Effects of initial abstraction ratio in SCS-CN method on modeling the impacts of urbanization on peak flows Navideh Noori, Latif Kalin, Puneet Srivastava, Charlene LeBleu Proceedings ASCE World Environmental & Water Resources Congress, May -,, Albuquerque, NM MASGP--8
Mississippi-Alabama Sea Grant Consortium Project Number: R/CEH- Program Year: Grant Number: NAOAR778
Effects of Initial Abstraction Ratio in SCS-CN Method on Modeling the Impacts of Urbanization on Peak Flows Navideh Noori, Latif Kalin, Puneet Srivastava, Charlene Lebleu Graduate Student, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 89, nzn@auburn.edu Associate Professor, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 89, kalinla@auburn.edu Associate Professor, Biosystems Engineering, Auburn University, Tom E. Corley Building, Auburn, Alabama 89, srivapu@auburn.edu Associate Professor, Landscape Architecture, Auburn University, Dudley Hall Auburn, AL 89, leblecm@auburn.edu ABSTRACT The initial abstraction ratio (λ) used in the SCS-CN method plays an important role in calculation of runoff volume and consequently hydrograph peak. The recommended value for λ in the SCS handbook is.. However, recent studies suggest that λ varies between. and., closer to the lower bound. Yet, the SCS recommended value of. is still being widely used in many hydrologic models. In this study, we explored the effects of variation in λ on simulating urbanization impacts in a coastal Alabama watershed with HEC-HMS. The potential impacts of future development scenarios on peak flows were scrutinized for λ values of. and.. Results showed that the use of λ =. provides superior results when model generated hydrographs and peak flows were compared to observed counterparts. INTRODUCTION Land use/cover (LULC) is influenced by vegetation changes and anthropogenic activities such as clear cutting for agriculture and urban development. Changes in LULC could lead to changes in rainfall-runoff characteristics of a watershed, which consequently affects the peak flow and flow volume. Many studies have focused on the impacts of LULC changes on flow hydrograph. For instance, Brinkinshaw et. al. () assessed the hypothesis that the effect of forest cover on flood peaks becomes less important as the size of the hydrological event increases. They applied the physically based spatially distributed SHETRAN catchment model to a watershed located in Chile. They found that the larger the size of the event, the lesser is the effect of the land use. Muller and Reinstorf () proposed a scenario-based modeling approach to analyze the impacts of possible future land use changes on flood hazard in a Chilean watershed. They applied the hydrological model HEC-HMS and compared their output to two reference studies. Three LULC scenarios were
developed, ) climate change, ) afforestation activities, and ) developing residential areas in the central parts of the basin. The residential development was identified based on field surveys, analysis of the physiogeographic setting and conversations with the local population. Results showed increase in peak flow for each of three scenarios in comparison to the reference conditions. Similarly, Olang et al. () estimated the effect of historical land cover changes on peak discharge and flow volume in Kenya using HEC-HMS model. The results showed that the detected land cover changes have increased peak discharges and runoff volumes within the watershed. The effect was severe in areas with higher rates of deforestation and agricultural expansion. However, the relative increase in the simulated peak discharge diminished with increasing rainfall amounts. One of the most popular methods used in assessing the impacts of LULC changes on flow hydrograph and estimating the runoff volume is the SCS-CN method (SCS, 97). This method has been widely used for computation of direct runoff volumes. It estimates the rainfall excess as a function of cumulative rainfall, land use, vegetation, and antecedent soil moisture condition. Q = (P+I a) (P I a +S) I a = λs S = CN P I a, else Q = where, Q = direct runoff (mm), P = total rainfall depth (mm), S = potential maximum retention (mm), I a = initial abstraction (mm) and λ = initial abstraction ratio, and CN = curve number (SCS, 97). The empirical value of λ suggested by SCS (97) is.. Since its inception, this method has been revised several times. Several studies have probed the validity of the λ value of.. Jiang () using event rainfall-runoff data from 7 watersheds in the United States found that a λ value of about. is more appropriate than λ=. for runoff calculations. Most recently, Fu et. al. () investigated the value of λ using rainfall-runoff data to obtain the most reasonable estimation of runoff for a study area located in China. Their results also indicated that the event runoff predicted using λ=. was more accurate than that for λ=.. In contrast, the annual runoff predicted by a λ value of. was not more accurate than that using λ=.. They mentioned that more studies are needed to assess the impacts of λ variations on model performance of SCS-CN method. In this paper, the effects of variation in λ on the flow hydrograph and peak flows were investigated in a coastal Alabama watershed. More specifically, the role of variability in λ on modeling urbanization impacts was scrutinized using the rainfall-runoff model HEC-HMS. Developed areas and areas that could potentially develop in the near future were identified and their impacts on flow hydrograph and peak flow were explored.
