Articles | Volume 19, issue 7
https://doi.org/10.5194/hess-19-3153-2015
© Author(s) 2015. 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-19-3153-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
20 Jul 2015
Research article |
| 20 Jul 2015
Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework
M. S. Raleigh
CORRESPONDING AUTHOR
National Center for Atmospheric Research, Boulder, Colorado, USA
J. D. Lundquist
Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
M. P. Clark
National Center for Atmospheric Research, Boulder, Colorado, USA
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Cécile B. Ménard, Richard Essery, Alan Barr, Paul Bartlett, Jeff Derry, Marie Dumont, Charles Fierz, Hyungjun Kim, Anna Kontu, Yves Lejeune, Danny Marks, Masashi Niwano, Mark Raleigh, Libo Wang, and Nander Wever
Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, https://doi.org/10.5194/essd-11-865-2019, 2019
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This paper describes long-term meteorological and evaluation datasets from 10 reference sites for use in snow modelling. We demonstrate how data sharing is crucial to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The ease of use, availability, and quality of the datasets will help model developers quantify and reduce model uncertainties and errors.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
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Hydrol. Earth Syst. Sci., 23, 2601–2614, https://doi.org/10.5194/hess-23-2601-2019, https://doi.org/10.5194/hess-23-2601-2019, 2019
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We find that Nash–Sutcliffe (NSE)-based model calibrations result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. The use of Kling–Gupta efficiency (KGE) results in annual peak flow estimates that are better than from NSE, with only a slight degradation in performance with respect to other related metrics.
Cécile B. Ménard, Richard Essery, Alan Barr, Paul Bartlett, Jeff Derry, Marie Dumont, Charles Fierz, Hyungjun Kim, Anna Kontu, Yves Lejeune, Danny Marks, Masashi Niwano, Mark Raleigh, Libo Wang, and Nander Wever
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
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Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, https://doi.org/10.5194/hess-21-5293-2017, 2017
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We introduce a data set describing the landscape of 671 catchments in the contiguous USA: we synthesized various data sources to characterize the topography, climate, streamflow, land cover, soil, and geology of each catchment. This extends the daily time series of meteorological forcing and discharge provided by an earlier study. The diversity of these catchments will help to improve our understanding and modeling of how the interplay between catchment attributes shapes hydrological processes.
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Lieke Melsen, Adriaan Teuling, Paul Torfs, Massimiliano Zappa, Naoki Mizukami, Martyn Clark, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 20, 2207–2226, https://doi.org/10.5194/hess-20-2207-2016, https://doi.org/10.5194/hess-20-2207-2016, 2016
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In this study we investigated the sensitivity of a large-domain hydrological model for spatial and temporal resolution. We evaluated the results on a mesoscale catchment in Switzerland. Our results show that the model was hardly sensitive for the spatial resolution, which implies that spatial variability is likely underestimated. Our results provide a motivation to improve the representation of spatial variability in hydrological models in order to increase their credibility on a smaller scale.
Lieke A. Melsen, Adriaan J. Teuling, Paul J. J. F. Torfs, Remko Uijlenhoet, Naoki Mizukami, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 20, 1069–1079, https://doi.org/10.5194/hess-20-1069-2016, https://doi.org/10.5194/hess-20-1069-2016, 2016
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A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan
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H. V. Gupta, C. Perrin, G. Blöschl, A. Montanari, R. Kumar, M. Clark, and V. Andréassian
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Rainfall and snowfall spatial estimation for hydrological purposes is often compromised in cold mountain regions due to inaccessibility, creating sparse gauge networks with few high-elevation gauges. This study developed a framework for quantifying gauge network uncertainty, considering elevation to aid in future gauge placement in mountain regions. Results show that gauge placement above 2000 m is the most cost-effective measure to decrease gauge network uncertainty in the Canadian Rockies.
Kevin K. W. Cheung, Fei Ji, Nidhi Nishant, Jin Teng, James Bennett, and De Li Liu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-286, https://doi.org/10.5194/hess-2024-286, 2024
Revised manuscript accepted for HESS
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This study evaluates two reanalysis datasets, which are critical in climate, weather research and water resources analysis, for the Australian region in simulating mean precipitation and six selected precipitation extremes. While spatial patterns of mean precipitation are well reproduced, substantial biases exist in precipitation variability, trends and extremes. Caution in applying these datasets is thus advised in terms of the latter aspects.
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Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023, https://doi.org/10.5194/hess-27-3375-2023, 2023
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We present the results of a large visual inspection campaign of 674 streamflow time series in France. The objective was to detect non-natural records resulting from instrument failure or anthropogenic influences, such as hydroelectric power generation or reservoir management. We conclude that the identification of flaws in flow time series is highly dependent on the objectives and skills of individual evaluators, and we raise the need for better practices for data cleaning.
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The present study refers to the region of Bologna, where the availability of a 209-year-long daily rainfall series allows us to make a unique assessment of global climate models' reliability and their predicted changes in rainfall and multiyear droughts. Our results suggest carefully considering the impact of uncertainty when designing climate change adaptation policies for droughts. Rigorous use and comprehensive interpretation of the available information are needed to avoid mismanagement.
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Land–atmosphere (L–A) interactions typically focus on daytime processes connecting the land state with the overlying atmospheric boundary layer. However, much prior L–A work used monthly or daily means due to the lack of daytime-only data products. Here we show that monthly smoothing can significantly obscure the L–A coupling signal, and including nighttime information can mute or mask the daytime processes of interest. We propose diagnosing L–A coupling within models or archiving subdaily data.
Lei Xu, Nengcheng Chen, Chao Yang, Hongchu Yu, and Zeqiang Chen
Hydrol. Earth Syst. Sci., 26, 2923–2938, https://doi.org/10.5194/hess-26-2923-2022, https://doi.org/10.5194/hess-26-2923-2022, 2022
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Precipitation forecasting has potential uncertainty due to data and model uncertainties. Here, an integrated predictive uncertainty modeling framework is proposed by jointly considering data and model uncertainties through an uncertainty propagation theorem. The results indicate an effective predictive uncertainty estimation for precipitation forecasting, indicating the great potential for uncertainty quantification of numerous predictive applications.
Thibault Lemaitre-Basset, Ludovic Oudin, Guillaume Thirel, and Lila Collet
Hydrol. Earth Syst. Sci., 26, 2147–2159, https://doi.org/10.5194/hess-26-2147-2022, https://doi.org/10.5194/hess-26-2147-2022, 2022
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Increasing temperature will impact evaporation and water resource management. Hydrological models are fed with an estimation of the evaporative demand of the atmosphere, called potential evapotranspiration (PE). The objectives of this study were (1) to compute the future PE anomaly over France and (2) to determine the impact of the choice of the method to estimate PE. Our results show that all methods present similar future trends. No method really stands out from the others.
Jing Xu, François Anctil, and Marie-Amélie Boucher
Hydrol. Earth Syst. Sci., 26, 1001–1017, https://doi.org/10.5194/hess-26-1001-2022, https://doi.org/10.5194/hess-26-1001-2022, 2022
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The performance of the non-dominated sorting genetic algorithm II (NSGA-II) is compared with a conventional post-processing method of affine kernel dressing. NSGA-II showed its superiority in improving the forecast skill and communicating trade-offs with end-users. It allows the enhancement of the forecast quality since it allows for setting multiple specific objectives from scratch. This flexibility should be considered as a reason to implement hydrologic ensemble prediction systems (H-EPSs).
Emixi Sthefany Valdez, François Anctil, and Maria-Helena Ramos
Hydrol. Earth Syst. Sci., 26, 197–220, https://doi.org/10.5194/hess-26-197-2022, https://doi.org/10.5194/hess-26-197-2022, 2022
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We investigated how a precipitation post-processor interacts with other tools for uncertainty quantification in a hydrometeorological forecasting chain. Four systems were implemented to generate 7 d ensemble streamflow forecasts, which vary from partial to total uncertainty estimation. Overall analysis showed that post-processing and initial condition estimation ensure the most skill improvements, in some cases even better than a system that considers all sources of uncertainty.
Haowen Yue, Mekonnen Gebremichael, and Vahid Nourani
Hydrol. Earth Syst. Sci., 26, 167–181, https://doi.org/10.5194/hess-26-167-2022, https://doi.org/10.5194/hess-26-167-2022, 2022
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The development of high-resolution global precipitation forecasts and the lack of reliable precipitation forecasts over Africa motivates this work to evaluate the precipitation forecasts from the Global Forecast System (GFS) over the Niger river basin in Africa. The GFS forecasts, at a 15 d accumulation timescale, have an acceptable performance; however, the forecasts are highly biased. It is recommended to apply bias correction to GFS forecasts before their application.
