Search Results (434)

This study investigates the transformation of river runoff and its sensitivity to changes in large-scale atmospheric circulation in the Zhaiyk–Caspian water management basin during the period of 1951–2023. The analysis is based on hydrometeorological observations data, the Vangengeim–Girs classification of macro-circulation patterns, and the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices. Correlation analysis, the Mann–Kendall test, Sen’s slope estimator, and the Pettitt test were applied to identify trends, structural shifts, and the spatial coherence of hydroclimatic changes. The results show that interannual variability in river runoff is characterized by a degree of spatial coherence, with correlation coefficients between annual streamflow records at most gauging stations reaching up to 0.95. It is demonstrated that the most pronounced changes in the hydrological regime occur during the cold season and are expressed in a statistically significant increase in winter runoff, while no significant long-term trend in annual runoff is observed. Structural shifts in winter runoff are predominantly associated with the late 1990s, whereas changes in the temperature regime are detected earlier and exhibit spatial coherence. The findings indicate that the contemporary transformation of river runoff is primarily driven by rising air temperatures and the associated intra-annual redistribution of flow. 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

This study reconstructs annual streamflow variability in the Yenice Stream Basin (northwestern Türkiye) for the period 1809–2020 using tree-ring data, substantially extending the short instrumental record (1979–2020). Three moisture-sensitive conifer chronologies were integrated using principal component analysis (PCA), and the first two principal components were employed as predictors in a multiple linear regression model calibrated against observed streamflow. The model explains a significant proportion of interannual variability (R² = 0.39; adjusted R² = 0.36; p < 0.001). Temporal stability was assessed using a 30-year moving-window correlation analysis, which reveals consistently positive and statistically significant relationships across all subperiods, indicating a stable and persistent calibration relationship through time. Years exceeding ±1 standard deviation account for approximately 24% of the record, while extreme events (±2 standard deviations) represent about 5%. The reconstruction identified several extreme events, including severe drought years (e.g., 1840, 1887, and 1907) and extremely wet years (e.g., 1896 and 1936). Among these, 1887 stands out as one of the most severe drought years, while the period 1927–1928 represents a persistent low-flow episode. The reconstruction provides a long-term perspective on streamflow variability and contributes baseline information for regional water resource planning and hydroclimatic risk assessment. Full article

The snow module of the HYDROTEL (version 2.8.x-078-00-4.1.15.5551) hydrological model was modified to incorporate a multilayer structure composed of ice and air layers within the snowpack, as well as to account for the impact of freezing rain on snow cover. This study examines whether this enhanced physical representation of snow processes improves the accuracy of streamflow simulations. The analysis was conducted across ten watersheds in Quebec, Canada. The multilayer snow model consistently improved low-flow simulations during both calibration and validation periods and enhanced the representation of the falling limb during the calibration period. However, the monolayer snow model performs slightly better during the rising limb of the freshet season for the calibration phase. In addition, the multilayer configuration reduced the bias of the cumulative freshet volumes and annual maximum freshet discharge. Overall, the multilayer snow model achieved comparable performance to the monolayer model for high-flow simulations while outperforming it for low-flow conditions, leading to a more accurate representation of freshet volumes and falling limb dynamics. Full article

