Additional data sources and model structure: help or hindrance? Olga Semenova State Hydrological Institute, St. Petersburg, Russia Pedro Restrepo Office of Hydrologic development, NOAA, USA James McNamara Boise State University, USA

Objectives Test the Hydrograph model in semi-arid snow- dominated watershed Study the effect of additional observations on the quality of the streamflow simulation results Answer the question, if the model developed for completely different geographical settings can handle the additional data in a satisfactory way without change of its fixed structure?

Dry Creek watershed, Idaho, USA

Dry Creek Catchment Area: 28 km 2 Elevation Range: m Grasses, shrubs, and conifer forests vary with aspect and elevation Low Elevation Grass Mid Elevation Shrub High Elevation Forest

Available data 963 mm 77% Snow High Elevation 335 mm 32% Snow Low Elevation Air Temperature Relative Humidity Wind Speed/Direction Solar Radiation Net Radiation Soil Moisture Soil Temperature Precipitation Snow Depth Hydrometeorological Data

State Hydrological Institute, St. Petersburg, Russia Hydrograph model R Single model structure for watersheds of any scale Adequacy to natural processes while looking for the simplest solutions Minimum of manual calibration Forcing data: precipitation, temperature, relative humidity Output results: runoff, soil and snow state variables, full water balance

Watershed discretization Representative pointsRunoff formation complexes

Lower Weather station (1151 m), soil, 5 cm depth,

Lower Weather station, soil state variables 30 cm depth,

Lower Weather station, soil state variables 100 cm depth,

Main soil characteristics and parameters Initial (SSURGO DB) Calibrated valueObservations Soil type LoamSandy loamSandy loam to loam Soil depth 70 cm120 cm130 cm Density (kg/m 3) 2700No change Porosity (volume content = VC) for upper stratum 0.40 in average, 0.48 for upper stratum Specific heat conductivity (Wt/m degree) Specific heat capacity (J/kg degree) Water holding capacity (VC) 0.12 – – 0.25 Wilting point (VC) 0.03 – – 0.08 (calibrated by strata) Infiltration coefficient (mm/min) 7.1No change0.2 – 11 (2 in average) Evaporation coefficient (10 -8 m/mbar s) 0.40 – – 0.40 Strata evaporation ration 0.40 for the 1st0.35 1)solar radiation input to effective air T changed from 1 to 0.5 2) added correction factor to snow 1.4, rain 1.2 Additionally calibrated:

Snow state variables, Tree Line station (1651 m) Simulated and observed snow depth (m)

Lower Gauge Annual Water Balance Precipitation mm(% of P) Streamflow mm(% of P) Groundwater Recharge mm(% of P) ET mm(% of P) 635 (1)169 (0.23)37 (0.09)429 (0.69) Aishlin and McNamara (2010) Groundwater recharge assessed by chloride mass balance Q R P-(ET+Q+R) =0

Distributed Water Balance C1E C2E C2M C1W TL BG LG Evapotranspiration (ET) Groundwater Recharge (R) Streamflow (Q) Treeline catchment “loses” approximately 44% of annual precipitation to deep groundwater recharge

? Riparian vegetation

Handling of Riparian Vegetation Assume Riparian vegetation transpires at the potential rate from May through August Increases linearly from 0 on 1 May to the potential rate on 31 May Decreases linearly from the potential rate on Sept 1 st to 0 on Sept 30. Assume evapotranspiration losses from riparian vegetation directly affect streamflow Used climatological pan evaporation, with k=0.7. Average seasonal water use Approach followed compares favorably with measured cottonwood water (966mm) and and open water evaporation (1156mm) use in the San Pedro River Basin (Arizona) 1 1 “Hydrologic Requirements of and Evapotranspiration by Riparian Vegetation along the San Pedro River, Arizona” Fact Sheet , USGS, May 2007

RiparianVegetation Evapotranspiration Riparian Vegetation Evapotranspiration

RiparianVegetation Losses-Detail Riparian Vegetation Losses-Detail

Runoff: final results

Runoff: final results

Model versus wrong observations… Lower gage 2mgage

Model versus wrong observations…

Conclusions The Hydrograph model produces reliable soil moisture and temperature, snow water equivalent and streamflow simulations without changes to the model structure. We handled water usage from riparian vegetation by post-processing the data. The model can handle that situation with its algorithm for simulating shallow groundwater. This will be done later on. Use of models which require modest amount of parameter adjustment serves also as a quality control for observations Overall, simulation results were satisfactory, with minor amount of parameter calibration.