Search Results (309)

Baseflow separation was performed for 42 small catchments completely built up of either crystalline rocks or folded/unfolded Paleogene flysch rocks. Three different methods were applied—Local minimum (BFI), Kille’s and Rambert’s. Mean total annual runoff in individual catchments varied from 179 to 1132 mm, with an average of 498 mm. Taking into account results for the whole dataset, baseflow participated in 45% ± 15% ratio of the total runoff. Local minimum and Kille’s method results were quite similar: both showed average baseflow participating on 39%/40% of total runoff in unfolded Paleogene catchments, on 29%/29% in folded flysch and 44%/45% in catchments with crystalline basement. Rambert’s method results were 10% to 12% higher from the previous two, reaching 50% in unfolded flysch Paleogene catchments, 41% in folded flysch and 56% in crystalline catchments. Differences might be caused by the nature of Rambert’s method, which is based on recession curves analyses, while the previous two result from discharge statistics. Still, usually only less than 50% of unevaporated precipitation is able to infiltrate and recharge groundwater resources in crystalline rocks and flysch sediments, and folded flysch rocks are sometimes able to absorb only 10–20% of unevaporated precipitation. Full article

This study developed a novel framework integrating UAV-derived orthophotography, deep learning-based substrate classification, two-dimensional hydraulic modeling, Froude number (Fr) analysis, and multispecies habitat suitability assessment to evaluate the effects of channel modification and precipitation on fish habitats in Gaoliao Creek, eastern Taiwan. Habitat changes under baseflow and rainfall-induced high-flow conditions were quantified using Fr-based hydraulic habitat availability and Habitat Suitability Index (HSI)- and Combined Habitat Suitability Index (CHSI)-based habitat suitability. Channel modification transformed the channel from a deep and slow-flowing system into a shallower and faster-flowing environment. Under baseflow conditions, the proportion of available habitat meeting the adopted hydraulic criteria decreased from 81.6% to 73.9%, whereas the CHSI-derived proportion of weighted usable area (PUA) increased from 0.300 to 0.323 due to favorable substrate composition. During rainfall events, habitat availability and suitability declined markedly during peak flows and recovered as discharge receded. Compared with the pre-engineering channel, the modified channel exhibited greater sensitivity to short-term hydrological fluctuations but effectively prevented overbank flooding during the selected extreme rainfall event. These findings highlight the trade-off between flood-control benefits and ecological resilience and emphasize the importance of maintaining habitat heterogeneity in river management. Because the analyses were based on a single typhoon-related rainfall event and lacked direct biological validation, the results should be interpreted as event-specific predictions requiring further verification. Full article

Integrated watershed management relies on distributed hydrological models to simulate water transport processes and support decision-making. However, model reliability is often constrained by the resolution and quality of input data, particularly soil information. High-resolution soil datasets remain scarce in many regions of Sub-Saharan Africa, limiting the representation of spatial soil heterogeneity in hydrological simulations. This study evaluates the effect of detailed soil information derived using the Soil–Land Inference Model (SoLIM) on the performance of the Soil and Water Assessment Tool (SWAT) in the Sigi River watershed, a topographically complex watershed in northeastern Tanzania. Two model setups were compared: (i) a high-resolution SoLIM-based soil dataset and (ii) the coarser global ISRIC SoilGrids database. The SoLIM-informed model better reproduced hydrographs and flow duration curves and showed stronger parameter sensitivities, achieving superior calibration performance (NSE = 0.87, PBIAS = 8.7%) compared to SoilGrids (NSE = 0.86, PBIAS = 11.1%). Hydrological component analysis further revealed that SoLIM enhanced baseflow (181 vs. 60 mm/year) and percolation (349 vs. 135 mm/year) while reducing surface runoff (263 vs. 474 mm/year). These findings demonstrate that high-resolution soil data measurably improve the representation of subsurface processes and moderately improve streamflow performance, especially for baseflow and low-flow regimes; reduce model uncertainty; and improve the robustness of SWAT simulations, thereby supporting more effective watershed management in data-scarce and heterogeneous landscapes. Full article