Hadush Meresa, Conor Murphy, Rowan Fealy, and Saeed Golian
Hydrol. Earth Syst. Sci., 25, 5237–5257, https://doi.org/10.5194/hess-25-5237-2021, https://doi.org/10.5194/hess-25-5237-2021, 2021
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The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis.
Qichun Yang, Quan J. Wang, Kirsti Hakala, and Yating Tang
Hydrol. Earth Syst. Sci., 25, 4773–4788, https://doi.org/10.5194/hess-25-4773-2021, https://doi.org/10.5194/hess-25-4773-2021, 2021
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Forecasts of water losses from land surface to the air are highly valuable for water resource management and planning. In this study, we aim to fill a critical knowledge gap in the forecasting of evaporative water loss. Model experiments across Australia clearly suggest the necessity of correcting errors in input variables for more reliable water loss forecasting. We anticipate that the strategy developed in our work will benefit future water loss forecasting and lead to more skillful forecasts.
Mostafa Tarek, François Brissette, and Richard Arsenault
Hydrol. Earth Syst. Sci., 25, 3331–3350, https://doi.org/10.5194/hess-25-3331-2021, https://doi.org/10.5194/hess-25-3331-2021, 2021
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It is not known how much uncertainty the choice of a reference data set may bring to impact studies. This study compares precipitation and temperature data sets to evaluate the uncertainty contribution to the results of climate change studies. Results show that all data sets provide good streamflow simulations over the reference period. The reference data sets also provided uncertainty that was equal to or larger than that related to general circulation models over most of the catchments.
Liming Wang, Songjun Han, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 25, 375–386, https://doi.org/10.5194/hess-25-375-2021, https://doi.org/10.5194/hess-25-375-2021, 2021
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It remains unclear at which timescale the complementary principle performs best in estimating evaporation. In this study, evaporation estimation was assessed over 88 eddy covariance monitoring sites at multiple timescales. The results indicate that the generalized complementary functions perform best in estimating evaporation at the monthly scale. This study provides a reference for choosing a suitable time step for evaporation estimations in relevant studies.
Okjeong Lee, Jeonghyeon Choi, Jeongeun Won, and Sangdan Kim
Hydrol. Earth Syst. Sci., 24, 5077–5093, https://doi.org/10.5194/hess-24-5077-2020, https://doi.org/10.5194/hess-24-5077-2020, 2020
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The uncertainty of the model interpreting rainfall extremes with temperature is analyzed. The performance of the model focuses on the reliability of the output. It has been found that the selection of temperatures suitable for extreme levels plays an important role in improving model reliability. Based on this, a methodology is proposed to quantify the degree of uncertainty inherent in the change in rainfall extremes due to global warming.
Chao Gao, Martijn J. Booij, and Yue-Ping Xu
Hydrol. Earth Syst. Sci., 24, 3251–3269, https://doi.org/10.5194/hess-24-3251-2020, https://doi.org/10.5194/hess-24-3251-2020, 2020
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This paper studies the impact of climate change on high and low flows and quantifies the contribution of uncertainty sources from representative concentration pathways (RCPs), global climate models (GCMs) and internal climate variability in extreme flows. Internal climate variability was reflected in a stochastic rainfall model. The results show the importance of internal climate variability and GCM uncertainty in high flows and GCM and RCP uncertainty in low flows especially for the far future.
Marc Schleiss, Jonas Olsson, Peter Berg, Tero Niemi, Teemu Kokkonen, Søren Thorndahl, Rasmus Nielsen, Jesper Ellerbæk Nielsen, Denica Bozhinova, and Seppo Pulkkinen
Hydrol. Earth Syst. Sci., 24, 3157–3188, https://doi.org/10.5194/hess-24-3157-2020, https://doi.org/10.5194/hess-24-3157-2020, 2020
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A multinational assessment of radar's ability to capture heavy rain events is conducted. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. Results show a fair agreement, with radar underestimating by 17 %-44 % on average compared with gauges. Despite being adjusted for bias, five of six radar products still exhibited strong conditional biases with intensities of 1–2% per mm/h. Median peak intensity bias was significantly higher, reaching 44 %–67%.
Xudong Zhou, Jan Polcher, Tao Yang, and Ching-Sheng Huang
Hydrol. Earth Syst. Sci., 24, 2061–2081, https://doi.org/10.5194/hess-24-2061-2020, https://doi.org/10.5194/hess-24-2061-2020, 2020
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This article proposes a new estimation approach for assessing the uncertainty with multiple datasets by fully considering all variations in temporal and spatial dimensions. Comparisons demonstrate that classical metrics may underestimate the uncertainties among datasets due to an averaging process in their algorithms. This new approach is particularly suitable for overall assessment of multiple climatic products, but can be easily applied to other spatiotemporal products in related fields.
Lionel Berthet, François Bourgin, Charles Perrin, Julie Viatgé, Renaud Marty, and Olivier Piotte
Hydrol. Earth Syst. Sci., 24, 2017–2041, https://doi.org/10.5194/hess-24-2017-2020, https://doi.org/10.5194/hess-24-2017-2020, 2020
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An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. We present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows and considerable variability among catchments and across lead times.
Shaoning Lv, Bernd Schalge, Pablo Saavedra Garfias, and Clemens Simmer
Hydrol. Earth Syst. Sci., 24, 1957–1973, https://doi.org/10.5194/hess-24-1957-2020, https://doi.org/10.5194/hess-24-1957-2020, 2020
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Passive remote sensing of soil moisture has good potential to improve weather forecasting via data assimilation in theory. We use the virtual reality data set (VR01) to infer the impact of sampling density on soil moisture ground cal/val activity. It shows how the sampling error is growing with an increasing sampling distance for a SMOS–SMAP scale footprint in about 40 km, 9 km, and 3 km. The conclusion will help in understanding the passive remote sensing soil moisture products.
Thanh Le and Deg-Hyo Bae
Hydrol. Earth Syst. Sci., 24, 1131–1143, https://doi.org/10.5194/hess-24-1131-2020, https://doi.org/10.5194/hess-24-1131-2020, 2020
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Here we investigate the response of global evaporation to main climate modes, including the Indian Ocean Dipole (IOD), the North Atlantic Oscillation (NAO) and the El Niño–Southern Oscillation (ENSO). Our results indicate that ENSO is an important driver of evaporation for many regions, while the impacts of NAO and IOD are substantial. This study allows us to obtain insight about the predictability of evaporation and, hence, may help to improve the early-warning systems of climate extremes.
Jean-Philippe Baudouin, Michael Herzog, and Cameron A. Petrie
Hydrol. Earth Syst. Sci., 24, 427–450, https://doi.org/10.5194/hess-24-427-2020, https://doi.org/10.5194/hess-24-427-2020, 2020
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The amount of precipitation falling in the Indus River basin remains uncertain while its variability impacts 100 million inhabitants. A comparison of datasets from diverse sources (ground remote observations, model outputs) reduces this uncertainty significantly. Grounded observations offer the most reliable long-term variability but with important underestimation in winter over the mountains. By contrast, recent model outputs offer better estimations of total amount and short-term variability.
Kamal Ahmed, Dhanapala A. Sachindra, Shamsuddin Shahid, Mehmet C. Demirel, and Eun-Sung Chung
Hydrol. Earth Syst. Sci., 23, 4803–4824, https://doi.org/10.5194/hess-23-4803-2019, https://doi.org/10.5194/hess-23-4803-2019, 2019
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This study evaluated the performance of 36 CMIP5 GCMs in simulating seasonal precipitation and maximum and minimum temperature over Pakistan using spatial metrics (SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency) for the period 1961–2005. NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 were identified as the most suitable GCMs for simulating all three climate variables over Pakistan.
Sungmin O and Ulrich Foelsche
Hydrol. Earth Syst. Sci., 23, 2863–2875, https://doi.org/10.5194/hess-23-2863-2019, https://doi.org/10.5194/hess-23-2863-2019, 2019
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We analyze heavy local rainfall to address questions regarding the spatial uncertainty due to the approximation of areal rainfall using point measurements. Ten years of rainfall data from a dense network of 150 rain gauges in southeastern Austria are employed, which permits robust examination of small-scale rainfall at various horizontal resolutions. Quantitative uncertainty information from the study can guide both data users and producers to estimate uncertainty in their own rainfall dataset.
