Kiran Chinnayakanahalli Predicting Hydrologic Flow Regime for Biological Assessment at Ungauged Basins in the Western United States Kiran Chinnayakanahalli David G. Tarboton Civil and Environmental Engineering Department, Utah State University, Logan, UT Charles P. Hawkins Department of Aquatic, Watershed, and Earth Resources, Utah State University, Logan, UT

hy quantify hydrologic flow regime? Vital to the composition, structure and functioning of stream ecosystems Classify streams and watersheds Relate to biotic variability Extrapolate site-specific data to stream reaches having similar characteristics Quantify stream impairment

A A pproach Develop statistical models that relate streamflow statistics to climatic variables We will be developing statistical models that relate the flow regime variables (that will be defined in the following slides) to watershed attributes (area, channel network properties etc), climatic variables (mean annual precipitation, average temperature etc), and soil variables (soil thickness, permeability). Existing work (Streamstats, National Flood frequency program etc) generally estimate high and low streamflow values. These variables though important are not the only ones that are vital to the biota of the stream Existing work Streamstats http://water.usgs.gov/osw/streamstats/ National Flood Frequency program USGS Statistical Regionalization Methods Kroll et al. Watershed characteristics database. watershed attributes (watershed morphology, channel network) basin geology/soils variables

ata – HCDN gauges in the west US Total number of HCDN gauges in Western US = 491. Area range: 15.7 km2 – 489346 km2 Off them 425 have area ≤ 5000km2 We will be developing our models for the gauged Hydro Climatic Data Network points in the Western US. There are 491 gauges with the area varying from 15.7km2 to 489000km2. We will be working with the watersheds with area less than or equal to 5000km2. No. of watersheds Watershed area in km2

low regime F F M F D T R Flow regime: Magnitude Frequency Duration Timing Rate of change M F D T R Water Quality Energy Sources Physical Habitat Biotic Interactions Stream ecologists look at the hydrology of the stream in terms of five component that could be related to the biota. We choose our hydrologic indices such that the complete set would be representative of all the components (M, F, D, T & R) of the flow regime. The other criterion while selecting HI (Hydrologic indices) was that they should be sparsely correlated with one another in order to capture the entire gamut of variation in the stream flow. Ecological Integrity Figure from Poff, N. L., et al. (1997). "The Natural Flow Regime: A Paradigm for Conservation and Restoration of River Ecosystems." BioScience 47: 769-784.

that define the flow regime 1 BFI M 2 DAYCV R 3 QMEAN M 4 ZERODAY M 5 Q1.67 M & F 6 Colwell’s index-T 7 7Qmin M 8 7Qmax M 9 NOR- no. of reversals R 10 Flood frequency F ydrologic indices that define the flow regime Flow regime: Magnitude Frequency Duration Timing Rate of change BFI (%) – Base flow index M F D T R These are our 10 choices for HI. Due to time constraint, I will be presenting only those, that I think are different from what hydrologists generally use. Flow regime: Magnitude Frequency Duration Timing Rate of change N –total number of years of record, QDMINy - Minimim Q in the year y, QAVEy = Mean Q in the year y, M F D T R

ydrologic indices H H BFI M DAYCV R QMEAN M Q1.67 M & F 2 DAYCV R 3 QMEAN M 4 ZERODAY M 5 Q1.67 M & F 6 Colwell’s index-T 7 7Qmin M 8 7Qmax M 9 NOR- no. of reversals R 10 Flood frequency F ydrologic indices Colwell’s index: P=C+M Predictability (P), Constancy (C), and Contingency (M) Maximum constancy, P=1, C = 1 and M = 0 Maximum contingency, P=1, C = 0 and M = 1 Flow Flow Time Time Flow regime: Magnitude – M, Frequency – F, Duration – D, Timing – T, Rate of change - R

atershed attribute derivation W W atershed attribute derivation Digital Elevation Model Analysis Climate Soils PRISM TauDEM STATSGO Watershed area Main channel length Main channel slope Mean elevation Relief Drainage density - channelization threshold determined objectively using constant drop analysis Basin shape Hypsometric curve indices Outlet elevation Mean annual precipitation Monthly mean temperature Monthly maximum and minimum temperature Soil thickness Available water capacity Permeability Bulk density

