Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1529-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-1529-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Mar 2021
Research article |
| 26 Mar 2021
| 26 Mar 2021
Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates
Analysis and Prediction Branch, U.S. Geological Survey, Lakewood, CO 80225, USA
Hydrologic Science and Engineering, Colorado School of Mines, Golden, CO 80401, USA
William Farmer
Analysis and Prediction Branch, U.S. Geological Survey, Lakewood, CO 80225, USA
Jessica Driscoll
Analysis and Prediction Branch, U.S. Geological Survey, Lakewood, CO 80225, USA
Terri S. Hogue
Hydrologic Science and Engineering, Colorado School of Mines, Golden, CO 80401, USA
Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO 80401, USA
Related authors
Samuel Saxe, Terri S. Hogue, and Lauren Hay
Hydrol. Earth Syst. Sci., 22, 1221–1237, https://doi.org/10.5194/hess-22-1221-2018, https://doi.org/10.5194/hess-22-1221-2018, 2018
Short summary
Short summary
We investigate the impact of wildfire on watershed flow regimes, examining responses across the western United States. On a national scale, our results confirm the work of prior studies: that low, high, and peak flows typically increase following a wildfire. Regionally, results are more variable and sometimes contradictory. Our results may be significant in justifying the calibration of watershed models and in contributing to the overall observational analysis of post-fire streamflow response.
Claudia Rebecca Corona and Terri Sue Hogue
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-256, https://doi.org/10.5194/hess-2024-256, 2024
Preprint under review for HESS
Short summary
Short summary
Stream water temperature (SWT) is a key indicator of water quality that benefits public use and aquatic life health. Advances in computer modeling have helped improve our understanding of SWT dynamics, but challenges remain. Recently, scientists have begun to use machine learning (ML) in SWT modeling, but it is unclear how ML is increasing our understanding of SWT causes and effects. This work reviews the application of ML in SWT modeling and discusses where there is still room for improvement.
William H. Farmer, Thomas M. Over, and Julie E. Kiang
Hydrol. Earth Syst. Sci., 22, 5741–5758, https://doi.org/10.5194/hess-22-5741-2018, https://doi.org/10.5194/hess-22-5741-2018, 2018
Short summary
Short summary
This work observes that the result of streamflow simulation is often biased, especially with regards to extreme events, and proposes a novel technique to reduce this bias. By using parallel simulations of relative streamflow timing (sequencing) and the distribution of streamflow (magnitude), severe biases can be mitigated. Reducing this bias allows for improved utility of streamflow simulation for water resources management.
Samuel Saxe, Terri S. Hogue, and Lauren Hay
Hydrol. Earth Syst. Sci., 22, 1221–1237, https://doi.org/10.5194/hess-22-1221-2018, https://doi.org/10.5194/hess-22-1221-2018, 2018
Short summary
Short summary
We investigate the impact of wildfire on watershed flow regimes, examining responses across the western United States. On a national scale, our results confirm the work of prior studies: that low, high, and peak flows typically increase following a wildfire. Regionally, results are more variable and sometimes contradictory. Our results may be significant in justifying the calibration of watershed models and in contributing to the overall observational analysis of post-fire streamflow response.
William H. Farmer
Hydrol. Earth Syst. Sci., 20, 2721–2735, https://doi.org/10.5194/hess-20-2721-2016, https://doi.org/10.5194/hess-20-2721-2016, 2016
Short summary
Short summary
The potential of geostatistical tools, leveraging the spatial structure and dependency of correlated time series, for the prediction of daily streamflow time series at unmonitored locations is explored. Simple geostatistical tools improve on traditional estimates of daily streamflow. The temporal evolution of spatial structure, including seasonal fluctuations, is also explored. The proposed method is contrasted with more advanced geostatistical methods and shown to be comparable.
P. Vahmani and T. S. Hogue
Hydrol. Earth Syst. Sci., 18, 4791–4806, https://doi.org/10.5194/hess-18-4791-2014, https://doi.org/10.5194/hess-18-4791-2014, 2014
P. D. Micheletty, A. M. Kinoshita, and T. S. Hogue
Hydrol. Earth Syst. Sci., 18, 4601–4615, https://doi.org/10.5194/hess-18-4601-2014, https://doi.org/10.5194/hess-18-4601-2014, 2014
S. R. Lopez, T. S. Hogue, and E. D. Stein
Hydrol. Earth Syst. Sci., 17, 3077–3094, https://doi.org/10.5194/hess-17-3077-2013, https://doi.org/10.5194/hess-17-3077-2013, 2013
Related subject area
Subject: Global hydrology | Techniques and Approaches: Uncertainty analysis
Leveraging multi-variable observations to reduce and quantify the output uncertainty of a global hydrological model: evaluation of three ensemble-based approaches for the Mississippi River basin
Information content of soil hydrology in a west Amazon watershed as informed by GRACE
Diagnostic evaluation of river discharge into the Arctic Ocean and its impact on oceanic volume transports
The 63-year changes in annual streamflow volumes across Europe with a focus on the Mediterranean basin
Multivariable evaluation of land surface processes in forced and coupled modes reveals new error sources to the simulated water cycle in the IPSL (Institute Pierre Simon Laplace) climate model
Historical and future changes in global flood magnitude – evidence from a model–observation investigation
A global-scale evaluation of extreme event uncertainty in the eartH2Observe project
Assessment of precipitation error propagation in multi-model global water resource reanalysis
The potential of global reanalysis datasets in identifying flood events in Southern Africa
Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model
Global change in streamflow extremes under climate change over the 21st century
Have precipitation extremes and annual totals been increasing in the world's dry regions over the last 60 years?
Sensitivity of future continental United States water deficit projections to general circulation models, the evapotranspiration estimation method, and the greenhouse gas emission scenario
Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use
Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach
Flood and drought hydrologic monitoring: the role of model parameter uncertainty
Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration
Climate change impacts on runoff in West Africa: a review
Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis
Disinformative data in large-scale hydrological modelling
The impact of climate mitigation on projections of future drought
Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data
Monitoring and quantifying future climate projections of dryness and wetness extremes: SPI bias
Improving runoff estimates from regional climate models: a performance analysis in Spain
A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models
Error characterisation of global active and passive microwave soil moisture datasets
Assessment of soil moisture fields from imperfect climate models with uncertain satellite observations
Petra Döll, Howlader Mohammad Mehedi Hasan, Kerstin Schulze, Helena Gerdener, Lara Börger, Somayeh Shadkam, Sebastian Ackermann, Seyed-Mohammad Hosseini-Moghari, Hannes Müller Schmied, Andreas Güntner, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 28, 2259–2295, https://doi.org/10.5194/hess-28-2259-2024, https://doi.org/10.5194/hess-28-2259-2024, 2024
Short summary
Short summary
Currently, global hydrological models do not benefit from observations of model output variables to reduce and quantify model output uncertainty. For the Mississippi River basin, we explored three approaches for using both streamflow and total water storage anomaly observations to adjust the parameter sets in a global hydrological model. We developed a method for considering the observation uncertainties to quantify the uncertainty of model output and provide recommendations.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
Short summary
Short summary
The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
Hydrol. Earth Syst. Sci., 26, 279–304, https://doi.org/10.5194/hess-26-279-2022, https://doi.org/10.5194/hess-26-279-2022, 2022
Short summary
Short summary
We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
Daniele Masseroni, Stefania Camici, Alessio Cislaghi, Giorgio Vacchiano, Christian Massari, and Luca Brocca
Hydrol. Earth Syst. Sci., 25, 5589–5601, https://doi.org/10.5194/hess-25-5589-2021, https://doi.org/10.5194/hess-25-5589-2021, 2021
Short summary
Short summary
We evaluate 63 years of changes in annual streamflow volume across Europe, using a data set of more than 3000 stations, with a special focus on the Mediterranean basin. The results show decreasing (increasing) volumes in the southern (northern) regions. These trends are strongly consistent with the changes in temperature and precipitation.
