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Abstract: Quantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types.

This research seeks to comprehensively assess the water footprint and environmental sustainability of millet-based ethanol production in India, responding to the growing interest in resilient and nutritious alternatives. Utilizing a life cycle assessment approach, the study will scrutinize water usage across the entire production process, from millet cultivation to ethanol extraction. The primary aim is to gain an in-depth understanding of water consumption at each stage, pinpointing areas for enhancement and efficiency. Rajasthan exhibited the highest total water footprint at 6596 m3/ton, primarily due to its substantial ETc value, while Andhra Pradesh showcased the lowest at 1906 m3/ton, owing to its high average yield. Haryana displayed significant water usage during crop production, with a water footprint of 5870 m3/ton, where WFgreen, WFblue, and WFgrey contributed 3730 m3/ton, 2010 m3/ton and 130 m3/ton respectively. Uttar Pradesh demonstrated a comparatively lower total water footprint of 2564 m3/ton. These findings are anticipated to offer valuable insights into the viability and sustainability of millet-derived ethanol as an eco-friendly biofuel option in India, guiding policymakers, researchers, and stakeholders in promoting its adoption and addressing environmental challenges linked with conventional fuel sources. [ABSTRACT FROM AUTHOR]

This study examines the spatiotemporal variability of drought and associated physical processes over the Arabian Peninsula (AP). For this purpose, we computed the standardized precipitation evapotranspiration index (SPEI) for the period 1951–2020 using the Climate Research Unit and fifth generation ECMWF atmospheric reanalysis datasets. By applying rotated empirical orthogonal function analysis on the SPEI data, we identified four homogeneous and coherent drought regions. The droughts in the northern regions follow a relatively similar temporal evolution as compared to those in the southern region. All four sub-regions of the AP exhibit a significant drying trend (p < 0.01) with an abrupt acceleration in drought frequency and intensity over the last two decades. The increase in droughts is associated with the reduction of synoptic activity and an increase in the high pressure over the AP. Seasonally, potential evapotranspiration is the dominant driver of summer droughts in the AP, whereas both precipitation and temperature are important for driving winter droughts. The summer droughts, mainly over the northern AP, are due to the occurrence of an anomalous equivalent barotropic high associated with anomalous dry and hot conditions. However, anomalous dry conditions in winter are a result of an anomalous paucity of winter storms caused by the weakening of the sub-tropical jets. [ABSTRACT FROM AUTHOR]

A short period of exposure to elevated CO2 is known to decrease evapotranspiration via stomatal closure. Based on theoretical evaluation of a canopy transpiration model, we hypothesized that this decrease in the evapotranspiration of rice under elevated CO2 was greater under higher temperature conditions due to an increased sensitivity of transpiration to changes in CO2 induced by the greater vapour pressure deficit. In a temperature gradient chamber‐based experiment, a 200 ppm increase in CO2 concentration led to 0.4 mm (−7%) and 1.5 mm (−15%) decreases in 12 h evapotranspiration under ambient temperature and high temperature (+3.7°C) conditions, respectively. Model simulations revealed that the greater vapour pressure deficit under higher temperature conditions explained the variations in the reduction of evapotranspiration observed under elevated CO2 levels between the temperature treatments. Our study suggests the utility of a simple modelling framework for mechanistic understanding of evapotranspiration and crop energy balance system under changing environmental conditions. Summary statement: Interactive effects of CO2 and temperature on canopy evapotranspiration were observed in large chamber‐based experiments, using a microlysimeter technique. A canopy transpiration model revealed decreased humidity under warm conditions enhanced the sensitivity of canopy transpiration to rising CO2. [ABSTRACT FROM AUTHOR]

As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant–environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r2 = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal‐based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing‐based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields. Summary statement: Using both trait and flux measurements, we have designed a new vegetation index: the combined nitrogen and drought index. This index describes changes in leaf optical properties during nitrogen and or drought stress that can be used to improve transpiration modelling during abiotic stress. [ABSTRACT FROM AUTHOR]

The frequency and severity of extreme climatic phenomena, including droughts, have increased across the globe. The aim of this study is to detect changes in the severity and trends of short- and medium- term droughts (3 and 12 months) across Iran (1990–2019) and project the near (2059–2030) and far (2095–2066) future changes. SPI and SPEI indices were used for this purpose. The non-parametric Mann–Kendall test was used for trend analysis, and the G * (Geties and Ord) index was exerted for hot spot analysis. The results revealed that in 1990–2020, except for 3 month SPI (SPI3) index, the SPI and SPEI trends tended to be negative and statistically significant at the 0.05 significance level. During the same period, specified and significant hot spots of the indices were formed in the northern and northwestern Iran, and significant cold spots were concentrated only in Zabol and Birjand stations in eastern Iran. Near and far future minimum and maximum monthly precipitation projections for four selected stations show increases in monthly maximum and minimum temperatures and decreases in precipitation at all stations, though the decrease in precipitation for Esfahan station only with RCP8.5. The Projected trends of 3 and 12 months SPI and SPEI (SPI3, SPI12, SPEI3 and SPEI12) in near and far future show the dominance of significant negative trends. Projected trends of SPI and SPEI for the near future was negative for all stations, though it was positive and significant for far future. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) estimation is essential for managing agricultural water demand at the basin scale and allocating irrigation water. Many uncertainties, such as those related to the model's structure, initial conditions, and parameter set, cascade into the ET calculation, producing unreliable results, even though water modelers and managers depend on stand-alone ET estimation models for planning and management. Utilizing an ensemble-based data assimilation (EDA) methodology, this study investigated how remotely-sensed ET can enhance simulations of the popular Surface Energy Balance Algorithm for Land (SEBAL) ET model while taking uncertainties into account. This watershed-scale study was carried out in the Maroon Basin situated in southwestern Iran. The SEBAL model was employed to simulate ET. The particle filter-based DA method was then applied to enhance the model's performance. Afterward, the SWAT model was utilized to simulate the performance of products and runoff using ET taken from the SEBAL model. The study findings demonstrated that employing DA in SEBAL ET produced a more reliable and accurate model simulation. These findings paved the way for future research by highlighting the significance of digital farming tools in the management of water resources and sound agricultural planning and management. [ABSTRACT FROM AUTHOR]

Carbon exchange between forest ecosystems and the atmosphere play an important role in global carbon cycle, which is difficult to be accurately quantified due to the large uncertainties in scaling up site-scale observations or filling-up measurement gaps. A process-oriented model equipped with comprehensive processes to explicitly simulating coupled carbon, nitrogen and water cycling, is hypothesized to reduce the uncertainties in quantification of forest carbon fluxes. To test this hypothesis, the Catchment Nutrient Management Model – DeNitrification-DeComposition (CNMM-DNDC), as a hydro-biogeochemical model that dynamically couples the carbon, nitrogen, phosphorous and water cycling processes, was updated in this study by incorporating a new forest growth module derived from the Biome-BGC model and validating the updates using multiple-year continuous observations of carbon and water fluxes at the site scale. The updated model has improved the processes of photosynthesis, litter decomposition, allocation, respiration and mortality to more effectively capture the transformation and transportation of nutrients in plant-soil-water continuum. The observed gross primary productivity (GPP), ecosystem respiration (ER), net ecosystem carbon dioxide exchange (NEE) and evapotranspiration (ET) of three typical forest sites subject to subtropical and temperate climates in eastern Asia (2003–2010) were used for the model calibration and validation. Compared with the original model in validation, the updated model showed significant improvements in simulating the daily dynamics and inter-annual variations of each variable, with the NRMSE values decreased by 46 % and 54 %, 65 % and 37 %, 4 % and -6 %, and 38 % and -3 % for GPP, ER, NEE, and ET on daily and annual scales, respectively. The comparable performances of both model versions for annual NEE emphasizes the importance of validating each component of carbon fluxes to avoid the offsetting of model errors. The canopy average specific leaf area, fraction of leaf nitrogen in Rubisco, annual leaf and fine root turnover fraction, maximum stomatal conductance and the ratio of carbon to nitrogen in leaves and fine roots were identified as the sensitive eco-physiological parameters affecting the simulations of GPP and ER. In addition, the meteorological variables of solar radiation, humidity and air temperature also showed strong influences on the simulated GPP and ER. The relatively satisfactory performances demonstrated that the modified model has the ability to capture the daily dynamics and inter-annual variations of carbon fluxes for forests in temperate and subtropical zones, which is essential for estimating the emissions of greenhouse gases at the regional or global scales. [ABSTRACT FROM AUTHOR]