Suspended sediment plays a critical role in river ecosystem health, nutrient transport, and water quality, while also affecting navigation infrastructure and reservoir sedimentation in regulated rivers. A sound understanding of sediment dynamics in complex river systems consisting of natural lakes and engineered navigation structures remains a critical challenge for river management and water quality assessment. This study investigates the longitudinal patterns of suspended sediment concentration (SSC) along a ~500-km reach of the Upper Mississippi River containing Lake Pepin and multiple lock-and-dam structures. In this study, we analyze remotely sensed SSC estimates from the RivSED database (2001–2019). The SSC datasets were then integrated with in situ streamflow measurements and potential soil erosion to characterize sediment supply and transport dynamics and relate with upstream contributing watershed’s attributes. Results reveal distinct sediment behavior patterns: (1) Lake Pepin functions as a significant sediment trap, creating a clear discontinuity in SSC with mean concentrations decreasing from ~25 mg/L upstream to ~13 mg/L within the lake; (2) longitudinal SSC profiles show re-establishment patterns downstream of the lake, reaching ~23 mg/L approximately 100 km below the outlet; (3) strong positive correlation (r = 0.80, R² = 0.64, p < 0.001) exists between watershed sediment export and river-reach-scale sediment fluxes. Temporal analysis across these upstream monitoring stations shows sediment export rates ranging from 10,000 to 200,000 tons/year, with notable inter-annual variability driven by discharge patterns. This research demonstrates the utility of combining a spectrum of datasets for exploring sediment dynamics in complex riverine systems. Though the current study is a case study, the study results provide crucial insights for navigation management, ecosystem health assessment, and watershed management strategies in similar settings. Full article

Analysis of the hydroclimatic variations in complex topographic and climatic regimes is important in determining the freshwater availability and its response. Although several previous studies have assessed the changing patterns of hydroclimatic variables in South Asian River basins, most of them have considered traditional statistical methods, which may inadequately reflect potential non-linear hydroclimatic trends. This study determines long-term variations in precipitation, temperature, and streamflow in the Soan River Basin of Pakistan, using three decades of in situ records (1991–2020). A non-parametric (Mann–Kendall) trend test along with an artificial neural network (ANN) approach was used to check the linear and non-linear trends. The results exhibited that the basin was getting warmer at a consistent rate, although the amount of precipitation varied significantly with location and season. The annual average amount of precipitation over the entire basin was decreasing at the rate of −7.33 mm/year. As compared to the westerly season, the trend of monsoon precipitation was less certain. Changes in streamflow patterns generally demonstrated the consequences of changing precipitation and rising temperature patterns. The annual average streamflow was decreasing at the rate of −0.47 (−1.30) m³/year, as per the results of MK (ANN). A moderate positive correlation between precipitation and streamflow indicates that precipitation mainly governed the flows in the basin. The results of the MK test and the machine-learning approach demonstrated the similar decreasing tendencies of hydroclimatic variables. However, the ANN approach more precisely demonstrates the non-linear behavior of hydroclimatic variables. It was concluded that the streamflow patterns were considerably responsive to the warming of the Soan River Basin, as well as to the changing behavior of precipitation. These findings emphasized the significance of integrating statistical and machine-learning approaches to enhance the comprehension of hydroclimatic trends. Results of this research could be applicable in sustainable management and planning of the water resources within the basin. Full article

Populations in areas with limited hydrological data face ongoing challenges related to water supply and management, with climate change increasing the risks of floods and droughts. New remote sensing and modeling tools can improve land and water management in these regions, especially when combined with limited ground measurements and local knowledge of extreme events. This study examined hydrological extremes and land cover change impacts in the Grande Rivière du Nord watershed, Haiti, using satellite and model-based data. Precipitation extremes were obtained from the Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG; 2000–2025), and streamflow data were sourced from the Group on Earth Observation Global Water Sustainability (GEOGLOWS) system and bias-corrected with a small historical hydrologic database. Annual maximum series were created and fitted with Gumbel, Lognormal, and Generalized Extreme Value (GEV) distributions using the L-moment method. Goodness-of-fit tests identified the best models, and precipitation amounts for return periods of 2–100 years were estimated. The precipitation maxima aligned with locally reported extreme events, and GEV provided the best overall fit. Using the bias-corrected streamflow, a hydrologic model was calibrated and validated and then applied to land cover change scenarios. Simulations suggest that moderate land-use change can increase peak flows beyond channel capacity, raising flood risk and informing adaptation planning in northern Haiti, which has limited data. Full article

