The Added Value of Different Data Types for Calibrating and Testing a Hydrologic Model in a Small Catchment. Issue 10 (8th October 2020) - Széles, B.; Parajka, J.; Hogan, P.; Silasari, R.; Pavlin, L.; Strauss, P.; Blöschl, G.
Abstract: This study investigated the added value of different data for calibrating a runoff model for small basins. The analysis was performed in the 66 ha Hydrological Open Air Laboratory, in Austria. An Hydrologiska Byråns Vattenbalansavdelning (HBV) type, spatially lumped hydrologic model was parameterized following two approaches. First, the model was calibrated using only runoff data. Second, a step‐by‐step approach was followed, where the modules of the model (snow, soil moisture, and runoff generation) were calibrated using measurements of runoff and model state variables and output fluxes. These measurements comprised laser‐based measurements of precipitation, satellite and camera observations of snow, ultrasonic measurements of snow depth, eddy covariance measurements of evapotranspiration, time domain transmissometry‐based soil moisture measurements, time‐lapse photography of overland flow, and groundwater level measurements by piezometers. The two model parameterizations were evaluated on annual, seasonal, and daily time scales, in terms of how well they simulated snow, soil moisture, evapotranspiration, overland flow, storage change in the saturated zone, and runoff. Using the proposed step‐by‐step approach, the relative runoff volume errors in the calibration and validation periods were 0.00 and −0.01, the monthly Pearson correlation coefficients were 0.92 and 0.82, and the daily logarithmic Nash Sutcliffe efficiencies were 0.59 and 0.18, respectively. By using different sources of data besides runoff, the overall process consistency improved, compared to the case when only runoff was used for calibration. Soil moisture and evapotranspiration observations had the largest influence on simulated runoff, while the parameterization of the snow and runoff generation modules had a smaller influence. Key Points: A new framework is presented for stepwise runoff model parameter estimation from observed runoff and model state variables and fluxes For the study catchment, correlation coefficient of monthly runoff in the validation period is 0.82, and the relative volume error is −1% Combination of soil moisture and evapotranspiration observations had the largest influence on parameter estimation