Aifeng Lv, Bo Qu, Shaofeng Jia, and Wenbin Zhu
Hydrol. Earth Syst. Sci., 23, 883–896, https://doi.org/10.5194/hess-23-883-2019, https://doi.org/10.5194/hess-23-883-2019, 2019
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ENSO-related changes in daily precipitation regimes are currently ignored by the scientific community. We analyzed the anomalies of daily precipitation and hydrological extremes caused by different phases of ENSO events, as well as the possible driving mechanisms, to reveal the influence of ENSO on China's daily precipitation regimes. Our results provide a valuable tool for daily precipitation prediction and enable the prioritization of adaptation efforts ahead of extreme events in China.
Micheal J. Simpson and Neil I. Fox
Hydrol. Earth Syst. Sci., 22, 3375–3389, https://doi.org/10.5194/hess-22-3375-2018, https://doi.org/10.5194/hess-22-3375-2018, 2018
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Many researchers have expressed that one of the main difficulties in modeling watershed hydrology is that of obtaining continuous, widespread weather input data, especially precipitation. The overarching objective of this study was to provide a comprehensive study of three weather radars as a function of range. We found that radar-estimated precipitation was best at ranges between 100 and 150 km from the radar, with different radar parameters being superior at varying distances from the radar.
Vimal Mishra, Reepal Shah, Syed Azhar, Harsh Shah, Parth Modi, and Rohini Kumar
Hydrol. Earth Syst. Sci., 22, 2269–2284, https://doi.org/10.5194/hess-22-2269-2018, https://doi.org/10.5194/hess-22-2269-2018, 2018
Sanjib Sharma, Ridwan Siddique, Seann Reed, Peter Ahnert, Pablo Mendoza, and Alfonso Mejia
Hydrol. Earth Syst. Sci., 22, 1831–1849, https://doi.org/10.5194/hess-22-1831-2018, https://doi.org/10.5194/hess-22-1831-2018, 2018
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We investigate the relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7). For this purpose, we develop and implement a regional hydrologic ensemble prediction system (RHEPS). Overall analysis shows that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.
Kevin Sene, Wlodek Tych, and Keith Beven
Hydrol. Earth Syst. Sci., 22, 127–141, https://doi.org/10.5194/hess-22-127-2018, https://doi.org/10.5194/hess-22-127-2018, 2018
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The theme of the paper is exploration of the potential for seasonal flow forecasting for large lakes using a range of stochastic transfer function techniques with additional insights gained from simple analytical approximations. The methods were evaluated using records for two of the largest lakes in the world. The paper concludes with a discussion of the relevance of the results to operational flow forecasting systems for other large lakes.
Fan Yang, Hui Lu, Kun Yang, Jie He, Wei Wang, Jonathon S. Wright, Chengwei Li, Menglei Han, and Yishan Li
Hydrol. Earth Syst. Sci., 21, 5805–5821, https://doi.org/10.5194/hess-21-5805-2017, https://doi.org/10.5194/hess-21-5805-2017, 2017
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In this paper, we show that CLDAS has the highest spatial and temporal resolution, and it performs best in terms of precipitation, while it overestimates the shortwave radiation. CMFD also has high resolution and its shortwave radiation data match well with the station data; its annual-mean precipitation is reliable but its monthly precipitation needs improvements. Both GLDAS and CN05.1 over mainland China need to be improved. The results can benefit researchers for forcing data selection.
Rachel Bazile, Marie-Amélie Boucher, Luc Perreault, and Robert Leconte
Hydrol. Earth Syst. Sci., 21, 5747–5762, https://doi.org/10.5194/hess-21-5747-2017, https://doi.org/10.5194/hess-21-5747-2017, 2017
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Meteorological forecasting agencies constantly work on pushing the limit of predictability farther in time. However, some end users need proof that climate model outputs are ready to be implemented operationally. We show that bias correction is crucial for the use of ECMWF System4 forecasts for the studied area and there is a potential for the use of 1-month-ahead forecasts. Beyond this, forecast performance is equivalent to using past climatology series as inputs to the hydrological model.
Judith Eeckman, Pierre Chevallier, Aaron Boone, Luc Neppel, Anneke De Rouw, Francois Delclaux, and Devesh Koirala
Hydrol. Earth Syst. Sci., 21, 4879–4893, https://doi.org/10.5194/hess-21-4879-2017, https://doi.org/10.5194/hess-21-4879-2017, 2017
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The central part of the Himalayan Range presents tremendous heterogeneity in terms of topography and climatology, but the representation of hydro-climatic processes for Himalayan catchments is limited due to a lack of knowledge in such poorly instrumented environments. The proposed approach is to characterize the effect of altitude on precipitation by considering ensembles of acceptable altitudinal factors. Ensembles of acceptable values for the components of the water cycle are then provided.
Jefferson S. Wong, Saman Razavi, Barrie R. Bonsal, Howard S. Wheater, and Zilefac E. Asong
Hydrol. Earth Syst. Sci., 21, 2163–2185, https://doi.org/10.5194/hess-21-2163-2017, https://doi.org/10.5194/hess-21-2163-2017, 2017
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This study was conducted to quantify the spatial and temporal variability of the errors associated with various gridded precipitation products in Canada. Overall, WFDEI [GPCC] and CaPA performed best with respect to different performance measures, followed by ANUSPLIN and WEDEI [CRU]. Princeton and NARR demonstrated the lowest quality. Comparing the climate model-simulated products, PCIC ensembles generally performed better than NA-CORDEX ensembles in terms of reliability in four seasons.
Danlu Guo, Seth Westra, and Holger R. Maier
Hydrol. Earth Syst. Sci., 21, 2107–2126, https://doi.org/10.5194/hess-21-2107-2017, https://doi.org/10.5194/hess-21-2107-2017, 2017
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This study assessed the impact of baseline climate conditions on the sensitivity of potential evapotranspiration (PET) to a large range of plausible changes in temperature, relative humidity, solar radiation and wind speed at 30 Australian locations. Around 2-fold greater PET changes were observed at cool and humid locations compared to others, indicating potential for elevated water loss in the future. These impacts can be useful to inform the selection of PET models under a changing climate.
Vojtěch Svoboda, Martin Hanel, Petr Máca, and Jan Kyselý
Hydrol. Earth Syst. Sci., 21, 963–980, https://doi.org/10.5194/hess-21-963-2017, https://doi.org/10.5194/hess-21-963-2017, 2017
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The study presents validation of precipitation events as simulated by an ensemble of regional climate models for the Czech Republic. While the number of events per season, seasonal total precipitation due to heavy events and the distribution of rainfall depths are simulated relatively well, event maximum precipitation and event intensity are strongly underestimated. This underestimation cannot be explained by scale mismatch between point observations and area average (climate model simulations).
Martin Hanel, Petr Máca, Petr Bašta, Radek Vlnas, and Pavel Pech
Hydrol. Earth Syst. Sci., 20, 4307–4322, https://doi.org/10.5194/hess-20-4307-2016, https://doi.org/10.5194/hess-20-4307-2016, 2016
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The paper is focused on assessment of the contribution of various sources of uncertainty to the estimated rainfall erosivity factor. It is shown that the rainfall erosivity factor can be estimated with reasonable precision even from records shorter than recommended, provided good spatial coverage and reasonable explanatory variables are available. The research was done as an update of the R factor estimates for the Czech Republic, which were later used for climate change assessment.
Jean-Philippe Vidal, Benoît Hingray, Claire Magand, Eric Sauquet, and Agnès Ducharne
Hydrol. Earth Syst. Sci., 20, 3651–3672, https://doi.org/10.5194/hess-20-3651-2016, https://doi.org/10.5194/hess-20-3651-2016, 2016
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Possible transient futures of winter and summer low flows for two snow-influenced catchments in the southern French Alps show a strong decrease signal. It is however largely masked by the year-to-year variability, which should be the main target for defining adaptation strategies. Responses of different hydrological models strongly diverge in the future, suggesting to carefully check the robustness of evapotranspiration and snowpack components under a changing climate.
