Search Results (29)

Growing agricultural water demand, driven by climate change and land-use intensification, is accelerating global water scarcity and threatening food and environmental security. This study quantifies spatiotemporal changes in crop water requirements (CWR) and irrigation water requirement (IWR) from 1980 to 2017 for wheat, maize, and soybean. A corrected FAO crop coefficient method was used to estimate global CWR, while the logarithmic mean Divisia index (LMDI) was applied to decompose its drivers into climate and crop area changes. IWR was calculated to evaluate the increasing water stress in four representative river basins: the Haihe (HRB), Yellow (YRB), Mississippi (MRB), and Ganges (GRB) river basins. Multiple linear regression models were used to identify dominant drivers of water stress. Results show that from 1980 to 2017, CWR increased significantly for maize (+210 × 10⁸ m³) and soybean (+523 × 10⁸ m³) primarily due to crop area expansion, while wheat CWR declined (−109 × 10⁸ m³). Area growth contributed over +850 × 10⁸ m³ to global CWR increases. At the basin scale, IWR rose notably in HRB, YRB, and GRB, but declined in MRB. Regression analysis confirms that crop area change was the dominant driver of variations in IWR, particularly for soybean in HRB and maize in YRB, while precipitation exerted strong negative effects in some regions. This study provides a scalable framework for diagnosing agricultural water stress and its key drivers, supporting climate adaptation and irrigation planning under global change. Full article

The present study aimed to investigate the occurrence of eight potentially toxic elements (PTEs) in selected varieties of watermelon (Citrullus lanatus var. Arka Shyama and Crimson Sweet) and muskmelon (Cucumis melo var. Cantaloupe and Kajri) grown near riverbanks in the Yamuna and Ganga River basins of Northern India. For this purpose, samples of melon fruits were collected from ten sampling sites from May to June 2024 and analyzed using ICP-OES. The results showed that the levels of PTEs varied significantly across the sampling sites, with muskmelons exhibiting slightly higher concentrations compared to watermelons. Specifically, the concentration (mg/kg dry weight) ranges for the watermelon varieties were Cd (0.05–0.20), Cr (0.40–1.10), Cu (1.50–4.90), Pb (0.01–0.11), As (0.01–0.08), Fe (80.00–120.00), Mn (9.00–15.80), and Zn (5.00–18.00). For muskmelons, the ranges were Cd (0.05–0.23), Cr (0.40–1.00), Cu (2.40–4.80), Pb (0.01–0.08), As (0.02–0.08), Fe (80.00–120.00 g), Mn (9.00–15.00), and Zn (8.00–18.00). In particular, the variability coefficients (CV%) indicated differential contamination in Crimson Sweet. On the other hand, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) tools facilitated the identification of sites of significant contamination and their respective interactions. The health risk studies using the health risk index (HRI), dietary intake modeling (DIM), and the target hazard quotient (THQ) also revealed no significant health risk of eight PTEs in melon fruits. Therefore, this study provides valuable insights into the biomonitoring of PTE contamination in widely consumed summer fruits of Northern India and the subsequent health risk assessment. Full article

In this study, the JiaoGang Basin in the Yangtze River Delta plains of the river network area was the research object. A basin water level simulation model was constructed based on the physical mechanism model and Mike software, and the parameters were calibrated and validated. Based on the dataset produced by the physical model, three types of ML models, Support Vector Machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), were constructed, trained, validated, and compared with the physical model. The results showed that the physical mechanism model met the water level simulation accuracy requirements at most stations. In the training and validation periods, the RF water level simulation and GBDT water level simulation models had root mean square errors (RMSEs) of all stations less than 0.25 and the Nash–Sutcliffe coefficient (NSE) of all stations was greater than 0.7. The physical mechanism model and ML water level simulation models can simulate the water level in the JiaoGang Basin better. The RF and GBDT models considerably outperform the physical mechanism model in terms of the peak simulation errors and peak present time errors, and the fluctuations of the ML water level simulation models (RMSE and NSE) are minor compared to those of the physical mechanism model. Full article

