BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-europe-2025//talk//JZRLA 8 BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-europe-2025-JZRLA8@talks.staging.osgeo.org DTSTART;TZID=CET:20250717T160000 DTEND;TZID=CET:20250717T163000 DESCRIPTION:Monitoring inland water areas is crucial for ecosystem health a nd water resources management\, particularly under impacts of global clima te change. However\, traditional water management plans prioritize large w ater bodies due to the labor-intensive nature of data collection and analy sis. Consequently\, shallow lakes are often overlooked despite their criti cal role in local ecosystems. This situation is critical\, as shallow lake s like Ilgın Lake have unique importance for migratory bird populations a nd irrigation-dependent agricultural livelihoods. Recent advancements in c loud-based platforms like Google Earth Engine (GEE) enable efficient\, sca lable remote sensing analyses and democratize access to a wide range of da ta sources. This study leverages the GEE Python API and free and open-sour ce Python libraries (e.g.\, geemap\, scipy\, pymannkendall\, pingouin) to present a scalable workflow for assessing hydrological and water quality d ynamics in shallow lakes. The methodology is demonstrated through a 40-yea r (1985-2024) case study of Ilgın Lake in Central Anatolia\, Türkiye. Il gın Lake is a vital resource for regional agriculture\; however\, its sha llow nature increases vulnerability to climate change and human activities \, necessitating continuous monitoring. This lake is also classified as a protected area and a nitrate vulnerable zone under the European Union Wate r Framework Directive (WFD). Despite this designation\, to the best of our knowledge\, there is no specific conservation action plan or regular in-s itu water quality monitoring program. \n\nWe conducted a long-term analysi s (1985-2024) of water area changes and water quality parameters to invest igate their relationship with key climate factors. Annual water areas were derived using the Modified Normalized Difference Water Index (MNDWI) appl ied to Landsat 5/7/8 satellite images\, with dynamic Otsu thresholding (Ot su\, 1979\; Xu\, 2006). The Otsu method is reliable\, especially for shall ow lakes\, as it automatically selects the best threshold by maximizing in ter-class variance between water and non-water pixels. A total of 347 Land sat scenes were processed using the GEE Python API\, incorporating cloud m asking and gap-filling for Landsat 7 scan-line corrector off data. The acc uracy of water area extraction was validated using high-resolution Google Earth image with random sampling points. Based on 250 sample points\, a bi nary confusion matrix was constructed\, and overall accuracy (96.0%) and k appa coefficient (0.887) were calculated. Trends were analyzed using non-p arametric statistical methods (Mann-Kendall and Theil-Sen)\, and correlati ons with key climate variables (total precipitation\, mean temperature) we re assessed using the ERA5 (ECMWF Reanalysis Fifth Generation) dataset. Wa ter quality within water-masked areas was assessed via the Normalized Diff erence Chlorophyll Index (NDCI) (Mishra and Mishra\, 2012) for chlorophyll and the Normalized Difference Turbidity Index (NDTI) (Lacaux et. al.\, 20 07) for turbidity. Relationships between climate variables\, water area\, and water quality were evaluated using Pearson correlation and multiple li near regression. Partial correlation analysis was used to isolate the effe cts of temperature and precipitation. Multiple linear regression was used to quantify the combined influence of temperature and precipitation on wat er area variations.\n\nThe results showed that Ilgın Lake experienced a s ignificant decrease in water area (p < 0.05) at a rate of -9.54 hectares/y ear. The lake lost 31% of its area between 1985 and 2024. Annual mean temp erature showed a significantly increasing trend (p < 0.01) at a rate of 0. 05 °C per year. For water quality\, chlorophyll concentrations (NDCI) sig nificantly increased (p < 0.01)\, indicating intensifying eutrophication. These trends are related to agricultural runoffs and warmer temperatures. The temperature was found to be negatively correlated with water area (r= -0.45) and positively correlated with NDCI (r= 0.40). Multiple linear regr ession revealed that temperature and precipitation explain 21% of the annu al water area variability (p < 0.05). Incorporating 1-year precipitation l ags improved the explanatory power (R2= 0.34)\, highlighting delayed hydro logical responses in shallow lakes. The remaining unexplained variance (66 %) suggests additional anthropogenic drivers\, such as agricultural water use and runoff. This aligns with public documentation under Türkiye’s E U WFD commitments\, as Ilgın Lake is designated as a nitrate vulnerable z one and protected area.\n\nThese findings underscore the vulnerability of shallow lakes like Ilgın Lake to ecological degradation\, driven by both climatic variations and human activities. Their limited water depth increa ses risks to sustainable agriculture\, biodiversity\, and local socio-econ omic conditions. The proposed workflow utilizes open datasets on the cloud -based GEE platform and open-source Python tools\, ensuring cost-effective scalability. All code and workflow are publicly available as Jupyter Note book on GitHub (https://github.com/earth-obs/lake-gee-hydrology-water-qual ity) under the open source MIT license. This approach provides valuable in sights into sustainable water resource management plans\, especially for r egions where field data is unavailable. This study aligns with the EU WFD goals by providing cost-effective and scalable sources for monitoring wate r bodies listed under Annex V. We conclude that water resource monitoring studies should focus not only on the hydrological context but also on wate r quality status\, as both are essential for holistic water management. Ad ditionally\, shallow lakes like Ilgın play a critical role in preserving natural habitats and sustaining local agricultural livelihoods. Future wor k will extend this framework to higher spatial and spectral resolution sat ellite imagery (e.g.\, Sentinel-2) and additional shallow lakes across Eur ope. DTSTAMP:20260527T195034Z LOCATION:PA01 (Quarticle) SUMMARY:Assessing long-term hydrological dynamics and water quality using G oogle Earth Engine: A case study of Ilgın Lake (1985-2024) - Omer Faruk A tiz URL:https://talks.staging.osgeo.org/foss4g-europe-2025/talk/JZRLA8/ END:VEVENT END:VCALENDAR