Water Resources Research Articles

Terminal deoxynucleotidyl transferase-mediated CRISPR sensing platform for simple and point-of-care detection of cobalt pollution

The excessive use of cobalt in various chemical industries and arbitrary discharge of industrial wastewater have led to increased cobalt pollution in soil and water resources, increasing the risk of human exposure to high concentrations of cobalt and necessitating an urgent need for on-site monitoring platform for cobalt pollution. In this study, the terminal deoxynucleotidyl transferase (TdT)-CRISPR platform has been developed. In this platform, cobalt as a cofactor of TdT, can significantly improve the tailing efficiency of TdT-mediated extension. Therefore, when cobalt is present, the detection probe can be extended with poly(T) tails through the TdT-mediated extension, which can be subsequently served as the DNA activator for Cas12a, leading to the cleavage of fluorescence reporter molecules and triggering turn-on fluorescence signals. Consequently, this dual amplification sensing strategy of TdT-CRISPR platform demonstrated exceptional sensitivity (0.83 nM) and high specificity for cobalt over other ions. Furthermore, the method was successfully employed for the detection of cobalt in tap water and river samples. CRISPR-lateral flow assays (CRISPR-LFAs) were evaluated in this study for the simple and point-of-care detection of cobalt pollution. The assays are capable of detecting cobalt concentrations as low as 50 nM, which is significantly lower than the environmental standards of 16.9 μM, through strip analysis with the naked eye. These results commonly suggest that the TdT-CRISPR platform holds significant promise for monitoring cobalt pollution, providing a robust and sensitive solution for on-site detection and contributing to the mitigation of cobalt contamination risks in environmental matrices.

Development of free flowing granular hybrid functionalized clay sorbent filters for chromium removal from waste water

Chromium (VI) contamination in water resources at industrial sites poses significant environmental and health risks. Conventional sorbents often suffer from limitations such as clogging, back pressure, poor kinetics, and challenges in coupling with flow systems and regeneration. This study focuses on developing, characterizing, and applying free-flowing granular (FFG) amidoxime-functionalized montmorillonite (Mt) clay sorbents (AO-Mt-H-C) to enhance chromium (VI) removal from contaminated waste water. Batch adsorption experiments demonstrated that AO-Mt-H-C outperformed montmorillonite clay modified with quaternary ammonium alone (Mt-H-C), achieving a Cr (VI) removal efficiency of 50.6 mg g−1 compared to 44.3 mg g−1, due to the synergistic interactions of quaternary ammonium and amidoxime functionalities. To address the limitations of conventional sorbents, such as clogging and poor kinetics, an FFG filter column was developed and tested, showing effective Cr (VI) removal across various water sources with minimal interference in drinking and groundwater. In industrial wastewater with higher matrix loads, over 99 % removal efficiency was achieved with a 25 % increase in sorbent dosage. Desorption and reusability assessments confirmed the AO-Mt-H-C's effectiveness across three cycles. Mathematical modelling using Thomas and Yoon–Nelson equations supported the design of customized treatment systems. Scalability was demonstrated by treating 1.6 m3 of textile industrial effluents (100 mg L−1 Cr (VI)) with quantitative removal efficiency (>99.5 %). This study illustrates that the hybrid functionalization and free-flowing granulation of clay sorbent materials significantly enhance Cr (VI) removal, providing valuable insights for advanced water pollution mitigation and environmental sustainability.

Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.

Analysis of Industrial Water Use Efficiency Based on SFA–Tobit Panel Model in China

Over the past two decades, the industrial sector of China has experienced rapid development, which has correspondingly led to a significant increase in water resource consumption. To better understand the dynamics of industrial water use, and formulate appropriate water resource conservation and management policies, it is necessary to evaluate the evolution of industrial water use efficiency and its influencing factors in China. Given the high sensitivity and accuracy of the stochastic frontier analysis (SFA) model for efficiency assessment, the Tobit model is more suitable for regression analyses of truncated data. This study employed the SFA–Tobit panel model to evaluate the industrial water use efficiency of provinces in China from 2003 to 2021. The results indicate that national industrial water use efficiency improved from 0.41 to 0.65 during the study period. All provinces showed significant improvements, with developed provinces exhibiting higher industrial water use efficiency than undeveloped provinces. Regionally, the eastern areas demonstrated superior industrial water use efficiency compared to the western regions, with the central regions having the lowest overall water use efficiency. Moreover, the efficiency gap between regions has been narrowing. The national industrial water-saving potential is estimated at 31.306 billion cubic meters, with Jiangsu province having the highest saving potential at 3.709 billion cubic meters. In comparison, Beijing has the lowest at just 32,000 cubic meters. The Tobit regression results reveal that economic development and technological progress positively contribute to increased industrial water use efficiency. In contrast, water use intensity, openness, and urbanization levels negatively impacted the improvement of industrial water use efficiency. Therefore, it is necessary to increase investment in technological innovation, strictly control industrial water intensity, appropriately balance import and export trade with urbanization levels, and promote sustainable economic development. This study can provide effective support for the subsequent green transformation of China’s industry.

