basin.


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Quality assessment statement
---
These are the key outcomes of this assessment

* The assessment indicates the C3S Lake Water Levels dataset is suitable to detect and characterise flood events, identifying the 2020 Lake Victoria flood event and ranking it as the third-highest 180-day lake level rise in the historical record (1948–2023), with only 1962 and 1998 recording greater increase.

* Differences between DAHITI and C3S-LWL v5.0 are more pronounced in the early record (1992–2002), due to limited satellite coverage and distinct processing approaches, but these differences shrink substantially after 2002. In recent years, the two datasets show very close agreement.
```


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Methodology
---
First, the C3S LWL and the DAHITI datasets of Lake Victoria’s water levels were compared. Both datasets are based on satellite altimetry but differ in their processing algorithms. We evaluated the two datasets in terms of temporal completeness and analysed the causes of their discrepancies. This comparison was motivated by the fact that DAHITI was used in Pietroiusti et al. (2024) [[1]](https://esd.copernicus.org/articles/15/225/2024/) for extreme event attribution (EEA) of the 2020 Lake Victoria flood. Our goal was to assess whether the C3S LWL dataset could serve as a suitable alternative to DAHITI for this type of application. To extend the record, a reconstructed lake level dataset was also built using HYDROMET data, which covers a period prior to satellite-based products. Finally, since lake level data is primarily required during the event definition step of the EEA process, we focused on this first step and tested its implementation using the C3S LWL dataset. The same procedure as on Pietroiusti et al. (2024) [[1]](https://esd.copernicus.org/articles/15/225/2024/) was followed for the event definition, which involved the calculation and ranking of the annual block maximum of the chosen variable, which was the rate of change in water levels over a time window.

The analysis and results are organised in the following steps, which are detailed in the sections below:

**[](section-1)**
 * Download C3S–LWL v5.0 satellite-lake-water-level data for Lake Victoria.

**[](section-2)**
 * Load and preprocess DAHITI data.
 * Plot monthly count of data recorded for both the DAHITI and C3S data.
 * Plot C3S and DAHITI’s Water Level and show the maximum difference.

**[](section-3)**
 * Load and preprocess HYDROMET data.
 * Calculate the average difference between HYDROMET and C3S-LWL overlapping data and correct the HYDROMET dataset based on this.
 * Interpolate to fill missing gaps in HYDROMET.
 * Plot reconstructed time series.

**[](section-4)**
 * Apply the annual block maxima to the reconstructed time series.
 * Compare with the results from Pietroiusti et al. (2024) [[1]](https://esd.copernicus.org/articles/15/225/2024/).


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 1. Data request and download > Download data
---
```text
100%|██████████| 1/1 [00:00<00:00, 20.35it/s]
```

(section-2)=


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 2. Comparison of C3S LWL with the DAHITI dataset > Load and preprocess DAHITI data
---
This assessment applies the methods developed by Pietroiusti et al. (2024) [[1]](https://esd.copernicus.org/articles/15/225/2024/), whose data are openly available via [Zenodo](https://zenodo.org/records/10793917). In that study, the DAHITI dataset [[2]](https://hess.copernicus.org/articles/19/4345/2015/hess-19-4345-2015.html) was employed. Accordingly, a comparison of Lake Victoria’s water level time series from C3S-LWL v5.0 and DAHITI will be carried out. The DAHITI time series for Lake Victoria can be accessed and downloaded from the [DAHITI portal](https://dahiti.dgfi.tum.de/en/2/water-level-altimetry/).

For reproducibility, the specific DAHITI dataset used in this assessment is located in the [Zenodo](https://zenodo.org/records/10793917) repository at: lakevic-eea-data-zenodo\lakevic-eea-data-zenodo\lakevic-eea-analysis\lakelevels\DAHITI_lakelevels_070322.csv.

Convert date column to datetime and set as index
Create scaled integer water level
Keep original as meters
Error as integer
Reorder columns

```text
water_level  error  water_level_m
datetime
1992-09-27      1135000      0       1135.000
1992-10-07      1135023      0       1135.023
1992-10-17      1135032      0       1135.032
1992-10-27      1134958      0       1134.958
1992-11-06      1135035      0       1135.035
...                 ...    ...            ...
2022-01-22      1136143      0       1136.143
2022-02-01      1136145      0       1136.145
2022-02-11      1136199      0       1136.199
2022-02-21      1136227      0       1136.227
2022-03-03      1136229      0       1136.229

[1058 rows x 3 columns]
```


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 2. Comparison of C3S LWL with the DAHITI dataset > Monthly temporal completeness
---
First dataset: xarray object
Second dataset: Pandas DataFrame
Create subplots with 1 row and 2 columns
Plot first bar chart
Plot second bar chart
Adjust layout

*Figure 1. Comparison of count of monthly values over time available in the C3S-LWL v5.0 dataset (on the left) and in DAHITI (on the right).*

The DAHITI dataset exhibits a relatively consistent number of monthly observations, typically ranging from 1 to 4 throughout its entire temporal span. In contrast, the C3S-LWL v5.0 dataset shows variability in the number of monthly values, depending on the availability of satellite missions over time.
Up until the end of 2010, only one value per month was recorded. From 2011 to 2015, this number increased to mostly three observations per month, likely reflecting additional unfiltered data from the Jason-2 mission. A significant increase in observation frequency is evident from 2016 onward, corresponding to the introduction of Jason-3 and Sentinel-3A satellites. The C3S-LWL v5.0 dataset contains only one gap, in 2006, indicating strong overall temporal completeness.

The differing number of monthly observations between the datasets can be explained by the fact that DAHITI applies interpolation using a Kalman filter, a statistical technique that predicts the water level at the next time step based on previous observations and associated uncertainties [[1]](https://esd.copernicus.org/articles/15/225/2024/). This allows DAHITI to maintain a nearly uniform temporal resolution, achieving approximately three observations per month even during periods with limited satellite overpasses. On the other hand, the C3S-LWL v5.0 dataset does not interpolate. The observations from the different satellite missions are processed in three sequential steps, each applying thresholds and filtering criteria to remove bad-quality data. As a result, only high-quality data are retained in the final dataset. Because older satellite sensors generally had lower accuracy, a larger fraction of early observations is filtered out, whereas later missions contribute more usable data. More in depth information on the processing steps to make the C3S-LWL v5.0 dataset can be found in the Algorithm Theoretical Basis Document [(ATBD)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=425328942).


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 2. Comparison of C3S LWL with the DAHITI dataset > Plot C3S and DAHITI's Water Level
---
Extract time series from xarray (da)
Final touches

*Figure 2. Lake Victoria's water level from 1992 to present from C3S-LWL v5.0 and DAHITI.*

Extract time series from xarray (da)
Align by nearest timestamp
Drop rows where no match was found
Compute difference
Ensure the datetime column is in datetime format
Define periods
Calculate average differences using the datetime column

