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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

FIND: A Synthetic weather generator to control drought Frequency, Intensity, and Duration

Water systems worldwide are experiencing climate change-induced shifts in drought properties like frequency, intensity, and duration, affecting water security and reliability. To develop and test effective drought preparedness plans, researchers often use synthetic weather generators to create hydrological scenarios that explore drought variability beyond historical records. Existing weather generators typically allow users to adjust streamflow statistics like percentiles or temporal correlation but do not directly control drought properties of frequency, intensity, and duration. To fill this gap, we propose FIND (Frequency, INtensity, and Duration) synthetic weather generator. FIND incorporates a standardized drought index to directly and in dependently control drought frequency, intensity, and duration in generated streamflow time series while preserving observed hydrological variability. Use cases for FIND include i) water systems analysis applications that seek to train and test drought strategies under historical and plausible future drought conditions, and ii) bottom-up vulnerability studies relating system vulnerability outcomes to specific changes in drought properties of frequency, intensity, and duration. Here, we demonstrate FIND’s versatility through three experiments: replicating historically observed drought properties, generating streamflow scenarios for multiple sites preserving correlation between their drought conditions, and generating a set of scenarios with direct and independent changes in drought properties. FIND source code is openly available for applications beyond the scope of this paper.

42 ENGINEERING↗
DOE OSTI2022

Machine learning for postprocessing ensemble streamflow forecasts

Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles, distributed hydrological model, and machine learning to generate ensemble streamflow forecasts at medium-range lead times (1–7 days). We demonstrate the application of machine learning as postprocessor for improving the quality of ensemble streamflow forecasts. Our results show that the machine learning postprocessor can improve streamflow forecasts relative to low-complexity forecasts (e.g., climatological and temporal persistence) as well as standalone hydrometeorological modeling and neural network. The relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low–moderate flows, and the warm season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Monitoring Areal Snow Cover Using NASA Satellite Imagery

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Harshburger, Brian J.↗
NASA NTRSDate not supplied

Harmonized Sentinel-1 SAR Global River Geometry and Inundation Database

Satellite-based observations on river geometries are sporadic in time, space, or both. Most satellite-based surface water maps, river widths, water surface elevations (WSE), slopes, and bathymetry are asynchronized in time and space. The current configuration of satellites such as Sentinel-6 measured the WSE but is missing the river width, slopes, and depths. To advance hydrological sciences research, there is a need to produce a harmonized time series of river geometry data of non-SWOT satellites in partnership with the upcoming SWOT mission. The SWOT satellite will measure river width, height, and slope but missing river depth measurements in space and time. Further, none of these current satellites measure the WSE, river width, and slopes synchronously. In this work, we use the Sentinel-1 SAR satellite data archive from 2015 to the present to create a global river width and surface water database at the reach scale. A modified version of the Sentinel SAR surface water classification algorithm from ASF is used to quantify the surface water extent on the stream approximately every six days (at the equator) at 10m spatial resolution globally. This 10m water mask is fed into a workflow to quantify the river widths, surface water inundations, slopes, and synthetic bathymetry in SWORD (SWOT River Database) stream networks. A Satellite HAND is used to address the cloud obscured surface water observations using a trained machine learning algorithm. We use WSE derived from the Global Water Monitor from NASA GSFC, Hydroweb from LEGOS, and ICESat-2 to harmonize the WSE observation. And Landsat-8/9 and Sentinel-2 water observations to fill the gaps in the Sentinel-1 SAR database. We use Congo River Basin as a test case where we have more than 500 radar altimetry-based WSE, continuous series of Sentinel-1, ICESat-2, Landsat-8/9, and Sentinel-2 observations. A Congo River hydrologic model is used to generate the streamflow discharge. The satellite observed river reaches are assimilated with the stream flows computed by the routing models. And the downstream reaches in the river network without satellite observations get optimized for discharge/river geometry at each observation cycle. Our final product is a harmonized river geometry dataset (reach's water extent, WSE, slope, synthetic bathymetry) for Congo Basin's SWORD reaches.

Chandana Gangodagamage↗
DOE OSTI2025

An Integrated Hydroclimatic Assessment of Future Reservoir and Hydropower Operations in the U.S.