Diurnal changes in cloud cover in eastern Gabon and their impacts on energy balance, light availability and water demand: a case study of the 2022 dry season

Abstract Western Central Africa is atypical of the equatorial domain as the main dry season is cloudier than the rainy seasons. To understand this cloud cover's diurnal evolution, we set-up an infrared camera and acquired measurements of the total cloud cover fraction (TCF) and cloud optical depth at Bambidie, Gabon (0°44’30.5” S,12°58’12.4” O) from May to October 2022. Diurnal variations in TCF can be summarized into four types, mostly discretized through the timing and duration of clouds clearing in the afternoon (Early afternoon Clearing: EaC, Late afternoon Clearing: LaC and Clear Night: CNi) while one type (No Clearing: NoC) shows overcast conditions all day long. Meteorological measurements show that NoC days record 50W/m2 less shortwave incoming surface radiation resulting in daytime temperatures 1°C lower than the seasonal norm, but 20% more diffuse light and 0.5mm/day less ETo. Conversely, EaC days record 50W/m2 more shortwave incoming surface radiation leading to temperatures 1.5°C higher than the seasonal norm, but 40% more direct light. The larger water demand (0.5mm/day more ETo) is partly compensated by more frequent rainfall at night-time. The SAFNWC satellite estimates well capture the TCF variations for most of the 4 types. They confirm that TCF is dominated by very low and low clouds whose dissipation in the afternoon and evolution into fractional and cumuliform convective clouds explains the clearings on EaC and LaC days. Satellite estimates also show that the 4 types of days extracted at Bambidie are representative of a larger-scale cloud cover evolution in Western Central Africa, with a W-E gradient in the timing of afternoon cloud dissipation.

The Optimal Drought Hardening Intensity and Salinity Level Combination for Tomato (Solanum lycopersicum L.) Cultivation under High-Yield, High-Quality and Water-Saving Multi-Objective Demands.

The extreme weather and the deteriorating water environment have exacerbated the crisis of freshwater resource insufficiency. Many studies have shown that salty water could replace freshwater to partly meet the water demand of plants. To study the effects of early-stage drought hardening and late-stage salt stress on tomatoes (Solanum lycopersicum L.), we conducted a 2-year pot experiment. Based on the multi-objective demands of high yield, high quality, and water saving, yield indicators, quality indicators, and a water-saving indicator were selected as evaluation indicators. Three irrigation levels (W1: 85% field capacity (FC), W2: 70% FC, W3: 55% FC) and three salinity levels (S2: 2 g/L, S4: 4 g/L, S6: 6 g/L) were set as nine treatments. In addition, a control treatment (CK: W1, 0 g/L) was added. Each treatment was evaluated and scored by principal component analysis. The results for 2022 and 2023 found the highest scores for CK, W2S2, W3S2 and CK, W2S4, W2S2, respectively. Based on response surface methodology, we constructed composite models of multi-objective demands, whose results indicated that 66-72% FC and 2 g/L salinity were considered the appropriate water-salt combinations for practical production. This paper will be beneficial for maintaining high yield and high quality in tomato production using salty water irrigation.

