CIVE 641 Advanced Surface Water Hydrology. Term Project Report. Continuous Simulation of DPHM-RS for Blue River Basin

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1 CIVE 641 Advanced Surface Water Hydrology Term Project Report Continuous Simulation of DPHM-RS for Blue River Basin Submitted to T.Y. Gan, PhD, FASCE Professor, Dept. of Civil and Environmental Engineering University of Alberta Submitted by Md. Zahidul Islam (ID: ) December 5, 7

2 Abstract Semi-distributed physically based hydrologic model DPHM-RS was applied to the Blue River Basin, Oklahoma, USA in a continuous simulation mode. Instead of gauged precipitation data multisensor NEXRAD and gauged precipitation data was used. The model was calibrated for soil property and the calibrated simulated hydrograph shows a correlation.95 with the observed hydrograph at basin outlet. The calibrated model was applied for the entire hydrologic year The simulated runoff shows overall correlation.6 with the observed value. It was found that the correlation is higher for high flow season than low flow season (.67). It was concluded that the spatial variability of precipitation and temporal variability of initial soil moisture should taken into account to get a better result. 1. Introduction Transformation of rainfall to runoff involves many hydrologic components such as evaporation, soil moisture, surface and sub-surface flow and also the interaction with energy fluxes at various time and spatial scales [1].In order to quantify the runoff from the watershed deterministic, lumped conceptual rainfall-runoff models (CRR) and system conceptual models or unit hydrographs have been used as practical tools. These models required more calibration as they are approximation of nature and thus less physics involved. On the others hand the fully distributed models use spatially distributed parameters of physical relevance and more accurate hydrologic prediction can be expected than CRR models. But these models required excessive demand of data and also obtaining the model parameters for all grids of the spatial domain. Partly to avoid some of the problems of distributed models and partly to exploit the potential of the satellite data in hydrologic modeling a semi-distributed, physically based hydrologic model DPHM-RS was developed by Biftu and Gan [1, & ]. The description of this model will be presented in section. DPHM-RS was applied to the semi-arid, Paddle River Basin, Central Alberta and good agreement was found between simulated and observed runoff as well as between simulated surface temperature and net radiation with the observed [1]. Later on DPHM-RS was partly applied to Blue River Basin (BRB), Oklahoma, USA for simulating some storm events and again good agreements between simulated and measured runoff was observed [3]. This study applies the DPHM-RS partially to the Blue River Basin to address the following issues: i. Whether the DPHM-RS is applicable for a continuous simulation mode. ii. The effect of multi-sensor (NEXRAD and gauge) precipitation data on the predicted stream flow.

3 . Model Components of DPHM-RS The model DPHM-RS is divided into six components. They have been shown in Fig. 1 The detail description of these model components could be found in [4]. Here they are presented in a summarized way in the following sections..1 Interception of precipitation Fig.1: Model Component of DPHM-RS [1] Precipitation falling from the atmosphere is partly intercepted by the canopy before it reaches the ground surface. The amount of precipitation, P intercepted by the canopy before reaching the ground surface depends upon the type and density of vegetation. The canopy storage is filled by precipitation and discharged by evaporation and drainage (see Figure ). In DPHM-RS, the water balance for the canopy is described by dc dt f ` = δ a k exp B ` a bc@ S C ; C S (1) Where, p is the precipitation rate, e wc is the wet canopy evaporation rate, S is the canopy water storage which related with leaf area index (LAI), C is the actual amount of water 3

4 intercepted by canopy. k and b are Rutter drainage parameters[5], and δ is the free throughfall coefficient.. Evapotranspiraion Fig.: Rutter interception model [1] The actual evapotranspiraion is estimated from the two-source model of Shuttleworth and Gurney [6]. In this model the actual evaporation from the land surface and transpiration from the vegetation canopy computed separately. The estimation is based on the amount of sensible (H), latent (L e ) and ground (G s ) heat flux available for ET at three layers, viz. as above canopy, within canopy and at soil surface, and modified for transport capacity and moisture availability on a catchment scale. Fig.3: Two source model of Shuttleworth and Gurney [1] The energy balance equation above canopy, within canopy and at soil sensible are given by respectively, R L H = () R L H c = (3) R L e E H G = (4) Where, the net radiation R n is partitioned into net radiation on the canopy, R nc and on the soil, R ns. This model solves the five simultaneous non-linear equations to obtain the surface 4