```text
Average difference for 1992-2002 (TOPEX/Poseidon): 0.334 m
Average difference for 2002-2016: 0.043 m
Average difference for 2016-2023: 0.002 m
```

The difference in water level estimates for Lake Victoria between the DAHITI and C3S-LWL v5.0 datasets varies noticeably over time (see *Figure 2*). A larger discrepancy is observed during the first decade of the time series (1992-2002), when the only satellite data available originated from the TOPEX/Poseidon mission. During this period, the average difference between the two datasets is 33.2 cm in average, with C3S values consistently higher than those from DAHITI.

This early discrepancy appears to stem from differences in the algorithms and retracking methods used by the two datasets. DAHITI, in some cases, applies an improved 10% threshold retracker for better perfomance on inland water bodies, depending on the specific case. However, specific information on whether the 10% retracker was applied to Lake Victoria could not be confirmed, since available documentation only details its application for lakes in the Americas. Additionally, DAHITI employs a Kalman filter to interpolate and smooth its time series, further influencing the results [[2]](https://hess.copernicus.org/articles/19/4345/2015/hess-19-4345-2015.html). In contrast, according to the [ATBD](https://confluence.ecmwf.int/pages/viewpage.action?pageId=425328942), the C3S-LWL v5.0 dataset uses a ocean model retracking for large lakes such as Lake Victoria.

However, this difference diminishes noticeably after 2002 (less than 10 cm in average), as newer satellite missions introduced more frequent and accurate measurements. The influence of processing seems to be much smaller in later years, likely because the improvements in sensor technology and data availability reduce the impact of such methodological differences.

These findings are supported by the [PQAR](https://confluence.ecmwf.int/pages/viewpage.action?pageId=428248112), which includes Lake Victoria under the Southern Africa region. According to the PQAR, the Pearson correlation coefficient between DAHITI and C3S in this region is very close to 1, and the Unbiased Root Mean Square Error (URMSE) remains below 25%, indicating strong overall agreement in this region.

(section-3)=


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 3. Time series reconstruction
---
The temporal coverage of the C3S-LWL dataset varies by lake, with a target record length of more than 25 years and a minimum threshold of 10 years. To increase statistical confidence and improve robustness, other available datasets can be merged with C3S-LWL to form a single continuous record.

The code on this section is based on the work of Pietroiusti et al. (2024) [[1]](https://esd.copernicus.org/articles/15/225/2024/).


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 3. Time series reconstruction > Load and preprocess HYDROMET data
---
From January 1, 1948 to August 1, 1996, daily in situ water level measurements at the Jinja station were obtained from the WMO Hydrometeorological Survey (hereafter referred to as HYDROMET) [[3]](https://library.wmo.int/records/item/54830-hydrometeorological-survey-of-the-catchments-of-lakes-victoria-kyoga-and-albert?offset=32). These measurements, originally recorded as water depth at the lake’s outflow, were later converted to meters above sea level by adding a geoid correction of 1122.887 m, following the approach of Vanderkelen et al. (2018) [[4]](https://hess.copernicus.org/articles/22/5509/2018/).

The HYDROMET dataset is located in the [Zenodo](https://zenodo.org/records/10793917) repository at: lakevic-eea-data-zenodo\lakevic-eea-data-zenodo\lakevic-eea-analysis\lakelevels\Jinja_lakelevels_Van.txt.

Pre-process data
Replace HYDROMET zeros with NaN, so that Matplotlib won't connect the points

```text
water_level    meas
date
1948-01-01     1134.097  11.210
1948-01-02     1134.102  11.215
1948-01-03     1134.062  11.175
1948-01-04     1134.052  11.165
1948-01-05     1134.077  11.190
...                 ...     ...
1996-07-28     1134.777  11.890
1996-07-29     1134.757  11.870
1996-07-30     1134.752  11.865
1996-07-31     1134.717  11.830
1996-08-01     1134.747  11.860

[17746 rows x 2 columns]
```


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 3. Time series reconstruction > Merging the datasets together
---
*Figure 3. HYDROMET (1948-1996) and C3S (1992-2023) datasets timeseries before correction.*

To obtain the reconstructed dataseries of Lake Victoria's water levels it is necessary to perform a correction to HYDROMET data based on the overlaping period (1992-1996).

Merge the two time-series, key on date, keep all observations (outer)
Get only the overlapping observations

```text
HYDROMET      C3S
date
1992-09-28  1134.567  1135.32
1992-10-18  1134.572  1135.30
1992-11-15  1134.597  1135.36
1992-12-16  1134.672  1135.43
1993-01-14  1134.767  1135.54
```