The engineering of rivers by dams is a formative feature of human-nature systems and the interconnectivity of water, energy, and the climate. Sufficient and broad-based representations of dams in large-scale hydrological models prove essential to mapping their extensive regulation of river flow and biogeochemistry and gauging climate-linked provisions, including freshwater supply and hydropower. We present an integrated modeling framework to investigate future streamflow and hydropower generation in the Contiguous U.S. (1990–2075), leveraging an ensemble of six downscaled and bias-corrected General Circulation Models (GCMs) from the high-end SSP585 scenario of the CMIP6. To achieve this, we develop a reservoir operations and parameterization scheme for 1,384 dams in a high-resolution river network, including simulated hydropower generation for 326 dams. For the GCM ensemble mean, we simulate a widespread increase in regulated streamflow into the late-century (11% annual and 17% in winter for the dam median) with region-specific changes in summer streamflow that feature prominent declines in the Northwest (−7%). Mediation by reservoirs is shown to dampen intra-annual streamflow changes, delivering additional summer releases that partially mitigate declining flows. Total hydropower generation is projected to increase modestly (+3%), with boosted generation in the winter (+9%) and spring (+5%) offsetting declined summer generation (−3.4%), suggesting strong adaptation potential for hydropower in the future energy portfolio. Further analysis reveals that the choice of GCM, particularly in western regions, has significant bearing on projected streamflow and hydropower changes.

13 HYDRO ENERGY↗
NASA NTRS2017

Covariability of Climate and Streamflow in the Upper Rio Grande from Interannual to Interdecadal Timescales

Study region: The Upper Rio Grande (URG) flows from its headwaters in Colorado, U.S., and provides an important source of water to millions of people in the U.S. states of Colorado, New Mexico, Texas, and also Mexico. Study focus: We reassess the explanatory power of the relationship of sea surface temperatures (SST) on URG streamflow variability on interannual to interdecadal timescales. We find a significant amount of the variance of spring-summer URG streamflow cannot be fully explained by SST. New hydrological insights: We find that the interdecadal teleconnection between SST and streamflow is more clear than on interannual timescales. The highest ranked years tend to be clustered during positive phases of the Pacific Decadal Oscillation (PDO). During the periods of decadal high flow (1900-1920, and 1979-1995), Pacific SST resembles a positive PDO pattern and the Atlantic a negative Atlantic Multidecadal Oscillation (AMO) pattern; an interbasin pattern shown in prior studies to be conducive to high precipitation and streamflow. To account for the part of streamflow variance not explained by SST, we analyze atmospheric Reanalysis data for the months preceding the highest spring-summer streamflow events. A variety of atmospheric configurations are found to precede the highest flow years through anomalous moisture convergence. This lack of consistency suggests that, on interannual timescales, weather and not climate can dominate the generation of high streamflow events.

AMO↗
NASA NTRS1985

Paleohydrologic Implications of Valley Networks of Mars

Valley networks in the heavily cratered terrains of Mars represent an ancient epoch of hydrologic conditions greatly different from those of today. Available crater counts on the valley networks indicate formation during the high flux of impacting bodies charaterizing the early heavy bombardment phase of Martian history. Two populations of valleys are recognized in the equatorial regions of Mars: pristine and degraded. The latter probably formed at the very end of the heavy bombardment phase, extending into the post-heavy bombardment by formation in the intercrater plains. Pristine valleys generally form segments of larger networks with degraded components. This suggests that valley formation was a prolonged process coeval with the heavy bombardment period and extending just beyond that period in martian history. The pristine networks and pristine portions of compound networks on Mars show morphological attributes consistent with an origin by headward growth through spring sapping. On Earth spring sapping occurs where groundwater out-streamflow can be generated by insolation changes associated with orbital parameters or with geothermal effects, such as might be associated with impact or with the volcanic emplacement of the intercrater plains. Thus, it is appropriate to specify the most conservation deviation from modern hydrologic conditions on Mars that could account for the ancient epoch of valley formation.

