Regional Water Resources Management Research Articles

Numerical comparison of streamflow drought index (SDI) and standardized streamflow index (SSI) for evaluation of Isfahan drought status

ABSTRACT Hydrological droughts are the most serious and alarming variation of droughts due to their numerous long-term negative impacts on natural ecosystems, agricultural capacities, and the water regime of a specific region. Isfahan province, in Iran, is highly prone to this type of drought, and its arid climate has already made the efficient water management in this area a challenging task. Due to the lack of a uniform method for drought evaluation, various indices have been suggested by different researchers over the last few decades. The streamflow drought index (SDI) and standardized streamflow index (SSI) are the two primary hydrological drought indices that have been used in many regions with similar climatic conditions to Isfahan province. Their reliability in previous case studies and the easy computational process have made them suitable drought measurement tools for Isfahan. In this study, these two indices are calculated based on the obtained data from 10 hydrometric stations in Isfahan, for the timeframe (1988–2018). The analysis of SSI and SDI in this region shows consistent and station-specific drought patterns, with moderate drought conditions prevailing. These findings offer valuable insights for regional water resource management and climate adaptation, guiding policies to mitigate the impact of droughts.

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

Accurate forecasting of fluctuations in groundwater table is crucial for the effective management of regional water resources. This study explores the potential of utilizing remotely sensed satellite data to predict and forecast water table variations. Specifically, two Artificial Neural Network (ANN) models were developed to simulate water table fluctuations at two distinct well sites, namely BA Ea 18 and FR Df 35 in Maryland. One model leveraged the relationship between variations in brightness temperature and water table depth, while the other model was founded on the association between changes in soil moisture and water table depth. These models were trained and validated using recorded water table depths from the aforementioned wells, brightness temperature data acquired from the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E), and soil moisture information generated using the Land Data Assimilation System (LDAS). All models exhibited strong performance in predicting and forecasting water table fluctuations, with root mean square errors ranging from 0.043 m to 0.047 m for a 12-month forecasting horizon. Sensitivity tests revealed that the models displayed greater sensitivity to uncertainties in water table depth compared to uncertainties in both brightness temperature and soil moisture content. This underscores the feasibility of constructing an ANN-based water table prediction model, even in cases where high-resolution remotely sensed data is unavailable. In such situations, the model’s efficacy is contingent on the compatibility of the time series trends in data, such as brightness temperature or soil moisture, with those observed at the study site.

Spatial and Temporal Assessment of Baseflow Based on Monthly Water Balance Modeling and Baseflow Separation

Baseflow is the part of streamflow that is mainly replenished by groundwater. The protection of the biological environment and the growth of its water resources greatly depend on the spatial and temporal evolution of baseflow. Therefore, the Baizhiao (BZA) and Shaduan (SD) catchments of the Jiaojiang River Basin (JRB) in the Zhejiang province of China were selected as study areas. The ABCD model and Eckhardt method were used to calculate baseflow and baseflow index (BFI). The temporal and spatial evolution patterns of baseflow were analyzed through statistical analysis and the Mann–Kendall test. The results showed that the ABCD model performs well in simulating overall hydrological processes on the monthly streamflow at BAZ and SD stations with NSE (Nash–Sutcliffe Efficiency) values of 0.82 and 0.83 and Pbias (Percentage Bias) values of 9.2% and 8.61%, respectively. The spatial–temporal distribution of the BFI indicates the higher baseflow contribution in upstream areas compared to downstream areas at both stations. The baseflow and BFI had significant upward trends at the BZA and SD stations in the dry season, while their trends were not uniform during the wet period. These findings are essential guidance for water resource management in the JRB regions.