STUDY AREA The study area is the Eight-Mile Creek watershed, located in south west Alabama near the city of Mobile (Figure ). Eight-Mile Creek joins the Chickasaw Creek in its junction with Mobile River flowing into the Gulf of Mexico. A significant portion of this watershed is located within the city limits of Mobile, Prichard and Chickasaw. The watershed has an area of 89. km with the elevation range of to m. Frequent flooding in the study area has been a chronic problem. This is only expected to worsen due to expected urban developments in the near future. The eastern part of the watershed is expected to keep growing, although the development in the central and western part of the watershed is slow. Figure. The study area. WATERSHED MODEL Effects of urbanization in a mesoscale watershed located in southwest coastal Alabama was characterized and modeled with HEC-HMS. The HEC-HMS is the US Army Corp of Engineers Hydrologic Modeling System computer program developed by the Hydrologic Engineering Center. This program simulates the precipitationrunoff and routing processes of watershed systems. The model includes precipitation option describing an observed precipitation event, loss models estimating the volume of water, direct runoff models and hydrologic routing models. Two effective components are climatic parameters and watershed physiographic factors. The climatic parameters are the intensity, rainfall duration and spatial distribution rainfall. Physiographic parameters include the type of land use, watershed area, basin shape, height, slope, direction and the type of drainage network. All of these factors are effective in both the surface runoff volume and peak discharge. HEC-HMS has several options to map the rainfall-runoff relationship in a watershed, with the SCS- CN method being one of them. The exponential recession method within HEC-HMS was used to represent the baseflow processes. The recession model shows the process of drainage from natural storage in a watershed (Linsley et al, 98). Routing of the channel flow was performed using the kinematic-wave model (USACE, 979).
Rainfall-Runoff data Hourly precipitation data from the NCDC Mobile Regional Airport station (ID: 78) was used in driving the HEC-HMS model. In order to assess the accuracy of the model predicted flow volumes and peak discharges, four years of streamflow data monitored by USGS from 99 to on the Eight-Mile Creek (USGS 7) was used. This period has seen some significant size rain events, including a hurricane. CN Estimation The key variable in application of the SCS-CN method is the curve number (CN), which varies with soil type, LULC and antecedent moisture conditions of the soil. To create a curve number grid of the study watershed, Soil Survey Geographic Database (SSURGO) and National Land Cover Dataset (NLCD ) were used. The SSURGO data was downloaded from the Natural Resource Conversation Service website: http://soildatamart.nrcs.usda.gov/ containing the spatial data and tabular data. These data were organized by exporting them into a geodatabase. Besides, the land use grid was converted into the polygon feature class to be merged with prepared soil data. In the next step, a look-up table was prepared containing curve numbers for different combinations of land uses and soil groups. It should be mentioned that as wetland areas typically have high water tables standing at or near the land surface, they were characterized as a low permeable areas in this study, and the related CN value was set as 8. Then, HEC-GeoHMS, a public domain extension of ESRI s ArcGIS software and spatial analyst extension, was activated in ArcGIS toolbar and the merged feature class and the look-up table were used to create the curve number grids. Table shows the percentage of each type of coverage for NLCD inside the Eight-Mile Creek watershed. Table. LULC in the Eight-Mile Creek watershed based on NLCD. Land Cover Type Class No. Percentage Open water.9% Developed, Open Space.8% Developed, low intensity 7.7% Developed, medium intensity.