Search Results (405)

The Athabasca River flows through the Lower Athabasca Region (LAR) in Alberta, Canada, which is characterized by variable inter-annual weather, long winters and short summers. LAR is important for the extraction of energy resources and industrial activities that lead to environmental concerns, including river pollution and exploitation. This study attempts to forecast the Athabasca River at Fort McMurray and understand the suitability of HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) in cold weather regions, characterized by poorly gauged streams. Daily temperature and precipitation records (1971–2014) were employed in two calibration–validation schemes: (1) a temporally dependent partition (1971–2000 for calibration; 2001–2014 for validation) and (2) a temporally independent partition (alternating years assigned to calibration and validation). The temporally independent approach achieved superior performance, with a Nash–Sutcliffe efficiency of 0.88, outperforming previously developed regional models. HEC-HMS successfully reproduced hydrologic dynamics and peak discharge events under conditions of sparse hydroclimatic data and limited computational inputs, underscoring its robustness for operational forecasting in data-scarce, cold-climate catchments. However, long-term projections may be subject to uncertainty due to the exclusion of anticipated changes in land use and climate forcing. These results substantiate the applicability of HEC-HMS as a cost-effective and reliable tool for hydrological modeling and flow forecasting in support of water resource management, particularly in regions subject to industrial pressures and associated environmental impacts. Full article

Climatic parameters influence the δ¹³C value of sugar in grape must. With the aim of investigating this dependence, grape must samples were collected from two viticultural Italian areas (Oltrepò Pavese, Lombardia region and Illasi–Mezzane, Veneto region), which share similar soil mineralogical compositions. Water uptake by the plant is the primary factor affecting the δ¹³C values of sugar: the greater the water availability, the lower the δ¹³C value. This is supported by a correlation between the δ¹³C values and the climatic water balance (BIC_c), which is defined as the difference between daily rainfall and crop evapotranspiration. Pre-harvest atmosphere was also sampled at both sites to determine its concentration and δ¹³C value. Using the Farquhar model, enrichment factors and ε_CO2-sugar were calculated. A moderate correlation was found between cumulative rainfall and the associated values of the enrichment factor: approximately 60% of the variation in sugar δ¹³C can be attributed to water uptake and to the δ¹³C values of atmospheric CO₂. Rainfall alone showed an even stronger correlation with ε_CO2-sugar, suggesting that water availability is the dominant factor influencing the sugar δ¹³C. Full article

The hydrology of the transboundary region encompassing the western Red River Basin headwaters, such as Devils Lake Basin (DLB) in North America, is complex and highly sensitive to climate variability, impacting water resources, agriculture, and flood risk. Understanding hydrological shifts in this region is critical, particularly given recent hydroclimatic changes. This study aimed to simulate and analyze key hydrological processes and their evolution from 1981 to 2020 using an integrated modeling approach. We employed the NASA Land Information System (LIS) framework configured with the Noah-MP land surface model and the HyMAP routing model, driven by a combination of reanalysis and observational datasets. Simulations revealed a significant increase in precipitation inputs and consequential positive net water storage trends post-1990, indicating increased water retention within the system. Snow dynamics showed high interannual variability and decadal shifts in average Snow Water Equivalent (SWE). Simulated streamflow exhibited corresponding multi-decadal trends, including increasing flows within a major DLB headwater basin (Mauvais Coulee Basin) during the period of Devils Lake expansion (mid-1990s to ~2011). Furthermore, analysis of decadal average seasonal hydrographs indicated significant shifts post-2000, characterized by earlier and often higher spring peaks and increased baseflows compared to previous decades. While the model captured these trends, validation against observed streamflow highlighted significant challenges in accurately simulating peak flow magnitudes (Nash–Sutcliffe Efficiency = 0.33 at Mauvais Coulee River near Cando). Overall, the results depict a non-stationary hydrological system responding dynamically to hydroclimatic forcing over the past four decades. While the integrated modeling approach provided valuable insights into these changes and their potential drivers, the findings also underscore the need for targeted model improvements, particularly concerning the representation of peak runoff generation processes, to enhance predictive capabilities for water resource management in this vital region. Full article

