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.

4 modules developed so far: Module 1 – General Features and Role of Hydropower Projects and Systems; Module 2 – Load-Resource Analysis; Module 3 – Operating Objectives and Principles for Water Management; Module 4 – Hydrologic Data

We postulate that true FFA uncertainty may be larger than currently appreciated and that different components of the modeling chain such as model choice, parameter values or initial conditions impact FFAs by different amounts. We propose to explore key components of the modeling chain: 1) expanding from one model to a multi-model ensemble, 2) varying model parameters, and 3) varying initial conditions for each model structure. Furthermore, uncertainty and sensitivity characteristics likely vary across hydroclimatic regime. To address this hypothesis, we will use continuous ensemble simulations across model structures with parameter perturbations to drive a stochastic event simulation framework to reveal true FFA uncertainty and understand sensitivities across several case-study basins spanning the hydroloclimatology of the 17 western states.

Can the use of sUAS combined with satellite data effectively improve calibration of distributed hydrologic models? Previous work has focused on developing techniques to control variables such as evapotranspiration using satellite remote sensing. The critical component is the equilibrium surface temperature which is normally calibrated using thermal remote sensing data from MODIS (MODerate resolution Imaging Spectroradiometer) or AATSR (Advanced Along-Track Scanning Radiometer) which have a 1 km ground sample distance. This study seeks to improve thermal calibration techniques using much finer-resolution thermal measurements derived from a sUAS. The use of a sUAS will allow for the collection of high resolution imagery (< 1 cm – 1 m or more depending upon research goals) of landscape components, repeatedly and during desired time frames. Thermal data from a sUAS will provide much greater spatial and temporal resolution than satellite-based measurements, and will be obtained at a relatively low cost. This study seeks to determine the added benefit of improved spatial and temporal resolution of observations, evaluated through the existing model calibration framework of a sub-daily, sub-kilometer hydrologic model with complex terrain and vegetation, typical of many mountain headwaters systems experiencing change in the West.

This research will address the overarching question of whether observed past climate trends have induced sufficient non-stationarity in climate and hydrologic systems in the western US to alter their predictability, which has consequences for water management. Key question include the following: When and where in the western US are hydroclimate trends and/or decadal variability leading to systematic biases in statistical and model-based waters supply forecasts? What practical adjustments can be made to current forecasting approaches to make seasonal water supply forecasts robust in the face of such phenomena? In particular, can hydrologic sensitivities to temperature be accounted for by leveraging operational temperature forecasts as streamflow predictors or weighting factors in conventional seasonal forecasting procedures? What are the marginal benefits of improvements in seasonal streamflow predictions for water management in the Upper Rio Grande River basin?