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Urban areas are increasingly getting vulnerable to floods due to high-intensity precipitation and increasing concretization. To identify the flood vulnerability status of urban micro-watersheds for an improved mitigation strategy, we propose a Flood Vulnerability Index (FVI) with readily available urban infrastructure and hydrological data. The criteria variables for FVI calculation include urban infrastructure data (building and road density), run-off retention capacity, the fraction of vegetation cover, and open spaces. The Soil Conservation Service Curve Number (SCS-CN) method has been applied to estimate run-off retention using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. The weighted linear combination of the criteria variables was used to derive the FVI of each micro-watershed. The Analytical Hierarchical Process (AHP) has been utilized for the weight assignment. This method is applied to the Hyderabad City area, India. The city is densely populated and often devastated by urban floods. Results indicate that out of 85 micro-watersheds classified in the region, 24 are highly vulnerable with FVI > 3, requiring immediate flood mitigation action. A near-future flood mitigation strategy is required for 36 micro-watersheds with FVI in the range of 2–3. The remaining 25 micro-watersheds are relatively less vulnerable with FVI

Flow simulation and forecasting accuracy using rainfall-runoff models is one of the main challenges in hydrological modelling, especially when focused on the flow simulation at a short time scale. These uncertainties are typically dependent on the model design, the technique used for estimating model parameters, the process of considering rainfall variability and errors in the discharge data used for the calibration. Indeed, the uncertainty associated with the discharge derived from the rating curve is ignored in many earlier rainfall-runoff models. In this paper, we provide a quantitative approach to rigorously investigate the effect that the rating curve uncertainty model has on the auto-calibration of Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model at hourly time scale. The multisegment BaRatin rating curve, based on the Bayesian analysis, was used to construct the most probable (MaxPost) rating curve with the bounds uncertainty for hydrometric station of Allala watershed. This allows establishing a new discharge hydrograph with its uncertainty bounds that are subsequently used in HEC-HMS calibration, to provide model parameters with confidence interval and to evaluate the model prediction accuracy. In HEC-HMS model, soil conservation service-curve number (SCS-CN) was applied to compute the runoff losses, while the SCS unit hydrograph (SCS-UH) method was used to estimate the direct runoff at the basin outlet. The model calibration process was carried out for the flood event of 2002 using four objective functions and validated for three independent events. We found that the calibration of the initial abstraction values (IA) varied between −15.16% and 20% when assessing the uncertainties associated with the rating curve, whereas the calibrated curve number values (CN) varied between −5.18% and 7.8%. The confidence interval for the CN and IA were extended from 65.71 to 74.81 and from 26.95 to 19.08, respectively. Results highlighted that the rating curve uncertainty has significant impact on the HEC-HMS model calibration parameters. Rigorous consideration of this uncertainty can improve considerably the model ability to predict the hourly discharge hydrographs.

The present study proposes a Geographic Information System-based runoff and sediment yield model. The model derives the study watershed characteristics using the Digital Elevation Model. The model calculates the runoff (using the NRCS curve number method) and sediment yield (using MUSLE) on each cell (pixel) of the DEM and routes these hydrological parameters over the overland flow cells into the drainage channels to the watershed outlet using Time-Area histogram method. The backend and frontend of the model is developed in Python and HTML + JavaScript code, respectively. The model develops the runoff hydrograph and sediment graph at the watershed outlet. The performance of the developed model has been assessed by comparing the model output with the observed runoff and sediment yield measured at the outlet of forest micro-watershed located in Shivalik foot hills of North-West India. The statistical analysis reveals the reasonably well performance of the developed model in simulating runoff and sediment yield as is corroborated by low values of PBIAS, MAPE, MBE and RMSE and high values of correlation coefficient and model efficiency.

Land-use changes often have significant impact on the water cycle, including changing groundwater/surface-water interactions, modifying groundwater recharge zones, and increasing risk of contamination. Surface runoff in particular is significantly impacted by land cover. As surface runoff can act as a carrier for contaminants found at the surface, it is important to characterize runoff dynamics in anthropogenic environments. In this study, the relationship between surface runoff and groundwater recharge in urban areas is explored using a top-down water balance approach. Two empirical models were used to estimate runoff: (1) an updated, advanced method based on curve number, followed by (2) bivariate hydrograph separation. Modifications were added to each method in an attempt to better capture continuous soil-moisture processes and explicitly account for runoff from impervious surfaces. Differences between the resulting runoff estimates shed light on the complexity of the rainfall–runoff relationship, and highlight the importance of understanding soil-moisture dynamics and their control on hydro(geo)logical responses. These results were then used as input in a water balance to calculate groundwater recharge. Two approaches were used to assess the accuracy of these groundwater balance estimates: (1) comparison to calculations of groundwater recharge using the calibrated conceptual HBV Light model, and (2) comparison to groundwater recharge estimates from physically similar catchments in Switzerland that are found in the literature. In all cases, recharge is estimated at approximately 40–45% of annual precipitation. These conditions were found to closely echo those results from Swiss catchments of similar characteristics.

Sensitivity analysis (SA) can identify the most critical parameters for crop growth model output, thus helping to improve model calibration efficiency. However, when combined with different production conditions, especially adverse conditions such as water stress and fertilizer stress, parameter sensitivity remains unclear. This study (i) assessed the sensitivity of the output response of the CERES-Maize model to the input parameters, particularly the effect of water and fertilizer stress on the SA results and (ii) evaluated the model performance based on the SA results. The results indicated that water stress had a considerable effect on SA, whereas nitrogen stress had little effect on SA. P5, G3, and P2 had significant effects on yield, maximum aboveground biomass (AGB), daily AGB, daily leaf area index (LAI), and daily actual evapotranspiration (ETc). Under water stress, soil drainage rate, soil runoff curve number, and photosynthesis factor greatly affected the output response of CERES-Maize. Compared with the calibration of maize cultivar coefficients, CERES-Maize with additional consideration of soil parameter calibration was more accurate. The model evaluation results revealed that the simulated LAI, yield, and soil water content were consistent with the actual measured values. These findings can provide a reference for the calibration of CERES-Maize model parameters under water and fertilizer stress.

use of a non-zero hydrologic response unit (HRU) threshold is an effective way of reducing unmanageable HRU numbers and simplifying computational cost in the Soil and Water Assessment Tool (SWAT) hydrologic modelling. However, being less representative of watershed heterogeneity and increasing the level of model output uncertainty are inevitable when minor HRU combinations are disproportionately eliminated. This study examined 20 scenarios by running the model with various HRU threshold settings to understand the mechanism of HRU threshold effects on watershed representation as well as streamflow predictions and identify the appropriate HRU thresholds. Findings show that HRU numbers decrease sharply with increasing HRU thresholds. Among different HRU threshold scenarios, the composition of land-use, soil, and slope all contribute to notable variations which are directly related to the model input parameters and consequently affect the streamflow predictions. Results indicate that saturated hydraulic conductivity, average slope of the HRU, and curve number are the three key factors affecting stream discharge when changing the HRU thresholds. It is also found that HRU thresholds have little effect on monthly model performance, while evaluation statistics for daily discharges are more sensitive than monthly results. For daily streamflow predictions, thresholds of 5%/5%/5% (land-use/soil/slope) are the optimum HRU threshold level for the watershed to allow full consideration of model accuracy and efficiency in the present work. Besides, the results provide strategies for selecting appropriate HRU thresholds based on the modelling goal.

