Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

We investigated the contribution of medium range weather forecasts with lead times of up to 14 days to seasonal hydrologic prediction skill over the conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP) based experiments were performed for the period 1980–2003 usin...

Full description

Bibliographic Details
Main Authors: S. Shukla, N. Voisin, D. P. Lettenmaier
Format: Article
Language:English
Published: Copernicus Publications 2012-08-01
Series:Hydrology and Earth System Sciences
Online Access:http://www.hydrol-earth-syst-sci.net/16/2825/2012/hess-16-2825-2012.pdf
id doaj-b2926dd303794948bddd970b8a0d4824
record_format Article
spelling doaj-b2926dd303794948bddd970b8a0d48242020-11-24T23:53:58ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382012-08-011682825283810.5194/hess-16-2825-2012Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skillS. ShuklaN. VoisinD. P. LettenmaierWe investigated the contribution of medium range weather forecasts with lead times of up to 14 days to seasonal hydrologic prediction skill over the conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP) based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts for runoff [SM] forecasts generally varies from 0 to 0.8 [0 to 0.5] as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.http://www.hydrol-earth-syst-sci.net/16/2825/2012/hess-16-2825-2012.pdf
collection DOAJ
language English
format Article
sources DOAJ
author S. Shukla
N. Voisin
D. P. Lettenmaier
spellingShingle S. Shukla
N. Voisin
D. P. Lettenmaier
Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill
Hydrology and Earth System Sciences
author_facet S. Shukla
N. Voisin
D. P. Lettenmaier
author_sort S. Shukla
title Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill
title_short Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill
title_full Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill
title_fullStr Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill
title_full_unstemmed Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill
title_sort value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill
publisher Copernicus Publications
series Hydrology and Earth System Sciences
issn 1027-5606
1607-7938
publishDate 2012-08-01
description We investigated the contribution of medium range weather forecasts with lead times of up to 14 days to seasonal hydrologic prediction skill over the conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP) based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts for runoff [SM] forecasts generally varies from 0 to 0.8 [0 to 0.5] as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.
url http://www.hydrol-earth-syst-sci.net/16/2825/2012/hess-16-2825-2012.pdf
work_keys_str_mv AT sshukla valueofmediumrangeweatherforecastsintheimprovementofseasonalhydrologicpredictionskill
AT nvoisin valueofmediumrangeweatherforecastsintheimprovementofseasonalhydrologicpredictionskill
AT dplettenmaier valueofmediumrangeweatherforecastsintheimprovementofseasonalhydrologicpredictionskill
_version_ 1725468064594526208