Hiroki Mizuochi, Agnès Ducharne, Frédérique Cheruy, Josefine Ghattas, Amen Al-Yaari, Jean-Pierre Wigneron, Vladislav Bastrikov, Philippe Peylin, Fabienne Maignan, and Nicolas Vuichard
Hydrol. Earth Syst. Sci., 25, 2199–2221, https://doi.org/10.5194/hess-25-2199-2021, https://doi.org/10.5194/hess-25-2199-2021, 2021
Hong Xuan Do, Fang Zhao, Seth Westra, Michael Leonard, Lukas Gudmundsson, Julien Eric Stanislas Boulange, Jinfeng Chang, Philippe Ciais, Dieter Gerten, Simon N. Gosling, Hannes Müller Schmied, Tobias Stacke, Camelia-Eliza Telteu, and Yoshihide Wada
Hydrol. Earth Syst. Sci., 24, 1543–1564, https://doi.org/10.5194/hess-24-1543-2020, https://doi.org/10.5194/hess-24-1543-2020, 2020
Short summary
Short summary
We presented a global comparison between observed and simulated trends in a flood index over the 1971–2005 period using the Global Streamflow Indices and Metadata archive and six global hydrological models available through The Inter-Sectoral Impact Model Intercomparison Project. Streamflow simulations over 2006–2099 period robustly project high flood hazard in several regions. These high-flood-risk areas, however, are under-sampled by the current global streamflow databases.
Toby R. Marthews, Eleanor M. Blyth, Alberto Martínez-de la Torre, and Ted I. E. Veldkamp
Hydrol. Earth Syst. Sci., 24, 75–92, https://doi.org/10.5194/hess-24-75-2020, https://doi.org/10.5194/hess-24-75-2020, 2020
Short summary
Short summary
Climate change impact modellers can only act on predictions of the occurrence of an extreme event in the Earth system if they know the uncertainty in that prediction and how uncertainty is attributable to different model components. Using eartH2Observe data, we quantify the balance between different sources of uncertainty in global evapotranspiration and runoff, making a crucial contribution to understanding the spatial distribution of water resources allocation deficiencies.
Md Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Jan Polcher, Clément Albergel, Emanuel Dutra, Gabriel Fink, Alberto Martínez-de la Torre, and Simon Munier
Hydrol. Earth Syst. Sci., 23, 1973–1994, https://doi.org/10.5194/hess-23-1973-2019, https://doi.org/10.5194/hess-23-1973-2019, 2019
Short summary
Short summary
This study investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period of 11 years based on six global hydrologic models and five precipitation datasets. Results show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure.
Gaby J. Gründemann, Micha Werner, and Ted I. E. Veldkamp
Hydrol. Earth Syst. Sci., 22, 4667–4683, https://doi.org/10.5194/hess-22-4667-2018, https://doi.org/10.5194/hess-22-4667-2018, 2018
Short summary
Short summary
Flooding in vulnerable and data-sparse regions such as the Limpopo basin in Southern Africa is a key concern. Data available to local flood managers are often limited, inconsistent or asymmetrically distributed. We demonstrate that freely available global datasets are well suited to provide essential information. Despite the poor performance of simulated discharges, these datasets hold potential in identifying damaging flood events, particularly for higher-resolution datasets and larger basins.
Emiliano Gelati, Bertrand Decharme, Jean-Christophe Calvet, Marie Minvielle, Jan Polcher, David Fairbairn, and Graham P. Weedon
Hydrol. Earth Syst. Sci., 22, 2091–2115, https://doi.org/10.5194/hess-22-2091-2018, https://doi.org/10.5194/hess-22-2091-2018, 2018
Short summary
Short summary
We compared land surface model simulations forced by several meteorological datasets with observations over the Euro-Mediterranean area, for the 1979–2012 period. Precipitation was the most uncertain forcing variable. The impacts of forcing uncertainty were larger on the mean and standard deviation rather than the timing, shape and inter-annual variability of simulated discharge. Simulated leaf area index and surface soil moisture were relatively insensitive to these uncertainties.
Behzad Asadieh and Nir Y. Krakauer
Hydrol. Earth Syst. Sci., 21, 5863–5874, https://doi.org/10.5194/hess-21-5863-2017, https://doi.org/10.5194/hess-21-5863-2017, 2017
Short summary
Short summary
Multi-model analysis of global streamflow extremes for the 20th and 21st centuries under two warming scenarios is performed. About 37 and 43 % of global land areas show potential for increases in flood and drought events. Nearly 10 % of global land areas, holding around 30 % of world’s population, reflect a potentially worsening hazard of flood and drought. A significant increase in streamflow of the regions near and above the Arctic Circle, and decrease in subtropical arid areas, is projected.
Sebastian Sippel, Jakob Zscheischler, Martin Heimann, Holger Lange, Miguel D. Mahecha, Geert Jan van Oldenborgh, Friederike E. L. Otto, and Markus Reichstein
Hydrol. Earth Syst. Sci., 21, 441–458, https://doi.org/10.5194/hess-21-441-2017, https://doi.org/10.5194/hess-21-441-2017, 2017
Short summary
Short summary
The paper re-investigates the question whether observed precipitation extremes and annual totals have been increasing in the world's dry regions over the last 60 years. Despite recently postulated increasing trends, we demonstrate that large uncertainties prevail due to (1) the choice of dryness definition and (2) statistical data processing. In fact, we find only minor (and only some significant) increases if (1) dryness is based on aridity and (2) statistical artefacts are accounted for.
