Actual Evapotranspiration Research Articles

A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022

Droughts cause multiple ecological and social damages. Drought indices are key tools to quantify drought severity, but they are mainly limited to timescales of monthly or longer. However, shorter-timescale (e.g., daily) drought indices enable more accurate identification of drought characteristics (e.g., onset and cessation time) and help timely potential mitigation of adverse effects. Here, we propose a dataset of a daily drought index named daily evapotranspiration deficit index (DEDI), which is produced for global land areas from 1979 to 2022 using actual and potential evapotranspiration data. Validation efforts show that the DEDI dataset can well identify dry and wet variations in terms of spatial patterns and temporal evolutions when compared with other available drought indices on a daily scale. The dataset also has the capability to capture recent drying trends and to detect ecology- or agriculture-related droughts. Overall, the DEDI dataset is a step forward in facilitating drought monitoring and early warning at higher temporal resolution than other compared existing products.

Average monthly recharge, surface runoff, and actual evapotranspiration estimation using WetSpass-M model in Low Folded Zone, Iraq

Abstract The evaluation of the spatial and temporal distribution of groundwater recharge is required to develop the regional groundwater model for more precise simulations of various management scenarios. WetSpass-M (Water and Energy Transfer between Soil, Plants, and Atmosphere under Steady-State Conditions), which is a GIS-based spatially distributed water balance model, is deployed to evaluate monthly groundwater recharge, surface runoff, and actual evapotranspiration in the Low Folded Zone from 2000 to 2019. ArcGIS software prepares the essential relevant input data for the Wetspass-M model as grid maps. They include monthly climatological measurements (precipitation, temperature, and wind speed), land cover distribution, soil map, groundwater depth, topography, and slope. The mean annual groundwater recharge, evapotranspiration, and runoff were found to be 128, 131, and 72 mm, respectively. Accordingly, recharge accounts for 39% of the precipitation, while the rest, 40 and 21%, become evapotranspiration and surface runoff, respectively. WetSpass-M model results are meant to enable integrated groundwater modeling. The study of simulation data demonstrates that the WetSpass-M model accurately simulates the hydrological water budget components of the Low Folded Zone. In addition, a better understanding of the simulated spatial and temporal distribution of water balance components is beneficial for managing and planning the available water resources of the Low Folded Zone in Iraq, which faces water scarcity threats.

Climate-growth relation and isotopic △13carbon responses of field-grown Pinus caribaea var. hondurensis and Pinus taeda to nutrient availability

This study aimed to characterize the development of Pinus caribaea var. hondurensis and Pinus taeda grown under two fertilization regimes (fertilized and control treatments) at field-grown condition. The growth, climate-growth relation, and intrinsic water use efficiency (WUEi) of the trees were assessed by a combined analysis of cross-sectional area (CSA) increment, water balance and carbon isotope discrimination (Δ13C ‰). The effect of fertilization on tree growth regime was assessed using inventory data collected yearly over 8 years. Additionally, monthly data of wood increments, monitored in nonfertilized stands over a period of 6 years, were utilized for correlations with meteorological variables (air temperature, rainfall, vapor pressure deficit, solar radiation and potential and actual evapotranspiration). The growth of Pinus caribaea and P. taeda was strongly influenced by multiple meteorological variables related to water availability and evapotranspiration. Differences in carbon isotope composition between treatments revealed that fertilized Pinus spp. displayed higher Δ13C values in some years, indicating improved water uptake efficiency due to increased nutrient availability. Pinus caribaea outperformed P. taeda in wood productivity and exhibited a heightened responsiveness to fertilization. In general, P. caribaea was shown to be more water-use efficient than P. taeda, as it is able to use smaller amounts of water for greater wood production.