Louise Arnal, Maria-Helena Ramos, Erin Coughlan de Perez, Hannah Louise Cloke, Elisabeth Stephens, Fredrik Wetterhall, Schalk Jan van Andel, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3109–3128, https://doi.org/10.5194/hess-20-3109-2016, https://doi.org/10.5194/hess-20-3109-2016, 2016
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Forecasts are produced as probabilities of occurrence of specific events, which is both an added value and a challenge for users. This paper presents a game on flood protection, "How much are you prepared to pay for a forecast?", which investigated how users perceive the value of forecasts and are willing to pay for them when making decisions. It shows that users are mainly influenced by the perceived quality of the forecasts, their need for the information and their degree of risk tolerance.
K. Sunilkumar, T. Narayana Rao, and S. Satheeshkumar
Hydrol. Earth Syst. Sci., 20, 1719–1735, https://doi.org/10.5194/hess-20-1719-2016, https://doi.org/10.5194/hess-20-1719-2016, 2016
Vincent Roth and Tatenda Lemann
Hydrol. Earth Syst. Sci., 20, 921–934, https://doi.org/10.5194/hess-20-921-2016, https://doi.org/10.5194/hess-20-921-2016, 2016
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The Soil and Water Assessment Tool (SWAT) suggests using the CFSR global rainfall data for modelling discharge and soil erosion in data-scarce parts of the world. These data are freely available and ready to use for SWAT modelling. However, simulations with the CFSR data in the Ethiopian Highlands were unable to represent the specific regional climates and showed high discrepancies. This article compares SWAT simulations with conventional rainfall data and with CFSR rainfall data.
J. Kim and S. K. Park
Hydrol. Earth Syst. Sci., 20, 651–658, https://doi.org/10.5194/hess-20-651-2016, https://doi.org/10.5194/hess-20-651-2016, 2016
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This study examined the uncertainty in climatological precipitation in East Asia, calculated from five gridded analysis data sets based on in situ rain gauge observations from 1980 to 2007. It is found that the regions of large uncertainties are typically lightly populated and are characterized by severe terrain and/or very high elevations. Thus, care must be taken in using long-term trends calculated from gridded precipitation analysis data for climate studies over such regions in East Asia.
M. F. Rios Gaona, A. Overeem, H. Leijnse, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 19, 3571–3584, https://doi.org/10.5194/hess-19-3571-2015, https://doi.org/10.5194/hess-19-3571-2015, 2015
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Commercial cellular networks are built for telecommunication purposes. These kinds of networks have lately been used to obtain rainfall maps at country-wide scales. From previous studies, we now quantify the uncertainties associated with such maps. To do so, we divided the sources or error into two categories: from microwave link measurements and from mapping. It was found that the former is the source that contributes the most to the overall error in rainfall maps from microwave link network.
S. H. Alemohammad, K. A. McColl, A. G. Konings, D. Entekhabi, and A. Stoffelen
Hydrol. Earth Syst. Sci., 19, 3489–3503, https://doi.org/10.5194/hess-19-3489-2015, https://doi.org/10.5194/hess-19-3489-2015, 2015
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This paper introduces a new variant of the triple collocation technique with multiplicative error model. The method is applied, for the first time, to precipitation products across the central part of continental USA. Results show distinctive patterns of error variance in each product that are estimated without a priori assumption of any of the error distributions. The correlation coefficients between each product and the truth are also estimated, which provides another performance perspective.
S. Garrigues, A. Olioso, J. C. Calvet, E. Martin, S. Lafont, S. Moulin, A. Chanzy, O. Marloie, S. Buis, V. Desfonds, N. Bertrand, and D. Renard
Hydrol. Earth Syst. Sci., 19, 3109–3131, https://doi.org/10.5194/hess-19-3109-2015, https://doi.org/10.5194/hess-19-3109-2015, 2015
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Land surface model simulations of evapotranspiration are assessed over a 12-year Mediterranean crop succession. Evapotranspiration mainly results from soil evaporation when it is simulated over a Mediterranean crop succession. This leads to a high sensitivity to the soil parameters. Errors on soil hydraulic properties can lead to a large bias in cumulative evapotranspiration over a long period of time. Accounting for uncertainties in soil properties is essential for land surface modelling.
W. Gong, Q. Duan, J. Li, C. Wang, Z. Di, Y. Dai, A. Ye, and C. Miao
Hydrol. Earth Syst. Sci., 19, 2409–2425, https://doi.org/10.5194/hess-19-2409-2015, https://doi.org/10.5194/hess-19-2409-2015, 2015
S. O. Los
Hydrol. Earth Syst. Sci., 19, 1713–1725, https://doi.org/10.5194/hess-19-1713-2015, https://doi.org/10.5194/hess-19-1713-2015, 2015
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The study evaluates annual precipitation (largely rainfall) amounts for the tropics and subtropics; precipitation was obtained from ground observations, satellite observations and numerical weather forecasting models.
- Annual precipitation amounts from ground and satellite observations were the most realistic.
- Newer weather forecasting models better predicted annual precipitation than older models.
- Weather forecasting models predicted inaccurate precipitation amounts for Africa.
A. Kann, I. Meirold-Mautner, F. Schmid, G. Kirchengast, J. Fuchsberger, V. Meyer, L. Tüchler, and B. Bica
Hydrol. Earth Syst. Sci., 19, 1547–1559, https://doi.org/10.5194/hess-19-1547-2015, https://doi.org/10.5194/hess-19-1547-2015, 2015
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The paper introduces a high resolution precipitation analysis system which operates on 1 km x 1 km resolution with high frequency updates of 5 minutes. The ability of such a system to adequately assess the convective precipitation distribution is evaluated by means of an independant, high resolution station network. This dense station network allows for a thorough evaluation of the analyses under different convective situations and of the representativeness error of raingaue measurements.
C. H. Wu, G. R. Huang, and H. J. Yu
Hydrol. Earth Syst. Sci., 19, 1385–1399, https://doi.org/10.5194/hess-19-1385-2015, https://doi.org/10.5194/hess-19-1385-2015, 2015
T. Antofie, G. Naumann, J. Spinoni, and J. Vogt
Hydrol. Earth Syst. Sci., 19, 177–193, https://doi.org/10.5194/hess-19-177-2015, https://doi.org/10.5194/hess-19-177-2015, 2015
P. López López, J. S. Verkade, A. H. Weerts, and D. P. Solomatine
Hydrol. Earth Syst. Sci., 18, 3411–3428, https://doi.org/10.5194/hess-18-3411-2014, https://doi.org/10.5194/hess-18-3411-2014, 2014
Cited articles
Archer, G. E. B., Saltelli, A., and Sobol, I. M.: Sensitivity measures,anova-like Techniques and the use of bootstrap, J. Stat. Comput. Simul., 58, 99–120, https://doi.org/10.1080/00949659708811825, 1997.
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., Pierce, D. W., Hidalgo, H. G., Bonfils, C., Santer, B. D., Das, T., Bala, G., Wood, A. W., Nozawa, T., Mirin, A. A., Cayan, D. R., and Dettinger, M. D.: Human-induced changes in the hydrology of the western United States, Science, 319, 1080–1083, https://doi.org/10.1126/science.1152538, 2008.
Baroni, G. and Tarantola, S.: A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study, Environ. Model. Softw., 51, 26–34, https://doi.org/10.1016/j.envsoft.2013.09.022, 2014.
Bastola, S., Murphy, C., and Sweeney, J.: The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments, Adv. Water Resour., 34, 562–576, https://doi.org/10.1016/j.advwatres.2011.01.008, 2011.
Benke, K. K., Lowell, K. E., and Hamilton, A. J.: Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model, Math. Comput. Model., 47, 1134–1149, https://doi.org/10.1016/j.mcm.2007.05.017, 2008.
Beven, K. and Binley, A.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992.
Bohn, T. J., Livneh, B., Oyler, J. W., Running, S. W., Nijssen, B., and Lettenmaier, D. P.: Global evaluation of MTCLIM and related algorithms for forcing of ecological and hydrological models, Agr. Forest Meteorol., 176, 38–49, https://doi.org/10.1016/j.agrformet.2013.03.003, 2013.
Bolstad, P. V., Swift, L., Collins, F., and Régnière, J.: Measured and predicted air temperatures at basin to regional scales in the southern Appalachian mountains, Agr. Forest Meteorol., 91, 161–176, https://doi.org/10.1016/S0168-1923(98)00076-8, 1998.
Bret-Harte, S., Shaver, G., and Euskirchen, E.: Eddy Flux Measurements, Ridge Station, Imnavait Creek, Alaska – 2010, Long Term Ecological Research Network, https://doi.org/10.6073/pasta/fb047eaa2c78d4a3254bba8369e6cee5, 2010a.
