Search Results (1,487)

Drought is a recurrent hazard in the Vietnamese Mekong Delta (VMD), with major implications for agriculture, water resources, and rural livelihoods. This study assesses drought variability in Kien Giang Province, Vietnam, from 1992 to 2024 using two multiscale indicators: the Multivariate Standardized Precipitation Index (MSPI) and the Multivariate Standardized Precipitation Evapotranspiration Index (MSPEI). Principal Component Analysis (PCA) was applied to Standardized Precipitation Index (SPI)- and Precipitation Evapotranspiration Index (SPEI)-based time series spanning multiple accumulation periods (3–48 months) to derive integrated drought signals and to reduce redundancy across timescales. The results show that the first principal component (PC1) captured a high proportion of total variance across stations, indicating strong coherence in drought dynamics across the province. Both MSPI and MSPEI successfully identified major historical drought episodes, particularly the severe events of 2004–2005 and 2015–2016. However, the two indices differed in their temporal behaviour: MSPI responded more directly to precipitation deficits, whereas MSPEI showed slower post-drought recovery in recent years, suggesting greater sensitivity to evaporative demand and climatic water-balance stress. These differences indicate that evapotranspiration-sensitive indices may provide added analytical value in warming coastal environments. Overall, the combined multiscale framework offers a robust basis for drought monitoring, comparative assessment, and water-resource planning in Kien Giang and other drought-prone coastal delta settings. Full article

Climate change and population growth are intensifying water scarcity in arid regions, yet previous analyses focusing on a single driver may not fully capture the compounded effects of climatic and anthropogenic factors. This study integrates water-balance analysis, trend analysis, and correlation-based statistical analysis to examine the combined effects of hydroclimatic anomalies and socioeconomic activities on water resource dynamics in ecologically vulnerable Northwest China. Our results show that despite increasing precipitation, warming-associated increases in evapotranspiration, together with irrigation-based water use accounting for 89.8% of total consumption, have offset the potential runoff gains, suggesting that agricultural water use is a major anthropogenic contributor to regional water stress. Based on these findings and a comparative review of representative arid-region practices in Israel, Australia, and Saudi Arabia, we propose a technology-market-institution tripartite governance framework for Northwest China. This framework is intended to support more proactive adaptation in regional water management and to provide a context-specific reference for advancing SDG 6 and SDG 13 in dryland regions. Full article

Climate change poses an escalating threat to global water resources, with semi-arid regions such as Morocco being particularly vulnerable due to high climatic variability and limited adaptive capacity. In these regions, including the Tassaoute watershed in central Morocco, data scarcity and uncertainties related to data availability and quality frequently hinder robust assessments of climate change impacts. Recent advances in data science and remote sensing offer promising alternatives to overcome these limitations. This study investigates the potential of the PERSIANN-CDR satellite-derived precipitation product for assessing climate change impacts on water resources. The capability of PERSIANN-CDR to reproduce observed precipitation patterns and associated hydrological responses is evaluated through a comparative analysis using observed precipitation data. Results indicate that PERSIANN-CDR generally underestimates peak precipitation events and total rainfall amounts compared to in situ observations. Runoff is simulated using two hydrological models: GR2M (Génie Rural 2 parameters Mensuel) and the Thornthwaite water balance method, both driven by observed meteorological data and PERSIANN-CDR precipitation. The future water availability was assessed using 5 climate models, under two scenarios: RCP4.5 and RCP8.5 for the periods 2030–2060 and 2061–2090. Results show a marked temperature increase of 2–3 °C across all models, accompanied by a general decline in precipitation ranging from −30% to −60% under RCP4.5 and −20% to −80% under RCP8.5. These climatic changes translate into substantial reductions in runoff, with stronger decreases projected under the high-emission scenario and during the dry season. Monthly analyses reveal pronounced seasonal contrasts, highlighting the increased sensitivity of low-flow periods to climate forcing. Overall, runoff is projected to decrease by 50–90%, with model and data-source differences highlighting the importance of multi-model and satellite-derived approaches in data-sparse regions. These results emphasize the utility of satellite precipitation datasets in guiding climate-adaptive water management strategies. Full article