Flood warning systems can provide an effective and low-cost solution to protect urban residents especially in developing countries suffering from severe flood damages caused by weak capacity of flood defense structures. Rainfall Threshold for Flood Warning (RTFW) is an essential key of accurate flood warning system. This study proposed an easy-to-use mathematical equation for RTFW for small urban watersheds based on computer simulations. First, a coupled 1D–2D dual-drainage model was used to simulate the flooded area in nine watersheds of Seoul, Korea corresponding to 540 scenarios of various synthetic rainfall events and watershed imperviousness. Then, the results of the 101 simulations that caused the critical flooded depth (0.25 m–0.35 m) were used to develop the equation that relates the value of RTFW to the rainfall event temporal variability (represented as coefficient of variation or CV) and the watershed imperviousness (represented as NRCS Curve Number or CN). The results suggest that (1) RTFW exponentially decreases as the rainfall CV increases; (2) RTFW linearly decreases as the watershed CN increases; and that (3) RTFW is dominated by CV when the rainfall has low temporal variability (e.g., CV 0.4). For validation, the proposed equation was applied for the flood warning system with two storm events occurred in 2010 and 2011 over 239 watersheds in Seoul. The system showed the hit, false and missed alarm rates at 69% (48%), 31% (52%) and 6.7% (4.5%), respectively for the 2010 (2011) event.

Hydrological modeling academic studies have focused on the response to human-caused land use changes. The effects of land use change on flood degree in the catchment basin of Ekbatan Dam were investigated in this study, which looked at changes that occurred in 1985, 2000, and 2015. A combination of remote sensing and the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) was used to achieve this goal. First, Landsat satellite images and sensors from Thematic Mapper, Enhanced Thematic Mapper Plus (ETM +), and Operational Land Imager were used to create land use maps for the target years. The weighted curve numbers (CN), a parameter related to infiltration, were then calculated for land uses. The extracted CN value, along with physiographic parameters and rainfall-runoff data, was then imported into the HEC-HMS model to simulate the effect of land use changes on runoff volume. After calibration and validation of the model using five (5) flood events, the simulation results showed an increase in the discharge peak volume of 64.3, 67.3, and 70.5 (m3/s) during the years 1985, 2000, and 2015, respectively, which resulted in an increase in the runoff height in these years as well.

The need for conserving water rises as the resource becomes scarcer. The most essential step in order to manage this resource is to estimate watershed runoff. Runoff and rainfall are the main two sources that maintain ground water and recharge it. Hydrologists in the Kurdistan Region face the challenge of unavailability of data (most watersheds in the area are ungauged) or inaccurate data. The surface runoff of the study area was simulated via soil conservation service-curve number (SCS-CN) technique integrated into geographical information system (GIS). Surface runoff and curve number maps were made using the GIS created and processed data from land use/land cover (LU/LC), daily rainfall, hydrologic soil group and slope thematic maps. The results showed that the urbanization of the Sulaymaniyah watershed has increased the impermeability of the land by 40.9% for the period from 1999 to 2019. The runoff depth was 40.2% higher in 2019 as compared to 1999 due to the increase of the impermeable surface area. Research has also revealed a growth in built-up areas with a decrease of coverage in vegetation, resulting in a greater depth of surface runoff in urban catchment areas.

This paper attempts to show a relationship between Soil Conservation Service (SCS) parameter curve number (CN), potential evapotranspiration (PET), and time duration (D) using the data of four Ethiopian watersheds. CN values were determined for various durations from observed rainfall-runoff data, and PET was derived for different watersheds utilizing the Thornthwaite method employing temperature data. The results indicate that the CN values decline exponentially with increasing PET and duration D and vice versa for all three wetness conditions of the watersheds. This study confirmed that as the duration upsurges, the CN values diminution due to the more extended time accessible for water loss in the watershed. The proposed simple model based on CN-PET-Duration also strongly supports the derived relationship for all watersheds for three wetness conditions. This relationship is also useful for the estimation of PET from the available published CN values.

Water is extremely critical for human survival. It is therefore important to efficiently manage integrated water resources, which includes basins, watersheds, and wadies. In this study, the freely available data was used, which included a digital elevation model, soil and geological maps, satellite imageries, and daily precipitation records. Furthermore, remote sensing techniques and geographic information system were also integrated in this study. The linear and areal aspects, along with the longitudinal profile were also included, and the hydrologic soil groups (HSGs), land use/land cover (LULC), and curve number (CN) were identified for the main basin of the Riyadh capital. The morphometric analysis indicated that the total area of the main basin was 8500 km2, with 40 watersheds and the highest stream order as eighth. The drainage density of the main basin was 1.57 km/km2. Impermeable surface constituted most of the basin area, as group D was the most prevalently found HSG, constituting 71% of the total basin area. Additionally, the main basin had a highly steep slope, with varying elevations. Furthermore, four types of LULC were identified in the basin namely urban-based, barren land, cultivated land, and ways. The CN for every cell of the main basin ranged from 64 to 98, and the basin weighted CN was found to be 92. The potential direct runoff ranged from 10 to 59 mm. More than 70% of the total area was found to be vulnerable to high and very high runoffs (> 35 mm). These values confirmed the occurrence of flood events, particularly in urban areas and cultivated lands.

This study presents a procedure to estimate more accurate direct surface runoff from large rainfall (>25.4 mm)-runoff events. Improved models (M5-M7) are derived by coupling two concepts: (i) initial abstraction as 2% of the total rainfall and (ii) runoff coefficient = degree of saturation. Performance of ten different models including the original SCS-CN method (M1), Mishra and Singh 2002 (M2), Mishra et al. 2006 (M3), Ajmal et al. 2016 (M4), improved models (M5-M7) and their simplified forms (M8-M10) is evaluated using large (7687) number of rainfall events derived from 98 watersheds of USDA-ARS to assess the accuracy of runoff estimation. Quantitatively, it is assessed using seven performance indices, viz., R2, NSE, PBIAS, RMSE, NRMSE, RSR, and MAE; categories; and Ranking and Grading System (RGS). The resulting high values of R2, RSR, RGS, and lowest values of NSE, PBIAS, RMSE, NRMSE, and MAE for the improved models (M5-M7) reveal that improved models performed better than the existing models (M1-M4). Similarly, based on different performance categories, all improved models exhibited superior performance in most of the watersheds than did the existing models. Sensitivity analysis indicated CN to be the most sensitive parameter of the improved model. The proposed model is seen to have overcome the limitations of the original and its previous versions intended for large events and can thus be used for estimating runoff more accurately.

The accumulation of floatable litter load captured in gross pollutant trap (GPT) due to the hydrological runoff has become challenging due to rapid development and alteration of land use changes. The application of soil conservation service curve number (SCS-CN) method has been widely used to estimate the direct runoff from the given rainfall especially when there is no runoff measured within the catchment. The results found that there was enormous variation on floatable litter load between different months and individual GPT. There was a high accumulation of floatable litter load on August 2015 with 495 kg/month and at K17 with 510 kg. Both of these conditions was significantly influenced by rainfall depth, hydrological parameters such as initial abstraction, potential maximum retention, and average weighted curve number (CNaw) values from different types of land use within their respective catchment area for individual GPT. Thus, the outcomes from this study can be very useful and beneficial for related government agencies and various stakeholders in ameliorating this critical and challenging environmental issue in terms of operation and management; technical operation; the selection of GPT prior to installation in other areas including the design, costs and maintenance.

Hydrological models are a conceptual representation of a simplified hydrological cycle. The hydrological cycle is the water cycle that circulates water from the land surface to the atmosphere and back again to the land. With the use of Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS), an event-based model and a continous hydrological model were established in Zijinguan watershed of Daqinghe River basin. To study the loss methods provided by HEC-HMS and its appropriateness for model fitting, is the main objective of this paper. The watershed was delineated with HEC-GeoHMS in ArcGIS, and its properties were extracted from a Digital Elevation Model (DEM) of 30 m × 30 m. The HEC-HMS includes a soil conservation service curve number (SCS-CN) method and a soil moisture calculation (SMA) loss method, simulating the event and continuous runoff, respectively. SCS Unit hydrograph and Muskingum were used for flow routing. Specifically, eight rainfall events were selected for calibrating (6 events) and verifying (2 events) the event-based model. Similarly, for the continuous model the wet seasons of eight different years were used for calibration and verification. The calibrated parameters of the events model were used in the continuous model. The soil moisture and evapotranspiration data were decoded from the satellite data to set in the continuous model. The performance of SCS-CN and SMA models was compared. During the calibration period, the values of NSE, PEV and PEPF range from 0.605 to 0.744, 3.1 to 13.58%, and 11.104 to 27.72%, and during validation they are 0.527 to 0.634, 4.35% to 5.01%, and 13.66% to 27.88%, for SCS-CN model. For the SMA model, the values of NSE, PEV and PEPF during calibration range from 0.434 to 0.604, 2.879 to 34.326%, −4.831 to 57.48%, and during validation they are 0.094 to 0.624, −19.52% to −12.55%, and 40.213% to 50.15%. Overall, the performance of SCS-CN model is found more satisfactory than that of SMA model.

