Soil Conservation Service Curve Number Model Research Articles

Consideration of vegetation interception of rainfall within the SCS-CN model: Application to the west bank of Dianchi Lake

Study regionThe west bank of Dianchi Lake, China. Study focusThe Soil Conservation Service Curve Number (SCS-CN) model is widely used to simulate rainfall runoff of mountain floods. The determination of the CN parameter within the SCS-CN model is key to accurately simulating runoff. CN is closely related to regional land use, soil type, antecedent moisture conditions and slope. Therefore, this study established an optimized SCS-CN model by comprehensively considering the above factors, particularly the interception of rainfall by vegetation. New hydrological insights for the region(1) The original CN value resulted in underestimation of surface runoff in the study area [Nash-Sutcliffe Efficiency Coefficient (NSE) = 0.2260], indicating that regional differences reduce the applicability of the original CN value; (2) the optimized model considering the slope, antecedent rainfall, and other single or combined factors generated simulations of runoff that were closer to observations (NSE = 0.6811); the accuracy of the CN value can be effectively improved by combining various factors; however, since the CN had not yet reached the optimal value, simulations remained inaccurate; (3) consideration of interception of rainfall by vegetation in the model greatly improved the accuracy of simulated runoff [NSE = 0.8620]; indicating that the impact of vegetation interception of rainfall on surface runoff cannot be ignored. The results demonstrated that the SCS-CN model can provide more accurate simulations of mountain flood runoff in the study area after considering multiple factors, such as vegetation interception of rainfall.

A comparison of the SCS-CN-based models for hydrological simulation of the Aghanashini River, Karnataka, India

Abstract This present study investigates different techniques for estimating the surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method for the Aghanashini River in Karnataka, India. The SCS-CN method is a simplified approach for runoff estimation, but it does not take into account the actual moisture content in the soil. Consequently, insignificant moisture level changes could induce significant variations in the runoff. The study analyzes six different models based on the SCS-CN method, including the original SCS-CN model and several variations with added features (SCS-CN with slope correction, SCS-CN with λ-optimization, Mishra and Singh, Michel-Vazken -Perrin (MVP), Activation Soil Moisture Accounting SCS-CN). The accuracy of each model was compared using several goodness-of-fit statistics. Furthermore, based on the flood frequency analysis, three large flood events were reported in 2005, 2013, and 2014. The results showed that the MVP model was the best-performing method in simulating runoff. The outcomes of this study can provide valuable information to the local authorities in making informed decisions about flood forecasting and water conservation.

Assessing the impacts of land use-land cover changes on direct surface runoff: a remote sensing approach in Khulna City.

The increased risks of storm flood occurrences in large cities are the result of land use changes due to rapid urbanization. This study examines the influence of land use changes in Khulna City Corporation (KCC) area on surface runoff over a period of 15 years, from 2005 to 2020. Land use-land cover (LULC) maps for 2005, 2010, 2015, and 2020 were created employing support vector machine (SVM)-based supervised image classification using time-series satellite data, and the surface runoff was determined using Soil Conservation Service-Curve Number model. The major land use change drivers of surface runoff were determined through a correlation analysis. Surface runoff was observed to follow a similar trend as that of impervious urban areas, which went up by 5.44% from 2005 to 2020 (17.00 mm increment in average runoff) and the opposite trend was found in vegetation land cover, which declined by 13.34% in areal extent throughout the study period. In comparison with other types of land use, surface runoff changes were most significantly associated with the changes in urban impervious areas and vegetation land use-land cover (LULC) class. In fast-growing cities across the world, and especially in developing nations, the results of this study may serve as a guide for urban storm flood management and urban planning efforts.

ASSESSING THE IMPACT OF URBANIZATION ON DIRECT RUNOFF IN DISTRICT 8, HO CHI MINH CITY

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.

