Search Results (77)

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

Understanding the spatial and seasonal dynamics of surface water bodies is imperative for addressing water security challenges in water-scarce regions. This study aimed to evaluate the efficacy of multi-date Sentinel-2-derived spectral indices, specifically the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and Sentinel 2 Water Index (SWI), in conjunction with landscape metrics for mapping spatial and seasonal fluctuations in surface water bodies. Google Earth Engine (GEE) was employed for this assessment. The research achieved impressive overall accuracies, ranging from 96 to 100% for both dry and wet seasons, highlighting the robustness of the methodology. The study revealed significant differences in water bodies in terms of size and coverage between the dry and wet seasons. Surprisingly, the dry season exhibited a higher prevalence of water bodies when compared to the wet season, indicating unexpected patterns of water availability in the region and the substantial heterogeneity of water bodies. Meanwhile, the wet season was characterized by extensive coverage. These findings challenge conventional assumptions about water resource availability during different seasons. Based on the findings, the study recommends that water resource management strategies in semi-arid regions consider the observed seasonal variability in water bodies. Policymakers and stakeholders should adopt adaptive management approaches to address the unique challenges posed by differing water body dynamics in dry and wet seasons. Future research endeavors should explore the underlying factors driving these seasonal fluctuations and assess the potential long-term impacts on water availability. This can help to develop more resilient and sustainable water security strategies to cope with changing climate conditions in semi-arid tropical environments. Full article

In arid regions, water scarcity necessitates reliance on surface runoff as a vital water source. Studying the impact of climate change on surface runoff can provide a scientific basis for optimizing water use and ensuring water security. This study investigated runoff patterns in the upper-middle Amu Darya River (UADR) from 1960 to 2015. Special emphasis was placed on the effects of climatic factors and the role of major atmospheric circulation indices, such as the Eurasian Zonal Circulation Index (EZI), Niño 3.4, and the Indian Ocean Dipole (IOD). The results show a significant linear decreasing annual trend in runoff at a rate of 2.5 × 10⁸ m³/year, with an abrupt change in 1972. Runoff exhibited periodic characteristics at 8–16 and 32–64 months. At the 8–16-month scale, runoff was primarily influenced by precipitation (PRE), actual evapotranspiration (AET), and snow water equivalent (SWE), and, at the 32–64-month scale, Niño 3.4 guided changes in runoff. In addition, El Niño 3.4 interacted with the EZI and IOD, which, together, influence runoff at the UADR. This study highlights the importance of considering multiple factors and their interactions when predicting runoff variations and developing water resource management strategies in the UADR Basin. The analysis of nonlinear runoff dynamics in conjunction with multiscale climate factors provides a theoretical basis for the management of water, land, and ecosystems in the Amu Darya Basin. Full article

This paper presents an analysis of regional terrestrial water storage (TWS) changes and drought characteristics in Southwest China, encompassing Sichuan Province, Chongqing Municipality, Yunnan Province, and Guizhou Province. Existing geodetic datasets, such as those from the Gravity Recovery and Climate Experiment (GRACE) and its successor satellites (GRACE Follow-On), as well as Global Navigation Satellite System (GNSS) data, face significant challenges related to limited spatial resolution and insufficient station distribution. To address these issues, we propose a novel inversion method that integrates GNSS and GRACE/GFO data by establishing virtual stations for GRACE/GFO data and determining the weight values between GNSS and GRACE/GFO using the Akaike Bayesian Information Criterion (ABIC). This method allows for estimating the TWS changes from December 2010 to June 2023 and monitoring drought conditions in conjunction with hydrometeorological data (precipitation, evapotranspiration, and runoff). The results show strong correlations between TWS changes from the joint inversion and GNSS (0.98) and GRACE/GFO (0.69). The Joint Drought Severity Index (Joint-DSI) indicates five major drought events, with the most severe occurring from July 2022 to June 2023, with an average deficit of 86.133 km³. Extreme drought primarily impacts Sichuan and Yunnan, driven by abnormal precipitation deficits. The joint inversion methodology presented in this study provides a practical approach for monitoring TWS changes and assessing drought characteristics in Southwest China. This paper leverages the complementary strengths of GNSS and GRACE/GFO data and offers new insights into regional water resource management and drought detection. Full article