Seungwoo Chang, Wendy D. Graham, Syewoon Hwang, and Rafael Muñoz-Carpena
Hydrol. Earth Syst. Sci., 20, 3245–3261, https://doi.org/10.5194/hess-20-3245-2016, https://doi.org/10.5194/hess-20-3245-2016, 2016
Short summary
Short summary
Projecting water deficit depends on how researchers combine possible future climate scenarios such as general circulation models (GCMs), evapotranspiration estimation method (ET), and greenhouse gas emission scenarios. Using global sensitivity analysis, we found the relative contribution of each of these factors to projecting future water deficit and the choice of ET estimation method are as important as the choice of GCM, and greenhouse gas emission scenario is less influential than the others.
Hannes Müller Schmied, Linda Adam, Stephanie Eisner, Gabriel Fink, Martina Flörke, Hyungjun Kim, Taikan Oki, Felix Theodor Portmann, Robert Reinecke, Claudia Riedel, Qi Song, Jing Zhang, and Petra Döll
Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, https://doi.org/10.5194/hess-20-2877-2016, 2016
Short summary
Short summary
The assessment of water balance components of the global land surface by means of hydrological models is affected by large uncertainties, in particular related to meteorological forcing. We analyze the effect of five state-of-the-art forcings on water balance components at different spatial and temporal scales modeled with WaterGAP. Furthermore, the dominant effect (precipitation/human alteration) for long-term changes in river discharge is assessed.
Dave MacLeod, Hannah Cloke, Florian Pappenberger, and Antje Weisheimer
Hydrol. Earth Syst. Sci., 20, 2737–2743, https://doi.org/10.5194/hess-20-2737-2016, https://doi.org/10.5194/hess-20-2737-2016, 2016
Short summary
Short summary
Soil moisture memory is a key aspect of seasonal climate predictions, through feedback between the land surface and the atmosphere. Estimates have been made of the length of soil moisture memory; however, we show here how estimates of memory show large variation with uncertain model parameters. Explicit representation of model uncertainty may then improve the realism of simulations and seasonal climate forecasts.
N. W. Chaney, J. D. Herman, P. M. Reed, and E. F. Wood
Hydrol. Earth Syst. Sci., 19, 3239–3251, https://doi.org/10.5194/hess-19-3239-2015, https://doi.org/10.5194/hess-19-3239-2015, 2015
Short summary
Short summary
Land surface modeling is playing an increasing role in global monitoring and prediction of extreme hydrologic events. However, uncertainties in parameter identifiability limit the reliability of model predictions. This study makes use of petascale computing to perform a comprehensive evaluation of land surface modeling for global flood and drought monitoring and suggests paths forward to overcome the challenges posed by parameter uncertainty.
H. Müller Schmied, S. Eisner, D. Franz, M. Wattenbach, F. T. Portmann, M. Flörke, and P. Döll
Hydrol. Earth Syst. Sci., 18, 3511–3538, https://doi.org/10.5194/hess-18-3511-2014, https://doi.org/10.5194/hess-18-3511-2014, 2014
P. Roudier, A. Ducharne, and L. Feyen
Hydrol. Earth Syst. Sci., 18, 2789–2801, https://doi.org/10.5194/hess-18-2789-2014, https://doi.org/10.5194/hess-18-2789-2014, 2014
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
A. Kauffeldt, S. Halldin, A. Rodhe, C.-Y. Xu, and I. K. Westerberg
Hydrol. Earth Syst. Sci., 17, 2845–2857, https://doi.org/10.5194/hess-17-2845-2013, https://doi.org/10.5194/hess-17-2845-2013, 2013
I. H. Taylor, E. Burke, L. McColl, P. D. Falloon, G. R. Harris, and D. McNeall
Hydrol. Earth Syst. Sci., 17, 2339–2358, https://doi.org/10.5194/hess-17-2339-2013, https://doi.org/10.5194/hess-17-2339-2013, 2013
H. Xie, L. Longuevergne, C. Ringler, and B. R. Scanlon
Hydrol. Earth Syst. Sci., 16, 3083–3099, https://doi.org/10.5194/hess-16-3083-2012, https://doi.org/10.5194/hess-16-3083-2012, 2012
F. Sienz, O. Bothe, and K. Fraedrich
Hydrol. Earth Syst. Sci., 16, 2143–2157, https://doi.org/10.5194/hess-16-2143-2012, https://doi.org/10.5194/hess-16-2143-2012, 2012
D. González-Zeas, L. Garrote, A. Iglesias, and A. Sordo-Ward
Hydrol. Earth Syst. Sci., 16, 1709–1723, https://doi.org/10.5194/hess-16-1709-2012, https://doi.org/10.5194/hess-16-1709-2012, 2012
S. N. Gosling, R. G. Taylor, N. W. Arnell, and M. C. Todd
Hydrol. Earth Syst. Sci., 15, 279–294, https://doi.org/10.5194/hess-15-279-2011, https://doi.org/10.5194/hess-15-279-2011, 2011
W. A. Dorigo, K. Scipal, R. M. Parinussa, Y. Y. Liu, W. Wagner, R. A. M. de Jeu, and V. Naeimi
Hydrol. Earth Syst. Sci., 14, 2605–2616, https://doi.org/10.5194/hess-14-2605-2010, https://doi.org/10.5194/hess-14-2605-2010, 2010
G. Schumann, D. J. Lunt, P. J. Valdes, R. A. M. de Jeu, K. Scipal, and P. D. Bates
Hydrol. Earth Syst. Sci., 13, 1545–1553, https://doi.org/10.5194/hess-13-1545-2009, https://doi.org/10.5194/hess-13-1545-2009, 2009
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.
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.:
TerraClimate, a high-resolution global dataset of monthly climate and
climatic water balance from 1958–2015, Scientific Data, 5, 170191, 2018.
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Al Bitar, A., Kerr, Y. H., Merlin, O., Cabot, F., and Wigneron, J.-P.: Global
drought index from SMOS soil moisture,
Geoscience and Remote Sensing Symposium (IGARSS), 21 February 2013, Melbourne, Australia, 2013.
Alsdorf, D. E., Rodrigues, E., and Lettenmaier, D. P.: Measuring surface
water from space, Rev. Geophys., 45, 2, https://doi.org/10.1029/2006RG000197, 2007.
Archfield, S. A., Clark, M., Arheimer, B., Hay, L. E., McMillan, H., Kiang,
J. E., Seibert, J., Hakala, K., Bock, A., Wagener, T., Farmer, W. H.,
Andréassian, V., Attinger, S., Viglione, A., Knight, R., Markstrom, S.,
and Over, T.: Accelerating advances in continental domain hydrologic
modeling, Water Resour. Res., 51, 10078–10091, https://doi.org/10.1002/2015WR017498, 2015.
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B.,
Hirschi, M., and Betts, A. K.: A revised hydrology for the ECMWF model:
Verification from field site to terrestrial water storage and impact in the
integrated forecast system, J. Hydrometeorol., 10, 623–643,
https://doi.org/10.1175/2008JHM1068.1, 2009.
