BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-europe-2026//talk//QADHQX BEGIN:VTIMEZONE TZID:EET BEGIN:STANDARD DTSTART:20001029T050000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:EET TZOFFSETFROM:+0300 TZOFFSETTO:+0200 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:EEST TZOFFSETFROM:+0200 TZOFFSETTO:+0300 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-europe-2026-QADHQX@talks.osgeo.org DTSTART;TZID=EET:20260630T143000 DTEND;TZID=EET:20260630T150000 DESCRIPTION:Agriculture accounts for approximately 70% of global freshwater withdrawals and remains highly sensitive to climate variability. Therefor e\, timely estimates of agricultural water use are crucial for effective b asin planning\, water-stress diagnostics\, and informed climate adaptation strategies.\n\nIn Colombia\, the National Water Study (Estudio Nacional d el Agua\, ENA) is the official instrument for quantifying water demand acr oss sectors. It relies on an FAO-56 soil–water balance approach that req uires extensive input data and numerous intermediate calculations (IDEAM 2023). Because this method is so operationally demanding\, the ENA is publ ished only once every four years and suffers from a significant reporting lag. For instance\, the ENA 2022 report relies on data from 2020. Conseque ntly\, these estimates are often outdated by the time of publication\, and lack the capacity to capture intra-annual variability driven by phenomena such as El Niño and La Niña.\n\nThis research therefore investigates wh ether freely available satellite data can support the ENA reporting\, enab ling more continuous and timely monitoring. The study evaluates FAO's WaPO R (Water Productivity through Open access to Remotely sensed-derived data) through an open-source\, reproducible framework of publicly available not ebooks. It combines WaPOR's monthly actual evapotranspiration and intercep tion (AETI) and precipitation products with ENA's agro-climatic crop mask to restrict the analysis to agricultural areas and perform pixel-level cal culations of water consumption. \n\nThe resulting blue water volumes and i rrigation water withdrawals were aggregated at the sub-basin level and com pared with official ENA estimates for 2020. Apart from the country-level a nalysis\, the methodology was applied to a specific zoom-in area located w ithin the Magdalena province. This area is characterized by a high dominan ce of banana and oil palm (Cruz 2020)\, which allowed a specific compariso n between WaPOR-derived evapotranspiration values and the expected agricul tural patterns. This selection was based on two main criteria: first\, the area exhibits a relatively homogeneous surface according to the FAO-WaPOR (L3-AETI-M - spatial resolution of 30m) layer which facilitates the spati al analysis and interpretation of results\; however\, it should be noted t hat the Level 2 (L2-AETI-M - spatial resolution of 100m) product will be used for the analysis\, consistent with the methodology applied across the entire national territory. Second\, crop mask data from IDEAM reveal that the region contains a significant proportion of two key permanent crops: oil palm and banana \n\nThe results show a strong spatial agreement (R² = 0.83)\, indicating consistent identification of priority basins\, althoug h WaPOR estimates are approximately 66% of ENA values\, with similar patte rns for irrigation withdrawals. This systematic offset is consistent with the conceptual difference between the two approaches: ENA estimates potent ial crop water demand under optimal conditions using FAO-56 crop coefficie nts\, while WaPOR captures actual evapotranspiration under real field cons traints. This difference becomes evident when analysing seasonal behaviour : in the Caribbean and Magdalena basins\, which concentrate the largest ag ricultural areas\, spatial agreement is notably high during the dry season but drops significantly with the onset of the first rainy season. \n\nThe detailed analyses for the Magdalena area shows that WaPOR-derived evapotr anspiration values are slightly lower than ENA estimates for both banana a nd oil palm\, consistent with the national findings. The seasonal structur e of this disagreement reveals that January is the only month where the re lationship inverts\, with WaPOR marginally exceeding ENA\, a pattern consi stent with dry-season dynamics in which low atmospheric humidity allows Wa POR's energy balance approach to capture relatively high actual ET. From F ebruary onwards\, ENA overtakes WaPOR\, and the gap widens progressively t hrough the wet season transition\, reaching its annual peak in May — pre cisely when the first rainy season is established — and a secondary peak in October\, coinciding with the second rainfall peak over the region. Th is seasonally structured bias confirms that the difference between the two approaches is not a random but a response to Colombia's climate variabili ty.\n\nThe developed framework produces spatial outputs and results using freely available tools and data sources such as the WaPOR data which is provided in near-real-time layers of actual evapotranspiration\, biomass p roduction\, and water productivity at resolutions from 30 m to 300 m globa lly. The potential of these products for agricultural water monitoring is not new — WaPOR has already been applied in other contexts\, such as the assessment of irrigation performance at a sugarcane estate in Mozambique (Chukalla et al. 2022) but its application at a national scale in Colombia for water demand reporting remains largely unexplored. This study aims to contribute to that evidence base in Latin America\, and to motivate furth er exploration of WaPOR's potential in other regions where timely\, low-co st alternatives to conventional water accounting methods are needed. By op enly sharing the methodological framework through accessible notebooks\, t his research actively promotes reproducibility and collaborative science\, which are core tenets of the FOSS4G community. It empowers local water au thorities\, researchers\, and policymakers in data-scarce regions to indep endently verify\, adapt\, and scale the approach to their specific hydrocl imatic contexts. Furthermore\, integrating these Python-based workflows wi th QGIS demonstrates how open-source ecosystems can bridge the gap between complex satellite data and operational water management\, ultimately demo cratizing access to critical climate adaptation tools. DTSTAMP:20260605T011141Z LOCATION:A01 SUMMARY:Evaluating the application of FAO-WaPOR data to support Colombia’ s National Water Study on water consumption in the agricultural sector - L aura Agudelo URL:https://talks.osgeo.org/foss4g-europe-2026/talk/QADHQX/ END:VEVENT END:VCALENDAR