Scarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49–0.55 (kg/m³) for irrigated wheat, 0.27–0.34 for rainfed wheat, 1.7–2.2 for apples, 1.2–1.7 for grapes, 5.5–6.2 for sugar beets, and 0.67–1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.

@en

This study explores recent changes in evaporative demand and water availability across 100 river sub-basins in India by partitioning the actual evapotranspiration (AET) into green water evapotranspiration (ET-Green) and blue water evapotranspiration (ET-Blue). For computation of ET-Green and ET-Blue, the Budyko framework is applied to long-term scenario (2003–2017) and to intra-annual averaged series (i.e. 2003-2007, 2008–2012 and 2013–2017). For the Budyko analysis, the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Global Land Data Assimilation System (GLDAS) AET and Climate Research Unit Global Data Assimilation System (CRU GDAS) Potential ET (PET) climate forcing variables have been utilized. Multiple hydro-climatic indicators, such as dryness index (DI), evaporative index (EI), and responsivity with respect to theoretical Budyko curve are computed and they show substantial variations across sub-basins from far past time (2003) to recent past (2017). The changes in DI and EI highlight the diversity in evaporative demand and dryness condition across the country. Results reveal that India's evaporative water demand is largely influenced by ET-Green (up to 65%) that depends mainly on precipitation. At the same time, in many river sub-basins, ET-Blue that depends on external sources of water like diversion or stored water, is significant. The shape parameter (ω) of Fu's Budyko equation, that can be utilized for the future assessment of ET-Green and ET-Blue, has been optimized. The results of this study would of immense value for sustainable irrigation water management and improving water use efficiency in agriculture and overall water availability in river basins in India.

@en

An exhaustive evaluation of water resources is a prerequisite for evidence-informed planning and implementing sustainable management strategies. However, the lack of sufficient information on water supply and consumption, alongside the technical limitations on comprehensive accounting for inter-relations and interactions between the subsystems, has resulted in decisions with often long-lasting outcomes and undesirable consequences. Water accounting is a tool for systematic acquisition, analysis, and reporting of water-related information to fill the existing knowledge gap on water flows and fluxes. In this study, Water Accounting Plus (WA+) framework is applied to the western region of Lake Urmia, a dying hyper-saline lake, to assess water use and crop water productivity (CWP) from 2010 to 2016. Remotely sensed information along with a distributed hydrological model (SPHY) is used to fill the information gap on water resources and inform effective policy actions. Our analysis reveals that the agricultural sector is neither productive nor efficient while there is a considerable scope to ameliorate water productivity and beneficial water use by adopting proper water management practices. Average CWP values for wheat, sugarbeet, vineyard, and apple vary between 0.38 and 0.55, 5.1–5.6, 1.5–1.7 and 1.9–2.3 (kg/m³), respectively while storage changes show consistent depletion, especially during dry year, up to 117 MCM. The results indicate that a 40% reduction in blue water use is achievable to supply additional water to revive Lake Urmia. This study highlights the importance of water accounting and information flow for decision-makers, practitioners, and farming communities to define practical targets and enhance productivity in water-scarce regions.

@en

Strategic planning of water management at the river-basin scale requires (1) measurement and accounting of individual hydrological processes, (2) quantification of water resources, and (3) their optimal allocation. Scalable Water Balances from Earth Observations (SWEO) is an open-access parameterization enabling automated reporting of water footprints and Sustainable Development Goal (SDG) indicators. We present its systematic arrangement and input datasets, and demonstrate its accuracy by independent riverflow measurements. We also review some achievements in remote sensing for hydrology during the last 50 years in quantifying hydrological and water management processes, flows, fluxes and changes in storage from various independent sources; and append mathematical formulations.