atabase D D = f , , Explanatory variables Response variables Watershed morphology Hydrologic indices Climate Soils = f , , BFI DAYCV QMEAN Q1.67 ZERODAY Colwell’s index 7Qmin 7Qmax NOR Flood frequency Watershed area Main channel length Main channel slope Mean elevation Relief Drainage density Basin shape Hypsometric curve indices Outlet elevation Mean annual precipitation Monthly mean temperature Monthly maximum and minimum temperature Soil thickness Available water capacity Permeability Bulk density Our database consists of HI (our response variables) and the watershed attributes, climatic data & soils data (our predictor variables). These quantities would be derived/computed for the 425 HCDN gauge points located in the Western US. Response variables Explanatory variables

nalysis - Correlation between hydrologic flow regime variables   BFI DAYCV QMEAN Zero day Q1.67 Flood Frequency P C M 7Qmin 7Qmax NOR 1.00 -0.40 0.03 -0.27 -0.10 0.34 -0.21 -0.20 0.00 0.35 -0.08 0.07 -0.25 0.63 -0.18 -0.15 0.39 0.72 -0.56 -0.48 0.91 -0.24 -0.30 -0.37 0.13 0.82 0.95 0.27 Zeroday -0.13 -0.05 0.29 0.54 -0.42 -0.14 -0.32 -0.31 0.09 0.58 0.97 0.23 Flood Frequency -0.04 -0.01 -0.03 -0.28 -0.09 0.78 -0.36 -0.29 -0.51 0.06 0.14 0.65 Results from the preliminary analysis. Results are based on a 343 gauge points , a subset of 425 HCDN gauges, for which I was able to put together the database that has all the HI values quantified and 5 watershed attributes that were readily available. The premise for our selection of HI, was to have least correlation between them. The results mostly agree with it, but there are some indices that are highly correlated with some of the other variables. We may have to decide, if we have to retain them or select some other variable that might be a better descriptor. Red ones show high correlation.

A A nalysis- Correlation between hydrologic flow regime and watershed attributes   Watershed area,km2 Channel slope, m/km channel length, km Gauge elevation, m Annual mean precip, mm BFI 0.044 0.050 -0.007 0.297 -0.064 DAYCV -0.110 0.072 -0.082 -0.318 -0.247 Qmean 0.609 -0.316 0.615 -0.269 0.547 Qmean/km2 0.075 -0.156 0.106 -0.376 0.905 Zeroday -0.099 0.059 -0.248 -0.195 Q1.67 0.492 -0.304 0.543 -0.366 0.581 Flood freq -0.019 0.190 -0.069 0.426 -0.387 P -0.143 0.054 -0.172 0.256 -0.451 C -0.183 0.087 -0.197 -0.051 -0.426 M -0.055 0.445 0.004 7Qmin 0.488 -0.246 0.459 -0.121 0.424 7Qmax 0.611 -0.332 0.639 -0.326 0.524 NOR 0.214 -0.286 0.253 0.142 0.242 The watershed attributes used here are quantified to be important predictor variables by the Streamstats package. The table shows that Magnitude related flow regime variables are relatively better correlated with the considered watershed attributes. Not surprisingly these attributes are considered important by Streamstats that mostly estimates magnitude related flow variables.

nalysis – results from stepwise linear regression   Watershed area in km2 Channel slope, m/km Channel length, km Gauge elevation, m Avg. Ann. precipitation mm R2 BFI  X X  1 0.09 DAYCV 3 2 0.32 Qmean 0.67 Qmean/km2 0.82 Zeroday 0.2 Q1.67 Flood Freq 0.22 P C 0.3 M 0.27 7Qmin 0.42 7Qmax 0.65 NOR 4 0.25 Numbers in blue shows the importance of the variables in the regression equation. 1-most important 4 –being least. X-not used in the regression model All regression are significant at 95% confidence level. 1,2,3,4- parameter importance in the model with 1-most important, X- Not used

C C onclusions Available methods do not quantify all the stream variables that are important to stream biota at the ungauged basins. Selected hydrologic indices show potential to capture the different aspects of the flow regime. Initial linear regression models showed better results for magnitude related hydrologic indices.

F F uture work To classify watersheds based on hydrological flow regime variables Quantify how effectively the classification based on hydrology performs in partitioning the naturally occurring biotic variation.

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