Articles | Volume 21, issue 1
https://doi.org/10.5194/hess-21-1-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-1-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Review article
| 
02 Jan 2017
Review article |
| 02 Jan 2017
Rain or snow: hydrologic processes, observations, prediction, and research needs
Department of Natural Resources and Environmental Science, University of Nevada, 1664 N. Virginia Street, Reno, Nevada, USA
Michael L. Kaplan
Division of Hydrologic Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, Nevada, USA
P. Zion Klos
Department of Forest, Rangeland, and Fire Sciences, University of Idaho, 875 Perimeter Drive, Moscow, Idaho, USA
Timothy Link
Department of Forest, Rangeland, and Fire Sciences, University of Idaho, 875 Perimeter Drive, Moscow, Idaho, USA
James P. McNamara
Department of Geosciences, Boise State University, 1910 University Dr., Boise, Idaho, USA
Seshadri Rajagopal
Division of Hydrologic Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, Nevada, USA
Rina Schumer
Division of Hydrologic Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, Nevada, USA
Caitriana M. Steele
Jornada Experimental Range, New Mexico State University, Las Cruces, New Mexico, USA
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We use repeat airborne lidar surveys (which provide high resolution topography) to map the seasonal snowpack and estimate mass loss from glaciers, snowfields, rock glaciers, and other forms of perennial snow and ice in the U.S. Rocky Mountains. Our results show that topography, especially wind drifting, is a fundamental driver of differences in snow persistence, glaciation, and streamflow across five mountain watersheds.
Gary Sterle, Julia Perdrial, Dustin W. Kincaid, Kristen L. Underwood, Donna M. Rizzo, Ijaz Ul Haq, Li Li, Byung Suk Lee, Thomas Adler, Hang Wen, Helena Middleton, and Adrian A. Harpold
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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.
A. A. Harpold, J. A. Marshall, S. W. Lyon, T. B. Barnhart, B. A. Fisher, M. Donovan, K. M. Brubaker, C. J. Crosby, N. F. Glenn, C. L. Glennie, P. B. Kirchner, N. Lam, K. D. Mankoff, J. L. McCreight, N. P. Molotch, K. N. Musselman, J. Pelletier, T. Russo, H. Sangireddy, Y. Sjöberg, T. Swetnam, and N. West
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Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
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Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025, https://doi.org/10.5194/hess-29-2133-2025, 2025
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Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025, https://doi.org/10.5194/hess-29-2023-2025, 2025
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Bamidele Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 29, 1659–1683, https://doi.org/10.5194/hess-29-1659-2025, https://doi.org/10.5194/hess-29-1659-2025, 2025
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We studied how soil and weather data affect land model simulations over Africa. By combining soil data processed in different ways with weather data of varying time intervals, we found that weather inputs had a greater impact on water processes than soil data type. However, the way soil data were processed became crucial when paired with high-frequency weather inputs, showing that detailed weather data can improve local and regional predictions of how water moves and interacts with the land.
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025, https://doi.org/10.5194/hess-29-1637-2025, 2025
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Deforestation has a significant impact on climate, yet its effects on drought remain less understood. This study investigates how deforestation affects drought across various climate zones and timescales. Findings indicate that deforestation leads to drier conditions in tropical regions and wetter conditions in arid areas, with minimal effects in temperate zones. Long-term drought is more affected than short-term drought, offering valuable insights into vegetation–climate interactions.
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 29, 1295–1317, https://doi.org/10.5194/hess-29-1295-2025, https://doi.org/10.5194/hess-29-1295-2025, 2025
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The study provides a detailed characterisation of flash drought in the UK for 1969–2021. The spatio-temporal distribution and trends of flash droughts are highly variable, with important regional and seasonal contrasts. In the UK, flash drought development responds primarily to precipitation variability, while the atmospheric evaporative demand plays a secondary role. We also found that the North Atlantic Oscillation is the main circulation pattern controlling flash drought development.
Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley J. Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci., 29, 1201–1220, https://doi.org/10.5194/hess-29-1201-2025, https://doi.org/10.5194/hess-29-1201-2025, 2025
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Future extreme rainfall events are influenced by changes in both absolute and relative humidity. The impact of increasing absolute humidity is reasonably well understood, but the role of relative humidity decreases over land remains largely unknown. Using hourly observations from France and the Netherlands, we find that lower relative humidity generally leads to more intense rainfall extremes. This relation is only captured well in recently developed convection-permitting climate models.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci., 29, 1135–1158, https://doi.org/10.5194/hess-29-1135-2025, https://doi.org/10.5194/hess-29-1135-2025, 2025
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Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.
Lorenzo Silvestri, Miriam Saraceni, Bruno Brunone, Silvia Meniconi, Giulia Passadore, and Paolina Bongioannini Cerlini
Hydrol. Earth Syst. Sci., 29, 925–946, https://doi.org/10.5194/hess-29-925-2025, https://doi.org/10.5194/hess-29-925-2025, 2025
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This work demonstrates that seasonal forecasts of soil moisture are a valuable resource for groundwater management in the areas of the Central Mediterranean where longer memory timescales are found. In particular, they show significant correlation coefficients and forecast skill for the deepest soil moisture at 289 cm depth. Wet and dry events can be predicted 6 months in advance, and, in general, dry events are better captured than wet events.
Mohammad A. Farmani, Ali Behrangi, Aniket Gupta, Ahmad Tavakoly, Matthew Geheran, and Guo-Yue Niu
Hydrol. Earth Syst. Sci., 29, 547–566, https://doi.org/10.5194/hess-29-547-2025, https://doi.org/10.5194/hess-29-547-2025, 2025
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Soil moisture memory (SMM) shows how long soil stays moist after rain, impacting climate and ecosystems. Current models often overestimate SMM, causing inaccuracies in evaporation predictions. We enhanced a land model, Noah-MP, to include better water flow and ponding processes, and we tested it against satellite and field data. This improved model reduced overestimations and enhanced short-term predictions, helping create more accurate climate and weather forecasts.
Felipe Lobos-Roco, Jordi Vilà-Guerau de Arellano, and Camilo del Río
Hydrol. Earth Syst. Sci., 29, 109–125, https://doi.org/10.5194/hess-29-109-2025, https://doi.org/10.5194/hess-29-109-2025, 2025
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Water resources are fundamental for the social, economic, and natural development of (semi-)arid regions. Precipitation decreases due to climate change obligate us to find new water resources. Fog harvesting (FH) emerges as a complementary resource in regions where it is abundant but untapped. This research proposes a model to estimate FH potential in coastal (semi-)arid regions. This model could have broader applicability worldwide in regions where FH could be a viable water source.
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard
Hydrol. Earth Syst. Sci., 29, 85–108, https://doi.org/10.5194/hess-29-85-2025, https://doi.org/10.5194/hess-29-85-2025, 2025
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We assess 63 gridded ground (G), satellite (S), and reanalysis (R) climate datasets. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; however, R outperformed G when underlying data had low station density. G outperformed S or R datasets, although better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.
