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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data and scripts associated with the manuscript evaluating the hydrologic responses of the Pacific Northwest watersheds to wildfires (v2)

This data package is associated with the publication “Evaluating Post-fire Watershed Response to Varying Burn Severity and Precipitation Regimes Using Fully-distributed and Integrated Hydrologic Models” submitted to Journal of Hydrology (Li et al. 2025). In this study, we employed the Advanced Terrestrial Simulator (ATS), an integrated watershed model that couples surface flow, subsurface flow, and canopy biophysical processes, to investigate post-fire hydrologic responses in a few selected watersheds with varying burn severity.The data package contains the required input data (meteorological forcing, Leaf Area Index, wildfire burn severities, etc.) to run the model, configuration files, the Jupyter notebooks in Python to pre-process and post-process data, the figures in the manuscript, and the modeling output files. The variables include watershed-averaged evapotranspiration, watershed-averaged surface/subsurface/canopy water content, and river discharge at watershed outlet.The data package contains a file-level metadata that lists and describes all the files contained in the data package (ATS_flmd.csv), a data dictionary file that defines columns headers across all csv files contained in the data package (ATS_dd.csv), a data package level readme file (the current file), and four zipped folders.The ‘data’ folder provides data needed to run the model in .h5, .i2s, .xyz, .shp, and .exo formats. The sub-folders are for each data types. The ‘model’ folder provides input files (.xml format) and essential model outputs. Each sub-folder provides the files from each simulated watershed. The ‘notebooks’ folder provides the Jupyter notebooks (.ipynb format) for pre- and post- processing model files, and for producing the figures in the manuscript. The ‘figures’ folder provides the figures associated with manuscript in .pdf and .png formats.The ‘model’ folder and the ‘data’ folder have been split into 5GB-large pieces using the Linux command ‘split -b 5120m model.zip model.zip.’ and ‘split -b 5120m data.zip data.zip.’, respectively. They can be merged back using the Linux command ‘cat model.zip.* > model.zip’ and ‘cat data.zip.* > data.zip’, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Continental Scale Hydrostratigraphy: Comparing Geologically Informed Data Products to Analytical Solutions

Abstract This study synthesizes two different methods for estimating hydraulic conductivity (K) at large scales. We derive analytical approaches that estimate K and apply them to the contiguous United States. We then compare these analytical approaches to three‐dimensional, national gridded K data products and three transmissivity (T) data products developed from publicly available sources. We evaluate these data products using multiple approaches: comparing their statistics qualitatively and quantitatively and with hydrologic model simulations. Some of these datasets were used as inputs for an integrated hydrologic model of the Upper Colorado River Basin and the comparison of the results with observations was used to further evaluate the K data products. Simulated average daily streamflow was compared to daily flow data from 10 USGS stream gages in the domain, and annually averaged simulated groundwater depths are compared to observations from nearly 2000 monitoring wells. We find streamflow predictions from analytically informed simulations to be similar in relative bias and Spearman's rho to the geologically informed simulations. R ‐squared values for groundwater depth predictions are close between the best performing analytically and geologically informed simulations at 0.68 and 0.70 respectively, with RMSE values under 10 m. We also show that the analytical approach derived by this study produces estimates of K that are similar in spatial distribution, standard deviation, mean value, and modeling performance to geologically‐informed estimates. The results of this work are used to inform a follow‐on study that tests additional data‐driven approaches in multiple basins within the contiguous United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Role of Bedrock Circulation Depth and Porosity in Mountain Streamflow Response to Prolonged Drought

Quantitative understanding is lacking on how the depth of active groundwater circulation in bedrock affects mountain streamflow response to a multi-year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage-limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022

This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Groundwater and Surface Water Flow (GSFLOW) model files to explore bedrock circulation depth and porosity in Copper Creek, Colorado

