Explore the vast range of titles available.

Estimating peak discharge ( Q p ) and design flood in small tributary sub-basins is challenging owing to limited observed streamflow data. To address this, the synthetic unit hydrograph (SUH) concept was introduced that helps to estimate Q p of direct surface runoff (DSRO) hydrograph by routing the excess rainfall to the basin’s outlet, facilitating the construction of hydraulic structures in areas lacking observed rainfall-runoff data. Therefore, the present study evaluates the performances of three types of SUHs i.e., CWC-UH, Nash-GIUH and SCS-UH in estimating DSRO hydrographs, with an emphasis on Q p and time to peak ( T P ) during a storm event in ten tributary sub-basins of one of the most flood-affected river, Shilabati in Eastern India. The results of these three models exhibit striking similarities in the shapes of DSRO hydrographs derived from the SCS-UH and the Nash-GIUH models compared to the CWC-UH. The Nash-GIUH model stands out as the superior model due to the strong correlation (R 2  = 0.86) between the ratio of Q p and T p and the observed flood extents (flood-inundated area) for all sub-basins. In the Nash-GIUH-based DSRO hydrographs, Sub-basin-9 witnesses the highest Q p (334.64 m 3 s − 1 ) with short T p (19 h) ( Q p / T p ratio = 17.61) followed by Sub-basin-10 ( Q p / T p ratio = 13.50). Similarly, 31.67% and 19.51% of the total areas of Sub-basin-9 and − 10, respectively, were affected by flood inundation in the past. Therefore, the association between the shape of hydrographs and flood extents depicts that the Nash-GIUH-based rainfall-runoff model can effectively estimate floods in areas lacking streamflow data.

Rainfall and surface runoff are the two most important components, which control the groundwater recharge of the basin. The long-term groundwater recharge of an aquifer gets affected by the population growth, irregular agriculture activities and industrialization. Hence, estimation of rainfall-surface runoff is very much essential for proper groundwater management practices. In the present study, Soil Conservation Service Curve Number (SCS-CN) model was employed in combination with geospatial techniques to estimate rainfall-surface runoff for the Lower Bhavani River basin in South India. To develop the SCS-CN model, rainfall data were obtained for 33 years (1983–2015) from 22 rain gauge stations spread over the basin. IRS LISS-IV satellite data of 5.8 m spatial resolution were used to analyze the land use/land cover (LU/LC) behavior. Based on the soil properties, four Hydrological Soil Groups (HSG) were identified in the basin which is most significant for surface runoff estimation. Curve Number (CN) values were obtained for various Antecedent Moisture Conditions (AMC) such as dry condition (AMC I), average condition (AMC II) and wet condition (AMC III). Spatial distribution of CN values was plotted using Geographical Information System (GIS) for the entire Lower Bhavani Basin to assess the surface runoff potential. The results indicate that the annual rainfall varies from 267 mm (2002) to 1528.6 mm (2005), and the annual surface runoff varies from 102.04 mm (1985) to 463.02 mm (2010). The SCS-CN model outputs predict that the average surface runoff of the basin is 211.99 mm, and the average surface runoff volume is 81,995,380 m3. The study also indicates that nearly 53% of the basin area is dominated by high to very high surface runoff potential. Finally, the output of surface runoff potential was validated with the Average Groundwater Level Fluctuation (AGLF) observed in 57 wells spread over the entire basin. The basin AGLF ranges from 2.32 to 21.72 m. The surface runoff potential categories are satisfactorily matching with the AGLF categories. Moderate surface runoff as well as moderate AGLF zones mostly occupy the central portion of the basin, which possess good groundwater potential. However, the high surface runoff zones in the basin lead more surface water flow into the river channels, which reduce the infiltration rate and decline the water table. This problem can be solved by constructing suitable artificial groundwater recharge structures across the river channels in the high surface runoff potential areas.

SCS-curve number (CN) is one of the most well-liked and commonly applied methods for estimating surface runoff. The present study aims to calculate surface runoff using SCS-CN watershed-based calculation and geospatial technology in the Kurumballi sub-watershed Shivamogga District of Karnataka, India. The study area covers about an area of 47.67 sq. km. The union of land use/land cover classification with hydrological soil groups (HSG) yields the runoff estimation by the SCS-CN curve approach. This method calculates the runoff volume from the land surface flows into the river or streams. Moreover, the study area’s delineation of runoff potential zones was done using the thematic integration method. Different thematic layers were used, including lithology, geomorphology, soil, slope, land use and land cover, drainage, surface water bodies, groundwater contour, and isohyetal maps. Furthermore, associating it with the SCS-CN technique, the total surface runoff volume of the study area was estimated. The total surface runoff volume in the study area is 21065849.7 m3. To this study, thematic integration with the SCS-CN approach to estimate runoff for watersheds is valuable for improving water management and soil conservation.