The goal of this study is to investigate the usefulness of the relatively new 30 m spatial and <5.7-day temporal resolution Harmonized Landsat Sentinel-2 (HLS) dataset for calculating vegetation index-based crop coefficients (KcVI) for estimating field scale crop evapotranspiration (ETc). Increased spatial and temporal resolution ETc estimates are needed for improving irrigation scheduling, monitoring impacts of water conservation programs, and improving crop yield. The crop coefficient (Kc) method is widely used for estimating ETc. Remote sensing vegetation indices (VI) are highly correlated to Kc and allow the creation of a KcVI but the approach is limited by the availability of high temporal and spatial resolutions. We selected and calculated sixteen commonly used VIs using HLS data and regressed them against field-measured ET for alfalfa in the Mesilla Valley, New Mexico to create linear KcVI models. All models showed good agreement with Kc (r2 > 0.67 and RMSE < 0.15). ETc prediction resulted in an MAE ranging between 0.35- and 0.64-mm day−1, an MSE ranging between 0.20- and 0.75-mm day−1 and an MAPD ranging between 10.0 and 16.5%. The largest differences in predicted ETc occurred early in the growing season and during cutting periods when the spectral signal could be influenced by soil background or irrigation events. The results suggest that applying the KcVI approach to the HLS dataset can help fill in the data gap in remote sensing ET tools. Future work should focus on assessing additional crops and integration into other tools such as the emerging OpenET platform. [ABSTRACT FROM AUTHOR]

Frequent drought has posed widespread, large-scale and extremely severe impact on the ecological environment under global climate and human activities. This study aims to analyze the spatiotemporal dynamic evolution of drought in China. A threshold of -0.5 was chosen to identify drought conditions. A raster was set as the drought state at standardized precipitation evapotranspiration index (SPEI) less than -0.5. The start and end times of drought were varied for each raster within a single event, indicating the dynamic and evolving drought. At the same time, some rasters exited the drought states, while others entering. The contribution rates of drought to rasters were calculated at a given time, in order to capture the dynamic changes in drought. Three perspectives were considered: the extent of drought impact, duration, and severity. The SPEI was then used to assess the drought, in order to introduce the concept of persistent drought rasters. Large-scale and persistent drought events were identified to investigate the spatiotemporal dynamic evolution from 1961 to 2020, together with their response mechanisms to the atmospheric circulation. The research findings can be summarized as follows: 1) A total of 47 events occurred between 1961 and 2020. The 1970s witnessed the highest frequency of large-scale persistent drought events, with 10 occurrences, while the 2000s and 2010s each experienced 9 such events. The average duration of these drought events was 7.14 months, with the longest average duration in the 1960s at 11.83 months. The average extent was 28.66%, with the highest average extent index reaching 33.75% in the 2000s.The average severity was -2.86 for the 47 large-scale persistent drought events. Between 1980 and 2010, the average area affected by persistent drought events each decade showed a continuous expanding trend. Between 1970 and 2020, the average duration of persistent drought events each decade was concentrated between 4 to 8 months. 2) Before the 1990s, the lag time of Oceanic Niño Index's impact on spring precipitation was 3 and 9 months. From then on, the ONI's influence on summer and autumn precipitation extended to 6, 9, and 12 months. Once La Niña events occurred, there was the decrease in the spring precipitation, thereby increasing the frequency of spring droughts in China. 3) There was a resonance period of 16-48 months between ONI and SPEI3 during the years 1961-1976, 1980-1988, 1994-2001, and 2006-2020. The ONI index also demonstrated the strongest correlation with SPEI3 in this timeframe. While the WP index exhibited the weakest correlation with SPEI3 during the same periods.The research results help further understand the causes and patterns of drought in China, thereby enhancing the scientific basis and precision of drought prevention and mitigation policies. [ABSTRACT FROM AUTHOR]

Forests are critical to water resources, but high evapotranspiration (ET) can reduce water yield. Thinning and prescribed fire reduce forest density and often reduce ET, promoting higher water yield. However, results from such treatments have been inconsistent, possibly because of unknown interactions among individual ET components. We compare water budget components of longleaf pine (Pinus palustris Mill.) woodlands with frequent prescribed fire to the water budget components of fire‐excluded stands. We hypothesized that fire exclusion would result in higher ET due to increased midstory transpiration (Et) and interception (Ei), and higher evaporation from litter (Ilitter). Reference plots were burned every two years while treatment plots had fire excluded for 15–20 years. Fire treatments were repeated in two sites representing a soil moisture gradient, noted as mesic and xeric. We measured woody Et using sap flux, and we modeled groundcover Et using physiological models. We measured Ei of canopy and groundcover layers, modeled Es litter biomass, and constructed a total component‐based water budget for each site and treatment. Compared with reference plots, midstory Et was 300%–800% higher in fire exclusion plots. Groundcover Et was ~80% less than reference treatments, countering the effects of midstory growth on total ET. Stand Ei followed similar trends, with groundcover Ei in reference plots countering the effects of midstory and litter Ei in fire exclusion plots. As expected, total ET in the xeric site was 18% higher in fire exclusion plots. However, ET in the mesic site was 16% lower in the fire exclusion plots due to high groundcover Et and Ei in reference plots. Thus, our results show that fire exclusion changes total forest ET, but the size and direction of the effect vary depending on the balance between midstory and groundcover transpiration and interception. These results highlight the importance of groundcover in ecosystem function in low‐density forests and may help explain inconsistent results from studies of water yields following thinning and fire. While prescribed fire is a valuable tool in forest management, we suggest that the effects of fire on ET are complex and require careful accounting of all water fluxes within a forest ecosystem. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays a crucial role in the regional water-energy cycle, illustrating intricate interactions among climate, vegetation and soil. Eddy covariance (EC) technology is a primary method for measuring ET. However, incomplete data due to adverse weather conditions and equipment malfunctions are common. This study utilizes the full-factorial method to address the ET gaps at 339 sites across multiple global flux networks, and the filled ET data are compared with three ET products: the Land component of the Fifth Generation of European Reanalysis (EAR5-Land), Global Land Evaporation Amsterdam Model (GLEAM), and Breathing Earth System Simulator (BESS). Results indicate high consistency between filled ET data and three ET products at 264 out of 339 sites. The absolute average mean error (|MAE|) and root mean system error (|RMSE|) are 0.32 mm/d and 0.92 mm/d, respectively. Among the remaining 75 sites, 49 sites exhibit better agreement between filled ET and measured ET data than ET products in terms of seasonal variations and numerical ranges. Further verification is needed for the reliability of filled ET data at the other 26 sites due to a scarcity of measured ET data. Overall, the gap-filled ET data from 313 sites (2210 site-years) demonstrate high-quality. These sites exhibit a strong correlation between available energy and turbulent fluxes, with R2, MAE, and RMSE for different surface types ranging from 0.84 to 0.94, 21.49 to 28.67 W/m2, and 28.37 to 36.91 W/m2, respectively. And the average energy balance closure rate is 0.73, indicating a relatively high degree of closure in the energy balance. These 313 sites with high-quality filled ET data can be utilized for ET model validation, ET product verification, water demand assessment, and other related tasks. The filled ET dataset can be publicly accessed at https://doi.org/10.57760/sciencedb.11651 (Wang & Jiang, 2024). [ABSTRACT FROM AUTHOR]