Characteristics of warming and humidification in the Yellow River's upper reaches and their impact on surface water resources

AbstractBased on multi‐source data, including observation, analysis, model calculation, satellite remote sensing, and scenario estimation data, this study has systematically analysed the warming and dry–wet change characteristics of the Yellow River's upper reaches in the last 60 years. The causes and features of the current warming and humidification and their impact on surface water resources are also discussed. This study found that since 1961, the average temperature in the Yellow River's upper reaches has increased at a rate of 0.37°C/10a, which is more significant than the increases observed for the world, China, and even northwest China in the same period. At the beginning of this century, the rate of change in precipitation in the Yellow River's upper reaches reversed direction from decreasing to increasing, with a significant increase (35 mm/10a) since 2000. Therefore, climate change has shown a significant warming and humidification trend in the upper reaches of the Yellow River. From the dynamic change perspective, the Yellow River's upper reaches were warming and drying in each climatic period from 1961 to 2010; however, the tendency towards dryness slowed period by period. In contrast, the region showed consistent warming and humidification from 1991 to 2020. From the perspective of multiple time scales, the change in average temperature is mainly dominated by the multi‐decadal scale. In contrast, precipitation is dominated by interannual change. The increase in the multi‐decadal component in average temperature and precipitation explains the current warming and humidification. Precipitation changes in the upper reaches of the Yellow River Basin are mainly affected by the westerly circulation, the East Asian summer monsoon, and the South Asian summer monsoon circulation systems. However, precipitation was affected by different circulation factors on different time scales. On an interannual scale, it was mainly affected by the South Asian summer monsoon circulation. On an interdecadal scale, it was mainly affected by the westerly wind and the East Asian summer monsoon circulation. On a multi‐decadal scale, it was jointly influenced by the westerly wind, the East Asian summer monsoon, and the South Asian summer monsoon circulation. Under the control of multi‐decadal synergistic enhancement of the westerly, the East Asian summer monsoon, and the South Asian summer monsoon circulations, the vertical field shows an abnormal upward movement. There has been a significant abnormal easterly and southerly airflow that transports water vapour, and cold air is transported along the northwest airflow at the back of the Mongolian cyclone, which makes the climate in the upper reaches of the Yellow River more humid. The warming and humidifying of the upper reaches of the Yellow River has significantly increased runoff and lake area and significantly degraded the frozen soil. The surface water budget in the Yellow River's upper reaches has significantly responded to the current warming and humidification. The actual surface evapotranspiration and surface runoff increased significantly, but the net surface water budget still showed a decreasing trend. The increase in precipitation did not change the surface water deficit.

Effects of Climate and Land-Use Change on the Supply and Demand Relationship of Water Provision Services in the Yellow River Basin

The Yellow River Basin (YRB) has undergone profound climate and land-use change. These transformations are anticipated to affect the availability of water resources, potentially causing substantial perturbations to the equilibrium between water availability and societal needs. Consequently, research is warranted to explore the ramifications of climate and land-use change on the water provision service, particularly their impacts on the delicate equilibrium between supply and demand. To quantify the water supply–demand relationship, this study introduces the water supply and demand index (WSDR). This study examines the impacts of climate and land-use change on the actual evapotranspiration, water yield, and WSDR in the YRB from 1995 to 2019. According to the results, the YRB experienced an increase in forest land by 4.72%, grassland by 1.71%, and a substantial surge of 595.36% in construction land; however, cropland witnessed a decrease of 5.95%. The climate exhibited a discernible wetting trend (3.38 mm/year, p < 0.01). The actual evapotranspiration significantly increased by 2.45 mm/year (p < 0.01), but this increase was not substantial enough to result in a decline in the water yield, since precipitation also increased. The annual water demand depth demonstrated a statistically significant increasing trend (0.13 mm/year, p < 0.01), but this increase was not sufficient to cause a decrease in the WSDR, since the water yield also increased (0.94 mm/year). Land use had a significantly negative effect on the water yield and WSDR, but this negative effect was offset by the positive effects of climate variability. At the basin scale, there exists a surplus of water resources (WSDR > 0). Nevertheless, regions grappling with water resource deficits (WSDR < 0) have witnessed an increase from 18% in 1995 to 26% in 2019. This indicates that the primary water resource problem in the YRB is the uneven spatial distribution of water resources. Hence, implementing spatial management strategies at larger scales may be instrumental in mitigating water scarcity in the region. These findings can serve as a valuable reference for the management of water resources, as well as for basin planning and construction.