This study investigates the paradoxical water security challenges in western Chilean Patagonia, where the regional abundance of water resources masks significant vulnerabilities of drinking water systems. We conducted an integrated socio-hydrological analysis over rural (APR) and urban (APU) drinking water systems, which provide water to approximately 846,000 people. We georeferenced 343 drinking water intake points, from which 51.6% are sourced from groundwater, and 45.8% from surface waters (2.6% other sources). An eco-hydrological characterization was conducted on the 147 watersheds supplying water to the surface intake points. Watersheds were characterized by their main hydrological, morphological, and land cover features, as well as by their level of anthropization (AI) and water stress index (WSI). Social dimensions were captured through structured interviews with 117 APR directorate leaders regarding their perceptions of infrastructure, governance, climate change, and local water management challenges. Our findings suggest that water availability in Patagonia creates a mirage of water security. AI and WSI indicate high variability in the status of water sources, with 25% of watersheds showing high levels of anthropization and 33% with medium to high levels of water stress, making it relevant to explore the results through a combination of hydroclimatic, longitudinal, and latitudinal gradients. A novel analysis linking WSI and AI to governance perceptions was conducted, finding significant inverse correlations between WSI and both technical capacity and users’ participation. Despite the region’s evident abundance of water resources, rural communities consistently express concerns regarding supply sustainability, infrastructure deficiencies, insufficient technical support, and climate change risks to current and future water availability, all of which constrain water security in Chilean Patagonia. Full article

The Yangtze River Basin is vital to China’s water security and flood management yet lacks a basin-wide quantitative assessment of long-term hydroclimatic changes. This study uses the high-resolution CMFD 2.0 dataset and the VIC model to evaluate spatiotemporal trends in precipitation and natural streamflow from 1951 to 2020. The results show a significant increase in annual precipitation in the upper basin (1.10 mm yr⁻¹, p < 0.05), particularly during the wet season, with spatially concentrated increases along the eastern Tibetan Plateau. The VIC model performed reliably across major stations, with NSE > 0.9 and PBIAS within ±10% during calibration. Natural streamflow trends are spatially heterogeneous: upper mainstream stations (e.g., Shigu, Panzhihua, Zhutuo) exhibit significant increases (6.25–14.58 m³/s per year), while lower stations remain stable or decline. Seasonally, wet-season streamflow increased in the upper basin, whereas dry-season streamflow decreased in the middle and lower reaches. At Yichang, natural simulations reveal growing seasonal extremes, with rising wet-season and declining dry-season flows (−19.06 m³/s yr⁻¹). Human interventions have partially offset these extremes. Since 1990, observed peak discharge at Yichang during the wet season has decreased by 10.04% compared to natural streamflow, while the dry-season minimum discharge has increased by 27.63%. This shows that large reservoirs help reduce flood peaks and increase low flows. These findings highlight the intensifying impacts of climate variability and human regulation on hydrological processes and provide a scientific basis for adaptive water resource management in large river basins. Full article

This research presents a comprehensive spatiotemporal assessment of the effects of climate change and anthropogenic pressures on the water surface area and quality of the Sidi Salem Dam, the largest reservoir in Northern Tunisia. Located within a sub-humid to Mediterranean humid bioclimatic zone, the dam plays a vital role in regional water supply, irrigation, and flood control. Utilizing a 40-year dataset (1985–2025), this study integrates multi-temporal satellite imagery and geospatial analysis using Geographic Information System (GIS) and remote sensing (RS) techniques. The temporal variability of the dam’s surface water extent was monitored through indices such as the Normalized Difference Water Index (NDWI). The analysis was further supported by climate data, including records of precipitation, temperature, and evapotranspiration, to assess correlations with observed hydrological changes. The findings revealed a significant reduction in the dam’s surface area, from approximately 37.8 km² in 1985 to 19.8 km² in 2025, indicating a net loss of 18 km² (47.6%). The Mann–Kendall trend test confirmed a significant long-term increase in annual precipitation, while annual temperature showed no significant trend. Nevertheless, recent observations indicate a decline in precipitation during the most recent period. Furthermore, Pearson correlation analysis revealed a significant negative relationship between precipitation and temperature, suggesting that wet years are generally associated with cooler conditions, whereas dry years coincide with warmer conditions. This hydroclimatic interplay underscores the complex dynamics driving reservoir fluctuations. Simultaneously, land use changes in the catchment area, particularly the expansion of agriculture, urban development, and deforestation have led to increased surface runoff and soil erosion, intensifying sediment deposition in the reservoir. This has progressively reduced the dam’s storage capacity, further diminishing its water storage efficiency. This study also investigates the degradation of water quality associated with declining water levels and climatic stress. Indicators such as turbidity and salinity were evaluated, showing clear signs of deterioration resulting from both natural and human-induced processes. Increased salinity and pollutant concentrations are primarily linked to reduced dilution capacity, intensified evaporation, and agrochemical runoff containing fertilizers and other contaminants. Full article

