Soil Conservation Service Curve Number Research Articles

Pengendalian Banjir di Daerah Aliran Sungai (DAS) Ampal Kota Balikpapan: Analisa Kapasitas Sungai

Ampal acts as the main channel in ampal drainage system. This river is located in urban areas that serves to drain the water from the upstream to the sea (Makassar Strait). Channel reduction, occurred in the middle to downstream channel, caused by sedimentation leads to capacity reduction, further its unability to drain the flood. The purpose of this study is to determine the river capacity as an alternative flood prevention. Hydrology and hydraulics data are used in the analysis process. In flood discharge calculation, the SCS-CN (Soil Conservation Service - curve number) runoff calculation method is used. Based on the data and analysis, the maximum flood discharge values flowing in the Ampal catchment are 100.20 m3/s for the middle section and 170.40 m3/s at the downstream. The central part of the Ampal River capacity is 60.69 m3/sec. and 168.50 m3/sec. at the downstream. Using three additional bozem, the problem at Section 3 Ampal River can be solved, with 7.70 m3/s flood discharge reduction.

Integrating GIS-Based MCDA Techniques and the SCS-CN Method for Identifying Potential Zones for Rainwater Harvesting in a Semi-Arid Area

An increasing scarcity of water, as well as rapid global climate change, requires more effective water conservation alternatives. One promising alternative is rainwater harvesting (RWH). Nevertheless, the evaluation of RWH potential together with the selection of appropriate sites for RWH structures is significantly difficult for the water managers. This study deals with this difficulty by identifying RWH potential areas and sites for RWH structures utilizing geospatial and multi-criteria decision analysis (MCDA) techniques. The conventional data and remote sensing data were employed to set up needed thematic layers using ArcGIS software. The soil conservation service curve number (SCS-CN) method was used to determine surface runoff, centered on which yearly runoff potential map was produced in the ArcGIS environment. Thematic layers such as drainage density, slope, land use/cover, and runoff were allotted appropriate weights to produced RWH potential areas and zones appropriate for RWH structures maps of the study location. Results analysis revealed that the outcomes of the spatial allocation of yearly surface runoff depth ranging from 83 to 295 mm. Moreover, RWH potential areas results showed that the study areas can be categorized into three RWH potential areas: (a) low suitability, (b) medium suitability, and (c) high suitability. Nearly 40% of the watershed zone falls within medium and high suitability RWH potential areas. It is deduced that the integrated MCDA and geospatial techniques provide a valuable and formidable resource for the strategizing of RWH within the study zones.

Identification of specific areas for water conservation measures using Geoinformatics approach

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

Response of surface runoff to land use and land cover change and its impact on Daihai Lake shrinkage in Inner Mongolia, China

Daihai Lake (DL), the third largest inland lake in Inner Mongolia, has shrunk severely given the conditions of no significant decrease in annual precipitation. On the basis of field surveys and Landsat remote sensing images, this study analyzed land use and land cover (LULC) changes in six periods of the DL watershed area during 1986–2019. Then, with the combination of the LULC results and the Soil Conservation Service–Curve Number method, we discussed the change characteristics and influence factors of the surface runoff in the watershed and the reason for DL shrinkage. From 1986 to 2019, the water area of DL decreased rapidly at a rate of −2.22 km2/year, and the area of farmland and grassland in the watershed changed strongly along the opposite trend. The surface runoff in the DL watershed was reduced by approximately 513.6 million m3 during the last 34 years. In addition to local rainfall, groundwater is also an important water supply to the lake and accounts for a greater proportion. The average recharge rate of groundwater to the lake is approximately 64.55 million m3 per year in the past 34 years. The area change of the DL is related to the runoff reduction, huge evaporation, and lake water use in thermoelectric power generation. The local precipitation cannot enter the groundwater in DLW. Large-scale exploitation of external groundwater for agricultural irrigation and industrial production is another important cause of lake degradation.

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.

Simulation of rainfall–runoff process for an ungauged catchment using an event-based hydrologic model: A case study of koraiyar basin in Tiruchirappalli city, India

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

Spaceborne C-band SAR remote sensing-based flood mapping and runoff estimation for 2019 flood scenario in Rupnagar, Punjab, India.

