BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.geoinfo-lab.org//foss4g-asia-2024//FD8QKU BEGIN:VTIMEZONE TZID:+07 BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:+07 TZOFFSETFROM:+0700 TZOFFSETTO:+0700 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-D3LAUM@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T145000 DTEND;TZID=+07:20241216T151000 DESCRIPTION:Global remotely sensed evapotranspiration (RS-ET) products are increasingly pivotal in enhancing the accuracy and scope of hydrological m odeling\, particularly in regions where traditional ground-based streamflo w data are sparse or non-existent. These products play a pivotal role in u nderstanding the dynamics of the climate-soil-vegetation system\, where ev apotranspiration constitutes a substantial portion of water loss following precipitation events. Their extensive spatial coverage and accessibility have significantly expanded the capability to predict hydrological dynamic s in ungauged basins\, offering insights that were previously inaccessible through in-situ observations alone.\n\nDespite their benefits\, RS-ET pro ducts are tempered by inherent uncertainties\, primarily stemming from bia ses that vary across datasets and geographical regions. These biases manif est as either overestimation or underestimation compared to ground truth m easurements\, posing challenges for the accurate calibration of hydrologic al models. Traditional approaches in hydrological modeling commonly utiliz e absolute ET values directly derived from RS-ET products for model calibr ation\, without accounting for potential biases. However\, the reliability of such direct calibrations is contingent upon the quality and accuracy o f the RS-ET data\, which remains uncertain in many cases.\n\nTo address th ese challenges\, this study shifts the focus from absolute ET values to ut ilizing spatial structural information embedded within RS-ET data\, partic ularly emphasizing spatial autocorrelation\, which refers to the tendency of ET values at nearby locations to exhibit similarities. Employing the lo cal Moran's I index\, a spatially weighted autocorrelation statistic that is insensitive to biases\, we capture the spatial structure of ET data acr oss sub-basins. Additionally\, a composite Kling-Gupta Efficiency (KGE) me tric\, integrating absolute ET values and spatial autocorrelation informat ion in a weighted manner\, is employed for calibrating hydrological models .\n\nThree calibration schemes are thus designed to analyze the effectiven ess of spatial autocorrelation in hydrological modeling: one focusing sole ly on absolute ET values\, another solely on spatial autocorrelation\, and a combined approach. Testing these schemes for hydrological modeling with four RS-ET products in the Meichuan basin—MOD16\, GLASS\, and SSEBop wi th large biases\, and PMLV2 with minimal bias—the study demonstrates var ying effectiveness across these schemes.\n\nFor RS-ET products with substa ntial biases\, hydrological modeling using spatial autocorrelation proved to be the optimal solution. It achieved a higher KGE and lower Percent Bia s (PBIAS) on simulated streamflow compared to using solely the absolute ET value or the combined approach. Conversely\, for RS-ET products with mini mal biases\, hydrological models calibrated using both the combined approa ch were considered the preferred solutions. This approach can result in a high KGE\, similar to that obtained from spatial autocorrelation informati on alone\, while maintaining a reasonable PBIAS. Therefore\, we recommend calibrating hydrological models using both absolute ET values and spatial autocorrelation information in regions where ground ET observations are av ailable. This approach enhances the robustness and reliability of hydrolog ical predictions\, mitigating the influence of biases inherent in RS-ET pr oducts.\n\nIn contrast\, in scenarios where the quality of RS-ET products is unknown\, we suggest calibrating using only spatial autocorrelation inf ormation\, thereby circumventing potential biases and improving model accu racy under such circumstances. Moreover\, methodologically\, the study con tributes by demonstrating the efficacy of the local Moran's I index in cap turing the spatial structure of ET data within hydrological sub-basins. Th is geostatistical measure not only quantifies spatial autocorrelation but also identifies clusters and patterns of ET values\, thereby enriching our understanding of spatial variability in hydrological processes.\n\nFurthe rmore\, the comprehensive analysis of the composite KGE index underscores the significant contribution of spatial autocorrelation information to hyd rological modeling\, surpassing the influence of absolute ET values in enh ancing model performance. In conclusion\, the spatial autocorrelation-base d approach presented in this study represents a significant advancement in the application of global RS-ET products for hydrological modeling. By le veraging spatial structural information and mitigating biases inherent in RS-ET data\, this approach not only improves the accuracy of hydrological predictions but also enhances the practical utility of RS-ET products in d iverse hydrological contexts. Future research directions may explore addit ional spatial statistical techniques and incorporate a broader array of RS -ET datasets to further refine and validate these findings across differen t geographical settings and hydrological conditions. DTSTAMP:20260610T100328Z LOCATION:Room34-1102 SUMMARY:Leveraging spatial autocorrelation information of remotely sensed e vapotranspiration for mitigating the impact of data uncertainty on hydrolo gical modeling - Yan He URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/D3LAUM/ END:VEVENT END:VCALENDAR