Event and Continuous Hydrologic Modeling with HEC-HMS

Transcription

1 Event and Continuous Hydrologic Modeling with HEC-HMS Xuefeng Chu, A.M.ASCE 1 ; and Alan Steinman 2 Abstract: Event hydrologic modeling reveals how a basin responds to an individual rainfall event e.g., quantity of surface runoff, peak, timing of the peak, detention. In contrast, continuous hydrologic modeling synthesizes hydrologic processes and phenomena i.e., synthetic responses of the basin to a number of rain events and their cumulative effects over a longer time period that includes both wet and dry conditions. Thus, fine-scale event hydrologic modeling is particularly useful for understanding detailed hydrologic processes and identifying the relevant parameters that can be further used for coarse-scale continuous modeling, especially when long-term intensive monitoring data are not available or the data are incomplete. Joint event and continuous hydrologic modeling with the Hydrologic Engineering Center s Hydrologic Modeling System HEC-HMS is discussed in this technical note and an application to the Mona Lake watershed in west Michigan is presented. Specifically, four rainfall events were selected for calibrating/verifying the event model and identifying model parameters. The calibrated parameters were then used in the continuous hydrologic model. The Soil Conservation Service curve number and soil moisture accounting methods in HEC-HMS were used for simulating surface runoff in the event and continuous models, respectively, and the relationship between the two rainfall-runoff models was analyzed. The simulations provided hydrologic details about quantity, variability, and sources of runoff in the watershed. The model output suggests that the fine-scale 5 min time step event hydrologic modeling, supported by intensive field data, is useful for improving the coarse-scale hourly time step continuous modeling by providing more accurate and well-calibrated parameters. DOI: / ASCE :1 119 CE Database subject headings: Watersheds; Hydrologic models; Geographic information systems; Runoff. Introduction Watershed hydrologic modeling and the associated model calibration and verification require a large set of spatial and temporal data e.g., topography, land use/covers, soils, rainfall, and flow monitoring data. In practice, however, the availability and quality of these data are often an issue one needs to cope with. Sometimes, one has to compromise the overall modeling quality because of insufficient high-resolution data for developing, calibrating, and validating the model. Under these circumstances, it is critical to develop an effective modeling strategy that not only takes full advantage of the available data but also maximizes the accuracy of modeling. The goal of the current study is to develop such a strategy by combining fine-scale event and coarse-scale continuous hydrologic modeling with the Hydrologic Engineering Center s Hydrologic Modeling System HEC-HMS USACE-HEC This approach has been applied to the Mona Lake watershed, located in west Michigan. Event hydrologic modeling for a basin characterizes finer-scale hydrologic processes and reveals how the basin 1 Assistant Professor, Dept. of Civil Engineering, North Dakota State Univ., th Ave. North, Fargo, ND 58105; formerly, Annis Water Resources Institute, Grand Valley State Univ., Muskegon, MI xuefeng.chu@ndsu.edu 2 Professor and Director, Annis Water Resources Institute, Grand Valley State Univ., 740 W. Shoreline Dr., Muskegon, MI steinmaa@gvsu.edu Note. Discussion open until July 1, Separate discussions must be submitted for individual papers. The manuscript for this technical note was submitted for review and possible publication on January 31, 2007; approved on April 17, This technical note is part of the Journal of Irrigation and Drainage Engineering, Vol. 135, No. 1, February 1, ASCE, ISSN /2009/ /$ responds to an individual rainfall event e.g., quantity of surface runoff, peak, timing of the peak, and detention. Thus, event hydrologic modeling is useful for better understanding the underlying hydrologic processes and identifying the relevant parameters. Also, intensive fine-scale hydrologic monitoring data for certain rainfall events, which are essential to the calibration of the event hydrologic model, are easily obtained. In contrast, continuous hydrologic