Actual evapotranspiration (ET) is a major component of the water balance. While several international flux measurement programs have been executed in the tropical rain forest of the Amazon, those measurements represent the evaporative process at a few selected sites only. The aim of this study is to obtain the spatial distribution of ET, using remote sensing techniques, across the entire Amazon River Basin. Results from six global ET products based on remote sensing techniques (GLEAM, SEBS, ALEXI, CMRSET, MOD16, and SSEBop) were merged to obtain an ensemble prediction of the ET rates for the complex and inaccessible environment of the Amazon at a spatial resolution of 250 m. The study shows that the basinwide average ET is 1316 mm/year with a standard deviation of 192 mm/year. This new ET-Amazon product was validated against seven different historic flux tower measurements. The energy balance closure of the in situ measurements varied between 86 and 116%. Only months with more than 70% completeness of in situ measurements were considered for validation. Different procedures for closure correction were included in the analyses. The correlation between measured and remotely sensed ET is good (R 2 > 0.97 for consecutive periods of 2 to 12 months), and the bias correction is negligible for the energy balance residual method, which seemed most favorable. Monthly ET values have more uncertainty. The monthly RMSE values vary between 7.4 and 27.8 mm/month (the average RMSE is 22.2 mm/month), and the coefficient of determination (R 2) varies between 0.48 and 0.87 (the average R 2 is 0.53). The ET from the water balance is 1380 mm/year, being − 64 mm/year difference and 4.6% less than ET derived from the water balance. The evaporation from the Amazon basin inside Brazil is 5063 km 3 /year, followed by Peru with 1165 km 3 /year. ET-Amazon shows more spatial details and accuracy than alternative global ET products such as LandFlux-EVAL, Model Tree Ensemble (MTE), and WACMOS-ET. This justifies the development of new regional ET products.
The assessment of water withdrawals for irrigation is essential for managing water resources in cultivated tropical catchments. These water withdrawals vary seasonally, driven by wet and dry seasons. A land use map is one of the required inputs of hydrological models used to estimate water withdrawals in a catchment. However, land use maps provide typically static information and do not represent the hydrological seasons and related cropping seasons and practices throughout the year. Therefore, this study assesses the value of seasonal land use maps in the quantification of water withdrawals for a tropical cultivated catchment. We developed land use maps for the main seasons (long rains, dry, and short rains) for the semi-arid Kikuletwa catchment, Tanzania. Three Landsat 8 images from 2016 were used to develop seasonal land use land cover (LULC) maps: March (long rains), August (dry season), and October (short rains). Quantitative and qualitative observation data on cropping systems (reference points and questionnaires/surveys) were collected and used for the supervised classification algorithm. Land use classifications were done using 20 land use and land cover classes for the wet season image and 19 classes for the dry and short rain season images. Water withdrawals for irrigated agriculture were calculated using (1) the static land use map or (2) the three seasonal land use maps. Clear differences in land use can be seen between the dry and the other seasons and between rain-fed and irrigated areas. A difference in water withdrawals was observed when seasonal and static land use maps were used. The highest differences were obtained for irrigated mixed crops, with an estimation of 572 million m3/year when seasonal dynamic maps were used and only 90 million m3/year when a static map was used. This study concludes that detailed seasonal land use maps are essential for quantifying annual irrigation water use of catchment areas with distinct dry and wet seasonal dynamics.
Evapotranspiration (ET) is a major hydrologic flux used in water resources planning and irrigation management. While recent advances in remote sensing (RS) have enabled availability of high spatial and temporal resolution ET data, a lack of information related to error in the estimations has made it challenging to use this data for on-farm irrigation management decision making. In this study, three commonly used single-source RS ET models (pySEBAL-a new version of Surface Energy Balance Algorithm for Land; SEBS-Surface Energy Balance System algorithm; and METRIC -Mapping Evapotranspiration at High Resolution with Internalized Calibration) were used to estimate daily actual evapotranspiration (ET a ) for almond, processing tomato, and maize in the Central Valley of California. Model evaluation wasconducted by comparing the predicted ET a from RS with in-situ measured ET a using surface renewal. Results indicated that the RS-based ET a estimations for all three models were within acceptable levels of uncertainty and agreed well with surface renewal estimates except for the underestimation by pySEBAL and METRIC during early season growth stages of processing tomatoes. This underestimation was attributed to the lack of accuracy when using single source ET models under lower vegetation cover condition (when ET is dominated by soil evaporation). Better estimates of ET a with pySEBAL and METRIC were detected at full cover, which explains the applicability of these two models to irrigation management during peak crop water demand. SEBS performed the best among the three RS-based models for daily ET a estimation for all crops. This suggests that SEBS-based ET a estimates can be adopted in operational irrigation management programs for farms that have not installed in field ET sensors such as Tule Sensors (Tule Technologies Inc.). In addition, RS based ET is spatially distributed which can help to identity spatial variability between different irrigation zones.
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