```text
Average Difference (C3S - HYDROMET) = 77.8  ±  3.0  cm
```

Snap the two timeseries together, add avg diff to HYDROMET

Left Plot: Full time series
Right Plot: Zoomed section

*Figure 4. HYDROMET (1948-1996) and C3S (1992-2023) datasets timeseries after correction.*

The average difference between the two datasets is 77.8 ± 3.0 cm [[1]](https://esd.copernicus.org/articles/15/225/2024/). The HYDROMET dataset was corrected using this value.

Correct for difference
Overwriting time-series with C3S where available

To have only one value per day
Make only one list with HYDROMET+C3S data

Round to 2 sig figs

Calculate basic statistics for the 'water_level' column
Calculate 10% trimmed mean (removes lowest and highest 10% of values)
Calculate percentiles

Interpolate to daily res and round to 2 sig figs

Plot the complete water levels timeseries with the statistics calculated

*Figure 5. Reconstructed Lake Victoria's water levels timeseries (1948-2023).*

A single dataset combining both sources was created. For the overlap period between 1992 and 1997, C3S LWL-v5.0 data were used when available. The gaps were filled by interpolation to have a daily resolution.

(section-4)=


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 4. Probabilistic extreme event analysis
---
Pietroiusti et al. (2024) [[1]](https://esd.copernicus.org/articles/15/225/2024/) assessed the influence of anthropogenic climate change on the 2020 Lake Victoria floods using the extreme event attribution framework outlined by Philip et al. (2020) [[5]](https://ascmo.copernicus.org/articles/6/177/2020/) and van Oldenborgh et al. (2021) [[6]](https://doi.org/10.1007/s10584-021-03071-7). The methodology follows a structured sequence of steps: (i) defining the event, (ii) estimating probabilities and trends based on observational data, (iii) validating climate models, (iv) conducting attribution using multiple models and methods, and (v) synthesizing the findings into clear attribution statements.

In this assessment, only the first step, event definition, is performed, using reconstructed Lake Victoria water level time series from the HYDROMET dataset and the C3S LWL-v5.0 product. The aim is to assess whether the C3S LWL-v5.0 dataset is suitable for use in attribution analyses of this kind. While the remaining steps involve model simulations and additional data sources that are beyond the scope of this assessment, the assumption is that if the observational component is consistent, the full attribution framework could, in principle, be applied using this dataset as well.


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 4. Probabilistic extreme event analysis > Event definition
---
The variable chosen for analysis is the rate of change in water levels (ΔL) over a time window (Δt), with Δt set to 180 days since most of the level rise in the 2020 Lake Victoria flooding occurred during the six-month period between November 2019 and May 2020 [[1]](https://esd.copernicus.org/articles/15/225/2024/). The code computes ΔL for every possible 180-day interval in the dataset, groups these values by year, and identifies the single largest 180-day change for each year, this is the the annual block maximum. The annual block maxima are then ranked to compare the magnitude of events across years. Finally, the ranking and maximum ΔL/Δt for 2020 are reported.

Set the time window (dt)
Extract annual block maxima
Keep only years with full data
Calculate rank of each year
Print 2020 event details

```text
🔹 2020 ΔL/Δt (180 days): 1.210 m
🔹 2020 Rank: 3 out of 74 years
```

Ensure rolling mean exists
Get top 3 values
Step plot for annual maxima (red)
Rolling mean (green)
Top 3 events (black markers + labels)
Labels, title, grid

*Figure 6. Annual block maxima time series $(\Delta L / \Delta t)_{\text{max}}$ with Δt=180 d for the period 1897–2021 and 10-year rolling mean of the time series (using C3S-LWL v5.0 data).*

attachment:e4f5e3f5-af36-4613-9df5-03a4f99fde20.png
```
*Figure 7. Annual block maxima time series $(\Delta L / \Delta t)_{\text{max}}$ with Δt=180 d for the period 1897–2021 and 10-year rolling mean of the time series (using DAHITI data). Source: Pietroiusti et al. (2022) [[7]](https://cris.vub.be/ws/portalfiles/portal/95073120/Geography_MSc_thesis_Rosa_Pietroiusti_final.pdf)*

The results of this analysis are consistent with those reported by Pietroiusti et al. (2024) [[1]](https://esd.copernicus.org/articles/15/225/2024/). According to the C3S-LWL v5.0 dataset, the year 2020 recorded the third-largest 180-day lake level rise, surpassed only by 1962 and 1998. The magnitude of the 2020 rise was 1.21 meters, which matches the value obtained by Pietroiusti et al. using the DAHITI dataset.

However, some discrepancies emerge when comparing other years. For instance, in 2000, the C3S-LWL v5.0 data shows a negative value, indicating a consistent decrease in lake levels throughout the year (see *Figure 6*). Conversely, the corresponding figure in Pietroiusti et al. (2022) [[7]](https://cris.vub.be/ws/portalfiles/portal/95073120/Geography_MSc_thesis_Rosa_Pietroiusti_final.pdf) (see *Figure 7*) shows a slightly positive value.

These differences likely stem from variations in the underlying datasets, as discussed in [](section-2). In 2000, TOPEX/Poseidon was still the only mission providing data for the C3S-LWL, with only one value per month. Meanwhile, DAHITI applied Kalman-filtered interpolation, resulting in a denser time series. Because no reliable in situ data are available for this period, it is difficult to determine which dataset more accurately reflects reality.

Nevertheless, the agreement between both datasets on the 2020 block maximum strengthens confidence in the reliability of this result. Although C3S-LWL and DAHITI use different processing algorithms, the increased availability of satellite observations in recent years (particularly after 2016) has resulted in greater similarity between their outputs. With more frequent and higher-quality measurements from multiple missions, the differences between the datasets diminish, making the 1.21 meter increase observed in 2020 not only consistent across sources, but also giving more confidence in the result.


