Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-233-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-233-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Jan 2023
Development and technical paper |
| 10 Jan 2023
| 10 Jan 2023
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Department of Geography, University of Utah, Salt Lake City, UT, USA
John Horel
Department of Atmospheric Sciences, University of Utah, Salt Lake
City, UT, USA
Patrick Kormos
Colorado Basin River Forecast Center NOAA National Weather Service,
Salt Lake City, UT, USA
Andrew Hedrick
Northwest Watershed Research Center, USDA Agricultural Research
Service, Boise, ID, USA
Ernesto Trujillo
Department of Geosciences, Boise State University, Boise, ID, USA
Northwest Watershed Research Center, USDA Agricultural Research
Service, Boise, ID, USA
S. McKenzie Skiles
Department of Geography, University of Utah, Salt Lake City, UT, USA
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Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
EGUsphere, https://doi.org/10.5194/egusphere-2024-1473, https://doi.org/10.5194/egusphere-2024-1473, 2024
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones, are often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
Joachim Meyer, McKenzie Skiles, Jeffrey Deems, Kat Boremann, and David Shean
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-281, https://doi.org/10.5194/hess-2021-281, 2021
Revised manuscript not accepted
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Seasonally accumulated snow in the mountains forms a natural water reservoir which is challenging to measure in the rugged and remote terrain. Here, we use overlapping aerial images that model surface elevations using software to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the utility of aerial images to improve our ability to capture the amount of water held as snow in remote and inaccessible locations.
Joachim Meyer, S. McKenzie Skiles, Jeffrey Deems, Kat Bormann, and David Shean
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-34, https://doi.org/10.5194/tc-2021-34, 2021
Manuscript not accepted for further review
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Snow that accumulates seasonally in mountains forms a natural water reservoir and is difficult to measure in the rugged and remote landscapes. Here, we use modern software that models surface elevations from overlapping aerial images to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the potential value of aerial images for understanding the amount of water held as snow in remote and inaccessible locations.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
EGUsphere, https://doi.org/10.5194/egusphere-2024-1473, https://doi.org/10.5194/egusphere-2024-1473, 2024
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones, are often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
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The Cryosphere, 18, 1925–1946, https://doi.org/10.5194/tc-18-1925-2024, https://doi.org/10.5194/tc-18-1925-2024, 2024
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The Cryosphere, 16, 3801–3814, https://doi.org/10.5194/tc-16-3801-2022, https://doi.org/10.5194/tc-16-3801-2022, 2022
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Snow grain size is important to determine the age and structure of snow, but it is difficult to measure. Snow grain size can be found from airborne and spaceborne observations by measuring near-infrared energy reflected from snow. In this study, we use the SNICAR radiative transfer model and a Monte Carlo model to examine how snow grain size measurements change with snow structure and solar zenith angle. We show that improved understanding of these variables improves snow grain size precision.
Leonie Kiewiet, Ernesto Trujillo, Andrew Hedrick, Scott Havens, Katherine Hale, Mark Seyfried, Stephanie Kampf, and Sarah E. Godsey
Hydrol. Earth Syst. Sci., 26, 2779–2796, https://doi.org/10.5194/hess-26-2779-2022, https://doi.org/10.5194/hess-26-2779-2022, 2022
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Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds
The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, https://doi.org/10.5194/tc-16-43-2022, 2022
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The amount of water within a snowpack is important information for predicting snowmelt and wet-snow avalanches. From within a controlled laboratory, the optimal method for measuring liquid water content (LWC) at the snow surface or along a snow pit profile using near-infrared imagery was determined. As snow samples melted, multiple models to represent wet-snow reflectance were assessed against a more established LWC instrument. The best model represents snow as separate spheres of ice and water.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Joachim Meyer, McKenzie Skiles, Jeffrey Deems, Kat Boremann, and David Shean
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-281, https://doi.org/10.5194/hess-2021-281, 2021
Revised manuscript not accepted
Short summary
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Seasonally accumulated snow in the mountains forms a natural water reservoir which is challenging to measure in the rugged and remote terrain. Here, we use overlapping aerial images that model surface elevations using software to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the utility of aerial images to improve our ability to capture the amount of water held as snow in remote and inaccessible locations.
