Water Resources in a Changing Climate: NSF ESPCoR VI Hydroclimatology V. Sridhar, Xin Jin, David Hoekema, Sumathy Sinnathamby, Muluken Muche R. Allen, Wenguang Zhao M. Germino

Background and Context Region-wide warming Precipitation change Decline of snowpack Earlier spring runoff and Decline in summer streamflow trends

Research Questions How will future climate change impact water resources?  Hydro - Climate  Hydro - Economic / Policy  Hydro - Ecology

Research Questions Hydro-climate interactions  What are the relationships between climate change, vegetation, snow pack, and the resulting stream flows in managed and unmanaged river systems?  How will aquifer systems exchange with surface and groundwater under various climate change scenarios?  What will both the supply and demand on water be in these systems under various climate change scenarios?

How will fire and invasive species (cheat grass, some bunchgrasses) impact:  Rates and durations of ET fluxes from desert systems?  Changes in infiltration patterns for precipitation?  Interactions of ET, infiltration, thermal profiles and microbial populations and feedbacks?  Erosion and Sedimentation in Tributaries of the Snake and Salmon basins? Hydroclimate Bio Interactions

Focus Area Research 1: Advance our ability to model surface energy balance processes Tasks: Use of scintillometer and eddy covariance (EC) systems to measure sensible (H) and latent (LE) heat fluxes in desert and timber Research 2:Advancement in Basin scale Hydrologic Forecasting – verification and operation under climate change scenarios Task1 : Operate, test and calibrate the CIG Variable Infiltration Capacity (VIC) model and combine with the groundwater model Task2: Implement VIC –groundwater model and evaluate the reservoir optimization techniques

Scintillometry Use to Retrieve Sensible Heat Flux (H) over large, integrated transects Use to improve components in METRIC and VIC  Reduce variances Use to derive ET experimentally  Determine how soil water meters out from desert and lodgepole

Transmitter Receiver Surface Energy Balance Processes----Large aperture scintillometer-- transmitter (left) and 3 intercompared receivers (right) purchased by Idaho EPSCoR RII Scintillometers to measure surface heat flux densities

Scintillometer – Idaho RII Deployments Three systems (BSU, ISU, UI) Three deployments  Sage brush -- Snake River Plain  Cheat grass or recent burn – Snake River Plain  Timber – Upper Reaches

Idaho RII: Sagebrush Deployment Sage brush ecosystem located west of Hollister, Idaho.

Raft River Site Cheat grass

LodgePole Pine Site --- Macks Inn Area

Scintillometers measure only Sensible Heat flux (H) ET is calculated as a residual of the energy balance ET = R n – G – H Net radiation, R n, and soil heat flux, G, must be measured All error in R n, G and H transfer into ET

Preliminary VIC model calibration & Flowchart for sensitivity analysis Choose parameters to be calibrated. In this study, 7 parameters were chosen recommendation Construct n x p Jacobian matrix and calculate CSS j Identify the most sensitive parameters (with the biggest CSS j ) end Choose observation locations to be used as reference. In this study, 6 locations were chosen Calculate sensitivity for each observation y i (i=1,…,n) over each parameter b j (j=1,…,p). Divide the range of each parameter into equal space Run VIC and routing with one parameter changed and the others unchanged. Obtaining new RMSE. Divide the Snake River Basin into 6 sub-basins and select the new set of parameters that make the RMSE minimum and apply it to the VIC WsT s3 b inf T s2 DsDs D smax T s1 2.54× × × × × × ×10 4 ParameterD smax DsDs b inf WsT s1, T s2, T s3 Unitmm/day%NA%m Range>0 to ~30>0 to 1>0 to ~0.4>0 to 10.1 – 1.5 Ws fraction of maximum soil moisture where non-linear baseflow occurs