The Priestley–Taylor model of the Jet Propulsion Laboratory (PT-JPL) evapotranspiration (ET) model is relatively simple and has been widely used based on meteorological and satellite data. However, soil moisture (SM) constraints include a vapor pressure deficit (VPD) that causes large uncertainty. In this study, we proposed a PT-SinRH model by introducing a sine function of air relative humidity (RH) to replace RHVPD to characterize SM constraints, which can improve the accuracy of ET estimations. The PT-SinRH model is validated by eddy covariance (EC) data from 2000–2020. These data were collected by AmeriFlux at 28 sites on the conterminous United States (CONUS), and the land cover types of the sites vary from croplands to wetlands, grasslands, shrub lands and forests. The validation results from daily scale-based on-site and satellite data inputs showed that the PT-SinRH model estimates fit the observations with a coefficient of determination (R2) of 0.55, root-mean-square error (RMSE) of 17.5 W/m2, bias of −1.2 W/m2 and Kling–Gupta efficiency (KGE) of 0.70. Additionally, the PT-SinRH model based on reanalysis and satellite data inputs has an R2 of 0.49, an RMSE of 20.3 W/m2, a bias of −8.6 W/m2 and a KGE of 0.55. The PT-SinRH model showed better accuracy when using the site-measured meteorological data than when using reanalysis meteorological data as inputs. Additionally, compared with the PT-JPL model, the results demonstrate that our approach, i.e., PT-SinRH, improved ET estimates, increasing the R2 and KGE by 0.02 and decreasing the RMSE by about 0.6 W/m2. This simple but accurate method permits us to investigate the decadal variation in regional ET over the land. [ABSTRACT FROM AUTHOR]

Against the backdrop of global warming and vegetation restoration, research on the evapotranspiration mechanism of the Yellow River basin has become a hot topic. The Budyko-Fu model is widely used to estimate basin-scale evapotranspiration, and its crucial parameter ω is used to characterize the underlying surface and climate characteristics of different basins. However, most studies only use factors such as the normalized difference vegetation index (NDVI), which represents the greenness of vegetation, to quantify the relationship between ω and the underlying surface, thereby neglecting richer vegetation information. In this study, we used long time-series multi-source remote sensing data from 1988 to 2015 and stepwise regression to establish dynamic estimation models of parameter ω for three subwatersheds of the upper Yellow River and quantify the contribution of underlying surface factors and climate factors to this parameter. In particular, vegetation optical depth (VOD) was introduced to represent plant biomass to improve the applicability of the model. The results showed that the dynamic estimation models of parameter ω established for the three subwatersheds were reasonable (R2 = 0.60, 0.80, and 0.40), and parameter ω was significantly correlated with the VOD and standardized precipitation evapotranspiration index (SPEI) in all watersheds. The dominant factors affecting the parameter in the different subwatersheds also differed, with underlying surface factors mainly affecting the parameter in the watershed before Longyang Gorge (BLG) (contributing 64% to 76%) and the watershed from Lanzhou to Hekou Town (LHT) (contributing 63% to 83%) and climate factors mainly affecting the parameter in the watershed from Longyang Gorge to Lanzhou (LGL) (contributing 75% to 93%). The results of this study reveal the changing mechanism of evapotranspiration in the Yellow River watershed and provide an important scientific basis for regional water balance assessment, global change response, and sustainable development. [ABSTRACT FROM AUTHOR]

The water footprint is an increasingly demanded environmental sustainability indicator for certifications and labels in agricultural production. Processing tomatoes are highly water-intensive, and existing studies on water footprint have uncertainties and do not consider the impact of different irrigation configurations (e.g., surface drip irrigation (SDI) and subsurface drip irrigation (SSDI)) and irrigation strategies. This study presents a two-year experimental investigation to determine the water footprint of processing tomatoes grown in southern Spain (Andalusia) and the impact of SSDI and deficit irrigation. Five irrigation treatments were established: SDI1 (surface drip irrigation without water limitation), SDI2 (surface drip irrigation without water limitation adjusted by soil moisture readings), SSDI1 (subsurface drip irrigation without water limitation and a dripline depth of 15 cm), SSDI2 (similar to SSDI1 but with mild/moderate water deficit during the fruit ripening stage), and SSDI3 (subsurface drip irrigation without water limitation and a dripline depth of 35 cm (first year) and 25 cm (second year)). Measurements included crop vegetative growth, leaf water potential, leaf gas exchange, nitrate concentration in soil solution, and crop yield and quality. The soil water balance components (actual evaporation, actual transpiration, deep drainage), necessary for determining the total crop water footprint, were simulated on a daily scale using Hydrus 2D software. Results indicated that SSDI makes more efficient use of irrigation water than SDI. The water footprint of SSDI1 was 20–35% lower than that of SDI1. SSDI2 showed similar water footprint values to SDI1 under highly demanding environmental conditions and significantly lower values (≈40%) in a year with lower evaporative demand. The dripline depth in SSDI was critical to the water footprint. With a 35 cm installation depth, SSDI3 had a significantly higher water footprint than the other treatments, while the values were similar to SSDI1 when the depth was reduced to 25 cm. [ABSTRACT FROM AUTHOR]

The climatic conditions in Hunan province are complex and the probability of drought occurrence is high. To reveal the probability characteristics of drought events in various climatic regions of Hunan province (central Hunan, northeast Hunan, northwest Hunan, western Hunan, and southern Hunan), based on meteorological data from 97 meteorological stations in Hunan province during 1961-2020, standardized precipitation evapotranspiration index (SPEI) and Copula function were used to extract the duration and severity of drought events, and to study the co-occurrence recurrence periods of drought duration and severity were studied in each climatic region of Hunan province. The results showed that: (1) along the direction of southern Hunan, central Hunan, northeast Hunan, northwest Hunan, and western Hunan, the number of drought events in the climatic region was from more to less, and the value range of drought events duration and severity was from small to large. (2) When fitting the optimal marginal distribution function of drought duration and severity respectively, the number of stations fitted by GEV function exceeds 60%, and the fitting effect of the optimal marginal distribution function of drought severity was better than that of drought duration. When the joint function of drought duration and the optimal marginal distribution of severity was established, the Frank Copula function was applied to more than 50% of the sites. (3) Southern Hunan was prone to shorter duration and greater severity of drought events, while western Hunan, northwest Hunan, and some areas of northeast Hunan were prone to longer duration and greater severity of drought events. Central Hunan was not only prone to drought events with long duration and high severity but also to drought events with short duration and high severity in the southern part of central Hunan. There were differences in the probability characteristics of drought events in different climatic regions in Hunan province, and measures can be taken to reduce the harm of drought. [ABSTRACT FROM AUTHOR]

The South American Monsoon System, which transports moisture from Amazonia to Central-West Brazil, is an important moisture source for the summer rainy season in this region. While local evapotranspiration also contributes to the atmospheric moisture supply, the balance between local and remote sources during the onset of the rainy season remains uncertain. Our research aimed to quantify the role of local evapotranspiration in initiating the rainy season in Central-West Brazil. By utilizing data from various sources, such as remote sensing (MODIS), modern reanalysis (ECMWF's ERA5), and composite products of rainfall (CHIRPS), and analyzing them in a comparative way, we conclusively found that local evapotranspiration is the only relevant source of moisture to the atmosphere during the dry-to-wet season transition, preceding the establishment of the monsoon system. [ABSTRACT FROM AUTHOR]