Bret-Harte, S., Shaver, G., and Euskirchen, E.: Eddy Flux Measurements, Fen Station, Imnavait Creek, Alaska – 2010, Long Term Ecological Research Network, https://doi.org/10.6073/pasta/dde37e89dab096bea795f5b111786c8b, 2010b.
Bret-Harte, S., Euskirchen, E., Griffin, K., and Shaver, G.: Eddy Flux Measurements, Tussock Station, Imnavait Creek, Alaska – 2011, Long Term Ecological Research Network, https://doi.org/10.6073/pasta/44a62e0c6741b3bd93c0a33e7b677d90, 2011a.
Bret-Harte, S., Euskirchen, E., and Shaver, G.: Eddy Flux Measurements, Fen Station, Imnavait Creek, Alaska – 2011, Long Term Ecological Research Network, https://doi.org/10.6073/pasta/50e9676f29f44a8b6677f05f43268840, 2011b.
Bret-Harte, S., Euskirchen, E., and Shaver, G.: Eddy Flux Measurements, Ridge Station, Imnavait Creek, Alaska – 2011, Long Term Ecological Research Network, https://doi.org/10.6073/pasta/5d603c3628f53f494f08f895875765e8, 2011c.
Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting, J. Glaciol., 38, 13–22, 1992.
Burles, K. and Boon, S.: Snowmelt energy balance in a burned forest plot, Crowsnest Pass, Alberta, Canada, Hydrol. Process., 25, 3012–3029, https://doi.org/10.1002/hyp.8067, 2011.
Butts, M. B., Payne, J. T., Kristensen, M., and Madsen, H.: An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation, J. Hydrol., 298, 242–266, https://doi.org/10.1016/j.jhydrol.2004.03.042, 2004.
Campolongo, F., Saltelli, A., and Cariboni, J.: From screening to quantitative sensitivity analysis. A unified approach, Comput. Phys. Commun., 182, 978–988, https://doi.org/10.1016/j.cpc.2010.12.039, 2011.
Cheng, F.-Y. and Georgakakos, K. P.: Statistical analysis of observed and simulated hourly surface wind in the vicinity of the Panama Canal, Int. J. Climatol., 31, 770–782, https://doi.org/10.1002/joc.2123, 2011.
Christopher Frey, H. and Patil, S. R.: Identification and Review of Sensitivity Analysis Methods, Risk Analysis, 22, 553–578, https://doi.org/10.1111/0272-4332.00039, 2002.
Chuanyan, Z., Zhongren, N., and Guodong, C.: Methods for modelling of temporal and spatial distribution of air temperature at landscape scale in the southern Qilian mountains, China, Ecol. Model., 189, 209–220, https://doi.org/10.1016/j.ecolmodel.2005.03.016, 2005.
Clark, M. P. and Slater, A. G.: Probabilistic Quantitative Precipitation Estimation in Complex Terrain, J. Hydrometeorol., 7, 3–22, https://doi.org/10.1175/JHM474.1, 2006.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007WR006735, 2008.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water Resour. Res., 47, W07539, https://doi.org/10.1029/2011WR010745, 2011a.
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple working hypotheses for hydrological modeling, Water Resour. Res., 47, W09301, https://doi.org/10.1029/2010WR009827, 2011b.
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., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015a.
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, 2015b.
Cukier, R. I., Fortuin, C. M., Shuler, K. E., Petschek, A. G., and Schaibly, J. H.: Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients, I Theory, J. Chem. Phys., 59, 3873–3878, https://doi.org/10.1063/1.1680571, 1973.
Dadic, R., Mott, R., Lehning, M., Carenzo, M., Anderson, B., and Mackintosh, A.: Sensitivity of turbulent fluxes to wind speed over snow surfaces in different climatic settings, Adv. Water Resour., 55, 178–189, https://doi.org/10.1016/j.advwatres.2012.06.010, 2013.
Dee, D. P.: Bias and data assimilation, Q. J. Roy. Meteorol. Soc., 131, 3323–3343, https://doi.org/10.1256/qj.05.137, 2005.
Deems, J. S., Painter, T. H., Barsugli, J. J., Belnap, J., and Udall, B.: Combined impacts of current and future dust deposition and regional warming on Colorado River Basin snow dynamics and hydrology, Hydrol. Earth Syst. Sci., 17, 4401–4413, https://doi.org/10.5194/hess-17-4401-2013, 2013.
Déry, S. and Stieglitz, M.: A note on surface humidity measurements in the cold Canadian environment, Bound.-Lay. Meteorol., 102, 491–497, https://doi.org/10.1023/A:1013890729982, 2002.
Di Baldassarre, G. and Montanari, A.: Uncertainty in river discharge observations: a quantitative analysis, Hydrol. Earth Syst. Sci., 13, 913–921, https://doi.org/10.5194/hess-13-913-2009, 2009.
Duan, Q., Sorooshian, S., and Gupta, V.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015–1031, https://doi.org/10.1029/91WR02985, 1992.
Durand, M. and Margulis, S. A.: Correcting first-order errors in snow water equivalent estimates using a multifrequency, multiscale radiometric data assimilation scheme, J. Geophys. Res., 112, D13121, https://doi.org/10.1029/2006JD008067, 2007.
Durand, M. and Margulis, S. A.: Effects of uncertainty magnitude and accuracy on assimilation of multiscale measurements for snowpack characterization, J. Geophys. Res., 113, D02105, https://doi.org/10.1029/2007JD008662, 2008.
Elsner, M. M., Gangopadhyay, S., Pruitt, T., Brekke, L. D., Mizukami, N., and Clark, M. P.: How Does the Choice of Distributed Meteorological Data Affect Hydrologic Model Calibration and Streamflow Simulations?, J. Hydrometeorol., 15, 1384–1403, https://doi.org/10.1175/JHM-D-13-083.1, 2014.
Essery, R., Morin, S., Lejeune, Y., and B Ménard, C.: A comparison of 1701 snow models using observations from an alpine site, Adv. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013.
Euskirchen, E. S., Bret-Harte, M. S., Scott, G. J., Edgar, C., and Shaver, G. R.: Seasonal patterns of carbon dioxide and water fluxes in three representative tundra ecosystems in northern Alaska, Ecosphere, 3, 19 pp., https://doi.org/10.1890/ES11-00202.1, 2012.
Feld, S. I., Cristea, N. C., and Lundquist, J. D.: Representing atmospheric moisture content along mountain slopes: Examination using distributed sensors in the Sierra Nevada, California, Water Resour. Res., 49, 4424–4441, https://doi.org/10.1002/wrcr.20318, 2013.
Flerchinger, G. N., Xaio, W., Marks, D., Sauer, T. J., and Yu, Q.: Comparison of algorithms for incoming atmospheric long-wave radiation, Water Resour. Res., 45, W03423, https://doi.org/10.1029/2008WR007394, 2009.
Flint, A. L. and Childs, S. W.: Calculation of solar radiation in mountainous terrain, Agr. Forest Meteorol., 40, 233–249, https://doi.org/10.1016/0168-1923(87)90061-X, 1987.
Foglia, L., Hill, M. C., Mehl, S. W., and Burlando, P.: Sensitivity analysis, calibration, and testing of a distributed hydrological model using error-based weighting and one objective function, Water Resour. Res., 45, W06427, https://doi.org/10.1029/2008WR007255, 2009.
Foscarini, F., Bellocchi, G., Confalonieri, R., Savini, C., and Van den Eede, G.: Sensitivity analysis in fuzzy systems: Integration of SimLab and DANA, Environ. Model. Softw., 25, 1256–1260, https://doi.org/10.1016/j.envsoft.2010.03.024, 2010.
Fridley, J. D.: Downscaling Climate over Complex Terrain: High Finescale (< 1000 m) Spatial Variation of Near-Ground Temperatures in a Montane Forested Landscape (Great Smoky Mountains), J. Appl. Meteorol. Clim., 48, 1033–1049, https://doi.org/10.1175/2008JAMC2084.1, 2009.
Georgakakos, K. P., Seo, D., Gupta, H., Schaake, J., and Butts, M. B.: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles, J. Hydrol., 298, 222–241, https://doi.org/10.1016/j.jhydrol.2004.03.037, 2004.
Goodison, B. E., Louie, P. Y. T., and Yang, D.: WMO solid precipitation measurement intercomparison: Final report, in: Instrum. Obs. Methods Rep. 67, WMO/TD-No. 872, p. 211, World Meteorol. Organ., Geneva, Switzerland, 1998.
