Soil Conservation Service Curve Number Research Articles

Activation soil moisture accounting (ASMA) for runoff estimation using soil conservation service curve number (SCS-CN) method

In this study, the concept of activation soil moisture (ASM) has been conceptualised by coupling the Soil Moisture Accounting (SMA) concept with the static infiltration component (Fc) for simulating rainfall-runoff process. The ASM has been defined as the height of soil moisture barrier (or the amount of soil moisture deficit), which must be fulfilled before runoff can start. Most of the SCS-CN inspired methods, including the original one do not consider ASM in their formulation to simulate rainfall-runoff process. To account for ASM, here, we develop an activation soil moisture accounting (ASMA) based method (ASMA-SCS-CN) by coupling the SMA concept of Michel-Vazken-Perrin (MVP) method with the static infiltration (Fc) based Mishra-Singh (MS) method, which presents a fuller picture of SMA system. The performance of the ASMA-SCS-CN method is compared with the original SCS-CN method, MS method and MVP method by applying a large dataset of 56,343 storm events from 164 small to large watersheds in the United States using goodness-of-fit statistics in terms of Nash-Sutcliffe efficiency (NSE), the root mean square error (RMSE), normalized RMSE (nRMSE), percent bias (PBIAS), mean absolute error (MAE), standard error (SE) and RMSE-observations standard deviation ratio (RSR). The ASMA-SCS-CN method has the highest median value of NSE (0.71; varying from 0.11 to 0.97) with inter-quartile range (IQR) as (0.62–0.80) followed by MVP with NSE (0.67; varying from 0.10 to 0.0.96) and IQR as (0.57–0.74), MS with NSE (0.61; varying from 0.02 to 0.97) and IQR range as (0.46–0.72), and SCS-CN with NSE (0.58; varying from 0,01 to 0.92) and IQR as (0.44–0.69). The ASMA-SCS-CN method is found to have lowest mean and median values of RMSE, nRMSE, MAE, SE and RSR than the MVP, MS and SCS-CN method. The PBIAS values of the ASMA-SCS-CN and MVP methods are lower than that of MS and SCS-CN method. In addition, the performance of all four methods is further evaluated based on the watershed characteristics such as landuse, soil type, drainage area, and mean rainfall and the results show that in all cases the ASMA-SCS-CN method performs much better than the rest of the methods. Overall, the improved performance of ASMA-SCS-CN can be attributed to the inclusion of SMA along with the static infiltration component for representing the complete picture of SMA system in modelling rainfall-runoff process.

Impact of Land use Land cover change on Storm Runoff Generation: A case study of suburban catchments of Pune, Maharashtra, India

Urbanization has evolved as one of the key factors responsible for the environmental health not only for the urban centres, but also for the suburban regions. In the recent years, most of the suburban regions in India are experiencing flash floods during the high intensity low duration rainfall condition. Such situations of flash floods in the urban and suburban areas are referred to as storm runoff. This paper deals with the computation of storm runoff in the suburban catchments of the Pune City. Five catchments, known for flash floods in the recent years, are identified for the present work. Storm runoff estimations in these catchments have been done using LandsatTM data of 1989 and 2011. Population-calibrated impervious surfaces (IS) were extracted for these catchments, and runoff has been calculated using the Soil Conservation Service-Curve Number method. Major changes in the land use land cover pattern in these 22 years have been detected, with a net growth in built-up area of almost 10 times from 1989 to 2011, leading to the increase in IS in the catchment. The impact of increase in built-up area and IS has augmented storm runoff in the catchment. Estimated runoff values increased from 461.8 m3 to 1068.52 m3 (Baner), 2026.4 m3 to 3638.73 m3 (Kharadi), 2947.5 m3 to 4736.46 m3 (Kondhwa Ghorpadi ), 1021.1 m3 to 2039.57 m3 (Wadgaon Sheri) and 1176.89 m3 to 3691.18 m3 (Wadgaon Budruk) from 1989 to 2011. Thus, it is quite evident that the growth in built-up area and impervious surfaces has enhanced the capacity of suburban basins to generate more runoff.