Managing water resources amidst the pressures of climate change and human activities is a significant challenge, especially in regions experiencing shrinking lakes, deteriorating water quality, and ecological degradation. This review focuses on achieving integrated river basin management by learning from the governance experiences of typical watersheds globally, using the Hulun Lake Basin as a case study. Hulun Lake, China’s fifth-largest lake, experienced severe ecological problems from 2000 to 2009 but saw improvements after comprehensive management efforts from 2012 onward. This review systematically explores methods to address water resource, environment, and ecological challenges through the lenses of data acquisition, mechanism identification, model simulation, and regulation and management. Drawing lessons from successful basins such as the Rhine, Ganges, Mississippi, and Murray–Darling, the review proposes key goals for comprehensive management, including establishing extensive monitoring networks, developing predictive models, and creating contingency plans for routine and emergency management. Leveraging advanced technologies like satellite imagery and IoT sensors, alongside continuous improvement mechanisms, will ensure the sustainable use and protection of river basins. This review provides a detailed roadmap for achieving comprehensive watershed management in Hulun Lake, summarizing effective strategies and outcomes from data acquisition to regulation, thus serving as a model for similar regions globally. Full article

The Ganges–Brahmaputra estuary, located in the northern Bay of Bengal, is situated within the largest delta in the world. This river basin features a complex river system, a dense population, and significant variation in watershed vegetation cover. Human activities have significantly impacted the concentration of total suspended matter (TSM) in the estuary and the ecological environment of the adjacent bay. In this study, we utilised the Landsat series of satellite remote sensing data from 1990 to 2020 for TSM retrieval. We applied an atmospheric correction algorithm based on the general purpose exact Rayleigh scattering look-up-table (LUT) and the shortwave-infrared (SWIR) bands extrapolation to Landsat L1 products to obtain high-precision remote sensing reflectance. In conjunction with the normalised difference vegetation index (NDVI), precipitation, and discharge data, we analysed the variation and influencing mechanisms of TSM in the Ganges–Brahmaputra estuary and its surrounding areas. We revealed notable seasonal variation in TSM in the estuary, with higher concentrations during the wet season (May–October) compared to the dry season (the rest of the year). Over the period from 1990 to 2020, the NDVI in the watershed exhibited a significant upward trend. The outer estuarine regions of the Hooghly River and Meghna River displayed significant decreases in TSM, whereas the Baleswar River, which flows through mangrove areas, showed no significant trend in TSM. The declining trend in TSM was mainly attributed to land-use changes and anthropogenic activities, including the construction of embankments, dams, and mangrove conservation efforts, rather than to runoff and precipitation. Surface sediment concentration and chlorophyll in the northern Bay of Bengal exhibited slight increases, which means the limited influence of terrestrial inputs on long-term change in surface sediment concentration and chlorophyll in the northern Bay of Bengal. This study emphasises the impact of human activities on the river–estuary–coast continuum and sheds light on future sustainable management. Full article

Satellite precipitation products (SPPs) have been widely evaluated at regional scales. However, there have been few quantitative comprehensive evaluations of SPPs using multiple indices. Ten high-resolution SPPs were quantitatively and comprehensively evaluated from precipitation occurrence and series indices using an improved rank score (RS) method in the data-scarce Qinghai–Tibetan Plateau (QTP). The new observation network, along with a number of national basic stations, was applied for SPP evaluation to obtain more reliable results. The results showed that the GPM and MSWEP showed the strongest overall performance, with an RS value of 0.75. CHIRPS and GPM had the strongest performance at measuring precipitation occurrence (RS = 0.92) and series (RS = 0.75), respectively. The optimal SPPs varied in evaluation indices, but also concentrated in the MSWEP, GPM, and CHIRPS. The bias of SPPs was markedly in the QTP, with relative error generally between −80% and 80%. In general, most SPPs showed the ability to detect precipitation occurrence. However, the SPPs showed relatively weak performance at measuring precipitation series. The mean Kling–Gupta efficiency of all stations was <0.50 for each SPP. The SPPs showed better performance in monsoon-affected regions, which mainly include the Yangtze, Yellow, Nu–Salween, Lancang–Mekong, Yarlung Zangbo–Bramaputra, and Ganges river basins. Performance was relatively poor in the westerly circulation areas, which mainly include the Tarim, Indus, and QTP inland river basins. The performance of SPPs showed a seasonal pattern during the year for most occurrence indices. The performance of SPPs in different periods was opposite in different indices. Therefore, multiple indices representing different characteristics are recommended for the evaluation of SPPs to obtain a comprehensive evaluation result. Overall, SPP measurement over the QTP needs further improvement, especially with regard to measuring precipitation series. The proposed improved RS method can also potentially be applied for comprehensive evaluation of other products and models. Full article