The ‘Normalized Antecedent Precipitation Index (NAPI)’ model developed based on water balance equation was found capable to predict runoff yields from ungauged catchment when its parameters estimated from the gauged catchment are updated using the linear relationship of geomorphologic parameters of an ungauged to that of the gauged catchment, and cumulative geomorphologic index (CGI). The CGI was developed by assigning a relative weight on each geomorphologic parameter multiplied by the ratio of characteristic value of that parameter of the ungauged and gauged catchment. Influence of land-use and land-cover (LULC) on the model’s parameters was also analyzed by developing an index for LULC. The NAPI model has three parameters and its mathematical structure has rational form and the parameters possessed resonance with curve number (CN) of the SCS (Soil Conservation Services) model. The NAPI model demonstrated ability to simulate rainfall-runoff events both as direct and inverse problem. Performances of the model to predictions of runoffs from ungauged catchments were also tested with the data of two observation sites of the Bina basin in Madhya Pradesh (India) considering the data of one site as the runoffs from the ungauged catchment. The results exhibited a close match between the computed and observed values when the model’s parameters were also updated by the index of LULC.

The progressive decrease in natural surfaces to create space for urban dwellers with the consequential changes caused to hydrological regime of an area is taking a toll on environmental resources. The Natural Resources Conservation Service (NRCS) Curve Number (CN) model (earlier known as Soil Conservation Service Curve Number, SCS-CN) is used in the present study for assessing temporal variations in surface runoff in upper catchment of Rispana River, Dehradun city, India. Also, the composite CN is calculated using vegetation-impervious surface-soil (V-I-S) fraction derived from spectral normalisation and linear spectral mixture analysis method while assigning initial CN for hydrologic soil-cover complex for five years during 1991 to 2018. The results indicated a steady rise in impervious surface fraction and consequent surface runoff (8.25%) from 1991 to 2018, while urban green space fraction has shown depleting trends in north and central part of the city. The level of imperviousness highlighted an over 60% of central part of the city as impervious, which gradually decreases outwards from the city. A positive correlation is observed between surface runoff and ISA (R2 varies between 0.62 and 0.89, p

Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naive Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data.

Freshwater is a major concern in arid and semi-arid regions of the West Bank due to political conditions where the Israeli occupation controls the groundwater resources and physical drivers such as land use/land cover (LULC) changes. The Nablus Mountains region receives between 175 and 739.1 mm of annual rainfall. Hence, direct runoff is high and untapped because of surface and rainfall characteristics. This study is aimed to identify the spatial distribution of LULC changes and its impact on potential rainfall–runoff water at the sub-watershed scale for eleven main watersheds in the Nablus Mountains. Remote sensing data (Landsat 5, 7, and 8 images from 1984, 2000, and 2016, respectively) were employed to detect and measure LULC changes. Geographic information systems (GIS) and soil conservation service curve number (SCS-CN) methods were employed to calculate the potential runoff of 487 rainy days (2008–2018). Seven LULC classes were identified using the supervised classification method in GIS (10.5). The findings of this study revealed an increase of 116%, 22%, and 8% in urban areas, bare lands, and agriculture areas, respectively, and a decrease of 14% and 22% in pastures areas, and forests, respectively, between 1984 and 2016. Furthermore, there was a significant increase of 5.8% in the potential annual runoff between 1984 and 2016 due to LULC changes which is around 7.8 M3M. The runoff estimation model indicated that the upstream sub-watersheds of Qana, Dair-Balout, and Zoumar watersheds are highly suitable locations for rainwater harvesting and flash floods mitigating projects.

Streamflow modeling is essential in developing long-term water resource management plans. Especially in the inaccessible area like Toba sub-watershed, Ethiopia, modeling is an alternative means of obtaining streamflow data. Knowing of the hydrological response of a watershed would help to resolve potential water resources problems associated with floods, droughts, and availability of water for irrigation, hydropower, domestic and industrial use, and to develop the adaptation and preparedness strategies to meet these challenges, in case of their occurrence. In the present study, the Soil and Water Assessment Tool (ArcSWAT version 2012) model was used to simulate streamflow from Toba sub-watershed located in the southwest part of Ethiopia. The model Sensitivity analysis, calibration, and validation made using a sequential uncertainty fitting (SUFI-2) method of the ArcSWAT Calibration Uncertainty program known as SWAT-CUP2012. Groundwater delay (GW_DELAY) and curve number (CN2) were the first and the second most sensitive parameters. Calibration and validation results indicated a good much between the measured and simulated streamflow data with R2, 0.82 and 0.70 and NSE, 0.72, and 0.68, respectively. The mean monthly stream simulated at Toba station was 58.71m3/s and 55.91m3/s during calibration and validation, respectively, and the mean annual runoff of the Toba river was 434.38 mm.

The changes that occur in various forms of land classes are known as land-use and land-cover changes (LULC). Such changes have a substantial impact on the contours of an urban basin and consequently have an effect on the surface runoff of the rainfall that occurs in the area. The runoff features obviously deteriorate because of the decrease in initial abstraction, and an increase in imperviousness causes enhanced runoff. The particular challenges in the urban basin are specificity calculation and quantification of surface runoff. Geospatial techniques are adopted along with the Soil Conservation Service-curve number (SCS-CN) technique to reliably predict and accurately measure runoff. The GIS is used to prepare the transformed layers of land classes from remotely sensed data. In this explorative study, LULC change and its impact on the urban Koraiyar basin, Tiruchirappalli city, South India, is studied by using SCS-CN with the aid of GIS techniques. As the basin that passes is located in a developing city, rapid changes in LULC are observed in and around the periphery of the basin. The LULC changes and their impact on surface runoff are analyzed using GIS with multi-dated Landsat satellite images for the years 1986–2016 at intervals of every 10 years. The supervised classification algorithm is used to develop LULC maps. From the study, it is observed that there is a continuous increase in settlement area of 1.04% from 1986 to 2016, especially in the northern part of the basin. A Markov model analysis is done from the historically developed LULC maps to predict the anticipated future LULC changes for the years 2026 and 2036 along with an estimation of the surface runoff. The predicted maps show that there is likely to be an increase in settlement area of 1.04% and decrease in 12.29% of agricultural land. The various thematic layers like slope, soil and curve number (CN) maps are also prepared using GIS. The composite CNs are generated for various land classes in the basin from 1986 to 2036. The increase in CN from 72 to 74.76 and their influence on runoff are studied. Finally, the study attempts to reliably estimate the present and future LULC status and its effect on surface runoff in the Koraiyar river basin.

In view of an exponential increase in the negative impacts of flash-floods globally, the present work aims at the identification of flash-floods-prone river reaches in the Beas river basin, Himachal Pradesh, India using a multi-criteria indexing technique. The flood hazard index (FHI) was computed by implementing analytical hierarchy process (AHP) model on 6 hydrologic parameters influencing flood hazard, namely rainfall intensity, curve number (CN) grid, time of travel, slope, Manning's roughness coefficient and drainage density. The CN grid (empirical parameter to estimate direct surface runoff) was used as one of the parameters which depend upon the land use, hydrologic soil group and hydrologic conditions. It is imperative to mention that remote sensing and geographical information system (GIS) techniques played a crucial role in the preparation of these 6 parameter layers. The AHP model calculates the normalized weights for each parameter using pair-wise comparison matrices. The rainfall intensity and curve number were the factors having the highest normalized weight of 34.52 each. Subsequently, the estimated weights of the parameters and hazard level-wise rating scores were used in a GIS environment to generate FHI. The generated FHI raster was masked using floodplain layer within geomorphology map and river buffer to identify flash-floods-affected river reaches. The generated flash-floods map was validated by historical flash-floods ground points, field observations and remote sensing data. The results depicted that the river reaches in the north and east of the Beas basin are susceptible to flash-floods which are mainly governed by heavy rainfall intensity and high runoff characteristics. The river stretches namely Bahang–Manali (Beas), Kullu–Bhuntar (Beas) and Manikaran–Kheer-Ganga (Parvati) have been categorized into very high and high flash-floods zones. Decreasing trend of normalized differential vegetation index (NDVI) was observed for river reaches falling within the very high and high zones indicating the vegetation loss post successive flash-floods events. The river order 2 lies in the very high and high flash-floods zones, indicating the fact that the contribution of tributaries is significant to flood events. Flash-floods map will serve as catastrophic product, which will help policymakers to take suitable measures to reduce the risk of flash-floods.