modeling synthesizes hydrologic processes and phenomena i.e., synthetic responses of the basin to a number of rain events and their cumulative effects over a longer time period that includes both wet and dry conditions. In addition, calibration and verification of a continuous hydrologic model over a long time period often require considerable monitoring data. For many small watersheds, however, such long-term monitoring data may not be available, may not be continuous, or may not have sufficient resolution small time-interval data. Thus, a combination of event and continuous hydrologic modeling takes advantage of the two modeling methods and data availability. In particular, the parameters that are well calibrated in event models will help improve the continuous hydrologic modeling. The Watershed Modeling System WMS 1999 and the latest version of HEC-HMS USACE-HEC 2006 are used in this hydrologic modeling study. The Watershed Modeling System is a comprehensive modeling environment for watershed-scale hydrologic analysis that incorporates several commonly used hydrologic models e.g., Hydrologic Engineering Center HEC-1, Technical Release TR -20, TR-55, National Flood Frequency NFF, and Hydrological Simulation Program FORTRAN HSPF and facilitates processing of various geographic information system GIS data, automated watershed delineation, computation of hydrologic parameters, and hydrologic modeling. HEC-HMS, the successor to HEC-1, is a precipitation-runoffrouting model that represents a drainage basin as an intercon- JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING ASCE / JANUARY/FEBRUARY 2009 / 119

2 Fig. 1. Location of the Mona Lake watershed nected system of hydrologic and hydraulic components and simulates the surface runoff response of the basin to precipitation USACE-HEC The Mona Lake watershed covers km 2 and is dominated by forest, residential, and agricultural cropland land-use types. The soils are mostly Rubicon and Au Gres sands RsB, AsB, and Ra Steinman et al. 2003, which possess relatively low runoff potential and high infiltration capability. Mona Lake itself covers an area of 2.65 km 2 and receives inflows from a number of tributaries and storm drains Steinman et al ; the lake connects directly to Lake Michigan via a small channel. Major tributaries include Black Creek BC, Little Black Creek LBC, Cress Creek CC, and Ellis Drain ED Fig. 1. Black Creek drains the largest area and it also receives discharge of treated wastewater from the Muskegon County Wastewater Management System WWMS. Methods and Model Development SCS-CN Event Model versus SMA Continuous Model in HEC-HMS The Soil Conservation Service curve number method SCS-CN, USDA 1986 is essentially an empirical, one-parameter CN, event rainfall-runoff model. The dimensionless curve number takes into account, in a lumped way, the effects of land use/cover, soil types, and hydrologic conditions on surface runoff, and relates direct surface runoff to rainfall. The SCS-CN method has been widely used for estimating rainfall-generated surface runoff in watershed hydrologic modeling. In spite of the popularity of the SCS-CN method, it is controversial; its limitations, application conditions, abuses, and future directions have been discussed by many researchers e.g., Ponce and Hawkins 1996; Garen and Moore The SCS-CN method is selected in this study primarily because it allows one to fully utilize the available spatially distributed GIS data for the Mona Lake watershed that also can be easily processed by using the Windows-based tools in WMS. The SCS-CN model is available in HEC-HMS for simulating direct surface runoff from a storm event i.e., event rainfall-runoff model. To facilitate continuous hydrologic modeling, a soil moisture accounting SMA model has been incorporated in HEC-HMS USACE-HEC, 2000, Basically, SMA in HEC-HMS is a lumped bucket-type model that represents a subbasin with well-linked storage layers/buckets accounting for canopy interception, surface depression storage, infiltration, evapotranspiration, as well as soil water and groundwater percolation. In this study, the SCS-CN and SMA models were used for event and continuous hydrologic modeling, respectively. The event modeling focused on understanding how the hydrologic system responded to individual storm events on a fine time scale and identifying