Identification of sources and transport pathways of heavy metals and major ions is crucial for effective water quality monitoring, particularly in large river systems. The Ganges river basin, the largest and the most populous river basin in India, remains poorly studied in this regard. We conducted a basin-level analysis of major ions, heavy metals, and stable isotopes of nitrate in the Ganges during the pre-monsoon season to constrain the sources and quantify the inorganic chemical composition of the river during its lean flow. Bedrock weathering, anthropogenic interferences, water contribution through tributaries, and surface water-groundwater interaction were identified as the major driver of metal and ion variability in the river. Heavy metals showed the highest concentrations in the upper section of the river, whereas ionic loads were the most variable in the middle. We find a significant impact of tributaries on the metal and ion concentrations of the Ganges in its lower reaches. Isotopic analysis of dissolved nitrate suggested synthetic fertilizers and industrial wastes as the main sources. We find that the otherwise clean waters of the Ganges can show high ionic/metallic concentrations at isolated stretches (As: up to 36 µg/L), suggesting frequent monitoring in the source region to maintain water quality. Except for water collected from the Yamuna and Kannauj in the middle stretch and the Alaknanda and Rishikesh in the upper stretch, the WQI showed acceptable water quality for the sampled stations. These findings provide an insight into the modifications of dissolved inorganic chemical loads and their sources in different sections of the basin, needed for mitigating site-specific pollution in the river, and a roadmap for evaluating chemical loads in other rivers of the world. Full article

Rice landraces, including Asian rice (Oryza sativa L.) and African rice (Oryza glaberrima Steud.), provide important genetic resources for rice breeding to address challenges related to food security. Due to climate change and farm destruction, rice landraces require urgent conservation action. Recognition of the geographical distributions of rice landraces will promote further collecting efforts. Here we modelled the potential distributions of eight rice landrace subgroups using 8351 occurrence records combined with environmental predictors with Maximum Entropy (MaxEnt) algorithm. The results showed they were predicted in eight sub-regions, including the Indus, Ganges, Meghna, Mekong, Yangtze, Pearl, Niger, and Senegal river basins. We then further revealed the changes in suitable areas of rice landraces under future climate change. Suitable areas showed an upward trend in most of study areas, while sub-regions of North and Central China and West Coast of West Africa displayed an unsuitable trend indicating rice landraces are more likely to disappear from fields in these areas. The above changes were mainly determined by changing global temperature and precipitation. Those increasingly unsuitable areas should receive high priority in further collections. Overall, these results provide valuable references for further collecting efforts of rice landraces, while shedding light on global biodiversity conservation. Full article

Numerous studies have demonstrated that a complex distribution of water resources, regional development, and management mechanisms create significant management challenges for transboundary river basins. We utilized diagnostic thinking to examine the water management issues of the 14 main transboundary watersheds in three regions (Northeast, Northwest, and Southwest) of China. Our four diagnosis points were water quantity, water quality, ecological stability and human health, and cooperation among watershed stakeholders. We found that the watersheds faced varying water management issues. The Indus and Ganges basins have the worst problems, whereas the Tarim basin’s ecological environment is the most vulnerable and the Ob basin is the fittest. Therefore, depending on each basin’s results, we provide practical water management ideas for each. Furthermore, we summarized and classified the geographical settings of each basin and determined the water management issues in each major region in China. Our results provide direction for both new research on and cooperation with transboundary basin water management. Full article

Elevated arsenic concentrations in groundwater in the Ganga–Brahmaputra–Meghna (GBM) river basin of India has created an alarming situation. Considering that India is one of the largest consumers of groundwater for a variety of uses such as drinking, irrigation, and industry, it is imperative to determine arsenic occurrence and hazard for sustainable groundwater management. The current study focused on the evaluation of arsenic occurrence and groundwater arsenic hazard for the Ganga basin employing Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models. Furthermore, arsenic hazard maps were prepared using a Kriging interpolation method and with overlay analysis in the GIS platform based on the available secondary datasets. Both models generated satisfactory results with minimum differences. The highest hazard likelihood has been displayed around and along the Ganges River. Most of the Uttar Pradesh and Bihar; and parts of Rajasthan, Chhattisgarh, Jharkhand, Madhya Pradesh, and eastern and western regions of West Bengal show a high arsenic hazard. More discrete results were rendered by the AHP model. Validation of arsenic hazard maps was performed through evaluating the Area Under Receiver Operating Characteristics metric (AUROC), where AUC values for both models ranged from 0.7 to 0.8. Furthermore, the final output was also validated against the primary arsenic data generated through field sampling for the districts of two states, viz Bihar (2019) and Uttar Pradesh (2021). Both models showed good accuracy in the spatial prediction of arsenic hazard. Full article