Floods are one of the most disastrous and dangerous catastrophes faced by humanity for ages. Though mostly deemed a natural phenomenon, floods can be anthropogenic and can be equally devastating in modern times. Remote sensing with its non-evasive data availability and high temporal resolution stands unparalleled for flood mapping and modelling. Since floods in India occur mainly in monsoon months, optical remote sensing has limited applications in proper flood mapping owing to lesser number of cloud-free days. Remotely sensed microwave/synthetic aperture radar (SAR) data has penetration ability and has high temporal data availability, making it both weather independent and highly versatile for the study of floods. This study uses space-borne SAR data in C-band with VV (vertically emitted and vertically received) and VH (vertically emitted and horizontally received) polarization channels from Sentinel-1A satellite for SAR interferometry-based flood mapping and runoff modeling for Rupnagar (Punjab) floods of 2019. The flood maps were prepared using coherence-based thresholding, and digital elevation map (DEM) was prepared by correlating the unwrapped phase to elevation. The DEM was further used for Soil Conservation Service-curve number (SCS-CN)-based runoff modelling. The maximum runoff on 18 August 2019 was 350mm while the average daily rainfall was 120mm. The estimated runoff significantly correlated with the rainfall with an R2 statistics value of 0.93 for 18 August 2019. On 18 August 2019, Rupnagar saw the most devastating floods and waterlogging that submerged acres of land and displaced thousands of people.

Implications of event-based loss model structure on simulating large floods

Event-based rainfall-runoff models used for design flood estimation and operational flood forecasting commonly represent losses (the amount of rainfall which does not appear as runoff) using lumped, parsimonious models. In gauged catchments, these loss values are typically estimated by calibrating models to observed flood events. Studies which compare loss models tend to focus on which model best reproduces observed events, however, flood modelling often requires estimating events larger than those used for calibration. It is thus of interest to examine how loss models perform over a range of flood magnitudes, particularly larger floods. Here, we evaluate the efficacy of different loss models by exploring the dependence between event magnitude and model performance. We assess four loss models commonly used in flood modelling: Initial Loss Continuing Loss (ILCL), Initial Loss Proportional Loss (ILPL), Soil Conservation Service Curve Number (SCSCN) and Probability Distributed Model (PDM).The four loss models were incorporated into a lumped rainfall runoff model that was calibrated to observed storm events for 35 small, rural Australian catchments using a total of 876 unique storm events. In calibration, ILPL was found to best reproduce observed hydrographs, however the difference in performance between the four different models in terms of NSE was small. Dependence between event magnitude and model performance was explored by re-simulating observed events with the loss model parameters fixed to represent typical catchment conditions. These typical parameters were estimated from the median of the observed parameter ranges in each catchment with the largest five events excluded so that the impact of extrapolating model parameters to events larger than which they were calibrated could be explored. When these typical parameters were applied to large flood events the ILCL model displayed minimal dependence between model performance and event magnitude. In contrast, ILPL, SCSCN and PDM all clearly displayed an underprediction bias which increased with increasing event size. We explain this behaviour through the loss model structure of ILCL where ongoing loss (the loss which occurs after runoff begins) is constant and independent of rainfall fvmagnitude. In contrast, the three remaining models represent ongoing loss as a proportion of rainfall. As the difference in magnitude between small and large floods increases with climate change, extrapolation of flood models is likely to become a more pertinent issue in the future. We conclude that loss models which are independent of event magnitude are better suited to flood modelling applications where extrapolation is required.

Analyzing the Impact of Temporal Landcover Changes on the Runoff Component of a Catchment Using Geospatial Techniques

Globally, the issue of urbanization has been a matter of concern over the past few decades. Unplanned urban growth alters the natural drainage pattern and the water cycle, consequently, giving rise to floods. The floods are attributed to heavy precipitation but, human activities act as a catalyst in creating an imbalance in the environmental harmony. Predominantly, urban floods in the developing nations are known to affect quite a large population and infrastructure. Nagpur, in Maharashtra, India has been facing hydro-meteorological events in the last decade along with a persistent reduction of natural land and rapid urban growth. The paper attempts to establish a relationship between temporal landcover change for years 1995, 2005, 2015 and increased surface runoff eventually causing urban floods through application of the Soil Conservation Services Curve Number method. These years marked a remarkable urban development in Nagpur hence were considered. The outcome demonstrated a prominent urban sprawl of 12.84 percent and a severe loss of 16.32 percent in natural vegetation between 1995 and 2015. This paper examines the relationship between urban expansion and runoff at the catchment level by integrating land use/cover mapping with the hydrological model. Although the potential impact is expected, this study can further confirm it and facilitate policymakers developing policies to mitigate flooding-caused damages.