Joachim Meyer, S. McKenzie Skiles, Jeffrey Deems, Kat Bormann, and David Shean
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-34, https://doi.org/10.5194/tc-2021-34, 2021
Manuscript not accepted for further review
Short summary
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Snow that accumulates seasonally in mountains forms a natural water reservoir and is difficult to measure in the rugged and remote landscapes. Here, we use modern software that models surface elevations from overlapping aerial images to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the potential value of aerial images for understanding the amount of water held as snow in remote and inaccessible locations.
C. Jason Williams, Frederick B. Pierson, Patrick R. Kormos, Osama Z. Al-Hamdan, and Justin C. Johnson
Earth Syst. Sci. Data, 12, 1347–1365, https://doi.org/10.5194/essd-12-1347-2020, https://doi.org/10.5194/essd-12-1347-2020, 2020
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Data were collected at three sites over 10 years to evaluate ecologic impacts of tree encroachment on rangelands and assess impacts of tree-removal practices on vegetation, surface conditions, and hydrologic/erosion processes. The dataset includes 1300 rainfall simulation and 838 overland-flow experiments paired with vegetation, surface cover, and soil data across point to hillslope scales. The data advance hydrology/erosion process understanding and are a source for model development/testing.
Joseph M. Cook, Andrew J. Tedstone, Christopher Williamson, Jenine McCutcheon, Andrew J. Hodson, Archana Dayal, McKenzie Skiles, Stefan Hofer, Robert Bryant, Owen McAree, Andrew McGonigle, Jonathan Ryan, Alexandre M. Anesio, Tristram D. L. Irvine-Fynn, Alun Hubbard, Edward Hanna, Mark Flanner, Sathish Mayanna, Liane G. Benning, Dirk van As, Marian Yallop, James B. McQuaid, Thomas Gribbin, and Martyn Tranter
The Cryosphere, 14, 309–330, https://doi.org/10.5194/tc-14-309-2020, https://doi.org/10.5194/tc-14-309-2020, 2020
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Melting of the Greenland Ice Sheet (GrIS) is a major source of uncertainty for sea level rise projections. Ice-darkening due to the growth of algae has been recognized as a potential accelerator of melting. This paper measures and models the algae-driven ice melting and maps the algae over the ice sheet for the first time. We estimate that as much as 13 % total runoff from the south-western GrIS can be attributed to these algae, showing that they must be included in future mass balance models.
Patrick R. Kormos, Danny G. Marks, Mark S. Seyfried, Scott C. Havens, Andrew Hedrick, Kathleen A. Lohse, Micah Sandusky, Annelen Kahl, and David Garen
Earth Syst. Sci. Data, 10, 1197–1205, https://doi.org/10.5194/essd-10-1197-2018, https://doi.org/10.5194/essd-10-1197-2018, 2018
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Thirty-one years of hourly gridded (10 m) air temperature, relative humidity, dew point temperature, precipitation amount, and precipitation phase data are presented for the Reynolds Creek Experimental Watershed, Idaho, which is part of the Critical Zone Observatory network. The air temperature, relative humidity, and precipitation are distributed from weather station measurements. This dataset covers a wide range of weather extremes in the rain–snow transition zone from 1984 to 2014.
Patrick R. Kormos, Danny G. Marks, Frederick B. Pierson, C. Jason Williams, Stuart P. Hardegree, Alex R. Boehm, Scott C. Havens, Andrew Hedrick, Zane K. Cram, and Tony J. Svejcar
Earth Syst. Sci. Data, 9, 91–98, https://doi.org/10.5194/essd-9-91-2017, https://doi.org/10.5194/essd-9-91-2017, 2017
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Data are presented that are essential to assessing the impacts of western juniper encroachment and woodland treatments in the interior Great Basin region of the western USA. This woodland expansion into sagebrush ecosystems influences the vegetation community and the hydrology and soil resources of an area, which affect wildlife habitat, ecosystem quality, and local economies. Data include weather, snow, and stream time series, as well as lidar-derived topographic and vegetation height data.
