Reference evapotranspiration (ET₀) is an important variable for water resources management and agricultural planning. Some regions, including Africa lack sufficient in-situ meteorological measurements to represent the climatic conditions. Open-access Global ET₀ data sets present a viable alternative that could potentially fill the gap. This study compares eight spatial ET₀ data sets against ET₀ estimated from 165 weather stations across Africa. Performance was assessed using statistical metrics, including R², Bias, RMSE, and RBias. Findings reveal that high-resolution data sets align better with in-situ data in temperate and tropical climates compared to low-resolution data sets. Results for arid regions appear to show low performance for all data sets, but results are less certain due to the availability of stations in this climate. This study also reveals that the input data contribute to 60–70% of the variability between data sets, with the remainder contributed by different model implementation, indicating the importance of good quality of input data.

Recent developments of higher-resolution and lower-latency reanalysis data allow mapping reference evapotranspiration (ET_o) over large areas in a near real-time manner. This study evaluates the ERA5, AgERA5 and GEOS5 reanalysis datasets for meteorological input in Africa and Southwest Asia by comparing between data products and with 174 in situ sites. The inter-comparison reveals non-stationary differences between datasets and highlights temporal inconsistencies in the GEOS5 data. When evaluated against in situ measurements, GEOS5 demonstrates lower accuracy compared with ERA5 and AgERA5. Additionally, while all datasets accurately estimate air temperature and pressure, they overestimate windspeed and solar radiation, and underestimate vapour pressure. The propagation of uncertainty estimates of ERA5 through the FAO56 ET_o equation shows particularly high uncertainty in the tropics. This study emphasizes the importance of applying multiple uncertainty assessment methods for better-informed use of reanalysis data, especially in data-scarce regions.

Nitrogen (N) is crucial for crop and ecosystem health in agricultural settings. Traditional remote sensing (RS) methods, using regression models and indices like NDVI, face challenges in transferability across time and space. This study aims to enhance in-season N concentration assessment by integrating RS data with a hybrid approach, combining the PROSPECT-PRO and 4SAIL models to create PROSAIL-PRO. This Radiative Transfer Model (RTM) excels in parsing leaf protein, crucial for accurate crop N content estimation. PROSAIL-PRO forms the basis for a robust learning database, guiding the training of Gaussian Process Regression (GPR) models—Bayesian-based machine learning known for precision and insights into uncertainties. The Gezira irrigation scheme in Sudan serves as a case study. Using Sentinel-2 bands, this research informs agricultural resource management and assesses crop health in the scheme. Fertilizer application data and yield records drawn from three farms within the Gezira scheme to form the basis for validation. Wheat, the primary crop in this context, experienced varying fertilizer application scenarios across these farms during the 2021-22 cropping season resulting in varying yields. Similar results were found in the crop N content and biomass estimation. GPR models, trained on PROSAIL-PRO, effectively predict above ground N content and biomass. Validation against field records shows promising outcomes, with GPR models exhibiting an RMSE of 7.9 kg/ha for N content and 0.54 tonnes/ha for yield estimation. Moreover, the model's spatiotemporal scalability was assessed, showing an RMSE of 1.01 tonnes/ha at the Nimra level and 1.6 tonnes/ha at the farm level, highlighting the applicability of this approach to larger areas. A significant correlation (0.7) was found between estimated N concentration and actual recorded yield at the field level, further corroborated by an 0.83 correlation at the Nimra level. These results emphasize the robustness of this hybrid modeling approach, particularly in linking nitrogen dynamics to primary productivity, as evidenced by stronger correlations between NPP and nitrogen content than between N content and fAPAR. This study therefore highlights the benefits of adoption hybrid modeling, based on PROSAIL-PRO, in global agricultural monitoring. The synergy found between remote sensing, radiative transfer modeling, and real-world dynamics promises a sustainable future for agriculture applications.

Jordan’s Amman-Zarqa (AZ) basin faces increasing water scarcity due to increasing demands and persistent groundwater over-abstractions for irrigation. To address this issue, water conservation has been set as a national strategy, and several initiatives aiming to conserve water in irrigated agriculture have been implemented in the basin’s highlands. This study evaluates the impact of water conservation technologies (WCTs) on irrigation water savings in the AZ basin highlands. Monthly data on irrigation application were collected from 22 farms over three crop seasons (2019–2022) for four dominant orchards. Farm-scale water savings were calculated and projected to the basin scale under two scenarios: a sustainability scenario aligning groundwater abstraction with irrigation needs under WCTs and an economic scenario expanding irrigated areas using the saved water. Results show that irrigation efficiency before the influence of WCTs was below 55%, with farmers applying an average of 1277 mm/year. After implementing WCTs and farmers fine-tuning their irrigation practices, irrigation application decreased to an average of 795 mm/year, resulting in 38% water savings. Projecting these savings basin-wide, WCTs could conserve 44 Mm³/year of water under the sustainability scenario. The results provide a solid basis for informing water conservation targets in this region. However, successful water conservation using WCTs depends on farmer-led testing to ensure reduced irrigation does not compromise crop yields. Pilot programs supported by trusted technical advice through farmer field schools and appropriate incentives can achieve sustainable water conservation in this region. Concurrently, monitoring is required to regulate irrigation expansion as it could undermine water savings.

