Development of computational infrastructure to support hyper-resolution large-ensemble hydrology simulations from local-to-continental scales

Metadata Updated: February 28, 2019

Development of computational infrastructure to support hyper-resolution large-ensemble hydrology simulations from local-to-continental scales

A move is currently afoot in the hydrologic community towards multi-model ensembles to capture the substantial uncertainty in environmental systems. However, the current generation of operational and experimental simulation platforms can be characterized as 'small ensemble' systems. Typically, fewer than five hydrologic models are used to characterize model uncertainty, and the multi-model systems have a poor probabilistic portrayal of risk. As a result, water resource management decisions based on these 'small ensemble' systems will often be suboptimal and in the worst case will simply be wrong. The relatively small size of the ensembles in current multi-model systems is largely dictated by the considerable difficulty and human resources needed to implement and parameterize individual hydrological models so that they can be operated within a common framework. We have developed an advanced hydrologic modeling approach, SUMMA (the Structure for Unifying Multiple Modeling Alternatives), which enables explicitly representing the ambiguities in a myriad of modeling decisions. SUMMA can be used to design ensemble systems with specific properties and improve the probabilistic characterization of risk. SUMMA is currently at Technical Readiness Level 3, and focused effort on computational infrastructure is necessary to develop the next-generation modeling system needed to support water resources planning and management throughout the USA. The goal of this project is to develop the computational infrastructure to enable hyper-resolution large-ensemble hydrology simulations from local-to-continental scales. By hyper-resolution we mean hydrology simulations on spatial scales of the order of 1 km and by large ensemble we mean a multi-model ensemble of the order of 100 ensemble members. The continental-scale domain for this project is the contiguous USA. The goal of hyper-resolution, large-ensemble, continental-domain hydrology simulations will be accomplished with focused effort on the following four tasks: (1) Satisfy data requirements of multiple modeling approaches (e.g., spatial parameter fields); (2) Improve SUMMA's numerical robustness and computational efficiency; (3) Embed SUMMA in the NASA Land Information System (NASA-LIS) to expand multi-model simulation capabilities; and (4) Evaluate and refine the multi-model ensemble. These tasks represent the obvious next steps to advance probabilistic continental-domain modeling capabilities at spatial scales relevant for water managers. The first two tasks of fulfilling data requirements for multiple modeling approaches and improving the computational performance of hydrologic models is necessary to enable hyper-resolution, large-ensemble, continental-domain hydrology simulations. The third task of embedding SUMMA in NASA-LIS takes advantage of NASA's state-of-the-art multi-model framework, especially its ensemble modeling and model benchmarking capabilities. It also extends the capabilities of NASA-LIS to support large multi-model ensembles. The final task to evaluate and refine the multi-model ensemble is necessary to improve the probabilistic characterization of risk. By using a common modeling core in SUMMA, the overhead associated with implementing alternative modeling approaches is significantly reduced, a major departure from current 'small ensemble' methods. By embedding SUMMA within NASA's Land Information System (LIS), meaningful 'large ensemble' systems will become a reality and can provide improved guidance for water resource management decisions by our development partners at the U.S. Army Corps of Engineers and the Bureau of Reclamation.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019

Metadata Source

Harvested from NASA Data.json

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Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_18682
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id bf46863a-f3d0-479e-a76e-dd1db150f17c
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2017-04-01
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash 3b41642fb11955174dc49065dc0b31cd4ef8311e
Source Schema Version 1.1

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