Precipitation variation: a key factor regulating plant diversity in semi-arid livestock grazing lands.

Livestock presence impacts plant biodiversity (species richness) in grassland ecosystems, yet extent and direction of grazing impacts on biodiversity vary greatly across inter-annual periods. In this study, an 8-year (2014-2021) grazing gradient experiment with sheep was conducted in a semi-arid grassland to investigate the impact of grazing under different precipitation variability on biodiversity. The results suggest no direct impact of grazing on species richness in semi-arid Stipa grassland. However, increased grazing indirectly enhanced species richness by elevating community dominance (increasing the sheltering effect of Stipa grass). Importantly, intensified grazing also regulates excessive community biomass resulting from increased inter-annual wetness (SPEI), amplifying the positive influence of annual humidity index on species richness. Lastly, we emphasize that, in water-constrained grassland ecosystems, intra-annual precipitation variability (PCI) was the most crucial factor driving species richness. Therefore, the water-heat synchrony during the growing season may alleviate physiological constraints on plants, significantly enhancing species richness as a result of multifactorial interactions. Our study provides strong evidence for how to regulate grazing intensity to increase biodiversity under future variable climate patterns. We suggest adapting grazing intensity according to local climate variability to achieve grassland biodiversity conservation.

Dynamics of carbon and water vapor fluxes in three typical ecosystems of Heihe River Basin, Northwestern China.

Understanding the dynamics of carbon and water vapor fluxes in arid inland river basin ecosystems is essential for predicting and assessing the regional carbon-water budget amid climate change. However, studies aiming to unravel the mechanisms driving the variations and coupling process of regional carbon-water budget in a changing environment in arid regions are limited. Here, we used the eddy covariance technique to analyze the relationship between CO2 and H2O fluxes in three typical ecosystems across the upper, middle, and lower reaches of an arid inland river basin in Northwestern China. Our results showed that all ecosystems acted as carbon sinks, with the alpine swamp meadow, cropland, and desert shrubland sequestrating -300.2 ± 0.01, -644.8 ± 2.9, and - 203.7 ± 22.5 g C m-2 yr-1, respectively. Air temperature (Ta) primarily controlled daily gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE) in the irrigated cropland during the growing season, while soil temperature (Ts) and vapor pressure deficit (VPD) regulated these parameters in the alpine swamp meadow and desert shrubland. Additionally, Ta and net radiation (Rn) controlled daily evapotranspiration (ET) in cropland, while Ts and Rn regulated ET at other sites. Consequently, carbon and water vapor fluxes of all three ecosystems tended to be energy-limited during the growing season. The differential responses of carbon and water vapor fluxes in the upper, middle, and lower reaches of these ecosystems to biophysical factors determined their distinct coupling and variations in water use efficiency. Notably, the desert shrub ecosystem in the lower reach of the basin maintained a stable balance between carbon gain and water loss, indicating adaptation to aridity. This study provides valuable insights into the underlying mechanisms behind the changes in carbon and water vapor fluxes and water-use efficiency in arid river basin ecosystems.

Comparative analysis in selecting best irrigation method to maximize tomato yield from various irrigation approaches in water scarce regions.

It is now essential for farmers to evaluate and compare different irrigation scheduling strategies in order to operate their irrigation systems in accordance with their resources and achievements. For tomato crops, a rigorous comparison was made between the yield of soil moisture and irrigation scheduling systems based on evapotranspiration. The experimental setup used a randomized complete block design (RCBD) with three replications, with water requirements of 100%, 75%, and 50%. Using a ReplogleBos-Clemmens (RBC) flume with an input rate of 1.62 l/s, water was applied to the furrows. The total applied amount in the Soil Moisture (SM) and Evapotranspiration (ET) based methods was 229.1 mm, and 280 mm, respectively. The collected data were assessed using ANOVA at a 5% significance level. For every technique, yield values, yield attributes, and agronomic traits were computed. The result indicates that there is no appreciable variation among the factors. The net yield advantage was highest at 75% SM and lowest at 25% SM. Furthermore, compared to the ET-based approach, it was demonstrated that the SM-based method conserved approximately 18.2% of irrigation water. As a result, when producing crops that require a lot of water, like tomatoes, in areas with limited water resources, SM-based irrigation scheduling is preferable to ET-based irrigation scheduling.

