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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An Alternative Ensemble Streamflow Prediction Approach Using Improved Subseasonal Precipitation Forecasts from the North America Multi-Model Ensemble Phase II

In this article, streamflow forecasting at a subseasonal time scale (10–30 days into the future) is important for various human activities. The ensemble streamflow prediction (ESP) is a widely applied technique for subseasonal streamflow forecasting. However, ESP’s reliance on the randomly resampled historical precipitation limits its predictive capability. Available dynamical subseasonal precipitation forecasts provide an alternative to the randomly resampled precipitation in ESP. Prior studies found the predictive performance of raw subseasonal precipitation forecast is limited in many regions such as the central south of the United States, which raises questions about its effectiveness in assisting streamflow forecasting. To further assess the hydrologic applicability of dynamical subseasonal precipitation forecasts, we test the subseasonal precipitation forecast from North America Multi-Model Ensemble Phase II (NMME-2) at four watersheds in the central south region of the United States. The subseasonal precipitation forecasts are postprocessed with bias correction and spatial disaggregation (BCSD) to correct bias and improve spatial resolution before replacing the randomly resampled precipitation in ESP for streamflow predictions. The performance of the resulting streamflow predictions is benchmarked with ESP. Evaluation is conducted using Kling–Gupta Efficiency (KGE), continuous ranked probability score (CRPS), probability of detection (POD), false alarm ratios (FARs), as well as reliability diagrams. Our results suggest that BCSD-corrected subseasonal precipitation forecasts lead to overall improved streamflow predictions due to added skills in winter and spring. Our results also suggest that BCSD-corrected subseasonal precipitation forecasts lead to improved predictions on the occurrence of high-percentile streamflow values above 75%. Overall, BCSD-corrected subseasonal precipitation has shown promising performance, highlighting its potential broader applications for river and flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km^2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology.

soil moisture↗
NASA NTRS2009

Contribution of Soil Moisture Information to Streamflow Prediction in the Snowmelt Season: A Continental-Scale Analysis

In areas dominated by winter snowcover, the prediction of streamflow during the snowmelt season may benefit from three pieces of information: (i) the accurate prediction of weather variability (precipitation, etc.) leading up to and during the snowmelt season, (ii) estimates of the amount of snow present during the winter season, and (iii) estimates of the amount of soil moisture underlying the snowpack during the winter season. The importance of accurate meteorological predictions and wintertime snow estimates is obvious. The contribution of soil moisture to streamflow prediction is more subtle yet potentially very important. If the soil is dry below the snowpack, a significant fraction of the snowmelt may be lost to streamflow and potential reservoir storage, since it may infiltrate the soil instead for later evaporation. Such evaporative losses are presumably smaller if the soil below the snowpack is wet. In this paper, we use a state-of-the-art land surface model to quantify the contribution of wintertime snow and soil moisture information -- both together and separately -- to skill in forecasting springtime streamflow. We find that soil moisture information indeed contributes significantly to streamflow prediction skill.

Reichle, Rolf↗
DOE OSTI2026

Flow Forecasting on New England’s Great River Hydro (CRADA 549) (Final Report)

Great River Hydro (GRH) is New England's largest conventional hydropower generator and operates sustainable hydropower systems in Vermont, New Hampshire, and Massachusetts. Several of GRH’s hydropower projects on the Connecticut River are largely constrained to operate as run-of-river; however, though a new FERC license they have opportunity in the form of flex hours where they are allowed to deviate operations to capture favorable market conditions or emergency operations. To maximize value from these new operations, GRH is considering new sources of forecasted inflow. Pacific Northwest National Laboratory (PNNL) is evaluating the quality of two commercial inflow forecasts for GRH under DOE Water Power Technology Office’s Notice of Opportunity for Technical Assistance: Improving Hydropower’s Value through Informed Decision Making. This project will provide an unbiased evaluation of the accuracy of inflow forecasts available through commercial vendors. GRH will use this information to support their decision on which inflow forecast to use under their new FERC license and to assess the overall value of the available inflow forecasting tools and practices. In this talk we will discuss details of the near real-time forecast evaluation process, the metrics involved in the evaluation, and some preliminary results. In addition, we’ll touch on the second phase of the project which involves evaluating scheduling strategies that identify windows of opportunity to change operations in ways that increase profit through participation in day ahead and real time markets.

