Welcome to WaRP, NHA’s new Waterpower Research Portal.

The existing Fryingpan-Arkansas Project RiverWare planning model was developed to support long-term water management and planning uses such as water supply and policy evaluation. In the model's current state, it doesn't support real-time operational uses. The primary objective of this project is to adapt the existing model to support uses for short-term operational decision-making, forecasting, probabilistic risk management, and administration so that the model can be used by Reclamation's Pueblo Field Office for these purposes with thorough documentation so this process can be used by model developers in the future.

This project will provide a scalable, fine-resolution, physics-based modeling framework to evaluate different potential hydropower investment and operational decisions in the face of hydrologic change. Specifically, the modeling framework will be able to quantify risk, at the plant and system levels; impacts of hydrologic conditions on hydropower and thermoelectric production; water temperature; and ecosystem resources.

This study includes three main components: a literature review of the use of streamflow ensembles; industry level surveys and interviews to benchmark the current use of ensembles; and an interactive Roadmap to guide users through the many different components of developing, verifying, processing, and using ensemble data.

To what extent do low-elevation snowpack contribute to streamflow forecast errors is current forecast models? What improvement in forecast skill can be gained by changing the spatial configuration of forecast models including improvements to their representation of low-elevation snow and to reservoir inflows? What improvement in forecast skill can be gained by incorporating in remotely-sensed and/or ground-based snow products into forecast models?

Research Need: In the Western US, warm-season precipitation has historically provided a secondary water source to snowmelt runoff (Serreze et al., 1999). However, increasing temperatures and decreasing snowpack suggest that it may gain importance for water resources management. As such, there is an interest in understanding the predictability of warm-season precipitation, as well as the sensitivity of water resources and management to this source. Understanding the predictability of warm-season precipitation is of particular interest in the U.S. Southwest, a region that is influenced by the North American Monsoon in summer (Adams and Comrie 1997). One of the barriers to using monsoon forecasts has been their low skill in simulating precipitation. However, it has been recommended that any examination of monsoon should consider large-scale circulation, rather than examining precipitation directly (Seneviratne et al. 2012). To this point, Prein (2019) identified large-scale conditions over the U.S. Southwest associated with monsoon precipitation anomalies, and found that they are robustly captured by NCAR's Community Earth System Model (CESM) and other general circulation models. This provides motivation to evaluate monsoon circulation patterns in forecast ensemble products.

The goal of the proposed research is to develop a new streamflow bias-correction method that can be used by Reclamation to create input time series for water resources models at a daily time step. The proposed method will avoid existing artifacts in the bias-corrected streamflow, such as discontinuities on month boundaries and inconsistent local inflows. The methodological advances are that the bias-correction method will develop corrections based on the dominant hydrologic process (e.g. snow melt) rather than on time-of-year (e.g. a correction based on month) and that the method will account for the connectivity between successive downstream locations to result in realistic incremental flows.