Do Water Transfer Projects Promote Water Use Efficiency? Case Study of South-to-North Water Transfer Project in Yellow River Basin of China

With a huge capital and labor input influx, inter-basin water transfer (IBWT) projects have been shown to effectively mitigate water stress and ensure the water demand for social and economic development in the receiving area. Whether they have promoted the improvement of regional water use efficiency (WUE) is crucial for sustainable management of regional water resources. Targeting the South-to-North Water Transfer Project (SNWTP), the largest and most ambitious inter-basin water transfer project in China, this study establishes quantitatively econometric models to analyze the impact of different water diversion projects, specifically the eastern route of the SNWTP (ER-SNWTP), middle route of the SNWTP (MR-SNWTP), and diversion from the main stream of the Yellow River (DYR), on the regional water consumption per unit of GDP; regional water stress, water use structure, economic structure, and urbanization level are used as control variables in different types of cities in the Yellow River Basin, and some intriguing results are found. While the overall water transfer project demonstrates a positive impact on water use efficiency, the effects of the three water transfer measures vary significantly. The ER-SNWTP does not exhibit a notable positive effect on regional water use efficiency, whereas the MR-SNWTP demonstrates a significant positive impact. Interestingly, the DYR has a notable negative influence on water use efficiency in developed cities. The water use structure, shaped by the pricing, scale, and policies of different projects, emerges as a pivotal factor in explaining these differences. Finally, this paper suggests that the impact of water transfer projects on the improvement of regional water use efficiency be viewed from a more comprehensive and developmental perspective.

Evapotranspiration increment was underestimated in China due to underrepresented land cover changes

Abstract Numerous evapotranspiration (ET) products have been produced using various approaches and diverse forcing data even as the magnitude and trends of ET show divergence. We simulated ET using updated land use and cover change (LUCC) data in China from 1900 to 2020. We found that China’s ET increased slightly from 1900 to 1980, but it increased rapidly after 1980 due to LUCC characterized by forest expansion (2.05 mm yr-1, P < 0.01). We also found that the ET trends derived from our simulation were significantly higher than other ET products (-0.70–1.47 mm yr-1, P < 0.01), implying that existing, long-term ET products might have underestimated ET trends in China during the post-1980 period because of underrepresented LUCC. These underestimated ET trends could introduce biases in the regional water budget and water resources management. We advocate for future studies to take into account the impacts of LUCC in global ET simulations.

Enhanced groundwater availability through rainwater harvesting and managed aquifer recharge in arid regions

Climate change in desert areas and semi-arid watersheds may offer a promising solution for the water scarcity problem that Bedouins and local inhabitants face. This study investigated the integrated water resources management in arid and semi-arid regions using rainwater harvesting in combination with the managed aquifer recharge (RWH-MAR) technique. The study also used recharge wells and storage dams to achieve the sustainability of groundwater supplies in the context of climate change and management of the flow to the Gulf of Suez. Therefore, different return periods of 10, 25, 50, and 100 years were considered for the annual flood volume resulting from those watersheds. Moreover, hydrologic modeling was carried out for the El Qaa plain area, South Sinai, Egypt, using the Watershed Modeling System (WMS) and the groundwater modeling of SEAWAT code. Our findings show that for different scenarios of climate change based on return periods of 10, 25, 50, and 100 years, the aquifer potentiality reached 24.3 MCM (million cubic meters) per year, 28.8 MCM, 36.7 MCM, and 49.4 MCM compared to 21.7 MCM at 2014 with storage of groundwater ranges 11.8%, 32.1%, 69%, and 127.4%, respectively. These findings have significant implications for the system of RWH-MAR and groundwater sustainability in El Qaa Plain, South Sinai. The RWH-MAR proved to be an effective approach that can be applied in different water-stressed and arid regions to support freshwater resources for sustainable future development and food security, as well as protect communities from extreme flash flood events.