This research explores the limitations and opportunities of Water Accounting Plus (WA+) for addressing water management issues in the MENA, focusing on Jordan. A comprehensive literature review and interview-based analysis were conducted to identify prevalent water management issues and evaluate information used in decision-making and strategy appraisals. The findings suggest that WA+ can enhance the spatio-temporal coverage of water resource assessments, refine estimates of irrigation water consumption, and facilitate demand management. Quantifying recharge and surface runoff requires integrating WA+ with hydrological models. Addressing climate change’s impact on future water resources requires integrating climate change projections with WA+.

Water resources assessments are essential for effective planning in water-scarce regions such as Jordan. Such assessments require sufficient data in space and time. The WaPOR-based Water Accounting Plus (WA +) framework is relevant as it integrates remote sensing data and the Pixel-Based Soil Water Balance model to simulate a basin’s water balance. However, since it relies on remote sensing, this framework only tracks water consumption in irrigated agriculture and does not consider non-irrigation water use and its return flow. This paper modifies the WaPOR-based WA + framework to include non-irrigation manmade consumption and its return flows. The modified framework provides a more comprehensive water budget for the Amman-Zarqa (AZ) basin, presented in a modified WA + resource base sheet for 2018 through 2021. The results show that water availability in the AZ basin is highly responsive to precipitation changes. Average precipitation was approximately 926 Mm³/year between 2018 and 2020, corresponding to an average available water of 485 Mm³/year. However, a reduction in average precipitation by 28% in 2021 corresponded to a reduction in available water to 243 Mm³/year. Nevertheless, substantial groundwater outflows to neighbouring basins may indicate that available water is being overestimated. Manmade consumption increased by 18% from 2018 to 2021, and the total demand exceeded the available supply by 150%. This underscores the pressing need to investigate supply augmentation and conservation methods. Future studies could focus on improving the representation of groundwater dynamics in the modified framework by improving groundwater dynamics in PixSWAB and testing the modified framework with other remote sensing datasets.

Evapotranspiration (ET), a key variable in both water and energy cycles. It is very challenging to measure or estimate in large regions. Among many approaches to estimate ET indirectly (e.g. through hydrological modelling), models that are based on satellite remote sensing data (RS) are increasingly being used. However, the RS-based models inherit uncertainty from many sources, such as the model’s algorithm and parameters, input satellite data, and processing techniques. It is challenging to assess this uncertainty due to limitations of validation data, high volume and high dimensionality of RS data. Many studies have evaluated uncertainty in RS-based estimation of ET using different methods and reference data. The suitability of methods and reference data subsequently affect the validity of these evaluations. Therefore, it is necessary to have an overview of different evaluation methods and their uses. This study aimed to systematically review original research papers that assessed uncertainty or accuracy of RS-ET model or data products. We categorized these papers and quantified based on (i) spatial and temporal scale of ET estimation, (ii) types of uncertainty, and (iii) methods used to assess uncertainty. Studies have been geographically concentrated in North Asia, North America, and Europe. Most studies used the validation method, which quantifies the discrepancy between pixel-based ET estimation with an in-situ estimation. Although a standardized validation approach for satellite-based ET estimates is not yet ready, most validation studies employed Eddy Covariance (EC) flux towers for reference estimation at field-scale. In regions where in-situ measurements are limited, many studies use the residual of the water balance as reference. However, few studies considered uncertainty in the reference estimation and mismatch of spatial and temporal scales. For monitoring agricultural fields, most RS-ET methods have been reported with high accuracy. When applying these methods to larger extent, additional assessments are required to better inform data users of the quality of RS-ET estimation. These include cross-validation, sensitivity, and uncertainty analyses. Overall, this review showed the progress in evapotranspiration estimation using satellite data in terms of uncertainty assessment.

The growing competition for finite land and water resources and the need to feed an ever-growing population require new techniques to monitor the performance of irrigation schemes and improve land and water productivity. Datasets from FAO's portal to monitor Water Productivity through Open access Remotely sensed derived data (WaPOR) are increasingly applied as a cost-effective means to support irrigation performance assessment and identify possible pathways for improvement. This study presents a framework that applies WaPOR data to assess irrigation performance indicators, including uniformity, equity, adequacy, and land and water productivity differentiated by irrigation method (furrow, sprinkler, and centre pivot) at the Xinavane sugarcane estate, Mozambique. The WaPOR data on water, land, and climate are in near-real time and spatially distributed, with the finest spatial resolution in the area of 100gm. The WaPOR data were first validated agronomically by examining the biomass response to water, and then the data were used to systematically analyse seasonal indicators for the period 2015 to 2018 on g1/48000gha. The WaPOR-based yield estimates were found to be comparable to the estate-measured yields with ±20g% difference, a root mean square error of 19±2.5gtgha-1 and a mean absolute error of 15±1.6gtgha-1. A climate normalization factor that enables the spatial and temporal comparison of performance indicators are applied. The assessment highlights that in Xinavane no single irrigation method performs the best across all performance indicators. Centre pivot compared to sprinkler and furrow irrigation shows higher adequacy, equity, and land productivity but lower water productivity. The three irrigation methods have excellent uniformity (g1/494g%) in the four seasons and acceptable adequacy for most periods of the season except in 2016, when a drought was observed. While this study is done for sugarcane in one irrigation scheme, the approach can be broadened to compare other crops across fields or irrigation schemes across Africa with diverse management units in the different agroclimatic zones within FAO WaPOR coverage. We conclude that the framework is useful for assessing irrigation performance using the WaPOR dataset.