Rasmus E. Benestad, Kajsa M. Parding, and Andreas Dobler
Hydrol. Earth Syst. Sci., 29, 45–65, https://doi.org/10.5194/hess-29-45-2025, https://doi.org/10.5194/hess-29-45-2025, 2025
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We present a new method to calculate the chance of heavy downpour and the maximum rainfall expected over a 25-year period. It is designed to analyse global climate models' reproduction of past and future climates. For the Nordic countries, it projects a wetter climate in the future with increased intensity but not necessarily more wet days. The analysis also shows that rainfall intensity is sensitive to future greenhouse gas emissions, while the number of wet days appears to be less affected.
Chien-Yu Tseng, Li-Pen Wang, and Christian Onof
Hydrol. Earth Syst. Sci., 29, 1–25, https://doi.org/10.5194/hess-29-1-2025, https://doi.org/10.5194/hess-29-1-2025, 2025
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This study presents a new algorithm to model convective storms. We used advanced tracking methods to analyse 165 storm events in Birmingham (UK) and reconstruct storm cell life cycles. We found that cell properties like intensity and size are interrelated and vary over time. The new algorithm, based on vine copulas, accurately simulates these properties and their evolution. It also integrates an exponential shape function for realistic rainfall patterns, enhancing its hydrological applicability.
Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner
Hydrol. Earth Syst. Sci., 28, 4903–4925, https://doi.org/10.5194/hess-28-4903-2024, https://doi.org/10.5194/hess-28-4903-2024, 2024
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This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.
Peter E. Levy and the COSMOS-UK team
Hydrol. Earth Syst. Sci., 28, 4819–4836, https://doi.org/10.5194/hess-28-4819-2024, https://doi.org/10.5194/hess-28-4819-2024, 2024
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Having accurate up-to-date maps of soil moisture is important for many purposes. However, current modelled and remotely sensed maps are rather coarse and not very accurate. Here, we demonstrate a simple but accurate approach that is closely linked to direct measurements of soil moisture at a network sites across the UK, to the water balance (precipitation minus drainage and evaporation) measured at a large number of catchments (1212) and to remotely sensed satellite estimates.
Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024, https://doi.org/10.5194/hess-28-4539-2024, 2024
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Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
Peyman Abbaszadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-209, https://doi.org/10.5194/hess-2024-209, 2024
Revised manuscript accepted for HESS
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The Hybrid Ensemble and Variational Data Assimilation framework for Environmental System (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three Southeastern U.S. watersheds during hurricanes, HEAVEN assimilates real-time USGS streamflow data, boosting forecast accuracy.
Ling Zhang, Lu Li, Zhongshi Zhang, Joël Arnault, Stefan Sobolowski, Anthony Musili Mwanthi, Pratik Kad, Mohammed Abdullahi Hassan, Tanja Portele, and Harald Kunstmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-278, https://doi.org/10.5194/hess-2024-278, 2024
Revised manuscript accepted for HESS
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To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.
Peng Huang, Agnès Ducharne, Lucia Rinchiuso, Jan Polcher, Laure Baratgin, Vladislav Bastrikov, and Eric Sauquet
Hydrol. Earth Syst. Sci., 28, 4455–4476, https://doi.org/10.5194/hess-28-4455-2024, https://doi.org/10.5194/hess-28-4455-2024, 2024
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We conducted a high-resolution hydrological simulation from 1959 to 2020 across France. We used a simple trial-and-error calibration to reduce the biases of the simulated water budget compared to observations. The selected simulation satisfactorily reproduces water fluxes, including their spatial contrasts and temporal trends. This work offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.
Marc Girona-Mata, Andrew Orr, Martin Widmann, Daniel Bannister, Ghulam Hussain Dars, Scott Hosking, Jesse Norris, David Ocio, Tony Phillips, Jakob Steiner, and Richard E. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2805, https://doi.org/10.5194/egusphere-2024-2805, 2024
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We introduce a novel method for improving daily precipitation maps in mountain regions and pilot it across three basins in the Hindu Kush Karakoram Himalaya (HKH). The approach leverages climate model and weather station data, along with statistical / machine learning techniques. Our results show this approach outperforms traditional methods, especially in remote, ungauged areas, suggesting it could be used to improve precipitation maps across much of the HKH, as well as other mountain regions.
He Sun, Tandong Yao, Fengge Su, Wei Yang, and Deliang Chen
Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024, https://doi.org/10.5194/hess-28-4361-2024, 2024
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Our findings show that runoff in the Yarlung Zangbo (YZ) basin is primarily driven by rainfall, with the largest glacier runoff contribution in the downstream sub-basin. Annual runoff increased in the upper stream but decreased downstream due to varying precipitation patterns. It is expected to rise throughout the 21st century, mainly driven by increased rainfall.
Simon Moulds, Louise Slater, Louise Arnal, and Andrew Wood
EGUsphere, https://doi.org/10.31223/X5X405, https://doi.org/10.31223/X5X405, 2024
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Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to four months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to four months ahead in many locations, although in general the skill declines with increasing lead time.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
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Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Yuna Lim, Andrea M. Molod, Randal D. Koster, and Joseph A. Santanello
EGUsphere, https://doi.org/10.5194/egusphere-2024-2312, https://doi.org/10.5194/egusphere-2024-2312, 2024
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To better utilize a given set of predictions, identifying “forecasts of opportunity” has great value. It can help anticipate when prediction skill will be higher. This study reveals that when strong L-A coupling is detected 3–4 weeks into a forecast, the prediction skill for surface air temperature at this lead increases across the Midwest and northern Great Plains. Regions experiencing strong L-A coupling exhibit warm and dry anomalies, leading to improved predictions of abnormally warm events.
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
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The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
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We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci., 28, 3457–3474, https://doi.org/10.5194/hess-28-3457-2024, https://doi.org/10.5194/hess-28-3457-2024, 2024
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Daily streamflow series for 661 near-natural French catchments are reconstructed over 1871–2012 using two ensemble datasets: HydRE and HydREM. They include uncertainties coming from climate forcings, streamflow measurement, and hydrological model error (for HydrREM). Comparisons with other hydrological reconstructions and independent/dependent observations show the added value of the two reconstructions in terms of quality, uncertainty estimation, and representation of extremes.
María Agostina Bracalenti, Omar V. Müller, Miguel A. Lovino, and Ernesto Hugo Berbery
Hydrol. Earth Syst. Sci., 28, 3281–3303, https://doi.org/10.5194/hess-28-3281-2024, https://doi.org/10.5194/hess-28-3281-2024, 2024
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The Gran Chaco is a large, dry forest in South America that has been heavily deforested, particularly in the dry Chaco subregion. This deforestation, mainly driven by the expansion of the agricultural frontier, has changed the land's characteristics, affecting the local and regional climate. The study reveals that deforestation has resulted in reduced precipitation, soil moisture, and runoff, and if intensive agriculture continues, it could make summers in this arid region even drier and hotter.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
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Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
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We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
Hydrol. Earth Syst. Sci., 28, 2579–2601, https://doi.org/10.5194/hess-28-2579-2024, https://doi.org/10.5194/hess-28-2579-2024, 2024
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The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, and Miroslav Trnka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1434, https://doi.org/10.5194/egusphere-2024-1434, 2024
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We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, which was clustered into 775/630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.
