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\"For integrated water resources management both blue and green water resources in a river basin and their spatial and temporal distribution have to be considered. This is because green and blue water uses are interdependent. In sub-Saharan Africa, the upper landscapes are often dominated by rainfed and supplementary irrigated agriculture that rely on green water resources. Downstream, most blue water uses are confined to the river channels, mainly for hydropower and the environment. Over time and due to population growth and increased demands for food and energy, water use of both green and blue water has increased. This book provides a quantitative assessment of green-blue water use and their interactions. The book makes a novel contribution by developing a hydrological model that can quantify not only green but also blue water use by many smallholder farmers scattered throughout the landscape. The book provides an innovative framework for mapping ecological productivity where gross returns from water consumed in agricultural and natural vegetation are quantified. The book provides a multi-objective optimization analysis involving green and blue water users, including the environment. The book also assesses the uncertainty levels of using remote sensing data in water resource management at river basin scale.\" --Back cover.

Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in a range of applications. However, it remains unclear whether DL models can produce physically plausible projections of streamflow under climate change. We investigate this question through a sensitivity analysis of modeled responses to increases in temperature and potential evapotranspiration (PET), with other meteorological variables left unchanged. Previous research has shown that temperature-based PET methods overestimate evaporative water loss under warming compared with energy budget-based PET methods. We therefore assume that reliable streamflow responses to warming should exhibit less evaporative water loss when forced with smaller, energy-budget-based PET compared with temperature-based PET. We conduct this assessment using three conceptual, process-based rainfall–runoff models and three DL models, trained and tested across 212 watersheds in the Great Lakes basin. The DL models include a Long Short-Term Memory network (LSTM), a mass-conserving LSTM (MC-LSTM), and a novel variant of the MC-LSTM that also respects the relationship between PET and evaporative water loss (MC-LSTM-PET). After validating models against historical streamflow and actual evapotranspiration, we force all models with scenarios of warming, historical precipitation, and both temperature-based (Hamon) and energy-budget-based (Priestley–Taylor) PET, and compare their responses in long-term mean daily flow, low flows, high flows, and seasonal streamflow timing. We also explore similar responses using a national LSTM fit to 531 watersheds across the United States to assess how the inclusion of a larger and more diverse set of basins influences signals of hydrological response under warming. The main results of this study are as follows: The three Great Lakes DL models substantially outperform all process-based models in streamflow estimation. The MC-LSTM-PET also matches the best process-based models and outperforms the MC-LSTM in estimating actual evapotranspiration. All process-based models show a downward shift in long-term mean daily flows under warming, but median shifts are considerably larger under temperature-based PET (−17 % to −25 %) than energy-budget-based PET (−6 % to −9 %). The MC-LSTM-PET model exhibits similar differences in water loss across the different PET forcings. Conversely, the LSTM exhibits unrealistically large water losses under warming using Priestley–Taylor PET (−20 %), while the MC-LSTM is relatively insensitive to the PET method. DL models exhibit smaller changes in high flows and seasonal timing of flows as compared with the process-based models, while DL estimates of low flows are within the range estimated by the process-based models. Like the Great Lakes LSTM, the national LSTM also shows unrealistically large water losses under warming (−25 %), but it is more stable when many inputs are changed under warming and better aligns with process-based model responses for seasonal timing of flows. Ultimately, the results of this sensitivity analysis suggest that physical considerations regarding model architecture and input variables may be necessary to promote the physical realism of deep-learning-based hydrological projections under climate change.

Quantifying the impact of climate change on evapotranspiration is necessary for devising accurate water and energy budgets in light of global warming. Nevertheless, in the Middle East and North Africa (MENA), little has been done to bridge this gap. This study, then, implements Penman and Budyko approaches to climatic data retrieved from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to assess evapotranspiration and water availability evolutions through the twenty-first century. Outcomes reveal that the MENA region is indeed vulnerable to a surge in temperature, which can increase evapotranspiration losses and decrease water availability. Under the shared socioeconomic pathway (SSP2-4.5), the potential evapotranspiration (PET) has been projected to increase throughout the MENA region by up to 0.37 mm per year during the middle of the twenty-first century (2021–2050) and by up to 0.51 mm per year during the end of the twenty-first century (2071–2100). Meanwhile, the actual evapotranspiration (AET) has been projected to increase by up to 0.3 (~0.2) mm per year before 2050 (2100). The trends in both projections (PET and AET) are exaggerated under SSP5-8.5. The analysis predicted a shortage of water availability (precipitation—AET), which is alarming for most MENA regions. Relative to the reference period (1981–2010), the decline in annual water availability would reach 26 (62) mm by 2100 under SSP2-4.5 (SSP5-8.5). The rise in temperatures appears to be the principal reason for MENA and water availability responses. This study’s outcomes can facilitate accurate and realistic predictions related to evapotranspiration and water availability, which are key elements in not only managing water resources but also in devising effective climate change mitigation and adaptation plans. Graphical abstract