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > Analysis and results > 4. Probabilistic extreme event analysis > Event definition
---
. With more frequent and higher-quality measurements from multiple missions, the differences between the datasets diminish, making the 1.21 meter increase observed in 2020 not only consistent across sources, but also giving more confidence in the result.

This assessment supports the suitability of the C3S-LWL v5.0 dataset for extreme event attribution applications, as the event-definition results closely match those from Pietroiusti et al. (2024) [[1]](https://esd.copernicus.org/articles/15/225/2024/). Since this step feeds directly into the rest of the workflow, similar outcomes would be expected in a full attribution analysis.


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > ℹ If you want to know more
---
* NASA (2021). [Lake Victoria’s Rising Waters](https://earthobservatory.nasa.gov/images/148414/lake-victorias-rising-waters)
* ACSA Uganda & Uganda Coalition for Sustainble Development (UCSD) (2020). [The Implication of Floods to Food Security During and the Aftermath of COVID-19 Pandemic in Uganda](https://acsa-ug.org/wordpress/wp-content/uploads/2020/06/THE-IMPLICATION-OF-FLOODS-TO-FOOD-SECURITY-DURING-AND-THE-AFTERMATH-OF-COVID19-PANDEMIC-IN-UGANDA.pdf)


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > ℹ If you want to know more > Key resources
---
Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)

Dataset documentation:

* [LWL v5.0 and LWL-S v1.0: Product User Guide and Specification (PUGS)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=425328964)
* [LWL v5.0 and LWL-S v1.0: Algorithm Theoretical Basis Document (ATBD)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=425328942)
* [LWL v5.0 and LWL-S v1.0: Product Quality Assessment Report (PQAR)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=428248112)


EQC Quality Assessment: "Lake Victoria’s 2020 Flood Event Analysis"
Dataset: satellite-lake-water-level [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Lake Victoria’s 2020 Flood Event Analysis > ℹ If you want to know more > References
---
[[1]](https://esd.copernicus.org/articles/15/225/2024/) Pietroiusti, R., Vanderkelen, I., Otto, F. E. L., Barnes, C., Temple, L., Akurut, M., Bally, P., van Lipzig, N. P. M., and Thiery, W. (2024). Possible role of anthropogenic climate change in the record-breaking 2020 Lake Victoria levels and floods, Earth Syst. Dynam., 15, 225–264.

[[2]](https://hess.copernicus.org/articles/19/4345/2015/hess-19-4345-2015.html) Schwatke, C., Dettmering, D., Bosch, W., and Seitz, F. (2015). DAHITI - an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry: , Hydrol. Earth Syst. Sci., 19, 4345-4364.

[[3]](https://library.wmo.int/records/item/54830-hydrometeorological-survey-of-the-catchments-of-lakes-victoria-kyoga-and-albert?offset=32) WMO-UNPD (1974). Hydrometeorological Survey of the Catchments of Lakes Victoria, Kyoga
and Albert: Vol 1 Meteorology and Hydrology of the Basin.

[[4]](https://hess.copernicus.org/articles/22/5509/2018/) Vanderkelen, I., Van Lipzig, N. P., and Thiery, W. (2018). Modelling the water balance of Lake Victoria (East Africa)-Part 1: Observational analysis. Hydrology and Earth System Sciences, 22(10):5509–5525.

[[5]](https://ascmo.copernicus.org/articles/6/177/2020/) Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M. (2020). A protocol for probabilistic extreme event attribution analyses, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203.

[[6]](https://doi.org/10.1007/s10584-021-03071-7) G. J. van Oldenborgh, K. van der Wiel, S. Kew, S. Philip, F. Otto, R. Vautard, A. King, F. Lott, J. Arrighi, R. Singh, and M. van Aalst. Pathways and pitfalls in extreme event attribution.  Climatic Change, vol. 166, no. 1, p. 13, 2021.

[[7]](https://cris.vub.be/ws/portalfiles/portal/95073120/Geography_MSc_thesis_Rosa_Pietroiusti_final.pdf) Pietroiusti, R., Vanderkelen, I., van Lipzig, N. P. M., and Thiery, W. (2022). Was the 2020 Lake Victoria flooding ‘caused’ by anthropogenic climate change? An event attribution study. M.Sc. thesis, Dept. of Hydrology and Climate, Vrije Universiteit Brussel and KU Leuven.

[[8]](https://link.springer.com/book/10.1007/978-1-4471-3675-0) Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics. Springer-Verlag London. ISBN: 978-1-85233-459-8.


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Quality assessment question(s)
---
* **Is the temporal completeness of the satellite-derived lake water level dataset for Lake Titicaca sufficient to support water resources management in the Titicaca–Desaguadero–Poopó–Salar de Coipasa (TDPS) System?**

The [lake water levels from 1992 to present derived from satellite observations](https://cds.climate.copernicus.eu/datasets/satellite-lake-water-level?tab=overview) (C3S-LWL v5.0) dataset provides long-term water level observations for numerous lakes worldwide. The Climate Data Record (CDR) of the C3S-LWL v5.0 dataset is updated annually. The dataset provides at least 10 years of temporal coverage. However, the specific temporal coverage and average time step vary by lake (time steps are irregular). The dataset provides one value of water level per time step.

This dataset can be useful for Integrated Water Resources Management (IWRM) applications such as understanding lake responses to seasonal dynamics, climate variability, and long-term climate change-factors which directly influence water availability for ecosystems and dependent human populations. To illustrate the potential applicability of the dataset, Lake Titicaca was selected as a case study. The lake forms part of the Titicaca–Desaguadero–Poopó–Salar de Coipasa (TDPS) hydrological system, which spans Peru and Bolivia. This system provides critical freshwater resources to millions of people, supporting agriculture, domestic use, and ecosystem services across the Altiplano. Lake Titicaca feeds the Desaguadero River, which flows into Lake Poopó. This hydrological system is complex and subject to multiple drivers (e.g., climate variability, land use change, population growth), which exert pressures on water availability and quality. These pressures affect the state of the system, such as lake levels, which in turn have impacts on ecosystems and human livelihoods [[1]](https://doi.org/10.3390/w8040144) [[2]](https://doi.org/10.1016/j.ejrh.2021.100927).