Barrett, A.: National Operational Hydrologic Remote Sensing Center, Snow Data
Assimilation System (SNODAS), products at NSIDC, Special Report, National
Snow and Ice Data Center, Boulder, Colorado, USA, available at:
https://nsidc.org/sites/nsidc.org/files/files/nsidc_special_report_11.pdf (last access: 1 November 2019), 2003.
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., and Ziese, M.: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013.
Beven, K.: Towards an alternative blueprint for a physically based digitally
simulated hydrologic response modelling system, Hydrol. Process.,
16, 189–206, https://doi.org/10.1002/hyp.343, 2002.
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol.,
320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006.
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions, Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015WR017173, 2015.
Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner,
W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P.,
Latron, J., Martin, C., and Bittelli, M.: Soil moisture estimation through
ASCAT and AMSR-E sensors: An intercomparison and validation study across
Europe, Remote Sens. Environ., 115, 3390–3408,
https://doi.org/10.1016/j.rse.2011.08.003, 2011.
Broxton, P. D., Zeng, X., and Dawson, N.: Why do global reanalyses and land
data assimilation products underestimate snow water equivalent?, J. Hydrometeorol., 17, 2743–2761, https://doi.org/10.1175/JHM-D-16-0056.1, 2016.
Carter, E., Hain, C., Anderson, M., and Steinschneider, S.: A water
balance–based, spatiotemporal evaluation of terrestrial evapotranspiration
products across the contiguous United States, J. Hydrometeorol.,
19, 891–905, https://doi.org/10.1175/JHM-D-17-0186.1, 2018.
Chang, A. T. C. and Rango, A.: Algorithm theoretical basis document (ATBD)
for the AMSR-E Snow Water Equivalent Algoritm, NASA/GSFC, available at:
https://nsidc.org/sites/nsidc.org/files/technical-references/amsr_atbd_snow.pdf (1 November 2019), 2000.
Chang, A. T. C., Kelly, R. E. J., Foster, J. L., and Hall, D. K.: Global SWE
monitoring using AMSR-E data, in: Proceedings IGARSS 2003, IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, 21–25 July 2003, 680–682, https://doi.org/10.1109/IGARSS.2003.1293880, 2003.
Chen, F., Barlage, M., Tewari, M., Rasmussen, R., Jin, J., Lettenmaier, D.,
Livneh, B., Lin, C., Miguez-Macho, G., Niu, G.-Y., Wen, L., and Yang, Z.-L.:
Modeling seasonal snowpack evolution in the complex terrain and forested
Colorado Headwaters region: A model intercomparison study,
J. Geophys. Res.-Atmos., 119, 13795–13819, https://doi.org/10.1002/2014JD022167, 2014.
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., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D.
J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., Maxwell, R. M.,
Shen, C., Swenson, S. C., and Zeng, X.: Improving the representation of
hydrologic processes in Earth System Models, Water Resour. Res.,
51, 5929–5956, https://doi.org/10.1002/2015WR017096, 2015b.
Covey, C., Achuta Rao, K. M., Cubasch, U., Jones, P., Lambert, S. J., Mann,
M. E., Phillips, T. J., and Taylor, K. E.: An overview of results from the
Coupled Model Intercomparison Project, Global Planet. Change, 37,
103–133, https://doi.org/10.1016/S0921-8181(02)00193-5, 2003.
Dai, Y., Zeng, X., Dickinson, R. E., Baker, I., Bonan, G. B., Bosilovich, M.
G., Denning, A. S., Dirmeyer, P. A., Houser, P. R., Niu, G., Oleson, K. W.,
Schlosser, C. A., and Yang, Z.-L.: The Common Land Model, B. Am. Meteorol. Soc., 84, 1013–1024, https://doi.org/10.1175/BAMS-84-8-1013, 2003.
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor,
G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping
of climatological temperature and precipitation across the conterminous
United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008.
Dawson, N., Broxton, P., Zeng, X., Leuthold, M., Barlage, M., and Holbrook,
P.: An evaluation of snow initializations in NCEP global and regional
forecasting models, J. Hydrometeorol., 17, 1885–1901,
https://doi.org/10.1175/JHM-D-15-0227.1, 2016.
Dawson, N., Broxton, P., and Zeng, X.: Evaluation of remotely sensed snow
water equivalent and snow cover extent over the contiguous United States, J. Hydrometeorol., 19, 1777–1791, https://doi.org/10.1175/JHM-D-18-0007.1, 2018.
Derin, Y. and Yilmaz, K. K.: Evaluation of multiple satellite-based
precipitation products over complex topography, J. Hydrometeorol.,
15, 1498–1516, https://doi.org/10.1175/JHM-D-13-0191.1, 2014.
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.
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.:
GSWP-2: Multimodel analysis and implications for our perception of the land
surface, B. Am. Meteorol. Soc., 87, 1381–1398, https://doi.org/10.1175/BAMS-87-10-1381, 2006.
Donat, M. G., Sillmann, J., Wild, S., Alexander, L. V., Lippmann, T., and
Zwiers, F. W.: Consistency of temperature and precipitation extremes across
various global gridded in situ and reanalysis datasets, J. Climate,
27, 5019–5035, https://doi.org/10.1175/JCLI-D-13-00405.1, 2014.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D.,
Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI soil moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction operational
mesoscale Eta model, J. Geophys. Res.-Atmos., 108, D22, https://doi.org/10.1029/2002JD003296, 2003.
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., Rutter, N., Pomeroy, J., Baxter, R., Stähli, M., Gustafsson,
D., Barr, A., Bartlett, P., and Elder, K.: SNOWMIP2: An evaluation of forest
snow process simulations, B. Am. Meteorol. Soc.,
90, 1120–1136, https://doi.org/10.1175/2009BAMS2629.1, 2009.
Fan, Y. and van den Dool, H.: Climate Prediction Center global monthly soil
moisture data set at 0.5∘ resolution for 1948 to present,
J. Geophys. Res., 109, D10102, https://doi.org/10.1029/2003JD004345, 2004.
Freeze, R. A. and Harlan, R. L.: Blueprint for a physically-based,
digitally-simulated hydrologic response model, J. Hydrol., 9,
237–258, https://doi.org/10.1016/0022-1694(69)90020-1, 1969.
Gao, H., Tang, Q., Ferguson, C. R., Wood, E. F., and Lettenmaier, D. P.:
Estimating the water budget of major US river basins via remote sensing,
Int. J. Remote Sens., 31, 3955–3978, https://doi.org/10.1080/01431161.2010.483488, 2010.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Guirguis, K. J. and Avissar, R.: A precipitation climatology and dataset
intercomparison for the western United States, J. Hydrometeorol.,
9, 825–841, https://doi.org/10.1175/2008JHM832.1, 2008.