Surface water–groundwater interactions within oasis–desert ecotones of arid regions play a pivotal role in sustaining regional water security and ecological stability. Taking the Niya River Basin in Xinjiang, Northwest China, as a representative inland watershed, this study systematically elucidates the mechanisms and seasonal dynamics of surface water–groundwater coupling under the combined influences of natural processes and anthropogenic activities. A total of 68 surface water and groundwater samples were collected during the dry, normal, and wet hydrological periods. Integrated hydrochemical characterization, mineral saturation index analysis, and stable isotope (δ²H and δ¹⁸O) mass balance modeling were employed to quantify recharge contributions and unravel hydrogeochemical evolution pathways. Results indicate that the waters in the study area are predominantly brackish to saline, with consistent dominant ionic assemblages (SO₄²⁻ and Na⁺) across all hydrological periods, highlighting evaporite dissolution as the primary control on solute composition. Hydrochemical evolution is jointly regulated by evaporation concentration, water–rock interactions, and cation exchange processes. Surface water chemistry reflects the combined effects of silicate weathering and evaporite dissolution, whereas groundwater chemistry is mainly governed by evaporite dissolution coupled with pronounced cation exchange. Stable isotope signatures reveal substantial secondary evaporation of regional precipitation prior to recharge. Frequent bidirectional recharge between surface water and groundwater was observed, exhibiting distinct seasonal transitions. During the dry period, groundwater provides significant baseflow support to surface water (48.6% in the oasis zone and 54.3% in the desert zone). In the normal period, recharge direction reverses, with surface water becoming the dominant source of groundwater recharge (99.0% in the oasis zone and 76.6% in the desert zone). In the wet period, spatial heterogeneity becomes evident: surface water continues to dominate groundwater recharge in the oasis zone (92.7%), whereas groundwater recharge to surface water prevails in the desert zone (50.5%). This study identifies a seasonally dynamic “discharge–infiltration–zonal regulation” bidirectional recharge pattern in arid inland river systems. The findings advance the mechanistic understanding of hydrological connectivity reconstruction within oasis–desert ecotones and provide a scientific basis for optimized regional water resource allocation and groundwater salinization risk mitigation. Full article

Many degraded streams in the Chesapeake Bay watershed have been structurally modified over the last two decades in an effort to reduce nutrient and sediment loads from urban catchments and contribute to the pollutant reduction goals of the Chesapeake Bay Total Maximum Daily Load (TMDL). The step-pool streamwater conveyance (SPSC) is a stream restoration design that has been extensively implemented in Maryland and the District of Columbia. In the summer of 2019, an SPSC was constructed in a degraded 800 m stream reach on the University of Maryland campus (i.e., Campus Creek). Precipitation, baseflow and stormflow runoff, and nutrient (nitrogen and phosphorus) and total suspended solid (TSS) concentrations were measured throughout pre- and post-restoration periods (~2 and 5 years, respectively) to determine the extent to which the SPSC structure reduced pollutant loads. A comparison of pre- (2018) versus post-restoration (2020) years with similar total annual rainfall volumes indicates that total annual runoff was 13% lower in the post-restoration period. Area yields of total nitrogen (TN), total phosphorus (TP) and TSS were 33, 39 and 59% lower, respectively, in the same pre- versus post-restoration comparison. 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

Agricultural rivers are often silent receivers of microplastics (MPs) from diffuse, non-point sources; however, their pollution dynamics during rainfall events remain poorly understood. In this study, MP transport was investigated at three sampling points in an agricultural river catchment, where mulching films are used, and sewage sludge is not applied. Sampling was conducted in the Umeda River and its tributaries during six sampling events. MP flux exhibited a strong positive correlation with river discharge (L–Q relationship; n = 1.49–1.61, R² = 0.67–0.87). The L–Q model indicates that a tenfold increase in discharge results in approximately a 600-fold increase in MP flux and a 1000-fold increase in total suspended solid flux. MP abundance during rainfall was up to four times higher than that during baseflow, ranging from 73 ± 64 to 200 ± 111 particles/m³, while peak flux reached 6736 particles/s, with an MP mass of 811 mg/s. Regarding particle characteristics, rainfall enhanced the heterogeneity of MPs, although fragments and polyethylene/polypropylene polymers remained consistently dominant across all hydrological stages. First-flush behavior was observed at HU, with more than half of the total MP mass exported within the initial 50% of the event flow volume. These findings help to inform mitigation strategies that should prioritize a reduction in upstream plastic inputs in order to effectively manage MP transport in agricultural rivers. Full article