The objective of this research is to deliver an unprecedented study of hourly SWAT model simulations of discharge in two local watersheds (around 50 km2). An urbanized watershed and a forest watershed across northwest Delaware and northeastern US were selected. Both watersheds are developed and vulnerable to flash flood. Very high resolution (2 m) topography and land use information were used to depict complex urban landscape. The model calibration and validation were performed on 2010 and 2011, starting with 2009 as model spin-up period. Satisfactory fitness results were achieved at both forest and urbanized watershed, with the Nash–Sutcliff coefficients of 0.68 and 0.77, R2 of 0.7 and 0.78, and percent bias of 1.5 and 9.3, respectively. The calibration results showed that the 2-m land use accurately captured the impervious surface due to 8.9% adjustment of curve number. But, greater steepness of slope derived from 2-m DEM leads to faster runoff than reality, which means 2-m DEM does not accurately depict the topography and finer detail DEM is needed to capture the local topographic variation. To reproduce the seasonality of discharge at hourly time step, additional examination of parameters influencing baseflow was needed as part of local sensitivity test. For the peak flow dynamic simulation, substantial adjustments were performed for parameters conditioning runoff travel time, such as Manning roughness. We found hourly SWAT is a reliable modelling tool for local level watersheds but needs deliberate attention to the input selection of land use and DEM and calibration process.

Climate projections point to modifications in the magnitude, frequency and timing of floods in the future. However, robust methodologies to quantify how climate change will modify the catchment response in flood events are required. Continuous hydrological modelling usually smooth magnitudes of extreme events. This paper proposes a methodology to improve the assessment of flood changes in the future driven by climate change. Climate change projections of the EURO-CORDEX programme obtained under the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) supplied are used. Four catchments located on the Douro River Basin have been considered as case studies. Precipitation and temperature projections have been bias corrected to reduce errors with observations in the control period (1971–2004). The HBV continuous hydrological simulation model has been used to simulate the soil moisture content on the day of occurrence of the maximum annual rainfalls in the four catchments. The modified curve number method has been utilized to obtain the changes expected in the future in flood magnitudes, considering the initial soil moisture contents estimated with the HBV model and the expected changes in annual maximum rainfalls. The methodology has been applied to the control period (1971–2004) to check the validity of the process. Then, the methodology has been applied to the future period (2011–2095), to quantify the changes expected in the future in flood magnitudes under climate change conditions. The combined use of the HBV continuous hydrological simulation with the modified curve number method improves the results provided by the HBV model. The proposed methodology allows a better characterization of the response of catchments in flood events. It also considers the expected variation in the antecedent moisture content in catchments in the future, driven by increasing temperatures and decreasing mean annual precipitations in the future. The results show that flood quantiles will increase in three of the four catchments considered.

Soil information is a vital input for crop models applications in various large area studies including climate change impact and food security. One of the global soil databases that provide full information for crop models is HC27 of IFPRI. The quality of the database has not been assessed for crop modeling so far. A tested crop simulation model (SSM-iCrop2) was used for this purpose that needs soil water related properties (i.e., depth, albedo, curve number for runoff, drainage coefficient, and soil water limits at saturation, drained upper limit and lower limit) for the simulation of crop properties. Actual data of two soil profiles from three different climate zones (locations) were used as model inputs to simulate potential yield, evapotranspiration (under rainfed conditions) or net irrigation water requirement (under irrigated conditions) of some important plant species (alfalfa, sugar beet, sugar cane, wheat, olive, soybean, apricot and chickpea) under rainfed and irrigated conditions of Iran. Results showed that the application of HC27 soil information in the SSM-iCrop2 model resulted in model output that was not different from the model output with actual soil information with respect to mean, variance, and distribution. No statistically significant difference was found in the simulation of various combinations of soil profiles-plant species-locations. It was concluded that HC27 information can be used in simulation studies with SSM-iCrop2 or other similar simple models for the simulation of potential yield, net irrigation water, or evapotranspiration that are commonly required for food security and climate change studies.

Hydrological runoff impacts on the accumulation of floatable litter in river system have not been extensively studied in Malaysia. Thus, the aim of this paper was to identify the variation on the accumulation of floatable litter load and its composition at floating debris boom (FDB) structure through the process of hydrological runoff. 14 days of passive sampling was performed periodically using the bucket conveyer machine. The rainfall event prior to the sampling date was used in Soil Conservation Service curve number (SCS-CN) method to generate the runoff volume. The results revealed that the highest accumulation of floatable litter load was 4654.60 kg/day during the first sampling. The plastic and organic waste load were recorded as the highest composition load with 881.50 kg/day during first sampling and 501.00 kg/day during second sampling, respectively. The floatable litter transport was demonstrated through the relationship between cumulative floatable litter loads and cumulative hydrological runoff during the first sampling with R2 = 0.9572, r = 0.978 (p

Flash floods pose significant threats to the socio-economic development of the Jalpaiguri and Darjeeling Districts. These districts situated in the piedmont zone of Sikkim–Darjeeling and Bhutanese Himalayas suffer from the flash floods, and those cause tremendous loss of life and property more or less every year. This study assessed flash flood risk of watersheds of the Himalayan foreland of Jalpaiguri and Darjeeling Districts in support of mitigation planning. Different hydro-geomorphological setup controls the magnitude, frequency and distribution of flash flood like topography, geomorphology, geology and climatology of the area under study. Land use/land cover and soils also have an integral relationship with run-off generation in the watersheds. Thus, we adopted holistic approach considering the topographic, hydrological, climatological, geological, soil and land use/land cover factors to assess the relative susceptibility to flash floods of the watersheds of Himalayan foreland of Jalpaiguri and Darjeeling Districts. Jaxa 30m DSM, Landsat8 OLI/TIRS and Sentinel 2A satellite images, digitized drainage network, geological, rainfall, soil and geomorphological map were analysed in GIS environment to infer lithology, land use, hydrological soil type and watershed morphometrics. The morphometric parameters were used to assign the relative susceptibility of the watersheds to flash flood, applying the weighted sum average method. Soil Conservation Service rainfall–runoff model of USDA and synthetic unit hydrograph were used to infer the hydrological response of the basin including curve number, runoff depth, time of concentration, lag time, peak discharge, etc. Final flash flood risk map was achieved by the integration of both the susceptibility maps. Higher weightage was given to the susceptibility map produced from run-off modelling and synthetic hydrograph parameters. The result shows that 63% of basins are fall in the high to very high categories of flash flood risk, 28% under medium and only 9% in the low categories of flash flood risk. Accuracy of the model was assessed using the flood inventory coupled with field diagnosis of past flood damages and available records. The resulting flash flood risk map could be used by the planners to adopt mitigation strategies to reduce the severity of the flash flood hazard.