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts. Full article

In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the capabilities of long short-term memory (LSTM) neural networks in conjunction with Internet of Things (IoT) paradigms. The efficacy of the LSTM model is juxtaposed with that of an advanced Deep Belief Network (DBN), as well as contemporary models like a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid model and a Transformer-based model, employing data sourced from a wastewater treatment facility located in Changsha. The empirical findings show that notwithstanding the comparable training durations used, the LSTM model exhibits a preeminent error rate of merely 7%, thus surpassing the DBN model (which has an error rate of 35%), the CNN-LSTM model (registering a 22% error rate), and the Transformer-based model (with a 17% error rate) in its predictive precision. This research underscores the potential of integrating an astute wastewater control system with IoT and LSTM models, thereby hinting at prospective enhancements in the sustainability and operational efficacy of wastewater treatment installations. Full article

This study evaluates the quality and vulnerability of groundwater within the Nakivale Sub-catchment of the transboundary Lake Victoria Basin in Southwestern Uganda. Groundwater quality assessment focuses on its suitability for both drinking and agricultural uses. Hydrochemical analysis of 19 groundwater samples revealed that 90% comply with World Health Organization drinking water standards, although localized contamination was noted, particularly in terms of total iron, nitrate, potassium, magnesium, and sulfates. The drinking groundwater quality index shows that over 90% of the samples fall within the good-to-excellent quality categories. Elevated nitrate levels and chloride–bromide ratios indicate human impacts, likely due to agricultural runoff and wastewater disposal. For irrigation, Sodium Adsorption Ratio analysis revealed medium-to-high salinity hazards in the region, while Sodium Percentage and other parameters indicated low-to-moderate risks of soil degradation. DRASTIC vulnerability assessments identified low contamination risks due to impermeable geological layers, steep terrain, slow groundwater recharge, deep aquifer depth, and clayey soil cover. These findings emphasize the need for conjunctive water resource management, including improved groundwater quality monitoring, public education on sustainable practices, and protective measures for recharge zones and areas highly susceptible to contamination. By addressing these issues, this study aims to preserve groundwater resources for domestic and agricultural use, ensuring long-term sustainability in the region. Full article

An innovative way to combat water scarcity brought on by population increase and climate change is rainwater harvesting (RWH), particularly in arid and semiarid areas. Currently, Pakistan is facing major water issues due to underprivileged water resource management, climate change, land use changes, and the sustainability of local water resources. This research aims to find out the suitable sites and options for RWH structures in the Quetta district of Pakistan by integrating the depression depth technique, Boolean analysis, and weighted linear combination (WLC) with hydrological modeling (HM), multicriteria analysis (MCA), a geographic information system (GIS), and remote sensing (RS). To find suitable sites for RWH, a collection of twelve (12) thematic layers were used, including the slope (SL), land use land cover (LULC), subarea (SA), runoff depth (RD), drainage density (DD), lineament density (LD), infiltration number (IFN), distance from built-up area (DB), distance from roads (DR), distance from lakes (DL), maximum flow distance (MFD), and topographic wetness index (TWI). The Boolean analysis and WLC approach were integrated in the GIS environment. The consistency ratio (CR) was calculated to make sure the assigned weights to thematic layers were consistent. Overall, results show that 6.36% (167.418 km²), 14.34% (377.284 km²), 16.36% (430.444 km²), 18.92% (497.663 km²), and 18.64% (490.224 km²) of the area are in the categories of very high, high, moderate, low, and very low suitability, respectively, for RWH. RWH potential is restricted to 25.35% (666.86 km²) of the area. This research also identifies the five (5) best locations for checking dams and the ten (10) best locations for percolation tanks on the streams. The conducted suitability analysis will assist stakeholders in selecting the optimal locations for RWH structures, facilitating the storage of water, and addressing the severe water scarcity prevalent in the area. This study proposes a novel approach to handle the problems of water shortage in conjunction with environmental and socioeconomic pressures in order to achieve the sustainable development goals (SDGs). Full article