Clarissa L. Enslin, Sarah E. Godsey, Danny Marks, Patrick R. Kormos, Mark S. Seyfried, James P. McNamara, and Timothy E. Link
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2016-44, https://doi.org/10.5194/essd-2016-44, 2016
Preprint withdrawn
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Weather data in mountainous rain-to-snow transition zones are limited, but vital for water resources. We present a 10-year dataset for this zone that includes hourly temperatures, relative humidity, stream flow, snow depth, precipitation, wind speed/direction, solar energy, and soil moisture at 11 stations. Average air temperatures are near freezing eight months each year, so that slight warming may determine whether rain falls instead of snow, affecting water supplies, ecosystems and fire risk.
E. Trujillo and M. Lehning
The Cryosphere, 9, 1249–1264, https://doi.org/10.5194/tc-9-1249-2015, https://doi.org/10.5194/tc-9-1249-2015, 2015
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In this article, we present a methodology for the objective evaluation of the error in capturing mean snow depths from point measurements. We demonstrate, using LIDAR snow depths, how the model can be used for assisting the design of survey strategies such that the error is minimized or an estimation threshold is achieved. Furthermore, the model can be extended to other spatially distributed snow variables (e.g., SWE) whose statistical properties are comparable to those of snow depth.
E. M. Neemann, E. T. Crosman, J. D. Horel, and L. Avey
Atmos. Chem. Phys., 15, 135–151, https://doi.org/10.5194/acp-15-135-2015, https://doi.org/10.5194/acp-15-135-2015, 2015
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This paper uses numerical model simulations to investigate the meteorological characteristics of the 31 January–6 February 2013 cold-air pool (also know as a temperature 'inversion') in the Uintah Basin, Utah, and the resulting high ozone concentrations. A number of factors that influence cold pools and pollutant concentrations in the Uintah Basin are discussed, including snow cover, ice fog, and thermally driven flows.
A. Hedrick, H.-P. Marshall, A. Winstral, K. Elder, S. Yueh, and D. Cline
The Cryosphere, 9, 13–23, https://doi.org/10.5194/tc-9-13-2015, https://doi.org/10.5194/tc-9-13-2015, 2015
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Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-190, https://doi.org/10.5194/gmd-2023-190, 2023
Revised manuscript accepted for GMD
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Accurate hydrological modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall-runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
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A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
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This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
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We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
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We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
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This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Cited articles
Anderson, E. A.: A point energy and mass balance model of a snow cover, United States, National Weather Service, https://repository.library.noaa.gov/view/noaa/6392 (last access: 28 February 2022), 1976.
Ayers, J., Ficklin, D. L., Stewart, I. T., and Strunk, M.: Comparison of
CMIP3 and CMIP5 projected hydrologic conditions over the Upper Colorado
River Basin, Int. J. Climatol., 36, 3807–3818,
https://doi.org/10.1002/joc.4594, 2016.
Bellaire, S., Jamieson, J. B., and Fierz, C.: Forcing the snow-cover model SNOWPACK with forecasted weather data, The Cryosphere, 5, 1115–1125, https://doi.org/10.5194/tc-5-1115-2011, 2011.
Bellaire, S., Jamieson, J. B., and Fierz, C.: Corrigendum to “Forcing the snow-cover model SNOWPACK with forecasted weather data” published in The Cryosphere, 5, 1115–1125, 2011, The Cryosphere, 7, 511–513, https://doi.org/10.5194/tc-7-511-2013, 2013.
Benjamin, S. G., Weygandt, S. S., Brown, J. M., Hu, M., Alexander, C. R.,
Smirnova, T. G., Olson, J. B., James, E. P., Dowell, D. C., Grell, G. A.,
Lin, H., Peckham, S. E., Smith, T. L., Moninger, W. R., Kenyon, J. S., and
Manikin, G. S.: A North American Hourly Assimilation and Model Forecast
Cycle: The Rapid Refresh, Mon. Weather Rev., 144, 1669–1694,
https://doi.org/10.1175/MWR-D-15-0242.1, 2016.
Brandt, W. T., Bormann, K. J., Cannon, F., Deems, J. S., Painter, T. H.,
Steinhoff, D. F., and Dozier, J.: Quantifying the Spatial Variability of a
Snowstorm Using Differential Airborne Lidar, Water Resour. Res., 56,
https://doi.org/10.1029/2019WR025331, 2020.