@en

Estimating precipitation at high spatial-temporal resolution is vital in manifold hydrological, meteorological and water management applications, especially over areas with un-gauged networks and regions where water resources are on the wane. This study aims to evaluate five downscaling methods to determine the accuracy and efficiency of which on generating high-resolution precipitation data at annual and monthly scales. To establish precipitation-Land surface characteristics relationship, environmental factors, including Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Digital Elevation Model (DEM), were considered as proxies in the spatial downscaling procedure. The downscaling algorithms, namely support vector machine (SVM), random forest (RF), geographically weighted regression (GWR), multiple linear regression (MLR) and exponential regression (ER), were implemented to downscale the version 7 of TRMM (Tropical Rainfall Measuring Mission) precipitation (3B43 V7 product) over Lake Urmia Basin (LUB) from 0.25° to 1 km spatial resolution. The downscaled precipitation data was validated against observations from meteorological stations. Monthly fractions derived from TRMM 3B43 were used to disaggregate 1 km annual precipitation to 1 km monthly precipitation. Furthermore, the best method was selected for calibration based on Geographical Difference Analysis (GDA) to assess the effectiveness of the calibration as a viable option. The results indicate that SVM not only outperforms the other methods, but also has good agreements with in-situ measurements compared to the original TRMM. The results confirm that inclusion of LST and geographic information along with NDVI can improve the downscaling performance. Downscaling and GDA calibration significantly improve the accuracy of TRMM 3B43 product at both spatial and temporal resolution and should be considered as an essential step in calibration of TRMM precipitation. Calibration at monthly scale yields slightly better results than calibration at annual scale and then disaggregating into monthly maps in terms of accuracy assessment.

@en

Agriculture is a major consumptive user of water through evapotranspiration (ET). Quantification of actual ET (ET_a) is of significant interest to better address water management and improve decision making in the agricultural sector. Many methods to estimate ET are available. However, due to the increasing need of validation of estimates of ET from various techniques, there arises a call for a critical comparison of ET_a estimated from a combination of methods. This research presents a critical comparison of ET_a estimates obtained using ground based (eddy covariance), remotely sensed (ET ensemble), and simulated data (a crop-soil-water balance model, SIMETAW#). The study focuses on experimental sites in Nebraska, Oklahoma and California (USA). Results show a relatively small and consistent variation between the three methods. The results are relatively close giving a reasonable impression of the expected ET_a at a site including variability which is useful to address agricultural practices. The estimates from remote sensing deviate from the other two methods since the method used is relatively new and requires further research to improve measurements and ET estimation. This study offers water managers at the sites new information for reliable, multi-source ET estimation for water resources planning and the study is in principle replicable at different locations.

@en

Traditional methods based on field campaigns are generally used to assess the performance of irrigation schemes in Burkina Faso, resulting in labor-intensive, time-consuming, and costly processes. Despite their extensive application for such performance assessment, remote sensing (RS)-based approaches remain very much underutilized in Burkina Faso. Using multi-temporal Landsat images within the Python module for the Surface Energy Balance Algorithm for Land model, we investigated the spatiotemporal performance patterns of the Kou Valley irrigation scheme (KVIS) during two consecutive cropping seasons. Four performance indicators (depleted fraction, relative evapotranspiration, uniformity of water consumption, and crop water productivity) for rice, maize, and sweet potato were calculated and compared against standard values. Overall, the performance of the KVIS varied depending on year, crop, and the crop’s geographical position in the irrigation scheme. A gradient of spatially varied relative evapotranspiration was observed across the scheme, with the uniformity of water consumption being fair to good. Although rice was the most cultivated, a shift to more sweet potato farming could be adopted to benefit more from irrigation, given the relatively good performance achieved by this crop. Our findings ascertain the potential of such RS-based cost-effective methodologies to serve as basis for improved irrigation water management in decision support tools.