Assessing impact of urbanisation on surface runoff using vegetation-impervious surface-soil (V-I-S) fraction and NRCS curve number (CN) model

The progressive decrease in natural surfaces to create space for urban dwellers with the consequential changes caused to hydrological regime of an area is taking a toll on environmental resources. The Natural Resources Conservation Service (NRCS) Curve Number (CN) model (earlier known as Soil Conservation Service Curve Number, SCS-CN) is used in the present study for assessing temporal variations in surface runoff in upper catchment of Rispana River, Dehradun city, India. Also, the composite CN is calculated using vegetation-impervious surface-soil (V-I-S) fraction derived from spectral normalisation and linear spectral mixture analysis method while assigning initial CN for hydrologic soil-cover complex for five years during 1991 to 2018. The results indicated a steady rise in impervious surface fraction and consequent surface runoff (8.25%) from 1991 to 2018, while urban green space fraction has shown depleting trends in north and central part of the city. The level of imperviousness highlighted an over 60% of central part of the city as impervious, which gradually decreases outwards from the city. A positive correlation is observed between surface runoff and ISA (R2 varies between 0.62 and 0.89, p < 0.05), whereas inverse relationship is observed with vegetative cover in all sub-basins (R2 varies between 0.61 and 0.91, p < 0.05). The study demonstrates the potential of NRCS-CN model with inputs drawn from V-I-S fraction for simulating surface runoff and understanding the urban flood risk. The work would be useful for the urban dwellers, administrators and planners for better planning and sustainable development of urban areas.

Soil Conservation Service Curve Number (SCS-CN) Method: Current Applications, Remaining Challenges, and Future Perspectives

Predicting runoff in ungauged or poorly gauged watersheds is one of the key problems in applied hydrology [...]

Effect of Land Use\Land Cover Changes on Estimated Potential Runoff in the Nablus Mountains Watersheds of Palestine: A Case Study

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

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.

Predictions of soil and nutrient losses using a modified SWAT model in a large hilly-gully watershed of the Chinese Loess Plateau

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.

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.

Inundation Analysis Method for Urban Mountainous Areas Based on Soil Conservation Service Curve Number (SCS-CN) Model Using Remote Sensing Data

Inundation Analysis Method for Urban Mountainous Areas Based on Soil Conservation Service Curve Number (SCS-CN) Model Using Remote Sensing Data

Application of the SCS–CN Method to the Hancheon Basin on the Volcanic Jeju Island, Korea

This study investigates three issues regarding the application of the SCS–CN (Soil Conservation Service–Curve Number) method to a basin on the volcanic Jeju Island, Korea. The first issue is the possible relation between the initial abstraction and the maximum potential retention. The second is the determination of the maximum potential retention, which is also closely related to the estimation of CN. The third issue is the effect of the antecedent soil moisture condition (AMC) on the initial abstraction, maximum potential retention and CN. All of these issues are dealt with based on the analysis of several rainfall events observed in the Hancheon basin, a typical basin on Jeju Island. In summary, the results are that, firstly, estimates of initial abstraction, ratio λ, maximum potential retention, and CN were all found to be consistent with the SCS–CN model structure. That is, CN and the maximum potential retention showed a strong negative correlation, and the ratio λ and the maximum potential retention also showed a rather weak negative correlation. On the other hand, a significant positive correlation was found between CN and the ratio λ. Second, in the case where the accumulated number of days is four or five, the effect of antecedent precipitation amount is clear. The antecedent five-day rainfall amount for the AMC-III condition is higher than 400 mm, compared to the AMC-I condition of less than 100 mm. Third, an inverse proportional relationship is found between the AMC and the maximum potential retention. On the other hand, a clear linear proportional relation is found between the AMC and CN. Finally, the maximum potential retention for the Hancheon basin is around 200 mm, with the corresponding CN being around 65. The ratio between the initial abstraction and the maximum potential retention is around 0.3. Even though these results are derived by analyzing a limited number of rainfall events, they are believed to properly consider the soil characteristics of Jeju Island.

Assessment of Rainfall–Runoff Due to the Impacts of Land-Use Changes by Integrated Geospatial Empirical Approach: Study on Koraiyar Basin, Tiruchirappalli City, India

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

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.