Calibration of Land-Use-Dependent Evaporation Parameters in Distributed Hydrological Models Using MODIS Evaporation Time Series Data

The land-use-specific calibration of evapotranspiration parameters in hydrologic modeling is challenging due to the lack of appropriate reference data. We present a MODIS-based calibration approach of vegetation-related evaporation parameters for two mesoscale catchments in western Germany with the physically based distributed hydrological model WaSiM-ETH. Time series of land-use-specific actual evapotranspiration (ETa) patterns were generated from MOD16A2 evapotranspiration and CORINE land-cover data from homogeneous image pixels for the major land-cover types in the region. Manual calibration was then carried out for 1D single-cell models, each representing a specific land-use type based on aggregated 11-year mean ETa values using SKout and PBIAS as objective functions (SKout > 0.8, |PBIAS| < 5%). The spatio-temporal evaluation on the catchment scale was conducted by comparing the simulated ETa pattern to six daily ETa grids derived from LANDSAT data. The results show a clear overall improvement in the SPAEF (spatial efficiency metric) for most land-use types, with some deficiencies for two scenes in spring and late summer due to phenological variation and a particularly dry hydrological system state, respectively. The presented method demonstrates a significant improvement in the simulation of ETa regarding both time and spatial scale.

Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using ground and remotely sensed data

The knowledge of crop evapotranspiration is crucial for several hydrological processes, including those related to the management of agricultural water sources. In particular, the estimations of actual evapotranspiration fluxes within fields are essential to managing irrigation strategies to save water and preserve water resources. Among the indirect methods to estimate actual evapotranspiration, ETa, the eddy covariance (EC) method allows to acquire continuous measurement of latent heat flux (LE). However, the time series of EC measurements are sometimes characterized by a lack of data due to the sensors' malfunctions. At this aim, Machine Learning (ML) techniques could represent a powerful tool to fill possible gaps in the time series. In this paper, the ML technique was applied using the Gaussian Process Regression (GPR) algorithm to fill gaps in daily actual evapotranspiration. The technique was tested in six different plots, two in Italy, three in the United States of America, and one in Canada, with different crops and climatic conditions in order to consider the suitability of the ML model in various contexts. For each site, the climate variables were not the same, therefore, the performance of the method was investigated on the basis of the available information. Initially, a comparison of ground and reanalysis data, where both databases were available, and between two different satellite products, when both databases were available, have been conducted. Then, the GPR model was tested. The mean and the covariance functions were set by considering a database of climate variables, soil water status measurements, and remotely sensed vegetation indices. Then, five different combinations of variables were analyzed to verify the suitability of the ML approach when limited input data are available or when the weather variables are replaced with reanalysis data. Cross-validation was used to assess the performance of the procedure. The model performances were assessed based on the statistical indicators: Root Mean Square Error (RMSE), coefficient of determination (R2), Mean Absolute Error (MAE), regression coefficient (b), and Nash-Sutcliffe efficiency coefficient (NSE). The quite high Nash Sutcliffe Efficiency (NSE) coefficient, and the root mean square error (RMSE) low values confirm the suitability of the proposed algorithm.

