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Ho Chi Minh City (HCMC) is among the cities that are most at risk of fl ooding worldwide. Urbanization processes have led to a change in land use, which in turn has resulted in an increase in impervious surfaces and runoff , thus again leading to the risk of fl ooding in the city. The aim of the article is to study the impact of urban development (on the example of District 8 of HCMC) on surface runoff using a combination of the interpretation of remote sensing images of the earth (ERS) Google Earth and the SCS-CN model (the Soil Conservation Service curve number). Theoretical models are used to analyze the relationship between the typology of buildings and areas of open and impervious surfaces. The interpretation of remote sensing images was carried out in the ArcGIS program. The method used to calculate surface runoff is the Soil Conservation Service Curve Number (SCS-CN) method developed by the US Soil Conservation Service and is suitable for assessing the eff ects of land-use/land cover change due to urbanization. The results of the study show the volume of surface runoff in areas with diff erent levels of urbanization in district 8 of HCMC, assessing the impact of urbanization processes on surface runoff and revealing new opportunities for managing this process. The combination of remote sensing interpretation and SCS-CN model makes it possible to assess the impact of urban development on surface runoff . Urbanization and an increase in built-up area strongly aff ect fl ooding, reducing the soil retention.

Runoff and erosion are the most important transport pathways of water, sediment, and associated pesticides from sloped agricultural fields. This results in the loss of fertile topsoil material, nutrients, irrigation water, and plant protection products (PPP) into adjacent surface water bodies. In the European and US risk assessment for the registration of PPP, runoff and erosion are numerically calculated with the simulation Pesticide Root Zone Model (PRZM) using the US Department of Agriculture (USDA) runoff curve number (CN) concept for the water movement and the MUSS equation to quantify the sediment transfer. This work presents an evaluation of maize field trials conducted in three seasons that considered micro-dams (i.e., small earthen dams between the rows; also known as "furrow diking," "furrow damming," etc.) and/or conservation tillage (via subsoiling) as mitigation measures to investigate the effects on the reduction in runoff and erosion. Measured quantitative reductions and event-wise calculated CN are presented. Furthermore, the trials were simulated using the PRZM over the complete vegetation period and runoff CN as well as parameter values of the MUSS erosion equation (a relative adaptation of the C-factor) were inversely estimated. Compared with the control plots (i.e., conventional tillage), micro-dams or conservation tillage reduced runoff by 24%-71% or 69%-89%, and erosion by 54%-81% or 91%-98%. Based on these data, a robust case can be made to lower CN or parameters in the MUSS equation for surface water exposure scenarios to consider the effects on predicted environmental concentrations (PECs) and estimated environmental concentrations (EECs). Mean resulting CN reductions by micro-dams or conservation tillage were ascertained to be 6% (±2.5%) or 12% (±3.0%), the C-factor was reduced by a factor of 0.1 (±0.15) or 0.48 (±0.19). Example calculations show reductions in the ranges of 11%-100% for PECs and 30%-98% for EECs. Integr Environ Assess Manag 2022;18:1348-1363. © 2021 Bayer AG Crop Science. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental ToxicologyChemistry (SETAC).

This paper describes the procedure for an extensive survey of hydropower potential locations to be employed at the originating hydropower development planning stage in a basin. The approach utilized spatial tools such as Arc-Hydro, Remote Sensing (RS), and GIS to characterize the Osse Sub-basin (OSB) and Natural Resources Conservation Service - Curve Number (NRCS-CN) hydrological model to compute the stream Peak discharge Qp. This method permits identification of Small Hydropower (SHP) potential locations by considering obtained catchment parameters along the drainage network in a run-of-river project that satisfies the requirement of establishing hydropower plants, which is difficult to achieve in traditional survey study. Results indicate 18 out of 148 points examined in OSB can produce between 677.485 kW and 7622.877 kW clean energy at 92% flow exceedance annually, preventing between 555.8 and 6543.0 tCO2/yr emission from polluting the environment if developed against GHG emission sources. The calculated project cost range is £ 937,167.48–4,048,733.99. This study establishes first clean development mechanism (CDM) procedure in Nigeria to help future activation of SHP resources by identifying SHP promising project sites that can earn saleable certified emission reduction (CER) credits in the optimization model named SHP Resource Assessment and Sustainability Analysis (SHPRASA).