Miscanthus is a perennial grass that can yield substantial amounts of biomass in land areas considered marginal. In the Coastal Plain region of North Carolina, marginal lands are typically located in coarse‐textured soils with low nutrient retention and water‐holding capacity, and high erosivity potential. Little is known about miscanthus water use under these conditions. We conducted a study to better understand the efficiency with which miscanthus uses natural resources such as water and radiant energy to produce harvestable dry biomass in comparison to corn, a typical commodity crop grown in the region. We hypothesized that under non‐limiting soil water conditions, miscanthus would have greater available energy and water use rates owing to its greater leaf area, thus leading to greater agronomic yields. Conversely, these effects would be negated under drought conditions. Our measurements showed that miscanthus intercepted more radiant energy than corn, which led to greater albedo (by 0.05), lower net radiation (by 4% or 0.4 MJ m−2 day−1), and lower soil heat flux (by 69% or 1.0 MJ m−2 day−1) than corn on average. Consequently, miscanthus had greater available energy (by 7% or 0.6 MJ m−2 day−1) and water use rates (by 14% or 0.5 mm day−1) than corn throughout the growing season on average, which partially confirmed our hypothesis. Greater water use rates and radiation interception by miscanthus did not translate to greater water‐use (1.5 g kg−1 vs. 1.6 g kg−1) and radiation‐use (0.9 g MJ−1 vs. 1.1 g MJ−1) efficiencies than corn. Compared to literature values, our data indicated that water and radiation availability were not limiting at our study site. Thus, it is likely that marginal land features present at the Coastal Plain region such as low soil fertility and high air temperatures throughout the growing season may constrain agronomic yields even if soil water and radiant energy are non‐limiting. [ABSTRACT FROM AUTHOR]

Rice–wheat rotation cropland is one of the most important agroecosystems in South China, the escalation of conflict between food demand augment and water supply shortage increased with climate change. Water use efficiency plays a more significant role in optimising water and carbon management. Thus, the diurnal and seasonal variations of water use efficiency were assessed by the 3‐year eddy covariance observations in the Shouxian National Observatory, a typical rice–wheat rotation station. The results revealed a 'U'‐shaped diurnal pattern of water use efficiency for winter wheat (Triticum aestivum L.) and rice (Oryza sativa L.). Seasonal water use efficiency had two peaks with the highest in the winter wheat‐growing season. The average water use efficiency for the rice–wheat rotation cropland was 2.85 g C kg−1 H2O over the whole year with 2.62 and 3.11 g C kg−1 H2O for winter wheat and rice, respectively. However, gross primary productivity and evapotranspiration of rice were higher than those of winter wheat. Temperature, photosynthetically active radiation were the principal impact factors of water use efficiency in the rice‐growing season. Comparatively, soil water and vapour pressure deficit dominated the water use efficiency changes in the winter wheat‐growing season. Our analyses can help understand the water use requirements for carbon assimilation on rice–wheat rotation cropland on the field scale. [ABSTRACT FROM AUTHOR]

The reliability of conventional drought indices is increasingly called into question as the stationarity of hydrologic series undergoes significant alterations and becomes irreversibly impacted by environmental changes. In the extant literature concerning Non‐Stationary Standardized Precipitation Evapotranspiration Indices (NSPEIs), researchers typically construct linear models incorporating location parameters and covariates; however, the simultaneous consideration of both location and scale parameters has been conspicuously infrequent. In this study, we developed a NSPEI using the generalized additive models in location, scale and shape. This index innovatively incorporates location and scale parameters that vary in response to changes in meteorological factors, thereby offering a more reliable and robust means to quantify drought characteristics under changing environmental conditions. The index was employed across representative sub‐watersheds within the western region of Liaoning Province and subjected to a comparative analysis alongside the conventional Standardized Precipitation Evapotranspiration Index (SPEI) to ascertain its reliability. The findings indicate that within the study area, both the precipitation and evapotranspiration series exhibit non‐stationary behaviour. Specifically, the precipitation data reveals an overall declining trend, while the evapotranspiration data demonstrates an ascending trend. Furthermore, NSPEI demonstrates superior performance over SPEI in the reconstruction of historical drought events within the western region of Liaoning Province, thereby providing a more accurate representation of the actual drought conditions experienced. The NSPEI estimated a larger drought peak, whereas the SPEI identified longer durations and greater severity of droughts. These findings provide valuable information for evaluating drought characteristics and managing water resources under changing environmental conditions. [ABSTRACT FROM AUTHOR]

Due to the lack of irrigation management techniques, the application of water in forest nurseries is generally carried out without control, resulting in great waste. This study aimed to develop and evaluate weighing mini-lysimeters (ML) to automate the irrigation system and determine the water demand and evaluation of the initial tree species seedling growth. The structure was built out of wood to fit a tray of 54 tubes measuring 280 cm2. It was equipped with a load cell with a nominal capacity of 10 kg, located in the center of the structure. The mass variations of the set were recorded on a micro SD card using an Arduino Mega board, which allowed for the automated irrigation management system through an algorithm. Two MLs were built, determining the water demand of Dalbergia nigra (DN) and Enterolobium contortisiliquum (EC) seedlings in the initial growth phase. After calibration, the MLs presented an absolute error and mean squared error below 1% of the total mass, allowing effective irrigation control. During the evaluation period, 1.47 L seedling-1 of DN and 1.45 L seedling-1 of EC were applied, reaching 89 days after emergence, height and stem diameter of 39.2 cm and 3.4 mm, and 19.3 cm and 4.6 mm, respectively. The weighing mini-lysimeters developed demonstrated the ability to automate the irrigation of tree species seedlings, reducing the amount of water applied in forest nurseries. [ABSTRACT FROM AUTHOR]

Cover plants, among the most commonly used plants in the realm of green spaces, are typically characterized by shallow roots. In this research, a simple and flexible algorithm has been introduced for calculating the basal crop coefficient and evaporation coefficient of shallowrooted plants without the use of (micro-) lysimeters. The presented algorithm requires measurements of moisture at three depths for short-term calculations and only surface layer moisture monitoring for long-term calculations. Using this algorithm, it is possible to calculate the potential evaporation and transpiration of the plant. Furthermore, the presented algorithm is independent of time steps. To evaluate this algorithm, nine experimental plots were utilized, including six Frankenia plots with full coverage and three bare soil plots during the peak water demand period. All experiments were conducted at the educational site of irrigation systems in the vicinity of the meteorological site of Ferdowsi University of Mashhad (FUM). For this purpose, the water content and irrigation of these plots was fulfilled (at most) every 48 hr. The results indicated that the presented algorithm has good capabilities for estimating the basal crop and evaporation coefficients. Additionally, the basal crop coefficient for Frankenia plant was found to be 1, and the evaporation coefficient was 0.58. Therefore, this method can be employed for estimating the water requirements of different plants without using (micro-) lysimeters. [ABSTRACT FROM AUTHOR]

The study gives an estimate of the effect of four methods most often used in hydrological models to calculate the potential evapotranspiration—Penman–Monteith, Priestley–Taylor, Oudin, and Hargreaves, which differ in complexity and meteorological data requirements—on the efficiency and the results of simulating the dynamics of water balance components. The methodological base of the hydrological simulation was the widely known HBV conceptual model, in which the actual evapotranspiration is associated with the current state of a storage, which simulates the dynamics of soil moisture content. The objects of the study were 18 catchments with areas from 2.4 to 755 km2, located within the former Primorskaya Water-Balance station, the data of which were used in calculations of potential evapotranspiration and simulations of runoff. The comparison of simulation results with observations showed that the model values of evapotranspiration at the use of physically sound methods are in better agreement with the Budyko's procedure of determination of potential evapotranspiration. The analysis of model sensitivity to the input potential evaportranspiration showed that the use of physically based methods to calculate the potential evapotranspiration has no general effect on the efficiency of runoff hydrograph simulation of the studied objects. At the same time, in the case when empirical models of evapotranspiration are used, the uncertainty in the results of calculation of potential evapotranspiration should be compensated for by model parameters that are less physically substantiated, which, in turn, can lead to distortion of the dynamics of water balance components and runoff genetic components. [ABSTRACT FROM AUTHOR]

The article focuses on using recurrent neural networks (RNNs) and sensitivity analysis for estimating monthly evapotranspiration in Fez, Morocco. Topics include the comparison of various machine learning models for evapotranspiration estimation, the application of sensitivity analysis to understand environmental factors, and the performance evaluation of Long Short-Term Memory (LSTM) networks in improving water management strategies.