Caroline Legrand, Benoît Hingray, Bruno Wilhelm, and Martin Ménégoz
Hydrol. Earth Syst. Sci., 28, 2139–2166, https://doi.org/10.5194/hess-28-2139-2024, https://doi.org/10.5194/hess-28-2139-2024, 2024
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Climate change is expected to increase flood hazard worldwide. The evolution is typically estimated from multi-model chains, where regional hydrological scenarios are simulated from weather scenarios derived from coarse-resolution atmospheric outputs of climate models. We show that two such chains are able to reproduce, from an atmospheric reanalysis, the 1902–2009 discharge variations and floods of the upper Rhône alpine river, provided that the weather scenarios are bias-corrected.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
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Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Kyungmin Sung, Max C. A. Torbenson, and James H. Stagge
Hydrol. Earth Syst. Sci., 28, 2047–2063, https://doi.org/10.5194/hess-28-2047-2024, https://doi.org/10.5194/hess-28-2047-2024, 2024
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This study examines centuries of nonstationary trends in meteorological drought and pluvial climatology. A novel approach merges tree-ring proxy data (North American Seasonal Precipitation Atlas – NASPA) with instrumental precipitation datasets by temporally downscaling proxy data, correcting biases, and analyzing shared trends in normal and extreme precipitation anomalies. We identify regions experiencing recent unprecedented shifts towards drier or wetter conditions and shifts in seasonality.
Baoying Shan, Niko E. C. Verhoest, and Bernard De Baets
Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, https://doi.org/10.5194/hess-28-2065-2024, 2024
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This study developed a convenient and new method to identify the occurrence of droughts, heatwaves, and co-occurring droughts and heatwaves (CDHW) across four seasons. Using this method, we could establish the start and/or end dates of drought (or heatwave) events. We found an increase in the frequency of heatwaves and CDHW events in Belgium caused by climate change. We also found that different months have different chances of CDHW events.
Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-304, https://doi.org/10.5194/hess-2023-304, 2024
Revised manuscript accepted for HESS
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The global soil information contributes to uncertainty in many models that monitor soil hydrothermal changes. Using the NOAH-MP model with two different global soil information, we show under-represented soil properties in wet loam soil, leading to dry bias in soil moisture. The dry bias is higher and drought categories are more severe in SOILGRIDS. We conclude that models should consider using detailed regionally-derived soil information, to reduce model uncertainties.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
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Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Urmin Vegad, Yadu Pokhrel, and Vimal Mishra
Hydrol. Earth Syst. Sci., 28, 1107–1126, https://doi.org/10.5194/hess-28-1107-2024, https://doi.org/10.5194/hess-28-1107-2024, 2024
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A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive damage of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
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Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
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We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
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We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Alexander Gelfan, Andrey Panin, Andrey Kalugin, Polina Morozova, Vladimir Semenov, Alexey Sidorchuk, Vadim Ukraintsev, and Konstantin Ushakov
Hydrol. Earth Syst. Sci., 28, 241–259, https://doi.org/10.5194/hess-28-241-2024, https://doi.org/10.5194/hess-28-241-2024, 2024
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Paleogeographical data show that 17–13 ka BP, the Caspian Sea level was 80 m above the current level. There are large disagreements on the genesis of this “Great” Khvalynian transgression of the sea, and we tried to shed light on this issue. Using climate and hydrological models as well as the paleo-reconstructions, we proved that the transgression could be initiated solely by hydroclimatic factors within the deglaciation period in the absence of the glacial meltwater effect.
Joeri B. Reinders and Samuel E. Munoz
Hydrol. Earth Syst. Sci., 28, 217–227, https://doi.org/10.5194/hess-28-217-2024, https://doi.org/10.5194/hess-28-217-2024, 2024
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Flooding presents a major hazard for people and infrastructure along waterways; however, it is challenging to study the likelihood of a flood magnitude occurring regionally due to a lack of long discharge records. We show that hydroclimatic variables like Köppen climate regions and precipitation intensity explain part of the variance in flood frequency distributions and thus reduce the uncertainty of flood probability estimates. This gives water managers a tool to locally improve flood analysis.
Mehrad Rahimpour Asenjan, Francois Brissette, Jean-Luc Martel, and Richard Arsenault
Hydrol. Earth Syst. Sci., 27, 4355–4367, https://doi.org/10.5194/hess-27-4355-2023, https://doi.org/10.5194/hess-27-4355-2023, 2023
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Climate models are central to climate change impact studies. Some models project a future deemed too hot by many. We looked at how including hot models may skew the result of impact studies. Applied to hydrology, this study shows that hot models do not systematically produce hydrological outliers.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
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Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz
Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, https://doi.org/10.5194/hess-27-3719-2023, 2023
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Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.
Kaltrina Maloku, Benoit Hingray, and Guillaume Evin
Hydrol. Earth Syst. Sci., 27, 3643–3661, https://doi.org/10.5194/hess-27-3643-2023, https://doi.org/10.5194/hess-27-3643-2023, 2023
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High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
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In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
Yuanhong You, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu
Hydrol. Earth Syst. Sci., 27, 2919–2933, https://doi.org/10.5194/hess-27-2919-2023, https://doi.org/10.5194/hess-27-2919-2023, 2023
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This study aims to investigate the performance of a genetic particle filter which was used as a snow data assimilation scheme across different snow climates. The results demonstrated that the genetic algorithm can effectively solve the problem of particle degeneration and impoverishment in a particle filter algorithm. The system has revealed a low sensitivity to the particle number in point-scale application of the ground snow depth measurement.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 2013.
Anderson, E.: Snow Accumulation and Ablation Model – Snow-17, available at: http://www.nws.noaa.gov/oh/hrl/nwsrfs/users_manual/part2/_pdf/22snow17.pdf, (last access: 22 August 2016), 2006,
Arkin, P. A. and Ardanuy, P. E.: Estimating climatic-scale precipitation from space: a review, J. Climate, 2, 1229–1238, 1989.
Arnold, J. G., Kiniry, J. R., Srinivasan R., Williams, J. R, Haney, E. B., and Neitsch S. L.: SWAT Input/Output Documentation, Texas Water Resources Institute, TR-439, available at: http://swat.tamu.edu/media/69296/SWAT-IO-Documentation-2012.pdf, (last access: 22 August 2016), 2012,
Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., and Dozier, J.: Mountain hydrology of the western United States, Water Resour. Res., 42, W08432, https://doi.org/10.1029/2005wr004387, 2006.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005.