Griffin, K., Bret-Harte, S., Shaver, G., and Euskirchen, E.: Eddy Flux Measurements, Tussock Station, Imnavait Creek, Alaska – 2010, Long Term Ecological Research Network, https://doi.org/10.6073/pasta/7bba82256e0f5d9ec3d2bc9c25ab9bcf, 2010.
Guan, B., Molotch, N. P., Waliser, D. E., Jepsen, S. M., Painter, T. H., and Dozier, J.: Snow water equivalent in the Sierra Nevada: Blending snow sensor observations with snowmelt model simulations, Water Resour. Res., 49, 5029–5046, https://doi.org/10.1002/wrcr.20387, 2013.
Guan, H., Wilson, J. L., and Makhnin, O.: Geostatistical Mapping of Mountain Precipitation Incorporating Autosearched Effects of Terrain and Climatic Characteristics, J. Hydrometeorol., 6, 1018–1031, https://doi.org/10.1175/JHM448.1, 2005.
Gupta, H. V., Wagener, T., and Liu, Y.: Reconciling theory with observations: elements of a diagnostic approach to model evaluation, Hydrol. Process., 22, 3802–3813, https://doi.org/10.1002/hyp.6989, 2008.
Hasenauer, H., Merganicova, K., Petritsch, R., Pietsch, S. A., and Thornton, P. E.: Validating daily climate interpolations over complex terrain in Austria, Agr. Forest Meteorol., 119, 87–107, https://doi.org/10.1016/S0168-1923(03)00114-X, 2003.
He, M., Hogue, T. S., Franz, K. J., Margulis, S. A., and Vrugt, J. A.: Corruption of parameter behavior and regionalization by model and forcing data errors: A Bayesian example using the SNOW17 model, Water Resour. Res., 47, W07546, https://doi.org/10.1029/2010WR009753, 2011a.
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, 2011b.
Herman, J. D., Kollat, J. B., Reed, P. M., and Wagener, T.: Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models, Hydrol. Earth Syst. Sci., 17, 2893–2903, https://doi.org/10.5194/hess-17-2893-2013, 2013.
Herrero, J. and Polo, M. J.: Parameterization of atmospheric longwave emissivity in a mountainous site for all sky conditions, Hydrol. Earth Syst. Sci., 16, 3139–3147, https://doi.org/10.5194/hess-16-3139-2012, 2012.
Hiemstra, C. A., Liston, G. E., and Reiners, W. A.: Observing, modelling, and validating snow redistribution by wind in a Wyoming upper treeline landscape, Ecol. Model., 197, 35–51, https://doi.org/10.1016/j.ecolmodel.2006.03.005, 2006.
Hutchinson, M. F., McKenney, D. W., Lawrence, K., Pedlar, J. H., Hopkinson, R. F., Milewska, E., and Papadopol, P.: Development and Testing of Canada-Wide Interpolated Spatial Models of Daily Minimum–Maximum Temperature and Precipitation for 1961–2003, J. Appl. Meteorol. Clim., 48, 725–741, https://doi.org/10.1175/2008JAMC1979.1, 2009.
Huwald, H., Higgins, C. W., Boldi, M.-O., Bou-Zeid, E., Lehning, M., and Parlange, M. B.: Albedo effect on radiative errors in air temperature measurements, Water Resour. Res., 45, W08431, https://doi.org/10.1029/2008WR007600, 2009.
Jackson, C., Xia, Y., Sen, M. K., and Stoffa, P. L.: Optimal parameter and uncertainty estimation of a land surface model: A case study using data from Cabauw, Netherlands, J. Geophys. Res., 108, 4583, https://doi.org/10.1029/2002JD002991, 2003.
Jansen, M. J.: Analysis of variance designs for model output, Comput. Phys. Commun., 117, 35–43, https://doi.org/10.1016/S0010-4655(98)00154-4, 1999.
Jepsen, S. M., Molotch, N. P., Williams, M. W., Rittger, K. E., and Sickman, J. O.: Interannual variability of snowmelt in the Sierra Nevada and Rocky Mountains, United States: Examples from two alpine watersheds, Water Resour. Res., 48, W02529, https://doi.org/10.1029/2011WR011006, 2012.
Jiménez, P. A., Dudhia, J., and Navarro, J.: On the surface wind speed probability density function over complex terrain, Geophys. Res. Lett., 38, L22803, https://doi.org/10.1029/2011GL049669, 2011.
Jing, X. and Cess, R. D.: Comparison of atmospheric clear-sky shortwave radiation models to collocated satellite and surface measurements in Canada, J. Geophys. Res., 103, 28817–28824, https://doi.org/10.1029/1998JD200012, 1998.
Jordan, R.: A One-Dimensional Temperature Model for a Snow Cover: Technical Documentation for SNTHERM.89, Special Report 91-16, US Army CRREL, Hanover, NH, USA, p. 58, 1991.
Kane, D. L., Hinzman, L. D., Benson, C. S., and Liston, G. E.: Snow hydrology of a headwater Arctic basin: 1. Physical measurements and process studies, Water Resour. Res., 27, 1099–1109, https://doi.org/10.1029/91WR00262, 1991.
Kavetski, D., Franks, S. W., and Kuczera, G.: Confronting input uncertainty in environmental modelling, in: Calibration of Watershed Models, edited by: Duan, Q., Gupta, H. V., Sorooshian, S., Roussea, A. N., and Turcotte, R., American Geophysical Union, Washington, D.C., 49–68, https://doi.org/10.1029/WS006p0049, 2002.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res., 42, W03407, https://doi.org/10.1029/2005WR004368, 2006a.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 2. Application, Water Resour. Res., 42, W03408, https://doi.org/10.1029/2005WR004376, 2006b.
Kelleher, C., Wagener, T., and McGlynn, B.: Model-based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments, Water Resour. Res., 51, 1–28, https://doi.org/10.1002/2014WR016147, 2015.
Koivusalo, H. and Heikinheimo, M.: Surface energy exchange over a boreal snowpack: comparison of two snow energy balance models, Hydrol. Process., 13, 2395–2408, https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2395::AID-HYP864>3.0.CO;2-G, 1999.
Kucherenko, S., Tarantola, S., and Annoni, P.: Estimation of global sensitivity indices for models with dependent variables, Comput. Phys. Commun., 183, 937–946, https://doi.org/10.1016/j.cpc.2011.12.020, 2012.
Kuczera, G. and Parent, E.: Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, J. Hydrol., 211, 69–85, https://doi.org/10.1016/S0022-1694(98)00198-X, 1998.
Kuczera, G., Renard, B., Thyer, M., and Kavetski, D.: There are no hydrological monsters, just models and observations with large uncertainties!, Hydrolog. Sci. J., 55, 980–991, https://doi.org/10.1080/02626667.2010.504677, 2010.
Landry, C. C., Buck, K. A., Raleigh, M. S., and Clark, M. P.: Mountain system monitoring at Senator Beck Basin, San Juan Mountains, Colorado: A new integrative data source to develop and evaluate models of snow and hydrologic processes, Water Resour. Res., 50, 1773–1788, https://doi.org/10.1002/2013WR013711, 2014.
Lapo, K. E., Hinkelman, L. M., Raleigh, M. S., and Lundquist, J. D.: Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance, Water Resour. Res., 51, 1649–1670, https://doi.org/10.1002/2014WR016259, 2015.
Lapp, S., Byrne, J., Townshend, I., and Kienzle, S.: Climate warming impacts on snowpack accumulation in an alpine watershed, Int. J. Climatol., 25, 521–536, https://doi.org/10.1002/joc.1140, 2005.
Leavesley, G. H.: Modeling the effects of climate change on water resources – a review, Climatic Change, 28, 159–177, https://doi.org/10.1007/BF01094105, 1994.
Lehning, M., Völksch, I., Gustafsson, D., Nguyen, T. A., Stähli, M., and Zappa, M.: ALPINE3D: a detailed model of mountain surface processes and its application to snow hydrology, Hydrol. Process., 20, 2111–2128, https://doi.org/10.1002/hyp.6204, 2006.
Li, J., Duan, Q. Y., Gong, W., Ye, A., Dai, Y., Miao, C., Di, Z., Tong, C., and Sun, Y.: Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis, Hydrol. Earth Syst. Sci., 17, 3279–3293, https://doi.org/10.5194/hess-17-3279-2013, 2013.
