Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach

Sanjib Sharma, Heather Gall, Jorge Gironás, Alfonso Mejia

Research output: Contribution to journalArticle

Abstract

Subseasonal-to-seasonal (S2S) water quantity and quality forecasts are needed to support decision and policy making in multiple sectors, e.g. hydropower, agriculture, water supply, and flood control. Traditionally, S2S climate forecasts for hydroclimatic variables (e.g. precipitation) have been characterized by low predictability. Since recent next-generation S2S climate forecasts are generated using improved capabilities (e.g. model physics, assimilation techniques, and spatial resolution), they have the potential to enhance hydroclimatic predictions. Here, this is tested by building and implementing a new dynamical-statistical hydroclimatic ensemble prediction system. Dynamical modeling is used to generate S2S flow predictions, which are then combined with quantile regression to generate water quality forecasts. The system is forced with the latest S2S climate forecasts from the National Oceanic and Atmospheric Administration's Climate Forecast System version 2 to generate biweekly flow, and monthly total nitrogen, total phosphorus, and total suspended sediment loads. By implementing the system along a major tributary of the Chesapeake Bay, the largest estuary in the US, we demonstrate that the dynamical-statistical approach generates skillful flow, nutrient load, and suspended sediment load forecasts at lead times of 1-3 months. Through the dynamical-statistical approach, the system comprises a cost and time effective solution to operational S2S water quality prediction.

Original languageEnglish (US)
Article number084016
JournalEnvironmental Research Letters
Volume14
Issue number8
DOIs
StatePublished - Jul 29 2019

Fingerprint

Suspended sediments
Climate
suspended sediment
Nutrients
Water Quality
Food
nutrient
Water quality
Estuaries
Flood control
climate
prediction
Water Supply
Policy Making
Physics
Agriculture
Water supply
Phosphorus
Decision Making
Nitrogen

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Public Health, Environmental and Occupational Health

Cite this

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abstract = "Subseasonal-to-seasonal (S2S) water quantity and quality forecasts are needed to support decision and policy making in multiple sectors, e.g. hydropower, agriculture, water supply, and flood control. Traditionally, S2S climate forecasts for hydroclimatic variables (e.g. precipitation) have been characterized by low predictability. Since recent next-generation S2S climate forecasts are generated using improved capabilities (e.g. model physics, assimilation techniques, and spatial resolution), they have the potential to enhance hydroclimatic predictions. Here, this is tested by building and implementing a new dynamical-statistical hydroclimatic ensemble prediction system. Dynamical modeling is used to generate S2S flow predictions, which are then combined with quantile regression to generate water quality forecasts. The system is forced with the latest S2S climate forecasts from the National Oceanic and Atmospheric Administration's Climate Forecast System version 2 to generate biweekly flow, and monthly total nitrogen, total phosphorus, and total suspended sediment loads. By implementing the system along a major tributary of the Chesapeake Bay, the largest estuary in the US, we demonstrate that the dynamical-statistical approach generates skillful flow, nutrient load, and suspended sediment load forecasts at lead times of 1-3 months. Through the dynamical-statistical approach, the system comprises a cost and time effective solution to operational S2S water quality prediction.",
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Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach. / Sharma, Sanjib; Gall, Heather; Gironás, Jorge; Mejia, Alfonso.

In: Environmental Research Letters, Vol. 14, No. 8, 084016, 29.07.2019.

Research output: Contribution to journalArticle

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