1 / 20

Geographical Database Development for the TxRR Surface Water Model

Geographical Database Development for the TxRR Surface Water Model. Richard Gu. Introduction of TxRR model. TxRR (Texas Rainfall-Runoff) Model: Based on the Soil Conservation Service’s Curve Number Method to estimate the direct runoff from a precipitation event. TxRR Model.

kaseem-sullivan
Download Presentation

Geographical Database Development for the TxRR Surface Water Model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Geographical Database Development for the TxRR Surface Water Model Richard Gu

  2. Introduction of TxRR model TxRR (Texas Rainfall-Runoff) Model: Based on the Soil Conservation Service’s Curve Number Method to estimate the direct runoff from a precipitation event.

  3. TxRR Model Precipitation P InitialAbstraction Direct runoff QD Soil Retention S Maximum Soil Moisture SMMAX Stream Flow Base Flow QB Soil Moisture SM Percolation

  4. Soil Moisture The depletion process of the soil moisture: SM2i=SM1i-1*exp(-amti) SM2i: soil moisture right before the I-th precipitation SM1i-1: soil moisture right after the I-1st precipitation am: monthly depletion factor for the m-th month ti: arrival time in days of the I-th precipitation

  5. Soil Retention Si=SMMAX-SM2i Si: soil retention SMMAX: maximum soil moisture

  6. Direct Runoff QDi=Pei2/(Pei+Si) Pei=Pi-Iai Iai=abst1*Si Qdi: direct runoff Pei: effective precipitation abst1: initial abstraction coefficient

  7. New Soil Moisture Soil moisture is renewed by the infiltration caused by new precipitation. The renewal process is described by: SM1i=SM2i+Fi Fi=Pi-Iai-QDi SM1i: new soil moisture right after ith precipitation Fi: infiltration

  8. Base Flow QB2=QB1*K t2-t1 K: recession constant QB2, QB1: base flow at time t2, t1 t2-t1: the elapse time

  9. Daily Streamflow Simulation NDAYS=INT(Tb/24)+1 Tb=5Tp, Tp=12+Tl, Tl=b*A 0.6 NDAYS: base time in days Tb: base time Tp: time to peak Tl: lag time A: drainage area

  10. Monthly Depletion Factor & Parameter Optimization • Important feature of TxRR model. • Monthly depletion factor used for monthly streamflow simulation. • Optimization based on the historical data.

  11. GIS Data Preprocessing • Goal: to prepare time-series streamflow & precipitation data for defined watershed. • Sources data: historical data of USGS & NCDC stations. • Sample area: Nueces Estuary. • Tools: Arc/Info, CRWR-Vector, Arcview Geoprocessing & Spatial Analyst.

  12. DSS database

  13. DSS Rainfall & stream flow, area% DSPLAY Macro NEW DSS file with Average rainfall & stream flow data for each watershed DSPLAY DSSMATH DSPLAY Macro REPGEN Data Retrieval Customized Reports Text file converter TxRR Input File

  14. Project Components TxRR model: source code in FORTRAN GIS: CRWR_Vector (Projection, Thiessen Polygon) Geoprocessing (Intersect two polygon coverages) Arcview Script (Create station point coverage)

  15. DSS: Data storage format tool (C/C++) Data format conversion from DSS file to TxRR input file (C/C++) Data format conversion from TxRR output file to DSS file (C/C++) DSPLAY: MS-DOS batch file & display control macro (Continue)

  16. Experience and Future Work DSS is efficient in time-series data storage. Data format conversion is not a big overhead. Display tools still need to be exploited. Avenue is not efficient in file reads/writes. An algorithm for combining historical data need to be developed. Data missing.

More Related