Global evapotranspiration models and their performance at different spatial scales: Contrasting a latitudinal gradient against global catchments

Actual evapotranspiration (AET) is a key variable in the global water balance, driving agricultural production and ecosystem health. It is a complex hydrologic process that depends on vegetation, climate, and available water conditions. Different moderate resolution global AET models have been developed to quantify water resources at large scales. In this work we evaluate five of these products, including MODIS, PML, SSEBop, TerraClimate, and a Synthesis AET using point and catchment-scale datasets based on flux towers. We also contrast water balance changes with total water storage (TWS) products. These comparisons cover different radiation and precipitation regimes over catchments around the world and along a strong climatic gradient in north-central Chile. We rank the models, contrast TWS datasets, and study differences related to scale in validation and the effect of rainfall and radiation on simulated values. Additionally, we use a Budyko framework to evaluate the AET products in terms of their agreement with expected water budgets. At different evaluation scales, AET estimates and observations agreed reasonably well, with the largest mean R2 of about 0.7 and errors of approximately 15% of the magnitude of the observed variables. MODIS and Synthesis AET had the highest R2 at the point (0.62) and at the catchment scales (0.71 and 0.59 for regional and global catchments), respectively, but were closely followed by PML. PML and TerraClimate led to the lowest magnitude errors at the point (RMSE = 0.78 mm day−1) and catchment scales (mean RMSE = 1.5 mm day−1), respectively. The rainfall gradient is reflected in a performance gradient. PML, MODIS, and TerraClimate gave consistent behaviour based on the Budyko curve, with a few arid catchments exceeding the water limit. The major conclusion is that remotely sensed AET outperforms flux tower AET extrapolation for water balance calculations at the catchment scale, which means that errors in satellite-based AET products tend to cancel out at larger spatial scales, which makes them viable alternatives for regional water balance studies. However, flux data integrated into AET models, such as the FluxCom model, leads to the lowest errors. The assimilation and downscaling of Gravity Recovery and Climate Experiment (GRACE) into the Global Land Data Assimilation System (GLDAS) leads to an improvement in regional results compared with other TWS products.

Evapotranspiration of Palmer amaranth (Amaranthus palmeri) in maize, soybean, and fallow under subsurface drip and center-pivot irrigation systems

AbstractPalmer amaranth (Amaranthus palmeri S. Watson) is a major biotic constraint in agronomic cropping systems in the United States. While crop–weed competition models offer a beneficial tool for understanding and predicting crop yield losses, within these models, certain weed biological characteristics and their responses to the environment are unknown. This limits understanding of weed growth in competition with crops under different irrigation methods and how competition for soil moisture affects crop growth parameters. This research measured the effect of center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) on the actual evapotranspiration (ETa) of A. palmeri grown in maize (Zea mays L.), soybean [Glycine max (L.) Merr.], and fallow subplots. Twelve A. palmeri plants were alternately transplanted 1 m apart in the middle two rows of maize, soybean, and fallow subplots under CPI and SDI in 2019 and 2020 in south-central Nebraska. Maize, soybean, and fallow subplots without A. palmeri were included for comparison. Soil-moisture sensors were installed at 0-0.30, 0.30-0.60, and 0.60-0.90-m soil depths next to or between three A. palmeri and crop plants in each subplot. Soil-moisture data were recorded hourly from the time of A. palmeri transplanting to crop harvest. The results indicate differences in A. palmeri ETa between time of season (early, mid-, and late season) and crop type across 2019 and 2020. Although irrigation type did not affect subplot data, the presence of A. palmeri had an impact on subplot ETa across both years, which can be attributed to the variable relationship between volumetric soil water content (VWC) and ETa throughout the growing season due to advancing phenological stages and management practices. This study provides important and first-established baseline data and information about A. palmeri evapotranspiration and its relation to morphological features for future use in mechanistic crop–weed competition models.