The objective of this assessment is to examine the temporal completeness of Lake Titicaca's water level data in the C3S-LWL v5.0 dataset and assess its ability to support Integrated Water Resources Management (IWRM) in the context of the TDPS system.


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Quality assessment statement
---
These are the key outcomes of this assessment

* The temporal completeness of the C3S–LWL v5.0 dataset improves significantly after 2016, following the deployment of Sentinel-3A and Jason-3, and is further enhanced by the launches of Sentinel-3B (2018) and Sentinel-6A (2020).

* The C3S-LWL dataset captures the seasonality of Lake Titicaca effectively, except in years with extended gaps in observations, such as 2012. If many months in a year have no data in the C3S-LWL dataset the results are visibly different to those of in-situ data.

* The C3S–LWL timeseries captures the major flooding events of 2003 and 2004 in Lake Titicaca, demonstrating its ability to represent cumulative seasonal peaks. However, its limited temporal resolution (specially before 2019) makes it unsuitable for detecting short-lived flash floods caused by isolated extreme precipitation events.

* The cumulative impact of major droughts, such as the 2023 event, on Lake Titicaca’s water level is evident in the C3S–LWL timeseries.

* The current temporal span and gaps in the dataset limit its ability to reliably detect and analyse ENSO-related effects on Titicaca lake's water levels.

```


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Methodology
---
C3S-LWL data for Lake Titicaca were downloaded and evaluated for temporal coverage, including median and maximum time steps and the occurrence of data gaps. Next, water level time series were plotted to examine seasonality and historically known extreme events, with visual comparisons made to in-situ observations from the Bolivian National Meteorological and Hydrological Service (SENAMHI) . The relationship between ENSO phases and lake levels was also explored following established approaches. Finally, percentile ranks were calculated for key performance indicators to assess data completeness, and the suitability of the dataset for similar analyses in other lakes, supporting Integrated Water Resources Management, was evaluated.

The analysis and results are organised in the following steps, which are detailed in the sections below:

**[](section-1)**
 * Download all available C3S–LWL v5.0 satellite-lake-water-level data for Lake Titicaca.

**[](section-2)**
 * Calculate performance indicators related to temporal resolution: median timestep and max. timestep for different periods of time.
 * Plot the number of water level records per month across the dataset’s full temporal span.
 * Analyse the causes of data gaps and changes in data availability over time using information from the dataset’s Documentation.

**[](section-3)**
 * Plot the water level time series, highlighting points relevant for analysing seasonality as well as historically reported extreme events.
 * Compare CS3-LWL and SENAMHI data visually.
 * Evaluate whether the dataset captures seasonality and extreme events.
 * Analyse the relationship between ENSO phases and Lake Titicaca’s water levels using an approach adapted from Gutierrez-Villarreal et al. (2024) [[3]](https://doi.org/10.1016/j.wace.2024.100710), and assess whether this relationship is evident in the C3S–LWL v5.0 dataset.

**[](section-4)**
 * Calculate the percentile ranks of Lake Titicaca across key performance indicators to assess its relative data completeness within the dataset.
 * Analysed the suitability of the dataset for applying similar assessments to other lakes in support of IWRM.


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 1. Request and download data > Download data
---
```text
100%|██████████| 1/1 [00:00<00:00,  3.98it/s]
```

Extract the DataArray for water_surface_height_above_reference_datum
Extract the DataArray for water_surface_height_uncertainty

(section-2)=


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 2. Temporal completeness > Performance indicators
---
The Product Quality Assessment Report ([PQAR](https://confluence.ecmwf.int/pages/viewpage.action?pageId=428248112)) for the dataset includes a table in Annex A presenting the performance indicators calculated for all lakes. These indicators are computed for the entire available period (1992–2023) and for the most recent 10-year period (2014–2023). The median and maximum timesteps are useful indicators for assessing temporal completeness. In this section, these indicators will be calculated for Lake Titicaca and compared with the information provided in the PQAR. The maximum timestep represents the largest gap between two observations, expressed as the maximum number of days without data.

Calculate time differences
Subset for 2014 to 2023
Print results

```text
All Data:
Median Timestep (days): 28
Max Timestep (days): 142
Timeseries Duration (years): 28.5972602739726

Subset 2014-2023:
Median Timestep (days): 14
Max Timestep (days): 93
```

The integer part of the results are the same as the ones presented in the PQAR.


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 2. Temporal completeness > Records per month
---
Resample to monthly frequency and count non-NaN values
Convert to Pandas for easier plotting
Plot as bars
Customize the x-axis labels to show only years

*Figure 1. Count of values available in the CDS water level dataset per month over time.*

A clear increase in the number of measurements per month can be observed from 2020 onwards. Therefore, the median and maximum timestep indicators were calculated again for two periods: 1995-2019 and 2020-2023.

Calculate time differences
Subset for 1995 to 2019
Print results
Subset for 2020 to 2023