Haddeland, I., Clark, D. B., Franssen, W., Ludwig, F., Voß, F., Arnell,
N. W., Bertrand, N., Best, M., Folwell, S., Gerten, D., Gomes, S., Gosling,
S. N., Hagemann, S., Hanasaki, N., Harding, R., Heinke, J., Kabat, P.,
Koirala, S., Oki, T., Polcher, J., Stacke, T., Viterbo, P., Weedon, G. P.,
and Yeh, P.: Multimodel estimate of the global terrestrial water balance:
Setup and first results, J. Hydrometeorol., 12, 869–884,
https://doi.org/10.1175/2011JHM1324.1, 2011.
Hofstra, N., Haylock, M., New, N., Jones, P., and Frei, C.: Comparison of six
methods for the interpolation of daily, European climate data, J. Geophys. Res., 113, D21, https://doi.org/10.1029/2008JD010100, 2008.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F.,
Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor
precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/JHM560.1, 2007.
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Nelkin, E. J.: The TRMM
Multi-Satellite Precipitation Analysis (TMPA), in: Satellite Rainfall
Applications for Surface Hydrology, edited by: Gebremichael, M. and
Hossain, F., Springer, Dordrecht, The Netherlands, 3–22,
https://doi.org/10.1007/978-90-481-2915-7_1, 2010.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Hsu, K., Kidd, C., Nelkin, E.
J., Tan, J., and Xie, P.: Algorithm Theoretical Basis Document (ATBD) for
Global Precipitation Climatology Project Version 3.0, Precipitation Data,
MEaSUREs project, Greenbelt, Maryland, USA, 26 pp., 2019.
Jian, X., Wolock, D., and Lins, H. F.: WaterWatch – Maps, graphs, and tables
of current, recent, and past streamflow conditions, Report, U.S. Geological Survey, Reston, Virginia,
USA, 2008.
Kader, G. D. and Perry, M.: Variability for categorical variables,
Journal of Statistics Education, 15, 2, https://doi.org/10.1080/10691898.2007.11889465, 2007.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, B. Am. Meteorol. Soc.,
77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J.,
Fiorino, M., and Potter, G. L.: NCEP–DOE AMIP-II Reanalysis (R-2), B. Am. Meteorol. Soc., 83, 1631–1644, https://doi.org/10.1175/BAMS-83-11-1631, 2002.
Kendall, M. G.: A new measure of rank correlation, Biometrika, 30,
81–93, https://doi.org/10.1093/biomet/30.1-2.81, 1938.
Kobayashi, S. and NCAR Research Staff: The Climate Data Guide: JRA-55,
available at: https://climatedataguide.ucar.edu/climate-data/jra-55, last access: 1 January 2019.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 reanalysis: General specifications and basic characteristics,
J. Meteorol. Soc. Jpn., 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Kollet, S., Sulis, M., Maxwell, R. M., Paniconi, C., Putti, M., Bertoldi,
G., Coon, E. T., Cordano, E., Endrizzi, S., Kikinzon, E., Mouche, E.,
Mügler, C., Park, Y.-J., Refsgaard, J. C., Stisen, S., and Sudicky, E.:
The integrated hydrologic model intercomparison project, IH-MIP2: A second
set of benchmark results to diagnose integrated hydrology and feedbacks, Water Resour. Res., 53, 867–890, https://doi.org/10.1002/2016WR019191, 2017.
Koster, R. D. and Suarez, M. J.: Modeling the land surface boundary in
climate models as a composite of independent vegetation stands, J. Geophys. Res.-Atmos., 97, 2697–2715, https://doi.org/10.1029/91JD01696, 1992.
Koster, R. D. and Suarez, M. J.: Energy and water balance calculations in
the Mosaic LSM, Goddard Space Flight Center, Greenbelt, Maryland, USA,
available at: https://gmao.gsfc.nasa.gov/pubs/docs/Koster130.pdf (last access: 1 October 2019), 1996.
Koster, R. D., Suarez, M. J., Ducharne, A., Stieglitz, M., and Kumar, P.: A
catchment-based approach to modeling land surface processes in a general
circulation model: 1. Model structure, J. Geophys. Res.-Atmos., 105, 24809–24822, https://doi.org/10.1029/2000JD900327, 2000.
Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., and Puma, M.
J.: On the nature of soil moisture in land surface models, J. Climate, 22, 4322–4335, https://doi.org/10.1175/2009JCLI2832.1, 2009.
La Fontaine, J. H., Hay, L. E., Viger, R. J., Regan, R. S., and Markstrom, S.
L.: Effects of climate and land cover on hydrology in the southeastern US:
Potential impacts on watershed planning,
J. Am. Water Resour. As., 51, 1235–1261, https://doi.org/10.1111/1752-1688.12304, 2015.
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial
water storage estimates, Water Resour. Res., 48, 4, https://doi.org/10.1029/2011WR011453, 2012.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation model, J. Geophys. Res., 99, 415–428, 1994.
Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Adreadis, K.
M., Maurer, E. P., and Lettenmaier, D. P.: A long-term hydrologically based
dataset of land surface fluxes and states for the conterminous United
States, J. Climate, 26, 9384–9392, https://doi.org/10.1175/JCLI-D-12-00508.1,
2013.
Manabe, S.: Climate and the ocean circulation: I. The atmospheric
circulation and the hydrology of the Earth's surface,
Mon. Weather Rev., 97, 739–774,
https://doi.org/10.1175/1520-0493(1969)097<0739:CATOC>2.3.CO;2, 1969.
Markstrom, S. L., Regan, S. R., Hay, L. E., Viger, R. J., Webb,
R. M. T., Payn, R. A., and La Fontaine, J. H.: PRMS-IV, the
Precipitation-Runoff Modeling System, Version 4, US Geological Survey,
Reston, Virginia, USA, available at: https://pubs.usgs.gov/tm/6b7/ (1 June 2019), 2015.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Maurer, E. P., Wood, A. W., Adam, J. C., Lettenmaier, D. P., and Nijssen, B.:
A long-term hydrologically-based data set of land surface fluxes and states
for the conterminous United States, J. Climate, 15, 3237–3251, 2002.
Maxwell, R. M., Putti, M., Meyerhoff, S., Delfs, J.-O., Ferguson, I. M.,
Ivanov, V., Kim, J., Kolditz, O., Kollet, S. J., Kumar, M., Lopez, S., Niu,
J., Paniconi, C., Park, Y.-J., Phanikumar, M. S., Shen, C., Sudicky, E. A.,
and Sulis, M.: Surface-subsurface model intercomparison: A first set of
benchmark results to diagnose integrated hydrology and feedbacks, Water Resour. Res., 50, 1531–1549, https://doi.org/10.1002/2013WR013725, 2014.
McCabe, G. J. and Markstrom, S. L.: A monthly water-balance model driven by
a graphical user interface, USGS Numbered Series, US Geological Survey,
Reston, Virginia, USA, 2007.