The Soil Conservation Service Curve Number (SCS-CN) model is widely used to estimate the watershed runoff. However, the model may result in inaccurate estimations when assuming a tabulated CN value...

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.

Daerah Aliran Sungai (DAS) mengalami peningkatan kerusakan akibat hubungan dan interferensi manusia yang berdampak pada kesetimbangan hidrologi sehingga terjadi bencana. Bencana banjir yang merupakan bencana hidrologi rutin terjadi di DAS Jambo Aye. Penelitian ini bertujuan untuk menganalisa hubungan karakteristik hidrologi DAS Jambo Aye terhadap potensi dan kejadian banjir di wilayah DAS Jambo Aye. Karakeristik kemiringan lereng, pergerakan tanah, analisis kelompok hidrologi tanah (HSG), Antecedent Precipitation Index (API) dan kurva limpasan digunakan sebagai indikator hidrologi untuk mengidentifikasi potensi banjir yang terjadi di DAS Jambo Aye. Analisis menunjukkan DAS Jambo Aye termasuk wilayah yang berpotensi mengalami banjir. Potensi banjir genangan terdapat di wilayah hilir yang mempunyai karakteristik lereng landai (12%), kapasitas infiltrasi sangat lambat (37,95%), nilai kurva limpasan tinggi (17%) dan tingkat kebasahan yang tinggi (26%). Banjir bandang berpotensi terjadi di wilayah hulu yang mempunyai karakteristik lereng curam, adanya pergerakan tanah dan curah hujan yang tinggi. Pengelolaan DAS perlu dilakukan di DAS Jambo Aye mengingat kejadian banjir yang sering terjadi.

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.

The estimation of the amounts of Surface runoff resulting from rainfall in the water basins is of great importance in water resources management. The study area (Bahr Al-Najaf) is located on the western edge of the plateau and the southwestern part of the city center of Najaf, with an area of 2729.4 (km2). The soil and water assessment tool (SWAT) with ArcGIS software was used to simulate the runoff coming from the three main valleys (Kharr (A and B)), Shoaib Al-Rahimawi, and Maleh), that contribute the flow to the study area. The results of the model showed that the SWAT software was successfully simulating the flow conditions based on the coefficient of determination (R2), the Nash coefficient (NSE), P-factor, and R-factor for calibration (validation) ranged between 0.59-0.62 (0.51-0.59), 0.59-0.66 (0,.60-0.62), 0.57-0.76 (0.62-0.76), and 0.58-0.74 (0.55-0.70) respectively for these valleys. Moreover, the sensitivity results revealed that the most sensitive parameters in (SWAT-CUP) SWAT calibration and uncertainty programs are the curve number (CN2) for the runoff, soil available water capacity (SOL_AWC), and Saturated hydraulic conductivity (Soil_k), according to the calibration results for the main three valleys related the study area. Three hypothesis scenarios were implemented according to the assumed amount of precipitation that would submit a water level of 16,18, and 22 (m.a.m.s.l.) which would result in filling with the bounded lake, the whole study area, and exceeding the boundaries to flood part of the ancient Najaf City.

Surface runoff is a primary driving factor for water regulation services on oil palm plantations as it determines the hydrological components and other biogeochemical process. Therefore, understanding on their interaction and contribution within the watershed system is important to support decision-making system. Here, we applied Soil and Water Assessment Tools (SWAT) model to simulate water regulation services for an intermittent micro-catchment dominated by oil palm plantation in Harapan Landscapes, Batanghari Regency, Jambi Province. In this study, we used two different runoff curve number (CN) approaches in the SWAT model, namely the soil moisture curve number (CN-SM) and the plant evaporation curve number (CN-ET), to evaluate their applicability and uncertainty for assessing water regulation services. SWAT was automatically calibrated and validated against daily observed streamflow data. The results showed that the model performed well as indicated by hydrograph visual interpretation and statistical indicators. The performance was good for calibration and validation for both approaches with high R2 and Nash-Sutcliffe Efficiency (NSE). Also, the uncertainty was acceptable with P-factor >70% and R-factor