This data package contains integrated hydrological model input and output files for Copper Creek, Colorado (24 km2), a tributary of the East River located in the headwaters of the Upper Colorado River Basin. The model code is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution and a daily timestep. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model contains 12 model layers and extends 400 m below land surface. The original Copper Creek model was modified to contain geologic layers representing saprolite, shallow bedrock, and deep bedrock. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. For the shallow case, median flow depths occur in the shallow saprolite at depths <8 m, while the deep case promotes a median groundwater flow depth of 100 m. With this modeling framework we compare streamflow response to a plausible worst-case drought lasting up to five years. Streamflow metrics of analysis include average streamflow, fraction of stream network that is dry, no-flow duration, average groundwater flow to streams and time to recovery following the drought. Results and implications are presented in a paper submitted to Geophysical Research Letters titled, "The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought" by Rosemary WH. Carroll, Andrew H. Manning and Kenneth H Williams. A Readme.txt file provides instructions on how to download all model files and execute each model scenario. In addition to the GSFLOW output/prms/copper_drought.csv file containing daily basin water stores and fluxes (refer to GSFLOW manual) and the output/prms/copper_drought_statvar.dat file with output defined in the gsflow3.control file (refer to GSFLOW Manual), output files also include spatially distributed daily values of total evapotranspiration, canopy evaporation, precipitation, snowfall, infiltration, snow water equivalent, potential evapotranspiration, recharge, sublimation, soil moisture, contributing interflow, water table elevations, changes in groundwater storage, groundwater evapotranspiration, interbasin groundwater flow (limited to the alluvium below the stream outlet), and surface-groundwater exchanges within the river system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Model Scripts for "Old-Aged Groundwater Contributes to Mountain Hillslope Hydrologic Dynamics"

The partitioning of water inputs between deep and shallow groundwater flow paths is a fundamental processes, yet is challenging to observe. Numerical models provide a valuable tool to further develop insights on these groundwater mixing processes. This repository contains scripts to run the ParFlow-CLM and EcoSLIM integrated hydrologic models along the Pumphouse Hillslope in the East River Watershed and associated python scripts to process model outputs. Files includes input decks for the associated models, python scripts, and text and css files to support the model runs and processing. The 2-D hillslope model simulates from 2000 to 2021 using transient forcing conditions from the publicly available NLDAS-2 dataset. We develop an ensemble of models with variable hydrogeologic and soil parameters. Model outputs from the ensemble of runs are compared to to water level observations at the PLM1 and PLM6 wells published at (https://data.ess-dive.lbl.gov/view/doi:10.15485/1866836) and groundwater mean ages published at (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1960042. The model results are used to evaluate hillslope water mass-balance transience and mixing dynamics between groundwater flow paths with young (<10 year) and old (>10 year) ages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Declining groundwater storage expected to amplify mountain streamflow reductions in a warmer world

Abstract Groundwater interactions with mountain streams are often simplified in model projections, potentially leading to inaccurate estimates of streamflow response to climate change. Here, using a high-resolution, integrated hydrological model extending 400 m into the subsurface, we find groundwater an important and stable source of historical streamflow in a mountainous watershed of the Colorado River. In a warmer climate, increased forest water use is predicted to reduce groundwater recharge resulting in groundwater storage loss. Losses are expected to be most severe during dry years and cannot recover to historical levels even during simulated wet periods. Groundwater depletion substantially reduces annual streamflow with intermittent conditions predicted when precipitation is low. Expanding results across the region suggests groundwater declines will be highest in the Colorado Headwater and Gunnison basins. Our research highlights the tight coupling of vegetation and groundwater dynamics and that excluding explicit groundwater response to warming may underestimate future reductions in mountain streamflow.