13 HYDRO ENERGY↗
NASA NTRS1974

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

The author has identified the following significant results. The area of snowcover on 10 individual drainage basins in the North Cascades, Washington, has been determined by use of a semi-automatic radiance threshold technique. The result is a unique record of the changing water storage as snow in these important hydrologic units, the runoff of which is utilized for hydroelectric power, dilution of wastes and heat, support of salmon migration, and irrigation. These data allow a new type of hydrologic modelling to proceed which should permit more accurate forecasts of streamflow. A new technique has been developed for measuring snow-covered area or snowline altitude semi-automatically. This variable contour overlay method permits the snowcover to be matched efficiently to the best fit contour of altitude. The motion of the Yentna Glacier during the concluding phase of its surge was successfully measured by a flicker technique using images of two dates. It appears that displacements as small as 100 to 200 m can be measured. Motion of the Tweedsmuir Glacier in Alaska was measured using ERTS-1 images enlarged to 1:50,000. Changes detected included a shock wave moving down the glacier, the margin expanding, the moraine pattern deforming, and the marginal valley deepening.

Meier, M. F.↗
NASA NTRS1974

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

The author has identified the following significant results. Snowlines on a small (6 sq km) drainage basin were accurately measured without use of digital processing, and snow patches as small as 150 m (maximum dimension) were correctly identified, proving that the resolution of ERTS is ample for snow mapping needs. The area of snow cover on 10 individual drainage basins in the North Cascades, Washington, has been determined at 12 different times; these data can be used for more accurate forecasts of streamflow. Progress has been made in distinguishing snow in trees using multispectral analysis. Motion of the surging Tweedsmuir Glacier was measured. Velocities ranged from 2 to 88 m per day; a zone of intense crevassing also appeared to spread up and down the glacier (at about 200 m per day upglacier). This tentative result may be of great importance to an understanding of surging glacier dynamics. ERTS images also show that the most recent debris flow (20-21 August 1973) from Mount Baker can be clearly discerned and mapped, in order to monitor this potential hazard.

Meier, M. F.↗
NASA NTRS1980

Evolution of snow sensors and the potential of microwave devices in operational telemetry networks

Both manually collected snow-course data and telemetered information from SNOTEL sites throughout the western United States are used to make forecasts of streamflow. Although these conventional methods have proven highly reliable over the years, they still exhibit some shortcomings. Active and passive microwave remote sensing systems recently developed and tests present the potential to eliminate some negative aspects associated with other types of sensors currently in use. Because of their ability to see through the snowpack they offer a unique opportunity to improve snowpack measurement techniques. As yet neither the active nor passive system is developed sufficiently to supplant existing methods in operational telemetry networks. Active microwave sensors are the most promising for the near future and are being used in research and development programs to study snow pillow performance in the SNOTEL system.

Shafer, B. A.↗
NASA NTRSDate not supplied

Lessons learned from replicating services for flood prediction and monitoring in Asia to the assessment of hurricane impacts in Central America

In October and November 2020, two dangerous back-to-back hurricanes, Eta and Iota, made landfall in Central America. The SERVIR program - a joint effort of NASA and the U.S. Agency for International Development, and whose motto is “connecting space to village” was able to leverage two tools originally developed for use in other regions for predicting and assessing the flood impacts of the hurricanes. The GEOGLoWS Streamflow Prediction tool - originally implemented in the Hindu Kush Himalayan region - was used for predicting potential flooding ahead of landfall by Eta and Iota. In conjunction, the Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods) framework - originally developed along with SERVIR-Mekong - was used for post-event flood mapping, leveraging its ability to map floods in cloud-covered areas using synthetic aperture radar (SAR) imagery from the Copernicus program. Both tools were used in support of disaster coordination efforts being led by the Central American Regional Disaster Prevention Center (CEPREDENAC), in conjunction with its sister agency, the Regional Water Resources Committee (CRRH). The support provided to regional entities - and to their stakeholder national governments - served as an example of rapid generation of Earth observation products for disaster response. Feedback on those products was also provided, especially in terms of the implications of (i) calibration of predicted river volumes, and (ii) the latency of the input Earth observation imagery and attempts to map the floods’ maximum extents. An upcoming NASA DEVELOP project will also seek to strengthen the capability of CEPREDENAC and CRRH to apply HYDRAFloods for future extreme events. The application of the tools also provides a useful case study on capacity building, in terms of how Earth observation data and models can be replicated across regions.

Capacity building↗
NASA NTRSDate not supplied

NASA SPoRT’s Streamflow-AI: Updates and Advancements

The NASA Short-term Prediction Research and Transition Center (SPoRT) has been running a near-real-time deep learning model, Streamflow-AI, that predicts stream heights at over 250 locations across the Eastern United States out to 7 days for several years. This project was born out of a research-to-operations/operation-to-research (R2O/O2R) paradigm within SPoRT through extensive collaboration with National Weather Service (NWS) and River Forecast Center (RFC) forecasters. The original intention of Streamflow-AI was to provide accurate medium range (>2 days) forecasts of river flooding events that are particularly challenging for NWS forecasters, because routine streamflow forecasts may not incorporate quantitative precipitation forecast (QPF) data beyond one or two days. Therefore, Streamflow-AI was designed to help fill the operational gap, producing routine 7 day river level forecasts using a suite of QPFs. Throughout the evolution of the project, research efforts have been tailored to meet the needs of the end users. This presentation will provide a brief overview of NASA SPoRT’s Streamflow-AI product, highlighting O2R/R2O concepts with a focus on recent developments including a 1-hr rapid update model and 15-min burn scar flooding modeling efforts.