Ketersediaan data menjadi hal yang krusial dalam analisis hidrologi. Banyak permasalahan menyangkut ketersediaan data yang seringkali ditemui di lapangan, seperti minimnya data, data yang tidak kontinyu, atau sebaran stasiun yang tidak merata. Seiring berkembangnya teknologi, permasalahan tersebut dapat diselesaikan dengan memanfaatkan data pengamatan satelit yang memiliki resolusi spasial dan temporal tinggi, cakupan luas, akses cepat, dan ekonomis. Akan tetapi, data satelit perlu divalidasi dengan data pengamatan nyata di lapangan. Penelitian ini dilakukan untuk validasi data satelit TRMM terhadap data observasi berbasis darat dengan membandingkan debit limpasan dari data hujan terukur di darat atau ARR ( Automatic Rainfall Recorder ) dengan data hujan TRMM, lalu dikoreksi dengan debit limpasan terukur di stasiun AWLR ( Automatic Water Level Recorder ) Gemawang. Debit limpasan dari hujan dihitung dengan menggunakan Metode SCS. Hasil penelitian menunjukan jeda waktu rata-rata pengukuran hujan TRMM dan ARR sekitar 8,5 jam. Ditemukan perbedaan bentuk hidrograf limpasanTRMM. Pada data 18 Januari 2018, terdapat kesalahan bentuk gelombang hidrograf (Ew) sebesar 11.843. Dari analisis indeks kesesuaian dan efisiensi, data satelit TRMM mendapat hasil koefisien korelasi rata-rata debit ARR-AWLR dan TRMM-AWLR tergolong rendah yaitu masing-masing sebesar 0,2416 dan 0,1041, sedangkan koefisien efisiensinya 1,67 yang dikategorikan sebagai data yang efisien. Availability of sufficient data as input data is important. Data availability tends to have several data problems, such as the lack of data availability, incomplete data, or the number of stations that are less scattered. As the development of the technology problems, those probelms can be solved by replacing ground-based observation data with satellite observations that have high spatial and temporal resolution, wide area coverage, fast access, and economics. This research was conducted to validate and correct TRMM satellite data on observation data at the AWLR Gemawang station with the SCS Method. The results of this study showed a delay in the average measurements of satellite rainfall and surface approximately 8.5 hours based on the data analysis used in this study. The results of the model error analysis can be concluded that TRMM rainfall data can be used in these needs. However, there is still an error in the TRMM data, which is on the data of January 18, 2018 which results in a hydrograph (Ew) waveform error of 11.843. From the conformity index and efficiency analysis, TRMM satellite data gets the correlation coefficient average ARR-AWLR debit of 0,2416 which is categorized as low efficiency data and TRMM-AWLR of 0,1041 which is categorized as quite low coefficient data, while the efficiency coefficient gets an average value 1,67 which is categorized as highly efficient optimization data. Availability of sufficient data as input data is important. Data availability tends to have several data problems, such as the lack of data availability, incomplete data, or the number of stations that are less scattered. As the development of the technology problems, those probelms can be solved by replacing ground-based observation data with satellite observations that have high spatial and temporal resolution, wide area coverage, fast access, and economics. This research was conducted to validate and correct TRMM satellite data on observation data at the AWLR Gemawang station with the SCS Method. The results of this study showed a delay in the average measurements of satellite rainfall and surface approximately 8.5 hours based on the data analysis used in this study. The results of the model error analysis can be concluded that TRMM rainfall data can be used in these needs. However, there is still an error in the TRMM data, which is on the data of January 18, 2018 which results in a hydrograph (Ew) waveform error of 11.843. From the conformity index and efficiency analysis, TRMM satellite data gets the correlation coefficient average ARR-AWLR debit of 0,2416 which is categorized as low efficiency data and TRMM-AWLR of 0,1041 which is categorized as quite low coefficient data, while the efficiency coefficient gets an average value 1,67 which is categorized as highly efficient optimization data.