Slightly Saline Water Improved Physiology, Growth, and Yield of Tomato Plants in Yellow Sand Substrate

Efficient utilization of saline water and yellow sand resources can enhance water and soil resource management while boosting crop yields in Xinjiang. This study conducted a two-season field experiment in Alar City, Xinjiang, from March to July 2023 and August 2023 to January 2024. The objective was to examine the effects of different irrigation water salinities (2, 3, 4, 5, and 6 g·L−1) on the physiology, growth, and yield of sand-cultured tomatoes grown in yellow sand slag. Groundwater irrigation with salinity levels of 0.8–1 g·L−1 was used as the control (CK). The results showed that the salinity of the substrate gradually increased with the salinity of irrigation water in each treatment. The salt accumulation increased by 59.5%, 82.5%, and 99.5% at the end of the experiment for T3 (4 g·L−1), T4 (5 g·L−1), and T5 (6 g·L−1), respectively, compared to CK. As the salinity of irrigation water increased, plant height, stem thickness, chlorophyll content, net photosynthesis rate, stomatal conductance, transpiration rate, and total yield of tomato showed an increasing and then decreasing trend, in which the total tomato yield of the T2 (3 g·L−1) treatment was significantly increased by 35.2% compared with that of CK between the two seasons. In contrast, as the salinity of irrigation water increased, the inter-cellular CO2 concentration of tomato leaves showed a decreasing and then increasing trend, with the T2 treatment having the lowest inter-cellular CO2 concentration. Pathway analysis revealed that appropriate salinity levels increased tomato yield by regulating inter-cellular CO2 concentration. Based on these findings, a 3 g·L−1 salinity level is recommended for irrigating sand-cultured tomatoes to maximize yellow sand resource use, address freshwater shortages, and optimize water and soil management in the Xinjiang region.

Estimation of the Potential for Soil and Water Conservation Measures in a Typical Basin of the Loess Plateau, China

Abstract: In the context of the large-scale management of the Loess Plateau and efforts to reduce water and sediment in the Yellow River, this study focuses on a typical watershed within the Loess Plateau. The potential for vegetation restoration in the Kuye River Basin is estimated based on the assumption that vegetation cover should be relatively uniform under similar habitat conditions. The potential for terrace restoration is assessed through an analysis of topographic features and soil layer thickness, while the potential for silt dam construction is evaluated by considering various hydrological and geomorphological factors. Based on these assessments, the overall potential for soil erosion control in the watershed is synthesized, providing a comprehensive understanding of target areas for ecological restoration within the Kuye River Basin. The study demonstrates that the areas with the greatest potential for vegetation restoration in the Kuye River Basin are concentrated in the upper and middle reaches of the basin, which are in closer proximity to the river. The total potential for terracing is 1013.85 km2, which is primarily distributed across the river terraces, farmlands, and gentle slopes on both sides of the riverbanks. Additionally, the potential for the construction of check dams is 14,390 units. The target areas for terracing measures in the Kuye River Basin are primarily situated in the middle and lower reaches of the basin, which are in closer proximity to the river. Conversely, the target areas for forest, grass, and check dams, as well as other small watershed integrated management measures, are predominantly located in the hill and gully areas on the eastern and southern sides of the basin. The implementation of the gradual ecological construction of the watershed, based on the aforementioned objectives, will facilitate the protection, improvement, and rational utilization of soil, water, and other natural resources within the watershed.

Portfolio Agriculture: A Model for Resilient Regional Agricultural Planning

Food security is threatened by climate change worldwide; consequently, agriculture and farming livelihoods must adapt to new and unpredictable conditions. These conditions vary along spatial scales, and since agricultural yields are sensitive to microclimate conditions, a locally tailored data-driven approach may be helpful. Furthermore, limited agricultural resources like water and labor increasingly constrain food production. This research proposes a regional portfolio model for identifying crop choices and regional portfolio compositions that align with known and forecasted microclimate variation in temperature and humidity. The model will enable farmers to assess tradeoffs between the financial returns and agricultural production risks. The goal of this work is to provide new insights into agricultural planning in the face of climate risk and limited access to water and labor resources. Three steps are taken. Firstly, regional agricultural land is divided into farming subunits, with each representing a terroir characterized by temperature and humidity. Then a simulated yield coefficient is used to assess the effect of microclimate variables on the yield of the different crops in the portfolio of each subunit. Secondly, farming resource allocation, represented by water and labor, across crops and farming subunits is optimized to maximize the yield and associated financial return from farming across the agricultural region. Finally, a resilient agricultural planning model is developed based on the assumed data for regional microclimate and agricultural resources. The results of this research can be used by regional farmers as a reference for selecting crop portfolios and resource allocations to maximize overall profit.