Flooding↗
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↗
NASA NTRS2011

NASA Water Resources Program

With increasing population pressure and water usage coupled with climate variability and change, water issues are being reported by numerous groups as the most critical environmental problems facing us in the 21st century. Competitive uses and the prevalence of river basins and aquifers that extend across boundaries engender political tensions between communities, stakeholders and countries. In addition to the numerous water availability issues, water quality related problems are seriously affecting human health and our environment. The potential crises and conflicts especially arise when water is competed among multiple uses. For example, urban areas, environmental and recreational uses, agriculture, and energy production compete for scarce resources, not only in the Western U.S. but throughout much of the U.S. and also in numerous parts of the world. Mitigating these conflicts and meeting water demands and needs requires using existing water resources more efficiently. The NASA Water Resources Program Element works to use NASA products and technology to address these critical water issues. The primary goal of the Water Resources is to facilitate application of NASA Earth science products as a routine use in integrated water resources management for the sustainable use of water. This also includes the extreme events of drought and floods and the adaptation to the impacts from climate change. NASA satellite and Earth system observations of water and related data provide a huge volume of valuable data in both near-real-time and extended back nearly 50 years about the Earth's land surface conditions such as precipitation, snow, soil moisture, water levels, land cover type, vegetation type, and health. NASA Water Resources Program works closely to use NASA and Earth science data with other U.S. government agencies, universities, and non-profit and private sector organizations both domestically and internationally. The NASA Water Resources Program organizes its projects under five functional themes. I) Streamflow and Flood Forecasting 2) Water Supply and Irrigation (includes evapotranspiration) 3) Drought 4) Water Quality 5) Climate and Water Resources. To maximize this activity NASA Water Resources Program works closely with other government agencies (e.g., the National Oceanic and Atmospheric Administration (NOAA); the U.S. Department of Agriculture (USDA); the U.S. Geological Survey (USGS); the Environmental Protection Agency (EPA), USAID, the Air Force Weather Agency (AFWA)), universities, non-profit national and international organizations, and the private sector. The NASA Water Resources program currently is funding 21 active projects under the functional themes (http://wmp.gsfc.nasa.gov & http://science.nasa.gov/earth-science/applied-sciences/).

Toll, David L.↗
DOE OSTI2021

Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS

With recent developments in computational techniques, Data-driven Machine Learning Models (DMLs) have shown great potential in simulating streamflow and capturing the rainfall-runoff relationship in given watersheds, which are traditionally fulfilled by Process-based Hydrologic Models (PHMs). There are debates on whether the DMLs can outperform and possibly replace the classical PHMs for streamflow simulation and river forecasting, but no clear conclusions have been made. This study aims to investigate whether the newer DMLs have any potential in further improving the simulation accuracy of classical PHMs, and vice versa. To do this, we compared a few popular PHMs and DMLs over four watersheds across the Continental US (CONUS) that are associated with different input, climate, and regional conditions. A total of five hydrologic models were chosen, including (1) two classical lumped models, i.e., the Sacramento Soil Moisture Accounting (SAC-SMA) and Xinanjiang (XAJ); (2) one modern distributed model, termed Coupled Routing and Excess Storage (CREST); (3) and two DMLs including an Artificial Neural Networks (ANN) and a deep learning model, termed Long Short Term Memory (LSTM). Our results demonstrated that the DMLs still significantly biased when using the baseline input scenario with the PHMs. However, the DMLs fed with delayed input scenarios had great potential and can reach high simulation accuracy. The DMLs, especially the ANN, outperformed other employed models under the rainfall-runoff relationship in which rainfall dominantly drives. Furthermore, the DMLs also showed better performance in the high-flow regime, while the PHMs had a better performance for the low-flow regime, implying both PHMs and DMLs have their own merits and are worthy of joint development. In general, our study indicated a great potential of using DMLs to simulate streamflow, but further studies are still needed to verify the transferability and scalability of DMLs in large-scale experiments, such as the Distributed Model Intercomparison Projects 1&2 conducted by National Weather Services but to compare modern DMLs and PHMs.

58 GEOSCIENCES↗
DOE OSTI2026

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗
NASA NTRS1975

Utilization of LANDSAT monitoring capabilities for snowcover depletion analysis

LANDSAT images for three snowmelt seasons were utilized to map and analyze snowcover depletion on a small river basin in southeastern Wyoming. Results indicate that snowcover-runoff curves established from repetitive LANDSAT coverage may be used in conjunction with streamflow data to provide low-cost seasonal runoff forecasts having a high degree of accuracy. Additionally, detectable variations within a snowpack might provide temporal estimates of peak flows.