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098–0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Changes in water conservation and possible causes in the Yellow River Basin of China during the recent four decades

As water quality declines and water resources become scarce, water conservation emerges as the central challenge of water resource management and sustainable development in the watershed ecosystem. The Yellow River, known as the “mother river” of China, of which the water conservation function has received significant public attention in the changing environmental conditions. In the study, the InVEST water yield model was utilized to evaluate the spatiotemporal dynamics of water conservation; the MK trend test and Sen’s slope were used to examine the trends of precipitation and water conservation, respectively. Additionally, the response of water conservation to climate, land use, and soil changes during the period 1981–2020 was also discussed. The results showed that under the comprehensive influence of various factors, the water conservation of the YRB showed a decreasing and then increasing trend, which was presumed to be associated with the Grain for Green Project. Water conservation showed an increasing trend in the upstream area of the study area while decreasing in the lower reaches in recent decades. The changes in water conservation function in the YRB resulted from the comprehensive influence of climate factors, soil conditions, and land use types. Besides, woodland and grassland provide the highest water conservation capacity, which initially increased and then decreased with the increase of slope. The spatial analysis emphasized the critical role of water conservation in the Yellow River Basin, particularly highlighting the source area (above Lanzhou Station), the southern tributaries of the Wei River, and the upper reaches of the Yiluo River as significant water conservation zones. These areas should be considered crucial and given priority in regional water resource management and ecological protection efforts. The findings of this study could provide a theoretical foundation for ecological protection and water security in the YRB, which are essential for sustainable development.

Rolling forecast of snowmelt floods in data-scarce mountainous regions using weather forecast products to drive distributed energy balance hydrological model

Snowmelt flood forecasting plays a crucial role in water resource management and utilization in arid regions. However, the accuracy of current snowmelt flood forecasts falls short of requirements due to complex mountainous terrain and limited observational data. In this study, We conducted a snowmelt flood simulation and forecasting experiment in a mountainous watershed in northern Xinjiang. First, we used site observations, ERA5-land reanalysis data and WorldClim data to produce historical meteorological forcing, calibrated the VIC-CAS model and improved routing model using historical observed runoff data, and conducted a detailed simulation of the hourly snowmelt flood process in the basin. Then, the latest grid forecast weather product developed by the China Meteorological Administration was integrated into the system to conduct a rolling forecast test of the snowmelt floods in the next 72 h during the snowmelt period in 2023. The remotely sensed snow cover and the freezing level measured by the sounding balloon were assimilated into the model in real time to correct the deviation of runoff simulation. The results show that during the model calibration period, the KGE of the simulated monthly runoff in the Kayertes River basin (KRB) from 2006 to 2011 was 0.83 and the NSE was 0.86. In the continuous hourly runoff simulation during the 2022 snowmelt period, the model well captured the intraday fluctuations of snowmelt runoff in the KRB, with the daily peak flow error within ± 20 % and the peak occurrence time controlled within ± 4 h. In the rolling forecast test in 2023, the daily peak flow error is within ± 20 % (the peak occurrence error during the peak period is even controlled within ± 5 %), and the peak occurrence time is within ± 4 h. Real-time assimilation of freezing level measured by sounding balloons and remotely sensed snow cover can effectively correct the deviations in simulated temperature and snow cover. This study can provide insights for runoff prediction in other high-altitude mountainous areas where data are scarce.

Tracing the sources and evaporation fate of surface water and groundwater using stable isotopes of hydrogen and oxygen

Recognizing the origins and movement processes of surface water and groundwater is crucial for understanding hydrochemical genesis, conserving water supplies, and managing water resources. Estimating the source water typically involves identifying the intersection of evaporation line (EL) and meteoric water line. However, there is currently confusion in determining the regional EL and selecting strategies for estimating the source water. This study aimed to explore the source of surface water and groundwater, as well as evaporation effect utilizing stable isotope tracing (δ2H and δ18O). The line-conditioned excess was adopted to differentiate evaporated water and non-evaporated water, then Craig-Gordon model and an analytical framework with Bayesian theory were used to investigate the source of surface water and groundwater and the evaporation influence. The findings revealed that surface water and groundwater in the northern region of the Weihe River suffered more sever evaporation impacts that the south, and the evaporated surface water (7.54 % to 27.34 %) with a wider range of mean evaporation ratio than evaporated groundwater (5.38 % to 8.52 %). Monsoon precipitation is the main contributor to both surface water (contribution ratio: 0.46) and groundwater (0.49) sources. This research provides specific information on evaporation and detailed insights into the source water of surface water and groundwater, aiding in understanding the evaporation effect during the hydrological cycle and facilitating the management of regional water resources.