Environmental Controls on Evapotranspiration and Its Components in a Qinghai Spruce Forest in the Qilian Mountains.

Qinghai spruce forests, found in the Qilian mountains, are a typical type of water conservation forest and play an important role in regulating the regional water balance and quantifying the changes and controlling factors for evapotranspiration (ET) and its components, namely, transpiration (T), evaporation (Es) and canopy interceptions (Ei), of the Qinghai spruce, which may provide rich information for improving water resource management. In this study, we partitioned ET based on the assumption that total ET equals the sum of T, Es and Ei, and then we analyzed the environmental controls on ET, T and Es. The results show that, during the main growing seasons of the Qinghai spruce (from May to September) in the Qilian mountains, the total ET values were 353.7 and 325.1 mm in 2019 and 2020, respectively. The monthly dynamics in the daily variations in T/ET and Es/ET showed that T/ET increased until July and gradually decreased afterwards, while Es/ET showed opposite trends and was mainly controlled by the amount of precipitation. Among all the ET components, T always occupied the largest part, while the contribution of Es to ET was minimal. Meanwhile, Ei must be considered when partitioning ET, as it accounts for a certain percentage (greater than one-third) of the total ET values. Combining Pearson's correlation analysis and the boosted regression trees method, we concluded that net radiation (Rn), soil temperature (Ts) and soil water content (SWC) were the main controlling factors for ET. T was mainly determined by the radiation and soil hydrothermic factors (Rn, photosynthetic active radiation (PAR) and TS30), while Es was mostly controlled by the vapor pressure deficit (VPD), atmospheric precipitation (Pa), throughfall (Pt) and air temperature (Ta). Our study may provide further theoretical support to improve our understanding of the responses of ET and its components to surrounding environments.

A comparative study of various drought indices at different timescales and over different record lengths in the arid area of northwest China.

Although many drought indices have been developed to monitor drought conditions, their applicability differs across climatic regions, which results in different characteristics of drought index assessments at different timescales and over different record lengths. Therefore, the applicability of precipitation-based and precipitation-evapotranspiration/temperature-based drought indices was examined in this study using the Generalized Extreme Value Index (GEVI), Homogeneity Index (HI) of precipitation and temperature, K index (K), precipitation anomaly percentage (Pa), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and China-Z Index (CZI) over different record lengths and at a timescale range of 1-24 months. In addition, the results that were obtained using these indices were compared with the Self-Calibrating Palmer Drought Severity Index (scPDSI). The stability, accuracy, and consistency of the different drought indices were evaluated using precipitation and evapotranspiration/temperature data (1-24 months) collected from different climatic regions in the arid area of northwest China in the 1961-2017 period. The results indicated that the Pa, CZI, K, and SPEI were more stable; the SPI, SPEI, and HI were more accurate; and the GEVI, SPI, HI, and SPEI were more consistent with the scPDSI. In addition, the results indicated that it is more appropriate to select a long record length (> 35 years) to monitor drought when sufficient data are available. However, defining constant drought classes may not be appropriate for all drought timescales. In fact, precipitation and evapotranspiration data from different timescales had different optimal distribution functions. The drought indices also demonstrated that they were applicable to a temperate arid area in the study region. In addition, the HI and SPEI better captured the precipitation and evapotranspiration/temperature characteristics, while the CZI, K, and Pa overestimated or underestimated the frequency of different drought classes at different timescales to some extent in the study region. The results of this study suggest that greater priority should be given to the precipitation-evapotranspiration-based indices. In addition, it is suggested to change the drought index class thresholds in future related studies on drought event recognition at different timescales to ensure more accurate drought monitoring.

Better estimation of evapotranspiration and transpiration using an improved modified Priestly-Taylor model based on a new parameter of leaf senescence in a rice field.