In the semi-arid Batna Belezma region of northeastern Algeria, groundwater is a vital resource for agriculture and drinking water. However, the climate leads to intense evaporation, which affects its quality. This study aims to identify the key hydrogeochemical processes that control groundwater composition in the Merouana Basin and to evaluate the predictive performance of machine learning (ML) models. A total of 30 groundwater samples were analyzed using multivariate statistical techniques, including Principal Component Analysis (PCA), and were modeled using PHREEQC to assess mineral saturation states. Additionally, ML-based regression models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB),were employed to predict groundwater chemistry. The results indicate that the dominant ion distribution follows the following trend: Ca²⁺ > Mg²⁺ > Na⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻. Alkaline earth metals (Ca²⁺ and Mg²⁺) constitute the major fraction of total dissolved cations, reflecting carbonate equilibrium and dolomite dissolution processes. In contrast, Na⁺ represents a smaller proportion of the cationic load; however, its hydro-agronomic significance is substantial due to its influence on sodium adsorption ratio (SAR) and soil permeability. The PHREEQC modeling showed that calcite and dolomite precipitation promote evaporite dissolution, while most samples remain undersaturated with respect to gypsum. The PCA results reveal high positive loadings of Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and EC, suggesting that ion exchange and seawater mixing are the primary controlling processes, with carbonate weathering playing a secondary role. To enhance predictive assessment, several supervised machine learning models were tested. Among them, the Random Forest model achieved the highest predictive performance (R² = 0.96) with low RMSE and MAE values, confirming its robustness and reliability. The results indicate that silicate weathering and mineral dissolution are the primary mechanisms governing groundwater chemistry. The integration of multivariate statistics and machine learning provides a comprehensive understanding of groundwater evolution and offers a reliable predictive framework for sustainable water resource management in semi-arid environments. Geochemical model performance showed a high global accuracy (GPI = 0.91), confirming a strong agreement between observed and simulated chemical data. However, the HH value (0.81) indicates some discrepancies, particularly for specific ions or extreme conditions. Full article

As a critical link between regional economic development and ecological security, understanding the dynamics of water retention is essential for sustainable water resource management in the Huaihe River Economic Belt. This study explores the spatio-temporal evolution and spatial explanatory factors of water retention across five temporal snapshots (2003, 2008, 2013, 2018, and 2023). Based on the InVEST model, we assessed water retention capacity at both grid and spatial development levels, thereby obtaining the retention characteristics of different land-use types and their responses to land-use transitions. Furthermore, a parameter-optimized geographical detector was employed to quantify the relative contributions of climatic-environmental and social-economic factors to the spatial variance of the modeled water retention index. Results indicate that the total water retention capacity exhibited significant interannual fluctuations, with the net capacity in 2023 being lower than the initial level in 2003. Retention values displayed obvious spatial heterogeneity, with high levels concentrated in the southwest and north and low levels distributed in the central area, closely mirroring precipitation distribution. While forest land exhibited the strongest unit water retention capacity, cropland contributed the most to the total volume (50.49%) due to its predominant areal proportion (73.92%). Notably, the conversion of forest to cropland was spatially associated with the most substantial loss in the modeled retention capacity. Soil saturated hydraulic conductivity and land-use type were identified as the dominant factors explaining the spatial variance of water retention. These findings underscore the methodological utility of coupling the InVEST model with a parameter-optimized geographical detector. For practical ecosystem management, the results suggest that spatial planning policies should strictly limit the conversion of ecological lands to agricultural use and prioritize targeted soil hydrological improvements in the central plains to secure long-term water resources. Full article

