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

Initial AbstractionPrecipitation P

Direct runoff QD

Base Flow QBStream FlowMaximum Soil

Moisture SMMAX

Soil Retention S

Soil Moisture SM

Percolation

Soil Moisture

The depletion process of the soil moisture:

SM2i=SM1i-1*exp(-mti)

SM2i: soil moisture right before the I-th precipitation

SM1i-1: soil moisture right after the I-1st precipitation

m: monthly depletion factor for the m-th month

ti: arrival time in days of the I-th precipitation

Soil Retention

Si=SMMAX-SM2iSi: soil retention

SMMAX: maximum soil moisture

Direct Runoff

QDi=Pei2/(Pei+Si)

Pei=Pi-Iai

Iai=abst1*SiQdi: direct runoff

Pei: effective precipitation

abst1: initial abstraction coefficient

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-QDiSM1i: new soil moisture right after ith precipitation

Fi: infiltration

Base Flow

QB2=QB1*K t2-t1

K: recession constant

QB2, QB1: base flow at time t2, t1

t2-t1: the elapse time

Daily Streamflow Simulation

NDAYS=INT(Tb/24)+1

Tb=5Tp, Tp=12+Tl, Tl=*A 0.6

NDAYS: base time in days

Tb: base time

Tp: time to peak

Tl: lag time

A: drainage area

Monthly Depletion Factor & Parameter Optimization

• Important feature of TxRR model.

• Monthly depletion factor used for monthly streamflow simulation.

• Optimization based on the historical data.

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.

Sub_AREA% NUECES_ID WS_ID USGS_O_ID0.16% 1 21010 112329.72% 1 21010 104227.03% 1 21010 104118.45% 1 21010 104024.64% 1 21010 103995.27% 2 20005 11234.73% 2 20005 1042

100.00% 3 24830 112369.25% 4 24820 112330.75% 4 24820 104294.37% 5 22012 1042100.00% 6 24810 1123100.00% 7 0 112389.52% 8 22013 112310.48% 8 22013 10420.16% 9 22010 112353.04% 9 22010 104346.80% 9 22010 104231.04% 10 22011 11235.47% 10 22011 112363.49% 10 22011 1043100.00% 11 22014 112394.83% 12 1 11235.17% 12 1 1043

100.00% 13 24850 1123

NUECES_ID Sub_AREA% WS_ID NCDCSTAT_I1 2.00% 21010 20141 23.45% 21010 20151 74.55% 21010 56612 100.00% 20005 20143 100.00% 24830 20144 50.30% 24820 20144 32.29% 24820 20155 94.37% 22012 20156 60.66% 24810 20146 39.34% 24810 32107 13.43% 0 20147 86.57% 0 32108 49.78% 22013 20148 50.22% 22013 20159 100.00% 22010 201510 28.68% 22011 165110 4.31% 22011 201410 1.28% 22011 201410 37.63% 22011 201510 28.10% 22011 321011 66.67% 22014 201411 33.33% 22014 321012 100.00% 1 321013 100.00% 24850 3210

DSS databaseImplementation of Pre-processing

Source Data CD

Text file Arcview GIS

Rainfall data &Stream flow data

Spatial Informationof gages

DSS file

DSS import:DSSUTL

Watershed Region Definition

Develop Thiessen Polygons

Export we ighted ar ea percentage for ea ch ga ge. (gage::area %)

DSS

DSS

DSSMATH

Rainfall & stream flow, area%

NEW DSS file with Average rainfall & stream flow data for each watershed

DSPLAY

REPGEN

Customized Reports

Data Retrieval

TxRR Input File

Text file converter

DSPLAY Macro

DSPLAY Macro

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)

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)

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.