Flood is one of the natural disasters that generates a lot of damages every year in different points of the world. Also, soil erosion is among the processes that threats the soil and water resources of the country. The performance of a small watershed is not same as the hydrologic response of a large watershed. Determining the amount of participation and prioritizing the sub-basins in terms of flood generation in the outlet of the basin can be a large help in correct locating the flood control and soil and water conservation projects and leads to decreasing the negative impacts of the flood control operations at unnecessary regions or at the regions with lower priority and also it prevents personal tastes. Therefore, determining the flood generator regions and prioritizing the sub-basins in terms of flooding and sedimentation potential is essential for better management of the watersheds. For this purpose, the aim of this research is to determine the amount of participation of the eastern sub-basins of the Gorganrud River Basin of Golestan province, Iran in flooding and sedimentation and their prioritization in terms of flooding and sedimentation potential using the multi-criteria decision-making methods. In this present study, we used area estimation indices, gravel coefficient, drainage density, basin average slope, basin average height, curve number, cover percentage, sediment yield, sediment delivery ratio, runoff height and concentration time. Indicators are considered to be important indicators affecting water permeability, runoff production and, consequently, the potential for flooding and sedimentation. Following the formation of decision matrix with 13 options (sub-basins) and 11 criteria (evaluation index), Technique For order Preference by Similarity to ideal Solution (TOPSIS), Simple Additive Weighting (SAW), Elimination Et Choice Translation Reality (ELECTRE) and Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) techniques were used to prioritize sub-basins. Borda and Copland methods were used to combine the rank of proposed techniques. Also, in order to validate the models, we estimated the percentage change and the intensity of the changes. The results showed that the highest runoff height index (0.179) and the average height index of the basin had the lowest weight (0.031), according to experts. Considering the results of the combined ranking of the proposed techniques, sub-basins 12, 1 and 2 are in first to third priority, respectively, and have a more critical situation than the rest of the sub-basins. Field studies clearly show the results of the research, because sub-basins 12 and 1 exhibit the highest erosion, poor soil and gradient. Also, zones with flood and sedimentation potential in the area showed that 49/31% of the area in high and very high risk. This study proves that multi-criteria decision-making methods and RS and GIS techniques are very suitable, precise, economically and temporally advantageous, and helpful tools to evaluate and prioritize the sub-basins in the soil erosion and soil and water conservation topics. Therefore, considering the multiple objective functions and the costly watershed management operations, it can be said that multi-criteria decision-making methods can be used for better management of watersheds in terms of biological and structural flood control operations. So, prioritization can be done based on a mathematical logic. The proposed method in this research is very suitable for watersheds without sufficient data. Hence, such researches which are low cost as well as quick can be used and watersheds can be prioritized for management and conservative acts.

Considering the severity of a catastrophic flood in the Teesta river in India earlier, it is indispensable to assess the runoff at the basin’s ungauged outlet, Mekhliganj. Almost every other year, the downstream of the river is in spate during the monsoon months. Among the three gauging stations in the basin, Teesta Bazaar, Domohani, and Mekhliganj, streamflow data is unavailable at the latter one. In this context, this study focuses on estimating the runoff of the Teesta river basin at its outlet, Mekhliganj, using Hydrologic Engineering Center-Hydrological Modeling System (HEC-HMS) software, verifying the output from the model through the measured streamflow data at the other two upstream gauging locations, Teesta Bazaar and Domohani. The runoff has been estimated for daily precipitation from May to October, applying Soil Conservation Service (SCS), curve number (CN), and SCS unit hydrograph method. Subsequently, the model has been calibrated for 2001 and 2006 to optimize both the Muskingum routing parameters. The model has been validated for some specific years like 2004, 2012, 2013, and 2016, which experienced extreme deluge. The results for both the upstream gauging stations bear a good correlation between the computed and observed data, evaluated through the statistical indicators such as percentage error in peak flow (PEPF), percentage error in volume (PEV), Nash-Sutcliffe (NSE), and R2, consequently justifying the simulated result at the outlet. This study is necessary for this vast watershed area with limited gauging stations and an ungauged outlet to understand the basin response and its derived consequence.

Sediment-Runoff process is highly variable and nonlinear in nature. In the present study an attempt has been made to develop a relationship between watershed stream length and Sediment Yield Index (SYI) and test it on Narmada watersheds, Madhya Pradesh, India. Area (A), Curve Number (CN) and stream length (SL) were utilized as input for model development. The three models (A model, CN model and simplified All India Soil and Land Use Survey (AISLUS) model including parameter SL) performed differently, with the coefficient of R2 equal to 0.01, 0.02 and 0.71 (Shakkar watershed), 0.11, 0.23 and 0.91 (Bamhani watershed), 0.06, 0.001 and 0.80 (Manot watershed) and 0.40, 0.05 and 0.66 (Mohgaon watershed), respectively. The logarithmic simplified AISLUS model incorporating parameter SL resulted with the coefficient of R2 as 0.76 (Shakkar watershed), 0.93 (Bamhani watershed), 0.84 (Manot watershed) and 0.66 (Mohgaon watershed). Therefore, the logarithm simplified AISLUS model was chosen as the best regression model for this study. It is observed that the simplified AISLUS model (logarithm form) incorporating parameter SL had a satisfactory efficiency as 76.35% (Shakkar watershed), 66.05% (Mohgaon watershed), 93.36% (Bamhani watershed), and 83.83% (Manot watershed) by Nash efficiency scale. The resulting higher Nash efficiency values support the versatility of the derived relationship and invoke assessment of SYI from the watershed stream length value. The prediction of SYI is important when adopting a suitable soil conservation measure in the watershed for minimizing soil erosion.

Rainfall runoff is a complex phenomenon in nature. It differs from place to place due to different topographical features and rainfall patterns. The Natural Resources Conservation Service - Curve Number (NRCS-CN) is a well-adopted model to account for direct runoff volume from storm events. There are several studies on determining the initial abstraction and the CN from observed rainfall-runoff data; however, few studies demonstrate their statistical characteristics. The major aim of this study is to determine the feasible range and the confidence intervals of the CN. We examined 660 rainfall-runoff events collected from six medium sized watersheds in South Korea. The interior point optimization algorithm was adopted to ascertain the optimum value of CN and the initial abstraction coefficient (λ). The obtained results show that the CN value ranged from 45 to 90 and the average λ = 0.12 was best suited for Korean watersheds. The estimated confidence intervals were highly significant and strongly recommended for Korean watersheds.

The objective of this study is to compare the effect of different spatial resolution of satellite images (Landsat-8, Sentinel-2 and Gaofen-1) for deriving LULC map and its effects on the performance of HEC-HMS lumped and Flood Hydro distributed models in flood hydrograph simulation in the Ammameh Watershed, Iran. The Soil Conservation Service Curve Number method was used for considering the rainfall loss rate in both models. The value of curve number was determined based on hydrologic soil groups and produced LULC maps from the satellite images with respect to soil moisture conditions. The performance of HEC-HMS lumped and Flood Hydro distributed models in flood hydrograph simulation was evaluated for 15 and five rainfall-runoff events, respectively. The simulation results of flood hydrographs in validation phase showed that both models provided acceptable results, and there were no significant differences among the results of three remotely sensed images for simulating the flood hydrographs.

Flood is ranked as the deadliest natural disaster that has been experienced by the urban basins in the world. Its detrimental effects can be minimized by appropriate modeling, analysis and management methods. Such modeling and analysis techniques help in flood risk assessment predicting flood occurrence, aid in the emergency preparation for evacuation and reduce damage from the impact of floods. Numerous modeling techniques are available for analyzing flood events, of which HEC-HMS software is chosen for this explorative study because of its simplicity and as it is a freely available open-source software. The present study aims to develop a rainfall–runoff simulation model by generating peak flow and volume of the extreme rainfall event that occurred on 22 November 1999 in the ungauged Koraiyar basin located south of Tiruchirappalli City in South India. The hydrographs are generated for the basin by using specified hyetograph and frequency storm method to identify the best method to be adopted in the study. Digital elevation model processed with geographic information system (GIS) and HEC-Geo HMS, which is an extension of GIS, is used for the analysis. Using the terrain processing tools in ArcGIS, the basin delineation and parameters such as slope and river length are extracted from the basin. The data generated during the HEC-Geo HMS process are the hydrologic parameters of Koraiyar basin, and it is imported to HEC-HMS modeling for generating peak flow and volume. In the modeling process, HEC-HMS has three modules, namely transform, loss and base flow. SCS curve number and SCS unit hydrograph are used to determine the losses and transformation of rainfall into the runoff process in the present study. The SCS method is adopted because of its simplicity and requirement of limited data approach for modeling. The peak flow and volume prepared from the model are compared with the standard Nash–Sutcliffe values. The frequency storm method has a Nash value of 0.7, which is higher than the value obtained from the specified hyetograph process, and it is chosen as a better model for generating flood peak and volume for different return periods in the basin. It can therefore be adopted for other studies of similar basin conditions.