5 temperature (T s ), canopy temperature (T c ), air temperature within canopy (T h ), evaporation from the soil surface and transpiration from vegetation..3 Soil moisture In DPHM-RS the soil profile is assumed to have three homogeneous layer viz. as active layer, transmission zone and groundwater zone. The top layer thickness is 15-3 cm and it simulates the rapid changes of soil moisture content under high frequency atmospheric forcing. The transmission zone lies between the base of the active layer and the top of the capillary fringe and it characterizes the seasonal changes of soil moisture. Fig.4 Conceptual representation of soil infiltration [1] Referring to Fig. 4, the water balance equation for the active layer and transmission layer is, dθ z 1 f 1 = X α dt j i X α j E bs e g 1 + w j = 1 j = 1 for c z 1 ; θ r θ 1 θ s (5) dθ z f = g v e dc ; z> for z = z 1 ; θ r θ θ s (6) Where, θ, θ are the moisture content of the active and transmission layer, θ 1 s and θ r are the saturated and residual soil moisture, i v is the infiltration rate, α j is the fraction of the land cover α bs is the fraction of the agricultural and pasture lands, e dc is the transpiration rate from the dry canopy and ψ c is the depth of the capillary fringe. If the capillary fringe lies with the active layer the transmission zone diminishes and the active layer water balance equation becomes, z dθ C 1 f 1 = X α dt j i X α j E bs e g 1 + w (7) j = 1 j = 1 for c = z 1C ; θ r θ 1 θ s 5

6 .4 Saturated subsurface flow The topographic soil or wetness index of Sivapalan et al. [7] is used in DPHM-RS to parameterize the spatial variability of topographic and soil properties and the average ground water table depth. The depth of the average water table for each sub-basin is given by, Where, X h ^\ j z i 1 f ln f z at e T i tanβ g i f Λ Y ^] is the initial average water table depth, ^[ ln h j at e T i tanβ g i f (8) k is the local topographic soil index determined from DEM data, Λis the catchment average topographic index, and f is the exponential decay of saturated hydraulic conductivity..5 Surface runoff The interflow is not considered in DPHM-RS. The only source of surface runoff is the saturated and unsaturated overland flow. The overland flow for bare soil (q bs ) is given by, q bs = p if θ 1 = θ s (9) C C q bs = f i if θ 1 <θ s and p> f i The overland flow for vegetated ground (q v ) is given by, B ` ac q v =δ p + k exp b C@ S,if θ 1 =θ s q v =δ p + k exp B ` a bc@ S f C,if θ <θ and δ + k exp B bc@ ` S ac C > f i 1 s p i (1) These runoff from is transferred into stream flow using an average lag function (response function) derived from kinematic wave equation. The response function was derived for each sub-basin by solving the kinematic wave equation at each grid of the sun-basin for a reference runoff. Fig. 5 shows the typical response function for sub-basin 1 of BRB. Kinemsatic Response Function for Sub-basin 1 1 Discharge (m3/s) Time (hr) Fig. 5: Response function for Sub-basin 1 6

7 The resulting runoff becomes a lateral inflow into the stream channel within the subbasin..6 Channel routing The Muskingum-Cunge routing method is used to route the flow through the drainage network. A four point iterative approach described in Ponce and Yevjevich [8] is used to evaluate the variable parameters. 3. Study Site: Blue River Basin The Blue River Basin is in the South Central Oklahoma, USA. The BRB was taken as study site because of two reasons; firstly, the basin is unregulated and secondly, all the meteorological, soil, and topographic data for this basin is provided by the Distributed Model Intercomparison Project: Phase (DMIP-) authority. The basin has a relatively flat terrain. Elevations ranges from 153 to 35 m above mean sea level. Total catchment area is 133 km. The major soil groups are Silty Clay loam (Sub-basin 1,, 3), Sandy Clay (Sub-basin 4) and Clay (Sub-basin 5, 6, 7). The dominant Vegetation is Woody Savanah which occupies almost 8% of the basin area. The average precipitation ranges from 4 mm in the extreme western panhandle to 14 mm in the southeastern corner of the state. Fig. 6: Blue River Basin with seven sub-basins 7

8 Energy Flux of Blue River Basin Station 1 5 Fluxes (w/m) Mar- 15-Mar- 16-Mar- 16-Mar- 17-Mar- 17-Mar- 18-Mar- 18-Mar- Date Rs Rn GF a) Shortwave radiation, net radiation and ground heat flux Wind Velocity of Blue River Basin Station V (m/s) Mar- 15-Mar- 16-Mar- 16-Mar- 17-Mar- 17-Mar- 18-Mar- 18-Mar- b) Wind velocity Date Temparatures of Blue River Basin Station 1 V Temparature ( C) Mar- 15-Mar- 16-Mar- 16-Mar- 17-Mar- 17-Mar- 18-Mar- 18-Mar- Date c) Air and soil temperature Ta Ts Relative Humidity of Blue River Basin Station 1 Relative Humidity (%) Mar- 15-Mar- 16-Mar- 16-Mar- 17-Mar- 17-Mar- 18-Mar- 18-Mar- Date d) Relative humidity Rh Fig. 7: Typical input data for Blue River Basin 8