Bryant, A. C., Painter, T. H., Deems, J. S., and Bender, S. M.: Impact of
dust radiative forcing in snow on accuracy of operational runoff prediction
in the Upper Colorado River Basin: DUST IN SNOW IMPACT ON STREAMFLOW,
Geophys. Res. Lett., 40, 3945–3949, https://doi.org/10.1002/grl.50773,
2013.
Bytheway, J. L., Kummerow, C. D., and Alexander, C.: A Features-Based
Assessment of the Evolution of Warm Season Precipitation Forecasts from the
HRRR Model over Three Years of Development, Weather Forecast., 32,
1841–1856, https://doi.org/10.1175/WAF-D-17-0050.1, 2017.
Carroll, R. W. H., Bearup, L. A., Brown, W., Dong, W., Bill, M., and
Willlams, K. H.: Factors controlling seasonal groundwater and solute flux
from snow-dominated basins, Hydrol. Process., 32, 2187–2202,
https://doi.org/10.1002/hyp.13151, 2018.
Chen, F., Barlage, M., Tewari, M., Rasmussen, R., Jin, J., Lettenmaier, D.,
Livneh, B., Lin, C., Miguez-Macho, G., Niu, G.-Y., Wen, L., and Yang, Z.-L.:
Modeling seasonal snowpack evolution in the complex terrain and forested
Colorado Headwaters region: A model intercomparison study, J. Geophys. Res.-Atmos., 119, 13795–13819, https://doi.org/10.1002/2014JD022167, 2014.
Cho, E., McCrary, R. R., and Jacobs, J. M.: Future Changes in Snowpack,
Snowmelt, and Runoff Potential Extremes Over North America, Geophys. Res. Lett., 48, e2021GL094985, https://doi.org/10.1029/2021GL094985,
2021.
Christensen, N. S. and Lettenmaier, D. P.: A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River Basin, Hydrol. Earth Syst. Sci., 11, 1417–1434, https://doi.org/10.5194/hess-11-1417-2007, 2007.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E.,
Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Gochis, D. J.,
Rasmussen, R. M., Tarboton, D. G., Mahat, V., Flerchinger, G. N., and Marks,
D. G.: A unified approach for process-based hydrologic modeling: 2. Model
implementation and case studies, Water Resour. Res., 51, 2515–2542,
https://doi.org/10.1002/2015WR017200, 2015.
Council, N. R.: Toward a new advanced hydrologic prediction service (AHPS),
The National Academies Press, Washington, D.C., 84 pp.,
https://doi.org/10.17226/11598, 2006.
Dettinger, M., Udall, B., and Georgakakos, A.: Western water and climate
change, Ecol. Appl., 25, 2069–2093,
https://doi.org/10.1890/15-0938.1, 2015.
Dozier, J. and Frew, J.: Rapid calculation of terrain parameters for
radiation modeling from digital elevation data, IEEE T.
Geosci. Remote, 28, 963–969,
https://doi.org/10.1109/36.58986, 1990.
Essery, R.: A factorial snowpack model (FSM 1.0), Geosci. Model Dev., 8, 3867–3876, https://doi.org/10.5194/gmd-8-3867-2015, 2015.
Feng, S. and Hu, Q.: Changes in winter snowfall/precipitation ratio in the
contiguous United States, J. Geophys. Res.-Atmos., 112,
https://doi.org/10.1029/2007JD008397, 2007.
Forthofer, J. M., Butler, B. W., Wagenbrenner, N. S., Forthofer, J. M.,
Butler, B. W., and Wagenbrenner, N. S.: A comparison of three approaches for
simulating fine-scale surface winds in support of wildland fire management.
Part I. Model formulation and comparison against measurements, Int. J.
Wildland Fire, 23, 969–981, https://doi.org/10.1071/WF12089, 2014.
Franz, K. J., Hogue, T. S., and Sorooshian, S.: Operational snow modeling:
Addressing the challenges of an energy balance model for National Weather
Service forecasts, J. Hydrol., 360, 48–66,
https://doi.org/10.1016/j.jhydrol.2008.07.013, 2008.