```text
Subset 1995-2019:
Median Timestep (days): 31
Max Timestep (days): 142

Subset 2020-2023:
Median Timestep (days): 12
Max Timestep (days): 93
```

The results show that for Lake Titicaca, from 1995 to 2019, there is approximately one measurement per month (median time step of 31 days). From 2020 onwards, the number of measurements per month increases to a median time step of 12 days.


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 2. Temporal completeness > Satellite missions over time
---
Define the satellite missions and their operational periods
Convert the mission data into a DataFrame
Sort the DataFrame by start date
Calculate durations in days
Create the figure and axes
Plot the bars (same as before)
Create a range of years for ticks
Set x-axis ticks to January of each year
Enable grid for x-axis
Axis labels and title

*Figure 2. Span of the satellite missions used to estimate water levels in the C3S–LWL v5.0 dataset. The time periods reflect the operational availability of each mission as described in the Algorithm Theoretical Basis Document ([ATBD](https://confluence.ecmwf.int/pages/viewpage.action?pageId=425328942)). A correction was applied to the end year of the TOPEX/Poseidon mission to reflect its actual data availability.*

According to the [PQAR](https://confluence.ecmwf.int/pages/viewpage.action?pageId=428248112), the median time step in the lake water level dataset decreases after 2016, indicating an increase in the number of recorded values. This improvement is attributed to the introduction of Sentinel-3A and Jason-3 missions, which provided more frequent overpasses with higher-quality sensors.

For Lake Titicaca, this enhancement is evident post-2016, as months with two records became more common. Following the launch of Sentinel-3B and Sentinel-6A, a further increase in data frequency was observed, with most months after late 2019 containing two records, and some months even having three or four (see *Figure 1*). This indicates a significant improvement in dataset completeness due to these newer missions.

Prior to 2016, the dataset exhibits more gaps, with several months lacking data. This can be explained by the limitations of earlier missions: TOPEX/Poseidon, Jason-1, Envisat, Jason-2, and SARAL. These missions had less precise instruments, making their measurements more susceptible to filtering. Although satellite coverage and sensor quality improved after 2016, a few gaps—such as those seen in 2021—may still occur. The observations from the different satellite missions are processed in three sequential steps, each applying thresholds and filtering criteria to remove bad-quality data. As a result, only high-quality data are retained in the final dataset. Because older satellite sensors generally had lower accuracy, a larger fraction of early observations is filtered out, whereas later missions contribute more usable data. More in depth information on the processing steps to make the C3S-LWL v5.0 dataset can be found in the Algorithm Theoretical Basis Document [(ATBD)](https://confluence.ecmwf.int/pages/viewpage.action?pageId=425328942).

(section-3)=


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 3. IWRM applications
---
Due to the irregular time steps, annual update frequency, and current absence of Interim Climate Data Records (ICDRs), the C3S–LWL v5.0 dataset is not well-suited for applications requiring high temporal resolution or real-time monitoring, such as flood early warning systems or rapid drought response mechanisms. With the upcoming release of ICDRs on the C3S platform, these limitations are likely to be overcome.

The Climate Data Records (CDRs) of the dataset have a long temporal span, making it valuable for applications that benefit from historical context and trend analysis. This includes assessing interannual variability, long-term climate influences, and understanding hydrological responses to large-scale drivers such as ENSO.


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 3. IWRM applications > Detect seasonality and extreme events
---
A time series of Lake Titicaca water levels was plotted, highlighting April and December values to illustrate seasonality, as well as the most severe historically reported extreme events.

Extract April and December data
Convert DataArray time coordinate to a pandas datetime index
--- Identify extremes ---
Plot time series
Add points for April and December with subtler colors
--- Add highlighted extremes with safe annotations ---
Remove duplicate labels in the legend
Force x-axis to show every year
Rotate ticks if needed

*Figure 3. Water level in the Titicaca Lake in the period 1995-2023 according to the C3S–LWL v5.0.*

In-situ water level data for Lake Titicaca are not openly available for direct download. Access to this information must be formally requested from the relevant governmental institutions in Peru and Bolivia. However, the Bolivian National Meteorological and Hydrological Service (SENAMHI) publishes a weekly bulletin that reports information on Lake Titicaca. The figure below, extracted from the SENAMHI bulletin of August 1, 2025, presents the time series of monthly average water levels recorded at the Huatajata Station [[4]](https://senamhi.gob.bo/meteorologia/boletines/hidrologico/LagoTiticaca/2025/Agosto/LAGO%20TITICACA%20-%20Informe%20Semanal%20%28Actualizado%2001_08_2025%29.pdf).

![LAKE LEVEL SENAMHI.jpg](attachment:188a8952-b0db-4164-aa44-a20c221455d2.jpg)

*Figure 4. Monthly averaged water level Lake Titicaca (1974-2025). Source: SENAMHI Bolivia (2025) [[4]](https://senamhi.gob.bo/meteorologia/boletines/hidrologico/LagoTiticaca/2025/Agosto/LAGO%20TITICACA%20-%20Informe%20Semanal%20%28Actualizado%2001_08_2025%29.pdf)*

An approximate comparison between the C3S-LWL data and the in-situ measurements was performed using Figures 3 and 4. To achieve this, the C3S-LWL figure was placed on top of SENAMHI’s figure with transparency, and the vertical and horizontal scales were adjusted to be identical. The time (x) axis was aligned to match SENAMHI’s figure, and the graph was then shifted vertically to best fit the most recent years of SENAMHI’s time series. This approach was necessary because the two datasets use different datums (approx. 1.5 m difference); since the most recent C3S data are considered more reliable, priority was given to visually aligning the later years. The result is presented in *Figure 5* below.

![LAKE LEVEL COMPARISON.jpg](attachment:b97f0a2a-4d26-48e1-9152-fb6c459b49bd.jpg)

*Figure 5. Overlap of SENAMHI's time series [[4]](https://senamhi.gob.bo/meteorologia/boletines/hidrologico/LagoTiticaca/2025/Agosto/LAGO%20TITICACA%20-%20Informe%20Semanal%20%28Actualizado%2001_08_2025%29.pdf) and Figure 3 using the C3S-LWL data.*

It can be observed in *Figure 5* that C3S–LWL values tend to be lower than those of SENAMHI prior to 2008. However, this observation is based on visual inspection only and cannot be corroborated numerically, as no quantitative comparison was applied on.

Convert time to a PeriodIndex (monthly)
Select only the chosen year
Count how many entries exist per month
All months for that year
Find missing ones

