Hydrograph Simulation with TOPMODEL

1. Hydrograph Simulation with TOPMODEL Geography 591: Watershed GIS Joel Sholtes

2. Hydrographs peak discharge and stage volume of runoff speed of flood wave A primary element of my research will be assessment of changes to discharge hydrographs as they move through a stream channel. Elements of interest of hydrographs include peak discharge and stage values for storm events. Also of interest is the time between the centroid of precipitation event (when plotted as volume vs time) and the peak discharge. This is called the time to concentration, and relates to how quickly water runs off within a catchment, and how flows coming from various parts of the catchment join together, which is affect by watershed shape and topography. Finally, the speed at which the flood wave travels downstream and its acceleration/de-acceleration are also of interest.A primary element of my research will be assessment of changes to discharge hydrographs as they move through a stream channel. Elements of interest of hydrographs include peak discharge and stage values for storm events. Also of interest is the time between the centroid of precipitation event (when plotted as volume vs time) and the peak discharge. This is called the time to concentration, and relates to how quickly water runs off within a catchment, and how flows coming from various parts of the catchment join together, which is affect by watershed shape and topography. Finally, the speed at which the flood wave travels downstream and its acceleration/de-acceleration are also of interest.

3. Stream Restoration and Floods Potential in reducing flood stage What restoration elements dissipate runoff energy or store flood waters? Is the scale of current restoration project adequate for this function? Stream restoration design can change how a stream conveys flood waters. There is potential in stream restoration for reusing the magnitude and speed of a flood wave.Stream restoration design can change how a stream conveys flood waters. There is potential in stream restoration for reusing the magnitude and speed of a flood wave.

4. Restoration Elements Though many other stream attributes must be addressed in any stream restoration design, various elements of restoration design can attenuate flood waves. Channel form can be altered to convey a much smaller flow, allowing for the stream to overflow its banks at a certain more common interval. This allows larger floods to leave the channel and spill into an adjoining floodplain or terrace where it is slowed down and temporarily stored. Reducing channel slope also allows water to travel more slowly, allowing for flood waves to be dispersed and reducing the peak. Stream patter (sinuosity) is a third element of stream design that can bring about flood wave attenuations. Increasing a stream�s sinuosity not only increases its length and the length a flood wave must travel, the meander bends cause transverse flows and turbulence, which lead to reductions in total stream power. Increasing channel and floodplain roughness will also serve to slow water movement down. All of these elements of restoration will cause a flood wave to slow its progression downstream and serve to store flood waters locally and temporarily on site. By doing so, downstream peak stage and discharge may be reduced. Though many other stream attributes must be addressed in any stream restoration design, various elements of restoration design can attenuate flood waves. Channel form can be altered to convey a much smaller flow, allowing for the stream to overflow its banks at a certain more common interval. This allows larger floods to leave the channel and spill into an adjoining floodplain or terrace where it is slowed down and temporarily stored. Reducing channel slope also allows water to travel more slowly, allowing for flood waves to be dispersed and reducing the peak. Stream patter (sinuosity) is a third element of stream design that can bring about flood wave attenuations. Increasing a stream�s sinuosity not only increases its length and the length a flood wave must travel, the meander bends cause transverse flows and turbulence, which lead to reductions in total stream power. Increasing channel and floodplain roughness will also serve to slow water movement down. All of these elements of restoration will cause a flood wave to slow its progression downstream and serve to store flood waters locally and temporarily on site. By doing so, downstream peak stage and discharge may be reduced.

5. Flood Attenuation Compare up with downstream hydrographs Reduced peaks Delayed Peaks Demonstration of effect of floodplain vegetation changes on flood wave progression.Demonstration of effect of floodplain vegetation changes on flood wave progression.