The Soil Conservation Service Curve Number (SCS-CN) method is frequently used for the estimation of direct surface runoff depth from the small watersheds. Coupling the SCS-CN method with the Soil Moisture Balance (SMB) method, new simple 2-parameters rainfall-runoff model and 3-parametrs rainfall-sediment yield models are derived for computation of runoff and sediment yield respectively. The proposed runoff (R2) and sediment yield (S2) models have been tested on a large set of rainfall-runoff and sediment yield data (98 storm events) obtained from twelve watersheds from different land use/land cover, soil and climatic conditions. The improved runoff (R2) and sediment yield (S2) models show superior results as compared to the existing Mishra et al. (S1) and original SCS-CN (R1) models. The results and analysis justify the use of the proposed models for field applications.

In flood management, analysis and modelling are needed, especially for the analysis of the occurrence of floods and decision making. One method for analyzing flood events can use the HEC–HMS software. In HEC–HMS, there are three sub-models, namely, losses, transform, and baseflow. In the development of the use of the HEC–HMS model, it still utilizes sub-model transform, where the peak rate factor (PRF) value is constant at 484 in modelling. The value of PRF is very dependent on the slope of the land (basin slope), where the value of this parameter is very varied depending on the physical condition of the area under study. Because of the fact, where the value of PRF should be varied depends on the slope in modelling stage; while the reality is still constant, this research is done. The research result shows that the role of PRF makes the modelling carried out using HEC–HMS produce comparable results as indicated by the objective function values in two study areas. The following is the comparison of the objective function of the Selorejo watershed in the March 2007 flood event between constant PRF 484 with variations in the values of PRF, RMSE (0.63 m; 0.59 m), CORREL (0.977; 0.976), and DELTAPEAK (43.04%; 41.46%). Whereas in the PDA Cipasang watershed, Nash–Sutcliffe (0.818; 0.820), RMSE (66.20 m3/s; 65.91 m3/s), CORREL (0.916; 0.917), and DELTAPEAK (15.55%; 15.35%). To utilize the variation of PRF into the model, based on the results of this study, the sub-model used is SCS Curve Number for Losses, SCS Units Hydrograph for Transform, and Recession Constant for Baseflow. It can be concluded that by including the influence of variations in PRF values resulting not a better model but only a slight improvement which is insignificant and a more complete model.

Curve number (CN) is a principal factor which is widely used in hydrology, specifically in the rainfall-runoff modelling. Its value varies based on soil moisture condition; soil hydrologic group, land use, and vegetation cover type. Remote sensing technology provides a tool to investigate spatiotemporal variations of land-cover. This may lead to generation of some continuous spatiotemporal CN datasets required for many hydrological applications. The purpose of this research is to develop an approach for estimating a monthly-distributed curve number via MODIS leaf area index (LAI) products. For calculating monthly CN, we investigated the relationship between monthly LAI, rainfall and CN under different computational scenarios of arithmetic mean, median, and geometric mean for estimating monthly LAI and rainfall. For this purpose, rainfall data for the period 2002–2016 were collected. Further, LAI data were obtained from MODIS for the same period. The performance of the modelled CN (correlation coefficients and Nash-Sutcliffe coefficient) was compared against obtained values of monthly rainfall and runoff in the SCS-CN method (observation-based CN). The findings of this study suggested that CN values obtained from the arithmetic mean of LAI and the geometric mean of rainfall yielded the best scenario (R2 = 0.916 and NS = 0.903 for calibration set; R2 = 0.926 and NS = 0.892 for validation set). Therefore, we suggest a simple appropriate method to generate monthly spatially distributed CN for hydrological applications.

In recent years, several empirical and mathematical methods have been developed to estimate runoff, among which the SCS curve number (SCS-CN) method is one of the simplest and most widely used methods. The SCS-CN depends mainly on a CN parameter which corresponds to various soil, land cover, and land management conditions, selected from look-up tables. An application of GIS and RS techniques along with filed investigations made it possible to enhance the method from a lumped one to the level of semi-distributed models in which a specific value can be assigned to each cell in raster maps. The up-to-date procedures require several datasets, field measurements and overlying issues which limits the use of SCS-CN in data-scarce regions. In this research a new method has been developed which estimates the SCS-CN over the catchment with a minimum input dataset and acceptable accuracy and is based on the saturation-excess concept, which is used in the semi-distributed model: TOPMODEL. The proposed method depends on three parameters, including ndrain (soil porosity), $${\bar{\text{z}}}$$ (average distance to watershed water table surface) and m (which controls the effective depth of the saturated soil) and one input dataset, the so-called topographic index. Results showed that the maximum and minimum differences between the basin-averaged CN based on the GIS and RS techniques and the proposed method for Kasilian and Jong watersheds are 12% and 0.3%, respectively. Also, the findings indicated that, of the three parameters of proposed method, the m parameter plays a key role and that by increasing this parameter the basin-averaged CN tends to decrease and vice versa. Because of the dependence on a topographic index, the proposed method is strongly affected by DEM resolution and there are significant differences between low and high-resolution DEMs. However, for a small scale watershed, similar to Kasilian, using DEMs with resolution lower than 100 m considerably decreases the above differences. As an overall conclusion, the proposed method provides acceptable values of SCS-CN which is important for running rainfall-runoff model in a data-limited or data-scarce regions. In addition, creating the gridded map for CN, which is required in most hydrological models, is one of the most important advantages of the proposed method.

Underground dams are constructions that prevent the natural flow of ground water and create underground water resources. Although lateral-flow volume is one of the main criteria to locate an underground dam, this factor is ignored in the most studies. In this research, the water balance equation was simulated using the SWAT model in the Daroongar watershed (northeastern Iran), and the paths of subsurface water flows were identified. Continuing by giving the weight to an 11-layer analytical network process (ANP), potential suitable locations for the construction of underground dams were determined. With the implementation of the SWAT model in the Daroongar watershed, R2, bR2, and NS (Nash Satklyf) coefficients were 0.77, 0.75, and 0.68, respectively, in the calibration phase; in the validation phase, they were 0.71, 0.67, and 0.61 in the same order. All these coefficients show that the efficiency of the SWAT model in simulating water balance is acceptable. Results of the sensitivity analysis of the SWAT model show 27 effective parameters, where the runoff curve number (CN2) was identified as the most important parameter. The results of the ANP model prove that subsurface flow, weighing 0.131, includes the most weight allocated. Results from the ANP model indicated that this is necessary for determining the extent of subsurface flow using the SWAT model. In addition, the reservoir volume and runoff volume, respectively, with a weight of 0.109 and 0.101 are more important than other factors. As a result, the greater the current subsurface stream, the more important it is in relation with other streams. The results showed that streams with ranks 3 and 4, which are located on slopes less than 15%, are suitable for the construction of underground dams.

The Soil Conservation Service Curve Number (CN) method is widely used to calculate the flood runoff in ungauged catchments. However, the existing CN calculation method has a disadvantage in that it cannot calculate the CNs considering the spatiotemporal variability of rainfall in the ungauged catchments. In this study, the authors used a distributed rainfall-runoff model and a simple runoff generation method to generate the hourly runoffs of the ungauged catchments considering the spatiotemporal variability of rainfall, and estimate the CNs of the ungauged catchments using the generated runoff and the CN back calculation method. As a result of calculating the CNs of the ungauged catchments for past 20 rainfall events, the CNs had a large variability even in the same catchment. In addition, the mean CNs for the 20 rainfall events of independent ungauged catchments differed even if the ungauged catchments were close to each other. The method of estimating the CNs of the ungauged catchments presented in this study properly reflects the characteristics of actual rainfall and catchment, and thus can be usefully used to estimate valid CN.