VIC model calibration results (all 6 locations, ) RMSE (cfs)r2r2 uncalibratedcalibrateduncalibratedcalibrated Heise Rexburg Milner Oxbow Parma Payette VIC model validation results (all 6 locations, ) RMSE (cfs)r2r2 uncalibratedcalibrateduncalibratedcalibrated Heise Rexburg Milner Oxbow Parma Payette

VIC model calibration results (at Heise) Default calibrated from University of Washington calibrated

Preliminary VIC results ( )

Parameter Selection for the SWAT Model Snowmelt and snow formation parameter Ground water parameter Soil parameter Surface Runoff parameter

SWAT Calibration and validation

Results

Decreasing trend in monthly discharges Salmon River watershed Snake River watershed

Average annual flow (cms) White bird Krassel Ranger Yellowpine ECHAM GISSIPSL A1b A2 B1 A1b A2 B1 A1b A2 B1

Average annual flow (cms) Oxbow Milner ECHAM GISS IPSL A1b A2 B1 A1b A2 B1

Oxbow Milner Mean Monthly flow (cms) Snake River watershed A1b A2 B1 A1b A2 B1

GUI development of ESPAM

Flow Distribution & Points of Interest (POI) Six Points of Interest 1)Heise (Snake River) 2)Rexburg (Henry’s Fork) 3)Milner (Snake River) 4)Parma (Boise River) 5)Payette (Payette River) 6)Oxbow (Snake River) 58% +32% 90 % Total Points of interest were chosen from which projected flows could be distributed to simulate upstream reach gain contributions. As represented in the chart below, we selected six points of interest that cover 90% of the flow in the upper SRB.

1)Monthly Natural flow (sum of upstream reaches) Where, NF m = monthly natural flow at reach d d = downstream reach (or point of interest) u = furthest upstream reach x i = any given reach between u and d 2)Annual Natural Flow (sum of monthly Natural Flows) Where, NF m,1 = natural monthly flow in October, NF M,2 = natural monthly flow in November…. Reach Gain Simulation Calculations The first step of the reach gain simulation method is to categorize flow based on a range of historic annual natural flows. The equations for calculating natural flow from IDWR historic reach gains are presented here.

Flow Categorization Henry’s Fork Flow Range per Category: 3000 (100 acre-feet) Minimum: Maximum: Mean: Dry< Wet> Flow categorization is based on annual flows while simulation of these flows are based on monthly distributions of the projected flow. Along the Henry’s Fork flows are categorized with a range of 300,000 acre-feet per category.

Predicting Minor Flows—Linear Model Flow W. = (2.18*(%Avg. Flow Ox.) )*W. Avg.

Model Validation

Irrigation Shortage Comparison: Historic vs. Simulated ( ) A comparison between SRPM calculated irrigation shortages as represented by historic and simulated reach gains reveals that the reach gain simulation method was able to provide perfect replication of historic irrigation shortages in the river between the years 1980 and FallsTetonHenry'sAboveLorenzoWillowBlkft MilnerBoiseNewYPayett River ForkLornezoBlkftCreekPrtnfMilnerMurphyRiverCanalRiverTOTAL His.0.3%0.9%0.6% 15.0%0.4%49.0%36.2%0.0%27.8%74.8%14.6%20.7% Sim.3.8%6.0%8.3%0.5%21.8%3.3%47.6%56.8%0.0%43.3%87.1%15.3%30.3% ∆Shor tage3.5%5.1%7.8%0.1%6.8%2.9%1.4%20.6%0.0%15.5%12.3%0.7%9.5%

Payette Watershed-Future Climate: Echam-5 Deadwood Dam Cascade Dam Black Canyon Dam

Future Climate Payette River

Future Shortages Payette River

VIC+MODFLOW flowchart Run VIC model to generate the infiltration, evapotransporation (ET), runoff and baseflow at each cell of unsaturated zone Run MODFLOW and generate recharge, water content Infiltration, ET Add a fraction of recharge from MODFLOW the baseflow in VIC output Run VIC routing model No Yes Stop Reaching time step limit Water content

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