Seasonal River Flow Forecasting Using Multi-model Ensemble Climate Data

摘要

Developing skilful seasonal (up to 6 month lead time) forecasting of river flows is important for many societal applications. Long-lead forecasts have potential to aid water management decision making and preparation for human response to hydrological extremes. The seasonal prediction of river flows has been a topic of increasing interest due to the recent 2004-06 drought and 2007 floods experienced in the UK.ududThe aim of this paper is to compare the relative skill of predictions of river flow using: (1) a multi-Global Climate Model (GCM) ensemble data set of seasonal hindcasts (i.e. DEMETER) and (2) downscaled multi-GCM data (also DEMETER) as input to a hydrological model. The period of 1980-2001 is the interval considered. The River Dyfi basin in West Wales, UK is the focus of this exploratory research. This basin is a near natural catchment, hence the climate-flow signal should be clearer. The DEMETER project is the source of the multi-model climate data, and this consists of 7 GCMs each with 9 ensemble members. Hindcasts with lead times up to 6 months are available from 1st February, 1st May, 1st August and 1st November initial conditions. Each hindcast was split into the first 3 and last 3 months, and the subsequent concatenation of the split hindcasts produced 2 time series (total of 7x9x2 ensemble series), which were used in the hydrological model. ududDEMETER daily precipitation (1–3 and 4–6 month series) and ‘observed’ potential evaporation (PE) data drive the Probability Distributed Model (PDM) to simulate daily river flow series. PDM is a lumped rainfall-runoff model that transforms rainfall and PE data to river flow at the basin outlet. PDM was calibrated with observations over a 10 year (1980–1990) period, and then validated over an independent 10 year (1991–2001) period.ududThe coarse resolution of the DEMETER data (standardised to 2.5˚ x 2.5˚ resolution) means that the atmospheric motions at sub-grid scales are not captured by the models. The large spatial disparity between the GCM grids and the scale of the study (471.3 km2) lead to important underestimation of precipitation by DEMETER models. This difference is addressed through the use of a statistical downscaling tool, the Statistical Downscaling Model (SDSM). The SDSM was calibrated on an independent re-analysis data set (ERA-40 from the ECMWF), as ERA-40 provides one of the best estimates of the real atmosphere (a spatial resolution comparable to that of DEMETER models was used for this calibration). Multiple linear regression models (one per month) were used to link large-scale DEMETER predictors (for each of the 9x7x2 ensemble members) with basin scale rainfall, and a stochastic weather generator produced downscaled rainfall time series. These new downscaled series are designed to more closely represent catchment rainfall. DEMETER precipitation data and downscaled data are inputted to the PDM to determine their relative river flow modelling skill.ududPreliminary results show that simulated river flows driven by DEMETER do indeed underestimate the observed flow. The downscaled series improves the hindcast skill, and little reduction in skill is seen when using a longer lead time hindcast. The results drawn from this research will have major implications for assessing (1) the potential skill expected from large scale GCM output, and (2) the relative improvement in skill of using downscaled versus non-downscaled precipitation data. Additionally, it will be possible to ascertain any degradation in the seasonal hindcast skill when using longer lead times.ud