Carroll, Rosemary W. H. (ORCID:0000000293028074)↗
DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

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↗
DOE OSTI2023

The Role of Topography in Controlling Evapotranspiration Age

Abstract Evapotranspiration (ET) age is a key metric of water sustainability but a major unknown partly due to the extreme difficulty in modeling it. Groundwater is found to be important in ET age variations in small‐scale studies, yet our understanding is insufficient because groundwater systems are nested across scales. Here, we conducted GPU‐accelerated particle tracking with integrated hydrologic modeling to quantify the variations in ET age at a regional scale of ∼0.4 M km 2 . Simulation results reveal topography‐driven flow paths shaping the spatial and temporal patterns of ET age variations. On ridges, where root zone decoupling with deep subsurface storage, ET age is generally young, with seasonal variations dominated by meteorological conditions. In the valley bottom, ET age is generally old, with significant subseasonal variations caused by the convergence of subsurface flow paths. On hillslopes with water table depths ranging from 1 to 10 m, ET age shows strong seasonal variations caused by the connections with lateral groundwater regulated by ET demand. Our modeling approach provides insights into the basic linkages between ET age and topography at large scale. Our work highlights the perspective of multiscale studies of ET age, suggesting new field experiments to test these process connections and to determine if such linkages warrant inclusion in Earth System Models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Continental Scale Hydrostratigraphy: Basin‐Scale Testing of Alternative Data‐Driven Approaches

Abstract Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data‐driven approaches for estimating hydraulic conductivity and subsurface properties at the continental‐scale, constructed from existing subsurface dataset components. Each subsurface configuration represents upper (unconfined) hydrogeology, lower (confined) hydrogeology, and the presence of a vertical flow barrier. Configurations are tested in two large‐scale U.S. watersheds using an integrated model. Model results are compared to observed streamflow and steady state water table depth (WTD). We provide model results for a range of configurations and show that both WTD and surface water partitioning are important indicators of performance. We also show that geology data source, total subsurface depth, anisotropy, and inclusion of a vertical flow barrier are the most important considerations for subsurface configurations. While a range of configurations proved viable, we provide a recommended Selected National Configuration 1 km resolution subsurface dataset for use in distributed large‐and continental‐scale hydrologic modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Mountain Basin Controls on the Snow-to-Streamflow Signal: An AIC-Weighted Multiple Linear Regression Framework

A regression-based analysis quantifies how basin characteristics modulate the snow-to-streamflow signal. First, we use the ERA5-Land reanalysis gridded product (European Centre for Medium Range Weather Forecasts reanalysis 5 -Land component) for 4,655 hydrologic unit code - 10 (HUC10) mountain basins across the western United States (US) for water years 1987–2024. Linear regressions are performed for peak snow water equivalent (SWE) and annual streamflow for each mountain basin. Models use ordinary least squares in Python’s statsmodels package. After which, an Akaike Information Criterion (AIC)–weighted ensemble multiple linear regression (MLR) framework with 47 watershed traits is used to predict the linear regression coefficient of determination (r-squared) defining the ability of peak SWE to predict annual streamflow across all mountain basin. Predictor sets are constrained to avoid multicollinearity by excluding models with variance inflation factors (VIF) greater than 5. Mountain basin traits included in the MLR include seasonal climate, topography, vegetation type and structure, and bedrock geology. Accepted models are considered if their AIC is within 2.0 of the model with the minimum AIC, or best model. To compare predictor influence across acceptable models, we computed standardized regression coefficients. To evaluate structural redundancy among models, we constructed binary inclusion vectors for each acceptable model, denoting whether a predictor was present (1) or absent (0). Core predictor variables are defined as occurring in at least 67% of the acceptable models. For this regional analysis, only one model was found acceptable, with higher snow-to-streamflow translation (higher r-squared) occurring in colder mountain basins with higher relative winter precipitation, more snow accumulation and a lower fraction of annual precipitation that falls in the spring and summer. The second component of the data package uses previously published, high-resolution output from an integrated hydrological model of the East River watershed using the U.S. Geological Survey Groundwater and Surface water Flow model (GSFLOW, doi:10.15485/1998576). East River MLR expands upon the approach described above to explore the response of five streamflow metrics—annual streamflow, runoff efficiency, 7-day minimum flow, low-flow duration, and non-perennial stream fraction to snow system indicators including peak SWE, snow-covered area, snow disappearance date, and the fraction of basin area characterized by low-to-no snow, as well as seasonal precipitation and temperature, and annual hydrologic variables representing soil moisture, evapotranspiration (ET), the partitioning of incoming precipitation to evapotranspiration (ET/P), groundwater storage, and groundwater inflow to streams. MLR was done on all water years (P0: 1987-2024) and for each period as determined in the split analysis using pooled regression techniques (P1: 1987-2011 and P2: 2012-2024) to evaluate shifting predictor variable emphasis on streamflow generation. Results indicate that since 2012, peak SWE has lost statistical strength in its prediction of annual streamflow and runoff efficiency, and the indirect influence of spring temperature has emerged as critically important. Low-flow metrics remain largely influenced by soil moisture, vegetation water use and groundwater inflows with summer precipitation becoming a direct influence on minimum summer flow. Together, these data and Python-based analysis tools provide a framework for identifying the key watershed characteristics that control how streamflow responds to snow from year to year. The package also helps quantify uncertainty in statistical models and assess how snow–streamflow relationships vary across regions and over time. This dataset contains comma-separated values files (.csv), text files (.txt), python code files (.py), figure files (.png), and shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .xml). Further details on file contents and MLR execution can be found in the readme file and the FLMD files. Work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Detecting Coastal Wetland Degradation by Combining Remote Sensing and Hydrologic Modeling