ESTIMATION OF POTENTIAL WATER AVAILABILITY AND WATER RESOURCES CARRYING CAPACITY FOR BOGOR CITY SPATIAL PLAN

The aim of the present research is to examine the effectiveness of rational method widely used in calculating water availability for spatial planning in Indonesia. The rational method is developed mainly for estimating the characteristics of drainage infrastructure instead of estimating water availability. The effectiveness of rational method and Soil-Conservation Service Curve Number (SCS-CN) method are compared by testing their performance in estimating water availability in the Upper Cisadane river basin in West Java, Indonesia. The results shows that calibrated SCS-CN model performs better than Rational model with R2 and NSE of 0,62 and 0,37. Result of model validation yields R2 = 0,73 and NSE = 0,52. The result suggests that SCS-CN performs better than the rational model in simulating the character of water catchment area and is suitable for model of choice in water availability estimation. Keywords : Bogor City, Regional Spatial Planning, Water availability, Water Balance, Water Resource Carrying Capacity

Surface Runoff Responses to Suburban Growth: An Integration of Remote Sensing, GIS, and Curve Number

Suburban growth and its impacts on surface runoff were investigated using the soil conservation service curve number (SCS-CN) model, compared with the integrated advanced remote sensing and geographic information system (GIS)-based integrated approach, over South Kingston, Rhode Island, USA. This study analyzed and employed the supervised classification method on four Landsat images from 1994, 2004, 2014, and 2020 to detect land-use pattern changes through remote sensing applications. Results showed that 68.6% urban land expansion was reported from 1994 to 2020 in this suburban area. After land-use change detection, a GIS-based SCS-CN model was developed to examine suburban growth and surface runoff estimation. The developed model demonstrated the spatial distribution of runoff for each of the studied years. The results showed an increasing spatial pattern of 2% to 10% of runoff from 1994 to 2020. The correlation between runoff co-efficient and rainfall indicated the significant impact of suburban growth in surface runoff over the last 36 years in South Kingstown, RI, USA, showing a slight change of forest (8.2% area of the total area) and agricultural land (4.8% area of the total area). Suburban growth began after 2000, and within 16 years this land-use change started to show its substantial impact on surface runoff. We concluded that the proposed integrated approach could classify land-use and land cover information to understand suburban growth and its potential impact on the area.

Event-based and continous flood modeling in Zijinguan watershed, Northern China

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.

Optimizing the soil conservation service curve number model by accounting for rainfall characteristics: a case study of surface water sources in Beijing.

The upper catchment of the Miyun reservoir is an important drinking water source in Beijing. In recent years, researchers have used the soil conservation service curve number (SCS-CN) model to calculate surface runoff for the district. Although the runoff forecasting accuracy was unsatisfactory, the lack of understanding of rainfall processes and their influence on runoff may explain the observed deviations. Our study sought to optimize and assess the SCS-CN model simulation accuracy for the district by proposing an SCS-CN calculation method for each runoff event (CNt) based on observation data for 253 rainfall and runoff events from 7 plots in the Miyun Shixia watershed. This study elucidated a significant positive correlation between the ratio of CNt and the average SCS-CN (CN1), as well as the ratio of the maximum X-minute rainfall amount (PX) to the total rainfall amount for each rainfall event (P). Furthermore, a calculation method involving power function equations between CNt/CN1 and PX/P was proposed for CNt. When X = 5min and the initial abstraction ratio (λ) = 0.01, the simulation performance of the optimized model was the highest, with a Nash-Sutcliffe efficiency coefficient of 0.791, which was significantly higher than that of the non-optimized SCS-CN model. The simulation performance for bare and cultivated land was higher than that of other land uses, with Ef values of 0.831 and 0.828, respectively. Future research should focus on improving the prediction accuracy of runoff events resulting from high-intensity and short-duration rainfall events.

Evaluation of parameters of SCS-CN-based models for prediction of runoff from watersheds of Jharkhand, India

Soil Conservation Service Curve number (SCS-CN) method is one of the popular methods to estimate the volume of direct surface runoff for a given rainfall. Recently, an improved version of SCS-CN model called as Sahu-Mishra-Eldho (SME) model was reported incorporating hydrologically sounder procedure for accounting antecedent moisture in the Mishra-Singh (MS) model, a modified version of the SCS-CN model. The present study estimates and evaluates the model parameters of the SME model, the MS model and the original SCS-CN model for the rainfall-runoff datasets of the selected four watersheds of Jharkhand (India). The model parameters were estimated by using the non-linear Marquardt algorithm of constrained least squares. The results indicated that for MS model and SME model, the optimum value of in all the four watersheds is zero. The optimum values of S for MS model for Adda-1, Chitankhari, Indra and Karimati watersheds are found to be 266.78, 256.61, 194.47 and 233.72 mm, respectively while the optimum values of S0 for SME model are found to be 194.39, 329.60, 214.51 and 259.90 mm, respectively. Further, the optimum values of in SME model are found to be 0.35, 1.15, 0.80 and 0.87, respectively. The value of greater than 1 in Chitakhari watershed indicates that there is a seepage inflow to this watershed from the nearby watershed.