Distinguishing the hydrological impacts of anthropogenic groundwater withdrawal from natural climate variability is a critical yet complex challenge in sustainable water resource management. This study quantitatively evaluated the watershed-scale hydrological response to the increased groundwater abstraction in the Han River basin (35,770 km²) of South Korea using the Soil and Water Assessment Tool (SWAT). Groundwater use datasets for the 1970s and 2010s were constructed using groundwater statistical yearbooks. By applying the groundwater use datasets under 2010s weather conditions, we effectively isolated the specific effects of human usage. The results indicated that a rise in the annual groundwater abstraction from 9.6 to 22.3 million m³ reduced the average streamflow by 6.59%. The baseflow and groundwater recharge were identified as the most sensitive components, decreasing by 20.7% and 20.8%, respectively. Notably, intensive summer withdrawal (53% of the annual total) depleted aquifer storage, directly exacerbating streamflow reductions during the autumn and winter seasons. A flow duration analysis further confirmed that the duration of the dry season—defined by the flow exceeded for 275 days (Q₂₇₅)—extended by 13 days, as the exceedance duration for the specific flow duration shifted from Q₂₇₅ to Q₂₆₃. These findings highlighted that excessive groundwater withdrawal compromises seasonal hydrological stability, necessitating integrated management strategies to secure the streamflow during critical dry periods. Full article

Water shortage is a critical problem that affects the sustainability of the agricultural sector in Northeastern Thailand, especially areas located far from major rivers. This study developed an integrated QSWAT–WEAP modeling system combined with GIS-based spatial analysis for water shortage risk assessment at the sub-district level in Maha Sarakham Province, covering 5292 sq.km and 133 sub-districts. The system bridges the institutional gap between hydrological sub-watershed boundaries and administrative jurisdictions, enabling model outputs that directly support local governance decision-making. The QSWAT model simulated sub-watershed streamflow while the WEAP model evaluated water balance against water demand from five sectors. QSWAT validation (2017–2023) achieved R² = 0.72, NSE = 0.70, and RSR = 0.54, while WEAP verification yielded R² of 0.66–0.80 and NSE of 0.65–0.71. In the spatial analysis, 106 of 133 sub-districts (79.7%) experienced low water availability. Agriculture was the dominant sector in terms of water demand, which has increased at an average rate of 1.7% annually and accounted for 88.7% of total demand in the study period. According to the temporal analysis, the dry season was the most critical period, with peak water shortage of 99.2% in March. Overall, 105 of 133 sub-districts (78.9%) were classified as having water shortages of moderate to very high severity. These findings provide a quantitative basis for sustainable water resource management planning and drought mitigation, thus helping to enhance agricultural sustainability in Maha Sarakham Province. Full article

In many scientific and engineering fields (e.g., measurement science), a probability density function often models a system comprising a signal embedded in noise. Conventional measures, such as the mean, variance, entropy, and informity, characterize signal strength and uncertainty (or noise level) separately. However, the true performance of a system depends on the interaction between signal and noise. In this paper, we propose a novel measure, called “inforpower”, for quantifying the system’s informational power that explicitly captures the interaction between signal and noise. We also propose a new measure of central tendency, called “information-energy center”. Closed-form expressions for inforpower and information-energy center are provided for ten well known continuous distributions. Moreover, we propose a maximum inforpower criterion, which can complement the Akaike information criterion (AIC), the minimum entropy criterion, and the maximum informity criterion for selecting the best distribution from a set of candidate distributions. Two examples (synthetic Weibull distribution data and Tana River annual maximum streamflow) are presented to demonstrate the effectiveness of the proposed maximum inforpower criterion and compare it with existing goodness-of-fit criteria. Full article