Baker, V. R.↗
DOE OSTI2025

Synthetic Streamflow Datasets Derived from DOE 9505 for Select Texas Basins

This dataset is generated using a Bayesian Hidden Markov Model trained on the DOE 9505 streamflow projection ensemble. A set of 21,000 streamflow realizations are generated for the Colorado, Sabine, and Trinity river basins in Texas. Separate models are trained either using the full 9505 ensemble or a subset based on three hyperparameters: bias correction, downscaling, and hydrological model.

drought↗
DOE OSTI2024

Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region

Abstract. Streamflow variability plays a crucial role in shaping the dynamics and sustainability of Earth's ecosystems, which can be simulated and projected by a river routing model coupled with a land surface model. However, the simulation of streamflow at large scales is subject to considerable uncertainties, primarily arising from two related processes: runoff generation (hydrological process) and river routing (hydraulic process). While both processes have impacts on streamflow variability, previous studies only calibrated one of the two processes to reduce biases in the simulated streamflow. Calibration focusing only on one process can result in unrealistic parameter values to compensate for the bias resulting from the other process; thus other water-related variables remain poorly simulated. In this study, we performed several experiments with the land and river components of the Energy Exascale Earth System Model (E3SM) over the Pantanal region to disentangle the hydrological and hydraulic controls on streamflow variability in coupled land–river simulations. Our results show that the generation of subsurface runoff is the most important factor for streamflow variability contributed by the runoff generation process, while floodplain storage effect and main-channel roughness have significant impacts on streamflow variability through the river routing process. We further propose a two-step procedure to robustly calibrate the two processes together. The impacts of runoff generation and river routing on streamflow are appropriately addressed with the two-step calibration, which may be adopted by developers of land surface and earth system models to improve the modeling of streamflow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies

Hydroelectric power generation in Western Canada significantly contributes to power grid operations of the North American Western Interconnection through substantial generation, some of which is exported to the United States (U.S.). However, the lack of publicly available hydropower generation datasets poses challenges for future market projections and resource adequacy evaluations. We present a simulation-based monthly power system model-ready hydropower generation dataset for 110 facilities in British Columbia and Alberta from 1981 to 2019. These monthly hydropower generation estimates are developed from integrated hydrologic model simulations of runoff and reservoir-operated streamflow, followed by scaling that considers diversion inflow constraints based on hydropower water license information. To address the lack of comparable hydropower generation records, we conduct step-by-step evaluations for simulated runoff, regulated streamflow, and hydropower generation using available observations or estimates. The presented hydropower dataset aims to enhance the representation of hydropower resources in Western Canada, supporting power grid system studies for the Western Interconnection of the U.S. and Canada.

13 HYDRO ENERGY↗
NASA NTRS2022

Leveraging Pre-storm Soil Moisture Estimates for Enhanced Land Surface Model Calibration in Ungauged Hydrologic Basins

Despite long-standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium-scale (200-10,000 km2) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre-storm) surface soil moisture (ASM) and the storm-scale runoff coefficient (RC; the fraction of storm-scale precipitation accumulation converted into streamflow). In humid and semi-humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm-scale RC observations (available only in basins that are both lightly regulated and gauged) with high-quality ASM values (available quasi-globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin-wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean-absolute error. Plain Text Summary Accurately forecasting the fraction of rainfall that runs off into streams, as opposed to infiltrates into the soil, is critical for flash-flood prediction, water-resource monitoring, and tracking the transport of nutrients from agricultural fields into local waterways. Such forecasting is typically performed by hydrologic models that attempt to represent the physical processes responsible for surface runoff generation. However, to provide accurate streamflow forecasts, these models typically need to be calibrated against actual streamflow observations. This is problematic given the relatively poor, and declining, global availability of stream gauges. This paper presents a novel model calibration strategy that uses soil moisture from remote sensing and numerical modeling in place of streamflow observations during calibration. This transition has significant practical advantages because, unlike streamflow observations, the soil moisture data are continuously available across space. Our results demonstrate that this new approach can significantly improve hydrologic models within humid and semi-humid basins lacking sufficient ground-based instrumentation for traditional streamflow calibration.

hydrologic models↗
DOE OSTI2026

Data From: "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater"

