Evaluation of Seasonal Streamflow Forecasting

Abstract

Long-term streamflow forecasts are essential for optimal management of water resources for various demands, including irrigation, fisheries management, hydropower production and flood warning. In this paper, a probabilistic forecast framework based on Ensemble Streamflow Prediction (ESP) technique is presented, with the basic assumption that future weather patterns will reflect those experienced historically. Hence, past forcing data (input to hydrological model) can be used with the current initial condition of a catchment to generate an ensemble of flow predictions. The present study employs the ESP-based approach using the TopNet hydrological model. The objective of this present paper is to evaluate and assess the uncertainty due to initial condition of the catchments and forcing (meteorological input to the model) data for (ESP) based streamflow forecasting using the TopNet hydrological model in New Zealand catchments. An ensemble of streamflow predictions which provide probabilistic hydrological forecasts, reflecting the intrinsic uncertainty in climate, with lead time up to three months is presented for the four catchments on New Zealand’s South Island. Verification of the forecast over the period 2000-2010 indicates a Ranked Probability Skill Score of 23% to 69% (over climatology) across the four catchments. In general, improvement in ESP forecasting skill over climatology is greatest in summer for all catchments studied. The major uncertainty associated with ESP forecast is combination of uncertainty due to initial state and climate forcing. The analysis indicates that the sensitivity of flow forecast to initial condition uncertainty depends on the hydrological regime experienced by the basin during the forecast period. On average, the relative importance of initial condition is greatest within two weeks to months of the start of the simulation for all catchment and all season. After this time period uncertainty in forecast is mainly due to uncertainty in forcing data. Finding of this study can be valuable tool for water resource managements.