Wide field application of curve number (CN) methodology in design flood estimation and forecasting studies is a well-accepted reality. The parameter CN is often regarded as a constant, but it is in fact a random variable, for it relies on a number of factors governing the process of rainfall-runoff. This paper suggests an alternative procedure to estimate design runoff (Q) using design storm (P) and design curve number (CN) values. To this end, design CN values for different durations and return periods have been derived from 25 years of daily annual maximum P-Q data of 10 Indian catchments. When compared, the computed design Q from design CN values was either very close to or slightly greater than the observed Q. The performance is also evaluated using coefficient of determination (R2), percentage of bias (PBIAS), and normalized Nash-Sutcliffe efficiency (NNSE). The magnitude of design CN is found to decrease for longer durations and smaller return periods, and vice versa, and such a behavior invoked the development of an empirical relation for estimation of design CN from given return period and duration for gauged as well as ungauged watersheds. The statistical analysis revealed the general extreme value (GEV) to fit best the P-CN series, whereas multiple probability distributions, viz., GEV, Gumbel max, log Pearson 3, and Gen Pareto fitted best the Q series. The 50-year, 100-year, and 200-year runoff estimated from the P-CN series exceeded only marginally those derived from the conventional Q series approach.

The accessibility of water resources is depleting everyday on a spatial and temporal scale, while water demand is expanding reliably. With this view, the investigation is an endeavour to identify the best locations appropriate for water conservation measures in West Midnapur, Purulia, and Bankura regions of West Bengal, India, using Geoinformatics approach. In this study, administrative boundaries have been considered to get the best locations for all water conservation measures. Then, these locations are shown district wise by considering administrative boundaries. In India, most of the funds are allocated based on administrative boundaries so it is necessary to suggest water conservation measures based on administrative boundaries. The Digital Elevation Model (DEM), Landsat 7 satellite imagery, and daily precipitation data of the year 2011 were used to get ready layers of the slope, and land use land cover. The runoff and runoff potential zones were determined using the Soil Conservation Service Curve Number (SCS-CN) method in the Geographic Information System (GIS) environment. The weights were assigned to thematic layers (slope and runoff coefficient) and ranks were given to these features; then, score was calculated to find out the Water Conservation Potential Index (WCPI). Thematic layers were integrated using ArcGIS software to generate water conservation potential maps for both surface water harvesting structures and groundwater recharge measures. Based on the values of WCPI, zones suitable for surface water harvesting structures and groundwater recharge measures were identified using slope, soil, drainage order, and rainfall as suitability criteria. The outcomes demonstrated that 479, 1884, 1400, 808, and 140 locations in all the three regions were suitable for percolation ponds, farm ponds, check dams, contour bunds, and contour trenching respectively. This research will be useful for policymakers to allocate government funds based on administrative boundaries.

In this study, a method for estimating the exponent “n” values of the catchment-area equations of four sub-basins within the poorly gauged Benin-Owena River Basin Development Authority (BORBDA) in Nigeria is presented to enable the estimation of flows at ungauged sites within the basin and the determination of small hydropower (SHP) potential at different locations in each sub-basin and the entire basin. Optimal prediction of streamflow characteristics in poorly gauged basin requires developing a methodology for extrapolation of data from gauged to ungauged sites within the basin. Four sub-catchments of BORBDA, a poorly gauged basin in Nigeria, were investigated using Remote Sensing (RS), Geographic Information System (GIS), statistical techniques, and Natural Resources Conservation Service-Curve Number (NRCS-CN) hydrological model. Discharge values at gauged sites (Qg) were obtained from recorded discharge values collected for 12 months at an established gauging station in each sub-basin. RS and GIS techniques were used to develop classification maps and obtain crucial data like curve number (CN), elevation, Hydrologic Soil Group (HSG), rainfall intensity, slope, area of gauged and ungauged required for evaluating spatial discharge (ungauged) utilizing NRCS-CN model. From the established model for each sub-basin, exponent “n” in the relationship between discharge and catchment area was obtained to be 0.23, 0.41, 0.71, and 0.74. Using the lumped modeling approach, which considers a watershed as a single unit for computation, where watershed parameters and variables were to be averaged produced “n” = 0.52 for BORBDA area, which is within the range of 0.5–0.85 suggested by previous researchers. Obtained BORBDA exponent “n” was validated for use in the entire basin through soil homogeneity test by generating BORBDA soil map which confirms the four sub-basins investigated share similar HSG A, B, and D with BORBDA. The exponent “n” value is useful for predicting flows in ungauged parts of the basin. The exponent “n” value obtained for the basin is helpful in the assessment of discharge and determination of SHP potential at different locations within the poorly gauged BORBDA basin, and the dissemination of the research findings will find practical use and guide to practicing hydrologists in Nigeria and locations around the world with similar challenges of poorly gauged basins particularly Africa and other developing countries.

The Koraiyar River basin is located in Tiruchirappalli city, one of the fastest-growing medium-sized cities in south India. Over the decades, the city has experienced sporadic flooding in vast areas due to the breaching of bunds when the river is in spate. The objective of this investigative study is to simulate rainfall–runoff in the Koraiyar basin watershed through the reliable Hydrologic Modelling System developed by the Hydrologic Engineering Center (HEC-HMS). Land-use and land-cover constantly change due to increasing population taken as one of parameter, its influence on CN, and their impact on surface runoff is analyzed in this study. The specified hyetograph method is adopted for the meteorological modelling; the Soil Conservation Services-Curve Number (SCS-CN) is selected to calculate the loss rate, and the SCS unit hydrograph method is adopted to simulate the runoff rate. Calibration and validation of the model are done to simulate assessed peak discharge values for comparison with actual observed values. The Nash–Sutcliffe efficiency coefficient is between 0.5 and 0.6, which indicates that the hydrological modelling results are satisfactory and acceptable for simulation of rainfall–runoff. The peak discharge is obtained for the single maximum rainfall event of about 100 mm in a day over the past 40 years, and hydrographs are generated.

Climate change can adversely affect the elements of water balance in river basins. This paper studies the effects of climate change on hydro-climatological components of the Gomti River basin using SWAT hydrological model. Model performance has been evaluated through sensitivity analysis, calibration, and validation. During calibration and validation time period, the Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2) values were obtained as 0.86, 0.87, and 0.79, 0.83, respectively. From the sensitivity analysis, it was found that SCS runoff curve number for antecedent moisture condition II (CN2) is the most sensitive parameter, followed by saturated hydraulic conductivity (SOL_K) and then available water capacity of the soil layer (SOL_AWC) etc. To assess the future climate change effects on hydrological variables, MIROC-ESM, MIROC-ESM-CHEM, MIROC_MIROC5, and MOHC_HADGEM2_ES Global Climate Models (GCMs) under RCP 4.5 and RCP 8.5 were applied in the calibrated SWAT model. The following trends were observed after applying the scenarios—the temperature is rising under RCP 4.5 (8.5) scenarios, near-century by + 0.24% (+ 0.65), mid-century by + 2.8% (+ 4.62), and end-century by + 5.25% (+ 10.98), respectively. Precipitation is declining under RCP 4.5 (8.5) by − 29.17% (− 29.65%), − 28.69% (− 28.88%), and − 23.82% (− 20.11%), respectively. Evapotranspiration under RCP 4.5 (RCP 8.5) is increasing by + 3.62% (+ 3.95%), + 4.30% (+ 5.38%), and + 5.49% (+ 6.03%), respectively. Consequently, streamflow under RCP 4.5 (RCP 8.5) is declining by − 31.27% (− 23.99%), − 27.45% (− 28.96%), and − 24.30% (− 18.03%), respectively in near, mid, and end century. The findings will be beneficial for managing future water resources in the Gomti River basin.