6. Rainfall Runoff Modeling Lumped Models Rational Method: Qpeak= CIA USDA SCS Method Semi-Distributed Models TOPMODEL HEC-HMS Spatially Distributed Models Runoff calculations made at each pixel Various Types of Rainfall Runoff Models: Lumped Rational Method After time of concentration has reached C= rational runoff coefficient that scale down runoff. Higher for more impervious surfaces. Can account for multiple land uses (weighted avgs) I= rainfall intensity (in/hr) A = Catchment area USDA Curve Method Based on parameters estimated for various types of agricultural settings, soils, crop cover, antecedent soil moisture, and practices. Both of these methods produce lumped values such as peak discharge and total runoff volume. Semi-Distributed Use spatial information such as channel length and rainfall distribution with lumped information such as landcover and areas associated with each cover type, to generate watershed outputs like discharge peak and volume, but can also produce hydrographs. TOPMODEL and HEC_HMS are semi-distributed models. Fully Distributed Models Use raster and vector data and perform pixel by pixel calculations for runoff processes such as evapotranspiration, precipitation infiltration and runoff. These types of models are much more computationally intensive than the former two versions.Various Types of Rainfall Runoff Models: Lumped Rational Method After time of concentration has reached C= rational runoff coefficient that scale down runoff. Higher for more impervious surfaces. Can account for multiple land uses (weighted avgs) I= rainfall intensity (in/hr) A = Catchment area USDA Curve Method Based on parameters estimated for various types of agricultural settings, soils, crop cover, antecedent soil moisture, and practices. Both of these methods produce lumped values such as peak discharge and total runoff volume. Semi-Distributed Use spatial information such as channel length and rainfall distribution with lumped information such as landcover and areas associated with each cover type, to generate watershed outputs like discharge peak and volume, but can also produce hydrographs. TOPMODEL and HEC_HMS are semi-distributed models. Fully Distributed Models Use raster and vector data and perform pixel by pixel calculations for runoff processes such as evapotranspiration, precipitation infiltration and runoff. These types of models are much more computationally intensive than the former two versions.

7. Semi-Distributed Models Screen shot from HEC-HMSScreen shot from HEC-HMS

8. TOPMODEL Assumptions: The �dynamics of the statured zone can be approximated by steady state representations�. The hydraulic gradient of the saturated zone is a factor of the local slope (tan�, Darcy�s Law) Distribution of down slope transmissivity changes with depth is approximated by an exponential relationship with saturation deficit Si. (Beven 1997)

9. TOPMODEL Calculates surface runoff discharges for classes of wetness index values TOPMODEL The catchment is divided classes of topographic index. Each class of index values are assumed to behave similarly hydrologically, that is, they will generate the same amount of runoff under the same set of circumstances. Precipitation and Evapotranspiration inputs are then used to generate surface runoff for these hydrologically similar areas. TOPMODEL The catchment is divided classes of topographic index. Each class of index values are assumed to behave similarly hydrologically, that is, they will generate the same amount of runoff under the same set of circumstances. Precipitation and Evapotranspiration inputs are then used to generate surface runoff for these hydrologically similar areas.

10. Stream Restoration Site UT to Tick Creek, Chatham Co Drainage Area: 0.276 km 1.14 km of an unnamed tributary to Tick Creek was restored for the Ecosystem Enhancement Program in 2006. It is a first order stream that drains approximately 0.276 km. 1.14 km of an unnamed tributary to Tick Creek was restored for the Ecosystem Enhancement Program in 2006. It is a first order stream that drains approximately 0.276 km.

11. Raster Processing in TAS High resolution LIDAR generated bare earth elevation point data for areas in Chatham County were acquired from the North Carolina Floodplain Mapping Program website. A 20x20ft (6.1x6.1m) DEM was generated from this point file by the IDW interpolation using a decay power of 3 and 7 nearest neighbors in ArcGIS 9.2. This DEM was imported into TAS in ESRI ascii format for further processing. The demonstration version of TOPMODEL that I acquired has a raster size limitation of 100x100 grid cells. The DEM was aggregated in TAS by a factor of two, resulting in 40x40ft (12.2x12.2m) grid cells. Pits were subsequently breached and a slope raster was generated to verify that no structured error existed in the DEM. Finally, a wetness index map was generated.High resolution LIDAR generated bare earth elevation point data for areas in Chatham County were acquired from the North Carolina Floodplain Mapping Program website. A 20x20ft (6.1x6.1m) DEM was generated from this point file by the IDW interpolation using a decay power of 3 and 7 nearest neighbors in ArcGIS 9.2. This DEM was imported into TAS in ESRI ascii format for further processing. The demonstration version of TOPMODEL that I acquired has a raster size limitation of 100x100 grid cells. The DEM was aggregated in TAS by a factor of two, resulting in 40x40ft (12.2x12.2m) grid cells. Pits were subsequently breached and a slope raster was generated to verify that no structured error existed in the DEM. Finally, a wetness index map was generated.