% Developed, high intensity.% Barren Land.8% Deciduous forest.% Evergreen forest.8% Mixed Forest.7% Shrub 7.% Grassland/ Herbaceous 7.% Hay/Pasture 8.8% Cultivated Crops 8.% Woody wetland 9 9.9% Emergent herbaceous wetland 9.7%
Initial Abstraction Ratio Adjustment To get the most accurate output from the rainfall-runoff model, the initial abstraction ratio was adjusted. λ value has a significant effect on the model accuracy. It varies from event to event and location to location. Jiang () indicated that the initial abstraction ratio value of. gives a better fit to the observed data for runoff generation. Hawkins et. al. (9) by citing Jiang () provides the following relationship CN. = 879[/CN. ], + where, CN. is the curve number obtained using λ=. and CN. is the curve number obtained using λ=.. Using both λ=. and., HEC-HMS was run for each of the runoff generating rainfall events over the 99- period. Table compares the observed and model simulated streamflow volumes obtained using both λ=. and.. Clearly, the model shows better accuracy with λ=.. Table. Observed and simulated streamflow volumes (mm). Observed Simulated λ=. Simulated, λ=. Events (mm) (mm) Error (%) (mm) Error (%) Mar-.. -9. - Mar-999. 9.7-9. -9 Mar-998.7. 8. 9 Jan-998. 9.7.8 -Nov-997.7. -. - Oct-997.. -7. - 9-May-997.9. -. - -Mar-997.9. -. - -Mar-997 7..8 -. - Feb-997.. -.8-7 7-Jan-997.. -7. - -Dec-99.. -. -7 Antecedent Runoff Condition (ARC) concept The obtained CN values are based on the average conditions of the watershed in terms of the wetness. For runoff estimation, the CN is adjusted according to the Antecedent Moisture Condition (AMC) which is expressed as an index based on seasonal limits for the total -day prior rainfall depths. For dry and wet conditions, Chow et. al. (998) suggested the following adjustments to the CN values, respectively..cn II CN I =.8CN II
CN II CN III = +.CN II Depending on the total amount of -day precipitation, AMC-II (CN II ), which represents average conditions, is converted to AMC-I (CN I ) and AMC-III (CN III ) using above equations. The AMC concept was recently modified and dropped for the Antecedent Runoff Condition (ARC) concept (Hawkins et. al., 9). The ARC was defined as an error band concept to encompass all sources of variation from the central trend of rainfall-runoff. The concept suggests ARC as error bands leading to about 7 percent of the runoff events falling between ARC-I and ARC-III. Hawkins et. al. (9) suggest that storms satisfying the following condition should be avoided and considered as small storms for the SCS-CN application. P S. Based on the above condition, small events were excluded from further analysis. Using CN I and CN III, the volume and peak streamflows were estimated and are shown in Figures and, respectively. Trend lines were added to the figures to represent the 7% confidence bands based on the ARC concept. If at least 7% of the runoff events fell between these bands, then the model operates with acceptable accuracy. As can be seen in both figures, more than 7% of the events are falling within the confidence bands. Simulated Streamflow Volume (cm) 8 ARC I ARC II ARC III 8 Observed Streamflow Volume (cm) Figure. Observed and model generated streamflow volumes.