Thompson, A. G.↗
NASA NTRS2011

Predicting Hydrological Drought: Relative Contributions of Soil Moisture and Snow Information to Seasonal Streamflow Prediction Skill

in this study we examine how knowledge of mid-winter snow accumulation and soil moisture conditions contribute to our ability to predict streamflow months in advance. A first "synthetic truth" analysis focuses on a series of numerical experiments with multiple sophisticated land surface models driven with a dataset of observations-based meteorological forcing spanning multiple decades and covering the continental United States. Snowpack information by itself obviously contributes to the skill attained in streamflow prediction, particularly in the mountainous west. The isolated contribution of soil moisture information, however, is found to be large and significant in many areas, particularly in the west but also in region surrounding the Great Lakes. The results are supported by a supplemental, observations-based analysis using (naturalized) March-July streamflow measurements covering much of the western U.S. Additional forecast experiments using start dates that span the year indicate a strong seasonality in the skill contributions; soil moisture information, for example, contributes to kill at much longer leads for forecasts issued in winter than for those issued in summer.

Koster, R.↗
NASA NTRS2010

The Contribution of Soil Moisture Information to Forecast Skill: Two Studies

This talk briefly describes two recent studies on the impact of soil moisture information on hydrological and meteorological prediction. While the studies utilize soil moisture derived from the integration of large-scale land surface models with observations-based meteorological data, the results directly illustrate the potential usefulness of satellite-derived soil moisture information (e.g., from SMOS and SMAP) for applications in prediction. The first study, the GEWEX- and ClIVAR-sponsored GLACE-2 project, quantifies the contribution of realistic soil moisture initialization to skill in subseasonal forecasts of precipitation and air temperature (out to two months). The multi-model study shows that soil moisture information does indeed contribute skill to the forecasts, particularly for air temperature, and particularly when the initial local soil moisture anomaly is large. Furthermore, the skill contributions tend to be larger where the soil moisture initialization is more accurate, as measured by the density of the observational network contributing to the initialization. The second study focuses on streamflow prediction. The relative contributions of snow and soil moisture initialization to skill in streamflow prediction at seasonal lead, in the absence of knowledge of meteorological anomalies during the forecast period, were quantified with several land surface models using uniquely designed numerical experiments and naturalized streamflow data covering mUltiple decades over the western United States. In several basins, accurate soil moisture initialization is found to contribute significant levels of predictive skill. Depending on the date of forecast issue, the contributions can be significant out to leads of six months. Both studies suggest that improvements in soil moisture initialization would lead to increases in predictive skill. The relevance of SMOS and SMAP satellite-based soil moisture information to prediction are discussed in the context of these studies.

Koster, Randal↗
DOE OSTI2026

Data and script associated with “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest”

This data package contains the data and code to use and run the Water Tracer enabled version of the Weather Research and Forecasting Hydrologic model (WT-WRF-Hydro) with the Sequential Precipitation Input Tagging (SPIT) framework. It is associated with the publication “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest” published in Scientific Reports (Butler et al., 2026; https://doi.org/10.1038/s41598-026-46539-1). We use the Continental U.S. (CONUSII; Rasmussen et al., 2021) dataset to force the model with an historical climate (2006–2013) and a future climate (2086–2093) with a representative carbon pathway (RCP) 8.5 scenario. We use the model to calculate water transit times in five headwater catchments within the U.S. Pacific Northwest. We also show key hydrologic and environmental variables that affect water transit times and changes in the future. Finally, we use observed data to validate the model such as stream water isotopes, snowpack characteristics, and stream discharge. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. The data package consists of 11 folders: (1) "Figures" contains the exported figures used in the manuscript; (2) "Model_Isotope_Date" contains the WT-WRF-Hydro isotope date used in model validation; (3) “Model_Outputs_Future” contains the WT-WRF-Hydro future climate outputs; (4) “Model_Outputs_Historical” contains the WT-WRF-Hydro historical climate outputs; (5) “Model_Outputs_Weights_Areas” contains the WT-WRF-Hydro weights per catchment used to calculate water transit times and isotopes in stream water; (6) “MODIS_data_scripts” contains data used to validate snow conditions in the study area; (7) “Observed_Flow_Data” contains the observed streamflow data used in model validation; (8) “Observed_Isotope_Data” contains the observed stream water isotope data used in model validation; (9) “Scripts” contains the Python scripts used to general results and the figures; (10) “Statistic_Outputs” contains the water transit time statistical outputs reported in this manuscript; (11) “Validation_SNOTEL” contains the SNOTEL data used in model validation. The files in this data package have the following file extensions: .tif, .txt, .csv, .pdf, .py, .jpg, and .png.

American River↗