This repository contains the data and code associated with the paper titled "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater," published in Nature Geoscience, 2026. This study seeks to answer how various ages of groundwater interact with mountainous streamflow in mountainous headwaters such as the East River. It includes various model-data processing scripts, primarily for ParFlow-CLM analysis of simulated water years 2015-2021, and two numerical warming experiments (+2.5 and +4.0 degrees C), including run scripts, forcing scripts, and post-processing, as well as comparison to observation datasets, detailed below. This data requires the use of R (.r, .rmd), Python (.py), Jupyter Notebook or Jupyter Lab (.ipynb), ParFLOW-CLM, EcoSLIM. Further information on the use of all file formats mentioned below (e.g. .tff. .nc) are provided within the associated scripts and directory where the files are located. Contents & Usage ASO/: ​​Contains the bash and python scripts used to convert airborne snow observatory (ASO) data (ASO, 2023) in various data formats (georeferenced tiff file, NetCDF, UTM, and to latitude/longitude) then regrided to the ParFlow equivalent grid. Output data are in regrid_regll_data.zip and subsequently visualized and analyzed in plot_and_compare.py for Supplementary Figures A14 and A15. The wksht_ASO_comparison.xlsx spreadsheet is used to calculate the data for Supplementary Figure A16. EcoSLIM/: Contains the scripts and input files to run the EcoSLIM particle tracking simulations (/run_scripts) and the post-processing python script (/plot_scripts/eco_agedist_plots.ipynb). Jasechko et al./: Contains the jupyter notebook (Extract_Elevation.ipynb) to determine the outlet elevations of the 260 watersheds used in Jasechko et al. (2016), and the corresponding table, Table_S1_Watersheds_alt.csv. Used to create Supplementary Information Figure A2. PLM_Wells/: Contains the QA/QC-ed groundwater level time series of the PLM-1 and PLM-6 Monitoring Wells from Faybishenko et al. (2023), reformatted to water years used for Supplementary Figures A19 and and A20. ParFlow/: Contains the input files and run scripts to run ParFlow-CLM (/run_scripts), the python and tool command language (Tcl) scripts to create and distribute the ParFlow forcing simulation files (/forcing), and various scripts and intermediary files to analyze the model outputs (/post_process). SQUIRE/: Contains the processing scripts and intermediary files for the Surface QUantitatIve pRecipitation Estimation (SQUIRE) data (Grover, 2023) used to generate Supplementary Figure A18. USGS_Streamflow/: Contains the raw and gap-filled United States Geological Survey streamflow data (U.S. Geological Survey, 2026) used at the Almont station (site number 09112500). Gap-filling is performed in the R script with data from the Taylor station (site number 09110000). (/USGS_09112500_EAST_RIVER_AT_ALMONT_GAP_FILLED/code_almont_streamflow_gap_fill.Rmd). discharge/: Contains the gap-filled discharge data at the Watershed Function SFA East River pumphouse site (Newcomer et al., 2022) used to generate Supplementary Figure A13 and to compute hourly Nash-Sutcliffe model efficiency coefficients (NSE) in Table A4. snotel_and_flux_tower/: Contains the snow telemetry data (U.S. Department of Agriculture, 2024) from the Butte (site ID 380) and Schofield (site ID 737) stations, reformatted by water year, accessed with the snotelr R package. Used to create Supplementary Figure A17. Also contains the flux tower observational data (FluxTower_Pumphouse_ESS-DIVE.ET_only.h.txt) from Ryken et al. (2022) and sap flux transpiration data (MaxB_Transpiration_5Sites.daily_sums.h.txt) from Ryken (2021), used to create Supplementary Figures A22 and A23, respectively. Raw EcoSLIM model outputs are in excess of 24TB, and are stored on National Energy Research Scientific Computing Center (NERSC) and publicly available via the external link provided in the paper.

atmospheric warming↗
DOE OSTI2026

Model Data Archive for Manuscript Titled "Evaluation of a Coupled Surface–Subsurface Hydrologic Model Using Dense Water‑Level Sensors in a Mixed Urban–Rural Watershed"

This archive provides scripts, input files, and datasets used for the implementation and evaluation of a fully coupled surface–subsurface hydrologic model in the Neches River Basin, southeast Texas. The study uses the Advanced Terrestrial Simulator (ATS) to simulate coupled surface–subsurface hydrologic processes over a mixed urban–rural watershed and evaluates model performance using a dense network of 136 in situ water-level sensors, nine U.S. Geological Survey (USGS) stream gauges, and SSEBop-derived evapotranspiration estimates during the period October 2014–June 2024. The workflow is implemented primarily in Python 3 using the Watershed Workflow package. The Jupyter notebooks can be executed using open-source software such as Anaconda JupyterLab or Visual Studio Code. Other data files include TXT, CSV, XML, SHP, TIF, NetCDF, HDF5, and ExodusII files, which can be processed using the provided Python scripts. ATS input files are provided in XML format and can be edited using any commonly used text editor. This archive contains: *Scripts and input files used to generate the ATS model setup, including watershed discretization, mesh generation, parameter mapping, and model configuration. *Jupyter notebooks used for preprocessing observational data, evaluating streamflow, water levels, and evapotranspiration, computing performance metrics, and generating the figures presented in the manuscript. *ATS simulation outputs and processed observational datasets, including OneRain and DD6 water-level sensors, USGS streamflow observations, GIS data, and supporting spatial datasets used throughout the study.