The increasing variability of precipitation, higher temperatures, and recurring droughts in the semi-arid regions due to climate change are leading to increased aridity, resulting in scarcer water resources for crops. The present study aimed to analyse the spatial distribution of climate variables related to water resources in the olive-growing areas throughout Extremadura, southwestern Spain. To perform this task, three climate variables were used: the potential evapotranspiration of the crop, the FAO aridity index, and the annual water requirement. Considering data from 58 weather stations located throughout Extremadura and 17 along boundaries with at least a 30-year length (within the 1991–2021 period), each variable was computed at each station. After calculating some descriptive statistics, a multivariate geostatistical (regression-kriging) algorithm, incorporating secondary information on elevation and latitude, was used to accurately map each climate variable. Later, temporal trends and their magnitude were analysed using the Mann–Kendall test and the Sen's estimator, respectively. The highest evapotranspiration and water requirements are located in the southern part of the region, which has large areas dedicated to olive cultivation. In the northern part of the region, there is greater spatial variability in evapotranspiration and, consequently, in water requirements for olive groves due to the more rugged topography. Similarly, the olive-growing areas with the highest aridity are also in the south of Extremadura. In most areas of Extremadura, olive cultivation requires appropriate irrigation for optimal productivity. According to evapotranspiration trends, the water requirements will become greater in the future. However, it is not guaranteed that the water supply will be sufficient in olive-growing areas where aridity is higher and water resources are scarce. The results of this study are very important for evaluating water deficit and water resources in vulnerable olive-growing areas throughout Extremadura. [ABSTRACT FROM AUTHOR]

The accuracy of the simulation of carbon and water processes largely relies on the selection of atmospheric forcing datasets when driving land surface models (LSM). Particularly in high-altitude regions, choosing appropriate atmospheric forcing datasets can effectively reduce uncertainties in the LSM simulations. Therefore, this study conducted four offline LSM simulations over the Tibetan Plateau (TP) using the Community Land Model version 4.5 (CLM4.5) driven by four state-of-the-art atmospheric forcing datasets. The performances of CRUNCEP (CLM4.5 model default) and three other reanalysis-based atmospheric forcing datasets (i.e. ITPCAS, GSWP3 and WFDEI) in simulating the net primary productivity (NPP) and actual evapotranspiration (ET) were evaluated based on in situ and gridded reference datasets. Compared with in situ observations, simulated results exhibited determination coefficients (R 2) ranging from 0.58 to 0.84 and 0.59 to 0.87 for observed NPP and ET, respectively, among which GSWP3 and ITPCAS showed superior performance. At the plateau level, CRUNCEP-based simulations displayed the largest bias compared with the reference NPP and ET. GSWP3-based simulations demonstrated the best performance when comprehensively considering both the magnitudes and change trends of TP-averaged NPP and ET. The simulated ET increase over the TP during 1982–2010 based on ITPCAS was significantly greater than in the other three simulations and reference ET, suggesting that ITPCAS may not be appropriate for studying long-term ET changes over the TP. These results suggest that GSWP3 is recommended for driving CLM4.5 in conducting long-term carbon and water processes simulations over the TP. This study contributes to enhancing the accuracy of LSM in water–carbon simulations over alpine regions. [ABSTRACT FROM AUTHOR]

Remotely sensed evapotranspiration (ETRS) shows promise for enhancing hydrological models, especially in regions lacking in situ streamflow observations. However, model calibration studies showed conflicting results regarding the ability of ETRS products to improve streamflow simulation. Rather than relying on model calibration, here we produce the first randomized experiment that explores the full streamflow–ET skill distribution, and also the first probabilistic assessment of the value of different global ETRS products for streamflow simulation. Using 280,000 randomized SWAT (Soil and Water Assessment Tool) model runs across seven catchments and four ETRS products, we show that the relationship between ET and streamflow skills is complex, and simultaneous improvement in both skills is only possible in a limited range. Parameter sensitivity analysis indicates that the most sensitive parameters can have opposite contributions to ET and streamflow skills, leading to skill trade-offs. Conditional probability assessment reveals that models with good ET skills are likely to produce good streamflow skills, but not vice versa. We suggest that randomized experiments such as ours should be performed before model calibration to determine whether using ETRS is worthwhile, and to help in interpreting the calibration results. [ABSTRACT FROM AUTHOR]

This study employs an event‐based approach to analyse drought propagation from meteorological to hydrological drought via agricultural drought in the semi‐arid Krishna River basin of India. The Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSMI) and Standardized Streamflow Index (SSI) representing meteorological, agricultural and hydrological drought, respectively, were estimated. Two different cases of drought propagation are analysed: meteorological‐to‐agricultural (SPEI‐to‐SSMI) and agricultural‐to‐hydrological (SSMI‐to‐SSI). The drought propagation is analysed using three‐time matrices, namely the time difference between initiation (∆ti2i$$ \Delta {t}_{i2i} $$), peak (∆tp2p$$ \Delta {t}_{p2p} $$) and termination (∆tt2t$$ \Delta {t}_{t2t} $$) at multiple timescales of 1, 3, 6, 9 and 12 months using different drought threshold values 0, −0.5, −1 and − 1.5, respectively, to delineate shifts from mild to extreme drought conditions in detail. The results indicate that the propagation time from SPEI‐to‐SSMI drought decreased for most of the tributaries using multiple timescales at different threshold values, whereas it increased significantly for SSMI‐to‐SSI drought. The drought propagation changes with respect to time as well as magnitude (intensity and severity). The propagation factor (PF), defined as the ratio of the average value of succeeding drought to preceding drought characteristics, has also been studied. For SPEI‐to‐SSMI drought, the duration PF shrinks across all tributaries using multiple timescales at different threshold values, whereas it expands for SSMI‐to‐SSI drought. On the other hand, the severity and intensity PF magnify for SPEI‐to‐SSMI drought, whereas it experiences dampening effects for SSMI‐to‐SSI drought. Thus, the proposed study provides valuable insights into drought propagation dynamics, aiding in managing and mitigating droughts in the semi‐arid river basin and elsewhere. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a key process in the eco-hydrological cycle of a basin and a reliable indicator of climate change. However, the spatiotemporal alterations of ET in the contiguous United States (CONUS) over the recent two decades remain largely uncertain. In this study, we used the recently proposed Priestley–Taylor (PT)-SinRH model to estimate the ET of CONUS during 2001–2022 based on satellite and reanalysis data. The results showed that the PT-SinRH model yielded superior performance at eddy covariance (EC) sites, and the root-mean-square error (RMSE) ranged from 6.0 to 33.5 W/m2, the Kling–Gupta efficiency (KGE) ranged from 0.22 to 0.66. The annual mean value of ET in CONUS from 2001 to 2022, estimated by the PT-SinRH model, was 42.54 W/m2, and the spatial pattern of seasonal and annual ET variations increased from west to east. From 2001 to 2022, seasonal and annual ET of CONUS showed linear trends, with an average increase of 0.76 W/m2/da (p < 0.05). The ET in the east of CONUS exhibited a rate of increase at 1.45 W/m2/da, and the ET in the west of CONUS exhibited a rate of increase at 0.42 W/m2/da (p < 0.05). Importantly, our analysis of ET trends highlights that the change of precipitation (P) and normalized difference vegetation index (NDVI) exerts a significant impact on the change of ET over CONUS. [ABSTRACT FROM AUTHOR]

In light of climate change and limited water resources, optimizing water usage in agriculture is crucial. This study models water productivity to help regional planners address these challenges. We integrate CROPWAT-based reference evapotranspiration (ETo) with Sentinel 2 data to calculate daily evapotranspiration and water needs for maize using soil and climate data from 2021 to 2023. The HYDRUS model predicted volumetric soil moisture content, validated against observed data. A 2D hydrodynamic model within HYDRUS simulated temporal and spatial variations in soil water distribution for maize at a non-irrigated site in Hungary. The model used soil physical properties and crop evapotranspiration rates as inputs, covering crop development stages from planting to harvest. The model showed good performance, with R² values of 0.65 (10 cm) and 0.81 (60 cm) in 2021, 0.51 (10 cm) and 0.50 (60 cm) in 2022, and 0.38 (10 cm) and 0.72 (60 cm) in 2023. RMSE and NRMSE values indicated reliability. The model revealed water deficits and proposed optimal irrigation schedules to maintain soil moisture between 32.2 and 17.51 V/V%. This integrated approach offers a reliable tool for monitoring soil moisture and developing efficient irrigation systems, aiding maize production's adaptation to climate change. [ABSTRACT FROM AUTHOR]