```text
Missing months in 2011: ['January 2011', 'April 2011']
Missing months in 2012: ['March 2012', 'April 2012', 'May 2012', 'August 2012', 'September 2012', 'December 2012']
Missing months in 2013: ['April 2013', 'December 2013']
```


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 3. IWRM applications > Detect seasonality and extreme events
---
', 'May 2012', 'August 2012', 'September 2012', 'December 2012']
Missing months in 2013: ['April 2013', 'December 2013']
```

**Seasonality:**  The annual cycle of rainfall over the TDPS system exhibits a marked seasonality, with a rainy season from December to March that accounts for over 75% of the annual precipitation, a dry season from May to August, and a pre-wet season from September to December [[3]](https://doi.org/10.1016/j.wace.2024.100710). Lake Titicaca's level is primarily influenced by variations in precipitation and high evaporation rates [[5]](https://doi.org/10.5194/hess-29-655-2025). Consequently, December (just before the onset of the rainy season) typically corresponds to the lake’s lowest water level in the hydrological year, while the highest level usually occurs around April, at the end of the rainy season and the start of the dry season. *Figure 3* shows that this seasonality is captured on years with data for April and December. Years with more complete records capture this seasonal cycle more reliably. The largest discrepancies between SENAMHI and C3S-LWL occur in 1996, likely due to lower-quality measurement instruments, and in 2011 and 2012, probably because many months lack observations in the C3S-LWL dataset (see *Figure 5*). In 2013, although April and December are missing, the remaining months still reproduce the seasonal pattern well. Overall, C3S-LWL captures the seasonality of Lake Titicaca effectively, except in years with extended gaps in observations, such as 2012.

**Floods of 2003 and 2004:** The C3S-LWL timeseries reflects the intense precipitation events that occurred in 2003 and 2004 (see *Figure 5*). At the beginning of 2003, extreme rainfall caused Lake Titicaca’s water level to rise significantly. By the end of January 2003, the Peruvian government declared a two-month state of emergency to support affected communities. The most impacted areas in Puno (the region where the Peruvian side of Lake Titicaca is located) included Huancané, Putina, Azángaro, Asillo, and San Antón. By late January, seven deaths and severe damage to infrastructure had been reported [[6]](https://reliefweb.int/report/peru/act-peru-12003-heavy-rainfall) [[7]](https://reliefweb.int/report/peru/flash-floods-leave-peruvian-farmers-devastated).

According to the National Institute of Statistics and Informatics of Peru (INEI), the mean inflow from tributary rivers into Lake Titicaca in January and February 2004 was 158.2% and 33.6% higher than the long-term average, respectively. These surges caused flooding in surrounding areas of the lake (Table 25 in [[8]](https://www.inei.gob.pe/media/MenuRecursivo/boletines/5340.pdf)).

**Limitations in flood detection:** The C3S–LWL dataset contains only one observation per month for most months prior to late 2019. Therefore, although the flooding events of 2003 and 2004 are visible in the peaks of *Figure 3*, short-lived flash floods that cause rapid water-level changes over shorter time windows are not captured. Similarly, SENAMHI’s data in *Figure 4* consist of monthly averages, which may also smooth out short-duration peaks. This averaging provides a clearer view of long-term lake levels with reduced noise but limits the ability to detect flash floods. The peaks observed in April 2003 and 2004 reflect the cumulative effect of increased inflows during the entire rainy season, whereas shorter-lived flooding events triggered by isolated extreme precipitation are likely underrepresented. A more in-depth analysis of extremes would require data with higher temporal resolution, which is beyond the scope of this study.


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 3. IWRM applications > Detect seasonality and extreme events
---
, whereas shorter-lived flooding events triggered by isolated extreme precipitation are likely underrepresented. A more in-depth analysis of extremes would require data with higher temporal resolution, which is beyond the scope of this study.

**Drought of 2023:** Conversely, the lowest point on the dataset reflects the severe drought experienced in 2023 in the TDPS, which led to a substantial drop in lake levels, reaching a minimum by the end of the year [[9]](https://www.copernicus.eu/fr/node/40141). This event had system-wide consequences, in Puno (Peru) and La Paz (Bolivia), up to 80% of potato and sweet potato and 90% of Andean grains were lost due to water scarcity [[3]](https://doi.org/10.1016/j.wace.2024.100710). From an IWRM perspective, capturing and understanding these extremes is critical for guiding investment in adaptive infrastructure and preparing multisectoral response strategies. It also underscores the need for transboundary coordination, as hydrological impacts in one part of the system inevitably propagate throughout the TDPS basin.


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 3. IWRM applications > Relation to ENSO
---
In the TDPS region, El Niño events are typically associated with drier conditions, while La Niña events often bring increased rainfall, particularly during the wet season (December-March). These precipitation patterns directly influence Lake Titicaca's hydrology. El Niño years tend to reduce inflows and lower lake levels, whereas La Niña years usually promote higher inflows and rising water levels. However, the 2022-2023 drought illustrates that La Niña can also be linked to severe dry conditions, particularly during the pre-wet season (September-November). Unlike its wet-season effects, La Niña during this early period is associated with decreased precipitation, which can delay the onset of the rainy season and heighten the risk of drought. Gutierrez-Villarreal et al. (2024) explain that the 2022-23 pre-wet season was one of the driest on record, with November 2022 marking the driest month in 58 years. As a result, Lake Titicaca’s seasonal rise was significantly suppressed, its water level increased by only 0.09 meters between December and April, one of the weakest rises since 1940. Notably, the lake level even declined between December and January, a rare occurrence during a period when levels typically rise [[3]](https://doi.org/10.1016/j.wace.2024.100710).