Precipitation is a fundamental element in terrestrial water circulation and ecosystem hydrological balance. The occurrence of concentrated precipitation is closely linked to vegetation growth and soil fertility rather than accumulated or averaged precipitation. Despite its importance, the characteristics of continuous precipitation and its specific effects on vegetation cover remain uncertain. In this study, we formulated a new continuous precipitation index system, including $C{P}_d$ (continuous precipitation days); $AC{P}_t$ (annual continuous precipitation times); $C{P}_a$ (continuous precipitation amount); and $FCP$ (frequency in different ranges of $AC{P}_a$). We utilized daily precipitation data from 467 meteorological stations across China, which were divided into eight vegetation type regions. We observed that the spatial distribution of continuous precipitation differed to varying degrees from accumulated precipitation. The national average of $MAC{P}_a$ for a single event was 16.7 mm, ranging from 3.8 mm in the temperate desert region to 37.1 mm in the tropical monsoon forest and rainforest region. Similarly, the national average of $MC{P}_d$ ($MMC{P}_d$) for a single event was approximately 2.3 or 9 days. At the regional level, the tropical monsoon forest and rainforest region experienced the longest $MMC{P}_d$. Furthermore, the national average of $MAC{P}_t$ occurrences for 1 year was 57.7 times, varying from 29.8 times in the temperate desert region to 77.9 times in the tropical monsoon forest and rainforest region. Vegetation responses to precipitation regimes exhibit significant regional heterogeneity across China. Our analysis reveals that $MAC{P}_t$ and $M{P}_a$ show markedly positive correlations with vegetation growth. In subtropical monsoon climate zones, particularly the Yunnan–Guizhou Plateau and Qinling Mountains, $MAC{P}_t$ demonstrates strong positive correlations (r = 0.6–1.0) with NDVI, where sustained rainfall provides stable moisture availability for vegetation. While a positive correlation between vegetation (NDVI) and mean annual consecutive precipitation is observed in some arid northern regions, in ecosystems such as the Loess Plateau (TG/TM), vegetation growth shows greater dependence on $M{P}_a$, highlighting the crucial role of total precipitation amount in water-limited ecosystems. Notably, extreme precipitation events display dual effects on vegetation dynamics. Prolonged heavy rainfall ($MMC{P}_d$/$MMC{P}_a$) exhibits significant negative impacts on NDVI (r = −1.0 to −0.6) in topographically complex regions, including the Hengduan Mountains and Yangtze River Basin (SE), likely due to induced soil erosion and waterlogging stress. Our findings underscore the importance of incorporating continuous precipitation indices to evaluate and forecast the influence of precipitation on ecosystem stability. This understanding is vital for developing informed conservation and management strategies to address current and future climate challenges. Full article

This study evaluates the impact of assimilating the Crustal Movement Observation Network of China (CMONOC) global navigation satellite system (GNSS) tropospheric products on heavy-rainfall simulation over the complex terrain of the Sichuan Basin. Using the Weather Research and Forecasting model with the WRF Data Assimilation (WRF/WRFDA) three-dimensional variational (3DVar) system, we conducted a control (CTRL) experiment and a data-assimilation (DA) experiment for a primary heavy-rainfall event during 10–12 August 2020. The DA experiment applied 6 h cycling assimilation of station-based zenith total delay (ZTD) and precipitable water vapor (PWV). Compared with CTRL, DA improved the placement of the primary rainband and the depiction of peak rainfall. On 10 August, the observed rainfall core (~40 mm) over the northwestern basin was underestimated in CTRL (~15 mm) but was strengthened in DA (~25 mm). Hourly verification at a threshold of 2 mm h⁻¹ showed a higher maximum Threat Score (TS) in DA (0.292) than in CTRL (0.250), and the largest instantaneous gain reached 0.061. For 72 h accumulated precipitation, TS was higher in DA across multiple thresholds (≥10, ≥25, ≥50, and ≥100 mm), with the most pronounced improvement for heavier rainfall categories. Diagnostic analysis indicates that GNSS assimilation introduces dynamically consistent low-level moistening and strengthened convergence at 850 hPa, together with a better-aligned vertical ascent structure during the key stage of the event. An additional heavy-rainfall event during 21–23 August 2021 was further examined as a compact robustness test, and the results showed a generally consistent improvement in precipitation distribution and TS after GNSS assimilation. Overall, the present results suggest that cycling assimilation of CMONOC GNSS ZTD/PWV products can provide effective moisture constraints and improve heavy-rainfall simulation over the Sichuan Basin in the examined cases. Full article