Hydrological modeling in deltaic regions remains challenging. This study assesses the impacts of climate change (CC) and land use/land cover change (LULC) on the Oueme Delta hydrosystem using the physics-based integrated model ParFlow-CLM. Surface runoff (SRO), evapotranspiration (ET), water table depth (WTD), and soil water content (SWC) were simulated and evaluated against ERA5 data using performance metrics such as correlation and Kling-Gupta efficiency (KGE). For historical simulations (1975, 2000, and 2013), land-use maps from the West Africa LULC Dynamics project and climate data from WFDE5 were employed. Future projections (2030, 2050, and 2085) relied on climate inputs from CMIP6 datasets, while LULC maps were extrapolated using a Markov chain approach. The model demonstrated strong performance in simulating key components of the water balance, particularly ET (daily scale: Correlation > 0.8; KGE > 0.6). Under constant climate conditions, a 20% reduction in forest cover between 1975 and 2013 showed a negligible impact on water resources. In contrast, CC exerted a substantial influence on the hydrological cycle: increased precipitation led to substantial rises in SRO and ET. Scenario-based projections indicate that LULC changes may amplify climate impacts. Specifically, a precipitation increase exceeding 50% combined with full reforestation could double SRO and increase flood risks. Conversely, a 50% decrease in precipitation coupled with complete deforestation could induce severe ecosystem water stress, reducing SWC by 3.9%. These findings highlight the need for integrated land and water management strategies and inform the development of effective policies for water resource conservation in the context of CC and LULC changes.

Surface runoff models are essential for designing water and soil protection measures. However, they often exhibit uncertainty in both parameterization and results. Typically, uncertainty is evaluated by comparing model realizations with measured data. However, this approach is constrained by limited data availability, preventing comprehensive uncertainty assessment. To overcome this limitation, we employed the generalized likelihood uncertainty estimation (GLUE) methodology to conduct sensitivity and uncertainty analyses on a series of surface runoff models. These models were based on an ensemble of artificial rainfall experiments comprising 77 scenarios with similar settings. We utilized the rainfall-runoff-erosion model SMODERP2D to simulate the experiments and employed Differential Evolution, a heuristic optimization method, to generate sets of behavioural models for each experiment. Additionally, we evaluated the sensitivity and uncertainty with respect to two variables; water level and surface runoff. Our results indicate similar sensitivity of water level and surface runoff to most parameters, with a generally high equifinality. The ensemble of models revealed high uncertainty in bare soil models, especially under dry initial soil water conditions where the lag time for runoff onset was the largest (e.g. runoff coefficient ranged between 0–0.8). Conversely, models with wet initial soil water conditions exhibited lower uncertainty compared to those with dry initial soil water content (e.g. runoff coefficient ranged between 0.6 – 1). Models with crop cover showed a multimodal distribution in water flow and volume, possibly due to variations in crop type and growth stages. Therefore, distinguishing these crop properties could reduce uncertainty. Utilizing an ensemble of models for sensitivity and uncertainty analysis demonstrated its potential in identifying sources of uncertainty, thereby enhancing the robustness and generalizability of such analyses.

Changes in surface runoff and its coupling relationship with atmospheric circulation have been an ongoing focus of climate change research. In this study, the Mann–Kendall test, Pettitt test, and quantile regression were used to analyze changes in surface runoff in the middle reaches of the Yangtze River Basin (MYRB) during 1902–2014 based on the global runoff reconstruction (GRUN) dataset. In addition, cross wavelet analysis was used to analyze the coupling relationships between four teleconnection indices from the National Oceanic and Atmospheric Administration (NOAA) website (i.e., East Central Tropical Pacific SST (El Niño 3.4), Pacific Decadal Oscillation (PDO), Eastern Pacific/North Pacific Oscillation Index (EP/NP) and multivariate ENSO Index (MEI V2)), and monthly average surface runoff series in the MYRB. The study produced several important results: (1) Trends in the annual surface runoff, from 1902 to 2014, in the MYRB have changed significantly, with the longest and most significant upward trend occurring from 1987 to 2000. (2) The annual series surface runoff mutation years varied across the MYRB (e.g., surface runoff in the central region changed abruptly around 1918, whereas surface runoff in the northern and southern regions changed abruptly around 2000). (3) Significant correlations (P < 0.05) between the surface runoff in the MYRB and the four representative teleconnection indices (e.g., El Niño 3.4, PDO, EP/NP, and MEI V2) indicate that surface runoff has been strongly linked to the indices. The significance of this study lies in its revelation of relationships between water resources system changes and climate change.

Surface runoff is the most significant environmental concern in the Rib Watershed. Hence, this study estimates runoff by combining geospatial information and the Soil Conservation Service Curve Number (SCS-CN) model in the watershed. Rainfall, land use, land cover, hydrologic soil group, maximum soil water retention, and CN values were processed using Arc GIS and ERDAS Imagine software accordingly. The model was validated using the coefficient of determination (R 2 ) and Nash-Sutcliffe efficiency (NSE). The R 2 (0.9861, 0.9508, and 0.9136) and NSE (0.7, 0.68, and 0.6) values for the periods (2018, 2020, and 2022), respectively, confirmed the good performance of the model. The result also showed runoff ranges from 497 mm/year to 1,258 mm/year. Therefore, the highest runoff is observed in most areas of Farta and Debre Tabor and in some parts of Lay Gayint, Fogera, and Kemkem districts. Consequently, this may result in a loss of soil moisture, a decline of surface and ground water, low crop yield and animal fodder, and unproductivity of the land. This, in turn, affects food security and the livelihoods of the community at large in the region. Therefore, well-planned watershed management practices should be put into practice by prioritizing runoff hotspot sites in the catchment.