A modeling framework was created for the development of spatially explicit aquatic exposure models for any region or country of interest for chemicals disposed of down the drain. The framework relies on globally available data sets for river flow and population, and locally available data sets for wastewater treatment infrastructure and domestic water use, and leverages the iSTREEM® chemical routing algorithm. The framework was applied to China and Japan as case study countries. Spatially explicit population data were obtained from WorldPop. River flows covering the spatial extent of the two countries were derived from a high-resolution surface runoff gridded data set that was based on the Curve Number approach and combined with the hydrology network for catchments and rivers from HydroBASINS and HydroSHEDS data sets. Publicly available data from government sources were used for estimating per capita water use and wastewater treatment infrastructure. To demonstrate the framework, the China model was used to predict the levels of the antifungal agent climbazole in rivers across the country, and the Japan model was used to predict river concentrations of linear alkylbenzene sulfonate. For both chemicals, the comparison of measured to modeled values showed good agreement, using linear regression analysis (R2 ≥ 0.96). The framework presented in this study provides a systematic and robust approach to develop spatially resolved exposure models that can be extrapolated to any country or region, allowing more accurate risk assessment of chemicals disposed down the drain by leveraging concentration distributions generated by the model. Integr Environ Assess Manag 2021;00:1-12. © 2021 SETAC.

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.

The present study was conducted in Baitarani basin up to Anandapur gauging station of Odisha covering an area of 8603.7 km2. Pre-processing of basin from digital elevation model (DEM) was done using HEC-Geo-HMS extension and spatial analyst tool in ArcGIS. These pre-processed files were then imported to HEC-HMS for simulating runoff. In this study, runoff simulation was done using two methods, viz., composite and distributed curve number (CN) approaches. SCS curve number method was used for computation of runoff volume, SCS UH method for direct runoff, constant- monthly varying base flow method for base flow and Muskingum method for flow routing. The model was calibrated and validated using both composite and distributed CN approaches. Data from 1st January, 2007 to 31st December, 2013 were used for calibration and 1st January, 2014 to 31st December, 2016 were used for validation. During the calibration period of composite CN approach, the statistical parameters like Nash-Sutcliffe efficiency (NSE), Coefficient of determination (R2), Percent bias (PBIAS) and RMSE-observations standard deviation ratio (RSR) were found to be 0.51, 0.63, 12.82 and 0.7, respectively and during the validation period they were found to be 0.53, 0.54, -19.73 and 0.7, respectively. In case of distributed CN approach, the statistical parameters like NSE, R2, PBIAS and RSR were found to be 0.62, 0.63, -8.64 and 0.6, respectively during the calibration period and 0.67, 0.66, -2.25 and 0.6, respectively during the validation period. The study indicated that distributed CN approach is more accurate than composite CN approach in simulation of runoff using HEC-HMS model.

The research is focused on the integrated use of satellite remote sensing, Geographic Information System (GIS), and extensive field observation techniques for a better understanding of the impacts ...

The present study aims to investigate the hydrological response of small coastal watersheds to storm events. Areas around the Mediterranean Sea are usually characterized by streams with intermittent flows and flash floods are common. Firstly, we analyze the geomorphological, soil and land cover characteristics of the watershed in order to estimate their effect on surface runoff. Furthermore, the rainfall characteristics of an extreme event that caused flash flooding in the past are analyzed. By combining these factors, we are able to predict the response of this basin to severe storm events. The study area is located in the island of Samos, in Eastern Greece, where flash flood events are usual and pose a risk to areas located around rivers. In this area runoff is intermittent, occurring mainly during storm events and there is a lack of discharge or other instrumental measurements. By applying the SCS-CN method we estimate the response of two of the largest watersheds in Samos Island, through the construction of a Synthetic Unit Hydrograph (SUH). Firstly, we examined the record of historic floods in the area, selecting a large flash flood event (November 2001) and then obtained the daily rainfall data, which are used by the SCS method for the calculations. We applied the SCS methodology in order to estimate various parameters (e.g. lag time, time of concentration, maximum discharge), which also required the calculation of the Curve Number (CN) for each watershed. During this event (136 mm rainfall), we calculated a direct runoff (excess rainfall) of 44%-48% for these watersheds. This methodology can be particularly useful in simulating the hydrological response of small Mediterranean watersheds and to introduce better strategies for the management of the whole drainage basin.

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.