This study investigates transboundary hydro-meteorological trends in the Upper Po River basin, adopting a multi-perspective framework to disentangle the joint evolution of climatic and hydrological drivers. We analyzed climatic variables from 25 weather stations (1950–2022) alongside streamflow data from 14 river sections (1911–2022). Trends were assessed using the Mann–Kendall test to detect monotonic changes and the Theil-Sen estimator to quantify magnitude, ensuring robustness against outliers. The results reveal pronounced warming, particularly in spring maximum temperatures with +0.95 ± 0.40 °C per decade, and +0.62 ± 0.35 °C per decade at the annual scale. Conversely, average and minimum daily temperatures show lower rates with, respectively, +0.50 ± 0.26 °C and +0.39 ± 0.27 °C at the annual scale. Consequently, potential evapotranspiration increased significantly (+15.1 ± 9.4 mm per decade), likely contributing to a marked decline in summer streamflow in 8 out of 14 sections. Correlation analysis confirms that snow dynamics modulate the hydrological response: precipitation drives discharge annually and in autumn, winter exhibits a weaker coupling, as winter precipitation is partially stored in the basin as snow, contributing to discharge during spring and summer. By focusing on this strategic region for European agriculture and industry, the study provides useful insights into the combined effects of warming and evapotranspiration on water availability for adaptation strategies. Full article

This paper reports the findings of the first initiative of developing a year-round streamflow forecasting system using the HBV hydrologic model in a data-scarce Ruvu catchment in Tanzania. Considering the importance of the Ruvu catchment as the main source of water to the fast-growing mega city of Dar es Salaam, the researchers in this study made the most of the available data and their joint previous application experience of the modelling framework for the purpose of setting up a reliable operational model. In addition, the researchers adopted a phased approach of developing the streamflow forecasting system, using HBV as a hydrological model, which resulted in a simplified model structure with minimized complexity. For instance, the snow routine was removed as it is not relevant to the study area, and a few parameters were reduced to improve model efficiency. As a measure to demonstrate model performance, in addition to the Nash–Sutcliffe Efficiency (NSE) parameter used for model calibration and verification, several other error functions and graphical displays were used. The model performance values, as measured by NSE for calibration and verification periods, are 0.85 and 0.82 for Ruvu Roadbridge (1H8A), and 0.80 and 0.82 for Kidunda (1H3), respectively, and all are classified as “Very Good”. In addition, the PBIAS of less than ±5% in calibration indicates excellent water balance simulation. Furthermore, the forecast’s performance in this study is evidenced by an annual forecast R² of 0.933, with operational meteorological forecasts improving to 0.962 with “perfect” precipitation; dry season performance with R² of 0.964, demonstrating high skill in baseflow-dominated periods; and the PBIAS for forecasts of 0.866, indicating a slight systematic under-forecasting correctable by a ~15% precipitation adjustment. Although the Ruvu catchment has been characterized by this study as a data-scarce catchment, the results of the operational hydrological forecasting system vary with season and quality of forecast meteorological data, and the model is already launched for operational use. As evidenced by these study findings, the journey from data scarcity to operational forecast provision in the Ruvu catchment demonstrates that the principal barriers are fundamentally institutional and capacity-related. The authors suggest that any future forecasting initiative should put much emphasis on both the understanding of the modelling framework to be used and adequate data collection and analysis, in a synergetic manner with all relevant agencies. And it is also recommended to be vigilant regarding changes in the catchment characteristics and model performance during its life cycle, as the performance of the developed model is only valid under the condition that it was calibrated and validated. Full article

This study integrates 90 years of hydrometeorological observations (1930/31–2019/20) with end-century projections (2080–2099) to evaluate climate-driven changes in the water balance of the Nitra River basin (2094 km²), Slovakia. Despite a modest 2–3% increase in annual precipitation from 1930/31–1959/60 to 1990/91–2019/20, mean annual runoff declined from 229 mm to 201 mm (≈−12%), primarily due to enhanced evapotranspiration stemming from a +1.08 °C basin-wide temperature increase. An empirical regression from 90 hydrological years shows that +100 mm in precipitation boosts runoff by ≈41 mm, while +1 °C in temperature reduces it by ≈13 mm. The BILAN monthly water balance model was calibrated for 1930/31–2019/20 to decompose runoff components. Over the 90-year period, the modeled annual runoff averaged 222 mm, comprising a 112 mm baseflow (50.4%), a 91 mm interflow (41.0%), and a 19 mm direct runoff (8.6%), underscoring the key role of groundwater and subsurface flows in sustaining streamflow. In the second part of our study, the monthly water balance model BILAN was recalibrated for 1995–2014 to simulate future runoff under three CMIP6 Shared Socioeconomic Pathways. Under the sustainability pathway SSP1-1.9 (+0.88 °C; +1.1% precipitation), annual runoff decreases by 8.9%. The middle-of-the-road scenario SSP2-4.5 (+2.6 °C; +3.1% precipitation) projects a 17.5% decline in annual runoff, with particularly severe reductions in autumn months (September −32.3%, October −35.8%, December −40.4%). The high-emission pathway SSP5-8.5 (+5.1 °C; +0.4% precipitation) yields the most dramatic impact with a 35.2% decrease in annual runoff and summer deficits exceeding 45%. These results underline the extreme sensitivity of a mid-sized Central European basin to temperature-driven evapotranspiration and the critical importance of emission mitigation, emphasizing the urgent need for adaptive water management strategies, including new storage infrastructure to address both winter floods and intensifying summer droughts. Full article