Natural aquifers used for human consumption are among the most important resources in the world. The Locumba basin faces significant challenges due to its limited water availability for the local population. In this way, the search for possible aquifer recharge zones is crucial work for urban development in areas that have water scarcity. To evaluate this problem, this research proposes the use of the hybrid Fuzzy AHP methodology in conjunction with the TOPSIS algorithm to obtain a potential aquifer recharge map. Ten factors that influence productivity and capacity in an aquifer were implemented, which were subjected to Fuzzy AHP to obtain their weighting. Using the TOPSIS algorithm, the delineation of the most favorable areas with high recharge potential was established. The result shows that the most influential factors for recharge are precipitation, permeability, and slopes, which obtained the highest weights of 0.22, 0.19, and 0.17, respectively. In parallel, the TOPSIS result highlights the potential recharge zones distributed in the Locumba basin, which were classified into five categories: very high (13%), high (28%), moderate (15%), low (28%), and very low (16%). The adapted methodology in this research seeks to be the first step toward effective water resource management in the study area. Full article

Rainfall variability and its underlying physical mechanisms are crucial for improving the predictive accuracy of July rainfall patterns in the Sichuan-Shaanxi (SS) region of Southwestern China. This study utilized observational 24 h accumulated rainfall data from China in conjunction with reanalysis products sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF). The purpose of this study was to elucidate the relationship between daily variations in the daily Asian-Pacific Oscillation (APO), atmospheric circulation, and daily rainfall patterns in the SS region, and to evaluate the impact of atmospheric circulation anomalies on these relationships. The results reveal a discernible intensification in the sea–land thermal contrast associated with atmospheric circulation anomalies transitioning from the daily extremely low APO (ELA) to the extremely high APO (EHA) days. These conditions lead to an increased presence of water vapor and widespread anomalies in rainfall that exceed normal levels in the SS region. Concurrently, the increase in stations experiencing extreme rainfall events (EREs) accounts for 21.3% of the overall increase in stations experiencing rainfall. The increase in rainfall amount contributed by EREs (RA-EREs) accounts for 73.5% of the overall increase in the total rainfall amount (TRA) across the SS region. Specifically, heavy rainfall (HR) and downpour rainfall (DR) during EREs accounted for 65.7% (HR) and 95.3% (DR) of the overall increase in the TRA, respectively. Relative to the ELA days, there was a substantial 122.6% increase in the occurrence frequency of EREs and a 23.3% increase in their intensity. The study suggests that the daily APO index emerges as a better indicator of July rainfall events in the SS region, with EREs significantly contributing to the overall increase in rainfall in this region. These findings indicate the importance of improving predictive capabilities for daily variability in the APO index and their correlation with rainfall events in the SS region. The results may inform the development of effective adaptation and mitigation strategies to manage the potential impacts of EREs on agriculture, water resources, sustainable development, and infrastructure in the region. Full article

The impact of the intercropping system on the soil–plant–atmosphere continuum (SPAC), encompassing soil evaporation, soil moisture dynamics, and crop transpiration, remains an area of uncertainty. Field experiments were conducted for two years in conjunction with the SIMDualKc (Simulation Dual Crop Coefficient) model to simulate two planting configurations: sole-cropped wolfberry (Lycium barbarum L.) (D) and wolfberry intercropped with alfalfa (Medicago sativa L.) (J). These configurations were subjected to different irrigation levels: full irrigation (W1, 75–85% θfc), mild deficit irrigation (W2, 65–75% θfc), moderate deficit irrigation (W3, 55–65% θfc), and severe deficit irrigation (W4, 45–55% θfc). The findings revealed that the JW1 treatment reduced the annual average soil evaporation by 32% compared with that of DW1. Additionally, mild, moderate, and severe deficit irrigation reduced soil evaporation by 17, 24, and 36%, respectively, compared with full irrigation. The intercropping system exhibited a more efficient canopy structure, resulting in reduced soil evaporation and alleviation of water stress to a certain extent. In terms of temporal dynamics, monocropping resulted in soil moisture levels from 1% to 15% higher than intercropping, with the most significant differences manifesting in the mid to late stages, whereas differences in the early stages were not statistically significant. Spatially, the intercropping system exhibited 7–19% lower soil water contents (SWCs) than sole cropping, primarily within the root water uptake zone within the 0–60 cm soil layer. The intercropping system showed an enhanced water absorption capacity for plant transpiration, resulting in a 29% increase in transpiration compared with sole cropping, thereby achieving water-saving benefits. These findings contribute to our understanding of the agronomic and environmental implications of intercropping wolfberry and alfalfa in arid regions and provide insights into optimizing water and soil resource management for sustainable agricultural practices. Full article