relevant hydrologic parameters. The SMA continuous hydrologic modeling operated over a longer period, which included a series of rainfall events and dry time periods on a coarse time scale. The main purpose of implementing joint SCS-CN event and SMA continuous modeling is to strengthen the overall modeling capability. Parameters that are well calibrated in the event modeling are further used to improve the continuous hydrologic modeling. The relationship between the SCS-CN and SMA methods can be analyzed as follows: The SCS-CN model can be expressed as USDA 1986 R = P 2 e P e + S P e = P I a I a = S S = 2, SI unit system,cm 4 CN where R cumulative runoff; P cumulative rainfall; P e cumulative effective rainfall P e 0; otherwise, R=0 ; S=potential maximum retention; I a =initial abstraction all initial losses: surface depression storage, vegetation interception, and infiltration ; =initial abstraction coefficient; and CN=curve number. For a default value of =, Eq. 1 becomes P 508/CN R = 5 P /CN According to the SMA method in HEC-HMS, rainfall contributes first to the canopy interception storage S c. Then, rainwater is available for infiltration, which is determined by infiltration capacity and soil storage S s. Any excess rainwater sequentially fills the surface depression storage S sf and eventually becomes surface runoff. The potential infiltration rate is given by USACE- HEC 2000 S sd t i t = i m 6 S s max S sd t = S s max S s t where i t =potential infiltration rate at time t the actual infiltration rate also depends on the water available for infiltration at time t ; i m =maximum infiltration rate; S smax =maximum soil water storage; S s t =soil water storage at time t; and S sd t =soil water storage deficit at time t. The infiltration rate equals zero when / JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING ASCE / JANUARY/FEBRUARY 2009

3 S sd t =0 i.e., S s t =S smax and reaches the maximum rate i m when S sd t =S smax i.e., S s t =0. From the definition of initial abstraction in the SCS-CN model and the computation procedures of the SMA model no evaporation during rainfall, one has I a = S c + S sf + F 0 8 F 0 = i 0 t 0 9 where F 0 =cumulative infiltration before surface runoff starts; i 0 =average infiltration rate before surface runoff starts; and t 0 =initial time. Substituting the expression of I a Eqs. 3 and 4 into Eq. 8, we have F 0 = 2,540/CN 25.4 S c S sf For =, Eq. 10 becomes 10 F 0 = 508/CN 5.08 S c S sf 11 For the entire rainfall event, total runoff can be expressed as i.e., R = P S c S sf F 0 F r R = P e F r F r = i r t r 14 where F r =cumulative infiltration after surface runoff initiates; i r =average infiltration rate after surface runoff starts; and t r =postrunoff time note that the entire time of the rainfall event T=t 0 +t r. According to Eqs. 1 and 13, one has thus, P e 2 P e + S = P e F r F r = P es P e + S Substituting Eqs. 2 and 3 into Eq. 16, one has or P S S F r = P S + S F r = RS P e Substituting = and Eq. 4 into Eq. 17, one has P 508/CN ,540/CN 25.4 F r = 19 P + 2,032/CN Thus, the cumulative infiltration over the entire rainfall event time T is given by Fig. 2. HEC-HMS conceptual model for the Mona Lake watershed F = F 0 + F r = 508/CN 5.08 S c S sf P 508/CN ,540/CN P + 2,032/CN The average infiltration rate over the entire event can be written as i = F 21 T Although SCS-CN and SMA utilize dissimilar methods for simulating surface runoff, infiltration, and other related hydrologic processes, the above-presented derivations do provide a way to better estimate the involved parameters in the SMA continuous model based on those well calibrated in the SCS-CN event model. Hydrologic Monitoring and Field Data Collection In this watershed study, eight sites Fig. 1 were selected for hydrologic monitoring three BC sites S1 S3 ; three LBC sites S4 S6 ; one CC site S7 ; and one ED site S8. The specific location of each site was determined based on a number of factors, such as shape and stability of stream channels, flow conditions, and accessibility. An Odyssey pressure and temperature recording system was installed for collecting stream water level and temperature data at each site. Streamflow also was manually measured and processed by using the Windows-based hydrologic software, HYDROL-INF Chu and Mariño Then, rating curves were developed and observed hydrographs Q t were computed for all monitoring sites, which were further utilized for model calibration. Watershed Delineation and Parameter Computation Using WMS, overland flow directions and accumulations were computed and the drainage network and subbasin boundaries were determined. The entire Mona Lake watershed was divided into thirteen subbasins: five BC subbasins Basins 1 5, five LBC subbasins Basins 6 10, one CC subbasin Basin 11, one ED subbasin Basin 12, and a subbasin adjacent to Mona Lake JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING ASCE / JANUARY/FEBRUARY 2009 / 121