Study region: This study focused on the Iznik Lake Watershed in northwestern Türkiye. Study focus: Climate change is increasingly affecting water resources worldwide, raising concerns about future hydrological sustainability. This study investigates the impacts of climate change on river streamflow in the Iznik Lake Watershed, a critical freshwater resource in northwestern Türkiye. To capture possible future conditions, downscaled climate projections were integrated with the SWAT+ hydrological model. Recognizing the inherent uncertainties in climate models and model parameterization, the analysis examined the relative influence of climate realizations, emission scenarios, and hydrological parameters on streamflow outputs. By quantifying both the magnitude of climate-induced changes and the contribution of different sources of uncertainty, the study provides insights that can guide decision-makers in future management planning and be useful for forthcoming modeling efforts. New hydrological insights for the region: Projections indicate wetter winters and springs but drier summers, with an overall warming trend in the study area. Based on simulations driven by four representative grid points, the results at the Karadere station, which represents the main inflow of the watershed, indicate modest changes in mean annual streamflow, ranging from −7% to +56% in the near future and from +19% to +54% in the far future. Maximum flows (Q_max) exhibit notable increases, ranging from +0.9% to +47% in the near future and from +21% to +63% in the far future, indicating a tendency toward higher peak discharges under future climate conditions. Low-flow conditions, especially in summer, exhibit the greatest relative variability due to near-zero baseline discharges. Relative change analysis revealed considerable differences in Karadere and Findicak sub-catchments, reflecting heterogeneous hydrological responses even within the same basin. Uncertainty analysis, conducted using both an ANOVA-based approach and Bayesian Model Averaging (BMA), highlighted the dominant influence of climate projections and potential evapotranspiration calculation methods, while land use change contributed negligibly to overall uncertainty. Full article

Understanding the local-scale impacts of climate change is critical for protecting water resources and ecosystems in vulnerable agricultural regions. This study investigates the Canagagigue Creek Watershed (CCW) in Southern Ontario, Canada, which is an area vital to the Grand River Basin yet threatened by sediment runoff, making it an ecologically sensitive area. We applied an integrated Statistical Downscaling Model (SDSM) and Soil and Water Assessment Tool (SWAT) (version 2012) approach under the IPCC A2 scenario to project impacts for the period 2025–2044. The results reveal a fundamental hydrological shift, and evapotranspiration is projected to claim nearly 70% of annual precipitation, leading to a ~30% reduction in total water yield. Seasonally, the annual streamflow peak is projected to shift from March to April, indicating a transition from a snowmelt-dominated to a rainfall-influenced system, while extended low-flow periods increase drought risk. Crucially, sediment yield at the watershed outlet is projected to decrease by 7.9–10.5%. The concomitant reduction in streamflow implies a weakened sediment transport capacity. However, this points to a heightened risk of increased in-stream deposition, which would pose a dual threat, (a) elevating flood risk through channel aggradation and (b) creating a long-term sink for agricultural pollutants that degrades water quality. By linking SDSM and SWAT, this study moves beyond generic predictions, providing a targeted blueprint for climate-resilient land and water management that addresses the complex, interacting challenges of water quantity. Full article