Synthetic wastewater was treated in lab-scale constructed wetlands (CWs) in sequencing batch mode to evaluate roles of aquatic plants for removing an azo dye: Reactive Yellow 86 (RY86). Under hydraulic retention time (HRT) of 5 days, removal by unplanted CWs was less than 20% for RY86 of 10–50 mg/L. The CWs planted with cattail and papyrus demonstrated RY86 removal of 50–68% and 73–84%, respectively. For wastewater containing 50 mg/L RY86, removal in the unplanted CW was <12%, even under a 15 day HRT, although it was 22–71% in CWs planted with cattail and 34–81% in CWs planted with cattail, with increasing values under HRTs of 1 day to 15 days. Both cattail and papyrus grew well, extending their roots in the CWs for 90 days. RY86-decolorizing microorganisms were detected in CW effluent. Overall, RY86 removal was positively correlated with evapotranspiration in the CWs, indicating the plant uptake as the main removal mechanism. Papyrus and cattail, especially the former, are suitable plants for CWs intended to treat RY86-containing wastewater. [ABSTRACT FROM AUTHOR]

Any change in a meteorological variable can affect an environmental element's evapotranspiration over time. This paper studies the dynamic causal relationships between evapotranspiration and meteorological parameters, which have long-term equilibrium relationships in Lahore, Pakistan, during the period from October 2004 to March 2020. The Augmented Dicky Fuller test reveals that the ARDL Approach to Cointegration is used for forecasting ET (kg m−2 s−1). The results depicted that geopotential height, surface air temperature, relative humidity at the surface, surface wind speed, tropopause height, surface skin temperature, and water vapor mass mixing ratio have a statistically significant long-run relationship with evapotranspiration. The results also revealed that cloud fraction, relative humidity at the surface, surface air temperature, total surface precipitation, water vapor mass mixing ratio, and tropopause height have a statistically significant short-run relationship with evapotranspiration. The CF (t - 1) has the strongest effect on ET (kg m−2 s−1) in the short and long run. Speed of adjustment ( ECT t - 1 ) shows a highly significant and negative relation, which indicates that after a 1 unit increase in the period, evapotranspiration (kg m−2 s−1) will go toward long-run equilibrium with a rate of 66.4%. The forecasted evapotranspiration time plot shows a long-term trend with seasonal components from April 2020 to March 2025. [ABSTRACT FROM AUTHOR]

Grain yield response to consumptive water use a.k.a. evapotranspiration (ETc) is a critical dimension of food‐water nexus in irrigated agroecosystems. With the largest water footprint, irrigated corn production in the U.S. must be assessed for its ETc and water use efficiency (WUE) at a scale meaningful for producers and water managers. Field experiments significantly inform our understanding of how corn yield responds to ETc at the field/plot‐level, but their collective synergism remains untapped. This analysis uses a literature synthesis of existing measured data from nationwide experiments that measured corn ETc, to build models that elucidate and predict corn ETc and WUE under diverse environment and on‐farm management. The resulting synthesis (n = 1,362) captured wide ranging conditions of mean aridity, environmental variability, management, and crop outcomes, providing an unprecedented opportunity to train data‐driven models. Random forest models effectively predicted seasonal total ETc and seasonal mean WUE using a handful of environmental and management‐based predictors, demonstrating RMSEs of 33 mm (5.1%) and 0.28 kg m−3 (10.4%), respectively. Irrigation depth was the most important followed by total precipitation received during the growing season in determining ETc and WUE. Trained on the largest compilation of experimental data sets of corn ETc, these data‐driven models represent nuanced corn water production functions that also account for weather and agronomy besides describing economic yield‐ETc response. Key Points: Measured corn ETc & water use efficiency (WUE) and underlying environmental and management conditions across all experimental research in the U.S. were synthesizedIrrigation depth was the most important predictor of ETc and WUE followed by total precipitation received during the growing seasonPresented nuanced crop water production functions that account for weather and management besides describing yield‐ETc response [ABSTRACT FROM AUTHOR]

Background: Accurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model. Results: Results indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R²) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R² values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively. [ABSTRACT FROM AUTHOR]

This study modelled the drivers of satellite-derived Net Primary Productivity (NPP). The NPP drivers used were human foot print (HFP), soil organic carbon (SOC), Total Nitrogen (TN), Total Phosphorus (TP), Potential evapotranspiration, (PET), and Aridity Index (AI). The results showed that inclusion of non-vegetated land cover types (LCT) in the modelling leads to establishment of wrong relationships between the NPP and its drivers, and wrong relationships among the drivers. Inclusion of the non-vegetated LCT revealed that 1) HFP had no relationship with NPP, CUE, and SOC; 2) PET had no relationship with all the three soil variables (SOC, TN, and TP), AI and the CUE; 3) PET and NPP were strongly and positively correlated; and 4) there was no relationship between the NPP and all the three soil variables and the AI. While exclusion of the non-vegetated LCT revealed that 1) HFP had negative relationship with NPP, CUE, and SOC; 2) PET had negative relationship with the CUE, AI and all the three soil variables; 3) PET and NPP were strongly negatively correlated; and 4) there was positive correlations between the NPP and all the three soil variables and AI. However, results of both the inclusion and exclusion of the non-vegetated LCT indicated that: 1) HFP did not correlate with TN, TP, PET, and AI; and 2) there was strong and positive correlation among all the three soils and AI. It is worth noting that exclusion of the non-vegetated LCT in the modelling established the most accurate results on the relationship between the NPP and its drivers; and on the relationships among the drivers. However, there is a need to replicate this study in different geographical regions to guarantee global generalization of the results. [ABSTRACT FROM AUTHOR]

The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m (q = 0.48). [ABSTRACT FROM AUTHOR]

Agriculture water use accounts for 70% of the total water withdrawal worldwide. The evapotranspiration during crop growth is one of the important hydrological processes in the agricultural water cycle. This study proposed the concept of artificial irrigation evapotranspiration of irrigated crops to describe that the evapotranspiration caused by irrigation water use. Irrigated crops rely on two kinds of water sources: precipitation and irrigation water. With the construction of irrigation schemes, the artificial irrigation evapotranspiration plays an increasingly important role in the dualistic water cycle system of irrigated cropland. To reveal the amount of artificial irrigation evapotranspiration of 17 categories of irrigated crops in China, this study proposed a new quantitative model system which was established based on traditional evapotranspiration models and soil water balance models. Based on the new model system, we calculated the annual artificial irrigation evapotranspiration of irrigated crops for the period 2013 to 2017 in China. The results showed that the proportion of artificial irrigation evapotranspiration to the total evapotranspiration of irrigated crops was 41.3%, whose value was 228.1 km3 a−1. The artificial irrigation evapotranspiration in different agricultural water management regions were 90.0 km3 a−1 in the northeast region, 86.0 km3 a−1 in the southeast region, and relatively low 52.2 km3a−1 in the west region. The results of this study can provide methods for water management and policy–making in agricultural irrigated areas, and it can also provide a preliminary understanding of the influence of human activities on the dualistic water cycle in cropland. [ABSTRACT FROM AUTHOR]