The adjustments in channel morphology under influence of vegetation dynamics, impacting natural sediment and flow regimes at local, catchment, and regional scales, are primarily driven by natural and anthropogenic factors. Limited knowledge exists regarding the historical channel adjustments along Himalayan glacier-dominated rivers. This study specifically concentrates on three distinct glacier-dominated river segments: Nubra in Jammu and Kashmir, Ganga-Bhagirathi in India, and Langtang-Khola in Nepal. The research adopts a supervised classification model initially developed by Mukhtar and extends the technique by applying it to four additional sources of satellite data with spatial resolutions ranging from 2.4 m to 30 m. This extension of the model is accomplished using the Google Earth Engine (GEE) platform to extract three main macro-units (base-flow channels, emerged sediment bars and vegetated surfaces) in fluvial corridors. Across different locations, the behavior of the rivers exhibited variability; however, possibly cyclic behavior in riparian vegetation cover was observed during the studied period. Surprisingly, in the subsequent period of 2016–2020, noticeable channel widening was observed in almost all reaches of the three river segments. Notably, the high meltwater runoff periods from 1989 to 2003 in the Nubra River segment induced vegetation erosion and channel widening. On the contrary, flood events during the early 21st century possibly lacked the duration and intensity required to impact vegetation growth in river corridors. This trend was also evident in the Ganga-Bhagirathi River, where the stable vegetation cover showed no major effects from the 2012 flood event. Despite the susceptibility of the Langtang-Khola river to landslides and earthquakes, the study reaches in Langtang-Khola River remained unaffected by these catastrophic events. Briefly, this study contributes to an enhanced understanding of the intricate dynamics of channels and vegetation in Himalayan glacier-dominated rivers, spanning diverse spatial and temporal scales, and elucidates their correlation with factors related to climate change. Full article

Groundwater models form the basis for investigating subsurface processes that relate to groundwater flow. Urban cover, however, usually inhibits the collection of new subsurface or geological data. Therefore, models usually depend on existing, poor-quality, or scarce datasets. The geological domain is an integral part of any groundwater model, and as such, understanding the model’s sensitivity to the geological interpretation is key to constraining uncertainty. This research uses a recent advancement in mitigating uncertainty in geological modeling to investigate how different geological interpretations affect groundwater model uncertainty. Using the Ouseburn catchment, Newcastle upon Tyne, UK, as a case study, it estimates baseflows and uses them to develop an ensemble of coupled distributed groundwater recharge and groundwater flow models using SWAc and MODFLOW, and performs a Monte Carlo analysis on the different model formulations. Results indicate that even though river baseflows are not highly affected, there is a connection between simulated groundwater level sensitivity and areas of high geological uncertainty. As the interest in the urban subsurface grows, constraining uncertainty in groundwater models is especially important for urban planning, policy making, water resources, and groundwater flooding protection. Therefore, constraining uncertainty from geological datasets is key to robust groundwater modeling. Full article

This study investigates how a Long Short-Term Memory (LSTM) model internally represents baseflow contributions in snowmelt-driven, semi-arid mountain basins with heterogeneous geologic characteristics. Five basins in the Sangre de Cristo Mountains of northern New Mexico, spanning fractured Precambrian bedrock and sedimentary-volcanic terrain, were used to evaluate both model performance and interpretability. Baseflow dynamics were inferred post hoc using the Baseflow Index (BFI) and a two-reservoir HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. Although baseflow components were not explicitly included in model training, internal cell state activations exhibited strong correlations with both shallow and deep baseflow components derived from the HEC-HMS model. To better understand how these relationships may change under climatic stress, BFI-based baseflow patterns were further analyzed under pre-drought and drought conditions. Results indicate that the internal LSTM states differentiated patterns consistent with short- and long-residence flow paths, reflecting physically interpretable hydrologic behavior. This work demonstrates the potential of LSTM models to provide valuable insights into baseflow generation and groundwater–surface water interactions, which is especially critical in water-scarce regions facing increasing drought frequency. Full article