Impact of the comprehensive remediation project on hydrological conditions in the lower reaches of the Ganjiang River.

This study undertakes a systematic analysis of the hydrological changes before and after the implementation of the Comprehensive Remediation Project in the lower reaches of the Ganjiang River. It focuses on changes in downstream inflow, ratios of flow distribution, and water levels, as well as water velocity near the gates. The results indicate a significant improvement in the spatial distribution of water resources in the lower reaches of the Ganjiang River. The project enhances the inflow from the northern and southern branches, positively influencing downstream water usage and the ecological environment. Building upon these findings, the study proposes operational recommendations tailored to different hydrological years, such as timely adjustments to the southern branch's water inflow and optimizing flow distribution ratios. This research provides a scientific basis for the implementation and dispatch of comprehensive remediation projects and offers insights into water resource management in similar regions.

Cryogenic Pits and Quaternary Glacial Evidence in Central Iran

Quaternary glaciation studies in Central Iran reveal that it is not implausible to anticipate glaciation traces in this region. It is feasible to provide conclusive evidence about the behavior of ice sheets by referring to geomorphological factors and climatic statistical analyses. Factors such as flatness and accidental geographical features contribute to the existence of ice sheets in the Namdan Fars region. The digital reconstruction of past climatic conditions، using the Wright method، along with the absence of lake terraces around Kaftar Lake، signifies the presence of ice instead of water in the lake. The multi-axial morphology of the Namdan Plain، the shape and form of main valleys and passes، and the existence of numerous glacial cirque traces in the surrounding heights are also indicators of this phenomenon. The lack of evidence of ancient settlements in the plain، despite the availability of water and fertile soil، can be attributed to the region's historical thermal conditions. Investigating ancient glaciers helps us understand the natural history of the land. This knowledge can aid planners in devising macro strategies for regional development and water resource management، as glacial performance has always been associated with optimal water levels. Although glacial activities can create aquifers، the movement of ice sheets or ice surfaces does not support the accumulation of sediments required for creating aquifers with sufficient water storage. Instead، their performance leads to the creation of suitable agricultural soil layers. In summary، regional glaciers play a positive role in transforming and creating favorable agricultural soils and can be beneficial in adapting to climatic changes and extending statistical periods.

Dynamic monitoring of surface area and water volume of reservoirs using satellite imagery, computer vision and deep learning

The development of surface area and estimated volume measurements for water reservoirs using advanced computational techniques has become an increasingly important task for reservoir operation. Thus, the objective of this study is to determine the surface area and estimate the volume of water reservoirs through the segmentation of multispectral satellite images using a Fully Convolutional Neural Network (FCN). This paper introduces a universally applicable framework that utilizes deep convolutional neural networks for segmenting water bodies, particularly focusing on areas in Paraíba State, Northeast Brazil, susceptible to severe droughts. The study utilized Sentinel-2 satellite data obtained from Google Earth Engine for the period between January 2019 and September 2023. The extraction of water surface area from satellite images was achieved using the U-Net model, a deep learning framework based on FCN. The accuracy of water surface area extraction was analyzed using the Intersection-Over-Union (IoU) metric, also known as Jaccard Index. Subsequently, reservoir volumes were calculated using elevation-area-volume curves. The evaluation metrics for the results included root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Pearson correlation coefficient (r). The network demonstrated high accuracy, achieving a 95% IoU on the test set. This methodology was applied to four reservoirs, yielding promising results that closely aligned the monitored reservoir curves derived from satellite imagery with observed field data. The findings of this study are significant for research on semiarid region changes, extending their relevance to a broader understanding of global environmental dynamics. This work not only contributes to the field of remote sensing in hydrology but also offers practical insights for effective water resource management in drought-prone regions.