Sea-level rise and climate change stresses pose increasing threats to coastal wetlands that are vital to wildlife habitats, carbon sequestration, water supply, and other ecosystem services with global significance. However, existing studies are limited in individual sites, and large-scale mapping of coastal wetland degradation patterns over a long period is rare. Our study developed a new framework to detect spatial and temporal patterns of coastal wetland degradation by analyzing fine-scale, long-term remotely sensed Normalized Difference Vegetation Index (NDVI) data. Then, this framework was tested to track the degradation of coastal wetlands at the Alligator River National Wildlife Refuge (ARNWR) in North Carolina, United States, during the period from 1995 to 2019. We identified six types of coastal wetland degradation in the study area. Most of the detected degradation was located within 2 km from the shoreline and occurred in the past five years. Further, we used a state-of-the-art coastal hydrologic model, PIHM-Wetland, to investigate key hydrologic processes/variables that control the coastal wetland degradation. The temporal and spatial distributions of simulated coastal flooding and saltwater intrusion confirmed the location and timing of wetland degradation detected by remote sensing. The combined method also quantified the possible critical thresholds of water tables for wetland degradation. The remote sensing–hydrologic model integrated scheme proposed in this study provides a new tool for detecting and understanding coastal wetland degradation mechanisms. Our study approach can also be extended to other coastal wetland regions to understand how climate change and sea-level rise impact wetland transformations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Scalable deep learning for watershed model calibration

Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R 2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Warming and snow loss increase reliance on old groundwater in a Colorado River headwater

Atmospheric warming is reducing snowpack, with uncertain effects on mountainous streamflow, a crucial water resource. Despite limited historical observations of groundwater–streamflow interactions above 2,500 m, new measurements in the Upper Colorado River headwaters indicate declining groundwater storage that is dated decades to millennia old. Here we use integrated hydrologic modelling spanning water years 2015–2021 to determine whether the loss of old-age groundwater buffers streamflow during low-snow years and whether that loss is exacerbated with warming. Results show that old-groundwater contributions to streams remain relatively steady through time, unlike the more variable contributions from young groundwater. Numerical experiments of increased surface air temperatures (+2.5 °C and +4 °C) increase rain–snow fractions and evapotranspiration and decrease runoff ratio by 2–3% per degree Celsius increase. As streamflow declines with warming, the age of groundwater supporting it gets older, in part owing to intermediate-aged (1–3 year) groundwater declining twice as fast. Simulations show that water table depths at higher elevations (>3,700 m) decline disproportionately and fail to recover even during wet years. These findings suggest altered groundwater–streamflow interactions with warming and snow loss, with implications for water resources.