Modeling the daily dynamics of grass growth of several species according to their functional type, based on soil water and nitrogen dynamics: Gras-Sim model definition, parametrization and evaluation

Gras-Sim model, through the environmental conditions and the dynamics of water and nitrogen in the soil, enables the prediction of the biomass yield in permanent grasslands. It was developed from existing models and simulates the dynamics of several grass species grouped into plant functional types (PFTs) A and B. Model inputs include weather data, fertilizer application, soil data, and cutting management. In contrast to previous models, Gras-Sim proposes a complete nitrogen balance at the field scale as well as a new formalism to estimate actual evapotranspiration based on the crop coefficient (Kc) for a better prediction of biomass production even under moderate stress. Gras-Sim was evaluated in this paper on the basis of data from experiments conducted between 2010 and 2018, on 3 sites fairly representative of the soil and climate conditions in Wallonia (Belgium). The relative root mean square error (RRMSE), normalized deviation (ND), and model efficiency (EF) across all cuts, sites, and PFTs were 29 %, 2 %, and 71 % respectively, for biomass production. Gras-Sim is a simple and efficient model that can be used as a starting point for the design of a decision support tool for better management of permanent grasslands.

A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation.

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.

Increasing actual evapotranspiration on the Loess Plateau of China: An insight from anthropologic activities and climate change

Actual evapotranspiration (ETa) is an important element of water circulation, and susceptible to environmental change. Precise estimation of ETa and identifying its key influence factors are of great importance. Loess Plateau has been largely affected by human activities and climate change, and the dynamics of ETa and its key influencing factors remain unknown in this region. The spatio-temporal variations of ETa over Loess Plateau from 2000 to 2020 were depicted on the basis of the ETWatch model. Then the individual contributions of climatic factors and human activities to ETa variation were quantified by applying generalized additive model. Results indicated that the annual average ETa during the period of 2000–2020 displayed a fluctuating increasing pattern from 386.67 mm in 2000 to 544.73 mm in 2020, with a growth rate of 6.25 mm⋅yr−1. Spatially, the ETa displays a downward pattern from southeast to northwest, which is highly similar to vegetation coverage with the coefficients of 0.766–0.832. The ETa of forest, grassland, cropland, shrub, barren land and impervious land exhibited positive or negative significantly correlations with their corresponding land cover area at the 0.05 or 0.01 level. Sunshine duration, temperature and precipitation are the top three factors showing high correlations with ETa. The results of contribution analysis suggested that the variations in ETa were largely controlled by LUCC in the Loess Plateau, where the LUCC accounted for 52.89% of the total ETa. The impacts of LUCC on ETa were greater than that of climate change on ETa, although climate change was dominant in a few years. These results could contribute to optimizing water resource management and developing various water-saving tactics in the Loess Plateau.

A Multi-sensor Analysis of Selected Reflectance-Based Crop Coefficient Models for Daily Maize Evapotranspiration Estimation

This study evaluated three reflectance-based crop coefficient models (RBCC) for daily maize actual evapotranspiration (ETa) estimates, using multispectral data from spaceborne, airborne, and proximal platforms. The goal was to identify the optimal multispectral sensor that gives more accurate daily ETa estimates. The remote sensing (RS) multispectral platforms included Landsat-8, Sentinel-2, Planet CubeSat, handheld multispectral radiometer (MSR), and unmanned aerial system or UAS, spatial resolution from 30 m to 0.03 m. Three RBCC models that use different vegetation indices as input variables were evaluated in the study. One RBCC uses the normalized difference vegetation index (NDVI). The second model uses the soil-adjusted vegetation index (SAVI), and the third model uses canopy cover (fc). The data for this study were from two maize research sites in Greeley and Fort Collins, Colorado, USA, collected in 2020 and 2021. The Greeley site had a subsurface drip system, while the Fort Collins site had surface irrigation (furrow). Daily maize ETa predictions were compared with observed daily maize ETa data from an Eddy Covariance system installed at each research site. Results indicated that, depending on the RS of ETa algorithm and platform, the optimal input RS data was different. The MSR sensor (1 m) provided the best remote sensing data (input) for the SAVI-based RBCC ETa model, with a maize ETa error (MBE±RMSE) of -0.13 (-3%)±0.67 (16%) mm/d. Sentinel-2 was the best sensor for the remaining two RBCC daily maize ETa algorithms, since the errors for the NDVI-based and fc-based RBCC models for maize ETa were 0.21 (5%)±0.78 (18%) mm/d and 0.59 (14%)±1.07 (25%) mm/d, respectively. These results indicate the need for methods to improve the spectral quality of the remote sensing data to improve spatial ETa estimates and advance sustainable irrigation water management.