Connectivity of evapotranspiration processes in a Brazilian dryland reservoir using remote sensing

Understanding the combined open water reservoir evaporation and riparian vegetation transpiration (evapotranspiration) is important for water resource management in semiarid regions like the Brazilian Dryland region. Existing research has explored reductions in open water evaporation due to riparian vegetation transpiration, but evidence for this interdependence in this region is not well understood. The aim of this study was to estimate the connectivity (impact and maximum distance of action) between riparian vegetation and its transpiration and reservoir evaporation, as well as the ratio of the volume losses by transpiration and evaporation, using Landsat 5 and 8 in a Brazilian Dryland environment. Evaporation and transpiration were estimated using the Surface Energy Balance System for Water and the Surface Energy Balance System models, respectively, from 1985 to 2020. A Class A Pan and the Penman-Monteith equation were used, for reference reservoir evaporation and vegetation transpiration, respectively. The occurrence of riparian vegetation was associated with the Normalized Difference Vegetation Index. Both models performed well, with root mean square error ≤ 0.80 mm/day. The riparian vegetation transpiration demonstrated a high negative correlation (R²>0.7) with evaporation, suggesting the vegetation acts to reduce evaporation (average of 30 %) up to a maximum distance of 90 m from the reservoir shoreline. This is explained by the adjacent riparian vegetation-induced reduction in radiation inputs due to shading at the reservoir margin, a lowering of wind speeds and increases in air humidity from transpiration. The ratio of riparian vegetation transpiration to reservoir evaporation within a buffer of 150 m is approximately equal. Combined riparian vegetation and reservoir evapotranspiration equates to the daily water supply for ∼275,000 people. The paper explains and quantifies the connectivity between riparian vegetation transpiration and reservoir evaporation, thereby providing a model for improved reservoir water balance estimates, which can aid regional water resources planning, development, and management.

Soil moisture dynamics and associated rainfall-runoff processes under different land uses and land covers in a humid mountainous watershed

Identifying soil moisture dynamics is critical for understanding watershed hydrological processes. Soil moisture responses to rainfall vary with land use and land covers (LULCs) and thus influence rainfall-runoff processes, however, the knowledge about how different LULCs affect such processes was less revealed, especially in humid areas. In this study, we investigated the characteristics of soil moisture content (SMC) and rainfall-related soil moisture responses under four typical LULCs, i.e., waxberry forest, farmland, sloping farmland, and bamboo forest, based on in-situ observations in a humid mountainous watershed. We then used the HYDRUS-1D model to analyze the patterns of the rainfall-infiltration processes and quantified corresponding runoff generation to reveal the partitioning between infiltration and runoff at the event scale. We found significant differences of the average SMC at multiple depths for four LULCs. The deep soil (80 cm) had the largest SMC for the waxberry forest, farmland, and sloping farmland, while the surface soil (10 cm) was the wettest soil layer for the bamboo forest. During the representative rainstorms, although soil moisture responses exhibited depth gradients, the earlier responses at some deep layers indicated the occurrence of preferential flow. Generally, the cumulative infiltration increased while the infiltration rate decreased with the rainfall grade. Farmland and bamboo forest showed the most and the least infiltration, respectively. These results suggested that both the rainfall properties and LULCs had a strong impact on the soil moisture responses. Meanwhile, the estimated average runoff coefficients of four typical LULCs increased with rainfall grade, and the sloping farmland generated the most runoff during the representative rainfall events. Our findings provide new insights into soil moisture dynamics and infiltration regimes under different LULCs in humid areas, which can contribute to hydrological modeling at the field-scale and regional water resource management.