Evapotranspiration (ET) is at the heart of the global water, energy, and carbon cycles. As ET is difficult and expensive to measure, it is crucial to develop estimation models that can be widely applied. Currently, an improved Priestley-Taylor (PT) model considers soil moisture stress, temperature constraints, and leaf senescence; however, its parameter (fs) for simulating crop senescence is based on empirical values, making it difficult to apply to different varieties and complex external conditions and thus challenging to generalize. We improved the parameters fs in the original model based on the chlorophyll decomposition that accompanies crop senescence through easily observable SPAD values (Soil-Plant Analysis Development readings) in the field. We validated the improved model by obtaining ET of different rice varieties in 2022 and 2023 using the energy balance residual method at the Free Air Concentration Enrichment Experimental (FACE) Facility located in Yangzhou City, China. The results showed that the simulation of leaf senescence using SPAD values was feasible and could be extended to different varieties. The new model using improved leaf senescence parameter for estimating ET and transpiration (T) in three plots (2022 and 2023) exhibited slightly enhanced accuracy, particularly at the later stages of crop growth. Moreover, the higher the T/ET ratio of the cropland, the more significant the improvement. This new development enhances the ability of PT models to estimate ET and T using readily available field observations and provides some suggestions for wider application in the field for other crop species.

Inside-out: Synergising leaf biochemical traits with stomatal-regulated water fluxes to enhance transpiration modelling during abiotic stress.

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.

Prediction of evapotranspiration variance in the Budyko framework with the incorporation of soil storage and runoff.

Water availability needs to be accurately assessed to understand and effectively manage hydrologic environments. However, the estimation of evapotranspiration (ET) is prone to errors due to the complex interactions that occur between the atmosphere, the Earth's surface, and vegetation cover. This paper proposes a novel approach for analyzing the sources of inaccuracy in estimating the annual ET using the Budyko framework (BF), particularly temporal variability in precipitation (P), potential evapotranspiration (EP), runoff (R), and the change in soil storage (ΔS). Error decomposition is employed to determine the individual contributions of P, R, EP, and ΔS to the ET error variance at 12 locations in the state of Illinois using a dataset covering a 22-year period. To the best of our knowledge, this study represents the first BF-based investigation that considers R in the error decomposition of the predicted ET variance. The ET error variance increases with the variance in the P and R in Illinois and decreases with the covariance between these two variables. In addition, when accounting for ΔS in the BF, the scenario in which ΔS affects the total available water (i.e., P) is reliable, with a low prediction error and a 13.87 % lower root mean square error compared with the scenario in which the effect of ΔS is negligible. We thus recommend the inclusion of ΔS and R as key variables in the BF to improve water budget estimations.

Improvement of evapotranspiration simulation study in the Hailar River basin under the influence of vegetation dynamics.

In arid and semi-arid areas with <400 mm of precipitation, evapotranspiration (ET) accounts for about 80% of precipitation and is the main water consumer in the watershed. However, vegetation greening in recent years will increase ET and exacerbate the aridity of the area by affecting soil moisture in the root system. Vegetation changes are regional and spatially heterogeneous, therefore, in order to characterize ET changes under vegetation dynamics, it is necessary to expand the spatial scale of ET simulation. However, widely used evapotranspiration simulation models, such as the Shuttleworth-Wallace model (SW model), are deficient in reflecting the direct and indirect effects of vertical (i.e., soil depths) and horizontal (i.e., vegetation dynamics) directions. Based on field sampling and constructed structural equation model (SEM), we found that vegetation dynamics affect evapotranspiration not only directly, but also indirectly by affecting soil moisture at different depths. On this basis, we defined the weighting coefficients of 0.85 and 0.15 for grassland vegetation zones, 0.3, 0.15, 0.20, 0.25, 0.10 for forest-grass interspersed zones, and 0.20, 0.55, 0.25 for forested zones, respectively, based on the SEM results. Different soil moisture weighting coefficients were defined within different vegetation type zones and the improved SW model is called S-W-α. Comparing the simulation results with the measured data, S-W-α improved the ET simulation accuracy in this region by 33.92% and the improved ET spatial trend can respond to the dynamic changes of vegetation. Replacing the ET module in the Block-wise use of TOPMODEL and Muskingum-Cunge method mode (BTOP model) with the modified S-W-α, the results show that the simulation accuracy of the improved model is increased by 25%, and the Nash is higher than 75% for both the rate period and the validation period, which realizes the extension of the model from the point scale to the basin scale. The modified model may provide technical support for simulation of evapotranspiration and management of ecosystem health in ecologically fragile areas.