Benchmarking Water-Limited Yield Potential and Yield Gaps of Shiraz in the Barossa and Eden Valleys

Background and Aims. Vineyard performance is impacted by water availability including the amount and seasonality of rainfall, evapotranspiration, and irrigation volume. We benchmarked water-limited yield potential (Yw), calculated yield gaps as the difference between Yw and actual yield, and explored the underlying environmental and management causes of these gaps. Methods and Results. The yield and its components in two sections of 24 Shiraz vineyards were monitored during three vintages in the Barossa zone (GI). The frequency distribution of yield was L-shaped, with half the vineyards below 5.2 t·ha−1, and an extended tail of the distribution that reached 24.9 t·ha−1. The seasonal ratio of actual crop evapotranspiration and reference evapotranspiration was below 0.48 in 85% of cases, with a maximum of 0.65, highlighting a substantial water deficit in these vineyards. A boundary function relating actual yield and seasonal rainfall was fitted to quantify Yw. Yield gaps increased with an increasing vine water deficit, as quantified by the carbon isotope composition of the fruit. The yield gap was smaller with higher rainfall before budburst, putatively favouring early-season vegetative growth and allocation to reproduction, and with higher rainfall between flowering and veraison, putatively favouring fruit set and berry growth. The gap was larger with higher rainfall and lower radiation between budburst and flowering. The yield gap increased linearly with vine age between 6 and 33 yr at a rate of 0.3 t·ha−1·yr−1. The correlation between yield gap and yield components ranked bunch weight ≈ berries per bunch > bunch number > berry weight; the minimum to close the yield gap was 185,000 bunches ha−1, 105 g bunch−1, 108 berries bunch−1, and 1.1 g berry−1. Conclusions. Water deficit and vine age were major causes of yield gaps. Irrigation during winter and spring provides an opportunity to improve productivity. The cost of dealing with older, less productive vines needs to be weighed against the rate of increase in yield gap with vine age. Significance of the Study. A boundary function to estimate water-limited yield potential returned viticulturally meaningful yield gaps and highlighted potential targets to improve vineyard productivity.

Sensitivity of runoff due to changes in the characteristics of the water balance in the Danube River region

Abstract Climate change is presently a widely discussed subject in relation to alterations in water storage capacity and the components of the hydrological balance within catchment areas. This research study was directed at two main objectives: 1. The indirect estimation of long-term mean annual runoff using an empirical model; 2. The determination of changes in the annual runoff regime of fifty Danube sub-basins. Monthly areal precipitation, discharges, and air temperature data from 1961 to 1990 were collected for selected headwater sub-basins of the Danube River. In the first part, Turc-type empirical equations for the estimation of the long-term average annual runoff R in the Danube basin were employed. The parameters of the empirical equations were determined through nonlinear regression. Given the underestimation of the actual (territorial, balance) evapotranspiration ET values determined from the balance equation, the precipitation totals were corrected by +10%. With a 10% increase in precipitation, the values of balance ET reached the values ET determined by the Budyko–Zubenok–Konstantinov method. In the second part, fifty equations for the estimation of changes in the average annual runoff, depending on increases in the air temperature and changes in the annual precipitation separately for each of the 50 sub-basins, were established. In conclusion, the results suggest that, on average, a 100 mm increase in the average annual rainfall in the Danube River headwater sub-basins, will cause a 50 mm increase in outflow, and a 1 °C increase in the average annual air temperature will lead to a 12 mm decrease in runoff.