Rills on the snow surface are a common phenomenon frequently reported by field observers. The interpretation of these field observations and an understanding of the underlying physical processes are important for forecasting routines and models used in avalanche warning as well as in hydrological and meteorological forecasting. Rills on the snow surface are typically associated with rain-on-snow (ROS) events and are often interpreted as an indicator of the approximate snowfall level. However, recent field observations of rills on the snow surface without significant liquid precipitation in the Austrian Alps challenge the assumption that ROS events are the sole cause of rill formation. In this study, we quantitatively compare liquid water input into the snowpack from melt processes to the amount of rain during a documented rill formation event. Using a combination of field observations, energy balance calculations, and model simulations, our results strongly suggest that, in this case study, meltwater was the predominant source of liquid water input and snowmelt the main driver of rill formation. Our results indicate that more than 97% of the total liquid water input originated from melt, while rain contributed only roughly 2%. These findings highlight the need for a revised interpretation of rill formation, suggesting that meltwater-driven rills may be more significant than previously assumed. Full article

The Juncal Norte Glacier (33°00′ S, 70°06′ W) is in the Dry Andes of central Chile within the Juncal Basin, a headwater watershed of the Aconcagua River, a semi-arid region experiencing an ongoing megadrought since 2010 and a 37% reduction in streamflow relative to pre-1990 baselines. This study provides the first glacier-specific annual melt and runoff estimate for Juncal Norte during mature megadrought conditions. Mass balance was estimated using a temperature index (positive degree day, PDD) model calibrated with automatic weather station (AWS) air temperature data and glacier hypsometry, assuming limited snow accumulation given that 2018–2019 precipitation and snow water equivalent (SWE) were extremely low relative to the long-term mean. Basin runoff was evaluated using a closure method comparing proglacial sub-basin-integrated discharge with modeled glacier melt volumes. Modeled glacier melt for 2018–2019 was equivalent to approximately 30% of observed annual discharge at the proglacial sub-basin, a disproportionate contribution given the glacier covers only 2.7% of the total basin area. The lower ablation zone (2900–4000 m), comprising 30% of glacier area, produced 90% of total melt volume. A + 1 °C temperature perturbation increased glacier-wide melt by 21.4%, confirming high climatic sensitivity. These results underscore the glacier’s critical but increasingly vulnerable buffering role for downstream water availability in the Dry Andes. Full article

Soil and water contamination with high nutrient concentrations from swine farms poses a risk to human and animal health. This study investigated the effects of biochar derived from young aromatic coconut husk (CH), coconut shell (CS), and their mixture (CHCS) on nutrient retention in biochar-amended soil columns for variable synthetic swine wastewater (SW) loading based on water use for piglets and fattening stalls. A 0.9 L leaching test column contained 3 g of each biochar type mixed with 300 g of soil. It was loaded daily with synthetic SW for 42 days at loading rates of 30 mL/day (piglet SW) and 60 mL/day (fattening SW). CH-amended soil was then selected to investigate the effect of rainfall rates at 0 (R0), 25 (R25), 70 (R70) and 140 (R140) mL/4 days on soil nutrient retention. Leachate was collected every 7 days to analyze nitrogen and phosphorus concentrations. The results showed that CH-amended soil had the highest retention of total nitrogen (TN) and phosphate among all treatments. For piglet SW, TN retention in CH-amended soil was 1.4–1.6 times higher than with CS and CHCS treatments, probably due to enhanced ammonium retention on exchangeable sites associated with the high cation exchange capacity of CH. High phosphate retention in CH-amended soil was linked to Ca²⁺ release from CH, facilitating phosphate precipitation. Moreover, CH-amended soil at R25 showed the highest ammonium retention but inhibited seed germination. Overall, CH-amended soil effectively retained nutrients and was suitable as a seedling growth medium, except under the R25 rainfall condition. Full article