Franz, K. J., Butcher, P., and Ajami, N. K.: Addressing snow model
uncertainty for hydrologic prediction, Adv. Water Resour., 33,
820–832, https://doi.org/10.1016/j.advwatres.2010.05.004, 2010.
Garen, D. C. and Marks, D.: Spatially distributed energy balance snowmelt
modelling in a mountainous river basin: estimation of meteorological inputs
and verification of model results, J. Hydrol., 315, 126–153,
https://doi.org/10.1016/j.jhydrol.2005.03.026, 2005.
Gowan, T. A., Horel, J. D., Jacques, A. A., and Kovac, A.: Using Cloud
Computing to Analyze Model Output Archived in Zarr Format, J.
Atmos. Ocean. Tech., 39, 449–462,
https://doi.org/10.1175/JTECH-D-21-0106.1, 2022.
Griessinger, N., Schirmer, M., Helbig, N., Winstral, A., Michel, A., and
Jonas, T.: Implications of observation-enhanced energy-balance snowmelt
simulations for runoff modeling of Alpine catchments, Adv. Water
Resour., 133, 103410, https://doi.org/10.1016/j.advwatres.2019.103410,
2019.
Harpold, A., Brooks, P., Rajagopal, S., Heidbuchel, I., Jardine, A., and
Stielstra, C.: Changes in snowpack accumulation and ablation in the
intermountain west, Water Resour. Res., 48,
https://doi.org/10.1029/2012WR011949, 2012.
Havens, S., Marks, D., Kormos, P., and Hedrick, A.: Spatial Modeling for
Resources Framework (SMRF): A modular framework for developing spatial
forcing data for snow modeling in mountain basins, Comput.
Geosci., 109, 295–304, https://doi.org/10.1016/j.cageo.2017.08.016,
2017.
Havens, S., Marks, D., FitzGerald, K., Masarik, M., Flores, A. N., Kormos,
P., and Hedrick, A.: Approximating Input Data to a Snowmelt Model Using
Weather Research and Forecasting Model Outputs in Lieu of Meteorological
Measurements, J. Hydrometeorol., 20, 847–862,
https://doi.org/10.1175/JHM-D-18-0146.1, 2019.
Havens, S., Marks, D., Sandusky, M., Hedrick, A., Johnson, M., Robertson,
M., and Trujillo, E.: Automated Water Supply Model (AWSM): Streamlining and
standardizing application of a physically based snow model for water
resources and reproducible science, Comput. Geosci., 144, 104571,
https://doi.org/10.1016/j.cageo.2020.104571, 2020.
Havens, S., Marks, D., Kormos, P., Hedrick, A., Trujillo, E., Johnson, M., Sandusky, M., and Robertson, M.: Spatial Modeling for Resources Framework (SMRF) (v0.9.1), Zenodo [code], https://doi.org/10.5281/zenodo.6543935, 2022a.
Havens, S., Marks, D., Sandusky, M., Johnson, M., Robertson, M., Hedrick, A., and Kormos, P.: Automated Water Supply Model (AWSM) (v0.10.0), Zenodo [code], https://doi.org/10.5281/zenodo.6543919, 2022b.
Havens, S., Meyer, J., Sandusky, M., Johnson, M., and Robertson, M.: UofU-Cryosphere/weather_forecast_retrieval: GMD submission (Version 20220512), Zenodo [code], https://doi.org/10.5281/zenodo.6543579, 2022c.
He, M., Hogue, T. S., Franz, K. J., Margulis, S. A., and Vrugt, J. A.:
Characterizing parameter sensitivity and uncertainty for a snow model across
hydroclimatic regimes, Adv. Water Resour., 34, 114–127,
https://doi.org/10.1016/j.advwatres.2010.10.002, 2011.
Hedrick, A. R., Marks, D., Havens, S., Robertson, M., Johnson, M., Sandusky,
M., Marshall, H., Kormos, P. R., Bormann, K. J., and Painter, T. H.: Direct
Insertion of NASA Airborne Snow Observatory-Derived Snow Depth Time Series
Into the iSnobal Energy Balance Snow Model, Water Resour. Res., 54,
8045–8063, https://doi.org/10.1029/2018WR023190, 2018.