12. Raster Processing in TAS DEM SCA WI Once pits where breached, flow accumulation computations were performed creating a specific contributing area raster. Finally the stream network was derived using a threshold value of 20 ft and 17 pixels of contributing area to generate the appropriate stream network. The study catchment�s stream was identified using a topographic map and comparing topographic features with those on the DEM. The watershed was then delineated using the study reach as a seed point. The above images were clipped from the original raster tile using the study catchment�s area. Once pits where breached, flow accumulation computations were performed creating a specific contributing area raster. Finally the stream network was derived using a threshold value of 20 ft and 17 pixels of contributing area to generate the appropriate stream network. The study catchment�s stream was identified using a topographic map and comparing topographic features with those on the DEM. The watershed was then delineated using the study reach as a seed point. The above images were clipped from the original raster tile using the study catchment�s area.

13. Data Input into TOPMODEL WI Raster and histogram Stream Gage Data to compare to output (nearby USGS gage, discharge scaled by drainage area) Precipitation Reconstruction of hourly rainfall from nearby co-op station (Siler City Airport) Evapotranspiration from incoming solar radiation and latent heat of evaporation relationship. Channel Length and Velocity (routing)

14. Discharge Hydrograph and Precipitation for Tick Creek flood event I am in the process of creating a stage discharge relationship for the outlet of this watershed. However, I do not have one yet and have turned to proxy data for this study. See next slide for data explication.I am in the process of creating a stage discharge relationship for the outlet of this watershed. However, I do not have one yet and have turned to proxy data for this study. See next slide for data explication.

15. Scaled Hydrologic Data Hourly precipitation and discharge data were acquired from nearby gages. Discharge data came from a USGS gage located 4.63 km upstream of the confluence of this UT with Tick Creek. Evapotranspiration was estimated by evaporation. Hourly solar radiation measurements were used in a latent heat energy conversion equation: ET = R / ?? , where R is the incoming solar radiation (Wm-2), ? is the density of water (1000 kgm-3), and ? is the latent heat of evaporation (2.26 MJ/kg). This produces a value with units of meters evaporated per second, which is converted to meters per hour. This hydrograph was scaled to the study catchment by use of relationship area and flood magnitude. A common practice in hydrology used to determine flood magnitude of a certain frequency at an ungaged site is to use a regional flood curve. Curves for a climactic region and for floods of a certain frequency (return interval) can be fitted regional stream discharge data, generating a power relationship for peak discharge as a function of drainage area. The drainage areas of the Tick Creek USGS stream gage and the study catchment were plugged into a North Carolina Piedmont regional curve. The ratio of the peak discharges between the two sites (the study catchment had a peak discharges that were about 4% of the Tick Creek Stream gage) was used to scale the input hydrograph to match an expected discharge rate for the study catchment. This scaling method is at best, a rough estimate. First there is the problem of timing and scale. Larger catchments have longer lag times to peak after a precipitation event. This means the time between a precipitation event and the peak of a hydrograph will likely be much shorter for a smaller catchment. Additionally, the shape of the receding curve will be much more skewed for a larger catchment, whereas smaller catchments tend to return to base flow much sooner. Hourly precipitation and discharge data were acquired from nearby gages. Discharge data came from a USGS gage located 4.63 km upstream of the confluence of this UT with Tick Creek. Evapotranspiration was estimated by evaporation. Hourly solar radiation measurements were used in a latent heat energy conversion equation: ET = R / ?? , where R is the incoming solar radiation (Wm-2), ? is the density of water (1000 kgm-3), and ? is the latent heat of evaporation (2.26 MJ/kg). This produces a value with units of meters evaporated per second, which is converted to meters per hour. This hydrograph was scaled to the study catchment by use of relationship area and flood magnitude. A common practice in hydrology used to determine flood magnitude of a certain frequency at an ungaged site is to use a regional flood curve. Curves for a climactic region and for floods of a certain frequency (return interval) can be fitted regional stream discharge data, generating a power relationship for peak discharge as a function of drainage area. The drainage areas of the Tick Creek USGS stream gage and the study catchment were plugged into a North Carolina Piedmont regional curve. The ratio of the peak discharges between the two sites (the study catchment had a peak discharges that were about 4% of the Tick Creek Stream gage) was used to scale the input hydrograph to match an expected discharge rate for the study catchment. This scaling method is at best, a rough estimate. First there is the problem of timing and scale. Larger catchments have longer lag times to peak after a precipitation event. This means the time between a precipitation event and the peak of a hydrograph will likely be much shorter for a smaller catchment. Additionally, the shape of the receding curve will be much more skewed for a larger catchment, whereas smaller catchments tend to return to base flow much sooner.

16. TOPMODEL Outputs This initial run generated a poor match between the modeled and observed hydrographs. TOPMODEL was not able to generate the estimated runoff. This initial run generated a poor match between the modeled and observed hydrographs. TOPMODEL was not able to generate the estimated runoff.