4 Basin 13 Figs. 1 and 2. Further, all geometric parameters, such as subbasin area, overland flow length, basin slope, and stream channel length and slope were computed. Curve numbers were computed for all subbasins based on their land use/covers, soil types, and hydrologic soil groups. Other parameters e.g., lag time and time of concentration also were estimated by using appropriate approaches in WMS. Event Hydrologic Modeling A 5 min time step was selected in the event hydrologic modeling. The SCS-CN loss method, the Clark transform method, and the recession baseflow method were selected for all subbasins. The SCS-CN method simulates rainfall excess and losses; the Clark unit hydrograph method Clark 1945 transforms the computed rainfall excess to direct runoff at the outlet of a subbasin; and the recession method utilizes an exponential recession model Chow et al to represent baseflow from a subbasin. To better represent the distinct flow characteristics of BC and smaller tributaries LBC, CC, and ED, three reach routing methods straddle-stagger progressive average-lag method USACE 1960, Muskingum method, and lag method were utilized in the modeling. Selection of rainfall events is a critical step for event hydrologic modeling and model calibration/verification. Selection depends on many factors, such as rainfall characteristics magnitude, duration, intensity, and temporal and spatial variability, watershed properties size, land use/covers, soil types, etc., and availability and completeness of rainfall and stream monitoring data. Four rainfall events were selected for calibration and verification of the event model. Two methods normalized objective function NOF Ibbitt and O Donnell 1971 and modeling efficiency EF Nash and Sutcliffe 1970 were used to quantify the goodness of fit between the simulated and observed flows. The values of NOF and EF, respectively, are given by NOF = 1 Q obs 1 Q obs,i Q sim,i 2 n i=1 n 22 EF=1 i=1 n Q obs,i Q sim,i 2 23 n i=1 Q obs,i Q obs 2 where Q obs,i =observed discharge; Q sim,i =simulated discharge; Q obs=mean of the observed discharge; and n=number of the observed or simulated data points. Note that if all observed discharges are the same as the simulated ones, the NOF and EF values equal 0 and 1, respectively. Continuous Hydrologic Modeling In the continuous hydrologic model, the simulation time period ranged from April 6, 2005 to September 15, 2005 and an hourly time step was used. As in the event model, the Clark transform method, the recession baseflow method, as well as the straddle stagger, Muskingum, and lag routing methods were selected in the continuous model. The relevant parameters calibrated in the event model were used. The SMA loss method was utilized for continuously simulating rainfall excess in this continuous model. Initial estimates of the parameters involved in the SMA method were determined primarily based on the relationship between the SCS-CN and SMA methods and the well-calibrated SCS-CN model parameters, the actual conditions e.g., soil properties, and Table 1. Normalized Objective Function NOF and Modeling Efficiency EF for the Event Model Event Watershed Outlet/site NOF EF 8/10-8/15 BC Outlet 2 S Outlet 3 S Outlet 4 S Calibration Average /26-8/30 BC Outlet 2 S Outlet 3 S Outlet 4 S Verification Average /28-10/1 LBC CC ED the suggestions provided in the HEC-HMS User s Manual USACE-HEC As in the event modeling, the NOF and EF methods also were applied to quantify the fit of the simulated hydrographs to the observed ones at the eight sites. Analysis of Results Outlet 7 S Outlet 9 S Outlet 10 S Outlet 11 S Outlet 12 S Calibration Average /26-8/30 LBC Outlet 9 S Outlet 10 S /30-7/3 LBC Outlet 7 S Outlet 10 S Verification Average /26-8/30 CC, ED Outlet 11 