Assessment of Crop Water Footprint and Actual Agricultural Water Consumption in Arid Inland Regions: A Case Study of Aksu Region

Water scarcity is a major issue in arid regions, and it is crucial to have an accurate understanding of water resource utilization for informed decision-making regarding water-related issues. However, due to various reasons such as inadequate measuring facilities, the actual agricultural water usage is often underestimated. The concept and methodology of the water footprint, based on a life-cycle perspective, provide a powerful tool for studying the actual water usage in agriculture. By utilizing the theories and models of water footprints, a method for reviewing the actual agricultural water usage in arid regions is proposed. Taking the Aksu region as an example, the evolution patterns and water consumption of 15 major crops over a 31-year period from 1990 to 2020 were calculated and analyzed. The research shows that the total water footprint of crops in the Aksu region has increased by nearly 3.13 times over the 31-year period, with significant accelerations in 2003 and 2016. The green water footprint accounts for an average of about 8% of the major crops, while the average water footprint of cotton accounts for over 57.2% of the total water footprint of major crops in the study area. Based on the calculation of the blue water footprint, the actual water usage in the Aksu region in 2020 was estimated to be 11.128 billion cubic meters, which is 1.30 times higher than the reported water usage, with groundwater extraction being 2.46 times higher than reported. This method of water footprint analysis for reviewing actual water usage and its application examples provide a methodological foundation and technical support for regional water resource management and policymaking.

Main directions for rational use of water resources in the Republic of Kazakhstan

The article identifies the main directions of the rational use of water resources in the Republic of Kazakhstan, specifically, the South Kazakhstan region. Rational water use is the process of efficient use of water resources, which guarantees the preservation and protection of water bodies from negative and excessive anthropogenic impact and at the same time provides the population and sectors of the national economy with water resources of adequate quality and in the amount that can guarantee not only satisfaction of needs, but also development of industry, energy, water transport and agriculture at the rates that are necessary for sustainable socio-economic development of the Republic of Kazakhstan as a whole. The analysis of the foundations of rational water use was carried out; the main economic instruments affecting the process of water use in Kazakhstan are considered, an overview of the structure of water resources management in the South Kazakhstan region is made. Currently, it is necessary to ensure the rational use of natural resources, their protection from pollution and degradation, as well as skillfully restore, multiply and control them for the benefit of human society. Water resources as an object of management are a complex phenomenon and require a special approach for the formation of governing bodies with a clear division of their sphere of influence and responsibility.

Identifying components and controls of streamflow in a cold and arid headwater catchment

Streamflow is increasingly vulnerable to climate change over cold-arid regions. To understand the underlying mechanisms of streamflow changes, it is essential to distinguish streamflow components and identify their drivers due to various meltwater and rainfall runoff processes. This study combines the conceptual temperature-index Snowmelt Runoff Model (SRM) and wavelet coherence analysis to unravel the impacts of environmental factors on streamflow and its components over the semiarid-and-cold headwater catchment of the Manas River in Northwest China during 2001–2015. Our results show that SRM reproduces monthly observed streamflow with determination coefficients of > 0.91, Nash-Sutcliffe Efficiency of > 0.82, and absolute relative error of < 5 % during SRM calibration (2001–2007) and validation (2008–2015) periods. The meltwater runoff and rainfall runoff contribute to 42 % and 31 % of the total streamflow, respectively. The wavelet-based spectral analysis, partial wavelet coherency analysis, and multiple-wavelet coherence together demonstrate that total streamflow (rainfall runoff) is predominantly influenced by snow cover area (precipitation) at a scale of 32 (4–16) months. But the meltwater runoff is simultaneously controlled by precipitation, potential evapotranspiration, and normalized difference vegetation index on scales of 4–16 and > 32 months. Our study provides technical support for streamflow partitioning and controlling factors identification, and has implication to sustainable water resources management in cold and arid regions.