Battaglia, A., Rustemeier, E., Tokay, A., Blahak, U., and Simmer, C.: PARSIVEL Snow Observations: A Critical Assessment, J. Atmos. Ocean. Tech., 27, 333–344, https://doi.org/10.1175/2009jtecha1332.1, 2010.
Berghuijs, W. R., Woods, R. A., and Hrachowitz, M.: A precipitation shift from snow towards rain leads to a decrease in streamflow, Nature Climate Change, 4, 583–586, https://doi.org/10.1038/nclimate2246, 2014.
Bergström, S.: The HBV model, in: Computer Models of Watershed Hydrology, edited by: Singh, V. P., Water Resour. Publications, Highlands Ranch, CO, 443–476, 1995.
Bernauer, F., Hurkamp, K., Ruhm, W., and Tschiersch, J.: Snow event classification with a 2D video disdrometer – A decision tree approach, Atmos. Res., 172, 186–195, 2016.
Berris, S. N. and Harr, R. D.: Comparative snow accumulation and melt during rainfall in forested and clear-cut plots in the Western Cascades of Oregon, Water Resour. Res., 23, 135–142, https://doi.org/10.1029/WR023i001p00135, 1987.
Bicknell, B. R., Imhoff, J. C., Kittle Jr., J. L., Donigian Jr., A. S., and Johanson, R. C.: Hydrological Simulation Program–Fortran, User's manual for version 11: US Environmental Protection Agency, National Exposure Research Laboratory, Athens, Ga., EPA/600/R-97/080, p. 755, 1997.
Boe, E. T.: Assessing Local Snow Variability Using a Network of Ultrasonic Snow Depth Sensors, Master of Science in Hydrologic Sciences, Geosciences, Boise State, 2013.
Boodoo, S., Hudak, D., Donaldson, N., and Leduc, M.: Application of Dual-Polarization Radar Melting-Layer Detection Algorithm, J. Appl. Meteorol. Climatol., 49, 1779–1793, https://doi.org/10.1175/2010jamc2421.1, 2010.
Borrmann, S. and Jaenicke, R.: Application of microholography for ground-based in-situ measurements in stratus cloud layers – a case study, J. Atmos. Ocean. Tech., 10, 277–293, 1993.
Braun, L. N.: Simulation of snowmelt-runoff in lowland and lower alpine regions of Switzerland, Diss. Naturwiss, ETH Zürich, Nr. 7684 0000, edited by: Ohmura, A., Vischer, D., and Lang, H., 1984.
Cao, Q., Hong, Y., Chen, S., Gourley, J. J., Zhang, J., and Kirstetter, P. E.: Snowfall Detectability of NASA's CloudSat: The First Cross-Investigation of Its 2C-Snow-Profile Product and National Multi-Sensor Mosaic QPE (NMQ) Snowfall Data, Prog. Electromagn. Res., 148, 55–61, https://doi.org/10.2528/pier14030405, 2014.
Cayan, D. R., Kammerdiener, S. A., Dettinger, M. D., Caprio, J. M., and Peterson, D. H.: Changes in the onset of spring in the western United States, B. Am. Meteorol. Soc., 82, 399–415, 2001.
Chandrasekar, V., Keranen, R., Lim, S., and Moisseev, D.: Recent advances in classification of observations from dual polarization weather radars, Atmos. Res., 119, 97–111, https://doi.org/10.1016/j.atmosres.2011.08.014, 2013.
Chen, S., Gourley, J. J., Hong, Y., Cao, Q., Carr, N., Kirstetter, P.-E., Zhang, J., and Flamig, Z.: Using citizen science reports to evaluate estimates of surface precipitation type, B. Am. Meteorol. Soc., 187–193, https://doi.org/10.1175/BAMS-D-13-00247.1, 2015.
Dai, A.: Temperature and pressure dependence of the rain-snow phase transition over land and ocean, Geophys. Res. Lett., 35, L12802, https://doi.org/10.1029/2008gl033295, 2008.
Ding, B., Yang, K., Qin, J., Wang, L., Chen, Y., and He, X.: The dependence of precipitation types on surface elevation and meteorological conditions and its parameterization, J. Hydrol., 513, 154–163, https://doi.org/10.1016/j.jhydrol.2014.03.038, 2014.
Eddington, L. W.: Satellite-Derived Moisture-Bogusing Profiles for the North Atlantic Ocean, DTIC Document, 1989.
Elmore, K. L.: The NSSL Hydrometeor Classification Algorithm in Winter Surface Precipitation: Evaluation and Future Development, Weather Forecast., 26, 756–765, https://doi.org/10.1175/waf-d-10-05011.1, 2011.
Fang, X., Pomeroy, J. W., Ellis, C. R., MacDonald, M. K., DeBeer, C. M., and Brown, T.: Multi-variable evaluation of hydrological model predictions for a headwater basin in the Canadian Rocky Mountains, Hydrol. Earth Syst. Sci., 17, 1635–1659, https://doi.org/10.5194/hess-17-1635-2013, 2013.
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarboton, D.: An overview of current applications, challenges, and future trends in distributed process-based models in hydrology, J. Hydrol., 537, 45–60, 2016.
Feiccabrino, J., Lundberg, A., and Gustafsson, D.: Improving surface-based precipitation phase determination through air mass boundary identification, Hydrol. Rese., 43, 179–191, https://doi.org/10.2166/nh.2012.060, 2013.
Feiccabrino, J., Gustafsson, D., and Lundberg, A.: Surface-based precipitation phase determination methods in hydrological models, Hydrol. Res., 44, 44–57, 2015.
Floyd, W. and Weiler, M.: Measuring snow accumulation and ablation dynamics during rain-on-snow events: innovative measurement techniques, Hydrol. Proc., 22, 4805–4812, https://doi.org/10.1002/hyp.7142, 2008.
Fritze, H., Stewart, I. T., and Pebesma, E.: Shifts in Western North American Snowmelt Runoff Regimes for the Recent Warm Decades, J. Hydrometeorol., 12, 989–1006, https://doi.org/10.1175/2011jhm1360.1, 2011.
Froidurot, S., Zin, I., Hingray, B., and Gautheron, A.: Sensitivity of Precipitation Phase over the Swiss Alps to Different Meteorological Variables, J. Hydrometeorol., 15, 685–696, https://doi.org/10.1175/jhm-d-13-073.1, 2014.
Garvelmann, J., Pohl, S., and Weiler, M.: From observation to the quantification of snow processes with a time-lapse camera network, Hydrol. Earth Syst. Sci., 17, 1415–1429, https://doi.org/10.5194/hess-17-1415-2013, 2013.
Giangrande, S. E., Krause, J. M., and Ryzhkov, A. V.: Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar, J. Appl. Meteorol. Climatol., 47, 1354–1364, https://doi.org/10.1175/2007jamc1634.1, 2008.