Dense water-level sensor network↗
DOE OSTI2020

Climate change alters flood magnitudes and mechanisms in climatically-diverse headwaters across the northwestern United States

Abstract Flooding caused by high streamflow events poses great risk around the world and is projected to increase under climate change. This paper assesses how climate change will alter high streamflow events by changing both the prevalence of different driving mechanisms (i.e. ‘flood generating processes’) and the magnitude of differently generated floods. We present an analysis of simulated changes in high streamflow events in selected basins in the hydroclimatically diverse Pacific Northwestern United States, classifying the events according to their mechanism. We then compare how the different classes of events respond to changes in climate at the annual scale. In a warmer future, high flow events will be caused less frequently by snowmelt and more frequently by precipitation events. Also, precipitation-driven high flow events are more sensitive to increases in precipitation than are snowmelt-driven high flow events, so the combination of the increase in both frequency and magnitude of precipitation-driven high flow events leads to higher flood likelihood than under each change alone. Our comparison of the results from two emissions pathways shows that a reduction in global emissions will limit the increase in magnitude and prevalence of these precipitation-driven events.

S Chegwidden, Oriana (ORCID:0000000313763835)↗
NASA NTRS2015

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition↗
DOE OSTI2026

Measuring Climate and Water Risk across the Bulk Power System

As climate impacts increase and power systems transition to renewables, planners and operators need insights into climate risks to power generation and infrastructure to ensure reliable decision-making in the short and long-term. We present a standardized, consistent mechanism for utilities and system operators to evaluate the climate- and water-related risks of their current and future grid assets. Using a risk-based approach on the combined outcomes of high-fidelity climate drivers together with water and power system models, we examine the temperature and water availability impacts within the contiguous United States to power system assets at the water basin level in three different time periods and report resulting outcomes on lost capacity across different expansion scenarios and climate models. The results indicate that air temperature has the highest effect on derating. Changes in streamflow do not have a large impact on generation capacity at the national level. Electric sector buildout scenarios each have a unique regional risk profile, depending on the technology mix and total capacity, although risks from high temperatures are significant for both traditional and renewable energy generation. Stakeholders can use this approach to monitor effects of generation capacity losses and potential impacts as climate, generation mix, and infrastructure change.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian subcontinental river basins

Water management activities affect the terrestrial water cycle by increasing water supply and hence changing the partitioning of surface water and energy budgets over irrigated agricultural land, as well as modifying streamflow seasonality through reservoir operations (for irrigation, hydropower generation, and other purposes). Indian sub-continental basins are strongly influenced by human activities. However, the dynamics of how anthropogenic activities interact with the terrestrial water cycle have not been sufficiently studied over Indian sub-continental Basins. In this study, we applied a well-evaluated water management model, coupled with a large-scale land surface hydrological model with irrigation and reservoir operation schemes to examine the impacts of irrigation and reservoir operations on terrestrial water budgets over the Indian sub-continental basins. We conducted two simulations: one NATURAL (i.e., no irrigation and no reservoirs) and water management scenario (i.e., with irrigation and with reservoir regulations, referred to as MANAGED) over 18 basins from 1951 to 2012. The water management impacts were evaluated by comparing a number of key water and energy budget terms such as evapotranspiration (ET), total runoff (TR), land surface temperature (LST), reservoir storage, and discharge. Our results show that water management activities have significant (p-value < 0.05) impacts on ET and LST in most of the river basins during the pre-monsoon season (February-May). Comparing to the NATURAL scenario, ET under the MANAGED scenario is significantly increased in most basins during pre- and post-monsoon seasons especially in semi-arid regions, and LST is reduced predominately in the pre-monsoon season when irrigation water demand is high.

Shah, Harsh L.↗
DOE OSTI2024

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

13 HYDRO ENERGY↗