Development and testing of a modified SWAT model based on slope condition and precipitation intensity

The SWAT model is a semi-distributed hydrological watershed model with an explicit physical mechanism, which is widely applied in areas characterized by a wide variety of precipitation distributions and slope conditions. However, the Soil Conservation Service-Curve Number (SCS-CN) value and precipitation initial abstraction ratio were not related to slope, soil moisture content, precipitation intensity, and land cover interception within the original SWAT model. Thus, a modified SCS-CN model is proposed by considering the slope, rainfall intensity, soil moisture, and interception effects of the landscape in this study, specifically revising the critical sensitive parameters including CN2 and initial abstraction ratio (λ). The modified SCS-CN model was added to the original SWAT model and named the SWAT-CH model, which is applied to Chao River watershed which experiences obvious differences in precipitation and slope. For this application, the daily streamflow was simulated for the calibration and validation periods of 1993–2005 and 2006–2013, respectively. The simulated variable is monthly runoff at watershed outlet, namely Xiahui hydrological station. The parameter sensitivity and uncertainty and the model applicability were analyzed using as Chao River watershed as a case study. The main results are as follows. ① Compared with the original SWAT model, the parameters of SOL-K, HRU-SLP, SLSUBBSN, and CANMX were identified as the most sensitive parameters for the runoff simulation in the new SWAT-CH model. ② The prediction efficiency of the SWAT-CH model surpasses that of the original SWAT model. The Nash-Sutcliffe efficiency, KGE index, and the PBIAS were 0.94, 0.83 and 4.55%, respectively for SWAT-CH during the calibration stage; while, those corresponding indexes were 0.91, 0.84, and 7.00% for the validation period. The modified SWAT-CH model can better reproduce the hydrological process than the SWAT model. In detail, the Nash-Sutcliffe efficiency, KGE index, and the PBIAS were improved by 0.02, 0.10, −4.01 percentage points for the calibration period, as well as by 0.18, 0.25, and −28.55 percentage points for the validation period in the modified SWAT-CH model compared with the original SWAT model. ③ Influenced by soil moisture, there is a substantial difference between the SWAT-CH and SWAT models in wet periods at various temporal scales. ④ Parameter uncertainty is the main source of the uncertainty in hydrological estimation for the Chao River watershed. The modified SWAT-CH model has, to some extent, reduced the effect of parameter uncertainty with increasing structure uncertainty from 6.00% for the SWAT model to 9.00% for the SWAT-CH model. The model adaptability has been enhanced by introducing terrace slope, precipitation intensity, and soil moisture for the modification of CN2 and the abstraction ratio, which makes the revised SWAT-CH model have better simulation performance in areas with the obvious characteristics of terrain & rainfall heterogeneity and drastic dry-wet cycles in the soil profile.

Using SCS-CN and Earth Observation for the Comparative Assessment of the Hydrological Effect of Gradual and Abrupt Spatiotemporal Land Cover Changes

In this study a comparative assessment of the impacts of urbanization and of forest fires as well as their combined effect on runoff response is investigated using earth observation and the Soil Conservation Service Curve Number (SCS-CN) direct runoff estimation method in a Mediterranean peri-urban watershed in Attica, Greece. The study area underwent a significant population increase and a rapid increase of urban land uses, especially from the 1980s to the early 2000s. The urbanization process in the studied watershed caused a considerable increase of direct runoff response. A key observation of this study is that the impact of forest fires is much more prominent in rural watersheds than in urbanized watersheds. However, the increments of runoff response are important during the postfire conditions in all cases. Generally, runoff increments due to urbanization seem to be higher than runoff increments due to forest fires affecting the associated hydrological risks. It should also be considered that the effect of urbanization is lasting, and therefore, the possibility of an intense storm to take place is higher than in the case of forest fires that have an abrupt but temporal impact on runoff response. It should be noted though that the combined effect of urbanization and forest fires results in even higher runoff responses. The SCS-CN method, proved to be a valuable tool in this study, allowing the determination of the direct runoff response for each soil, land cover and land management complex in a simple but efficient way. The analysis of the evolution of the urbanization process and the runoff response in the studied watershed may provide a better insight for the design and implementation of flood risk management plans.