Gilmore, M. S., Straka, J. M., and Rasmussen, E. N.: Precipitation Uncertainty Due to Variations in Precipitation Particle Parameters within a Simple Microphysics Scheme, Mon. Weather Rev., 132, 2610–2627, https://doi.org/10.1175/MWR2810.1, 2004.
Godsey, S. E., Kirchner, J. W., and Tague, C. L.: Effects of changes in winter snowpacks on summer low flows: case studies in the Sierra Nevada, California, USA, Hydrol. Proc., 28, 5048–5064, https://doi.org/10.1002/hyp.9943, 2014.
Grazioli, J., Tuia, D., and Berne, A.: Hydrometeor classification from polarimetric radar measurements: a clustering approach, Atmos. Meas. Tech., 8, 149–170, https://doi.org/10.5194/amt-8-149-2015, 2015.
Gusev, E. M. and Nasonova, O. N.: Parameterization of Heat and Water Exchange on Land Surface for Coupling Hydrologic and Climate Models, Water Resour., 25, 421–431, 1998.
Gutmann, E. D., Rasmussen, R. M., Liu, C., Ikeda, K., Gochis, D., Clark, P. P., Dudhia, J., and Gregory, T.: A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain, J. Clim., 25, 262–281, 2012.
Harder, P. and Pomeroy, J.: Estimating precipitation phase using a psychrometric energy balance method, Hydrol. Proc., 27, 1901–1914, https://doi.org/10.1002/hyp.9799, 2013.
Harder, P. and Pomeroy, J. W.: Hydrological model uncertainty due to precipitation-phase partitioning methods, Hydrol. Proc., 28, 4311–4327, 2014.
Hauser, D., Amayenc, P., and Nutten, B.: A new optical instrument for simultaneous measurement of raindrop diameter and fall speed distributions, Atmos. Oceanic Technol., 1, 256–259, 1984.
HEC-1: Flood Hydrograph Package, User's Manual, CPD-1A, Version 4.1, available at: http://www.hec.usace.army.mil/publications/ComputerProgramDocumentation/HEC-1_UsersManual_(CPD-1a).pdf, (last access: 22 August 2016), 1998,
Hedrick, A. R. and Marshall, H.-P.: Automated Snow Depth Measurements in Avalanche Terrain Using Time-Lapse Photography, 2014 International Snow Science Workshop, 2014.
Holden, Z. A., Abatzoglou, J. T., Luce, C. H., and Baggett, L. S.: Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain, Agr. Forest Meteorol., 151, 1066–1073, https://doi.org/10.1016/j.agrformet.2011.03.011, 2011.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The global precipitation measurement mission, B. Am. Meteorol. Soc., 95, 701–722, 2014.
Isaac, G. A., Joe, P. I., Mailhot, J., Bailey, M., Bélair, S., Boudala, F. S., and Wilson, L. J.: Science of nowcasting Olympic weather for Vancouver 2010 (SNOW-V10): A World Weather Research Programme project, Pure Appl. Geophys., 171, 1–24, https://doi.org/10.1007/s00024-012-0579-0, 2014.
Jepsen, S. M., Harmon, T. C., Meadows, M. W., and Hunsaker, C. T.: Hydrogeologic influence on changes in snowmelt runoff with climate warming: Numerical experiments on a mid-elevation catchment in the Sierra Nevada, USA, J. Hydrol., 533, 332–342, https://doi.org/10.1016/j.jhydrol.2015.12.010, 2016.
Joss, J. and Waldvogel, A.: Ein Spektograph fuer Niederschlagstropfen mit automatischer Auswertung, Pure Appl. Geophys., 68, 240–246, 1967.
Kalnay, E. and Cai, M.: Impact of urbanization and land-use change on climate, Nature, 423, 528–531, https://doi.org/10.1038/nature01675, 2003.
Kaplan, M. L., Vellore, R. K., Marzette, P. J., and Lewis, J. M.: The role of windward-side diabatic heating in Sierra Nevada spillover precipitation, J. Hydrometeorol., 13, 1172–1194, 2012.
Kapnick, S. B. and Delworth, T. L.: Controls of global snow under a changed climate, J. Clim., 26, 5537–5562, 2013.
Kidd, C.: On rainfall retrieval using polarization-corrected temperatures, Int. J. Remote Sens., 19, 981–996, https://doi.org/10.1080/014311698215829, 1998.
Kidd, C. and Huffman, G.: Global precipitation measurement, Meteorol. Appl., 18, 334–353, https://doi.org/10.1002/met.284, 2011.
Kienzle, S. W.: A new temperature based method to separate rain and snow, Hydrol. Proc., 22, 5067–5085, https://doi.org/10.1002/hyp.7131, 2008.
Kim, M. J., Weinman, J. A., Olson, W. S., Chang, D. E., Skofronick-Jackson, G., and Wang, J. R.: A physical model to estimate snowfall over land using AMSU-B observations, J. Geophys. Res.-Atmos., 113, D09201, https://doi.org/10.1029/2007jd008589, 2008.
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, https://doi.org/10.1029/2005wr004362, 2006.
Kite, G.: The HBV model, in: Computer Models of Watershed Hydrology, edited by: Singh, V.P., Water Resources Publications, Highlands Ranch, CO, 443–476, 1995.
Klos, P. Z., Link, T. E., and Abatzoglou, J. T.: Extent of the rain-snow transition zone in the western US under historic and projected climate, Geophys. Res. Lett., 41, 4560–4568, https://doi.org/10.1002/2014gl060500, 2014.
Knollenberg, R. G.: Some results of measurements of latent heat released from seeded stratus, B. Am. Meteorol. Soc., 51, 580–592, 1970.
Knowles, N., Dettinger, M. D., and Cayan, D. R.: Trends in snowfall versus rainfall in the Western United States, J. Clim., 19, 4545–4559, 2006.
Kongoli, C., Pellegrino, P., Ferraro, R. R., Grody, N. C., and Meng, H.: A new snowfall detection algorithm over land using measurements from the Advanced Microwave Sounding Unit (AMSU), Geophys. Res. Lett., 30, 1756–1763, https://doi.org/10.1029/2003gl017177, 2003.
Kongoli, C., Meng, H., Dong, J., and Ferraro, R.: A snowfall detection algorithm over land utilizing high-frequency passive microwave measurements-Application to ATMS, J. Geophys. Res.-Atmos., 120, 1918–1932, https://doi.org/10.1002/2014jd022427, 2015.
Kruger, A. and Krajewski, W. F.: Two-dimensional video disdrometer: A description, J. Atmos. Ocean. Tech., 19, 602–617, https://doi.org/10.1175/1520-0426(2002)019<0602:tdvdad>2.0.co;2, 2002.
Kulie, M. S., Milani, L., Wood, N. B., Tushaus, S. A., Bennartz, R., and L'Ecuyer, T. S.: A Shallow Cumuliform Snowfall Census Using Spaceborne Radar, J. Hydrometeorol., 17, 1261–1279, https://doi.org/10.1175/jhm-d-15-0123.1, 2016.