Hedrick, A. R., Marks, D., Marshall, H., McNamara, J., Havens, S., Trujillo,
E., Sandusky, M., Robertson, M., Johnson, M., Bormann, K. J., and Painter,
T. H.: From Drought to Flood: A Water Balance Analysis of the Tuolumne River
Basin during Extreme Conditions (2015–2017), Hydrol. Process.,
hyp.13749, https://doi.org/10.1002/hyp.13749, 2020.
Hubbard, S. S., Williams, K. H., Agarwal, D., Banfield, J., Beller, H.,
Bouskill, N., Brodie, E., Carroll, R., Dafflon, B., Dwivedi, D., Falco, N.,
Faybishenko, B., Maxwell, R., Nico, P., Steefel, C., Steltzer, H., Tokunaga,
T., Tran, P. A., Wainwright, H., and Varadharajan, C.: The East River,
Colorado, Watershed: A Mountainous Community Testbed for Improving
Predictive Understanding of Multiscale Hydrological–Biogeochemical
Dynamics, Vadose Zone J., 17, 0,
https://doi.org/10.2136/vzj2018.03.0061, 2018.
Ikeda, K., Rasmussen, R., Liu, C., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Miller, K., Arsenault, K., Grubišić, V., Thompson, G., and Guttman, E.: Simulation of seasonal snowfall over Colorado, Atmos. Res., 97, 462–477, https://doi.org/10.1016/j.atmosres.2010.04.010, 2010.
Ikeda, K., Rasmussen, R., Liu, C., Newman, A., Chen, F., Barlage, M.,
Gutmann, E., Dudhia, J., Dai, A., Luce, C., and Musselman, K.: Snowfall and
snowpack in the Western U.S. as captured by convection permitting climate
simulations: current climate and pseudo global warming future climate, Clim.
Dynam., 57, 2191–2215, https://doi.org/10.1007/s00382-021-05805-w, 2021.
Iwamoto, K., Yamaguchi, S., Nakai, S., and Sato, A.: Forecasting Experiments
Using the Regional Meteorological Model and the Numerical Snow Cover Model
in the Snow Disaster Forecasting System, J. Nat. Dis.
Sci., 30, 35–43, https://doi.org/10.2328/jnds.30.35, 2008.
Knowles, N., Dettinger, M. D., and Cayan, D. R.: Trends in Snowfall versus
Rainfall in the Western United States, J. Climate, 19, 4545–4559,
https://doi.org/10.1175/JCLI3850.1, 2006.
Kormos, P. R., Marks, D., McNamara, J. P., Marshall, H. P., Winstral, A.,
and Flores, A. N.: Snow distribution, melt and surface water inputs to the
soil in the mountain rain–snow transition zone, J. Hydrol., 519,
190–204, https://doi.org/10.1016/j.jhydrol.2014.06.051, 2014.
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018.
LANDFIRE: Existing Vegetation Type and Height Layer, LANDFIRE 1.4.0,
U.S. Department of the Interior, Geological Survey, and U.S. Department of
AgricultureData Product Mosaic Downloads, https://landfire.gov/getdata.php
(last access: 28 February 2022), 2014.
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A
physical SNOWPACK model for the Swiss avalanche warning: Part II. Snow
microstructure, Cold Reg. Sci. Technol., 35, 147–167,
https://doi.org/10.1016/S0165-232X(02)00073-3, 2002.
Li, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P.: How
much runoff originates as snow in the western United States, and how will
that change in the future?, Geophys. Res. Lett., 44, 6163–6172,
https://doi.org/10.1002/2017GL073551, 2017.
Link, T. and Marks, D.: Distributed simulation of snowcover mass- and
energy-balance in the boreal forest, Hydrol. Process., 13, 2439–2452,
https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2439::AID-HYP866>3.0.CO;2-1, 1999.
Liston, G. E. and Elder, K.: A Distributed Snow-Evolution Modeling System
(SnowModel), J. Hydrometeorol., 7, 1259–1276,
https://doi.org/10.1175/JHM548.1, 2006.
Marks, D. and Dozier, J.: Climate and energy exchange at the snow surface in
the Alpine Region of the Sierra Nevada: 2. Snow cover energy balance, Water Resour. Res., 28, 3043–3054, https://doi.org/10.1029/92WR01483, 1992.