Climate change substantially affects water resources and agriculture, highlighting the critical importance of assessing land suitability for surface irrigation. This study was initiated with the objective of assessing the present and future land suitability for surface irrigation in the Gardulla Zone of Southern Ethiopia, utilizing meteorological, topography, soil, land cover, and proximity data. The analytic hierarchy process and weighted overlay analysis were employed to assign factor weights, while future climate projections were downscaled via a statistical downscaling model (SDSM4.2) under the shared socio-economic pathways (i.e., SSP2-4.5 and SSP5-8.5) scenarios. Irrigation suitability mapping was performed via inverse distance-weighted interpolation. The results revealed that 8% of the area is highly suitable, 54.3% is moderately suitable, 30% is marginally suitable, and 2.3% is unsuitable under current climate conditions. In the future periods, under both SSP scenarios, highly suitable land increases (up to 9.7% and 10.3% by 2050s and 10.8% and 13.5% by the 2080s under SSP2-4.5 and SSP5-8.5, respectively), whereas unsuitable land decreases (down to 0.6% by 2080s under SSP5.8.5). In terms of area, highly to moderately suitable land expanded by 1357.6–6867.7 ha, depending on the scenario and timeframe. The study concludes that climate change is expected to affect the suitability of land for surface irrigation potential in the study area and similar hydroclimatic settings, highlighting the need for forward-looking policies and adaptation options. Therefore, it is recommended to promote climate-smart irrigation systems by integrating site-specific suitability mapping into regional land-use planning and prioritizing investment in small-scale, community-managed surface irrigation schemes that reduce water losses and ensure long-term agricultural sustainability. Full article

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1 > 0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article

Evapotranspiration (ET) variation is typically influenced by climatic factors, which are considered the primary drivers of agricultural water requirements. Any changes in ET rates directly affect crop water demands. In this study, temporal trends and magnitudes of key climatic variables, and their impacts on reference evapotranspiration (ETo) during 1981–2020, were evaluated across 36 districts of Punjab, Pakistan. Positive serial correlations, ranging from 0.29 to 0.48, were identified and removed using the pre-whitening technique. Increasing trends in maximum temperature (Tmax) and wind speed (WS) across Punjab and its subregions were observed, while relative humidity (RH) exhibited both increasing and decreasing trends. No significant trends were detected for the minimum temperature (Tmin). On a monthly scale, in the Southern Punjab (SP) region, Sen’s slope estimated an increase in ETo, ranging from 0.239 mm/year in November to 0.636 mm/year in May, at a significance level of α = 0.05 (5%). At the provincial scale, significant upward trends in ETo were observed for the annual, Kharif, and autumn seasons, with Z-values of 2.04, 2.16, and 3.13, respectively, at α = 0.05 and 0.01. It was determined that, on an annual scale in Punjab, ETo sensitivity to climatic parameters followed the following order: Tmax > wind speed (WS) > Tmin > RH. The best-fitted models for Tmax, Tmin, WS, and RH were Gaussian, exponential, and spherical. ETo was found to increase spatially from North to South Punjab, with an approximate rise of 70–80 mm/decade. The results provide a scientific basis for understanding hydroclimatic drivers of ETo in semi-arid regions and contribute to improving climate impact assessments on agricultural water use. The observed ETo increases, particularly in South Punjab and lower Central Punjab, highlight the need for region-specific irrigation scheduling and water allocation. These findings can guide cropping calendars, improve irrigation efficiency, and increase canal water supplies to high-ETo areas, supporting adaptive strategies against climate variability in Punjab. Full article