Leavesley, G. H., Restrepo, P. J., Markstrom, S. L., Dixon, M., and Stannard, L. G.: The Modular Modeling System (MMS): User's Manual, US Geological Survey, Denver, COOpen File Report 96-151, 1996.
Lempio, G. E., Bumke, K., and Macke, A.: Measurement of solid precipitation with an optical disdrometer, Adv. Geosci., 10, 91–97, 2007.
Lewis, J., Lakshmivarahan, S., and Dhall, S.: Dynamic Data Assimilation: A Least Squares Approach, Cambridge Univ. Press, 745 pp., 2006.
L'hôte, Y., Chevallier, P., Coudrain, A., Lejeune, Y., and Etchevers, P.: Relationship between precipitation phase and air temperature: comparison between the Bolivian Andes and the Swiss Alps/Relation entre phase de précipitation et température de l'air: comparaison entre les Andes Boliviennes et les Alpes Suisses, Hydrol. Sci. J., 50, 989–997, 2005.
Lin, Y.-L.: Mesoscale Dynamics, Cambridge University Press, 630 pp., 2007.
Liu, G.: Deriving snow cloud characteristics from CloudSat observations, J. Geophys. Res.-Atmos., 113, D00A09, https://doi.org/10.1029/2007jd009766, 2008.
Loffler-Mang, M., Kunz, M., and Schmid, W.: On the performance of a low-cost K-band Doppler radar for quantitative rain measurements, J. Atmos. Ocean. Tech., 16, 379–387, https://doi.org/10.1175/1520-0426(1999)016<0379:otpoal>2.0.co;2, 1999.
Luce, C. H. and Holden, Z. A.: Declining annual streamflow distributions in the Pacific Northwest United States, 1948–2006, Geophys. Res. Lett., 36, L16401, https://doi.org/10.1029/2009gl039407, 2009.
Lundquist, J. D., Neiman, P. J., Martner, B., White, A. B., Gottas, D. J., and Ralph, F. M.: Rain versus snow in the Sierra Nevada, California: Comparing Doppler profiling radar and surface observations of melting level, J. Hydrometeorol., 9, 194–211, https://doi.org/10.1175/2007jhm853.1, 2008.
Lynch-Stieglitz, M.: The development and validation of a simple snow model for the GISS GCM, J. Clim., 7, 1842–1855, 1994.
Marks, D., Link, T., Winstral, A., and Garen, D.: Simulating snowmelt processes during rain-on-snow over a semi-arid mountain basin, Ann. Glaciol., 32, 195–202, 2001.
Marks, D., Winstral, A., Reba, M., Pomeroy, J., and Kumar, M.: An evaluation of methods for determining during-storm precipitation phase and the rain/snow transition elevation at the surface in a mountain basin, Adv. Water Resour., 55, 98–110, 2013.
Martinec, J. and Rango, A.: Parameter values for snowmelt runoff modelling, J. Hydrol., 84, 197–219, 1986.
Martinec, J., Rango, A., and Roberts, R.: Snowmelt Runoff Model, User's Manual, available at: http://aces.nmsu.edu/pubs/research/weather_climate/SRMSpecRep100.pdf, (last access: 22 August 2016), 2008,
Matrosov, S. Y., Shupe, M. D., and Djalalova, I. V.: Snowfall retrievals using millimeter-wavelength cloud radars, J. Appl. Meteorol. Climatol., 47, 769–777, https://doi.org/10.1175/2007jamc1768.1, 2008.
Maurer, E. P. and Mass, C.: Using radar data to partition precipitation into rain and snow in a hydrologic model, J. Hydrol. Engin., 11, 214–221, https://doi.org/10.1061/(asce)1084-0699(2006)11:3(214), 2006.
McCabe, G. J. and Wolock, D. M.:General-circulation-model simulations of future snowpack in the western United States1, JAWRA J. Am. Water Resour. Assoc., 35, 1473–1484, 1999a.
McCabe, G. J. and Wolock, D. M.: Recent Declines in Western US Snowpack in the Context of Twentieth-Century Climate Variability, Earth Interact., 13, 1–15, https://doi.org/10.1175/2009EI283.1, 1999b.
McCabe, G. J. and Wolock, D. M.: Long-term variability in Northern Hemisphere snow cover and associations with warmer winters, Climatic Change, 99, 141–153, 2010.
McCabe, G. J., Clark, M. P., and Hay, L. E.: Rain-on-snow events in the western United States, Bull. Am. Meteorol. Soc., 88, 319–328, https://doi.org/10.1175/bams-88-3-319, 2007.
MIKE-SHE User Manual, available at: ftp://ftp.cgs.si/Uporabniki/UrosZ/mike/Manuals/MIKE_SHE/MIKE_SHE.htm, last acces: 22 August 2016.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Climate change – Stationarity is dead: Whither water management?, Science, 319, 573–574, https://doi.org/10.1126/science.1151915, 2008.
Minder, J. R.: The Sensitivity of Mountain Snowpack Accumulation to Climate Warming, J. Clim., 23, 2634–2650, https://doi.org/10.1175/2009jcli3263.1, 2010.
Minder, J. R. and Kingsmill, D. E.: Mesoscale Variations of the Atmospheric Snow Line over the Northern Sierra Nevada: Multiyear Statistics, Case Study, and Mechanisms, J. Atmos. Sci., 70, 916–938, https://doi.org/10.1175/jas-d-12-0194.1, 2013.
Mitchell K., Ek, M., Wong, V., Lohmann, D., Koren, V., Schaake, J., Duan, Q., Gayno, G., Moore, B., Grunmann, P., Tarpley, D., Ramsay, B., Chen, F., Kim, J., Pan, H.L., Lin, Y., Marshall, C., Mahrt, L., Meyers, T., and Ruscher, P.: Noah Land-Surface Model, User's Guide, version 2.7.1, available at: ftp://ftp.emc.ncep.noaa.gov/mmb/gcp/ldas/noahlsm/ver_2.7.1, (last access: 22 August 2016), 2005.
Mizukami, N., Koren, V., Smith, M., Kingsmill, D., Zhang, Z. Y., Cosgrove, B., and Cui, Z. T.: The Impact of Precipitation Type Discrimination on Hydrologic Simulation: Rain-Snow Partitioning Derived from HMT-West Radar-Detected Brightband Height versus Surface Temperature Data, J. Hydrometeorol., 14, 1139–1158, https://doi.org/10.1175/jhm-d-12-035.1, 2013.
Morrison, H., Curry, J., and Khvorostyanov, V.: A new double-moment microphysics parameterization for application in cloud and climate models, Part I: Description, J. Atmos. Sci., 62, 1665–1677, 2005.
Motoyama, H.: Simulation of seasonal snowcover based on air temperature and precipitation, J. Appl. Meteorol., 29, 1104–1110, 1990.
