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Comparison of blue and green water fluxes for different land use classes in a semi-arid cultivated catchment using remote sensing

机译:使用遥感的半干旱栽培集水中不同土地利用课程的蓝绿和绿水通量的比较

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Study areaKikuletwa catchment, Upper Pangani River Basin, Tanzania.Study focusThis study compared yearly blue and green water fluxes using four different methods: Senay’s method (SN) (Senay et al., 2016), van Eekelen method (EK) (van Eekelen et al., 2015), the Budyko method (Simons et al., 2020) and the Soil Water Balance (SWB) model (FAO and IHE Delft, 2019). The yearly blue and green water fluxes of different Land Use Land Cover (LULC) classes were estimated using an ensemble of seven global remote sensing-based evapotranspiration products (Ensemble ET) and the CHIRPS rainfall dataset. The Ensemble ET was created from seven global RS-based surface energy balance models (GLEAM, CMRS-ET, SSEBop, ALEXI, SEBS, ETMonitor and MOD16).New hydrological insightsOur study found that the EK method was able to map blue and green water fluxes with realistic results for irrigated and non-irrigation cultivated areas. Budyko and SWB gave too high blue water fluxes for the non-irrigated agricultural areas, whereas the Budyko and SWB models were not able to show a clear difference in blue-water fluxes in irrigated versus non-irrigated areas. On the other hand, the SN method estimated no blue water fluxes in more than half of the identified irrigated areas.Three of the four methods estimate the highest blue water fluxes (318–582 mm/y) in forested areas, while the SWB model estimates the highest blue water fluxes in irrigated banana and coffee (278 mm/y). Overall, we conclude that the EK method yielded the most realistic spatial pattern of blue-water fluxes when compared to the irrigated land use map, whereas SWB could be considered after further calibration if higher temporal data resolution (e.g. monthly) is required.
机译:研究Areakikuletwa集水区,上庞山河流域,坦桑尼亚.Study Focusthis学习使用四种不同的方法比较了蓝色和绿色水量:Senay的方法(SN)(Senay等,2016),范eekelen方法(ek)(van Eekelen et Al。,2015),Budyko方法(Simons等,2020)和土壤水平(SWB)模型(粮农组织和Ihe Delft,2019)。使用七个全球遥感蒸发产品(Ensemble et)和Chirps降雨数据集估计不同土地使用陆地覆盖(LULC)课程的年平蓝和绿色水势次估计。 Ensemble等是由七个全球RS的地表能量平衡模型(Gleam,CMRS-et,SSEBOP,Alexi,SEB,Etmonitor和Mod16)创建。新水文Insightsour的研究发现,EK方法能够映射蓝绿水具有灌溉和非灌溉培养区域的现实结果的助熔剂。 Budyko和SWB为非灌溉农业领域发出了太高的蓝色水势态,而Budyko和SWB模型无法显示出灌溉与非灌溉区域的蓝水量差异的明显差异。另一方面,Sn方法估计超过一半的鉴定灌溉区域的蓝水量。四种方法中的三种方法估计森林区域中的最高蓝水助水(318-582mm / y),而SWB模型估计灌溉香蕉和咖啡(278 mm / y)中最高的蓝色水势率。总的来说,我们得出结论,与灌溉土地使用图相比,EK方法产生了蓝水助熔剂的最现实的空间模式,而如果需要更高的时间数据分辨率(例如,每月),则可以考虑SWB。

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