The Beishan area of Gansu, China, is the primary candidate site for the geological disposal of China’s high-level radioactive waste (HLW). To assess the long-term safety of this repository, the evolutionary patterns of groundwater and the primary migration vector of radionuclides must be understood. Through experiments and hydrogeochemical simulations of snowmelt samples from the Qilian Mountains and deep rock samples from Beishan, we reveal different hydrochemical compositions and types of the snowmelt and deep groundwater. The results show that the hydrochemical type of Qilian Mountain snowmelt is SO₄–Na·Ca, whereas that of the deep groundwater in the Beishan is Cl·SO₄–Na, indicating substantial differences in the hydrochemical characteristics of the two samples. The water–rock interactions between snowmelt and granite are dominated by the dissolution of silicate minerals and the precipitation of carbonate minerals, accompanied by cation exchange and adsorption. After the interaction, the hydrochemical type of the snowmelt becomes SO₄–Na, with total dissolved solids (TDS) consistently maintained at ~500 mg/L, which is distinct from the TDS range of 1540–2045 mg/L observed for the deep groundwater in the Beishan. Under the experimental and simulation conditions set in this study, the water–rock interactions between Qilian Mountain snowmelt and Beishan granite cannot reproduce the hydrochemical characteristics of the deep groundwater in the Beishan. This study provides theoretical support for the hydrogeological safety assessment of HLW geological repositories. Full article

Data compatibility remains a major challenge in metabolomics, as commonly used measures of biological material—such as sample weight or cell count—are often poorly reproducible. Here, we systematically evaluated practical normalization strategies for GC × GC-MS-based metabolomic profiling of two widely used model cell lines: human hepatoblastoma (HepG2) and mesenchymal stromal cells (MSCs). We compared orthogonal biomass estimates, including total protein and double-stranded DNA quantified either directly in aliquots of the cell suspension lysate aliquots or in the post-extraction cell precipitate, alongside normalization based on extracted ion current (XIC). We also assessed three widely used extraction mixtures—methanol/chloroform/water (7:2:1); methanol/water (8:2); acetonitrile/isopropanol/water (3:3:2)—for metabolome coverage and normalization robustness. Under realistic biological variability, signal-to-biomass dependencies were moderate. In contrast, under strictly controlled conditions, DNA- and protein-based normalization yielded near-linear relationships with metabolite abundances (R² > 0.90), demonstrating that biological variability is the dominant source of dispersion rather than technical factors. Methanol/chloroform/water system provided the broadest metabolome coverage and strongest correlation with injected biomass. Based on these findings, we recommend normalization to total precipitate protein or DNA using the methanol/chloroform/water extraction protocol, with XIC as a complementary quality control metric. Full article

The homogeneous nature of L-proline organocatalysts restricts their application in aldol condensation due to poor recyclability and stability. Herein, L-proline was heterogenized by ionic intercalation into Mg–Al layered double hydroxides (LDHs), yielding a series of proline-intercalated catalysts with tunable layer structures. Co-precipitation and memory-effect reconstruction strategies were employed to regulate interlayer spacing and proline loading. The resulting catalysts exhibited efficient performance in the aldol condensation of furfural with ketones under mild conditions. The reconstructed catalyst re-Mg₄Al₁P achieved a furfural conversion of 88.67% and a total product yield of 85.54% at room temperature, with product selectivity exceeding 95%. Structural characterizations confirmed that proline was stabilized within the LDH interlayers via R–COO—Mg electrostatic interaction while preserving the secondary amine active site. Mechanistic analysis indicated that the reaction proceeded through enamine- or enol-mediated pathways depending on water content, while the layered LDH framework imposed geometric confinement that suppressed side reactions. Catalyst deactivation in aqueous systems was mainly attributed to proline leaching rather than structural collapse. Full article