@en

Reliable and readily available data on actual evapotranspiration (ET_a) over large-scale areas throughout the crop growing season are critical for improved agricultural irrigation and water resource management. On-site data collection is costly, labour-intensive, and very challenging in resource-limited conditions. Thus, open-source satellite-based approaches might be adopted as cost-effective alternatives. In this study, the performance of a cost-effective and open source satellite-based approach for estimating ET_a over a large-scale (1200 ha) irrigation system, the Kou Valley Irrigation Scheme (KVIS), in Burkina Faso was assessed. ET_a values over the critical irrigation period during the 2014 dry season (January-April) were estimated using the Python module for Surface Energy Balance Algorithm for Land model (PySEBAL). Then, they were compared against the Water Productivity Open-access (FAO-WaPOR), and United States Geological Survey-Famine Early Warning Systems Network Operational Simplified Surface Energy Balance (USGS-FEWS NET's SSEBop) ET_a over the same period at different temporal scales. Overall, ET_a values were satisfactorily estimated throughout the crop growth season across the Kou Valley irrigation scheme using PySEBAL. They spatially varied depending on the soil type and crop, with daily values ranging from 4.09 mm day⁻¹ to 7.7 mm day⁻¹, for a seasonal average of 619 mm. The finer spatial resolution (30 m) of PySEBAL outputs allowed better estimations compared to the FAO-WaPOR and SSEBop-based approaches. Our findings help ascertain the use of the PySEBAL model in semi-arid environment in Burkina Faso, and could serve as a basis for developing strategies for improved irrigation water management in countries experiencing similar conditions such as Burkina Faso.

@en

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.

@en

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 in-accessible environment of the Amazon at a spatial resolution of 250 m. The study shows that the basin-wide 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.

@en

Ecosystems provide social, ecological and economic benefits and are linked through the landscape. Landscape degradation and loss of biodiversity undermine ecosystem functioning especially in vital ecosystems such as the Mara Basin in Africa. Holistically monitored and assessed landscape provide values to support conservation strategy which could enhance sustainable resource utilisation. The study relied on high resolution 10-m Sentinel 2A, Landsat 30-m and SEBAL to generate biophysical parameters for valuation in the landscapes. A household survey was used to derive forest benefits to local livelihoods. The well conserved Trans Mara forest provided high ecological benefits but few social benefits to the local community. Tea buffers have proven to be an effective conservancy strategy for the forest ecosystem. The long term carbon-based benefits in conservation areas of rural Africa is hindered by communities’ high dependency on firewood and charcoal energy. The land policy on limited livestock access to the pastoralists in the conservancies should be based on the livestock carrying capacity of the lands. Further, the valuation maps can be used for land use planning where critical wildlife protection areas, movement corridors and mixed use zones of high livestock value can be identified. The holistic assessment of value of African landscape based on key functionalities and linked spatially over the landscape using remotely sensed data is indeed crucial to improve decision making and conservation strategy towards sustainable ecosystem management.@en

The storage and dynamics of water in the root zone control many important hydrological processes such as saturation excess overland flow, interflow, recharge, capillary rise, soil evaporation and transpiration. Consequently, the effect of the root zone parameterization on the runoff prediction of global hydrological or land surface models (hereafter, GHMs) can be quite large. Many studies have found that climate is the most important factor in explaining rooting depths of natural vegetation, and thus outweighs the importance of soil and vegetation type. Most GHMs, however, use only soil maps and look-up tables for root zone properties to parameterize the root zone. This is problematic, as the observations of the root zone may indirectly have some climate properties, but is still likely to contribute to errors as the data is scarce, often incomplete (i.e. not the entire vertical root profile), at the wrong scale (i.e. individual plant vs. ecosystem value vs. grid cell value), and it is uncertain which part of root zone is hydrologically active. Recent studies developed different optimization methods to a priori determine the root zone storage or rooting depth at the global scale. These studies also suggested that their values could potentially be directly exported to other GHMs. In this study we test which root zone storage and rooting depth would be optimal for the global hydrological model PCR-GLOBWB. We want to answer the following questions: • How much do the “best fit” root zone storages and rooting depths differ from each other with different model settings in PCR-GLOBWB? • Are calibrated root zone storage capacities and rooting depths for PCR-GLOBWB similar to root zone storage capacities and rooting depths obtained by other global studies? The optimal root zone parameters follow from taking the parameters that best reproduce the evaporative flux from a state-of-the-art ensemble of satellite remote sensing based gridded evaporation products. We find that the global spatial patterns of maximum root zone storage capacity are quite independent of other parameters in PCR-GLOBWB. These patterns are similar to the patterns found in recent global studies, but the absolute values are not. We believe that the future for GHMs lies in dynamically determining the root zone storage based on logical – climate based – optimization concepts that are tailor-made for each GHM independently. @en