S Outlet 12 S /30-7/3 CC, ED Outlet 11 S Outlet 12 S Verification Average Overall average The event hydrologic model was calibrated and verified using the observed flow data at the eight monitoring sites Sites S1 S8, Fig. 1. According to quantitative evaluation of the performance of the event hydrologic model Table 1, the overall NOF and EF averages for all rainfall events and all monitoring sites are and 0.844, respectively. The minimum NOF value is 66 for Event 8/26-8/30 at Site S3 and the highest EF value is for Event 6/30-7/3 at Site S6. Thus, good agreement between simulations and field data has been achieved in the event hydrologic modeling. The parameters calibrated in the event model were then used for continuous hydrologic modeling. Comparisons between the simulated and observed hydrographs at the three BC sites and five other sites three LBC sites, one CC site, and one ED site for the continuous hydrologic modeling are shown in Figs. 3 and 4, respectively. Similarly, performance of the continuous model was quantitatively evaluated by using the NOF and EF methods Table 2. The average NOF value for all eight monitoring sites is and the average EF value is 0.691, suggesting a fairly good agreement between the simulated and observed hydrographs. Site S4 has the highest NOF and the lowest EF because of the significant variations in the flow measurements in April Fig. 4 a, which can be attributed to various factors e.g., 122 / JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING ASCE / JANUARY/FEBRUARY 2009

5 diurnal changes in temperature and moisture. In addition, minor diurnal oscillations, induced by the varying discharge of treated wastewater from the WWMS, were observed at Sites S1 and S2 along Black Creek Figs. 3 a and b, but such oscillations were not represented in the continuous model because only daily discharge data were available for the simulation time period. Overall, the simulated hydrographs at the eight sites reflect the dominant trends and variations observed in the field flow data Figs. 3 and 4. The hydrologic modeling suggests that the overall percentage of rainfall excess for the entire Mona Lake watershed is 6.8% and the remaining portion of rainfall 93.2% is subject to various losses. The BC subbasin, which is the largest in the watershed Basins 1 5, has higher loss and lower excess percentages than the other subbasins. During the simulation period from April 6 to September 15, 2005, m 3 of water was generated from the 13 subbasins, of which 93.9% came from baseflow and 6.1% was contributed by direct runoff; baseflow was the primary source of runoff in the Mona Lake watershed. Summary and Conclusions a). Wolf-Lake Site (Outlet 2) b). Broadway Site (Outlet 3) c). US31 Site (Outlet 4) Date Simulated Observed Fig. 3. Comparison between simulated and observed hydrographs for the BC sites simulation period: April 6 September 15, 2005 In practice, long-term hydrologic monitoring data are not always available or they may not always be of sufficient frequency and duration for hydrologic modeling. How to implement effective and accurate hydrologic modeling when faced with such incomplete data is often an issue for modelers. In this study, both event and continuous hydrologic models were developed for the Mona Lake watershed in west Michigan by using WMS and the widely used HEC-HMS. However, it was found that for some small subbasins, a larger computation time scale such as an hourly time step used in the continuous model prevented the writers from 1 effectively identifying how these basins responded to a storm event and 2 accurately determining the time-related parameters e.g., time of concentration or lag time because their response time was shorter than the hourly time step. In contrast, smallscale, high-resolution storm event data 5 min time step used in Table 2. Normalized Objective Function NOF and Modeling Efficiency EF for the Continuous Model Outlet/site NOF EF Outlet 2 S Outlet 3 S Outlet 4 S Outlet 7 S Outlet 9 S Outlet 10 S Outlet 11 S Outlet 12 S Average the event model enabled the writers to refine the model calibration and identify parameters more accurately on a fine time scale, which sequentially improved the continuous hydrologic modeling over a much larger time scale. Thus, this modeling study suggests that a combination of fine-scale event and