Marks, D., Dozier, J., and Davis, R. E.: Climate and energy exchange at the
snow surface in the Alpine Region of the Sierra Nevada: 1. Meteorological
measurements and monitoring, Water Resour. Res., 28, 3029–3042,
https://doi.org/10.1029/92WR01482, 1992.
Marks, D., Kimball, J., Tingey, D., and Link, T.: The sensitivity of
snowmelt processes to climate conditions and forest cover during
rain-on-snow: a case study of the 1996 Pacific Northwest flood, Hydrol.
Process., 12, 1569–1587,
https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1569::AID-HYP682>3.0.CO;2-L, 1998.
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A spatially distributed energy balance snowmelt model for application in mountain basins, Hydrol. Process., 13, 1935–1959, https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C, 1999.
McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H.-P., Painter, T.
H., Molotch, N. P., Elder, K., Hiemstra, C., and Brucker, L.: Spatially
Extensive Ground-Penetrating Radar Snow Depth Observations During NASA's
2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite
Observations, Water Resour. Res., 55, 10026–10036,
https://doi.org/10.1029/2019WR024907, 2019.
Meyer, J. and Ragar, D.: UofU-Cryosphere/isnoda: GMD-final (Version 20221212), Zenodo [code], https://doi.org/10.5281/zenodo.7452230, 2022.
Miller, S. D., Wang, F., Burgess, A. B., Skiles, S. M., Rogers, M., and
Painter, T. H.: Satellite-Based Estimation of Temporally Resolved Dust
Radiative Forcing in Snow Cover, J. Hydrometeorol., 17,
1999–2011, https://doi.org/10.1175/JHM-D-15-0150.1, 2016.
Molotch, N. P. and Bales, R. C.: Scaling snow observations from the point to
the grid element: Implications for observation network design, Water Resour.
Res., 41, https://doi.org/10.1029/2005WR004229, 2005.
Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M., and Engel, R.: Dramatic
declines in snowpack in the western US, npj Clim. Atmos. Sci.,
1, 2, https://doi.org/10.1038/s41612-018-0012-1, 2018.
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K., and Rasmussen, R.:
Slower snowmelt in a warmer world, Nat. Clim. Change, 7, 214–219,
https://doi.org/10.1038/nclimate3225, 2017.
Musselman, K. N., Addor, N., Vano, J. A., and Molotch, N. P.: Winter melt
trends portend widespread declines in snow water resources, Nat. Clim.
Chang., 11, 418–424, https://doi.org/10.1038/s41558-021-01014-9, 2021.
NOAA: The High-Resolution Rapid Refresh (HRRR):
https://rapidrefresh.noaa.gov/hrrr/, last access: 28 February 2022.
Nowak, K., Bearup, L. A., Larsen, D., Garcia, D., Moore, C., and Baker, S.: Emerging Technologies in Snow, The Bureau of Reclamation, https://www.usbr.gov/research/docs/news/EmergingTechnologiesInSnowMonitoring_Report508.pdf, last access: 28 February 2022.
NRCS National Water and Climate Center: SNOTEL | SWE Data:
https://www.wcc.nrcs.usda.gov/snow/SNOTEL-wedata.html, last access: 28 February 2022.
Painter, T.: ASO L4 Lidar Snow Depth 50m UTM Grid, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/STOT5I0U1WVI, 2018.
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J.
S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann,
C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C.,
and Winstral, A.: The Airborne Snow Observatory: Fusion of scanning lidar,
imaging spectrometer, and physically-based modeling for mapping snow water
equivalent and snow albedo, Remote Sens. Environ., 184, 139–152,
https://doi.org/10.1016/j.rse.2016.06.018, 2016.
Painter, T. H., Skiles, S. M., Deems, J. S., Brandt, W. T., and Dozier, J.:
Variation in Rising Limb of Colorado River Snowmelt Runoff Hydrograph
Controlled by Dust Radiative Forcing in Snow, Geophys. Res. Lett., 45, 797–808,
https://doi.org/10.1002/2017GL075826, 2018.
Qu, X. and Hall, A.: On the persistent spread in snow-albedo feedback, Clim.
Dynam., 42, 69–81, https://doi.org/10.1007/s00382-013-1774-0, 2014.