Automated actual evapotranspiration estimation: Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms

Actual evapotranspiration (ETa) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ETa is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ETa values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ETa, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel attention mechanisms, including a novel activated spatial attention mechanism (ASPAM), with the U-Net architecture. Bypassing the need for meteorological inputs, the proposed framework uses Moderate Resolution Imaging Spectrometer (MODIS) products and Digital Elevation Model (DEM) data as inputs. To evaluate the performance of the models, they are applied to three study areas in the United States with various climatic characteristics. According to the results, during the spring and summer, when the target values have higher certainty, the estimations are highly promising, with R2 as high as 0.91 and MAPE as low as 6.40%. Furthermore, the proposed ASPAM module improves the accuracy of ETa estimations compared to attention gate (AG) and squeeze and excitation (SE) attention modules. The results also indicate that the MODIS raw products and derived vegetation and water indices can predict ETa within a reliable range of accuracy, with the addition of DEM data marginally enhancing the models' performance. The automatic workflow of this model makes it significantly easy to use, contributing to its applicability and generalizability for enhancing atmospheric research.

Recharge Estimation Approach in a Data-Scarce Semi-Arid Region, Northern Ethiopian Rift Valley

Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.

Estimation of Crop-coefficients and Evapotranspiration of Field Pea (Pisum sativum L.) Using Lysimeter and Empirical Models under Temperate Climate

During Rabi 2020-21, a field experiment was conducted at SKUAST-K, Shalimar, India, focusing on field Pea (Pisum sativum L.). The aim of this study was to determine the water requirement and single crop coefficient (Kc) of pea using a lysimeter setup. Four empirical models were employed to calculate the reference evapotranspiration and were then compared with the actual crop evapotranspiration at different growth stages. The Kc values for field pea were 0.50, 0.80, 1.15, and 1.10 during the initial, development, mid-season and late season stages, respectively. The water requirement was found as 239.9 mm for the whole cropping period of the pea. Among the models, the Penman Montieth crop evapotranspiration model exhibited the closest agreement with the corresponding values obtained in the field through water balance study, yielding RMSE, RSR, and NSE values of 0.97, 9.5, and 11.6, respectively. These findings highlight the significance of using Penman Monteith crop evapotranspiration model for estimating crop evapotranspiration in temperate regions.

Different types of meteorological drought and their impact on agriculture in Central China

Challenges in drought monitoring hinder social development. In this study, a robust drought monitoring framework composed of ten indicators was established based on the standard precipitation actual evapotranspiration (SPAEI) index calculated using the maximum entropy production (MEP) model. Additionally, the relationship between meteorological drought and agricultural development was analyzed. The results indicated that the seasonal SPAEI performed better than the standardized precipitation evapotranspiration index (SPEI) in terms of drought area, vegetation drought, and drought identification in Central China. The annual drought duration was between 3.4 and 4.5 months, and the annual drought frequency ranged from 1.1 to 1.9 times per year. The main drought pattern was divided into persistent light drought and short-term severe drought, with the latter dominating crop yields in Central China. Although persistent light drought contributes more to drought, short-term severe drought has a greater impact on crop yields. Thoroughly investigating regional drought patterns and assessing their impacts on agriculture will contribute to a better understanding of drought characteristics and provide valuable guidance for regional agricultural water resource allocation and management.

Assessing robustness in global hydrological predictions by comparing modelling and Earth observations

ABSTRACTHydrological modelling to support hypotheses on Earth system boundaries or the accelerating water crisis is nowadays done at the global scale, with difficulties associated to model uncertainties. Here we bring a robustness analysis of internal model variables as an additional tool for model evaluation using data from six Earth observation products and the global catchment model World-Wide HYPE in a comparative study. The assessment shows that: (i) variables have high agreement in mid-latitude temperate regions; (ii) the variables with higher agreement, and associated with good model performance in streamflow, were actual evapotranspiration, fractional snow cover and snow water equivalent; and (iii) changes in total water storage showed very poor agreement, probably due to an insufficient number of aquifers in the model set-up. We propose this procedure as a standard complementary method in global hydrological modelling, highlighting the importance of justifying models before using them for scenario analysis or water accounting.