coarse-scale continuous hydrologic simulations can be an effective way that not only fully takes advantage of the characteristics of distinct modeling approaches and the availability of various data, but also enhances the overall modeling capabilities. The event hydrologic model was calibrated and validated, which was then used for improving the continuous modeling over a longer time period. The quantitative evaluation of the goodness 0.6 a). Roberts Site (Outlet 7) b). Airline Site (Outlet 9) c). Hoyt Site (Outlet 10) d). Grand-Haven Site (Outlet 11) e). Rood Site (Outlet 12) Date Simulated Observed Fig. 4. Comparison between simulated and observed hydrographs for the LBC, CC, and ED sites simulation period: April 6 September 15, 2005 JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING ASCE / JANUARY/FEBRUARY 2009 / 123

6 of fit by the NOF and EF methods indicated that good agreements between the simulated and observed flows were achieved in both event and continuous simulations. As to the practical aspect of this application study, the following conclusions can be reached: 1 the continuous hydrologic modeling suggested that only 6.8% of rainwater was available for surface runoff; % of the runoff from the 13 subbasins originated from baseflow; and 3 Black Creek had hydrologic characteristics distinct from Little Black Creek, Cress Creek, Ellis Drain, and other small tributaries flowing into Mona Lake, which can be attributed to their dissimilar sizes, land use/land covers, soil types, and other hydrologic conditions. Black Creek was supported primarily by subsurface flow and characterized by a consistent, relatively stable pattern Fig. 3 and an obvious annual hydrologic cycle. In contrast, Little Black Creek and other small subbasins were particularly sensitive to rainfall showing a number of high short-duration peaks dominated by storm events Fig. 4, which could be attributed primarily to their small size and higher percentage of impervious area. Acknowledgments This project was supported by the Michigan Department of Environmental Quality and Grand Valley State University. The writers would like to thank Annoesjka Steinman, David Fongers, David Kendrick, Vivek Singh, Rick Rediske, Kurt Thompson, Rod Denning, Patrick Womble, Eric Nemeth, and Brain Hanson for their contributions to various aspects of this research. References Chow, V. T., Maidment, D. R., and Mays, L. W Applied hydrology, McGraw-Hill, New York. Chu, X., and Mariño, M. A Simulation of infiltration and surface runoff A Windows-based hydrologic modeling system HYDROL-INF. Examining the Confluence of Environmental and Water Concerns, Proc., 2006 World Environmental and Water Resources Congress, R. Graham, ed., ASCE, New York. Clark, C. O Storage and the unit hydrograph. Trans. Am. Soc. Civ. Eng., , Garen, D. C., and Moore, D. S Curve number hydrology in water quality modeling: Uses, abuses, and future directions. J. Am. Water Resour. Assoc., 41 2, Ibbitt, R. P., and O Donnell, T Fitting methods for conceptual catchment models. J. Hydr. Div., 97 9, Nash, J. E., and Sutcliffe, J. V River flow forecasting through conceptual models. Part I A discussion of principles. J. Hydrol., 10 3, Ponce, V. M., and Hawkins, R. H Runoff curve number: Has it reached maturity? J. Hydrol. Eng., 1 1, Steinman, A. D., et al Preliminary watershed assessment: Mona Lake watershed, Annis Water Resources Institute, Muskegon, Mich., Publication No. MR Steinman, A. D., et al An environmental assessment of an impacted, urbanized watershed: The Mona Lake Watershed, Michigan. Archiv Hydrobiol., 166 1, United States Army Corps of Engineers USACE Routing of floods through river channels. Engineering manual No , Washington, D.C. United States Army Corps of Engineers, Hydrologic Engineering Center USACE-HEC Hydrologic modeling system HEC-HMS technical reference manual, Davis, Calif. United States Army Corps of Engineers, Hydrologic Engineering Center USACE-HEC Hydrologic modeling system HEC-HMS user s manual, Davis, Calif. USDA Urban Hydrology for Small Watersheds. Technical Release 55 TR-55, Natural Resources Conservation Services, Washington, D.C. Watershed Modeling System WMS WMS V6.1 tutorials. Brigham Young Univ., Environmental Modeling Research Laboratory, Provo, Utah. 124 / JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING ASCE / JANUARY/FEBRUARY 2009