Multi-datasets to monitor and assess meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region.

This study analyzed the meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region from 1994 to 2016. In recent decades, this region has faced prolonged and severe droughts, leading to marked reductions in agricultural productivity and significant challenges to food security and water availability. The datasets employed included a digital elevation model, land use and cover data, soil characteristics, climatic data (temperature, wind speed, solar radiation, humidity, and precipitation), runoff data, images from the MODIS/TERRA and AQUA sensors (MOD09A1 and MODY09A1 products), and soil water content. A variety of methods and products were used to study these droughts: the meteorological drought was analyzed using the Standardized Precipitation Index (SPI) derived from observed precipitation data, while the hydrological drought was assessed using the Standardized Soil Index (SSI), the Nonparametric Multivariate Standardized Drought Index (NMSDI), and the Parametric Multivariate Standardized Drought Index (PMSDI). These indices were determined using water balance components, including streamflow and soil water content, from the Soil Water Assessment Tool (SWAT) model, and evapotranspiration data from the Surface Energy Balance Algorithm for Land (SEBAL). The findings indicate that the methodology effectively identified variations in water dynamics and drought periods in a headwater basin within Brazil's semiarid region, suggesting potential applicability in other semiarid areas. This study provides essential insights for water resource management and resilience building in the face of adverse climatic events, offering a valuable guide for decision-making processes.

Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress.

Soil water balance is an essential element to consider for the management of droughts and agricultural land use. It is important to evaluate the water consumption of a crop in each of its phenological phases and the status of water reserves during critical hydrologic periods. This study developed an agricultural drought index (Standardized Soil Moisture Deficit Index - SMODI) conceptualized with a water balance model considering the vegetation stress caused by soil moisture deficit. This contribution was based on meteorological information, soil moisture from satellite images, hydrophysical properties of the soil and crop evapotranspiration. Information from 61 weather stations located in the dry zone of Tolima was used for estimating the water balance. SMODI was compared with the most common drought indexes: Standardized Precipitation - Evapotranspiration Index (SPEI), the Palmer Self-Calibrated Drought Index (scPDSI), and other eleven macroclimatic indexes. Pearson's correlation coefficients (r), Tukey's test, and analysis of variance were applied to analyze the degree of association between SMODI and the contrasting indexes on a quarterly basis. SMODI considers factors influencing soil moisture distribution and retention and the water stress thresholds that plants have evolved to withstand during drought periods. Consequently, this integrated approach enhances the assessment of agricultural drought by relying on pertinent physical processes. SMODI identified extremely dry, severe, moderate and normal drought 5 %, 3 %, 20 % and 72 % respectively conditions in areas characterized by Entisols, Inceptisols, and Andisols, where rice and fruit crops and pasturelands are cultivated. The SMODI has a good correlation with macroclimatic indexes (0.70 < r < 0.74).

Do baseline assumptions alter the efficacy of green stormwater infrastructure to reduce combined sewer overflows?

Green stormwater infrastructure (GSI) is growing in popularity to reduce combined sewer overflows (CSOs) and hydrologic simulation models are a tool to assess their reduction potential. Given the numerous and interacting water flows that contribute to CSOs, such as evapotranspiration (ET) and groundwater (GW), these models should ideally account for them. However, due to the complexity, simplified models are often used, and it is currently unknown how these assumptions affect estimates of CSOs, GSI effectiveness, and ultimately planning guidance. This study evaluates the effect on estimates of CSOs and GSI effectiveness when different flows and hydrologic processes are neglected. We modified an existing EPA SWMM model of a combined sewer system in Switzerland to include ET, GW, and upstream inflows. Historical rainfall data over 30 years are used to assess volume and duration of CSOs with and without three types of GSI (bioretention basins, permeable pavements and green roofs). Results demonstrate that neglect of certain flows in modelling can alter CSO volumes from -15 % to 40 %. GSI effectiveness also varies considerably, resulting in differences in simulated percent of CSO volume reduced from 8 % to 35 %, depending on the GSI type and modeled flow or process. Representation of GW within models is particularly crucial when infiltrating GSI are present, as CSOs could increase in certain subcatchments due to higher GW levels from increased infiltration. When basing GSI planning decisions on modeled estimates of CSOs, all relevant hydrologic processes should be included to the extent possible, and uncertainty and assumptions should always be considered.