The future state of global evapotranspiration (ET) estimation under climate change remains uncertain. Current formulations primarily developed based on the high emission CMIP5 scenario, have been widely used to represent conditions under elevated greenhouse gas pathways. However, these formulations may not adequately capture the enhanced vegetation–climate interactions projected under the lower-emission scenarios of CMIP6. Without updates to account for evolving plant physiological responses to rising CO2, projections may overlook critical feedbacks between atmospheric CO2 concentrations, vegetation behavior, and hydrological processes. To address this, developing CMIP6-specific formulations is essential to leverage its improved datasets and reduce uncertainties in future ET simulations. In this study, we update the Penman-Monteith evapotranspiration (PM-ET) model by incorporating the CO2-vegetation coupling effect. This is achieved using outputs from four Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Results indicate a sustained historical increase in potential evapotranspiration (Ep). The inclusion of CO2 physiological effects reduces the deviation in projected ET trends by approximately 15 %–20 % compared to CMIP5-based frameworks, accounting for the increase in stomatal resistance driven by CO2 concentrations rising from ∼284 to ∼ 935 ppm. Furthermore, our model predicts an increasing dependence of ET projections on emission scenario, highlighting the growing influence of pathway-specific feedbacks. Overall, our approach demonstrates greater compatibility with CMIP6 simulations, allowing for more accurate representation of ET responses to future CO2 increases. These findings provide valuable insights for advancing the analysis of nonlinear vegetation-atmosphere interactions and hydrological uncertainty under climate and physiological forcings.

Potential evapotranspiration (PET) serves as a proxy for estimating actual evapotranspiration (AET) in hydrological model simulations and constitutes an input for drought analyses. Nonetheless, it is elusive how the inclusion of different PET models in a modeling chain, which encapsulates multiple general circulation models (GCMs) operating under varying emission scenarios, can affect drought projections. In this study, utilizing four GCMs, two representative concentration pathways (RCPs), and eleven widely used PET models, ensemble projections of the frequency of droughts in the Gordes, a semi-arid watershed located in Western Turkey, were derived for the period 2021–2050. The standardized precipitation evapotranspiration index (SPEI) was used to characterize meteorological drought, while the standardized runoff index (SRI) was preferred for inspecting hydrological drought. Using an analysis of variance decomposition, the contribution of each stage of the modeling chain to both meteorological and hydrological drought uncertainty was quantified. Results show that PET models expectedly produced large disparities in projected changes in the evapotranspiration regime. Even so, only the 25% uncertainty contribution of PET models to severe meteorological drought frequency can be deemed notable. Yet, their contribution to uncertainties in mild and moderate meteorological drought frequencies is rather marginal (≤ 5%) compared to what GCMs overwhelmingly do. It is also worth noting that SPEI and SRI respond differently to sources of uncertainty and that SRI suggests drought frequencies of significantly lower amounts compared to those of SPEI, possibly because the temperature dependence of AET, which SRI considers synthetically, is much less than that of PET.

Surface energy and water balance calculations across crop surfaces improve comprehension of water balance and facilitates water usage which is cost-effective. With this idea, field trails were conducted at Tamil Nadu Agricultural University, Coimbatore during two cropping seasons i.e., winter (Jan-March) and kharif (June-Sep) , 2022 to study crop development, energy balance partitioning and evapotranspiration rate at various growth stage of baby corn under different sowing windows. Energy balance components were studied using Bowen Ratio Energy Balance method (BREB). Crop evapotranspiration was measured and simulated using AquaCrop model. Results clearly indicated that maximum energy balance components such as net radiation ( Rn ), Latent Heat Flux ( LE ), Sensible heat flux ( H ), Ground heat flux ( G ) were recorded at maturity stage, among crop growing cycle. Early sowing accumulated more amount of energy balance than mid and late sown crops during both seasons. Daily Kc values varied significantly from 0.05 to 1.01 and 0.01 to 0.96 for winter and kharif seasons, respectively. Good correlation was observed between calculated and simulated daily crop evapotranspiration (Etc). The total measured Etc was 486 and 624.4 mm for winter and kharif , respectively whereas AquaCrop simulated ETc of 438.8 mm and 500.4 mm. The digital agricultural technologies like crop simulation models would be useful to increase the accuracy of ET calculation in agricultural water management. This study examines the effective approaches used in estimating ET for baby corn water management which could be made to boost the precision of ET estimation and achieve precise water management.