McCabe, G. J., Wolock, D. M., and Austin, S. H.: Variability of runoff-based
drought conditions in the conterminous United States, Int. J. Climatol., 37, 1014–1021, https://doi.org/10.1002/joc.4756, 2017.
McCabe, M. F., Ershadi, A., Jimenez, C., Miralles, D. G., Michel, D., and Wood, E. F.: The GEWEX LandFlux project: evaluation of model evaporation using tower-based and globally gridded forcing data, Geosci. Model Dev., 9, 283–305, https://doi.org/10.5194/gmd-9-283-2016, 2016.
McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R., Wagner, W., Lucieer, A., Houborg, R., Verhoest, N. E. C., Franz, T. E., Shi, J., Gao, H., and Wood, E. F.: The future of Earth observation in hydrology, Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, 2017.
Mendoza, P. A., Clark, M. P., Mizukami, N., Newman, A. J., Barlage, M.,
Gutmann, E. D., Rasmussen, R. M., Rajagopalan, B., Brekke, L. D., and Arnold,
J. R.: Effects of hydrologic model choice and calibration on the portrayal
of climate change impacts, J. Hydrometeorol., 16, 762–780,
https://doi.org/10.1175/JHM-D-14-0104.1, 2015.
Mesinger, F., Di Mego, G., Kalnay, E., Mitchell, K., Shafran, P. C.,
Ebisuzaki, W., Jović, D., Woollen, J., Rogers, E., Berbery, E. H., Ek,
M. B., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G.,
Parrish, D., and Shi, W.: North American Regional Reanalysis, B. Am. Meteorol. Soc., 87, 343–360, https://doi.org/10.1175/BAMS-87-3-343, 2006.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
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.
Mizukami, N., Clark, M. P., Newman, A. J., Wood, A. W., Gutmann, E. D.,
Nijssen, B., Rakovec, O., and Samaniego, L.: Towards seamless large-domain
parameter estimation for hydrologic models, Water Resour. Res., 53,
8020–8040, https://doi.org/10.1002/2017WR020401, 2017.
Mu, Q., Heinsch, F. A., Zhao, M., and Running, S. W.: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sens. Environ., 111, 519–536, https://doi.org/10.1016/j.rse.2007.04.015, 2007.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global
terrestrial evapotranspiration algorithm, Remote Sens. Environ.,
115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011.
Mudryk, L. R. and Derksen, C.: CanSISE Observation-Based Ensemble of
Northern Hemisphere Terrestrial Snow Water Equivalent, Version 2, NSIDC:
National Snow and Ice Data Center, Boulder, Colorado, USA, https://doi.org/10.5067/96ltniikJ7vd, 2017.
Mudryk, L. R., Derksen, C., Kushner, P. J., and Brown, R.: Characterization
of northern hemisphere snow water equivalent datasets, 1981–2010, J. Climate, 28, 8037–8051, https://doi.org/10.1175/JCLI-D-15-0229.1, 2015.
Mueller, B., Seneviratne, S. I., Jimenez, C., Corti, T., Hirschi, M.,
Balsamo, G., Ciais, P., Dirmeyer, P., Fisher, J. B., Guo, Z., Jung, M.,
Maignan, F., McCabe, M. F., Reichle, R., Reichstein, M., Rodell, M.,
Sheffield, J., Teuling, A. J., Wang, K., Wood, E. F., and Zhang, Y.:
Evaluation of global observations-based evapotranspiration datasets and IPCC
AR4 simulations, Geophys. Res. Lett., 38, 6, https://doi.org/10.1029/2010GL046230, 2011.
Muñoz Sabater, J.: ERA5-Land monthly averaged data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.68d2bb3, 2019.
National Operational Hydrologic Remote Sensing Center: Snow Data
Assimilation System (SNODAS) data products at NSIDC, Version 1, NSIDC:
National Snow and Ice Data Center, Boulder, Colorado, USA, 2004.
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015.
NCAR Research Staff: The Climate Data Guide: JRA-25, available at: https://climatedataguide.ucar.edu/climate-data/jra-25 (last access: 1 December 2019), 2016.
Omernik, J. M. and Griffith, G. E.: Ecoregions of the conterminous United
States: Evolution of a hierarchical spatial
framework, Environ. Manage., 54, 1249–1266, https://doi.org/10.1007/s00267-014-0364-1,
2014.
Onogi, K., Tsutsui, J., Koide, H., Sakamoto, M., Kobayashi, S., Hatsushika,
H., Matsumoto, T., Yamazaki, N., Kamahori, H., Takahashi, K., Kadokura, S.,
Wada, K., Kato, K., Oyama, R., Ose, T., Mannoji, N., and Taira, R.: The
JRA-25 Reanalysis, J. Meteorol. Soc. Jpn., 85, 369–432, https://doi.org/10.2151/jmsj.85.369, 2007.
Pan, M., Sahoo, A. K., Troy, T. J., Vinukollu, R., K., Sheffield, J., and
Wood, E. F.: Multisource estimation of long-term terrestrial water budget
for major global river basins, J. Climate, 25, 3191–3206,
https://doi.org/10.1175/JCLI-D-11-00300.1, 2012.
Peters-Lidard, C. D., Hossain, F., Leung, L. R., McDowell, N., Rodell, M.,
Tapiador, F. J., Turk, F. J., and Wood, A.: 100 years of progress in
hydrology, Meteor. Mon., 59, 25.1–25.51, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0019.1, 2018.
Prat, O. P. and Nelson, B. R.: Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012), Hydrol. Earth Syst. Sci., 19, 2037–2056, https://doi.org/10.5194/hess-19-2037-2015, 2015.
PRISM Climate Group, OSU: PRISM, available at: http://prism.oregonstate.edu (last access: 1 June 2019), 2004.
Regan, R. S., Markstrom, S. L., Hay, L. E., Viger, R. J., Norton, P. A.,
Driscoll, J. M., and La Fontaine, J. H.: Description of the National
Hydrologic Model for use with the Precipitation-Runoff Modeling System
(PRMS), Report, Reston, Virginia, USA, U.S. Geological Survey, 2018.
Reichle, R. H., Koster, R. D., De Lannoy, G. J. M., Forman, B. A., Liu, Q.,
Mahanama, S. P. P., and Touré, A.: Assessment and enhancement of MERRA
land surface hydrology estimates, J. Climate, 24, 6322–6338,
https://doi.org/10.1175/JCLI-D-10-05033.1, 2011.
Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P. P.,
Koster, R. D., and De Lannoy, G. J. M.: Assessment of MERRA-2 land surface
hydrology estimates, J. Climate, 30, 2937–2960,
https://doi.org/10.1175/JCLI-D-16-0720.1, 2017.