Evaluating future water availability in Texas through the lens of a data-driven approach leveraged with CMIP6 general circulation models.

Climate change is escalating the frequency and intensity of extreme precipitation events, significantly influencing the spatial and temporal distributions of water resources. This is particularly evident in Texas, a rapidly growing state with a pronounced west-east gradient in water supply. This study utilizes Coupled Model Intercomparison Project Phase 6 (CMIP6) data and data-driven methodology to improve projections of Texas's future water resources, focusing on actual evapotranspiration (AET) and water availability through enhanced Multi-Model Ensembles. The results reveal that the data-driven model significantly outperforms the CMIP5 and CMIP6 models across all skill metrics, underscoring the potential of data-driven methodologies in advancing climate science. Furthermore, the study provides an in-depth analysis of the projected changes in net water availability (NWA) and estimated water demand for different regions in Texas over the next six decades from 2015 to 2074, which reveal fluctuating patterns of water stress, with the regions (nine out of sixteen water planning regions in Texas, especially for the most populated regions) poised for heightened challenges in reconciling water demand and availability. While increasing trends are found in precipitation, AET, and NWA for the northern region of Texas based on SSP2-4.5, decreasing trends are found over the southern region for all three parameters based on SSP5-8.5. These findings underscore the importance of factoring both spatial and temporal variations in water availability and demand for effective water management strategies and the need for adaptive water management strategies for the changing water availability scenarios.

Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg-Marquardt method in Elazig, Turkey.

The phenomenon of evapotranspiration (ET) is closely linked to the issue of water scarcity, as it involves water loss through both evaporation and plant transpiration. Accurate prediction of evapotranspiration is of utmost importance in the strategic planning of agricultural irrigation, effective management of water resources, and precise hydrological modeling. The current investigation aims to predict the monthly ET values in the Elazig province by developing an artificial neural network (ANN) model utilizing the Levenberg-Marquardt method. Consequently, the values of temperature, precipitation, relative humidity, solar hour, and mean wind speed were utilized in forecasting evapotranspiration values by implementing ANN algorithms. This research makes a valuable contribution to the existing body of literature by utilizing an ANN model developed with the Levenberg-Marquardt method to estimate evapotranspiration. It has been discovered that evapotranspiration values are impacted by various factors such as temperature (minimum, average, maximum), relative humidity (minimum, average, maximum), wind speed, solar hour, and precipitation values, which are taken into consideration for prediction. The findings indicated that Elazig, Keban, Baskil, and Agin sites had R values of 0.9995, 0.9948, 0.9898, and 0.9994 in the proposed model. It was found that Elazig's MAPE ranged from 0 to 0.2288, Keban's was 0.0001 to 0.3703, Baskil's was between 0 and 0.4453, and Agin's was both 0 and 0.2784. The findings obtained from the proposed model are compatible with evapotranspiration values computed from the Hargreaves method (R2 = 0.996). The study's findings provide significant insights for planners and decision-makers involved in the planning and managing water resources and agricultural irrigation.

Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study.

Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan. A novel approach of the SL model, the Catboost Regressor (CBR) and three DL models: 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were adopted and coupled with a semi-supervised pseudo-labeling (PL) technique. Six scenarios were developed regarding different input combinations of meteorological variables such as air temperature (Tmin, Tmax, and Tmean), wind speed (U2), relative humidity (RH), sunshine hours duration (SSH), net radiation (Rn), and saturation vapor pressure deficit (es-ea). The results showed that the PL technique reduced the systematic error of SL and DL models during training for all the scenarios. The input combination of Tmin, Tmax, Tmean, and RH reflected higher performance than other combinations for all employed models. The CBR-PL model demonstrated good generalization abilities to predict daily ETo and was the overall superior model in the testing phase according to prediction accuracy, stability analysis, and less computation cost compared to DL models. Thus, the relatively simple CBR-PL model is highly recommended as a promising tool for predicting daily ETo in coastal regions worldwide which have limited climate data.