Five reanalysis datasets—National Centers for Environmental Prediction reanalysis II (NCEP-2), NCEP Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), Japanese 55-year Reanalysis Project (JRA-55), and National Aeronautics and Space Administration (NASA) Modern Era Reanalysis for Research and Applications Version-2 (MERRA-2)—are selected to estimate meteorological droughts of China using three drought indices—the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI). Drought indices, drought areas and drought severity estimated for China from these reanalysis datasets are assessed against corresponding results obtained from observed climate dataset of China using Nash–Sutcliffe efficiency (NSE), correlation coefficient, and the analysis of time series. Further, temperature, precipitation and potential evapotranspiration data of the five reanalysis datasets are also compared against the observed dataset. Drought indices and drought areas estimated from reanalysis datasets are generally more representative of historical droughts that had occurred in eastern China than in western China. However, the performance of these five reanalysis datasets in representing the drought severity is unsatisfactory in both western China and eastern China. SPEI is generally more representative than PDSI and SPI partly because temperature and potential evapotranspiration data of reanalysis datasets are generally better than precipitation data. PDSI is also based on a supply-and-demand model of soil moisture but estimating the demand of soil moisture is complicated. Therefore, SPEI is preferred over PDSI and SPI as the drought index to characterize the meteorological droughts of China. Climate data and meteorological drought characteristics of eastern China are best represented by JRA-55, while that of western China are best represented by MERRA-2. From 1980 to 2014, statistically significant increasing trends in annual drought areas and drought severity are detected from JRA-55 and observed climate datasets in eastern China, but they are only detected from observed dataset in western China.

Quantifying global terrestrial evapotranspiration (ET) relies on models with different levels of complexity. The water balance method offers a straightforward approach for benchmarking complex ET models, as evidenced by the widely-used terrestrial water-balance-based ET (ET TWB ) data. However, deriving ET TWB must rely on ground-observed runoff data, which is not feasible for ungauged or poorly-gauged regions. In this context, the atmospheric water balance (AWB) method offers an alternative for estimating ET, which can be applied to the entire global land area. Nevertheless, the accuracy of the AWB approach in estimating global ET remains poorly understood. In this study, we generated monthly atmospheric water-balance-based ET (ET AWB ) globally from 1983 to 2020 at a 0.25° resolution using multi-source data. Validations against the annual ET TWB of 56 large river basins suggest that ET AWB , estimated using the moisture convergence and atmospheric water vapor from the fifth generation of European Center for Medium-Range Weather Forecasts Reanalysis (ERA5) and the precipitation from four observation-based products, is overall accurate. Specifically, the AWB method yields Nash–Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and relative bias (RB) of 0.88, 89.5 mm year −1 , and 2%, respectively. These statistical metrics indicate that the AWB method is generally on par with current mainstream ET models. However, the AWB approach still has certain challenges in capturing the trend in ET. The ensemble mean ET AWB , estimated using the moisture convergence and atmospheric water vapor from ERA5 and four precipitation datasets, yields a global-averaged value of 619 ± 8 mm year −1 (excluding Antarctica) and shows an increase of 2.1% from 1983 to 2020, with a trend of 0.35 mm year −1 . Tropical regions exhibit pronounced interannual variability in ET AWB due to the internal climate variability influencing precipitation and moisture convergence. The current AWB approach  can potentially improve the understanding of regional and global ET processes, as it represents an independent approach to ET estimation, distinct from current remote sensing and land surface models.

Accurate estimation of evapotranspiration (ET) is essential both at the regional and local scales for many management tasks. Numerous methods for estimating ET with various complexities and combinations exist which may be broadly classified as direct and indirect methods. Information on ET estimation uncertainties cannot be overemphasized and ignoring them can misguide decision-making in management of water resources. This study reviews the uncertainties in ET estimations and suggests ways to reduce them. Identified in this study are uncertainties associated with ET methods and input data, uncertainties due to spatial and temporal scales, and uncertainties based on region. Many studies have the ET method related uncertainties. The ground-based techniques generally used as a standard for comparing other methods have considerable uncertainty (10–30%) associated with the input components. The errors from the input reflect in the estimated ET output irrespective of the model used. Datasets from satellite products are based on in-situ network forcing as well as on model’s estimation and remote sensing (RS), and they are prone to errors as a result of differences in in-situ measurements, scale, sensor calibration and basics of model theory and parametrization. Generally, uncertainties associated with ET were found to vary temporally. Also, homogeneity and stability of potential evapotranspiration (PET) were worse in space than in time, indicating that the temporal distribution of PET was more uniform and stable compared to spatial distribution. Some ET RS products showed less uncertainty in coarse resolution and comparatively high uncertainty in fine resolution. This study identified five ways to minimize uncertainties in ET estimations. Minimizing uncertainty in ET estimation will definitely improve planning, management and use of water resources especially where accurate estimations are required.

The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface–atmosphere interactions. However, its magnitude remains highly uncertain at the global scale. Here we apply an emergent constraint approach that integrates CMIP5 Earth system models (ESMs) with 33 field T/ET measurements to re-estimate the global T/ET value. Our observational constraint strongly increases the original ESM estimates (0.41 ± 0.11) and greatly alleviates intermodel discrepancy, which leads to a new global T/ET estimate of 0.62 ± 0.06. For all the ESMs, the leaf area index is identified as the primary driver of spatial variations of T/ET, but to correct its bias generates a larger T/ET underestimation than the original ESM output. We present evidence that the ESM underestimation of T/ET is, instead, attributable to inaccurate representation of canopy light use, interception loss and root water uptake processes in the ESMs. These processes should be prioritized to reduce model uncertainties in the global hydrological cycle.