9 4. Data Description Model parameters are derived from a combination of remotely sensed data, ground observations and radar data. Data required to run the model and sources are summarized in Table 1. Table 1: Data requirement of DPHM-RS (modified from [3]) Data Type Parameters Source Topographic Mean Altitude, Aspects, Flow direction, Surface slope, Drainage network, Topographic soil index DEM of USGS National Elevation Dataset Land use Spatial distribution of land use, classes, Surface Albedo, Surface emissivity, Leaf Area Index NASA LDAS, NOAA- AVHRR Satellite data Soil Properties Spatial distribution of soil types, Antecedent moisture content, Soil hydraulic properties US. State Soil Geographic (STATSGO) and Soil Propeties of Rawls and Brakensiek (15) Stream Flow Hourly streamflow data at the catchment outlet, Channel cross section USGS Meteorological Shortwave radiation, Wind speed, Air temperature, Ground temperature, Relative humidity, Net radiation, Ground heat flux North American Regional Reanalysis (NARR) Hourly Precipitation Multisensor (NEXRAD and gauge) Precipitation Data Figure 7 shows some of the typical data of blue river basin used in calibration of the model. 5. Model Setup At first the basin is divided into 7 sub-basins based on the digital elevation model (DEM) and stream network. Then the response functions for all sub-basins were derived. DPHM- RS simulation is based on the input data at 3 defined input stations (shown in Figure 8). The initial soil moisture for active zone and transmission zone was set as 6% of the saturated soil moisture. 9

10 Fig. 8: DPHM-RS Input stations for Blue River Basin 6. Model Calibration The routing of the surface runoff is based on the response function of unit rainfall excess developed for each sub-basin using the kinematic wave approach at the beginning of the simulation period. The response function was adjusted by changing the Manning s roughness parameters so as to improve the simulated peak with respect to the observed peak runoff at the basin outlet. The model was calibrated for runoff for a period of 3 days starting from 15 March 19 to 15 April, 19. In calibration process the soil parameter f (exponential decay of the saturated hydraulic conductivity) was adjusted to provide the proper base flow or recession curve. After each simulation the observed and simulated runoff was compared and water budget was calculated to see the percentage deviation of total volume of water. This results in a final f value of.45,.9 and 1.35 m -1 for clay, sandy clay and silty clay loam respectively. Figure 9 shows the variation of percentage deviation of total runoff volume with different f value for different types of soil. The f corresponding to minimum deviation was taken as calibrated final value. The 1

11 simulated calibrated hydrograph is shown in Figure 1, which has a correlation coefficient of.95. Calibration for f % Deviation in Total Water Volume (Mm3) f (/m) Silty Clay Loam Sandy Clay Clay Fig. 9: Calibration of model parameter f Hydrograph at basin outlet Discharge(m3/s) Mar- -Mar- 5-Mar- 3-Mar- 4-Apr- 9-Apr- 14-Apr- 19-Apr- Measured Date Simulated Fig. 1: Observed and simulated hydrograph at basin outlet after calibration 11

12 7. Model Application The calibrated model was applied to simulate the runoff for the entire hydrologic year The results are shown in Figure 11. It was found that the simulated and observed hydrograph are in a good agreement in the high flow period but the model overestimated the low flow. The correlation is.6 in this case which is much lower than that was in calibration stage. It was found that the correlation in high flow period is.67 which is higher than that in low flow periods. It might because of the fact that the initial soil moisture is assumed to be constant all through the simulation period which leads overestimation of runoff at the dry season. Hydrograph at basin outlet 3 5 Discharge(m3/s) Oct-96 3-Nov-96 9-Jan-97 3-Mar-97 9-May-97 8-Jul-97 6-Sep-97 Measured Date Simulated Fig. 11: Observed and simulated hydrograph at basin outlet 8. Conclusion and Recommendations The model simulates the runoff at high flow period with a higher correlation than the low flow period. So the model needs to be modified to consider the temporal variability of initial soil moisture. Instead of using NEXRAD precipitation data at three stations spatial variability of precipitation should taken into account. 1

13 9. References [1] Biftu, G.F., and Gan, T.Y., 4, A semi-distributed, physics-based hydrologic model using remotely sensed and Digital Terrain Data for semi-arid catchments, International Journal of Remote Sensing, Vol. 5, No., [] Biftu, G.F., and Gan, T.Y., 1, Semi-distributed, physically based, hydrologic modeling of the Paddle River Basin, Alberta, using remotely sensed data, Journal of Hydrology, 44, [3] Kalinga, O.A., and Gan, T.Y., 6, Semi-distributed modeling of basin hydrology with radar and gauged precipitation, Hydrologic Processes,, [4] Biftu, G.F., 19, Semi-distributed hydrologic modeling using remotely sensed data and GIS, Doctoral Dissertation, University of Alberta, Edmonton. [5] Rutter, A.J., Morton, A.J., and Robins, P.C., 1975, A predictive model of rain interception in forests, 1. Generalization of the model and comparison with observation in some coniferous and hardwood stands, Journal of Applied Ecology, 1, [6] Shuttleworth, J.W., and Gurney, R.J., 199, The theoretical relationship between foliage temperature and canopy resistance in sparse crop, Quarterly Journal of Royal Meteorology Society, 116, [7] Sivapalan, M., Wood, E.F., and Beven, K.J., 17, On hydrologic similarity, a scaled model of storm runoff prediction, Water Resources Research, 3(1), [8] Ponce, V.M., Yevjevich, V., 1978, Muskingum-Cunge method for variable parameters, Proc. ASCE 14(HY1). 13