Newman, A. J., Kucera, P. A., and Bliven, L. F.: Presenting the snowflake video imager (SVI), J. Atmos. Ocean. Tech., 26, 167–179, https://doi.org/10.1175/2008jtecha1148.1, 2009.
NOAA: NEXRAD Data Archive, Inventory and Access, available at: https://www.ncdc.noaa.gov/nexradinv/, last access: 11 October 2016.
Noh, Y. J., Liu, G. S., Jones, A. S., and Haar, T. H. V.: Toward snowfall retrieval over land by combining satellite and in situ measurements, J. Geophys. Res.-Atmos., 114, D24205, https://doi.org/10.1029/2009jd012307, 2009.
O'Gorman, P. A.: Contrasting responses of mean and extreme snowfall to climate change, Nature, 512, 416–418, 2014.
Olsen, A.: Snow or rain? – A matter of wet-bulb temperature, thesis, Uppsala Univ., Uppsala, Sweden, available at: http://www.geo.uu.se/luva/exarb/2003/Arvid_Olsen. pdf (last access: 22 August 2016), 2003.
Olson, W. S., Kummerow, C. D., Heymsfield, G. M., and Giglio, L.: A method for combined passive-active microwave retrievals of cloud and precipitation profiles, J. Appl. Meteorol., 35, 1763–1789, https://doi.org/10.1175/1520-0450(1996)035<1763:amfcpm>2.0.co;2, 1996.
Pachauri, R. K.: Intergovernmental panel on climate change (IPCC): Keynote address, Environ. Sci. Pollut. Res., 9, 436–438, 2002.
Pagano, T. C., Wood, A. W., Ramos, M. H., Cloke, H. L., Pappenberger, F., Clark, M. P., Cranston, M., Kavetski, D., Mathevet, T., Sorooshian, S., and Verkade, J. S.: Challenges of Operational River Forecasting, J. Hydrometeorol., 15, 1692–1707, https://doi.org/10.1175/jhm-d-13-0188.1, 2014.
Parajka, J., Haas, P., Kirnbauer, R., Jansa, J., and Bloeschl, G.: Potential of time-lapse photography of snow for hydrological purposes at the small catchment scale, Hydrol. Proc., 26, 3327–3337, https://doi.org/10.1002/hyp.8389, 2012.
Park, H., Ryzhkov, A. V., Zrnic, D. S., and Kim, K.-E.: The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS, Weather Forecast., 24, 730–748, https://doi.org/10.1175/2008waf2222205.1, 2009.
Pierce, D. W. and Cayan, D. R.: The uneven response of different snow measures to human-induced climate warming, J. Clim., 26, 4148–4167, 2013.
Pierce, D. W. and Cayan, D. R.: Downscaling humidity with localized constructed analogs (LOCA) over the conterminous united states, Clim. Dynam., 47, 411–431, 2016.
Pierce, D. W., Westerling, A. L., and Oyler, J.: Future humidity trends over the western United States in the CMIP5 global climate models and variable infiltration capacity hydrological modeling system, Hydrol. Earth Syst. Sci., 17, 1833–1850, https://doi.org/10.5194/hess-17-1833-2013, 2013.
Pipes, A. and Quick, M. C.: UBC watershed model users guide, Department of Civil Engineering, University of British Columbia, 1977.
Rajagopal, S. and Harpold, A.: Testing and Improving Temperature Thresholds for Snow and Rain Prediction in the Western United States, J. Am. Water Resour. Assoc., 2016.
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer, A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu, R., Hall, M., Ikeda, K., and Gutmann, E.: How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed, B. Am. Meteorol. Soc., 93, 811–829, https://doi.org/10.1175/BAMS-D-11-00052.1, 2012.
Reisner, J., Rasmussen, R. M., and Bruintjes, R.: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model, Q. J. R. Meteor. Soc., 124, 1071–1107, 1998.
Rojas, R., Feyen, L., Dosio, A., and Bavera, D.: Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations, Hydrol. Earth Syst. Sci., 15, 2599–2620, https://doi.org/10.5194/hess-15-2599-2011, 2011.
Safeeq, M., Mauger, G. S., Grant, G. E., Arismendi, I., Hamlet, A. F., and Lee, S. Y.: Comparing Large-Scale Hydrological Model Predictions with Observed Streamflow in the Pacific Northwest: Effects of Climate and Groundwater, J. Hydrometeorol., 15, 2501–2521, https://doi.org/10.1175/jhm-d-13-0198.1, 2014.
Sevruk, B.: Assessment of snowfall proportion in monthly precipitation in Switzerland, Zbornik meteoroloskih i Hidroloskih Radovav Beograd, 10, 315–318, 1984.
Shamir, E. and Georgakakos, K. P.: Distributed snow accumulation and ablation modeling in the American River basin, Adv. Water Resour., 29, 558–570, https://doi.org/10.1016/j.advwatres.2005.06.010, 2006.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the advanced research WRF version 3NCAR Tech. Note NCAR/TN-475+STR, 113, 65–83, 2008.
Skofronick-Jackson, G., Hudak, D., Petersen, W., Nesbitt, S. W., Chandrasekar, V., Durden, S., Gleicher, K. J., Huang, G.-J., Joe, P., Kollias, P., Reed, K. A., Schwaller, M. R., Stewart, R., Tanelli, S., Tokay, A., Wang, J. R., and Wolde, M.: Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEX): For Measurement's Sake, Let It Snow, B. Am. Meteorol. Soc., 96, 1719–1741, https://doi.org/10.1175/BAMS-D-13-00262.1, 2015.
SNTHERM: Online Documentation, available at: http://www.geo.utexas.edu/climate/Research/SNOWMIP/SUPERSNOW2/rjordan.html, last access: 22 August 2016.
Stewart, R. E.: Precipitation Types in the Transition Region of Winter Storms, B. Am. Meteorol. Soc., 73, 287–296, 1992.
Stewart, R. E., Theriault, J. M., and Henson, W.: On the Characteristics of and Processes Producing Winter Precipitation Types near 0 °C, B. Am. Meteorol. Soc., 96, 623–639, https://doi.org/10.1175/bams-d-14-00032.1, 2015.
Tague, C. L. and Band, LE.: RHESSys: Regional Hydro-Ecologic Simulation System – An Object Oriented Approach to Spatially Distributed Modeling of Carbon, Water, and Nutrient Cycling, Earth Interact., 8, 1–42, 2004.
Tarboton, D., Jackson, T., Liu, J., Neale, C., Cooley, K., and McDonnell, J.: A Grid Based Distributed Hydrologic Model: Testing Against Data from Reynolds Creek Experimental Watershed, Preprints AMS Conf. Hydrol, 79–84, 1995.