Mid–long-term streamflow prediction (MLSP) plays a critical role in water resource planning amid growing hydroclimatic and anthropogenic uncertainties. Although AI-based models have demonstrated strong performance in MLSP, their capacity to quantify predictive uncertainty remains limited. To address this challenge, a DeepAR-based probabilistic modeling framework is developed, enabling direct estimation of streamflow distribution parameters and flexible selection of output distributions. The framework is applied to two case studies with distinct hydrological characteristics, where combinations of recurrent model structures (GRU and LSTM) and output distributions (Normal, Student’s t, and Gamma) are systematically evaluated. The results indicate that the choice of output distribution is the most critical factor for predictive performance. The Gamma distribution consistently outperformed those using Normal and Student’s t distributions, due to its ability to better capture the skewed, non-negative nature of streamflow data. Notably, the magnitude of performance gain from using the Gamma distribution is itself region-dependent, proving more significant in the basin with higher streamflow skewness. For instance, in the more skewed Upper Wudongde Reservoir area, the model using LSTM structure and Gamma distribution reduces RMSE by over 27% compared to its Normal-distribution counterpart (from 1407.77 m³/s to 1016.54 m³/s). Furthermore, the Gamma-based models yield superior probabilistic forecasts, achieving not only lower CRPS values but also a more effective balance between high reliability (PICP) and forecast sharpness (MPIW). In contrast, the relative performance between GRU and LSTM architectures was found to be less significant and inconsistent across the different basins. These findings highlight that the DeepAR-based framework delivers consistent enhancement in forecasting accuracy by prioritizing the selection of a physically plausible output distribution, thereby providing stronger and more reliable support for practical applications. Full article

The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin. Full article

The Loess Plateau, possessing the world’s most extensive loess deposits, is highly vulnerable to accelerated soil erosion and vegetation loss triggered by extreme hourly rainfall (EHR) events due to the inherently erodible nature of its porous, weakly cemented sediment structure. EHR exacerbates soil erosion, induces flash flooding, compromises power infrastructure, and jeopardizes agricultural productivity. Through analysis of 43 years (1981–2023) of station observational data and ERA5 reanalysis, we present the first comprehensive assessment of EHR characteristics across the plateau. Results reveal pronounced spatial heterogeneity, with southeastern regions exhibiting higher EHR intensity thresholds and frequency compared to northwestern areas. EHR frequency correlates positively with elevation, while intensity decreases with altitude, demonstrating orographic modulation. Synoptic-scale background environment of EHR events is characterized by upper-level divergence, mid-tropospheric warm advection, and lower-tropospheric convergence, all of which are linked to summer monsoon systems. Temporally, EHR peaks in July during the East Asian summer monsoon and exhibits a bimodal diurnal cycle (0700/1700 LST). Long-term trends reveal a significant overall increase in the frequency of EHR events (~0.82 events a⁻¹). While an overall increase in EHR intensity is also observed, it fails to achieve statistical significance due to opposing regional signals. Collectively, these trends elevate the risks of slope failures and debris flows. Our findings highlight three priority interventions: (i) implementation of elevation-adapted early warning systems, (ii) targeted agricultural soil conservation practices, and (iii) climate-resilient infrastructure design for high-risk valleys—all essential for safeguarding this ecologically sensitive region against intensifying hydroclimatic extremes. Full article

Climate-driven hydrological extremes and anthropogenic interventions are increasingly altering streamflow regimes worldwide. While prior studies have explored climate or regulation effects separately, few have integrated multiple teleconnection indices and reservoir chronologies within a cross-basin comparative framework. This study addresses this gap by assessing long-term streamflow nonstationarity and its drivers at two key stations—Khabarovsk on the Amur River and Datong on the Yangtze River—representing distinct hydroclimatic settings. We utilized monthly discharge records, meteorological data, and large-scale climate indices to apply trend analysis, wavelet transform, percentile-based extreme diagnostics, lagged random forest regression, and slope-based attribution. The results show that Khabarovsk experienced an increase in winter baseflow from 513 to 1335 m³/s and a notable reduction in seasonal discharge contrast, primarily driven by temperature and cold-region reservoir regulation. In contrast, Datong displayed increased discharge extremes, with flood discharges increasing by +71.9 m³/s/year, equivalent to approximately 0.12% of the mean flood discharge annually, and low discharges by +24.2 m³/s/year in recent decades, shaped by both climate variability and large-scale hydropower infrastructure. Random forest models identified temperature and precipitation as short-term drivers, with ENSO-related indices showing lagged impacts on streamflow variability. Attribution analysis indicated that Khabarovsk is primarily shaped by cold-region reservoir operations in conjunction with temperature-driven snowmelt dynamics, while Datong reflects a combined influence of both climate variability and regulation. These insights may provide guidance for climate-responsive reservoir scheduling and basin-specific regulation strategies, supporting the development of integrated frameworks for adaptive water management under climate change. Full article