Reitz, M., Sanford, W. E., Senay, G. B., and Cazenas, J.: Annual estimates of
recharge, quick-flow runoff, and evapotranspiration for the contiguous US
using empirical regression equations, J. Am. Water Resour. As., 53, 961–983,
https://doi.org/10.1111/1752-1688.12546, 2017.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom,
S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J.,
Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P.,
Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz,
M., and Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for
Research and Applications, J. Climate, 24, 3624–3648,
https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004.
Rodell, M., Beaudoing, H. K., and NASA/GSFC/HSL: GLDAS CLM Land Surface Model
L4 3 hourly 1.0 × 1.0∘ Subsetted V001, Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, Maryland, USA, https://doi.org/10.5067/83NO2QDLG6M0, 2007.
Rodell, M., Beaudoing, H. K., L'Ecuyer, T. S., Olson, W. S., Famiglietti, J.
S., Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., Chambers,
D., Clark, E., Fetzer, E. J., Gao, X., Gu, G., Hilburn, K., Huffman, G. J.,
Lettenmaier, D. P., Liu, W. T., Robertson, F. R., Schlosser, C. A.,
Sheffield, J., and Wood, E. F.: The observed state of the water cycle in the
early twenty-first century, J. Climate, 28, 8289–8318, 2015.
Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J.,
Thorburn, P., Antle, J. M., Nelson, G. C., Porter, C., Janssen, S., Asseng,
S., Basso, B., Ewert, F., Wallach, D., Baigorria, G., and Winter, J. M.: The
Agricultural Model Intercomparison and Improvement Project (AgMIP):
Protocols and pilot studies, Agr. Forest Meteorol., 170,
166–182, https://doi.org/10.1016/j.agrformet.2012.09.011, 2013.
Running, S., Mu, Q., and Zhao, M.: MOD16A2 MODIS/Terra Net Evapotranspiration
8-day L4 Global 500 m SIN Grid V006 [Data set], NASA EOSDIS Land Processes DAAC, Sioux Falls, South Dakota, USA, https://doi.org/10.5067/MODIS/MOD16A2.006, 2017.
Running, S. W. and Coughlan, J. C.: A general model of forest ecosystem
processes for regional applications I. Hydrologic balance, canopy gas
exchange and primary production processes, Ecol. Model., 42,
125–154, https://doi.org/10.1016/0304-3800(88)90112-3, 1988.
Rutter, N., Essery, R., Pomeroy, J., Altimir, N., Andreadis, K., Baker, I.,
Barr, A., Bartlett, P., Boone, A., Deng, H., Douville, H., Dutra, E., Elder,
K., Ellis, C., Feng, X., Gelfan, A., Goodbody, A., Gusev, Y., Gustafsson,
D., Hellström, R., Hirabayashi, Y., Hirota, T., Jonas, T., Koren, V.,
Kuragina, A., Lettenmaier, D., Li, W.-P., Luce, C., Martin, E., Nasonova,
O., Pumpanen, J., Pyles, R. D., Samuelsson, P., Sandells, M., Schädler,
G., Shmakin, A., Smirnova, T. G., Stähli, M., Stöckli, R., Strasser,
U., Su, H., Suzuki, K., Takata, K., Tanaka, K., Thompson, E., Vesala, T.,
Viterbo, P., Wiltshire, A., Xia, K., Xue, Y., and Yamazaki, T.: Evaluation of
forest snow processes models (SnowMIP2), J. Geophys. Res.-Atmos., 114, D6, https://doi.org/10.1029/2008JD011063, 2009.
Saxe, S., Farmer, W. H., Driscoll, J. M., and Hogue, T. S.: Collection of
hydrologic models, reanalysis datasets, and remote sensing products
aggregated by ecoregion over the CONUS from 1900–2018, US Geological
Survey data release, https://doi.org/10.5066/P9588YM2, 2020.
Scanlon, B. R., Longuevergne, L., and Long, D.: Ground referencing GRACE
satellite estimates of groundwater storage changes in the California Central
Valley, USA, Water Resour. Res., 48, 4, https://doi.org/10.1029/2011WR011312, 2012.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., and
Ziese, M.: GPCC Full Data Reanalysis Version 6.0 at 0.5∘: Monthly
land-surface precipitation from rain-sauges built on GTS-based and historic
data, https://doi.org/10.5676/DWD_GPCC/FD_M_V7_050, 2011.
Sellers, P. J., Mintz, Y., Sud, Y. C., and Dalcher, A.: A Simple Biosphere
Model (SIB) for use within general circulation models,
J. Atmos. Sci., 43, 505–531,
https://doi.org/10.1175/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2, 1986.
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau,
J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.2307/2285891, 1968.
Senay, G. B.: Modeling landscape evapotranspiration by integrating land
surface phenology and a water balance algorithm, Algorithms, 1, 52–68,
https://doi.org/10.3390/a1020052, 2008.
Senay, G. B., Budde, M. E., and Verdin, J. P.: Enhancing the Simplified
Surface Energy Balance (SSEB) approach for estimating landscape ET:
Validation with the METRIC model, Agr. Water Manage., 98,
606–618, https://doi.org/10.1016/j.agwat.2010.10.014, 2011.
Senay, G. B., Bohms, S., Singh, R., K., Gowda, P. H., Velpuri, N. M., Alemu,
H., and Verdin, J. P.: Operational evapotranspiration mapping using remote
sensing and weather datasets: A new parameterization for the SSEB approach,
J. Am. Water Resour. As., 49, 577–591, https://doi.org/10.1111/jawr.12057, 2013.
Sheffield, J., Ferguson, C. R., Troy, T. J., Wood, E. F., and McCabe, M. F.:
Closing the terrestrial water budget from satellite remote sensing, Geophys. Res. Lett., 36, 7, https://doi.org/10.1029/2009GL037338, 2009.
Smith, R. A. and Kummerow, C. D.: A comparison of in situ, reanalysis, and
satellite water budgets over the Upper Colorado river basin, J. Hydrometeorol., 14, 888–905, https://doi.org/10.1175/JHM-D-12-0119.1, 2013.
Spearman, C.: The proof and measurement of association between two things,
Am. J. Psychol., 15, 72–101, 1904.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A
review of global precipitation data sets: Data sources, estimation, and
intercomparisons, Rev. Geophys., 56, 79–107,
https://doi.org/10.1002/2017RG000574, 2018.
Tedesco, M., Kelly, R., Foster, J. L., and Chang, A. T.: AMSR-E/Aqua Monthly
L3 Global Snow Water Equivalent EASE-Grids, Version 2, NASA National Snow
and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/AMSR-E/AE_MOSNO.002, 2004.
Thomas, B. F. and Famiglietti, J. S.: Identifying climate-induced
groundwater depletion in GRACE observations, Sci. Rep., 9, 4124,
https://doi.org/10.1038/s41598-019-40155-y, 2019.
Thornthwaite, C. W.: An approach toward a rational classification of
climate, Geogr. Rev., 38, 55–94, 1948.