A review of hydroclimate variability and changes in the Blue Nile Basin, Ethiopia.

Understanding the factors that influence hydroclimate variability is crucial for developing sustainable water management strategies in dynamic environments. The Blue Nile Basin, a significant freshwater resource in Africa, is facing challenges related to hydroclimate changes that impact sustainable development. Since the 1970s, the hydroclimate patterns of the region have undergone notable changes, prompting the need for a review of the literature on hydroclimate variability of the basin. Therefore, this study aims to offer a brief overview of the latest literature on hydroclimate variability and changes in the Blue Nile Basin. Based on the review of hydroclimate studies in the basin, it is evident that there have been significant advancements in our understanding of this complex system. However, the review also highlights that there are still areas of research that require further development to provide more comprehensive knowledge of the basin's hydroclimate. The projected intensification of hydroclimate change throughout the 21st century underscores the urgency for continued research efforts. The observed warming trend in the temperature of the basin and the discrepancies amongst research outputs on precipitation changes are important areas that require further investigation. Additionally, the inconsistency in reported changes in the watershed's hydrology and streamflow across the basin emphasizes the need for continued research to understand the factors behind these changes. Overall, this review provides valuable insights into the current state of hydroclimate studies in the basin and highlights the key areas for future research efforts to enhance our understanding of this vital system.

Re-intensification of flash drought in western China over the past decade: Implications of fluctuating wetting trend.

Climate changes and human activities have led to a rise of frequency and intensity of the global flash droughts, resulting in severe consequences for ecosystems, agriculture, and human societies. However, research dedicated to flash droughts in the dryland of western China is relatively limited, leaving their evolutionary characteristics and development processes of these phenomena unclear. To bridge this gap, this study analyzed the spatiotemporal characteristics of flash droughts in western China from 1981 to 2020, based on the standardized evapotranspiration stress index. Additionally, we investigated the development mechanisms by taking meteorological conditions and soil moisture into account. The findings revealed that the northern Qinghai-Tibet Plateau, western Qilian Mountains, and western and southern Loess Plateau are hotspots of flash droughts, characterized by rapid development rates. Across most of the study area, flash drought events persisted between 25 and 30 days. Adequate precipitation is necessary before the onset of flash droughts in western China, while water scarcity and high temperatures played crucial roles in driving the mid-stage of flash droughts. Within the context of the observed "warming and wetting" trend, the average flash droughts occurrence from 2011 to 2020 was approximately 16 % lower than that from 1981 to 1990, and there was a significant annual decrease in spatial coverage of 0.01 % per year. However, in the "wetting in west, drying in east" trend, the spatial coverage of flash droughts has shifted from a declining trend to an insignificant increasing trend since 2000 in the study area, with significant regional differences between the western and eastern regions. Over the past decade, flash droughts had once again intensified in the central Qinghai-Tibet Plateau and the Loess Plateau due to warming and fluctuating wetting trends, raising significant concerns for future ecosystem and agricultural water management in these regions.

Divergent trajectories of future global gross primary productivity and evapotranspiration of terrestrial vegetation in Shared Socioeconomic Pathways.