Modeling the Soil Response to Rainstorms after Wildfire and Prescribed Fire in Mediterranean Forests

The use of the Soil Conservation Service-curve number (SCS-CN) model for runoff predictions after rainstorms in fire-affected forests in the Mediterranean climate is quite scarce and limited to the watershed scale. To validate the applicability of this model in this environment, this study has evaluated the runoff prediction capacity of the SCS-CN model after storms at the plot scale in two pine forests of Central-Eastern Spain, affected by wildfire (with or without straw mulching) or prescribed fire and in unburned soils. The model performance has been compared to the predictions of linear regression equations between rainfall depth and runoff volume. The runoff volume was simulated with reliability by the linear regression only for the unburned soil (coefficient of Nash and Sutcliffe E = 0.73–0.89). Conversely, the SCS-CN model was more accurate for burned soils (E = 0.81–0.97), also when mulching was applied (E = 0.96). The performance of this model was very satisfactory in predicting the maximum runoff. Very low values of CNs and initial abstraction were required to predict the particular hydrology of the experimental areas. Moreover, the post-fire hydrological “window-of-disturbance” could be reproduced only by increasing the CN for the storms immediately after the wildfire. This study indicates that, in Mediterranean forests subject to the fire risk, the simple linear equations are feasible to predict runoff after low-intensity storms, while the SCS-CN model is advisable when runoff predictions are needed to control the flooding risk.

Evaluating the Impacts of Future Urban Expansion on Surface Runoff in an Alpine Basin by Coupling the LUSD-Urban and SCS-CN Models

Effective evaluations of the future urban expansion impacts (UEI) on surface runoff in alpine basins are full of challenges due to the lack of reliable methods. Our objective was to provide a new approach by coupling the Land Use Scenario Dynamics-urban (LUSD-urban) and Soil Conservation Service-Curve Number (SCS-CN) models to estimate the future UEI on surface runoff. Taking the Qinghaihu-Huangshui basin (QHB) in the Tibetan Plateau, China, as an example, we first applied the SCS-CN model to quantify the surface runoff in 2000 and 2018 and analyzed the changes in surface runoff. Next, we applied the LUSD-urban model to simulate urban expansion under five localized shared socioeconomic pathways (SSPs) from 2018 to 2050. Finally, we assessed the UEI on surface runoff in the QHB from 2018 to 2050. We found that coupling the LUSD-urban and SCS-CN models could effectually evaluate the future UEI on surface runoff. Compared with the combination of the Future Land Use Simulation (FLUS) and SCS-CN models, our method reduced the absolute evaluation errors from 3.40% and 11.78% to 0.18% and 4.23%, respectively. In addition, the results showed that future urban expansion will have severe impacts on surface runoff in the valley region. For example, as a result of urban expansion, the surface runoff in the Huangzhong, Xining, and Datong catchments will increase by 4.90–9.01%, 4.25–7.36%, and 2.33–3.95%, respectively. Therefore, we believe that the coupled model can be utilized to evaluate the future UEI on surface runoff in alpine basins. In addition, the local government should pay attention to flood risk prevention, especially in the valley region, and adopt reasonable urban planning with soft and hard adaptation measures to promote the sustainable development of alpine basins under rapid urban expansion.

Comparison of antecedent precipitation based rainfall-runoff models

Abstract The Soil Conservation Service Curve Number (SCS-CN) method is one of the popular methods for calculating storm depth from a rainfall event. The previous research identified antecedent rainfall as a key element that controls the non-linear behaviour of the model. The original version indirectly uses five days antecedent rainfall to identify the land condition as dry, normal or wet. This leads to a sudden jump once the land condition changes. To obviate this, the present work intends to improve the performance of antecedent rainfall-based SCS-CN models. Two forms of SCS-CN model (M1 and M2), two recently developed P-P5 based models (M3 and M4), and an alternate approach of considering P5 in the SCS-CN model (M5 and M6), as proposed here, were investigated. Based on the evaluation of several error metrics, the new proposed model M6 has performed better than other models. The performance of this model is evaluated using rainfall-runoff events of 114 watersheds located in the USA. The median value of Nash-Sutcliffe Efficiency was found as 0.78 for the M6 model followed by M5 (0.75), M3 (0.73), M4 (0.72), M2 (0.63) and M1 (0.61) model.