This study aims to evaluate the climate- and human-induced impacts on two contrasting river basins in India, specifically, the Ganges and the Godavari. Monthly discharge simulations from global hydrological models (GHMs), run with and without human influence using CMIP5 projections under the framework of the Inter-Sectoral Impact Model Intercomparison Project, are utilized to address the scientific questions related to the quantification of the future impacts of climate change and the historical impacts of human activities on these river basins. The five state-of-the-art GHMs were considered and subsequently used to evaluate the human and climate change impacts on river discharges (seasonal mean discharge and extreme flows) during the pre-monsoon, monsoon, and post-monsoon seasons under the RCP2.6 and RCP8.5 emission scenarios. Results showed that human impacts during the baseline period on long-term seasonal discharge in the Ganges and Godavari River basins for the pre-monsoon season are around 40% and 23%, respectively, and these impacts are stronger than the future climate change impact in the pre-monsoon season for the Ganges basin, whereas, for the Godavari basin, the same pattern is observed with some exceptions. The human impact in the course of the historical period on the pre-monsoon flows of both the Ganges and the Godavari are more significant than on the monsoon and post-monsoon flows. In the near future (2010–39) time slice, the impact of climate change on the streamflow of the Ganges is highest for the post-monsoon season (13.4%) under RCP 8.5 as compared to other seasons. For Godavari, in the near-future period, this impact is highest for the pre-monsoon season (18.2%) under RCP 2.6. Climate-induced changes in both of the basins during both the monsoon and post-monsoon seasons is observed to have a higher impact on future flows than direct human impact-induced changes to flow during the current period. High flows (31.4% and 19.9%) and low flows (51.2% and 36.8%) gain greater influence due to anthropogenic actions in the time of the pre-monsoon season compared to other times of year for the Ganges and Godavari basins, respectively. High flows for the Ganges during the near future time slice are most affected in the monsoon season (15.8%) under RCP 8.5 and, in the case of the Godavari, in the pre-monsoon season (18.4%) under the RCP 2.6 scenario. Low flows of the Ganges during the near-future period are most affected during the monsoon season (22.3%) and for the Godavari, low flows are affected most for the post-monsoon season (22.1%) under RCP 2.6. Uncertainty in the streamflow estimates is more pronounced for the Godavari basin compared to the Ganges basin. The findings of this study enhance our understanding of the natural and human-influenced flow regimes in these river basins, which helps the formation of future strategies, especially for inter-state and transboundary river management. Full article

The Ganges-Brahmaputra-Meghna (GBM) river basin is the world’s third largest. Literature show that changes in precipitation have a significant impact on climate, agriculture, and the environment in the GBM. Two satellite-based precipitation products, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Multi-Source Weighted-Ensemble Precipitation (MSWEP), were used to analyze and compare precipitation trends over the GBM as a whole and within 34 pre-defined hydrological sub-basins separately for the period 1983–2019. A non-parametric Modified Mann-Kendall test was applied to determine significant trends in monsoon (June–September) and pre-monsoon (March–May) precipitation. The results show an inconsistency between the two precipitation products. Namely, the MSWEP pre-monsoon precipitation trend has significantly increased (Z-value = 2.236, p = 0.025), and the PERSIANN-CDR monsoon precipitation trend has significantly decreased (Z-value = −33.071, p < 0.000). However, both products strongly indicate that precipitation has recently declined in the pre-monsoon and monsoon seasons in the eastern and southern regions of the GBM river basin, agreeing with several previous studies. Further work is needed to identify the reasons behind inconsistent decreasing and increasing precipitation trends in the GBM river basin. Full article