This study focuses on developing a climate-flood model to investigate and interpret the relationship and impact of climate on runoff/flooding at a sub-regional scale using multiple linear regression (MLR) with 30 years of hydro-climatic data for the Cross River Basin, Nigeria. Data were obtained from Nigerian Meteorological Agency (NIMET) for the following climatic parameters: annual average rainfall, maximum and minimum temperatures, humidity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure. These hydro-meteorological data were analysed and used as parameters input to the climate-flood model. Results from multiple regression analyses were used to develop climate-flood models for all the gauge stations in the basin. The findings suggest that at 95% confidence, the climate-flood model was effective in forecasting the annual runoff at all the stations. The findings also identified the climatic parameters that were responsible for 100% of the runoff variability in Calabar (R² = 1.000), 100% the runoff in Uyo (R² = 1.000), 98.8% of the runoff in Ogoja (R² = 0.988), and 99.9% of the runoff in Eket (R² = 0.999). Based on the model, rainfall depth is the only climate parameter that significantly predicts runoff at 95% confidence intervals in Calabar, while in Ogoja, rainfall depth, temperature, and evaporation significantly predict runoff. In Eket, rainfall depth, relative humidity, solar radiation, and soil temperatures are significant predictors of runoff. The model also reveals that rainfall depth and evaporation are significant predictors of runoff in Uyo. The outcome of the study suggests that climate change has impacted runoff and flooding within the Cross River Basin. Full article

Stable oxygen isotopes in tree rings (δ¹⁸O) serve as important proxies for climate change and offer unique advantages for climate reconstruction in arid and semi-arid regions. We established an annual δ¹⁸O chronology spanning 1964–2023 using Juniperus excelsa tree-ring samples collected from the Alborz Mountains in Iran. We analyzed relationships between δ¹⁸O and key climate variables: precipitation, temperature, Palmer Drought Severity Index (PDSI), vapor pressure (VP), and potential evapotranspiration (PET). Correlation analysis reveals that tree-ring δ¹⁸O is highly sensitive to hydroclimatic variations. Tree-ring cellulose δ¹⁸O shows significant negative correlations with annual total precipitation and spring PDSI, and significant positive correlations with spring temperature (particularly maximum temperature), April VP, and spring PET. The strongest correlation occurs with spring PET. These results indicate that δ¹⁸O responds strongly to the balance between springtime moisture supply (precipitation and soil moisture) and atmospheric evaporative demand (temperature, VP, and PET), reflecting an integrated signal of both regional moisture availability and energy input. The pronounced response of δ¹⁸O to spring evaporative conditions highlights its potential for capturing high-resolution changes in spring climatic conditions. Our δ¹⁸O series remained stable from the 1960s to the 1990s, but showed greater interannual variability after 2000, likely linked to regional warming and climate instability. A comparison with the δ¹⁸O variations from the eastern Alborz Mountains indicates that, despite some differences in magnitude, δ¹⁸O records from the western and eastern Alborz Mountains show broadly similar variability patterns. On a larger climatic scale, δ¹⁸O correlates significantly and positively with the Niño 3.4 index but shows no significant correlation with the Arctic Oscillation (AO) or the North Atlantic Oscillation (NAO). This suggests that ENSO-driven interannual variability in the tropical Pacific plays a key role in regulating regional hydroclimatic processes. This study confirms the strong potential of tree-ring oxygen isotopes from the Alborz Mountains for reconstructing hydroclimatic conditions and high-frequency climate variability. Full article