![Gutierrez2024.jpg](attachment:6ef06ea7-4202-4a54-9d6d-05c3936c0542.jpg)

*Figure 6. Lake Titicaca's geographical location and hydrograms. a) The Lake Titicaca, Desaguadero River, and Lake Poopó basins (TDPS system) boundary is delimited by a black line, and the lake area is represented by a black polygon. Red lines in the Andean cordillera denote altitudes of 3 000 m.a.s.l. Interannual increment of lake levels of b) April, c) February, and d) January, with respect to December. The dataset spans between 1914 and 2023. The purple horizontal lines represent the long-term (1914–2023) average of lake level differences. “n” refers to the calendar year. Selected El Niño (La Niña) years are tagged with red (blue) bars if the [El Niño 3.4 index from the HadiSST](https://psl.noaa.gov/data/timeseries/month/Nino34/) [[10]](https://doi.org/10.1029/2002jd002670). dataset is higher (lower) than 0.75 (−0.75) during at least 4 months between October n-1 and April n. Reproduced from Gutierrez-Villarreal et al. (2024), licensed under CC BY 4 [[3]](https://doi.org/10.1016/j.wace.2024.100710).*

The water levels used by Gutierrez-Villarreal et al. (2024) to make *Figure 6* are monthly lake level data from 1914 to 2023, measured at the Muelle Enafer station (3,800 m a.s.l.) on the Peruvian side of Lake Titicaca, and provided by Peru’s National Meteorology and Hydrology Service (SENAMHI) [[3]](https://doi.org/10.1016/j.wace.2024.100710). The figure illustrates how water levels in Lake Titicaca can be a valuable indicator for analysing the relationship between ENSO phases and extreme events in the TDPS system, an insight that is highly relevant for IWRM due to its implications for drought preparedness and long-term planning. A similar analysis was conducted here using the C3S–LWL v5.0 dataset, to assess whether the patterns and anomalies described in the original study can also be detected with this alternative data source.

Load Niño 3.4 SST Index from the HadISST1.1 (https://psl.noaa.gov/data/timeseries/month/Nino34/)

Extract April and December data

Rename ENSO column
Function to compute differences and ENSO-colored bar data
Mean per year
Calculate difference
Compute ENSO-based bar colors
Identify missing years
Compute all three
Only for the April plot, add marker and text
Horizontal line from y-axis to the bar at y_val
Add text above the 2023 bar
Shared x-label


EQC Quality Assessment: "Assessing Lake Titicaca’s water levels in support of water management"
Dataset: satellite-lake-water-level [CDS]
Aspect: completeness_q02  |  Category: Satellite_ECVs
Section: Assessing Lake Titicaca’s water levels in support of water management > Analysis and results > 3. IWRM applications > Relation to ENSO
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bar colors
Identify missing years
Compute all three
Only for the April plot, add marker and text
Horizontal line from y-axis to the bar at y_val
Add text above the 2023 bar
Shared x-label

*Figure 7. Yearly differences between water levels of the Titicaca Lake form the C3S-LWL v5.0 dataset in April, February, and January of year *n* and December of year *n-1*, for the period 1995-2023. Bars are color-coded based on ENSO conditions between October *n-1* and April *n*: red for El Niño years (≥ 4 months with Niño 3.4 index > 0.75), blue for La Niña years (≥ 4 months with index < −0.75), and gray for neutral years. Missing bars indicate years where data was unavailable for the respective months.*

Print missing years for each

```text
Missing years (no data for Apr(n) or Dec(n-1)): [1995, 1999, 2000, 2011, 2012, 2013, 2014]
Missing years (no data for Feb(n) or Dec(n-1)): [1995, 1996, 2000, 2007, 2011, 2013, 2014, 2022]
Missing years (no data for Jan(n) or Dec(n-1)): [1995, 1998, 2000, 2011, 2013, 2014]
```

In the work of Gutierrez-Villarreal et al. (2024) (see *Figure 6*), the blue bars representing La Niña years are generally taller than the red bars representing El Niño years. This suggests that water level gains during the wet season tend to be greater during La Niña years. Such a trend is clearly visible thanks to the extensive temporal coverage of the SENAMHI dataset, which spans more than 100 years. In contrast, this pattern is not noticeable on *Figure 7*, which uses the C3S-LWL v5.0 dataset. This is likely due to the shorter time span of the C3S dataset and the presence of data gaps, resulting in missing values for several years.

Additionally, some inconsistencies between the two figures can be noted. For instance, in 2012, *Figure 7* shows slightly negative differences for both Jan(*n*)-Dec(*n-1*) and Feb(*n*)-Dec(*n-1*), while according to the SENAMHI dataset (see *Figure 6*) these differences are clearly positive. This discrepancy indicates that the C3S-LWL v5.0 dataset may not be sufficiently accurate for these types of analyses, especially prior to 2016. As mentioned earlier, satellite instrument quality and measurement accuracy have improved significantly since 2016, which may explain some of the observed inconsistencies.

Despite these limitations, the C3S-LWL v5.0 dataset successfully captures the small negative difference in Jan(*n*)-Dec(*n-1*) for the year 2023, as well as the anomalously weak water level increase during the 2022-2023 wet season (see *Figure 7*). However, according to the C3S-LWL v5.0 dataset, the rise was 0.17 m, while the SENAMHI dataset recorded a lower value of 0.09 m. Although not identical, this similarity suggests that the C3S-LWL v5.0 dataset has some utility in identifying recent anomalies, even if it is not yet robust enough for long-term ENSO-related hydrological analysis in Lake Titicaca.

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