Long‐range experimental hydrologic forecasting for the eastern United States

TLDR

The study investigates a long‑range hydrologic forecasting strategy that feeds ensemble climate model outputs into a macroscale hydrologic model to generate runoff and streamflow predictions for water management. The approach uses NCEP/CPCC GSM monthly ensemble forecasts that are bias‑corrected by percentile comparison to observed climatology, downscaled to 1/8° resolution, disaggregated to daily time steps, and interpolated to the Variable Infiltration Capacity model grid, with a daily anomaly signal imposed through historic record resampling. During the 2000 southeastern drought, forecasts were dominated by persistence of initial hydrologic states, whereas under 1997‑98 El Niño the forecasts reflected both climate signal and antecedent land surface conditions; overall, the strategy qualitatively succeeded in translating climate signals to hydrologic variables for water management.

Abstract

We explore a strategy for long‐range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Monthly ensemble climate model forecasts produced by the National Centers for Environmental Prediction/Climate Prediction Center global spectral model (GSM) are bias corrected, downscaled to 1/8° horizontal resolution, and disaggregated to a daily time step for input to the Variable Infiltration Capacity hydrologic model. Bias correction is effected by evaluating the GSM ensemble forecast variables as percentiles relative to the GSM model climatology and then extracting the percentiles' associated variable values instead from the observed climatology. The monthly meteorological forecasts are then interpolated to the finer hydrologic model scale, at which a daily signal that preserves the forecast anomaly is imposed through resampling of the historic record. With the resulting monthly runoff and streamflow forecasts for the East Coast and Ohio River basin, we evaluate the bias correction and resampling approaches during the southeastern United States drought from May to August 2000 and also for the El Niño conditions of December 1997 to February 1998. For the summer 2000 study period, persistence in anomalous initial hydrologic states predominates in determining the hydrologic forecasts. In contrast, the El Niño‐condition hydrologic forecasts derive direction both from the climate model forecast signal and the antecedent land surface state. From a qualitative standpoint the hydrologic forecasting strategy appears successful in translating climate forecast signals to hydrologic variables of interest for water management.