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.

       Hydropower is an important renewable energy resource. It is low-carbon, emits nearly no particulate pollution, can ramp quickly, and is capable of storing energy across many hours or days. While it is a very valuable resource by itself, hydropower can also serve as a key enabler for the increased penetration of non-dispatchable renewable energy resources like wind and solar power. This project focused on developing an optimization-based coordinated control framework for a hydropower cascade. It consisted largely of two parts. The first is the development of the coordination scheme. The second is the simulation and state estimation tools that were developed to allow comparisons between historical operations and the operations dictated by the coordination scheme. The coordinated control scheme that we developed is based on a control technique known as Model Predictive Control (MPC), wherein a linear state space model is designed to model the hydraulics of a hydropower cascade. Here, the hydraulics model describes how water flows in a hydropower cascade change the reservoir elevations behind each hydropower plant. The model accounts for the delay between water discharged from the upstream plant affecting the forebay elevation of the downstream plant. The control scheme also accounts for the non-linear character of tailrace elevations. There is an obvious relationship between the amount of water discharged into the tailrace and the tailrace elevation. Our modeling work takes that a step further by identifying the conditions that lead to encroachment and modeling encroachment. Encroachment is when the downstream forebay backs up into the upstream tailrace, causing the tailrace elevation to be higher than it would be otherwise. The optimization scheme also accounts for the relationship between turbine discharge, hydraulic head, and powerhouse generation in a hydropower plant. Turbine discharge and hydraulic head are mapped to a corresponding amount of powerhouse generation using a three-dimensional piecewise planar function. This function is fit to historical operations data. Since the relationship between the three variables can be represented using a set of linear functions, the model for hydropower production can be integrated into a linear or quadratic program. This results in an optimization model that is both fast and accurate, an improvement over other coordinated control schemes that are based on nonlinear or mixed-integer programming. The objective function was formulated to minimize the sum of the squared turbine discharge and spill for each hydropower plant. The weights were chosen such that water was preferentially discharged from large surface area reservoirs to small surface area reservoirs. This allocates a certain volume of water such that it results in the maximum total hydraulic head. Weighting turbine discharges in this way is unique in the hydropower optimization literature. We tested the coordinated control scheme on the Mid-Columbia hydropower system. The MidColumbia consists of seven dams on the Columbia River in Eastern Washington State. Historical data on system operations allowed us to benchmark the performance of our coordination scheme with actual system operations. Further data was provided that allowed us to properly calibrate the parameters of our model, including forebay and tailrace curves, travel times, and hydropower production functions. Simulations were conducted for a five-day period with five-minute time resolution. The results of our simulations, in brief, can be condensed into four areas. 1. The hydraulic potential of the system (H/K) increased steadily over the course of the simulations. At the end of the simulation period, the total system H/K was 0.6% higher than in the historical case. This translates to several feet of additional hydraulic head. 2. The net energy stored in the cascade increased. Overall, the net energy benefit was 1708 MWh, or 0.33% of the total energy generated during the simulation period. In general, Grand Coulee ran an energy deficit (i.e., its forebay was lower in the optimized case than the historical case) and the remaining hydropower plants ran an energy surplus. 3. Ramping was reduced substantially. Quantitative measures indicated that ramping decreased substantially at every hydropower plant besides Grand Coulee. Qualitatively, the discharge profiles were much smoother in the optimized case than in the historical case. This method of operation could have substantial (but uncertain) benefits to hydropower plant owners and operators due to less unit cycling and ramping, which results in lower maintenance and repair costs. 4. System constraints were satisfied. The Mid-Columbia system is constrained at many times of the year due to environmental limits on turbine discharge, spill, and flow ramping. These limits are designed to ensure the health of salmon runs on the Columbia River and the spawning areas in the Hanford Reach downstream of Priest Rapids. One of the primary benefits of doing coordinated control in an optimization framework is that system constraints can be explicitly obeyed. This ensures that regulatory and legal bounds on system operations are satisfied completely. The second part of the research involved the development of a state estimation procedure for a hydropower cascade. Evaluating the coordinated control scheme necessitated developing a state estimation procedure to reconcile measured values of turbine discharge, spill, and forebay elevation. In lieu of being able to test the outputs of the coordinated control scheme on the actual Mid-Columbia system or on a high-fidelity simulator, an inherently inaccurate computer model must be used. This model will contain some modeling errors. Likewise, the measured flows and forebay elevations can be biased and noisy. These biases and noise levels are unknown and, a priori, we do not know which values can be trusted and to what extent. The state estimation procedure takes these values and the hydraulic model, and adjusts the measurements such that the model is open-loop stable and the estimated measurements are consistent with each other. The general idea is that one flow measurement is assumed to be the true flow through the system, and the other flows (upstream and/or downstream) are adjusted to reduce the residual error between the estimated flow and the measured flow. Constraints are added to the procedure to ensure that the estimated flow profile is similar to the measured flow profile. Results are given demonstrating the practical efficacy of the proposed state estimation method.

In stratified reservoirs, both dam tailwater discharge and thermal plant intake water quality and temperature can be highly dependent on structure depth. A twodimensional laterally-averaged model allows for better prediction of water quality over time at specific depths. Because high-fidelity models are typically too computationally expensive for direct inclusion within optimization algorithms, water quality is incorporated using one dimensional models are simple flow requirements. Water quality predictions can be incorporated within the optimization process through using surrogate modeling methods, in this application artificial neural network (ANN) models. ANNs are flexible machine learning tools for function approximation composed of a structure of neurons assembled within a multi-layer architecture. They are capable of handling large amounts of training data and modeling nonlinear dynamic systems, making ANNs a well-suited method for this application. This report illustrates the development of ANN models to emulate the hydrodynamic and water quality modeling capabilities of the high-fidelity, two-dimensional CE-QUAL-W2 (W2) model, as well as a linked riverine reservoir system optimization process which accounts for energy generation, water balance and hydraulics, and compliance point water quality. A process for hourly hydropower generation planning is demonstrated on a pair of reservoirs linked in series. The two reservoirs are U.S. Army Corps of Engineers projects with hydropower capabilities on the Cumberland River near Nashville, Tennessee, USA. The content presented here is largely a combination of technical papers previously presented at the HydroVision International conference (Shaw et al., 2015, 2016).

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.