Improved runoff curve numbers for a large number of watersheds of the USA

ABSTRACT This study presents a procedure to determine improved curve numbers (CNImp) for application of the enhanced Soil Conservation Service - Curve Number (SCS-CN) model developed by Verma et al. for improved runoff prediction. To this end, five different mathematical relationships, i.e. linear, exponential, power, logarithmic and polynomial, are developed for 57 US Department of Agriculture – Agricultural Research Service (USDA-ARS) watersheds to compute CNImp with the aid of CN derivable from the National Engineering Handbook, Section-4 (NEH-4) table (CNT) and from the enhanced SCS-CN model (CNV) with significant R 2 values. Using these relationships, CNImp values are computed for another 57 watersheds for validation. Based on common performance criteria, all five developed relationships for CNImp are found to be superior to the original model in runoff computation. When compared, CNImp are close to CNV and very different from CNT. Despite the fact all the relationships performed equally well, with similar performance statistics, the exponential relationship performed the best of all and the power relationship was the worst.

Challenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco

Abstract. The Mediterranean region is characterized by intense rainfall events giving rise to devastating floods. In Maghreb countries such as Morocco, there is a strong need for forecasting systems to reduce the impacts of floods. The development of such a system in the case of ungauged catchments is complicated, but remote-sensing products could overcome the lack of in situ measurements. The soil moisture content can strongly modulate the magnitude of flood events and consequently is a crucial parameter to take into account for flood modeling. In this study, different soil moisture products (European Space Agency Climate Change Initiative, ESA-CCI; Soil Moisture and Ocean Salinity, SMOS; Soil Moisture and Ocean Salinity by the Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la Biosphère, SMOS-IC; Advanced Scatterometer, ASCAT; and ERA5 reanalysis) are compared to in situ measurements and one continuous soil-moisture-accounting (SMA) model for basins located in the High Atlas Mountains, upstream of the city of Marrakech. The results show that the SMOS-IC satellite product and the ERA5 reanalysis are best correlated with observed soil moisture and with the SMA model outputs. The different soil moisture datasets were also compared to estimate the initial soil moisture condition for an event-based hydrological model based on the Soil Conservation Service curve number (SCS-CN). The ASCAT, SMOS-IC, and ERA5 products performed equally well in validation to simulate floods, outperforming daily in situ soil moisture measurements that may not be representative of the whole catchment soil moisture conditions. The results also indicated that the daily time step may not fully represent the saturation state before a flood event due to the rapid decay of soil moisture after rainfall in these semiarid environments. Indeed, at the hourly time step, ERA5 and in situ measurements were found to better represent the initial soil moisture conditions of the SCS-CN model by comparison with the daily time step. The results of this work could be used to implement efficient flood modeling and forecasting systems in semiarid regions where soil moisture measurements are lacking.

Rainfall-surface runoff estimation for the Lower Bhavani basin in south India using SCS-CN model and geospatial techniques

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.

The Effects of Rainfall Intensities and Duration on SCS-CN Model Parameters under Simulated Rainfall

The Soil Conservation Service curve number (SCS-CN) method has been widely used in rainfall-runoff modelling; however, the direct effects of rainfall intensities and duration, which are major factors in hydrological engineering design, on its parameters (initial abstraction ratio (λ) and potential maximum storage (S), the latter is transformed into curve number (CN)) have seldom been studied. In this study, we chose simulated rainfall experiments on runoff plots (30 mm/h, 180 min; 60 mm/h, 90 min; 90 mm/h, 60 min) to obtain synchronized rainfall-runoff data and calculated the parameters using general model fitting and event analysis methods every ten minutes throughout the duration. The results indicate that the parameters changed with rainfall intensities and duration. S decreased as the rainfall intensities increased. Under the same rainfall intensity, the S increased with the duration overall. The corresponding λ changed with rainfall intensities and duration, and has no obvious relationship under different rainfall intensities. Initial abstraction in the event analysis method is the same as the actual situation and we chose these parameters obtained with the event analysis method as our selected parameters. These selected parameters and the parameters obtained using Fu et al.’s method which was based on the standardized procedure in NEH-630 were evaluated by the Nash–Sutcliffe efficiency (NSE), the percentage deviation coefficient (PBIAS), and the ratio of the root mean square error to the standard deviation of measured data (RSR). The results show that the statistics for the selected parameters satisfied the evaluating standard, and have a better value, while the statistics for the parameters obtained by Fu et al.’s method declined as the rainfall intensity increased, and PBAIS was out of the standard range under 90 mm/h rainfall intensity condition. This indicates that the rainfall intensities and duration have important effects on the parameters of the model, and the parameters vary dynamically with the rainfall intensity and duration. These results could be useful for hydrological design in which engineers consider the return period (i.e., rainfall intensities and duration).