Magnesium phosphate cement (MPC) is a type of rapid-hardening inorganic cementitious material, which has important application value in rapid road repair, solidification of hazardous and radioactive waste, and other fields. However, it suffers from excessively fast setting and hardening and a short working time retention, which severely restrict its engineering application. Therefore, the development of high-efficiency set retarders is of great significance for optimizing MPC performance, enhancing its construction workability, and expanding its application scope. In this study, the effect of sodium polyacrylate (PAAS) on the setting and hardening of magnesium potassium phosphate cement (MKPC) was investigated by testing the setting time and fluidity at a low water-to-solid ratio (W/S = 0.18). Through pH and electrical conductivity measurements, combined with XRD, TG/DTG, and FTIR characterizations, we elucidated the retarding mechanism of PAAS on MKPC using a high water-to-solid ratio (W/S = 10). The results indicate that the setting time of MKPC is positively correlated with the PAAS dosage, whereas the fluidity and compressive strength exhibited a negative correlation with the PAAS dosage. Additionally, PAAS reduces the total heat release and the heat release rate of MKPC. The addition of PAAS increased the pH of the suspension, thereby reducing the solubility of MgO, but did not inhibit the dissolution of KH₂PO₄. The carboxylate groups in PAAS chemically reacted with Mg²⁺ on the surface of MgO to form magnesium carboxylate complexes (Mg-PAA), which remained as precipitates in the MKPC suspension system, thus reducing the amount of available Mg²⁺ participating in the hydration reaction. Furthermore, PAAS had no effect on the final precipitate composition at the end of hydration, which was composed of MgKPO₄·6H₂O and Mg₃(PO₄)₂·22H₂O in all cases. Full article

Soil moisture (SM) in the mountains is critical for agropastoral productivity, and it is subject to both large-scale climate gradients and fine-scale effects of terrain, vegetation and soil. However, how the climate, topography, soil and vegetation factors impact surface SM spatiotemporal dynamics remains elusive in mountainous terrains, due to their complex interactions. Based on multi-source datasets, this study employs the structural equation model to investigate the impact pathways of climate and vegetation factors on annual surface SM dynamics from the year 2000 to 2022 in the Tianshan Mountains of China (TS). We also utilize the factor and interaction detectors of Geographical Detector to explore the individual and interactive effects of climate, topography, soil and vegetation factors on the spatial pattern of the annual surface SM. Moreover, their integrated impacts on the spatiotemporal dynamics of annual surface SM were investigated based on the explanatory power from the factor detector and total effects from structural equation modeling. The results showed that the multi-year average surface SM was 0.21 m³·m⁻³ for the whole region, with greater values in areas with dense vegetation and high elevation. Annual surface SM exhibited significant increasing trends across different land cover classifications and elevation zones, which was directly influenced by vegetation greenness enhancement. Precipitation (PRE) and relative humidity (RH) also significantly influenced the temporal variations in surface SM through their indirect effect on vegetation greenness, while these indirect effects were much lower than the direct effect of vegetation greenness. RH, PRE and surface net solar radiation (SSR) showed strong individual and interactive effects on the spatial distribution of surface SM, particularly the interactive effects of RH and PRE with wind speed (WS). Surface SM was highly sensitive to RH and PRE in the central TS. Overall, vegetation greenness, PRE and RH were the main drivers of surface SM variations across both temporal and spatial scales, while SSR, total evaporation and WS primarily shaped its spatial distribution. These insights enhance our understanding of land–atmosphere interactions in mountainous areas and provide scientific references for sustainable agropastoral water resource management under global warming. Full article