Understanding the future trends of carbon and water fluxes between terrestrial ecosystems and the atmosphere is crucial for predicting Earth's climate dynamics. This study employs an advanced numerical approach to project global gross primary productivity (GPP) and evapotranspiration (ET) from 2001 to 2100 under various climate scenarios based on Shared Socioeconomic Pathways (SSPs). To improve predictions of vegetation dynamics, we introduce a novel model (CoLM-PVPM), an enhancement of the Common Land Model version 2014 (CoLM2014), incorporating a prognostic vegetation phenology model (PVPM). Compared to CoLM2014 that relies on satellite-based leaf area index (LAI) inputs, CoLM-PVPM predicts LAI time series using climate variables. Model validation using historical data from 2001 to 2010 demonstrates PVPM in capturing spatiotemporal variations in satellite LAI. Our modeling results indicate that annual averaged LAI and total GPP increase under SSP1-2.6 but decrease under SSP2-4.5, SSP3-7.0, and SSP5-8.5 by 2100. By comparison, annual total ET consistently increases under all SSP scenarios by 2100. Global annual averaged LAI is highly correlated with annual total GPP in all scenarios, while its correlation with annual total ET weakens in SSP2-4.5, SSP3-7.0, and SSP5-8.5. Global annual total vapor pressure deficit (VPD) and precipitation are highly correlated with annual total ET in all scenarios. As emission levels increase, the negative correlation between annual total VPD and GPP strengthens, while the correlation between annual total precipitation and GPP weakens. This research presents an improved model for predicting terrestrial vegetation processes and underscores the importance of low carbon emission scenarios in maintaining carbon-water balances in specific regions.

ENSO effects on the relationship between aerosols and evapotranspiration in the south of the Amazon biome.

The effects of the El Nino-Southern Oscillation (ENSO) events have local, regional, and global consequences for water regimes, causing floods or extreme drought events. Tropical forests are strongly affected by ENSO, and in the case of the Amazon, its territorial extension allows for a wide variation of these effects. The prolongation of drought events in the Amazon basin contributes to an increase in gas and aerosol particle emissions mainly caused by biomass burning, which in turn alter radiative fluxes and evapotranspiration rates, cyclically interfering with the hydrological regime. The ENSO effects on the interactions between aerosol particles and evapotranspiration is a critical aspect to be systematically investigated. Therefore, this study aimed to evaluate the ENSO effect on a site located on the southern portion of the Amazonian region. In addition to quantifying and testing possible differences between aerosols and evapotranspiration under different ENSO classes (El Niño, La Niña and Neutrality), this study also evaluated possible variations in evapotranspiration as a function of the aerosol load. A highly significant difference was found for air temperature, relative humidity and aerosol load between the El Niño and La Niña classes. For evapotranspiration, significant differences were found for the El Niño and La Niña classes and for El Niño and Neutrality classes. Under the Neutrality class, the aerosol load correlated significantly with evapotranspiration, explaining 20% of the phenomenon. Under the El Niño and La Niña classes, no significant linear correlation was found between aerosol load and evapotranspiration. However, the results showed that for the total data set, there is a positive and significant correlation between aerosol and evapotranspiration. It increases with a quadratic fit, i.e., the aerosol favors evapotranspiration rates up to a certain concentration threshold. The results obtained in this study can help to understand the effects of ENSO events on atmospheric conditions in the southern Amazon basin, in addition to elucidating the role of aerosols in feedback to the water cycle in the region.

Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data.

Crop evapotranspiration is a key parameter influencing water-saving irrigation and water resources management of agriculture. However, current models for estimating maize evapotranspiration primarily rely on meteorological data and empirical coefficients, and the estimated evapotranspiration contains uncertainties. In this study, the evapotranspiration data of summer maize were collected from typical stations in Northern China (Yucheng Station), and a back-propagation neural network (BP) model for predicting maize evapotranspiration was constructed based on meteorological data, soil data, and crop data. To further improve its accuracy, the maize evapotranspiration model was optimized using three bionic optimization algorithms, namely the sand cat swarm optimization (SCSO) algorithms, hunter-prey optimizer (HPO) algorithm, and golden jackal optimization (GJO) algorithm. The results showed that the fusion of meteorological, soil moisture, and crop data can effectively improve the accuracy of the maize evapotranspiration model. The model showed higher accuracy with the hybrid optimization model SCSO-BP compared to the stand-alone BP neural network model, with improvements of 2.7-4.8%, 17.2-25.5%, 13.9-26.8%, and 3.3-5.6% in terms of R2, RMSE, MAE, and NSE, respectively. Comprehensively compared with existing maize evapotranspiration models, the SCSO-BP model presented the highest accuracy, with R2 = 0.842, RMSE = 0.433 mm/day, MAE = 0.316 mm/day, NSE = 0.840, and overall global evaluation index (GPI) ranking the first. The results have reference value for the calculation of daily evapotranspiration of maize in similar areas of northern China.