Dam construction and operation are known to alter the hydrology of rivers and degrade riverine ecosystems. In recent decades, the call to reverse these negative impacts by re-operating dams has become stronger. Dams can support riverine ecosystems by releasing environmental flows (e-flows). Unfortunately, despite the development of numerous methodologies to determine e-flows and optimise dam releases, actual implementation has not followed suit. Integrating e-flow requirements in the design of new dams is relatively easier than changing operations of existing dams; however, re-operating existing dams is essential to restore ecosystems and ecosystem services that have already been affected by the construction and operation of dams. This study provides insights into how e-flows evolve from recommendation to practice through a systematic literature review on practical experiences to integrate e-flows in dam operations. Sixty-nine cases of successful dam re-operation have been identified, ranging from the well-documented case of the Glen Canyon Dam in the United States to less known cases such as the Katse Dam in Lesotho. We find that the most important factors that facilitate the successful implementation of e-flows are the existence of e-flows legislation or policy, the development of a research base in the form of an environmental impact study, and then flow experimentation. Illustrations of the important role of collaboration between various stakeholders and set timelines for implementation of recommendations are also given. These insights will inform how existing dams can be re-operated and governed more equitably and sustainably for both humans and the environment.

The concept of integrated water resource management (IWRM) attempts to integrate all elements of water resources. Different tools are developed to assist in developing sound IWRM plans. One such tool is multiobjective analysis using an integrated hydro-economic model (IHEM). However, IHEM mainly deals with the optimization of river flow (blue water) in a river basin. This paper linked a distributed model of green water (landscape water uses) in the upper catchment with mainly blue water uses in the lower catchment of the Pangani Basin. The results show that agricultural water use has the highest water productivity and competes with all other objective functions in the catchment. The generation of firm energy competes with the downstream ecosystem requirements. The integrated study shows that improving rainfed cropping through supplementary irrigation has comparable marginal water values to full-scale irrigation but are much higher compared with hydropower. However, hydropower has more benefits if used in conjunction with the environment. The methodological approach has increased the understanding of trade-offs between green and blue water uses that are highly interdependent in African landscapes.

Scarcity of information on the water productivity of different water, land, and other ecosystems in Africa, hampers the optimal allocation of the limited water resources. This study presents an innovative method to quantify the spatial variability of biomass production, crop yield, and economic water productivity, in a data scarce landscape of the Pangani Basin. For the first time, gross return from carbon credits and other ecosystem services are considered, in the concept of Economic Water Productivity. The analysis relied on the MODIS satellite data of 250 m and eight-day resolutions, and the SEBAL model, utilizing Monteith's framework for ecological production. In agriculture, irrigated sugarcane and rice achieved the highest water productivities in both biophysical and economic values. Rainfed and supplementary irrigated banana and maize productivities were significantly lower than the potential values, reflecting a wide spatial variability. In natural landscapes, forest and wetland showed the highest biomass production. However, the transition to economic productivity was low but showed the potential to increase significantly when non-market goods and services were considered. Spatially explicit information, from both biophysical and economic water productivity, provides a holistic outlook of the socio-environmental and the economic water values of a land-use activity, and can identify areas for improvement, and trade-offs in river basins.

To meet growing population demands for food and other agricultural commodities, agricultural land-use intensification and extensification seems to be increasing in the Abbay (Upper Blue Nile) basin in Ethiopia. However, the amount, location and degree of suitability of the basin for agriculture seem not well studied and/or documented. From global data sources, literature review and field investigation, a number of agricultural land suitability evaluation criteria were identified. These criteria were pre-processed as raster layers on a GIS platform and weights of criteria raster layers in determining suitability were computed using the analytic hierarchy process (AHP). A weighted overlay analysis method was used to compute categories of highly suitable, moderately suitable, marginally suitable and unsuitable lands for agriculture in the basin. It was found out that 53.8 % of the basin’s land coverage was highly suitable for agriculture and 23.2 % was moderately suitable. The marginally suitable and the unsuitable lands were at 11 and 12 % respectively. From the analysis, regions of the basin with high suitability as well as those with higher susceptibility for land degradation and soil erosion were identified.