Disentangling influences of driving forces on intra-annual variability in sediment discharge in karst watersheds

The intra-annual variability in sediment discharge was considerably influenced by the climate variability and vegetation dynamics. Because of the coupled or relationships between climatic and vegetation variables, it is still challenging to decouple the direct and indirect effects of climate variability and vegetation dynamics on hydrological and sediment transport processes. The purpose of this study is to decouple influences of individual driving force on intra-annual distribution of sediment discharge during 2003–2017 using the partial least squares structural equation model (PLS-SEM) method in four typical karst watersheds of Southwest China. The coefficient of variation (Cv), Completely regulation coefficient (Cr), Lorenz asymmetry coefficient and Gini coefficient were used to represent the intra-annual sediment discharge variability. Results showed that the monthly sediment discharge (190 % < Cv < 353 %) exhibited greater variability than its potential affecting factors (18 % < Cv < 101 %). From the PLS-SEM analysis, the water discharge, climate, and vegetation together explain 57 %–75 %, 64 %–79 %, and 53 %–80 % of the total variance in Cv, Cr, and Gini coefficient, respectively. Specifically, water discharge exerts the largest influence on sediment discharge variability (0.65 ≤ direct effect ≤0.97, P < 0.05), while vegetation dynamic mainly indirectly affects sediment discharge variability (−0.88 ≤ indirect effect ≤ −0.01) through influencing water discharge. The climate factors also principally indirectly affect the sediment discharge variability (−0.47 ≤ indirect effect ≤0.19) by affecting water discharge and vegetation. The PLS-SEM can effectively reveal the driving force and influencing mechanism of intra-annual sediment discharge changes, and provide an important reference for regional soil and water resources management in karst watersheds. Future studies can decouple the influences of the extreme climate, unique lithology, discontinuous soil, heterogeneous landscape, and special geomorphology on spatial variability in sediment discharge across different karst watersheds.

Widespread increase in plant transpiration driven by global greening

Global plant transpiration (PT) is a crucial component of the Earth's hydrological cycle and plays a significant role in regulating the exchange of water and energy between the land surface and the atmosphere. However, the long-term trend and the underlying driver of global PT remain unclear due to the significant uncertainties in estimating PT on a global scale. This study uses two sub-Mixture Density Networks (MDNc and MDNa) to predict vegetation canopy resistance (rc) and aerodynamic resistance (ra), then the predicted rc and ra are imported into the Penman-Monteith-Leuning (PML) model to simulate PT. The observed PT at 112 SAPFLUXNET sites are used to validate the performance of hybrid MDN-PML model. The verified MDN-PML model is further applied to map the spatial distribution of global PT and reconstruct a long-term (1990–2020) global PT dataset. The results indicate that the long-term average global PT is 397.2 ± 63.1 mm. During the period 1990–2020, the global PT exhibit a significant upward trend (0.79 ± 0.28 mm/year (P < 0.05)), which equates to a 6.0% increase compared with the long-term average global PT. A widespread trend of elevated PT is observed in approximately 70% of the global land surface. The trend attribution analysis results show that the change in leaf area index (LAI) can explain 66.2% of the global PT trend, indicating that elevated LAI due to global greening is the dominant factor contributing to the upward trend in global PT. The elevated LAI can be largely attributed to the CO2 fertilization effect induced by elevated atmospheric CO2 concentration. Additional analysis reveals that the increased global PT is more sensitive to CO2 fertilization effect in high LAI areas than in low LAI areas. Projected climate scenarios indicate that global land surface PT will continue to rise from 2023 to 2100, and the rate of increase in the future will be higher than in historical periods. The rising rates of global PT under the three Representative Concentration Pathway scenarios (RCP) 2.6, RCP4.5, and RCP8.5 climate change scenarios are 0.86 mm/year, 1.16 mm/year, and 1.45 mm/year, respectively, during the period 2023–2100. Our results highlight the impact of global change and vegetation greening on the global PT and hydrological cycle. This study is of great significance for the scientific response to the challenges of climate change for regional water resource management.