An Improved SCS-CN Method Incorporating Slope, Soil Moisture, and Storm Duration Factors for Runoff Prediction

Soil Conservation Service Curve Number (SCS-CN) is a popular surface runoff prediction method because it is simple in principle, convenient in application, and easy to accept. However, the method still has several limitations, such as lack of a land slope factor, discounting the storm duration, and the absence of guidance on antecedent moisture conditions. In this study, an equation was developed to improve the SCS-CN method by combining the CN value with the tabulated CN2 value and three introduced factors (slope gradient, soil moisture, and storm duration). The proposed method was tested for calibration and validation with a dataset from three runoff plots in a watershed of the Loess Plateau. The results showed the model efficiencies of the proposed method were improved to 80.58% and 80.44% during the calibration and validation period, respectively, which was better than the standard SCS-CN and the other two modified SCS-CN methods where only a single factor of soil moisture or slope gradient was considered, respectively. Using the parameters calibrated and validated by dataset of the initial three runoff plots, the proposed method was then applied to runoff estimation of the remaining three runoff plots in another watershed. The proposed method reduced the root-mean-square error between the observed and estimated runoff values from 5.53 to 2.01 mm. Furthermore, the parameters of soil moisture (b1 and b2) is the most sensitive, followed by parameters in storm duration (c) and slope equations (a1 and a2), and the least sensitive parameter is the initial abstraction ratio λ on the basis of the proposed method sensitivity analysis. Conclusions can be drawn from the above results that the proposed method incorporating the three factors in the SCS method may estimate runoff more accurately in the Loess Plateau of China.

Estimation of Initial Abstraction for Hydrological Modeling Based on Global Land Data Assimilation System–Simulated Datasets

AbstractInitial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

Surface runoff in urban areas: The role of residential cover and urban growth form

In the context of rapid urbanization and climate change, understanding the impact of urban dynamics on surface runoff is important. For future urban planning, there is a particular knowledge gap regarding the factors that affect surface runoff to improve urban resilience to local flooding. This study explored the impacts of different urban dynamics on surface water runoff considering the surface cover characteristics of different residential types (i.e., low- and high-density settlements) in the city of Munich, Germany. The surface cover characteristics were quantified using the ‘i-Tree Canopy’ method based on high-resolution aerial images. The surface runoff was simulated by the Soil Conservation Service Curve Number (SCS-CN) method. Meanwhile, the impacts of different urban dynamics on surface runoff were compared by incorporating a scenario-based urban dynamic modeling approach. The results suggest that, compared with low-density settlements, high-density settlement areas tend to generate more surface runoff within the neighborhood because they have considerably less vegetative surfaces and more built ones. As the development of low-density settlements leads to a greater loss of green and open spaces in the surrounding peri-urban areas, however, the compact growth scenarios characterized by high-density settlements have advantages over low-density scenarios in terms of overall surface runoff reduction at the city scale. Thus, the trade-off between the neighborhood and city scale should be considered when promoting nature-based solutions for surface runoff mitigation. Moreover, the approach presented here can be used as an effective planning tool supporting various urban contexts.

Correlation between runoff volume and land use changes using SCS-CN method for Sungai Gombak catchment

This research focuses on river modeling using the input precipitation and land use system Soil Conservation Service Curve Number (SCS-CN) using InfoWorks RS (IWRS) and Geographical Information System (GIS). The purpose of this study is to measure the volume of runoff based on the year selected for the Sungai Gombak model with changes in land use base. Sungai Gombak is part of the upper basin of the Sungai Klang. The Sungai Klang basin area has undergone significant urbanization, especially upstream in Sungai Batu, Sungai Gombak and Sungai Klang. This urbanization is the most significant changes in land use happening within the basin of the lake. For the Sungai Gombak catchment, the transformation of the undeveloped area that lead to Sungai Gombak changing the amount of runoff. Six rainfall stations were selected to model the river and then the average catchment rainfall for each catchment was interpolated using Thiessen method from the available rainfall station. Three rainfall events were chosen based on the flood history reported by the Department of Irrigation and Drainage (DID) in the Sungai Gombak. The results indicate that the CN value rises from year to year due to changes in landscape patterns that indirectly influence ecological processes and decrease the stream flow time concentration.

Estimation of potential runoff in Wadi Damad watershed, Jazan Province, southwestern Saudi Arabia

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.

Improved SMA-based SCS-CN method incorporating storm duration for runoff prediction on the Loess Plateau, China

Abstract In the Soil Conservation Service Curve Number (SCS-CN) method for estimating runoff, three antecedent moisture condition (AMC) levels produce a discrete relation between the curve number (CN) and soil water content, which results in corresponding sudden jumps in estimated runoff. An improved soil moisture accounting (SMA)-based SCS-CN method that incorporates a continuous function for the AMC was developed to obviate sudden jumps in estimated runoff. However, this method ignores the effect of storm duration on surface runoff, yet this is an important component of rainfall-runoff processes. In this study, the SMA-based method for runoff estimation was modified by incorporating storm duration and a revised SMA procedure. Then, the performance of the proposed method was compared to both the original SCS-CN and SMA-based methods by applying them in three experimental watersheds located on the Loess Plateau, China. The results indicate that the SCS-CN method underestimates large runoff events and overestimates small runoff events, yielding an efficiency of 0.626 in calibration and 0.051 in validation; the SMA-based method has improved runoff estimation in both calibration (efficiency = 0.702) and validation (efficiency = 0.481). However, the proposed method performed significantly better than both, yielding model efficiencies of 0.810 and 0.779 in calibration and validation, respectively.