Siirila-Woodburn, Erica R. [Lawrence Berkeley Nati↗
DOE OSTI2022

A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019

Abstract This article presents a hydrological reconstruction of the Upper Colorado River Basin with an hourly temporal resolution, and 1-km spatial resolution from October 1982 to September 2019. The validated dataset includes a suite of hydrologic variables including streamflow, water table depth, snow water equivalent (SWE) and evapotranspiration (ET) simulated by an integrated hydrological model, ParFlow-CLM. The dataset was validated over the period with a combination of point observations and remotely sensed products. These datasets provide a long-term, natural-flow, simulation for one of the most over-allocated basins in the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Projecting end-of-century climate extremes and their impacts on the hydrology of a representative California watershed

Abstract. In California, it is essential to understand the evolution of water resources in response to a changing climate to sustain its economy and agriculture and to build resilient communities. Although extreme conditions have characterized the historical hydroclimate of California, climate change will likely intensify hydroclimatic extremes by the end of the century (EoC). However, few studies have investigated the impacts of EoC extremes on watershed hydrology. We use cutting-edge global climate and integrated hydrologic models to simulate EoC extremes and their effects on the water-energy balance. We assess the impacts of projected driest, median, and wettest water years under Representative Concentration Pathway (RCP) 8.5 on the hydrodynamics of the Cosumnes River basin. Substantial changes to annual average temperature (>+2.5 ∘C) and precipitation (>+38 %) will characterize the EoC extreme water years compared to their historical counterparts. A shift in the dominant form of precipitation, mostly in the form of rain, is projected to fall earlier. These changes reduce snowpack by more than 90 %, increase peak surface water and groundwater storages up to 75 % and 23 %, respectively, and drive the timing of peak storage to occur earlier in the year. Because EoC temperatures and soil moisture are high, both potential and actual evapotranspiration (ET) increase. The latter, along with the lack of snowmelt in the warm EoC, causes surface water and groundwater storages to significantly decrease in summer, with groundwater showing the highest rates of decrease. These changes result in more ephemeral EoC streams with more focused flow and increased storage in the mainstem of the river network during the summer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Estimating Future Surface Water Availability Through an Integrated Climate‐Hydrology‐Management Modeling Framework at a Basin Scale Under CMIP6 Scenarios

Abstract Climate change and increasing water demand due to population growth pose serious threats to surface water availability. The biggest challenge in addressing these threats is the gap between climate science and water management practices. Local water planning often lacks the integration of climate change information, especially with regard to its impacts on surface water storage and evaporation as well as the associated uncertainties. Using Texas as an example, state and regional water planning relies on the use of reservoir “Firm Yield” (FY)—an important metric that quantifies surface water availability. However, this existing planning methodology does not account for the impacts of climate change on future inflows and on reservoir evaporation. To bridge this knowledge gap, an integrated climate‐hydrology‐management (CHM) modeling framework was developed, which is generally applicable to river basins with geographical, hydrological, and water right settings similar to those in Texas. The framework leverages the advantages of two modeling approaches—the Distributed Hydrology Soil Vegetation Model (DHSVM) and Water Availability Modeling (WAM). Additionally, the Double Bias Correction Constructed Analogues method is utilized to downscale and incorporate Coupled Model Intercomparison Project Phase 6 GCMs. Finally, the DHSVM simulated naturalized streamflow and reservoir evaporation rate are input to WAM to simulate reservoir FY. A new term—“Ratio of Firm Yield” (RFY)—is created to compare how much FY changes under different climate scenarios. The results indicate that climate change has a significant impact on surface water availability by increasing reservoir evaporation, altering the seasonal pattern of naturalized streamflow, and reducing FY.

54 ENVIRONMENTAL SCIENCES↗