Tarboton, D. G. and Luce, C. H.: Utah Energy Balance Snow Accumulation and Melt Model (UEB), available at: http://www.fs.fed.us/rm/boise/publications/watershed/rmrs_1996_tarbotond001.pdf, (last access: 22 August 2016), 1996,
Thériault, J. M. and Stewart, R. E.: On the effects of vertical air velocity on winter precipitation types, Nat. Hazards Earth Syst. Sci., 7, 231–242, https://doi.org/10.5194/nhess-7-231-2007, 2007.
Theriault, J. M. and Stewart, R. E.: A Parameterization of the Microphysical Processes Forming Many Types of Winter Precipitation, J. Atmos. Sci., 67, 1492–1508, https://doi.org/10.1175/2009jas3224.1, 2010.
Theriault, J. M., Stewart, R. E., and Henson, W.: On the Dependence of Winter Precipitation Types on Temperature, Precipitation Rate, and Associated Features, J. Appl. Meteorol. Clim., 49, 1429–1442, https://doi.org/10.1175/2010jamc2321.1, 2010.
Theriault, J. M., Stewart, R. E., and Henson, W.: Impacts of terminal velocity on the trajectory of winter precipitation types, Atmos. Res., 116, 116–129, https://doi.org/10.1016/j.atmosres.2012.03.008, 2012.
Thompson, E. J., Rutledge, S. A., Dolan, B., Chandrasekar, V., and Cheong, B. L.: A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation, J. Atmos. Ocean. Tech., 31, 1457–1481, https://doi.org/10.1175/jtech-d-13-00119.1, 2014.
Thompson, G., Rasmussen, R. M., and Manning, K.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme, Part I: Description and sensitivity analysis, Mon. Weather Rev., 132, 519–542, 2004.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme, Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, 2008.
Todini, E.: The ARNO Rainfall-runoff model, J. Hydrol., 175, 339–382, 1996.
Tung, C.-P. and Haith, D. A.: Global-warming effects on New York streamflows, J. Water Resour. Plann. Manage., 121, 216–225, 1995.
US Army Corps of Engineers: Summary Report of the Snow Investigation Hydrological Practices, 3rd Edn., Chapter 2, North Pacific Division, Portland, Oregon, 54–56, 1956.
Verseghy, D.: CLASS-The Canadian Land Surface Scheme, Version 3.4, Technical Documentation, Version 1.1, Environment Canada, available at: http://www.usask.ca/ip3/download/CLASS_v3_4_Documentation_v1_1.pdf, (last access: 22 August 2016), 2009.
VIC Documentation: Development and Maintenance Documentation, available at: https://vic.readthedocs.io/en/develop/, last access: 22 August 2016.
Wang, R., Kumar, M., and Marks, D.: Anomalous trend in soil evaporation in a semi-arid, snow-dominated watershed, Adv. Water Resour., 57, 32–40, 2013.
Wang, R., Kumar, M., and Link, T. E.: Potential trends in snowmelt generated peak streamflows in a warming climate, Geophys. Res. Lett. 43, 5052–5059, https://doi.org/10.1002/2016GL068935, 2016.
Wen, L., Nagabhatla, N., Lu, S., and Wang, S.-Y.: Impact of rain snow threshold temperature on snow depth simulation in land surface and regional atmospheric models, Adv. Atmos. Sci., 30, 1449–1460, https://doi.org/10.1007/s00376-012-2192-7, 2013.
White, A. B., Gottas, D. J., Strem, E. T., Ralph, F. M., and Neiman, P. J.: An automated brightband height detection algorithm for use with Doppler radar spectral moments, J. Atmos. Ocean. Tech., 19, 687–697, 2002.
White, A. B., Gottas, D. J., Henkel, A. F., Neiman, P. J., Ralph, F. M., and Gutman, S. I.: Developing a Performance Measure for Snow-Level Forecasts, J. Hydrometeorol., 11, 739–753, https://doi.org/10.1175/2009jhm1181.1, 2010.
Whiteman, C. D., Bian, X., and Zhong, S.: Wintertime evolution of the temperature inversion in the colorado plateau basin, J. Appl. Meteorol., 38, 1103–1117, 1999.
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed hydrology-vegetation model for complex terrain, Water Resour. Res., 30, 1665–1679, 1994.
Wilheit, T. T., Chang, A. T. C., King, J. L., Rodgers, E. B., Nieman, R. A., Krupp, B. M., Milman, A. S., Stratigos, J. S., and Siddalingaiah, H.: Microwave radiometric observation nea 19.35, 92 and 183 GHz of precipitation in tropical storm Cora, J. Appl. Meteorol., 21, 1137–1145, 1982.
Wood, A. W., Leung, L. R., Sridhar, V., and Lettenmaier, D.: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Climatic Change, 62, 189–216, 2004.
Wood, N., L'Ecuyer, T. S., Vane, D., Stephens, G., and Partain, P.: Level 2C snow profile process description and interface control document, 2013.
Yamazaki, T.: A One-dimensional Land Surface Model Adaptable to Intensely Cold Regions and its Applications in Eastern Siberia, J. Meteorol. Soc. Jap., 79, 1107–1118, 2001.
Yang, Z. L., Dickinson, R. E., Robock, A., and Vinniko, K. Y.: Validation of the Snow Submodel of the Biosphere–Atmosphere Transfer Scheme with Russian Snow Cover and Meteorological Observational Data, J. Clim., 10, 353–373, 1997.
Yarnell, S. M., Viers, J. H., and Mount, J. F.: Ecology and Management of the Spring Snowmelt Recession, Bioscience, 60, 114–127, https://doi.org/10.1525/bio.2010.60.2.6, 2010.
Ye, H., Cohen, J., and Rawlins, M.: Discrimination of Solid from Liquid Precipitation over Northern Eurasia Using Surface Atmospheric Conditions, J. Hydrometeorol., 14, 1345–1355, https://doi.org/10.1175/jhm-d-12-0164.1, 2013.
Yucel, I., Onen, A., Yilmaz, K. K., and Gochis, D. J.: Calibration and evaluation of a flood forecasting sytem: Utility of numerical weather prediction model, data assimilate, and satellite-based rainfall, J. Hydrol., 523, 49–66, https://doi.org/10.1016/j.hydrol.2015.01.042, 2015.
Zängl, G.: Interaction between dynamics and cloud microphysics in orographic precipitation enhancement: A high-resolution modeling study of two North Alpine heavy-precipitation events, Mon. Weather Rev., 135, 2817–2840, 2007.
Zanotti, F., Endrizzi, S., Bertoldi, G., and Rigon, R.: The GEOTOP snow module, Hydro. Proc., 18, 3667–3679, https://doi.org/10.1002/hyp.5794, 2004.
Short summary
The phase of precipitation as rain or snow is fundamental to hydrological processes and water resources. Despite its importance, the methods used to predict precipitation phase are inconsistent and often overly simplified. We review these methods and underlying mechanisms that control phase. We present a vision to meet important research gaps needed to improve prediction, including new field-based and remote measurements, validating new and existing methods, and expanding regional prediction.
The phase of precipitation as rain or snow is fundamental to hydrological processes and water...