In this study, a methodology has been proposed that can be used to estimate the peak unit discharge for all the watersheds in the northern part of Cyprus. Thus, the Soil Conservation Service method was used to determine the unit hydrographs of 366 watersheds available in the study area. Through the Digital Elevation Model with a spatial resolution of 12 m the watershed characteristics such as watershed boundaries, area, slope, and streams were defined. Runoff curve number maps for each watershed were created by the integration of the land use/land cover and soil data within a Geographic Information System technique. In the end, through genetic algorithm optimization, a dimensionally homogenous formula as a function of basin parameters was developed. The developed function can be used to estimate the unit discharge at ungauged watersheds of the northern part of Cyprus with a high degree of accuracy. Furthermore, an empirical equation based on morphometric watershed characteristics was also formulized to replace the time of concentration with parameters that directly affect the time, such as Bifurcation ratio and drainage density. The proposed empirical equation estimates the unit discharge in all the watersheds with the mean relative error of 11.97%.

There are various methods to estimate the curve number (CN) for flood studies. In the ungauged basins, hydrologists rely on the use of an NRCS-CN table called (CNdesign). The CNdesign, in this study, is estimated using remote sensing techniques and geographical information systems based on alluvium-rock-vegetation classification of natural basins. However, in gauged basins, it is common to use rainfall-runoff data through the application of the least-squares method (LSM) to get the best CN value (CNobs), or the asymptotic fitting method (AFM) to obtain asymptotic CN (CN∞). A comparison between these methods is made under the effect of changing both the coefficient of abstraction ratio, λ, and the effect of data sorting techniques to find out the best estimation of CN for reliable prediction of floods. A methodology has been developed to convert the NRCS-CN table values at λ = 0.2 to λ = 0.01 for arid basins. The relationship between the observed CN and the NRCS-CN table shows that estimating runoff using λ = 0.2 is best made by CN of the impervious area (CNimp = 90) instead of 98 (for urban areas) used in the literature. The highest value of CN between the methods is the CNdesign, then CNobs. CN∞ shows the lowest value. Therefore, for a safe design of the hydraulic structures, it is recommended to use CNdesign. However, for the simulation of the rainfall (P)-runoff (Q) process in the natural basins, it is recommended to use CNobs at the natural sorting of data pairs (P: Q). The root mean square error (RMSE) of CN is reduced from 11 at CNimp = 98 to 7 at CNimp = 90. This value reflects the infiltration process in the rocks due to the high density of fractures and fissures in the mountains in the area. The developed NRCS-CN table at λ = 0.01 reduces the RMSE of the estimated runoff depth by 57%.

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.

Desert precipitations are rare but torrential, and ephemeral streams are ungauged, by which synthetic hydrograph methods become essential for flood risk assessment. This study quantifies the impact of urbanization on generating flash floods in desert watersheds. A definition of flash flood was presented in terms of the relative strength of partial storm intensity to storm design frequency for existing stormwater drainage systems. A large desert catchment was chosen to generate synthetic surface runoff hydrograph records using HEC-HMS model. The catchment characteristics were extracted using GIS tools and digital elevation data. Intensity-duration-frequency curves were used for the catchment to simulate hypothetical storms of 24-h duration for different return periods. A sensitivity analysis was performed for curve number parameter to simulate urbanization level scenarios. The results show that it is possible to employ simple models to synthesize the impact of urbanization on desert flash floods with a drastic change in hydrograph peak discharge, time to peak, and runoff volume. The former hydrograph characteristic is found to be the most sensitive, with an increase by 316% in response to increase in curve number by 10%. This finding implies that a small variation in land cover due to urbanization may significantly increase the flash flood strength and potential damage to infrastructure and human life.

A unified and systematic approach for hydrological modeling is presented for assessing flood hazards in arid and semi-arid regions with wadi Itwad as a case study. The approach consists of the following steps: (1) estimation of the design storm duration, (2) estimation of the design rainfall depth, (3) development of the design storm hyetograph, (4) setup of the watershed hydrological system, (5) infiltration tests for estimate of soil characteristics, (6) land use and land cover analysis for curve number estimation, (7) how to incorporate dams (if any) and their reservoir characteristics for reservoir routing analysis, and (8) performing hydrological modeling under different scenarios, namely: lumped with and without the effect of dams and routed scenarios. An emphasis is made on the comparison between the SCS unit hydrograph and the unit hydrograph (UH) estimated for stream flow data. The UH method produces hydrographs that are typical of arid regions. It is characterized by a steep rising limb with a short time to peak, a rapid recession to zero base flow, and less flood volume due to transmission losses. The scenarios show that the lumped case overdesigns the protection schemes, while the lumped central basin with detention dams storing the flood water completely leads to underdesign in the downstream area. The routed scenario is more realistic, since it considers the detention dams with an overflow. The runoff volume (66.6 MCM) of the 10-year return period of the routed scenario is in the order of magnitude within the observed historical data (50 to 100 MCM) reported in the literature. The study concludes that the proposed approach is robust and takes into account the Saudi arid environment while performing flood risk assessment in KSA.

Background On sloped agricultural fields, water and sediment can be transported downhill as runoff and erosion. This process can cause losses of valuable top soil material, water resources for plant availability, and nutrients as well as transport of plant protection products (PPP) into adjacent surface water bodies. In the European and the US risk assessment for the registration of PPP, runoff and erosion are numerically calculated with the simulation model PRZM which uses the USDA runoff curve number (CN) concept for the water movement. Results from runoff field trials were used to estimate the effect of dedicated management practices in terms of mitigating runoff and erosion, i.e. creating micro-dams between the ridges of potato fields or in maize cultivation on model input parameters. Results Application of different cultivation and tillage techniques (micro-dams/bunds) showed a consistent decrease of the measured quantities of runoff, erosion, and PPP transport as well as of the calculated CN and predicted environmental concentrations in surface water. The results presented here support the approach to quantitatively consider in-field risk mitigation measures (if applied) in the context of regulatory surface water exposure calculations, as proposed by the SETAC MAgPIE workshop. Conclusion Based on these data, a robust case can be made to consider innovative runoff mitigation for risk assessment purposes by, e.g. lowering the CN in the exposure scenarios. In the assessment presented herein, an average decrease in the mean of the derived CN of 86 of 21 points (± 11, 10th percentile: 12) for potatoes could be derived. For maize, the mean calculated CN of 73 was lowered on average by 3 points.

This study aims to compute the potential runoff in the mountain Wadi Ḑamâd watershed located in the South Western of Saudi Arabia, using Soil Conservation Service-Curve Number (SCS-CN) method based on the maximum daily rainfall recorded during 47 years from 1970 to 2016 at the stations of Ḑamâd, Ayban, Wadi Ḑamâd and Jabal Fayfa, Malaki, and Al Aridhah. The SCS-CN method depends on land use, land cover, and soil complex characteristics of a watershed, and it is the hydrological model widely used for estimating runoff using curve number (CN). In the present study, SCS-CN method is used for estimating the potential runoff in the mountain drainage basin of Wadi Ḑamâd extends on area of 1084.0 km2. The CN was obtained at every studied station using the sheet 78 of the land resources map 1:500,000, and it is 62 at Ḑamâd, 66 at Ayban and Jabal Fayfa, and 70 at Wadi Ḑamâd, Malaki, and Al Aridhah. The potential maximum retention corresponding to these CN was 141.2 mm, 117.2 mm, and 108.9 mm. Therefore, the initial abstraction corresponding to the potential maximum retention is respectively 28.2 mm, 23.4 mm, 21.8 mm, and 23.4 mm. The results of this study reveal that the average of yearly rainfall varies from 171.3 to 538.1 mm, and the maximum daily rainfall is ranged between 37.4 and 77.5 mm at Malaki and Ḑamâd respectively. In the opposite, the excess rainfall and the direct runoff vary systematically with the rainfall. The maximum excess rainfall is ranged between 60.6 mm at Jabal Fayfa and 103.2 mm at Wadi Ḑamâd. However, the direct maximum runoff rises respectively from 20.6 to 50.2 mm.