Assessment and Modeling of Water Quality in the Niger River: A Comprehensive Study Using Water Quality Indices and Intelligent Techniques

The Niger River is essential to the livelihoods of those in Bamako and its neighboring areas, providing vital resources for drinking water, agriculture, livestock, industry, and fishing. Given its significance across these sectors, there is a pressing need to establish an effective water resource management strategy that incorporates a thorough qualitative assessment of the river's water quality. This research seeks to characterize the water quality of the Niger River by employing Water Quality Indices (WQI) and intelligent modeling techniques. To fulfil this objective, various physicochemical parameters, including pH, electrical conductivity (EC), nitrate (NO3-), nitrite (NO2-), and iron (Fe), were collected from 40 sampling points along the river during three distinct periods: December 2017, March 2018, and July 2018. The study utilized a weighted arithmetic approach to compute the WQIs, while the predictive models were developed using two of the most famous and effective modeling techniques namely Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In order to evaluate the predictive performance of the models, the dataset was partitioned into three distinct segments, allocating 60% for training purposes, 20% for validation, and the remaining 20% for testing, with the segments organized in a sequence from upstream to downstream. The performance of both models was evaluated using metrics such as the correlation coefficient (r²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The computed Water Quality Indices (WQIs) vary from 0.44 to 1887.40, indicating a diverse range of water quality across the samples analyzed. The classification of these samples reveals that 62.5% are considered excellent, while 15% are categorized as good, another 15% as poor, 2.5% as very poor, and 5% as unsuitable for consumption. Furthermore, the results derived from ANN with five inputs, one hidden layer (13 neurons) and one output (WQI) demonstrates superior efficiency in assessing water quality.

A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.

Hyperspectral Image Transects during Transient Events in Rivers (HITTER): Framework Development and Application to a Tracer Experiment on the Missouri River, USA

Rivers convey a broad range of materials, such as sediment, nutrients, and contaminants. Much of this transport can occur during or immediately after an episodic, pulsed event like a flood or an oil spill. Understanding the flow processes that influence the motion of these substances is important for managing water resources and conserving aquatic ecosystems. This study introduces a new remote sensing framework for characterizing dynamic phenomena at the scale of a channel cross-section: Hyperspectral Image Transects during Transient Events in Rivers (HITTER). We present a workflow that uses repeated hyperspectral scan lines acquired from a hovering uncrewed aircraft system (UAS) to quantify how a water attribute of interest varies laterally across the river and evolves over time. Data from a tracer experiment on the Missouri River are used to illustrate the components of the end-to-end processing chain we used to quantify the passage of a visible dye. The framework is intended to be flexible and could be applied in a number of different contexts. The results of this initial proof-of-concept investigation suggest that HITTER could potentially provide insight regarding the dispersion of a range of materials in rivers, which would facilitate ecological and geomorphic studies and help inform management.

SMGformer: integrating STL and multi-head self-attention in deep learning model for multi-step runoff forecasting

Accurate runoff forecasting is of great significance for water resource allocation flood control and disaster reduction. However, due to the inherent strong randomness of runoff sequences, this task faces significant challenges. To address this challenge, this study proposes a new SMGformer runoff forecast model. The model integrates Seasonal and Trend decomposition using Loess (STL), Informer’s Encoder layer, Bidirectional Gated Recurrent Unit (BiGRU), and Multi-head self-attention (MHSA). Firstly, in response to the nonlinear and non-stationary characteristics of the runoff sequence, the STL decomposition is used to extract the runoff sequence’s trend, period, and residual terms, and a multi-feature set based on ‘sequence-sequence’ is constructed as the input of the model, providing a foundation for subsequent models to capture the evolution of runoff. The key features of the input set are then captured using the Informer’s Encoder layer. Next, the BiGRU layer is used to learn the temporal information of these features. To further optimize the output of the BiGRU layer, the MHSA mechanism is introduced to emphasize the impact of important information. Finally, accurate runoff forecasting is achieved by transforming the output of the MHSA layer through the Fully connected layer. To verify the effectiveness of the proposed model, monthly runoff data from two hydrological stations in China are selected, and eight models are constructed to compare the performance of the proposed model. The results show that compared with the Informer model, the 1th step MAE of the SMGformer model decreases by 42.2% and 36.6%, respectively; RMSE decreases by 37.9% and 43.6% respectively; NSE increases from 0.936 to 0.975 and from 0.487 to 0.837, respectively. In addition, the KGE of the SMGformer model at the 3th step are 0.960 and 0.805, both of which can maintain above 0.8. Therefore, the model can accurately capture key information in the monthly runoff sequence and extend the effective forecast period of the model.