17. Saturated Area vs. Soil Transmissivity The modeled discharge peak is most sensitive to the estimate of the inform soil transmissivity parameter. The less transmissive the soil is, the more runoff is generated.The modeled discharge peak is most sensitive to the estimate of the inform soil transmissivity parameter. The less transmissive the soil is, the more runoff is generated.

18. Parameter Adjustments My first though on comparing the observed (scaled) discharge and the modeled hydrograph relates to the scaled discharge data I used. As discussed previously, the discharge hydrograph came from a catchment that had a much bigger drainage area. Flood flows are maintained for longer durations in larger catchments and there is a longer lag time between precipitation and peak discharge. The study catchment to which this was scaled is much smaller and a hydrograph generated from such a catchment is expected to be much steeper, shorter in duration, and respond more quickly. TOPMODEL does a decent job of demonstrating this expectation. However, the magnitude of the modeled peak only approached the scaled observed peak at very low soil transmissivity [Ln(To)] and very low soil transmissivity decay [m]. Overall, TOPMODEL under predicted the amount of runoff generated. By reducing the average soils transmissivity, Ln(To), more runoff was generated and the peak discharge increased. By reducing the �m� value, the difference between local saturation deficit and average deficit converges. �m� also affects how fast soil transmissivity decays with depth. Reducing �m� appears to allow runoff to runoff more quickly and spend less time in subsurface storage. Not shown on this slide is the affect of reducing the channel velocity. The current value is 3600 m/hr, a default. Increasing this number has no effect on the hydrograph. Reducing it spreads the hydrograph out and reduces the peak. This is a crude representation of flood peak attenuation.My first though on comparing the observed (scaled) discharge and the modeled hydrograph relates to the scaled discharge data I used. As discussed previously, the discharge hydrograph came from a catchment that had a much bigger drainage area. Flood flows are maintained for longer durations in larger catchments and there is a longer lag time between precipitation and peak discharge. The study catchment to which this was scaled is much smaller and a hydrograph generated from such a catchment is expected to be much steeper, shorter in duration, and respond more quickly. TOPMODEL does a decent job of demonstrating this expectation. However, the magnitude of the modeled peak only approached the scaled observed peak at very low soil transmissivity [Ln(To)] and very low soil transmissivity decay [m]. Overall, TOPMODEL under predicted the amount of runoff generated. By reducing the average soils transmissivity, Ln(To), more runoff was generated and the peak discharge increased. By reducing the �m� value, the difference between local saturation deficit and average deficit converges. �m� also affects how fast soil transmissivity decays with depth. Reducing �m� appears to allow runoff to runoff more quickly and spend less time in subsurface storage. Not shown on this slide is the affect of reducing the channel velocity. The current value is 3600 m/hr, a default. Increasing this number has no effect on the hydrograph. Reducing it spreads the hydrograph out and reduces the peak. This is a crude representation of flood peak attenuation.

19. Conclusions Local, site specific discharge data is necessary to properly calibrate TOPMODEL TOPMODEL is appropriate for modeling runoff in small catchments with quick runoff/discharge response times Hydrograph properties are sensitive to Ln(To) � changes infiltration capacity of catchement�s soils �m� � determines how quickly runoff leaves soil in cathcment and drains to channel Channel Velocity � alters runoff routing

20. References & Data Sources ANDERSON, B. G., I. D. Rutherford, and A. W. Western. 2006. An analysis of the influence of riparian vegetation on the propagation of flood waves. Environmental Modelling& Software 21, (9): 1290-6. Beven., K. 1997. TOPMODEL: A critique. Hydrological Processes 11, 1069-1085. North Carolina Ecosystem Enhancement Program. 2007. Restoration Project Monitoring Reports. www.nceep.net. Accessed April 12, 2008.. State Climate Office of NC. NC CRONOS Database. Siler City ECONET tower. www.nc-climate.ncsu.edu/cronos/?station=SILR. Accessed April 27, 2008. US ACOE. Hydrological Engineering Center. 2003. Hydrological Modeling System (HEC-HMS). User�s Manual. V. 3.1.1. USGS. National Water Information System: Web Interface. Stream Gauge 02101800 TICK CREEK. Accessed April 29th, 2008. Woltemade, C. J., and K. W. Potter. 1994. A WATERSHED MODELING ANALYSIS OF FLUVIAL GEOMORPHOLOGIC INFLUENCES ON FLOOD PEAK ATTENUATION. Water Resources Research 30, (6): 1933-42.