Influence of river cross-section data resolution on flood inundation modeling: Case study of Kashkan river basin in western Iran

In this study, a coupling of a hydrologic and hydraulic model was utilized to assess the impacts of river geometry data resolution on the flood inundation characteristics in a data-scarce environment. Hydrological modeling incorporates soil conservation service curve-number (SCS-CN) and the geomorphologic based instantaneous unit hydrograph model (GIUH) to compute the direct runoff hydrograph in Kashkan river basin located in western Iran. 1D HEC-Geo-RAS model was used and performed to simulate inundation extent of 100-yr floods (~1800 m3/s) along 40 km reach of Kashkan river with a ground survey of river cross-section (2000 cross-sections, each including 500 data-points). The effect of cross-section spacing (20 m to 5000 m) and dataset resolution along cross-section (5–500 points) on 100-yr (~1800 m3/s) flood inundation extent area (FIA), average flow depth (FD), and velocity (FV) in river centerline were scrutinized. Results indicate that coarser cross-section spacing (20–5000 m) showed an overestimating tendency to simulate FIA (~5.75%), FD (391.34%), and FV (31.2%). While increasing grid resolution (5–500 points) on cross-section tends to underestimate of FIA (17.84%),FD (84.13%), and FV (49.12%). The predominant effect of cross-section spacing on FIA, FD, and FV was found compared to the number of data-points on a cross-section. Results of this study could potentially be considered as a baseline for mapping of flood inundation extent in the environment that ground or remotely sensed high-resolution topographic data of river cross-section are scarce while making a trade-off between model accuracy, computational time, and cost of data compilation is crucial.

Variability of the Initial Abstraction Ratio in an Urban and an Agroforested Catchment

The Curve Number method is one of the most commonly applied methods to describe the relationship between the direct runoff and storm rainfall depth. Due to its popularity and simplicity, it has been studied extensively. Less attention has been given to the dimensionless initial abstraction ratio, which is crucial for an accurate direct runoff estimation with the Curve Number. This ratio is most often assumed to be equal to 0.20, which was originally proposed by the method’s developers. In this work, storm events recorded in the years 2009–2017 in two small Polish catchments of different land use types (urban and agroforested) were analyzed for variability in the initial abstraction ratio across events, seasons, and land use type. Our results showed that: (i) estimated initial abstraction ratios varied between storm events and seasons, and were most often lower than the original value of 0.20; (ii) for large events, the initial abstraction ratio in the catchment approaches a constant value after the rainfall depth exceeds a certain threshold value. Thus, when using the Soil Conservation Service-Curve Number (SCS-CN) method, the initial abstraction ratio should be locally verified, and the conditions for the application of the suggested value of 0.20 should be established.

Assessment of Soil and Water Conservation Practices in the Loess Hilly Region Using a Coupled Rainfall-Runoff-Erosion Model

Soil and water conservation practices (SWCPs) are widely used to control soil and water loss. Quantifying the effect of SWCPs and climate change on soil and water erosion is important for regional environmental management. In this study, the Soil Conservation Service Curve Number (SCS-CN) and the Modified Universal Soil Loss Equation (MUSLE) were employed to investigate the patterns of surface runoff and soil erosion with different SWCPs in the hilly region on the Loess Plateau of China. The impact of climate change under RCP4.5 and RCP8.5 emission scenarios was considered from 2020 to 2050. Surface runoff grew with the increased rainfall and rainfall erosivity, while soil erosion presented large variations between years due to uneven distribution of rainfall and rainfall erosivity under two scenarios. Different SWCPs significantly reduced surface soil and water loss. Compared with bare slopes, the reduction rates were 15–40% for surface runoff and 35–67% for soil erosion under RCP4.5 and RCP8.5 emission scenarios, respectively. The combination of shrub and horizontal terracing was recommended due to its low water cost for sediment control among seven SWCPs.