Rittger, K., Raleigh, M. S., Dozier, J., Hill, A. F., Lutz, J. A., and
Painter, T. H.: Canopy Adjustment and Improved Cloud Detection for Remotely
Sensed Snow Cover Mapping, Water Resour. Res., 56, e2019WR024914,
https://doi.org/10.1029/2019WR024914, 2020.
Ryken, A., Bearup, L. A., Jefferson, J. L., Constantine, P., and Maxwell, R.
M.: Sensitivity and model reduction of simulated snow processes: Contrasting
observational and parameter uncertainty to improve prediction, Adv.
Water Resour., 135, 103473,
https://doi.org/10.1016/j.advwatres.2019.103473, 2020.
Schirmer, M. and Jamieson, B.: Verification of analysed and forecasted winter precipitation in complex terrain, The Cryosphere, 9, 587–601, https://doi.org/10.5194/tc-9-587-2015, 2015.
Skiles, S. M. and Painter, T.: Daily evolution in dust and black carbon
content, snow grain size, and snow albedo during snowmelt, Rocky Mountains,
Colorado, J. Glaciol., 63, 118–132,
https://doi.org/10.1017/jog.2016.125, 2017.
Skiles, S. M. and Painter, T. H.: Toward Understanding Direct Absorption and
Grain Size Feedbacks by Dust Radiative Forcing in Snow With Coupled Snow
Physical and Radiative Transfer Modeling, Water Resour. Res., 55,
7362–7378, https://doi.org/10.1029/2018WR024573, 2019.
Skiles, S. M., Painter, T. H., Deems, J. S., Bryant, A. C., and Landry, C.
C.: Dust radiative forcing in snow of the Upper Colorado River Basin: 2.
Interannual variability in radiative forcing and snowmelt rates, Water Resour. Res., 48, https://doi.org/10.1029/2012WR011986, 2012.
Skiles, S. M., Painter, T. H., Belnap, J., Holland, L., Reynolds, R. L.,
Goldstein, H. L., and Lin, J.: Regional variability in dust-on-snow
processes and impacts in the Upper Colorado River Basin, Hydrol. Process., 29, 5397–5413, https://doi.org/10.1002/hyp.10569, 2015.
Stewart, I. T.: Changes in snowpack and snowmelt runoff for key mountain
regions, Hydrol. Process., 23, 78–94,
https://doi.org/10.1002/hyp.7128, 2009.
The National Elevation Dataset (NED), U.S. Department of the Interior,
Geological Survey, https://apps.nationalmap.gov/viewer/, last access: 28 February 2022.
Trujillo, E. and Lehning, M.: Theoretical analysis of errors when estimating snow distribution through point measurements, The Cryosphere, 9, 1249–1264, https://doi.org/10.5194/tc-9-1249-2015, 2015.
Trujillo, E. and Molotch, N. P.: Snowpack regimes of the Western United
States, Water Resour. Res., 50, 5611–5623,
https://doi.org/10.1002/2013WR014753, 2014.
U.S. Geological Survey: The National Map
https://www.usgs.gov/programs/national-geospatial-program/national-map, last
access: 28 February 2022a.
U.S. Geological Survey: Surface Water data for USA: USGS Surface-Water Daily Statistics:
https://waterdata.usgs.gov/nwis/dvstat/?site_no=09112500&referred_module=sw&format=sites_selection_links,
last access: 28 February 2022b.
Vernay, M., Lafaysse, M., Monteiro, D., Hagenmuller, P., Nheili, R., Samacoïts, R., Verfaillie, D., and Morin, S.: The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021), Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, 2022.
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012.
Winstral, A. and Marks, D.: Simulating wind fields and snow redistribution
using terrain-based parameters to model snow accumulation and melt over a
semi-arid mountain catchment, Hydrol. Process., 16, 3585–3603,
https://doi.org/10.1002/hyp.1238, 2002.
Winstral, A., Marks, D., and Gurney, R.: Assessing the Sensitivities of a
Distributed Snow Model to Forcing Data Resolution, J. Hydrometeorol., 15, 1366–1383, https://doi.org/10.1175/JHM-D-13-0169.1,
2014.
Short summary
Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction...
