Distributed Hydrologic Modeling Baxter E. Vieux, Ph.D., P.E., Professor School of Civil Engineering and Environmental Science University of Oklahoma 202 West Boyd Street, Room CEC

Biosketch Dr. Baxter E. Vieux, PhD, P.E. is a professor in the School of Civil Engineering and Environmental Science, University of Oklahoma. He specializes in the integration of computational hydrologic methods and visualization with Geographic Information Systems (GIS). Applications include simulation of water quality and flooding using WSR-88D radar estimates of rainfall. He was recently named Director of the International Center for Natural Hazards and Disaster Research, University of Oklahoma. Efforts to reduce impacts on civil infrastructures due to severe weather are being undertaken by this center with an initial focus on flooding. Prior to joining the faculty at the University of Oklahoma, he was a Visiting Assistant Professor at Michigan State University. He has performed consulting and collaborative research with agencies and private enterprises in the US and abroad in Japan, France, Nicaragua, and Poland. Over fifty publications appearing as book chapters (2), refereed journal articles (14, 3 in press), and conference proceedings (35, 2 in press) have been authored including a forthcoming text for Kluwer entitled: Distributed Hydrology Using GIS (expected 2000). He has been on the Editorial Board of Transactions in GIS since 1995, serves on the American Society of Civil Engineers Council on Natural Hazards and Disasters, and is Fellow and member of the Advisory Council of the Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma. He is a member of ASCE, NSPE, AGU, and AMS, Tau Beta Pi, Phi Kappa Phi, and ASEE. Prior to his academic career, ten years were spent in Kansas and Michigan with the USDA-Natural Resources Conservation Service (formerly, USDA-SCS) supervising design and construction of drainage, irrigation, soil conservation, and flood control projects.

Recipe for a flood Ingredients— Take a generous amount of rainfall Presoak the soil so it is saturated Add generous amounts of rainfall Stand back

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Flood disasters

More disasters

What constitutes a flash flood No firm criteria exist to discriminate between fast response and river floods Response time in the range of 1-6 hours As opposed to river floods, flash floods have a quick response to rainfall input Upland basins are most likely killers Slow-rise river floods have highest economic impact

Flooding --The Economist, 11March 2000 Last year natural disasters killed an estimated 100,000 people. In a typical year, floods claim half the victims of the world’s natural disasters.

Enabling Technologies Ingest, storage and processing of data streams from radar, satellite and other mesonet sensor systems Radar, automated sensors, remote sensing platforms are next generation technologies providing new data and information for mitigating the impact of flooding and drought Improved modeling, warning and information dissemination technologies

Why does one basin flood and another doesn’t Efficient drainage network Debris clogged main channel Denuded landscape or burned vegetation Urbanization effects on time and volume Steep topography Heavy rain over large areas

Basin Characteristics Factors that affect the basin response are— Drainage area Drainage network Slope Channel geometry and roughness Overland flow and roughness Vegetative cover Soil infiltration capacity Storage capacity

Runoff Mechanisms There are two runoff producing mechanisms: 1. Infiltration excess 2. Saturation excess Mountainous watersheds tend to be dominated by saturation excess. Infiltration excess dominates runoff in flatter agricultural watersheds.

Saturation Excess

Infiltration Excess

Horton Infiltration Equation

Hydraulics of Runoff Two basic flow types can be recognized: Overland flow This is conceptualized as thin sheet flow before the runoff concentrates in recognized channels. Channel flow The channel has hydraulic characteristics that govern flow depth and velocity.

Lumped modeling approach The following slides show how a lumped model may be used with distributed rainfall derived from WSR-88D There were no rain gauges in the vicinity of the basin. Flood magnitudes were modeled for design of a bridge and roadway re-alignment for the Oklahoma Department of Transportation

Cottonwood Creek Storm Total Oct 30 - Nov 1, 1998

Cottonwood Watershed

Storm Total Contours

HEC-HMS Model

Hydrograph

HEC-HMS 50-Year Storm

SCS CN increased from 79 to 90

Rainfall increased by 20%

Distributed Model Advantages Distributed has advantages because the spatial variability of precipitation input and controlling parameters are represented in the model. Incorporating spatial variability in a distributed model reduces the prediction variance. Physics-based models are generally more responsive to radar input than lumped models. River basin models based on 6-hour unit hydrographs are not suitable for basins with response times less than 6 hours. Distributed models require fewer storm events for calibration than lumped

Overall Goal The overall goal of distributed hydrology is to better represent the spatially distributed processes using maps of parameters and precipitation input. Distributed models tend to have better prediction variance than lumped models. Applications include simulation of flash floods, soil moisture, water resources.

Rainfall Infiltration Runon Runoff Stream Overland Direction Flow Characteristics Channel Characteristics - Cross-Section Geometry - Slope - Hydraulic Roughness * Rainfall excess at each cell - Soil infiltration rate - Rainfall rate - Runon from upslope Grid Cell Resolution Finite Elements Connectivity Watershed Runoff Simulation Runoff Simulation

Digital Elevation Model Resolution 1080 meter60 meter

Digital Watershed

OUTPUT Radar Rainfall (R) INPUT Land surface Soil Infiltration (I) Hydraulic Roughness (n) h Runoff Model Equations

Runoff Flow Rates Depth h is measured perpendicular to the bed and the velocity, V is parallel to the landsurface. Continuity equation — Manning Equation— n=hydraulic roughness So=land surface slope c=1 for metric, 1.49 english

Blue River Basin The 1200 km2 Blue River basin was delineated from a 3-arc second digital elevation model Aggregated to grid cell size = 270 m Hydrographs simulated for each sub-basin Runoff is computed for each grid cell Routed downslope through each cell eventually reaching the stream network and basin outlet

Lumped Versus Distributed Lumped modeling represents the basin and precipitation characteristics using single values of roughness, slope, and rainfall over each sub-basin. Distributed modeling represents the spatial variability within each sub-basin or basin using grid cells, TINS or other computational element.

Lumped model?

Research efforts 1. Soil moisture with feedback (SHEELS) 2. Data assimilation (LDAS/AMSR) 3. Real-time radar (QPESUMS) 4. Nonpoint water quality simulation of phosphorus transport 5. Calibration using optimal control theory-Optimal values are identified by comparing simulated and observed hydrographs 6. Radar/Rain gauge calibration using the river basin as validation…

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