Liston, G. E.: Representing Subgrid Snow Cover Heterogeneities in Regional and Global Models, J. Climate, 17, 1381–1397, https://doi.org/10.1175/1520-0442(2004)017<1381:RSSCHI>2.0.CO;2, 2004.
Liston, G. E. and Elder, K.: A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet), J. Hydrometeorol., 7, 217–234, https://doi.org/10.1175/JHM486.1, 2006.
Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework, Water Resour. Res., 43, W07401, https://doi.org/10.1029/2006WR005756, 2007.
Luce, C. H., Tarboton, D. G., and Cooley, K. R.: The influence of the spatial distribution of snow on basin-averaged snowmelt, Hydrol. Process., 12, 1671–1683, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1671::AID-HYP688>3.0.CO;2-N, 1998.
Lundquist, J. D. and Cayan, D. R.: Surface temperature patterns in complex terrain: Daily variations and long-term change in the central Sierra Nevada, California, J. Geophys. Res., 112, D11124, https://doi.org/10.1029/2006JD007561, 2007.
Lundquist, J. D., Wayand, N. E., Massmann, A., Clark, M. P., Lott, F., and Cristea, N. C.: Diagnosis of insidious data disasters, Water Resour. Res., 51, 3815–3827, https://doi.org/10.1002/2014WR016585, 2015.
Luo, W., Taylor, M. C., and Parker, S. R.: A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales, Int. J. Climatol., 28, 947–959, https://doi.org/10.1002/joc.1583, 2008.
Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., and Jonas, T.: Evaluating snow models with varying process representations for hydrological applications, Water Resour. Res., 51, 2707–2723, https://doi.org/10.1002/2014WR016498, 2015.
Mahat, V. and Tarboton, D. G.: Canopy radiation transmission for an energy balance snowmelt model, Water Resour. Res., 48, W01534, https://doi.org/10.1029/2011WR010438, 2012.
Mardikis, M. G., Kalivas, D. P., and Kollias, V. J.: Comparison of Interpolation Methods for the Prediction of Reference Evapotranspiration—An Application in Greece, Water Resour. Manage., 19, 251–278, https://doi.org/10.1007/s11269-005-3179-2, 2005.
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.
Matott, L. S., Babendreier, J. E., and Purucker, S. T.: Evaluating uncertainty in integrated environmental models: A review of concepts and tools, Water Resour. Res., 45, W06421, https://doi.org/10.1029/2008WR007301, 2009.
Meyer, J. D. D., Jin, J., and Wang, S.-Y.: Systematic Patterns of the Inconsistency between Snow Water Equivalent and Accumulated Precipitation as Reported by the Snowpack Telemetry Network, J. Hydrometeorol., 13, 1970–1976, https://doi.org/10.1175/JHM-D-12-066.1, 2012.
Mizukami, N., Clark, M. P., Slater, A. G., Brekke, L. D., Elsner, M. M., Arnold, J. R., and Gangopadhyay, S.: Hydrologic Implications of Different Large-Scale Meteorological Model Forcing Datasets in Mountainous Regions, J. Hydrometeorol., 15, 474–488, https://doi.org/10.1175/JHM-D-13-036.1, 2014.
Morin, S., Lejeune, Y., Lesaffre, B., Panel, J.-M., Poncet, D., David, P., and Sudul, M.: An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models, Earth Syst. Sci. Data, 4, 13–21, https://doi.org/10.5194/essd-4-13-2012, 2012.
Morris, M. D.: Factorial Sampling Plans for Preliminary Computational Experiments, Technometrics, 33, 161–174, https://doi.org/10.1080/00401706.1991.10484804, 1991.
Mott, R. and Lehning, M.: Meteorological Modeling of Very High-Resolution Wind Fields and Snow Deposition for Mountains, J. Hydrometeorol., 11, 934–949, https://doi.org/10.1175/2010JHM1216.1, 2010.
Niemelä, S., Räisänen, P., and Savijärvi, H.: Comparison of surface radiative flux parameterizations: Part I. Longwave radiation, Atmos. Res., 58, 1–18, https://doi.org/10.1016/S0169-8095(01)00084-9, 2001a.
Niemelä, S., Räisänen, P., and Savijärvi, H.: Comparison of surface radiative flux parameterizations: Part II. Shortwave radiation, Atmos. Res., 58, 141–154, https://doi.org/10.1016/S0169-8095(01)00085-0, 2001b.
Nossent, J., Elsen, P., and Bauwens, W.: Sobol' sensitivity analysis of a complex environmental model, Environ. Model. Softw., 26, 1515–1525, https://doi.org/10.1016/j.envsoft.2011.08.010, 2011.
Oudin, L., Perrin, C., Mathevet, T., Andréassian, V., and Michel, C.: Impact of biased and randomly corrupted inputs on the efficiency and the parameters of watershed models, J. Hydrol., 320, 62–83, https://doi.org/10.1016/j.jhydrol.2005.07.016, 2006.
Pappenberger, F. and Beven, K. J.: Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, https://doi.org/10.1029/2005WR004820, 2006.
Pappenberger, F., Beven, K. J., Ratto, M., and Matgen, P.: Multi-method global sensitivity analysis of flood inundation models, Adv. Water Resour., 31, 1–14, https://doi.org/10.1016/j.advwatres.2007.04.009, 2008.
Phillips, D. and Marks, D.: Spatial uncertainty analysis: propagation of interpolation errors in spatially distributed models, Ecol. Model., 91, 213–229, https://doi.org/10.1016/0304-3800(95)00191-3, 1996.
Rakovec, O., Hill, M. C., Clark, M. P., Weerts, A. H., Teuling, A. J., and Uijlenhoet, R.: Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 409–426, https://doi.org/10.1002/2013WR014063, 2014.
Raleigh, M. S.: Quantification of uncertainties in snow accumulation, snowmelt, and snow disappearance dates, PhD thesis, University of Washington, Washington, 2013.
Raleigh, M. S. and Lundquist, J. D.: Comparing and combining SWE estimates from the SNOW-17 model using PRISM and SWE reconstruction, Water Resour. Res., 48, W01506, https://doi.org/10.1029/2011WR010542, 2012.
Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Yu, W., Miller, K., Arsenault, K., Grubišić, V., Thompson, G., and Gutmann, E.: High-Resolution Coupled Climate Runoff Simulations of Seasonal Snowfall over Colorado: A Process Study of Current and Warmer Climate, J. Climate, 24, 3015–3048, https://doi.org/10.1175/2010JCLI3985.1, 2011.
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.
Rasmussen, R., Ikeda, K., Liu, C., Gochis, D., Clark, M., Dai, A., Gutmann, E., Dudhia, J., Chen, F., Barlage, M., Yates, D., and Zhang, G.: Climate Change Impacts on the Water Balance of the Colorado Headwaters: High-Resolution Regional Climate Model Simulations, J. Hydrometeorol., 15, 1091–1116, https://doi.org/10.1175/JHM-D-13-0118.1, 2014.
Razavi, S. and Gupta, H. V.: What do we mean by sensitivity analysis? The need for comprehensive characterization of "Global" sensitivity in Earth and Environmental Systems Models, Water Resour. Res., 51, 3070–3092, https://doi.org/10.1002/2014WR016527, 2015.
Reba, M. L., Marks, D., Seyfried, M., Winstral, A., Kumar, M., and Flerchinger, G.: A long-term data set for hydrologic modeling in a snow-dominated mountain catchment, Water Resour. Res., 47, W07702, https://doi.org/10.1029/2010WR010030, 2011.
Refsgaard, J. C., van der Sluijs, J. P., Brown, J., and van der Keur, P.: A framework for dealing with uncertainty due to model structure error, Adv. Water Resour., 29, 1586–1597, https://doi.org/10.1016/j.advwatres.2005.11.013, 2006.
Rosero, E., Yang, Z.-L., Wagener, T., Gulden, L. E., Yatheendradas, S., and Niu, G.-Y.: Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season, J. Geophys. Res., 115, D03106, https://doi.org/10.1029/2009JD012035, 2010.
Rosolem, R., Gupta, H. V., Shuttleworth, W. J., Zeng, X., and de Gonçalves, L. G. G.: A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis, J. Geophys. Res.-Atmos. 117, D07103, https://doi.org/10.1029/2011JD016355, 2012.
Saltelli, A.: Sensitivity analysis: Could better methods be used?, J. Geophys. Res., 104, 3789–3793, https://doi.org/10.1029/1998JD100042, 1999.