In the context of global climate change and ecological restoration projects, significant changes have been observed in the fractional vegetation cover (FVC) in the Yellow River basin. The increased vegetation growth accelerates water consumption, exacerbating drought and water scarcity issues, thereby heightening regional water resource shortage risks. This study targets the Yellow River basin in China, employing a pixel-based model to convert NDVI into FVC datasets. We establish a pixel-wise mathematical model for annual runoff and environmental factors based on residual analysis and methods like multiple linear regression. Using climate model data from CMIP6 as independent variables, in conjunction with the statistical model, we elucidate the spatiotemporal characteristics of annual runoff in the Yellow River basin under future climate scenarios. Our results indicate that, under four different climate scenarios, the average annual runoff in the Yellow River basin is projected to increase. The increases are quantified as 0.008 mm/a, 0.065 mm/a, 0.25 mm/a, and 0.24 mm/a for SSP126, SSP245, SSP370, and SSP585 scenarios, respectively. From 2022 to 2040, the spatial distribution of the runoff change rates under the SSP245 and SSP370 scenarios show an increasing trend in upstream areas such as the Qinhe and Longmen regions, with rates ranging from 6.00 to 8.61 mm/a. During the period from 2041 to 2060, all four climate scenarios indicate minimal changes in the runoff depth in the northern part of the Yellow River basin. From 2061 to 2080, under the SSP126 and SSP245 scenarios, the spatial distribution of the runoff shows significant increases in the river source area and a decreasing trend in the middle reaches, with rates ranging from 4.52 to 11.39 mm/a. For the period from 2081 to 2100, the runoff change rates vary significantly under the four climate scenarios. These findings provide a detailed understanding of how future climate scenarios could impact water resource distribution in the Yellow River basin, offering critical insights for regional water management and policy making to mitigate potential water scarcity challenges. Full article

Cereal grains and pulses are staple foods worldwide, being the primary supply of energy, protein, and fiber in human diets. The current practice of milling and fractionation yields large quantities of byproducts and waste, which are largely downgraded and end up as animal feeds or fertilizers. This adversely affects food security and the environment, and definitely implies an urgent need for a sustainable grain processing system to rectify the current issues, particularly the management of waste and excessive use of water and energy. The current review intends to discuss the limitations and flaws of the existing practice of grain milling and fractionation, along with potential solutions to make it more sustainable, with an emphasis on wheat and peas as common fractionation crops. This review discusses a proposed sustainable grain processing system for the fractionation of wheat or peas into flour, protein, starch, and value-added components. The proposed system is a hybrid model that combines dry and wet fractionation processes in conjunction with the implementation of three principles, namely, integration, recycling, and upcycling, to improve component separation efficiency and value addition and minimize grain milling waste. The three principles are critical in making grain processing more efficient in terms of the management of waste and resources. Overall, this review provides potential solutions for how to make the grain processing system more sustainable. Full article

Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management. Full article

The mapping of small-scale irrigation areas is essential for food security and water resource management studies. The identification of small-scale irrigation areas is a challenge, but it can be overcome using expert knowledge and satellite-derived high-spatial-resolution multispectral information in conjunction with monthly normalized difference vegetation index (NDVI) time series, and additional terrain information. This paper presents a novel approach to characterize small-scale irrigation schemes that combine expert knowledge, multi-temporal NDVI time series, multispectral high-resolution satellite images, and the random forest classifier in the Zamra catchment, North Ethiopia. A fundamental element of the approach is mapping small-scale irrigation areas using expert decision rules to incorporate the available water resources. We apply expert decision rules to monthly NDVI composites from September 2020 to August 2021 along with the digital elevation model (DEM) data on the slope, drainage order, and distance maps to derive the sample set. The samples were based on the thresholds obtained by expert knowledge from field surveys. These data, along with the four spectral bands of a cloud-free Planet satellite image composite, 12 NDVI monthly composites, slope, drainage order, and distance map were used as input into a random forest classifier which was trained to classify pixels as either irrigated or non-irrigated. The results show that the analysis allows the mapping of small-scale irrigation areas with high accuracy. The classification accuracy for identifying irrigated areas showed a user accuracy ranging from 81% to 87%, along with a producer accuracy ranging from 64% to 79%. Furthermore, the classification accuracy and the kappa coefficient for the classified irrigation schemes were 80% and 0.70, respectively. As a result, these findings highlight a substantial level of agreement between the classification results and the reference data. The use of different expert knowledge-based decision rules, as a method, can be applied to extract small-scale and larger irrigation areas with similar agro-ecological characteristics. Full article