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.

Effects of hillslope geometry on spatial infiltration using the TOPMODEL and SCS-CN models

ABSTRACTThe estimation of infiltration is a main issue in runoff simulation. The geometry of hillslopes (plan shape and profile curvature) may affect the responses, as well as infiltration over the hillslopes. In this study, the equations of TOPMODEL (a topography-based model) were applied to complex hillslopes to develop the complex TOPMODEL. This model was coupled with the SCS-CN (Soil Conservation Service Curve Number) model to examine the effects of geometry on infiltration and derive a saturation excess-based curve number (CN). The effects of plan shape and profile curvature upon the spatial distribution of CN and infiltration were studied. The results show that convergent hillslopes have 15.4% less infiltration and divergent hillslopes have 7.8% more infiltration than parallel ones. The infiltration over concave hillslopes is 13.5% lower and infiltration over convex hillslopes 5.8% higher than for straight ones. The degree of convergence/divergence has a greater effect on the CN compared to that of profile curvature.

Characteristics of impervious surface and its effect on direct runoff: a case study in a rapidly urbanized area

Abstract In recent years, many cities have experienced serious urban flood and non-point pollution issues due to hydrological process changes in rapidly urbanizing areas. Understanding the relationship between impervious surface and direct runoff is important for urban planning to protect the urban hydrological system. In this study, we used a mixed spectral decomposition method to interpret the long-term series of impervious surface of Shenyang, China. Direct runoff was evaluated by an improved SCS-CN (Soil Conservation Service curve number) model, and the relative influences of five underlying surface factors on the direct runoff of each period were analyzed by boosted regression trees. The overall impervious area was significantly increased in both the study area and built-up area from 1984 to 2015. The impervious ratio showed a decreasing trend in the built-up area and increasing trend in the whole study area. The runoff coefficient of the built-up area showed a significantly decreasing trend. The runoff ratio of the built-up area to the whole study area was increased dramatically, reaching 0.26 by 2015. NDVI (normalized distribution vegetation index), vegetation, and impervious surface were the most important urban surface conditions in the study area for direct runoff generation. The relative influence of impervious surface showed a rapidly increasing trend and then gradually decreased from 2000.

Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region

The effects of climate and land use/land cover (LULC) dynamics have directly affected the surface runoff and flooding events. Hence, current study proposes a full-packaged model to monitor the changes in surface runoff in addition to forecast of the future surface runoff based on LULC and precipitation variations. On one hand, six different LULC classes were extracted from Spot-5 satellite image. Conjointly, land transformation model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020 ones. On the other hand, the time series-autoregressive integrated moving average (ARIMA) model was applied to forecast the amount of rainfall in 2020. The ARIMA parameters were calibrated and fitted by latest Taguchi method. To simulate the maximum probable surface runoff, distributed soil conservation service-curve number (SCS-CN) model was applied. The comparison results showed that firstly, deforestation and urbanization have been occurred upon the given time, and they are anticipated to increase as well. Secondly, the amount of rainfall has non-stationary declined since 2000 till 2015 and this trend is estimated to continue by 2020. Thirdly, due to damaging changes in LULC, the surface runoff has been also increased till 2010 and it is forecasted to gradually exceed by 2020. Generally, model calibrations and accuracy assessments have been indicated, using distributed-GIS-based SCS-CN model in combination with the LTM and ARIMA models are an efficient and reliable approach for detecting, monitoring, and forecasting surface runoff.