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.

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.

Navigating the complexities of global and local water resources challenges requires collaboration and mutual learning among diverse knowledge systems and disciplines. However, Western philosophical approaches to generating knowledge have prevailed in water management and hydrology, often overlooking community priorities, practices and perspectives, and power asymmetries - including gender inequalities, racism, and colonial injustices. In this perspective paper, we explore the co-creation of water knowledge (CCWK) concept to value multiple and diverse forms of knowledge. We identify four overarching principles (inclusivity, openness, legitimacy, and actionability), highlighting the importance of establishing relationships and collaborative leadership, adopting key tools and techniques, and integrating knowledge for water resources management. Furthermore, we argue that prioritizing epistemic justice is essential for effective CCWK. To address these, we advocate for more interdisciplinary and reflexive research practices that challenge and disrupt Western scientific traditions shaped by functionalist and colonial legacies.

A growing scholarship suggests hydrological models have political power as they embed and reinforce specific understandings of water and society relations which, in turn, shape future visions of how and for whom water is to be managed. In this commentary, we explore how the power of models can be explicitly and constructively engaged with, thereby expanding their potential to support transformations to water justice and sustainability. To achieve this, we suggest understanding, analyzing, and doing hydrological modeling as a situated knowledge practice. We take inspiration from feminist scholarship that emphasizes that all forms of knowledge are inherently partial, situated within specific contexts, experiences, and circumstances, and shaped by power relations. Situating hydrological modeling, we argue, requires opening up modeling processes to ask where, how, for whom, and by whom models are developed and used, and how outcomes influence water distributions and conditions of access for different social groups. Situating also opens opportunities to explore what it would take for hydrological modeling to explicitly pursue justice and sustainability goals in context-specific and tangible ways. We present initial insights and invite further experimentation towards making models active agents of a more inclusive, transparent, and transformative water management.

Open-access remote sensing products provide data for transboundary water management. This study presents a comprehensive overview of the applications, uncertainties and implications of these remote sensing data products in the context of transboundary water management. Focusing on different stages within the transboundary cooperation continuum, we delineate the potential role and application of remote sensing data at the various stages of this cooperation. Despite the uncertainties and capacity requirements for data acquisition, processing and interpretation, we argue that remote sensing broadens opportunities to monitor, assess, forecast, track or validate compliance in transboundary basins, thereby challenging traditional notions of water data exclusivity.

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.