Statistical applications of physically based hydrologic models to seasonal streamflow forecasts

Abstract

[1] Despite advances in physically based hydrologic models and prediction systems, longstanding statistical methods remain a fundamental component in most operational forecasts of seasonal streamflow. We develop a hybrid framework that employs gridded observed precipitation and model-simulated snow water equivalent (SWE) data as predictors in regression equations adapted from an operational forecasting environment. We test the modified approach using the semidistributed variable infiltration capacity hydrologic model in a case study of California's Sacramento River, San Joaquin River, and Tulare Lake hydrologic regions. The approach employs a principal components regression methodology, adapted from the Natural Resources Conservation Service, which leverages the ability of the distributed model to provide an added dimension to SWE predictors in a statistical framework. Hybrid forecasts based on data simulated at grid points acting as surrogates for ground-based observing stations are found to perform comparably to those based on their observed counterparts. When a larger selection of grid points are considered as potential predictors, hybrid forecasts achieve superior skill, with the largest benefits in watersheds that are poorly represented in terms of ground-based observations. Forecasts are also found to offer overall improvement over those officially issued by California's Department of Water Resources, although their specific performance in dry years is less consistent. The study demonstrates the utility of physically based models within an operational statistical framework, as well as the ability of the approach to identify locations with strong predictive skill for potential ground station implementation.Citation: Rosenberg, E. A., A. W. Wood, and A. C. Steinemann (2011), Statistical applications of physically based hydrologic models to seasonal streamflow forecasts, Water Resour. Res., 47, W00H14,

Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

“…The Sierra Nevada serves as a barrier to moisture moving inland from the Pacific, has an ideal orientation for producing orographic precipitation, and thus exerts a strong influence on the upslope amplification of precipitation (Colle, 2004;Rotach and Zardi, 2007;Smith and Barstad, 2004). Recent studies provide insight on how orographic and topographic factors affect snow depth in the Alps (Grünewald et al, 2013(Grünewald et al, , 2014Lehning et al, 2011), suggesting that similar studies could be extended to the Sierra Nevada.…”

“…In situ, operational measurements of snow water equivalent (SWE) in the Sierra Nevada come from monthly manual snow surveys and daily snow-pillow observations (Rosenberg et al, 2011). Meteorological stations and remotesensing products also provide estimates of precipitation and snow accumulation (Guan et al, 2013).…”

The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia

“…In this example the catchment wetness is overestimated by total streamflow. Catchment wetness can be modelled more effectively for forecasting with so-called "dynamical" approaches (Rosenberg et al, 2011;Robertson et al, 2013a) that use soil-moisture accounting models (e.g. conceptual rainfall-runoff models forced by observed rainfall and evaporation) to improve estimates of catchment wetness and thereby improve forecasts.…”

Statistical forecast of seasonal discharge in Central Asia for water resources management: development of a generic linear modelling tool for operational use

“…However, no skilful forecast with 10 lead times greater than zero were possible. Rosenberg et al (2011) proposed a hybrid (statistical -hydrological model) framework for seasonal flow prediction in Californian catchments using accumulated precipitation in antecedent period and SWE modelled by a distributed hydrological model. These two predictors were linked to seasonal discharge by principal component and Z-score regression (Rosenberg et al, 2011).…”

“…Rosenberg et al (2011) proposed a hybrid (statistical -hydrological model) framework for seasonal flow prediction in Californian catchments using accumulated precipitation in antecedent period and SWE modelled by a distributed hydrological model. These two predictors were linked to seasonal discharge by principal component and Z-score regression (Rosenberg et al, 2011). The hybrid approach was found comparable and in some cases superior to a purely statistical approach, however, at the cost of effort for hydrological simulation of the SWE dynamics.…”