Simulated Peak Streamflow (cms) 9 8 ARCI ARC II ARCIII 7 8 Observed Peak Streamflow (cms) Figure. Observed and model generated peak discharges. Hydrographs Figure compares observed and simulated hydrographs of four sample events. As can be seen hydrographs generated using λ=. are much closer to the observed hydrographs. Note that no calibration has been carried out in generating these hydrographs. Streamflow (cms) 8 9: 8: : : : : : 9: 8: 8 Streamflow (cms) 7 Rainfall Observed Simulated (CN.) Simulated (CN.) : : : : : : March, 998 January, 997 Figure. Observed and HEC-HMS generated flow hydrographs with λ=. and. for some selected events. 7
9 8 7 Streamflow (cms) Streamflow (cms) 9 7 Rainfall Observed Simulated (CN.) Simulated (CN.) : : 8: 8: : : : : : : 8: : : : 8: : : March, March, 997 Figure (continued). Potential Impact of Future Development on Flow Hydrograph To explore the potential impacts of future developments, areas which are expected to develop in the near future were identified and the imperviousness of these areas was increased by percent. The CN grid map of was updated accordingly. Figure shows those areas with updated CN values. Figure. Areas with expected urban development (green color). Since higher accuracy was obtained with λ=., the impacts future developments on peaks flows were explored only with λ=.. Upstream and downstream developments were assessed separately to identify sensitive areas. As shown in Figure, the impacts of increased imperviousness in different parts of 8
watershed are not the same. Increased imperviousness near the outlet of the watershed appears to have a smaller impact on the peak flow hydrograph compared to increased imperviousness in the upstream. The fact that generated runoff from areas near the outlet needs less time to reach the outlet compared to upstream areas is probably the likely reason for this. Runoff from these areas leaves the watershed earlier than the time that the peak flow is observed at the watershed outlet. As a result, the impact of urban development near the outlet contributes to the rising stage of the flow hydrograph. Stream flow (cms) Streamflow (cms) Rainfall NLCD Upstream Development Downstream Development 8: : 8: : 8: : 8: : : : 8: : : 8: : : 8: December, 99 March, 997 Streamflow (cms) Rainfall NLCD Upstream Development Downstream Development : : 8: : : 8: : : 8: 7 8 9 Stream flow (cms) 8 8: : 8: : 8: : 8: : 7 8 March, 999 March, 998 Figure. Impacts of potential development on flow hydrographs for selected events. 9
CONCLUSIONS In this study, two values of λ,. and., were used with the HEC-HMS model to investigate their impacts on peak flow and flow hydrograph in Eight-Mile Creek watershed. The event runoff generated using λ=. fit the data much better and was more appropriate for estimating the peak flow. With no calibration at all, HEC-HMS was able generate flow hydrographs comparable to observed data for big storms. Model simulations with λ=. showed that the impact of increased imperviousness in the downstream parts of the watershed plays a smaller role than the areas upstream. ACKNOWLEDGMENT This work is a result of research sponsored in part by the National Oceanic and Atmospheric Administration, Department of Commerce under Grant #NAOAR778, the Mississippi-Alabama Sea Grant Consortium and Auburn University. The views expressed herein do not necessarily reflect the views of any of those organizations. REFERENCE Birkinshaw, S. J., Bathurst, J. C., Iroume, A., Palacios, H. () The effect of forest cover on peak flow and sediment discharge-an integrated field and modeling study in cenral-southern Chile. Hydrol. Proces.,, 8-97. Chow, V. T., Maidment, D. R., Mays, L. W. (988) Applied hydrology. McGraw- Hill, New York. Fu, S., Zhang, G., Wang, N., Luo, L. () Initial abstraction ratio in the SCS-CN method in the Loess Plateau of China. Trans. ASABE, (), -9. Hawkins, R.H., Ward, T.J., Woodward, D.E., Van Mullen, J.A. (9) Curve Number Hydrology- State of Practice. The ASCE/EWRI Curve Number Hydrology Task Committee. Jiang, R. () Investigation of Runoff Curve Number Initial Abstraction Ratio. MS thesis, Watershed Management, University of Arizona, pp. Linsely, R.K., Kohler, M.A., Paulhus, J.L.H. (98) Hydrology for Engineers. McGraw-Hill, New York, NY. Muller, A., Reinstorf, F. () Exploration of land-use scenarios for flood hazard modeling- the case of Santiago de Chile. Hydrol. Earth Syst. Sc., 8, 99. Olang, L. O. and Furst, J.F. () Effects of land cover change on flood peak discharges and runoff volumes: model estimates for the Nyando River Basin, Kenya. Hydrol. Process.,, 8 89. SCS (97) Hyrology, National Engineering Handbook. Washington, D.C.: USDA Soil Conservation Service. USACE (979) Introduction and application of kinematic wave routing techniques using HEC-, training document. HEC, Davis, CA.