Intermediate-range Climate Forecasting to Support Water Supply and Flood Control with a Regionally Focused Mesoscale Model
Project ID: 9682
Principal Investigator: Eric Rothwell
Research Topic: Water Supply Forecasting
Priority Area Assignments: 2015 (Climate Change and Variability Research), 2016 (Climate Change and Variability Research)
Funded Fiscal Years: 2015, 2016 and 2017
Keywords: water supply, flood control, climate forecasting, modeling, snow, operations
This Science and Technology project will address the research question: Can mesoscale weather prediction (NWP) models, appropriately tailored to regional characteristics, provide hydroclimate forecast data at improved accuracy and spatiotemporal coverage relative to data products currently in use?
This research question is motivated by the need for accurate and timely hydroclimate information to support streamflow forecasting and decision-making by water resource managers in the Western US. Key hydroclimatic variables of interest include precipitation, temperature, snow storage, soil moisture, evapotranspiration, runoff, solar radiation, and streamflow. There are significant limitations associated with existing data streams used by these decision-makers. A significant limitation is the coarse spatial resolution of these data in most landscapes of the Western US. The research question is informed by the results of Colorado Headwaters Project. Outcomes of that project include an outline of conditions that, when imposed on the computational domain and physics options of numerical weather prediction models, can lead to prediction errors that are on par with observational errors. The research question, therefore, is predicated on an assumption that the outcomes of the Colorado Headwaters Project are generally applicable to other regions. It is hypothesized that when appropriate physics options and spatial resolutions for the WRF model are determined for a region, outputs from the WRF model will be more accurate than commonly used National Weather Service Global Forecast System (GFS) data products, compared to ground-based observations and geospatial products, resulting in more relevant and accurate streamflow forecasts. The outputs of the WRF model will potentially add value to operations by providing hydroclimate data products at improved spatial resolutions (~1-3 km) and lead times (1-30 days) that will enhance streamflow prediction and water resource decisions.
Need and Benefit
Virtually the entire Western US and up to one-sixth of the global population depend on seasonal snowpacks for water supply. The water resources infrastructure of the Western US is designed to buffer variability in precipitation and snow storage, delivering reliable water supply to users, affording flood control for communities, and providing recreation opportunities to the public. Operation of water resource infrastructure must balance variability and change in precipitation and natural storage on one hand, with the multiple objectives of water supply, flood control, habitat, and other ecosystem and hydrologic services on the other.
At present, short-range (0-10 days) detailed information is often obtained from the National Weather Service's Global Forecasting System (GFS) while long-range (30 days and beyond) more qualitative information is often obtained from the NOAA Climate Prediction Center. There are two significant limitations to these datastreams: (1) GFS data are available only at spatial resolutions of 0.5 degrees (~40 km), significantly coarser than correlation lengths of precipitation associated with range-scale topography, and (2) there is a scarcity of detailed information in the intermediate-term (10-30 days). In recent years, significant progress has been made in improving the physical representation of atmospheric processes within and the spatial resolution captured by mesoscale numerical weather prediction models, such as the WRF model. Despite this progress, these models remain too numerically intensive to apply at continental scales while maintaining high spatial resolutions (i.e., < 4 km). What is needed in the near future, therefore, is a concerted suite of experiments to evaluate and identify the circumstances under which region-specific applications of models like WRF can provide data that is a demonstrable improvement over data products already in use. This will promote improved understanding and communication of the data accuracy and spatiotemporal resolution requirements to support water resource management by Reclamation.
This project will directly benefit Reclamation by assessing and evaluating the use of physically based models, which have the potential to increase reliability of forecasts under non-normal (e.g., post-fire, climate change-altered flow regimes, etc.) conditions. Moreover, the use of WRF (a community, open-source, mesoscale NWP) to produce the data required to drive these hydrological models has the unique potential to fill a gap in data availability in the 10-30 day time horizon and supply data at spatial resolutions significantly finer than the data products currently used for operational forecasting.
Contact the Principal Investigator for information about partners.
At least one peer-reviewed paper
Data outputs of the WRF model will be distilled to spatial locations and temporal intervals (e.g., daily, weekly, etc.) of use to operations personnel, and archived in a discoverable and accessible location.
The data management and sharing plan include: A) Types of data produced: Research activities will use existing and produce new datasets. These include: point time-series data, vector-based geospatial data representing point/line/polygon geometries and associated attributes, data analysis products, and gridded model output. Examples of these data products include gridded model outputs, gridded forecast data, time-series hydrometeorological and hydroclimate station observations, and gridded analysis products developed by other organizations. These data will be combined with or derived from data available from national and regional data repositories, including NASA DAACs (e.g. ORNL DAAC), UCAR Research Data Archive, the National Climatic Data Cen