Verification of a New Spatial Distribution Function of Soil Water Storage Capacity Using Conceptual and SWAT Models

The Soil Conservation Service Curve Number (SCS-CN) method is widely used in conceptual rainfall-runoff models for describing the runoff response with a curve, which is a function of the cumulative storm rainfall and antecedent wetness conditions. To improve the SCS-CN method, a new distribution function was recently proposed to unify the surface runoff modeling of the SCS-CN method and probability-distributed functions in the variable infiltration capacity (VIC) and Xin’anjiang models. This study aims to verify the new distribution function in a conceptual rainfall-runoff model and in the Soil and Water Assessment Tool (SWAT) by using real catchments. The Xunhe River basin in China and other basins in the United States were used as case studies. Results show that more observed variability in streamflow is captured when using the new spatial distribution function of soil water storage capacity in the conceptual runoff model. Specifically, there is a 9.8% average increase in the Nash-Sutcliffe efficiency (NSE), while simultaneously reducing the bias and mean relative absolute error (MRAE). When using the new distribution in SWAT, the model is able to better estimate the observed streamflow as indicated by higher NSE values for most of the basins. Akaike information criterion (AIC) is used for validating the goodness-of-fit when the number of parameters and model structure change. Further findings suggest that the estimated variance is more sensitive to the value of the new shape parameter a when soil water content is low in the early stage of rainfall. Therefore, the proposed new distribution function is shown to be effective in improving the accuracy of simulating streamflow for both conceptual and SWAT models.

Assessing the Effect of Land Use Change on Surface Runoff in a Rapidly Urbanized City: A Case Study of the Central Area of Beijing

The change in land use during the process of urbanization affects surface runoff and increases flood risk in big cities. This study investigated the impact of land use change on surface runoff in Beijing’s central area during the period of rapid urbanization from 1984 to 2019. Land use maps of 1984, 1999, 2009, and 2019 were generated by image classification of Landsat images. Surface runoffs were calculated with the Soil Conservation Service curve number (SCS-CN) model. Correlation analysis was used to identify the dominant factor of land use change affecting surface runoff. The result showed that the variation trend of surface runoff was consistent with the trend of impervious land in Beijing’s central area, which increased during 1984~2009 and decreased during 2009~2019. Correlation analysis showed that changes in surface runoff were most strongly correlated with changes in impervious surfaces when compared with the correlation of runoff with other types of land use. The results of this study may provide a reference for city flood control and urban planning in fast growing cities worldwide, especially in developing countries.

A streamflow hydrograph analysis and simulation for a study case watershed

The streamflow hydrograph analysis and simulation for a study case watershed was carried out using the hydrologic engineering centre - geospatial hydrologic modelling system (HEC-GeoHMS) and the hydrologic engineering centre - hydrologic modelling system (HEC-HMS) software coupling. HEC-GeoHMS is integrated in a geographic information system (GIS) so as to develop a terrain's pre-processing tool and to generate output files which are inputted to the hydrologic model. Soil map data, land use map and digital elevation model (DEM) were used in order to delineate the physical watershed's characteristics. The runoff estimation was based on the soil conservation service curve number (SCS-CN) approach while the watershed's outflow estimation was carried out by employing SCS unit hydrograph method. The rainfall - runoff modelling is applied on a storm event which occurred on 01/10/2005 at Kastaneri watershed. The coupled hydrological models simulated runoff hydrograph for the latter storm event whilst the CN and the peak discharge values were computed equal to 69.5 and 72.8 m3/s respectively.

Influence of Land Use Land Cover Change on Groundwater Recharge in the Continental Terminal Area of Abidjan, Ivory Coast

The process by which rainfall reaches the aquifer in a sedimentary area is infiltration. This process could be affected quantitatively or qualitatively by the changes in the land use land cover (LULC) as a result of anthropogenic activities which could affect groundwater reserves. This study focuses on the influence of LULC change on groundwater recharge in the context of urbanization and population growth. Four weather stations data and satellite image data were used in order to evaluate water infiltration which is the amount of water that reaches the piezometric surface from 1990 to 2016. The spatial-temporal LULC change in relation to urbanization sprawl was assessed based on a series of Landsat images for 1990, 2000 and 2016. The maximum likelihood pixel-based on classification method was used to analyze the spatial-temporal LULC dynamics. The Thiessen polygon method was used for the mean area precipitation computation. The recharge was determined using water balance method after determining the runoff based on the Soil Conservation Service curve number method. The results show an increase in built-up and agricultural land, while the forest and shrub areas declined with water body remaining unchanged over the period 1990-2016. The decline in forest could be imputed to the demographic and socio-economic growth as expressed by the expansion of agriculture and urbanization. Groundwater recharge and runoff results are respectively 34%, 20% in 1999; 21%, 46% in 2000 and 26%, 14% in 2016 of rainfall and show their strong dependence on precipitation and LULC change.