The flux of total suspended matter (TSM), F_TSM, output by several large rivers in Asia, has been in decline due to human activities. As the estuary of the Ganges–Brahmaputra River, the Padma River transports a significant amount of suspended matter (SM) to the Bay of Bengal each year. In this study, the TSM concentration (C_TSM) and F_TSM in the Padma River in the period 1991–2019 were calculated based on the data acquired by the Landsat series satellites and an empirical TSM algorithm model for large, high-turbidity rivers. The results showed that the maximum and minimum F_TSM values (318 ± 62 and 73 ± 29 mt, respectively) in the Padma River occurred in 2011 and 2015, respectively. On average, F_TSM in the Padma River decreased at an annual rate of 3.3 mt (p < 0.01). The impact of human activities on C_TSM contributed more significantly to the changes in F_TSM (R = 0.76) than natural factors (R = 0.44). Due to a lack of water conservancy facilities within the river basin, changes in the water and soil retention capacity due to the changes in vegetation coverage were an important human factor (R = −0.79). Full article

Long-term (2000–2019) assessment of aerosol loads and dominant aerosol types at spatiotemporal scales using multi-source datasets can provide a strong impetus to the investigation of aerosol loads and to the targeted prevention control of atmospheric pollution in densely populated regions with frequent anthropogenic activities and heavy aerosol emissions. This study uses multi-source aerosol datasets, including Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network (AERONET), to conduct a long-term variation assessment of aerosol load, high aerosol load frequency, and dominant aerosol types over Asia. The results indicate that regional aerosol type information with adequate spatial resolution can be combined with aerosol optical depth (AOD) values and heavy aerosol load frequency characterization results to explore the key contributors to air pollution. During the study period, the aerosol load over the North China Plain, Central China, Yangtze River Delta, Red River Delta, Sichuan Basin, and Pearl River Delta exhibited an increasing trend from 2000–2009 due to a sharp rise in aerosol emissions with economic development and a declining trend from 2010–2019 under stricter energy conservation controls and emissions reductions. The growth of urban/industrial (UI) type and biomass burning (BB) type aerosol emissions hindered the improvement of the atmospheric environment. Therefore, in future pollution mitigation efforts, focus should be on the control of UI-type and BB-type aerosol emissions. The Indus–Ganges River Plain, Deccan Plateau, and Eastern Ghats show a continuously increasing trend; however, the aerosol load growth rate of the last decade was lower than that of the first decade, which was mainly due to the decrease in the proportion of the mixed type aerosols. Full article

Availability of water in the Ganges River basin has been recognized as a critical regional issue with a significant impact on drinking water supply, irrigation, as well as on industrial development, and ecosystem services in vast areas of South Asia. In addition, water availability is also strongly linked to energy security in the region. Hence, quantification of spatial availability of water resources is necessary to bolster reliable evaluation of the sustainability of future thermal power plants in the Ganges River basin. This study focuses on the risks facing existing and planned power plants regarding water availability, applying climate change scenarios at the sub-basin and district level up to 2050. For this purpose, this study develops an integrated assessment approach to quantify the water-energy nexus in four selected sub-basins of the Ganges, namely, Chambal, Damodar, Gandak, and Yamuna. The results of simulations using Soil and Water Assessment Tools (SWAT) showed that future water availability will increase significantly in the Chambal, Damodar, and Gandak sub-basins during the wet season, and will negligibly increase in the dry season, except for the Yamuna sub-basin, which is likely to experience a decrease in available water in both wet and dry seasons under the Representative Concentration Pathway (RCP) 8.5 scenario. Changes in the water supply-demand ratio, due to climate change, indicated that water-related risks for future power plants would reduce in the Chambal and Damodar sub-basins, as there would be sufficient water in the future. For 19 out of 23 districts in the Chambal sub-basin, climate change will have a moderate-positive to high-positive impact on reducing the water risk for power plants by 2050. In contrast, existing and future power plants in the Yamuna and Gandak sub-basins will face increasing water risks. The proposed new thermal power installations, particularly in the Gandak sub-basin, are likely to face serious water shortages, which will adversely affect the stability of their operations. These results will stimulate and guide future research work to optimize the water-energy nexus, and will inform development and planning organizations, energy planning organizations, as well as investors, concerning the spatial distribution of water risks for future power plants so that more accurate decisions can be made on the location of future power plants. Full article