RColSim-v.1.2 User Manual

Zenodo (CERN European Organization for Nuclear Research) · 2026-01-04

RColSim: An R-Based Open-Source Regional Water Management Model for the Columbia River Basin

Journal of Open Research Software · 2026-04-01

We present RColSim, a script-based water management model that simulates the operation of reservoir systems of the Columbia River Basin located in the Pacific Northwest. The model simulates the operation of 46 major storage and run-of-the-river dams on the Columbia River, given rule curves and flow targets for system-wide flood control, irrigation, environmental, and hydroelectricity generation. RColSim is written in the R programming language and is publicly available on GitHub and Zenodo. The model can be downloaded, modified, and reused in accordance with the MIT open-source license terms of use. The model provides an open-source simulation tool for researchers, practitioners, and policymakers to investigate the ramifications of various planning and operation scenarios and stressors for Columbia River Basin stakeholders.

RColSim-v.1.2 User Manual

Zenodo (CERN European Organization for Nuclear Research) · 2026-01-04

Global Sensitivity Analysis of a Coupled Hydro-Economic Model and Groundwater Restriction Assessment

UNC Libraries · 2024-07-09

California's food-energy-water system: An open source simulation model of adaptive surface and groundwater management in the Central Valley

UNC Libraries · 2024-07-09

Roam: A Decision Support System for Digital Agriculture Systems

SSRN Electronic Journal · 2024-01-01

Realtime optimization and management system (ROAM): A decision support system for digital agriculture systems

Smart Agricultural Technology · 2024-04-20 · 5 citations

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity stemming from the challenge of integrating diverse, often non-interoperable hardware and software components. In order to tackle these complexities to increase farm efficiency and understand the tradeoffs of these new DA innovations we developed Realtime Optimization and Management System (ROAM), which is a decision-support system developed to find a Pareto optimal architectural design to build DA systems. To find the Pareto optimal solution, we employed the Rhodium Multi-Objective Evolutionary Algorithm (MOEA), which systematically evaluates the trade-offs in DA system designs. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined objectives and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a DA architecture design method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability.

Downscaling global land-use/cover change scenarios for regional analysis of food, energy, and water subsystems

Frontiers in Environmental Science · 2023-01-30 · 9 citations

Integrated assessment models (IAMs) capture synergies between human development and natural ecosystems that have important implications for the food-energy-water (FEW) nexus. However, their lack of fine-scale representation of water regulatory structure and landscape heterogeneity impedes their application to FEW impact studies in water-limited basins. To address this limitation, we developed a framework for studying effects of global change on regional outcomes for food crops, bioenergy, hydropower, and instream flows. We applied the new methodology to the Columbia River Basin (CRB) as a case study. The framework uses the Demeter land-use and land-cover change (LULCC) downscaling tool, which we updated so that water rights are spatially integrated in the land allocation process. We downscaled two LULCC scenarios (SSP2-RCP 4.5 and SSP5-RCP 8.5) under three levels of irrigation expansion: no expansion (historical extent), moderate expansion (all land presently authorized by a water right is irrigated), and maximum expansion (new water rights are granted to cover all irrigable land). The downscaled scenarios were evaluated using a hydrology-cropping systems model and a reservoir model coupled in a linear fashion to quantify changes in food and bioenergy crop production, hydropower generation, and availability of instream flows for fish. The net changes in each sector were partitioned among climate, land use, and irrigation-expansion effects. We found that climate change alone resulted in approximately 50% greater production of switchgrass for bioenergy and 20% greater instream flow deficits. In the irrigation-expansion scenarios, the combination of climate change and greater irrigated extent increased switchgrass production by 76% to 256% at the cost of 42% to 165% greater instream flow deficits and 0% to 8% less hydropower generation. Therefore, while irrigation expansion increased bioenergy crop productivity, it also exacerbated seasonal water shortages, especially for instream use. This paper provides a general framework for assessing benchmark scenarios of global LULCC in terms of their regional FEW subsystem outcomes.

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

Journal of Advances in Modeling Earth Systems · 2023-05-01 · 22 citations

Abstract Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance.

CLM5 CAMELS Basins Ensemble

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2023-01-01 · 1 citations

Land surface models such as Community Land Model Version 5 (CLM5) are essential tools for simulating the behaviors of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrologic parameters and the implications that these uncertainties have on water resources applications. To address this long-standing issue, we conduct a comprehensive hydrologic parameter uncertainty characterization (UC) of CLM5 over the hydroclimatic gradients of the Conterminous United States using five meteorological datasets. Key datasets produced from the UC experiment include a benchmark dataset of CLM5 default hydrological performance, parameter sensitivity identified for 28 hydrological metrics, and large ensemble outputs for hydrological predictions. The presented datasets can assist CLM5 calibration and to support broad applications such as evaluating vulnerabilities to droughts and floods. The dataset can be used to identify under what hydroclimate conditions parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how land runoff uncertainties interact with other Earth system processes. Please refer to the README.pdf for a description of the included files. Note that raw CLM5 model outputs for each forcing dataset are hosted on a Globus endpoint: https://app.globus.org/file-manager?destination_id=d22ef858-27b0-11ed-a910-fd3165076336.