The Xiliao River Basin is an important economic development area and ecological barrier in Northeast China. In recent years, it has experienced the serious degradation of hydrological function. Based on the hydrometeorological data, remote-sensing imagery, and water resource utilization data of the basin, the function evaluation model and standard deviation ellipse model were used to construct a hydrological function index system and evaluation model that combined ecological and socioeconomic indicators to analyse the evolution characteristics of hydrological functions. The results showed that the centre of precipitation function of the basin had shifted eastward; the evapotranspiration function was strong and showed an increasing trend in the hilly area in the upper reaches, watercourses, waters and their surrounding areas in the middle reaches, and most areas in the lower reaches; and the surface water function in the middle and lower reaches of the basin had weakened sharply since the beginning of the twenty-first century, and the degradation reached 70%. The groundwater function was stronger in the lower reaches than in the upper and middle reaches, and the degradation of groundwater function in the urban agglomeration area was is degraded by 10–30%. The overall hydrological function of the basin was higher in the southwest and lower in the east, and its degradation degree and scope in the urban agglomeration areas was further deepened and has expanded since 2000. This study enriches the research results on hydrological functions and provides a basis for the restoration and construction of the hydrological functions in the basin. [ABSTRACT FROM AUTHOR]

The increase in rainfall intensity caused by climate change, combined with high levels of urban soil sealing and the limited capacity of drainage systems, is significantly increasing flooding risk. Integrated stormwater management is a key factor in dealing with the consequences of climate change by mitigating flood risk based on nature-based solutions. An experimental site was designed to assess the hydrological response of vegetated swales depending on different catchment surfaces, and to evaluate the impact of spatial variability of the hydraulic conductivity (K). A hydrological model was developed on EPA SWMM and calibrated based on measured data in two experimental swales with different active surfaces (N6—50 m2 and N11—100 m2). The model validation was assessed with NSE scores higher than 0.7. The simulations considered various factors, such as the water level in the swales, the actual rainfall, the evapotranspiration, the swale geometry, the catchment area (Sa), and the hydraulic conductivities of the natural silty soil, to assess, for the heaviest rainfall event, the best swale morphological characteristics. The study examined the combined impact of K (8) and Sa (6) on swale storage capacity, designed with a 250 mm depth. The simulations showed that the 250 mm overflow limit was exceeded for N10 (90 m2) when K ≤ 2 × 10−6 m/s, and for N11 (100 m2) when K ≤ 4 × 10−6 m/s. These results provide valuable information on the optimal storage capacity based on the swales' geometrical and physical properties. [ABSTRACT FROM AUTHOR]

In order to gain a deeper understanding of the effects of environmental factors on the formation of Populus euphratica xylem, this study analysed the anatomical characteristics of Populus euphratica xylem and its response to environmental factors using wood anatomy as an example of Populus euphratica in the Yingsu section of the lower Tarim River. The results showed the following: (1) Throughout the growing season, the number of conduits of Populus euphratica in the two sample sites showed a slow increasing trend with the increase in groundwater burial depth, and the total conduit area showed an increasing and then decreasing trend with the increase in groundwater burial depth. The four indices of total, minimum, average, and maximum conduit areas in Populus euphratica xylem increased significantly with increasing temperatures during the growing season. (2) From the principal component analysis, the anatomical parameters of Populus euphratica xylem were found to be positively correlated with the depth of groundwater, average air temperature, maximum air temperature, minimum air temperature, surface temperature, relative humidity, and saturated water and air pressure deficit in the two sampling sites The most significant effects were found in the air temperature and depth of the groundwater. (3) The contribution of groundwater level and air temperature to different growth stages of Populus euphratica xylem was different. During the early part of the growing season of Populus euphratica, the air temperature was the main factor influencing the number of xylem conduits and total conduit area. As the growing season entered the middle stage, the air temperature and groundwater together affected the conduit parameters. In the later part of the growing season, groundwater became the most important factor affecting the number of conduits and the total conduit area. (4) The sensitivity analyses yielded a sensitive groundwater burial depth of 5.2 m for changes in the number of conduits in the xylem of the Populus euphratica and 5.9 m for changes in the total conduit area; the sensitive air temperature for the total conduit area in the xylem of the Populus euphratica was 22.0 °C, and the sensitive air temperature for the average conduit area was 18.5 °C. The results of this study can have important theoretical significance for understanding the Populus euphratica forests, as well as ecological water resource management in the lower Tarim River. They also provide scientific basis for the restoration and protection of Populus euphratica forests in the lower Tarim River. [ABSTRACT FROM AUTHOR]

Vegetation phenology is a biological factor that directly or indirectly affects the dynamic equilibrium between water and carbon fluxes in ecosystems. Quantitative evaluations of the regulatory mechanisms of vegetation phenology on water–carbon coupling are of great significance for carbon neutrality and sustainable development. In this study, the interannual variation and partial correlation between vegetation phenology (the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS)) and ET (evapotranspiration), GPP (gross primary productivity), WUE (water use efficiency; water–carbon coupling index) in the Huang-Huai-Hai and Yangtze River Basins in China from 2001 to 2019 were systematically quantified. The response patterns of spring (autumn) and growing season WUE to SOS, EOS, and LOS, as well as the interpretation rate of interannual changes, were evaluated. Further analysis was conducted on the differences in vegetation phenology in response to WUE across different river basins. The results showed that during the vegetation growth season, ET and GPP were greatly influenced by phenology. Due to the different increases in ET and GPP caused by extending LOS, WUE showed differences in different basins. For example, an extended LOS in the Huang-Huai-Hai basins reduced WUE, while in the Yangtze River Basin, it increased WUE. After extending the growing season for 1 day, ET and GPP increased by 3.01–4.79 mm and 4.22–6.07 gC/m2, respectively, while WUE decreased by 0.002–0.008 gC/kgH2O. Further analysis of WUE response patterns indicates that compared to ET, early SOS (longer LOS) in the Yellow River and Hai River basins led to a greater increase in vegetation GPP, therefore weakening WUE. This suggests that phenological changes may increase ineffective water use in arid, semi-arid, and semi-humid areas and may further exacerbate drought. For the humid areas dominated by the Yangtze River Basin, changes in phenology improved local water use efficiency. [ABSTRACT FROM AUTHOR]

Runoff and evapotranspiration (ET) are pivotal constituents of the water, energy, and carbon cycles. This research presents a 5-km monthly gridded runoff and ET dataset for 1998–2017, encompassing seven headwaters of Tibetan Plateau rivers (Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, and Indus) (hereinafter TPRED). The dataset was generated using the advanced cryosphere-hydrology model WEB-DHM, yielding a Nash coefficient ranging from 0.77 to 0.93 when compared to the observed discharges. The findings indicate that TPRED's monthly runoff notably outperforms existing datasets in capturing hydrological patterns, as evidenced by robust metrics such as the correlation coefficient (CC) (0.944–0.995), Bias (−0.68-0.53), and Root Mean Square Error (5.50–15.59 mm). Additionally, TPRED's monthly ET estimates closely align with expected seasonal fluctuations, as reflected by a CC ranging from 0.94 to 0.98 when contrasted with alternative ET products. Furthermore, TPRED's annual values exhibit commendable concordance with operational products across multiple dimensions. Ultimately, the TPRED will have great application on hydrometeorology, carbon transport, water management, hydrological modeling, and sustainable development of water resources. [ABSTRACT FROM AUTHOR]