This study evaluates groundwater quality in the Wadi Feiran Basin, Southwestern Sinai, by integrating hydrochemical analysis, pollution assessment, and human health risk assessment (HHRA) with morphometric characterization of surface runoff. Morphometric analysis shows that sub-basins vary in runoff potential, reflecting differences in size and topography. Groundwater quality exhibits significant variability, with pH, electrical conductivity, total dissolved solids, and total hardness exceeding World Health Organization (WHO) limits in several samples. Hydrochemical facies analysis indicates that silicate weathering, evaporite dissolution, ion exchange, saline intrusion, and anthropogenic contamination are the dominant processes shaping groundwater chemistry. Pollution assessment using Nemerov’s Pollution Index identifies nitrate and iron as key contaminants, with nitrate exceeding WHO standards in nearly half of the samples. HHRA reveals substantial non-carcinogenic risks, particularly for children, due to elevated nitrate levels, while long-term exposure also suggests potential carcinogenic effects. Overall, 60% of the sampled groundwater is unsuitable for drinking, underscoring the urgent need for monitoring and management strategies to protect public health and ensure sustainable groundwater use in the basin.

Pluvial floods caused by extreme overland flow inland account for half of all flood damage claims each year along with fluvial floods. In order to increase confidence in pluvial flood susceptibility mapping, overland flow models need to be intensively evaluated using observations from past events. However, most remote-sensing-based flood detection techniques only focus on the identification of degradations and/or water pixels in the close vicinity of overflowing streams after heavy rainfall. Many occurrences of pluvial-flood-induced damages such as soil erosion, gullies, landslides and mudflows located further away from the stream are thus often unrevealed. To fill this gap, a transferable remote sensing fusion method called FuSVIPR, for Fusion of Sentinel-2 & Very high resolution Imagery for Pluvial Runoff, is developed to produce damage-detection maps. Based on very high spatial resolution optical imagery (from Pléiades satellites or airborne sensors) combined with 10 m change images from Sentinel-2 satellites, the Random Forest and U-net machine/deep learning techniques are separately trained and compared to locate pluvial flood footprints on the ground at 0.5 m spatial resolution following heavy weather events. In this work, three flash flood events in the Aude and Alpes-Maritimes departments in the South of France are investigated, covering over more than 160 km2 of rural and periurban areas between 2018 and 2020. Pluvial-flood-detection accuracies hover around 75% (with a minimum area detection ratio for annotated ground truths of 25%), and false-positive rates mostly below 2% are achieved on all three distinct events using a cross-site validation framework. FuSVIPR is then further evaluated on the latest devastating flash floods of April 2022 in the Durban area (South Africa), without additional training. Very good agreement with the impact maps produced in the context of the International Charter “Space and Major Disasters” are reached with similar performance figures. These results emphasize the high generalization capability of this method to locate pluvial floods at any time of the year and over diverse regions worldwide using a very high spatial resolution visible product and two Sentinel-2 images. The resulting impact maps have high potential for helping thorough evaluation and improvement of surface water inundation models and boosting extreme precipitation downscaling at a very high spatial resolution.

Process-based hydrological models are of great importance to understand hydrological processes and support decision making. The LImburg Soil Erosion Model (LISEM) requires information on soil and land-use-related attributes to represent the transformation of rainfall into runoff for isolated rainfall events. This study aimed at evaluating LISEM for estimation of direct surface runoff (DSR) hydrographs in a watershed in Southern Brazil under the predominance of long-duration rainfall events, dominated by Argisols and with availability of a high-density rain gauge network. In addition, this study sought to: (i) suggest and evaluate a procedure for definition of initial soil moisture from antecedent 5-day rainfall depth; (ii) reduce the degree of subjectivity involved in the determination of some vegetation-related parameters by using remote sensing; and (iii) recommend a validation procedure. The saturated soil hydraulic conductivity and the Manning’s surface roughness coefficient were calibrated considering 11 rainfall–runoff events, whereas the validation was performed for 4 events from the average calibrated parameters. The Nash–Sutcliffe coefficient was used to assess both calibration and validation, resulting in average values of 0.64 and 0.58, respectively. It can be inferred from the results that the use of remote sensing to derive some LISEM parameters, along with the suggested schemes for definition of initial soil moisture and validation, was effective and provided sound results even for long-duration rainfall events. The results of this study and its methodological procedures can serve as a basis for other professionals who intend to use LISEM for both conducting detailed analyses of DSR hydrographs and supporting water resources management.