To address the challenge of predicting runoff processes in the middle reaches of the Yangtze River under the influence of complex river–lake relationships and human disturbances, this paper proposes a coupled model based on the Sparrow Search Algorithm-optimized Long Short-Term Memory neural network (SSA-LSTM) for daily runoff forecasting at the Jiujiang Hydrological Station. The input data were preprocessed through feature selection and sequence decomposition. Subsequently, the Sparrow Search Algorithm (SSA) was utilized to perform automated of key hyperparameters of the Long Short-Term Memory (LSTM) model, thereby enhancing the model’s adaptability under complex hydrological conditions. Experimental results based on multi-station hydrological and meteorological data of the middle reaches of the Yangtze River from 2009 to 2016 show that the SSA-LSTM achieves a Nash–Sutcliffe Efficiency (NSE) of 0.98 during the testing period (2016). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are reduced by 49.3% and 51.3%, respectively, compared to the standard LSTM. A comprehensive evaluation across different flow levels, utilizing Taylor diagrams and error distribution analysis, further confirms the model’s robustness. The model demonstrates robust performance across different flow regimes: compared to the standard LSTM model, SSA-LSTM improves the NSE from 0.45 to 0.88 in high-flow scenarios, exhibiting excellent capabilities in peak flow prediction and flood process characterization. In low-flow scenarios, the NSE is improved from −0.77 to 0.72, indicating more reliable prediction of baseflow mechanisms. The study demonstrates that SSA-LSTM can effectively capture hydrological nonlinear characteristics under strong river–lake backwater and human disturbances, providing a high-precision and high-efficiency data-driven method for runoff prediction in complex basins. Full article

The application of two-dimensional (2D) hydrologic and hydraulic modeling tools is increasing for overland flow simulation, as they represent spatial changes in depth, velocity, and flow conditions more accurately. Recently, the US Army Corps HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) added the capability to import an unstructured 2D mesh, which enables the routing of excess precipitation across the mesh, as a fully distributed hydrological model. In HEC-HMS, the 2D diffusion-wave component functions as a hydrologic transform representing overland flow routing. In contrast, HEC-RAS 2D (Hydrologic Engineering Center-River Analysis System), initially applied to river flow simulation, can apply either the 2D shallow-water equations or the 2D diffusion-wave option. Similarly to HEC-HMS, HEC-RAS also includes rain-on-grid (RoG) capability and infiltration algorithms, and in this fashion has some hydrological modeling capabilities. Still, while HEC-HMS is capable of representing extended-period hydrological simulations, HEC-RAS hydrological capabilities are limited to event-based simulations, as there are no provisions to represent abstractions such as evapotranspiration or groundwater/baseflow contributions together. Studies performing a direct comparison between the HEC-HMS RoG and HEC-RAS RoG approaches for representing urban hydrology remain scarce. This study aims to fill that gap by assessing their performance in Moore’s Mill Creek Watershed, in Lee County, Alabama, with a focus on continuous rainfall-runoff modeling. Both models run on the same unstructured mesh and use identical rainfall, terrain, land-use, and soil data. Model simulations are compared over an extended period to evaluate simulated depth, velocity, and flow hydrographs against field observations. The comparison shows HEC-HMS’s superior performance for extended simulation and provides practical guidance on parameter alignment, data needs, and tool selection. 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

Existing baseflow estimation methods—such as exponential recession models, linear reservoir approaches, and digital filtering techniques—seldom account for anthropogenic disturbances or evapotranspiration-induced streamflow alterations. To address this limitation, a physically based baseflow equation that explicitly integrates human water withdrawals and evapotranspiration losses has been introduced. The governing equation was reformulated from a nonlinear storage–discharge relationship and validated against multi-decadal streamflow records in the Middle Yellow River Basin (MYRB). Results demonstrate that the proposed model accurately reproduces observed recession behavior across diverse sub-basins (NSE ≥ 0.94; RMSE ≤ 152 m³ s⁻¹). By providing reliable baseflow estimates, the equation enables quantitative assessment of eco-hydrological benefits and informs cost-effective water-resource investments. Furthermore, long-term baseflow simulations driven by climate projections offer a scientific basis for evaluating climate-change impacts on regional water security. 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