Vegetation plays an essential role in terrestrial carbon balance and climate systems. Exploring and understanding relationships between vegetation dynamics and climate changes in Southwest China is of great significance for ecological environment conservation. Nonlinear relationships between vegetation and natural factors are extraordinarily complex in Southwest China with complicated topographic conditions and changeable climatic characteristics. Considering the complex nonlinear relationships, the Random Forest (RF) and an integration of Convolutional Neural Networks and Long Short-Term Memory network (CNN-LSTM) were used with multi-source data from 2000-2020. Performance of two models were compared with precision indicators, and influence of topographic and hydro-climatic factors on vegetation was quantified based on the optimal models. Results revealed that the Normalized Difference Vegetation Index had a significant negative correlation with elevation and a positive correlation with land surface temperature and evapotranspiration. According to precision indicators, the RF model (RF3) built with longitude, latitude, elevation, slope, temperature, precipitation, evapotranspiration and surface solar radiation as inputs outperformed other models. Relative importance of the eight natural factors was quantified based on the RF3, and results indicated that elevation, temperature and evapotranspiration were major factors that influenced vegetation growth. Responses of vegetation toward climatic variables exhibited significant seasonal change, and there were different decisive factors, which influenced vegetation growth in forests, grasslands and croplands. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal patterns and variations in vapor pressure deficit (VPD) is essential for effective water resource management in the face of climate change. VPD serves as a fundamental indicator of atmospheric dryness, directly impacting plant evapotranspiration rates, thereby affecting overall ecosystem functioning. In this study, VPD changes in five subregions and four ecological types of land in Yunnan from 1980 to 2021 were investigated using data from 108 meteorological stations. We found increasing trends in annual VPD that were larger during warmer spring and summer seasons. Among the subregions, the highland subtropical southern broadleaved evergreen forest ecoregion exhibited the largest drying trend (0.04 kPa / decade), while the subtropical (eastern) humid broadleaved evergreen forest ecoregion had the smallest drying trend (0.01 kPa / decade). Among the ecological barriers, the dry-hot valleys had significantly higher increases in VPD compared to other Yunnan regions. Increases in VPD from 1990s to 2000s in several regions of Yunnan exceeded atmospheric drying trends reported elsewhere. Additionally, we documented smaller VPD declines at higher elevations. Across different ecological land types, Shrubland exhibited the largest VPD trend (0.07 kPa / decade), while Forest displayed the smallest (0.03 kPa / decade). Decreased relative humidity through time explained 65% of the increase in VPD in Yunnan, while increasing temperatures accounted for 25%. These findings provide valuable insights into climatic dynamics of Yunnan, with implications for ecological, hydrological, and atmospheric studies. [ABSTRACT FROM AUTHOR]

Background Aims Methods Results Conclusion Neither the magnitude of boron (B) depletion in agricultural soils nor the driving factors of this process have been systematically studied before.The objectives of our study were to survey the extractable B (Be) in pristine soils as compared with cultivated soils, to estimate the yearly B depletion rate in agroecosystems, and to identify the edaphic, productive, and/or climatic factors determining that rate.Surface soil samples (0–20 cm layer) were taken from uncropped (UC) sites and nearby agricultural fields in 2011 and 2018 (AGR2011 and AGR2018, respectively) from the Argentinean Pampas. In these samples, Be and other edaphoclimatic and productive variables were determined, such as clay content, precipitation (PP), pH, evapotranspiration (ET), soil organic matter (SOM), and B removal with crop grains (RB).The Be concentration decreased with agricultural activity, because UC soils had an average Be value of 1.9 mg kg−1, whereas AGR2011 and AGR2018 soils had values of 1.3 and 0.9 mg kg−1, respectively. The depletion rate from 2018 to 2011 ranged from 0.01 to 0.06 mg kg−1 y−1, and the main factors associated with this process were RB, PP, soil acidification, and SOM depletion (promoting B depletion) and ET and clay (reducing depletion).At current rates, B depletion from these agricultural soils could compromise B availability for crops in the short‐to‐medium term, depending on regional differences caused by edaphoclimatic and productive differences (RB, PP, pH, SOM, ET, and clay). Urgent actions are required to halt and/or revert this soil degradation process. [ABSTRACT FROM AUTHOR]

Prolonged drought exacerbated by climate change in the Mesilla Valley, one of the major agricultural areas of New Mexico, USA, is causing a shortage of surface water from the Rio Grande for irrigation. Farmers in the Valley are using groundwater for irrigation and complementing it with limited surface water from the river (Rio Grande). Managing irrigation water better is vital to sustaining agriculture in the Valley. Remote sensing (RS)-based crop evapotranspiration (ETa) models offer significant advantages over traditional methods. The ET maps generated by these RS models provide valuable information that can be used to manage irrigation water and crops in water-scarce areas. This study used METRIC and SSEBop RS models to map the ET of alfalfa on a private farm that is managed as commonly practiced in the Valley. The integrated ET values of the two models are compared to those of the ETa measured using the eddy covariance method. The comparison showed that 91.55% of the variability in SSEBop ETa estimates can be explained by the variability in the METRIC ETa estimates, and the variability in eddy covariance ETa can explain 93.07% of the variability in METRIC ETa and 86.01% in the SSEBop Eta estimates. Both METRIC and SSEBop reflected the ETa of alfalfa during full growth and harvesting periods. However, the absolute percent mean relative difference (MRD) of ET was higher for two out of three cuttings by SSEBop (>32%) compared to those for METRIC and eddy covariance. The spatiotemporal variabilities in crop ET estimates using METRIC and SSEBop showed a need to improve on-farm irrigation conveyance and on-the-field irrigation efficiency. Overall, RS models can provide spatiotemporal maps of ET that can be used for decision-making to manage irrigation water better and improve crop yield on a field, farm, and regional scale. [ABSTRACT FROM AUTHOR]

The combination effect of straw mulching and supplemental irrigation on the soil water and heat, and the growth and productivity of mature apple trees on the Loess Plateau of China remains unclear. Field experiments were conducted in 2022 and 2023 to evaluate the combined effect of straw mulching and supplemental irrigation (two irrigation types, ring and double-row drip irrigation, and three irrigation levels: high, medium, and low irrigation level) on the soil water and temperature, growth, yield, and water productivity (WP) of a mature apple orchard. Local rainfed cultivation was used as the control (CK). The results showed that straw mulching increased soil moisture by 2.4–6.5% compared to the CK treatment. Supplemental irrigation significantly increased soil moisture in the 0–60 cm layer by 3.0–8.1%, and its effect increased with an increasing irrigation level. Straw mulching significantly reduced soil temperature by 7.8% compared to the CK treatment. Supplemental irrigation significantly increased the new shoot length and stem thickness of apple trees. Under straw mulching, a medium supplemental irrigation level significantly increased both apple yield and WP compared to the CK treatment. In this study area, it is recommended to choose a combination of straw mulching and a medium supplemental irrigation level. [ABSTRACT FROM AUTHOR]

More than half of global water use can be attributed to crop irrigation, and as the human population grows, so will the water requirements of agriculture. Improved irrigation will be critical to mitigating the impact of increased requirements. An ideal irrigation system is informed by measurements of water demand—a combination of water use and water status signals—and delivers water to plants based on this demand. In this review, examples of methods for monitoring water status are reviewed, along with details on stem and trunk water potential measurements. Then, methods for monitoring evapotranspiration (ET), or water use, are described. These methods are broken into coarse- and fine-scale categories, with a 10 m spatial resolution threshold between them. Fourteen crop ET technologies are presented, including examples of a successful estimation of ET in research and field settings, as well as limitations. The focus then shifts to water distribution technologies, with an emphasis on the challenges associated with the development of systems that achieve dynamic single plant resolution. Some attention is given to the process of choosing ET and water status sensing methods as well as water delivery system design given site characteristics and agronomic goals. This review concludes with a short discussion on the future directions of ET research and the importance of translating findings into useful tools for growers. [ABSTRACT FROM AUTHOR]

Drylands, where the water supplies are scarce relative to the atmospheric moisture demands, occupy approximately half of the area of China. With global warming, the response of atmospheric water cycle is a crucial concern for sustainable development in China's drylands. This study investigates the characteristics of the water cycle in China's drylands during 1980–2019, as well as future projections estimated by the Community Earth System Model (CESM) during 2020–2100. The results show that the air temperature in China's drylands has increased significantly since 1980, especially after 2000, with greater warming in the warm season. Meanwhile, China generally tend to be drier from 1980 to 2019 as a whole, especially in the cold season. Both precipitation (P) and evapotranspiration (E) have increased, but the net effect is that drylands become drier (negative P-E) and wetlands become wetter (positive P-E), respectively. In the future, drylands are projected to become more humid due to increased precipitation, showing a pattern of "western wetter and eastern drier" during 2020–2100. These results could improve understanding of the water cycle and provide some guidance for adapting to climate change in drylands. [ABSTRACT FROM AUTHOR]