Water on fire: losses and the post-war future of ecosystem services from water resources of Ukraine

Water on fire: losses and the post-war future of ecosystem services from water resources of Ukraine

Applicability of a Modified Gash Model for Artificial Forests in the Transitional Zone between the Loess Hilly Region and the Mu Us Sandy Land, China

Afforestation in the transitional zone between the loess hilly area and the Mu Us Sandy Land of China has reshaped the landscape and greatly affected eco-hydrological processes. Plantations are crucial for regulating local net rainfall inputs, thus making it necessary to quantify the closure loss of plantation species in drought and semi-arid areas. To quantify and model the canopy interception of these plantations, we conducted rainfall redistribution measurement experiments. Based on this, we used the modified Gash model to simulate their interception losses, and the model applicability across varying rainfall types was further compared and verified. Herein, Caragana korshinskii, Salix psammophila, and Pinus sylvestris plantations in the Kuye River mountain tract were chosen to measure the precipitation distribution from May to October (growing season). The applicability of a modified Gash model for different stands was then evaluated using the assessed data. The results showed that the canopy interception characteristics of each typical plantation were throughfall, interception, and stemflow. The relative error of canopy interception of C. korshinskii simulated by the modified Gash model was 8.79%. The relative error of simulated canopy interception of S. psammophila was 4.19%. The relative error of canopy interception simulation of P. sylvestris was 13.28%, and the modified Gash model had good applicability in the Kuye River Basin. The modified Gash model has the greatest sensitivity to rainfall intensity among the parameters of the C. korshinskii and S. psammophila forest. The sensitivity of P. sylvestris in the modified Gash model is that the canopy cover has the greatest influence, followed by the mean rainfall intensity. Our results provide a scientific basis for the rational use of water resources and vegetation restoration in the transitional zone between the loess hilly region and the Mu Us Sandy Land. This study is of import for the restoration and sustainability of fragile ecosystems in the region.

A novel BiVO4/vermiculite Z-scheme heterojunction with enhanced removal rate of dyes through adsorption photodegradation synergistic mechanism

Water pollution, exacerbated by industrialization, poses severe threats to aquatic ecosystems and human health, particularly due to persistent organic pollutants like dyes. To mitigate this issue, the development of effective and sustainable water treatment technologies is essential. This study presents the synthesis of a novel bismuth vanadate (BVO)/vermiculite (Vt) adsorption-photocatalytic self-cycling composite using a simple hydrothermal method. The composite's Z-scheme heterojunction structure, along with the introduction of surface oxygen vacancies, significantly reduces electron-hole recombination and enhances charge separation. These modifications lead to a 1.64-fold increase in adsorption capacity compared to Vt and a 6.4-fold improvement in photocatalytic efficiency over BVO. The material maintained a 76 % removal efficiency after five usage cycles and demonstrated high efficacy in methylene blue (MB) solutions, even at concentrations up to 300 mg/L. Moreover, the composite displayed stable performance under repeated use and in high-pollutant environments, highlighting its durability and practicality. These characteristics underscore its strong potential for widespread application in environmental remediation and sustainable water resource management, offering an innovative solution to the global challenge of water pollution.

Investigating a Century of Rainfall: The Impact of Elevation on Precipitation Changes (Northern Tuscany, Italy)

Precipitation is crucial for water resource renewal, but climate change alters their frequency and amounts, challenging societies for correct and effective water management. However, modifications of precipitation dynamics appear to be not uniformly distributed, both in space and time. Even in relatively small areas, precipitation shows the coexistence of positive and negative trends. Local topography seems to be a strong driver of precipitation changes. Understanding precipitation changes and their relationship with local topography is crucial for society’s resilience. Taking advantage of a dense and long-lasting (1920–2019) meteorological monitoring network, we analyzed the precipitation changes over the last century in a sensitive and strategic area in the Mediterranean hotspot. The study area corresponds to northern Tuscany (Italy), where its topography comprises mountain ridges and coastal and river plains. Forty-eight rain gauges were selected with continuous annual precipitation time series. These were analyzed for trends and differences in mean annual precipitation between the stable period of 1921–1970 and the last 30-year 1990–2019. The relationship between precipitation changes and local topography was also examined. The results show the following highlights: (i) A general decrease in precipitation was found through the century, even if variability is marked. (ii) The mountain ridges show the largest decrease in mean annual precipitation. (iii) The precipitation change entity over the last century was not homogenous and was dependent on topography and geographical setting. (iv) A decrease in annual precipitation of up to 400 mm was found for the mountainous sites.