Identification of groundwater recharge-based potential rainwater harvesting sites for sustainable development of a semiarid region of southern India using geospatial, AHP, and SCS-CN approach

Groundwater recharge-based rainwater harvesting (RWH) is a standout among the most capable structures to reduce water deficit issues and to build the accessibility of water for sustainable development in the semiarid regions because of absence of precipitation and flighty precipitation patterns. The delineation of potential recharge zone (PRZ) with RWH and selection of sites cause a greater dispute for the water stakeholders because of deficient framework. The essential thematic layer such as land use/land cover, soil, geology, vadose zone, drainage density, slope, and runoff were used for delineation of PRZ and RWH sites. Analytical hierarchical process (AHP) was carried out by assigning different weights to different thematic layers to determining the PRZ and RWH sites. The present study endeavours to decide the suitable areas for RWH using Soil Conservation Service curve number (SCS-CN) based on surface runoff with geospatial techniques. The enlargement of water asset is proposed by the development of RWH structures such as farm pond (FP), percolation tank (PT), check dam (CD), and gully plug (GP) having 73.53% (358.08 km2) appropriate for FP, 8.62% (42 km2) reasonable for PT, 17.48% (85.16 km2) reasonable for CD, and 0.35% (1.71 km2) for GP of the present study region. Based on integrated mission of sustainable development specification, 20 PT, 11 FP, and 4 CD were identified having 85%, 90%, and 100% accuracy in agreement between existing structures and predicted potential site. The present study approach demonstrates the higher accuracy for identification of RWH at any scale with sustainable water resources development.

Analysis of surface runoff potential in ungauged basin using basin parameters and SCS-CN method

The present research work was carried out to understand the influence of basin morphometric parameters on runoff potential in an ungauged basin using satellite images, topographical maps, and rainfall data combined with geospatial techniques. The upper Gosthani river basin is an ungauged basin which is located in the Eastern Ghats of Visakhapatnam District, Andhra Pradesh State, Southern India. The river Gosthani and its tributaries are draining through the basin area covering about 321.1 km2. The quantitative analysis of basin morphometry reveals that the area is under influenced by steep ground slopes, with moderate to less permeable rocks, leading to high runoff. The basin is elongated in shape resulting to flatter peak of flow for longer duration. The daily rainfall data during 2008–2016 were used in the estimation of runoff potential with the help of the Soil Conservation Service-Curve Number (SCS-CN) model. The weighted curve number was determined by the integration of land use and land cover, antecedent moisture condition, and hydrological soil groups. It was observed from the analysis that the overall increase in runoff corresponding to the rainfall. The area receives a good amount of rainfall, but most of it lost as surface runoff (nearly 40% of total rainfall) due to rapid overland flow and impermeable rocks. Analysis of morphometric parameters combined with SCS-CN-based approaches can be explored as an alternative for simulating the hydrological response of the basins.

A Calibrated, Watershed-Specific SCS-CN Method: Application to Wangjiaqiao Watershed in the Three Gorges Area, China

The Soil Conservation Service curve number ( S C S-C N) method is one of the most popular methods used to compute runoff amount due to its few input parameters. However, recent studies challenged the inconsistent runoff results obtained by the method which set the initial abstraction ratio λ as 0.20. This paper developed a watershed-specific S C S-C N calibration method using non-parametric inferential statistics with rainfall–runoff data pairs. The proposed method first analyzed the data and generated confidence intervals to determine the optimum values for S C S- C N model calibration. Subsequently, the runoff depth and curve number were calculated. The proposed method outperformed the runoff prediction accuracy of the asymptotic curve number fitting method, linear regression model and the conventional S C S-C N model with the highest Nash–Sutcliffe index value of 0.825, the lowest residual sum of squares value of 133.04 and the lowest prediction error. It reduced the residual sum of squares by 66% and the model prediction errors by 96% when compared to the conventional S C S-C N model. The estimated curve number was 72.28, with the confidence interval ranging from 62.06 to 78.00 at a 0.01 confidence interval level for the Wangjiaqiao watershed in China.