Thornton, M., Thornton, P. E., Wei, Y., Mayer, B. W., Cook, R. B., and
Vose, R. S.: Daymet: Monthly climate summaries on a 1 km grid for North
America, Version 3, ORNL DAAC, Oak Ridge, Tennessee, USA, 2018.
Thornton, P. E., Running, S. W., and White, M. A.: Generating surfaces of
daily meteorological variables over large regions of complex terrain, J. Hydrol., 190, 214–251, https://doi.org/10.1016/S0022-1694(96)03128-9, 1997.
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.
Thornton, P. E., Thornton, M. M., Mayer, B. W., Wei, Y., Devarakonda, R.,
Vose, R. S., and Cook, R. B.: Daymet: Daily surface weather data on a 1 km
grid for North America, Version 3, ORNL DAAC, Oak Ridge, Tennessee, USA,
2017.
U.S. Geological Survey: Watershed Boundary Dataset (WBD) data model, available at: https://nhd.usgs.gov/wbd.html (last access: 1 January 2018), 2016.
Velpuri, N. M. and Senay, G. B.: Partitioning evapotranspiration into green
and blue water sources in the conterminous United States, Sci. Rep., 7, 6191, https://doi.org/10.1038/s41598-017-06359-w, 2017.
Velpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S., and Verdin, J. P.: A
comprehensive evaluation of two MODIS evapotranspiration products over the
conterminous United States: Using point and gridded FLUXNET and water
balance ET, Remote Sens. Environ., 139, 35–49,
https://doi.org/10.1016/j.rse.2013.07.013, 2013.
Velpuri, N. M., Senay, G. B., Driscoll, J. M., Saxe, S., Hay, L., Farmer, W.,
and Kiang, J.: Gravity Recovery and Climate Experiment (GRACE) storage
change characteristics (2003–2016) over major surface basins and principal
aquifers in the conterminous United States, Remote Sens.-Basel, 11, 35–49, https://doi.org/10.3390/rs11080936, 2019.
Voss, K. A., Famiglietti, J. S., Lo, M.-H., de Linage, C., Rodell,
M., and Swenson, S. C.: Groundwater depletion in the Middle East from
GRACE with implications for transboundary water management in the
Tigris-Euphrates-Western Iran region, Water Resour. Res., 49,
904–914, https://doi.org/10.1002/wrcr.20078, 2013.
Vuyovich, C. M., Jacobs, J. M., and Daly, S. F.: Comparison of passive
microwave and modeled estimates of total watershed SWE in the continental
United States, Water Resour. Res., 50, 9088–9102,
https://doi.org/10.1002/2013WR014734, 2014.
Willmott, C. J. and Matsuura, K.: Terrestrial air temperature and
precipitation: Monthly and annual time series (1950–1999),
available at: http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html (1 June 2019), 2001.
Xia, Y., NCEP/EMC: NLDAS Primary Forcing Data L4 Hourly 0.125 × 0.125∘ V002, edited by: Mocko, D., Goddard
Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, Maryland, USA, https://doi.org/10.5067/6J5LHHOHZHN4, 2009.
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L., Alonge,
C., Wei, H., Meng, J., Livneh, B., Duan, Q., and Lohmann, D.:
Continental-scale water and energy flux analysis and validation for North
American Land Data Assimilation System project phase 2 (NLDAS-2): 2.
Validation of model-simulated streamflow, J. Geophys. Res.-Atmos., 117, D3, https://doi.org/10.1029/2011JD016051, 2012a.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo,
L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V.,
Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy
flux analysis and validation for the North American Land Data Assimilation
System project phase 2 (NLDAS-2): 1. Intercomparison and application of
model products, J. Geophys. Res.-Atmos., 117, D3, https://doi.org/10.1029/2011JD016048, 2012b.
Xia, Y.: NLDAS Mosaic Land Surface Model L4 Hourly 0.125 × 0.125∘ V002, Goddard Earth Sciences Data and
Information Services Center (GES DISC), Greenbelt, Maryland, USA, https://doi.org/10.5067/EN4MBWTCENE5, 2012c.
Xia, Y., Sheffield, J., Ek, M. B., Dong, J., Chaney, N., Wei, H., Meng, J.,
and Wood, E. F.: Evaluation of multi-model simulated soil moisture in
NLDAS-2, J. Hydrol., 512, 107–125, https://doi.org/10.1016/j.jhydrol.2014.02.027, 2014.
Xie, P. and Arkin, P. A.: Global precipitation: A 17-year monthly analysis
based on gauge observations, satellite estimates, and numerical model
outputs, B. Am. Meteorol. Soc., 78, 2539–2558, 1997.
Yang, Z.-L. and Dickinson, R. E.: Description of the Biosphere-Atmosphere
Transfer Scheme (BATS) for the Soil Moisture Workshop and evaluation of its
performance, Global Planet. Change, 13, 117–134,
https://doi.org/10.1016/0921-8181(95)00041-0, 1996.
Zaherpour, J., Mount, N., Gosling, S. N., Dankers, R., Eisner, S., Gerten,
D., Liu, X., Masaki, Y., Schmied, H. M., Tang, Q., and Wada, Y.: Exploring
the value of machine learning for weighted multi-model combination of an
ensemble of global hydrological models,
Environ. Modell. Softw., 114, 112–128, https://doi.org/10.1016/j.envsoft.2019.01.003,
2019.
Zaussinger, F., Dorigo, W., Gruber, A., Tarpanelli, A., Filippucci, P., and Brocca, L.: Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data, Hydrol. Earth Syst. Sci., 23, 897–923, https://doi.org/10.5194/hess-23-897-2019, 2019.
Zhang, Y., Peña-Arancibia, J. L., McVicar, T. R., Chiew, F. H. S., Vaze,
J., Liu, C., Lu, X., Zheng, H., Wang, Y., Liu, Y. Y., Miralles, D. G., and
Pan, M.: Multi-decadal trends in global terrestrial evapotranspiration and
its components, Sci. Rep., 6, 19124, https://doi.org/10.1038/srep19124, 2016.
Zhang, Y., Pan, M., Sheffield, J., Siemann, A. L., Fisher, C. K., Liang, M., Beck, H. E., Wanders, N., MacCracken, R. F., Houser, P. R., Zhou, T., Lettenmaier, D. P., Pinker, R. T., Bytheway, J., Kummerow, C. D., and Wood, E. F.: A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010, Hydrol. Earth Syst. Sci., 22, 241–263, https://doi.org/10.5194/hess-22-241-2018, 2018.
Short summary
We compare simulated values from 47 models estimating surface water over the USA. Results show that model uncertainty is substantial over much of the conterminous USA and especially high in the west. Applying the studied models to a simple water accounting equation shows that model selection can significantly affect research results. This paper concludes that multimodel ensembles help to best represent uncertainty in conclusions and suggest targeted research efforts in arid regions.
We compare simulated values from 47 models estimating surface water over the USA. Results show...