Hydrological models are effective tools for assessing the effects of soil and nutrient losses on land degradation. SWAT (Soil and Water Assessment Tool) model is widely used to simulate soil and nutrient losses caused by various management regimes. However, its performance of predicting nutrient loss has not been assessed adequately on the Loess Plateau. This study proposed a modified SWAT model by incorporating the modified Soil Conservation Service curve number method, the storm-based Chinese soil loss equation and the nutrient loss model. The observed daily data of runoff and sediment over 16 years and the monthly soluble phosphorus (P) and nitrate losses over 9 years and 4 years, respectively at the outlet of the upper Beiluo river (UBR) basin were used to assess the model performances. Global sensitivity and uncertainty analyses of parameters to runoff, sediment, soluble P and nitrate in the modified SWAT were conducted. The findings during calibration and validation showed that the modified SWAT was highly accurate in terms of model efficiency (calibration: 0.83, 0.83, 0.48, and 0.49; validation: 0.58, 0.57, 0.53, and 0.65) for runoff, sediment, soluble P loss and nitrate loss, respectively. High model efficiency indicated that the modified SWAT could accurately predict soil and nutrient losses at the river basin scale for the Loess Plateau. Moreover, the temporal variations from month to year and the spatial variations at the sub-basin scale for soil loss and the total N and P losses were analysed using the data simulated by the modified SWAT. The results indicated that the critical loss period occurred in July and August, and the Grain for Green project significantly affected the hydrological behaviour and reduced the soil and nutrient losses in the UBR basin.

The Benanain Watershed is located in East Nusa Tenggara with an area of 3,181 km2 and is divided into 29 sub-watersheds. The East Nusa Tenggara itself is an eastern region of Indonesia with a unique climate condition called semi-arid. The high rainfall intensity occurring in short duration results in large surface runoff and erosion. Floods and erosion in semi-arid areas due to sensitive soils to drought and heavy rainfall extremely. This paper presents the application of the Soil Conservation Services-Curve Number (SCS-CN) real-flood flows through a digital map of soil type, land use, topography, and the heterogeneity of physical condition, especially for ungauged watersheds. The method used is an approach empirical to estimate runoff from the relationship between rainfall, land use, and soil hydrology groups. This watershed has a large area that must analyze every sub-watershed. The land-use of the Benanain watershed is secondary dryland forest by 44.26% and the hydrological soil group on the B group classification with medium to high absorption potential by 46.502% from the total area. The curve number value of the Benanain Watershed ranges from 56.54 to 73.90, where the mean CN value of 65.32. The rainfall (mm) for the 29 sub-watersheds in the Benanain Watershed has decreased by about 74.65% when being surface runoff or only 25.35% of water becomes surface runoff. The relationship between rainfall depth and CN is classified as standard response and trend line (flat slope) equilibrium occurs when rainfall depth value of 56.71 mm and CN is close to 66.30. The high variability of intense rainfall between the rainy season and the dry season had a significant influence on the curve number value in a large watershed area. Further analysis will be more accurate if it is supported by long rainfall data and observation runoff data as a control. Doi: 10.28991/cej-2021-03091711 Full Text: PDF

The current paper deals with the evaluation of the BANCS erosion prediction model and its two components–the Bank Erosion Hazard Index (BEHI) and Near-Bank Stress (NBS) indices. To construct the erosion prediction curves, 18 experimental sections were established on the Kubrica Stream, district of Trencin, Slovakia. Each section was assessed through the NBS index and BEHI index and real annual bank erosion was measured using erosion toe pins. Subsequently, the relations between the BEHI and real annual bank erosion was assessed through regression and correlation analyses. The relation proved to be moderately strong, with the correlation coefficient (R) reaching 0.47. Further, the relation between the NBS index and real annual bank erosion was evaluated, which was also moderately strong, with R = 0.65. Based on the measured data, two erosion prediction curves were constructed, the first for moderate BEHI, with R = 0.69 and coefficient of determination (R2) of 0.47 and the second for high BEHI with R = 0.74 and R2 = 0.55. The prediction curves were based on data from one year of measurements and can, therefore, be used only for discharges that occurred within that year and in the region where the model was developed. In the current case, according to runoff Curve Numbers (CN), the real culmination discharge was Q = 1.88 m3/s, which is roughly equivalent to 1.5-year recurrence interval flow (Q1.5).

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.