Enhanced relationship between seasonal soil moisture droughts and vegetation under climate change over China

Climate change could intensify seasonal soil moisture droughts and subsequently degrade vegetation, but it can also facilitate vegetation growth with the rising temperature and CO2 concentrations. Therefore, the change of the vegetation-drought relationship under climate change remains elusive. Here, we investigate the effects of vegetation greening (i.e., increasing leaf area index (LAI)) on seasonal soil moisture drought trends by performing a set of land surface model simulations with the Conjunctive Surface-Subsurface Process model version 2 (CSSPv2), as well as the impact of seasonal soil moisture drought change on vegetation productivity via a statistical fitting model. Our results indicate that both the impact of increasing LAI on exacerbating seasonal droughts and the impact of increasing seasonal droughts on reducing vegetation productivity have been enhanced over China during 1961–2018. Specifically, increased LAI accounts for 48 % of the rising drought frequency through escalated evapotranspiration losses, with more pronounced effects in northern semiarid regions. The decrease in vegetation stomatal conductance caused by rising CO2 concentrations limits transpiration and alleviates 11 % of the increase in drought frequency, but it cannot fully offset the drought aggravation induced by increased vegetation water consumption under climate warming. Meanwhile, increased drought frequency and intensity have reduced the upward trend in vegetation productivity by 5 % over China, and by 8 % in water-limited northern regions. Our study shows the relationship between seasonal soil moisture droughts and vegetation over China has strengthened, indicating that vegetation greening will lead to increased water shortages and further impact vegetation growth and carbon sequestration. This challenges water resources management and environmental sustainability, especially in semiarid regions.

The Impact of Digital Technology on Water Resources Management: Evidence from China

Digital technology is gradually emerging as a new driving force in the field of water resources management. In this paper, we conduct a thorough analysis of panel data from 30 provinces in China spanning from 2013 to 2022. Utilizing the fixed-effects model, the mediation effect model, a panel threshold model, and a coupling coordination degree model, this study empirically examines the impact of digital technology on water resources management. The findings are as follows: (1) The direct impact of digital technology on water resources management is significantly positive at the 1% level, with notable regional variations. (2) Digital technology improves water management through green innovation. (3) In the process of digital technology promoting water resources management, green innovation exhibits a threshold effect, with an estimated threshold value of 1.840. (4) During the sample period, the national coupling coordination degree of digital technology and water resources management was barely coordinated, showing the following characteristics: Eastern China > Western China > Central China. These research conclusions will offer valuable insights and directions for advancing sustainable water resources management strategies and fostering the deep integration of digital technology and water resources management.

Water insecurity may exacerbate food insecurity even in water-rich environments: Evidence from the Bolivian Amazon

Globally, challenges with water and food are two of the most pressing problems people face. Yet hydrologically water-rich environments and rural environments are often overlooked in these discussions due to abundance of natural water resources. Here we test the relationship between water and food insecurity among 270 Tsimane' households in the Bolivian Amazon. Water challenges were evaluated with the Household Water Insecurity Experiences Scale (HWISE), water quality perception, objective water quality analyses, and water access via the JMP drinking water ladder. Food insecurity was measured with the Household Food Insecurity Access Scale (HFIAS), and quantitative measures of food frequency recall were used to further test the water and food insecurity relationship. Using multilevel mixed-effects linear regression, each point increase in HWISE score was associated with 0.47 point (95 % CI: 0.30, 0.62, p < 0.001) higher food insecurity, and households with access to improved water sources had between 1.25 and 1.36 points (95 % CI: −2.61, −0.01, p < 0.05) lower food insecurity compared to households reliant on surface water. These relationships held true independent of quantitative measures of both fish and meat consumption. Using mixed-effects logistic regression analyses, each point increase in HWISE score was associated with 43 % (95 % CI: 1.25–1.66, p < 0.001) increased odds of experiencing severe food insecurity. Households changing what was eaten due to experienced water problems was associated with 2.33 points (95 % CI: 0.41, 4.25, p < 0.05) higher food insecurity. This relationship held true independent of perceived water quality, indicating other structural water problems may be important here in the household water and food insecurity relationship. These results demonstrate that even in water-rich environments, like the Amazon, water and food insecurity are interconnected. Further, despite the challenging conditions, equitable structural interventions, like the development of improved water infrastructure, are critical for the provision of clean drinking water and may simultaneously help alleviate food insecurity.