Saltelli, A. and Annoni, P.: How to avoid a perfunctory sensitivity analysis, Environ. Model. Softw., 25, 1508–1517, https://doi.org/10.1016/j.envsoft.2010.04.012, 2010.
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S.: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Comput. Phys. Commun., 181, 259–270, https://doi.org/10.1016/j.cpc.2009.09.018, 2010.
Sauter, T. and Obleitner, F.: Assessment of the uncertainty of snowpack simulations based on variance decomposition, Geosci. Model Dev. Discuss., 8, 2807–2845, https://doi.org/10.5194/gmdd-8-2807-2015, 2015.
Schmucki, E., Marty, C., Fierz, C., and Lehning, M.: Evaluation of modelled snow depth and snow water equivalent at three contrasting sites in Switzerland using SNOWPACK simulations driven by different meteorological data input, Cold Reg. Sci. Technol., 99, 27–37, https://doi.org/10.1016/j.coldregions.2013.12.004, 2014.
Schoups, G. and Hopmans, J. W.: Evaluation of Model Complexity and Input Uncertainty of Field-Scale Water Flow and Salt Transport, Vadose Zone J., 5, 951–962, https://doi.org/10.2136/vzj2005.0130, 2006.
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160, https://doi.org/10.1029/1999WR900090, 1999.
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.
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, W07522, https://doi.org/10.1029/2012WR011986, 2012.
Slater, A. G. and Clark, M. P.: Snow Data Assimilation via an Ensemble Kalman Filter, J. Hydrometeorol., 7, 478–493, https://doi.org/10.1175/JHM505.1, 2006.
Slater, A. G., Schlosser, C. A., Desborough, C. E., Pitman, A. J., Henderson-Sellers, A., Robock, A., Vinnikov, K. Y., Entin, J., Mitchell, K., Chen, F., Boone, A., Etchevers, P., Habets, F., Noilhan, J., Braden, H., Cox, P. M., de Rosnay, P., Dickinson, R. E., Yang, Z.-L., Dai, Y.-J., Zeng, Q., Duan, Q., Koren, V., Schaake, S., Gedney, N., Gusev, Y. M., Nasonova, O. N., Kim, J., Kowalczyk, E. A., Shmakin, A. B., Smirnova, T. G., Verseghy, D., Wetzel, P., and Xue, Y.: The Representation of Snow in Land Surface Schemes: Results from PILPS 2(d), J. Hydrometeorol., 2, 7–25, https://doi.org/10.1175/1525-7541(2001)002<0007:TROSIL>2.0.CO;2, 2001.
Smith, P. J., Beven, K. J., and Tawn, J. A.: Detection of structural inadequacy in process-based hydrological models: A particle-filtering approach, Water Resour. Res., 44, W01410, https://doi.org/10.1029/2006WR005205, 2008.
Sobol', I.: On sensitivity estimation for nonlinear mathematical models, Matematicheskoe Modelirovanie, 2, 112–118, 1990.
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., and Xu, C.: Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications, J. Hydrol., 523, 739–757, https://doi.org/10.1016/j.jhydrol.2015.02.013, 2015.
Spank, U., Schwärzel, K., Renner, M., Moderow, U., and Bernhofer, C.: Effects of measurement uncertainties of meteorological data on estimates of site water balance components, J. Hydrol., 492, 176–189, https://doi.org/10.1016/j.jhydrol.2013.03.047, 2013.
Sturm, M. and Wagner, A. M.: Using repeated patterns in snow distribution modeling: An Arctic example, Water Resour. Res., 46, W12549, https://doi.org/10.1029/2010WR009434, 2010.
Sturm, M., Holmgren, J., and Liston, G. E.: A Seasonal Snow Cover Classification System for Local to Global Applications, J. Climate, 8, 1261–1283, https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2, 1995.
Tang, Y., Reed, P., Wagener, T., and van Werkhoven, K.: Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrol. Earth Syst. Sci., 11, 793–817, https://doi.org/10.5194/hess-11-793-2007, 2007.
Tarboton, D. and Luce, C.: Utah Energy Balance Snow Accumulation and Melt Model (UEB), Utah Water Res. Lab. and USDA For. Serv. Intermt. Res. Station, Logan, UT, p. 64, 1996.
Thornton, P. E., Hasenauer, H., and White, M. A.: Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria, Agr. Forest Meteorol., 104, 255–271, https://doi.org/10.1016/S0168-1923(00)00170-2, 2000.
Touhami, H. B., Lardy, R., Barra, V., and Bellocchi, G.: Screening parameters in the Pasture Simulation model using the Morris method, Ecol. Model., 266, 42–57, https://doi.org/10.1016/j.ecolmodel.2013.07.005, 2013.
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.
van Werkhoven, K., Wagener, T., Reed, P., and Tang, Y.: Characterization of watershed model behavior across a hydroclimatic gradient, Water Resour. Res., 44, W01429, https://doi.org/10.1029/2007WR006271, 2008.
Vrugt, J. A., Gupta, H. V., Bastidas, L. A., Bouten, W., and Sorooshian, S.: Effective and efficient algorithm for multiobjective optimization of hydrologic models, Water Resour. Res., 39, 1214, https://doi.org/10.1029/2002WR001746, 2003a.
Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S.: A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39, 1201, https://doi.org/10.1029/2002WR001642, 2003b.
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., and Verstraten, J. M.: Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, https://doi.org/10.1029/2004WR003059, 2005.
Vrugt, J. A., ter Braak, C. J. F., Clark, M. P., Hyman, J. M., and Robinson, B. A.: Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resour. Res., 44, W00B09, https://doi.org/10.1029/2007WR006720, 2008.
Vrugt, J. A., Braak, C. J. F., Gupta, H. V., and Robinson, B. A.: Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?, Stoch. Environ. Res. Risk A., 23, 1011–1026, https://doi.org/10.1007/s00477-008-0274-y, 2009.
Wayand, N. E., Hamlet, A. F., Hughes, M., Feld, S. I., and Lundquist, J. D.: Intercomparison of Meteorological Forcing Data from Empirical and Mesoscale Model Sources in the N.F. American River Basin in northern Sierra Nevada, California, J. Hydrometeorology, 14, 677–699, https://doi.org/10.1175/JHM-D-12-0102.1, 2013.
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.: An efficient method for distributing wind speeds over heterogeneous terrain, Hydrol. Process., 23, 2526–2535, https://doi.org/10.1002/hyp.7141, 2009.
Winstral, A., Marks, D., and Gurney, R.: Simulating wind-affected snow accumulations at catchment to basin scales, Adv. Water Resour., 55, 64–79, https://doi.org/10.1016/j.advwatres.2012.08.011, 2013.
Xia, Y., Yang, Z.-L., Stoffa, P. L., and Sen, M. K.: Using different hydrological variables to assess the impacts of atmospheric forcing errors on optimization and uncertainty analysis of the CHASM surface model at a cold catchment, J. Geophys. Res., 110, D01101, https://doi.org/10.1029/2004JD005130, 2005.
Yang, D., Kane, D. L., Hinzman, L. D., Goodison, B. E., Metcalfe, J. R., Louie, P. Y. T., Leavesley, G. H., Emerson, D. G., and Hanson, C. L.: An evaluation of the Wyoming Gauge System for snowfall measurement, Water Resour. Res., 36, 2665–2677, https://doi.org/10.1029/2000WR900158, 2000.
Yilmaz, K. K., Gupta, H. V., and Wagener, T.: A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, https://doi.org/10.1029/2007WR006716, 2008.
You, J., Tarboton, D. G., and Luce, C. H.: Modeling the snow surface temperature with a one-layer energy balance snowmelt model, Hydrol. Earth Syst. Sci., 18, 5061–5076, https://doi.org/10.5194/hess-18-5061-2014, 2014.
Zuzel, J. F. and Cox, L. M.: Relative importance of meteorological variables in snowmelt, Water Resour. Res., 11, 174–176, https://doi.org/10.1029/WR011i001p00174, 1975.
Short summary
A sensitivity analysis is used to examine how error characteristics (type, distributions, and magnitudes) in meteorological forcing data impact outputs from a physics-based snow model in four climates. Bias and error magnitudes were key factors in model sensitivity and precipitation bias often dominated. However, the relative importance of forcings depended somewhat on the selected model output. Forcing uncertainty was comparable to model structural uncertainty as found in other studies.
A sensitivity analysis is used to examine how error characteristics (type, distributions, and...