Estimation of runoff in a watershed is very important to manage the water resources efficiently. In this regard, surface runoff quantification is an essential study. The main objective of this study is to quantify the surface runoff of the catchment area of a well located in AEC & RI, TNAU, Kumulur, Trichy District of Tamil Nadu State, India. An attempt also made to analyze the surface runoff by SCS-CN event and annual basis as well as by modified runoff-coefficient method. This study identified the variation of runoff volume within different approaches of SCS-CN method and runoff coefficient method. By using GPS and GIS techniques catchment area of a well and slope direction was delineated. With the help of GIS tools and remote sensing technology with ground truth verifications, the land use/ land cover and soil maps were delineated for the study area. Sandy loam and sandy clay loam type of soils are predominating and HSG ‘C’ was identified for the study area. The highest CN value is 92 and the maximum runoff coefficient value is 0.95 for the Built-up land, the lowest CN value is 71 and the lowest runoff coefficient is 0.11 for the area covered with trees. Three AMC conditions were considered while estimating runoff volume by SCS-CN event approach. Among 15 years of rainfall data from 2004 to 2018, the highest runoff 38452.36 m3 was generated in the year 2005 and the lowest runoff 8718.29 m3 was generated in 2018 by SCS-CN event basis method. From this study between two concepts of SCS-CN and runoff coefficient models, the SCS-CN model with an event basis approach is yielding productive results. For quantifying surface runoff and for planning water conservation structures event basis calculations are more effective.

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.

In this paper, we present the result obtained by application of Remote Sensing (RS) data & Geographic Information System (GIS) procedure in the identification and selection of feasible sites for small hydropower (SHP) projects in Oyanmi River in the Benin-Owena drainage Basin in Nigeria, using Natural Resource Conservation Service-Curve Number (NRCS-CN) model. Catchment delineation, Sub-basin Area size, slope, Curve Number (CN), Land Use Land Cover (LULC), Digital Elevation Model (DEM), rainfall intensity, etc. were obtained by RS and Arc GIS tools. Data on rainfall were obtained from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) for the year 2018 and validated using Nigerian Meteorological Services Agency (NIMET) Lokoja synoptic station data and then utilized for the computation of simulated discharge. The simulated discharge were correlated with observed data obtained from a gauging station in the study area. The correlation between observed and simulated data yielded a good correlation result of 65% and 63% for rainfall and discharge respectively with good statistical significance P-value. Elevation values extracted from the DEM were used in calculating the available head utilized in evaluating the hydropower potentials across the 58 hydrometric mapped-out points. Based on the criteria that a viable SHP should have a minimum slope of 2% and 10 m available head, 11 points were identified in Oyanmi sub-basin with feasible power potential ranging from 393.849 kW to 16038.194 kW at 92% annual flow exceedance which if developed would improve access to energy and socio-economic development of the study area.

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 rainfall-runoff model is commonly used in flood discharge computation. One of the most frequently employed methods to estimate the flood discharge in the ungauged basin is NRCS-CN. This study is aimed to determine the CN value with/without soil drainage capacity from the soil information in SPT Indonesia. The location used is Pataruman watershed by calibrating the simulation discharge results with the observed discharge. Soil conditions in the Pataruman watershed show that the soil texture has a fine texture (HSG D), a slightly fine texture (HSG C), and a slightly coarse texture (HSG A). The average composite CN in the Pataruman watershed without soil drainage capacity was 85.58 while soil drainage capacity was 81.01. The results of the analysis show that with/without taking into account the soil drainage capacity in the Pataruman watershed there is no significant difference in discharge with a relation coefficient of 0.734 (without soil drainage capacity) and 0.732 (with soil drainage capacity). CN calculations with/without soil drainage capacity are recommended for small watersheds.

Lesti watershed is a sub basin of Brantas River located in Malang Regency, which is the main source of inflow and sediment loads for the Sengguruh Dam. Human activities change the type of land cover by deforestation for the expansion of agricultural and residential areas. It makes a rapid increasing of runoff and discharges that were potentially carrying sediment into Lesti River. To measure surface runoff in a watershed can be held by modeling rather than directly in the field, it is cheaper and more effective with accurate results. This study is based on Soil Conservation Service (SCS) formula to illustrate surface runoff level by knowing curve number distribution. Using models based on land use changes in 2010, 2012 and 2017, generated by AV SWAT software, shows that increasing CN value each year affects the surface runoff, so there is a relationship between land use and runoff. The average CN value in 2010 is 63.644, 2012 is 63.942, 2017 is 65.49, while the average surface runoff in 2010 is 800.28, 2012 is 823.26, 2017 is 828.009. Conservation treatment on the area with a high CN value can reduce the surface runoff. It shows that watershed performance is getting better