Articles | Volume 23, issue 2
https://doi.org/10.5194/hess-23-851-2019
© Author(s) 2019. 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-23-851-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2019
Research article |
| 13 Feb 2019
| 13 Feb 2019
Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product
Sanaa Hobeichi
CORRESPONDING AUTHOR
Climate Change Research Centre, University of New South Wales,
Sydney, NSW 2052, Australia
ARC Centre of Excellence for Climate System Science, University of New
South Wales, Sydney, NSW 2052, Australia
Gab Abramowitz
Climate Change Research Centre, University of New South Wales,
Sydney, NSW 2052, Australia
ARC Centre of Excellence for Climate Extremes, University of New South
Wales, Sydney, NSW 2052, Australia
Jason Evans
Climate Change Research Centre, University of New South Wales,
Sydney, NSW 2052, Australia
ARC Centre of Excellence for Climate Extremes, University of New South
Wales, Sydney, NSW 2052, Australia
Hylke E. Beck
Department of Civil and Environmental Engineering, Princeton
University, Princeton, NJ 08544, USA
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Yinglin Mu, Jason Evans, Andrea Taschetto, and Chiara Holgate
EGUsphere, https://doi.org/10.5194/egusphere-2025-2833, https://doi.org/10.5194/egusphere-2025-2833, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Lagrangian approaches have been increasingly employed due to their suitability for extreme events and climatological studies in finding moisture sources of precipitation. However, as these approaches track independent air parcels carrying moisture—rather than simulate processes based on governing physical equations—they rely on several underlying assumptions. This study tests these assumptions and refines the approaches to enhance their broader applicability.
Georgina Falster, Gab Abramowitz, Sanaa Hobeichi, Cath Hughes, Pauline Treble, Nerilie J. Abram, Michael I. Bird, Alexandre Cauquoin, Bronwyn Dixon, Russell Drysdale, Chenhui Jin, Niels Munksgaard, Bernadette Proemse, Jonathan J. Tyler, Martin Werner, and Carol Tadros
EGUsphere, https://doi.org/10.5194/egusphere-2025-2458, https://doi.org/10.5194/egusphere-2025-2458, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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We used a random forest approach to produce estimates of monthly precipitation stable isotope variability from 1962–2023, at high resolution across the entire Australian continent. Comprehensive skill and sensitivity testing shows that our random forest models skilfully predict precipitation isotope values in places and times that observations are not available. We make all outputs publicly available, facilitating use in fields from ecology and hydrology to archaeology and forensic science.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Philippe Ciais, and Daniel S. Goll
EGUsphere, https://doi.org/10.5194/egusphere-2025-2545, https://doi.org/10.5194/egusphere-2025-2545, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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Accurate estimates of global soil organic carbon (SOC) content and its spatial pattern are critical for future climate change mitigation. However, the most advanced mechanistic SOC models struggle to do this task. Here we apply multiple explainable machine learning methods to identify missing variables and misrepresented relationships between environmental factors and SOC in these models, offering new insights to guide model development for more reliable SOC predictions.
Rajesh Kumar Sahu, Hamza Kunhu Bangalath, Suleiman Mostamandi, Jason Evans, Paul A. Kucera, and Hylke E. Beck
EGUsphere, https://doi.org/10.5194/egusphere-2025-912, https://doi.org/10.5194/egusphere-2025-912, 2025
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This study evaluates 36 microphysics (MP) and boundary layer (BL) scheme combinations in the Weather Research and Forecasting (WRF) model for extreme rainfall over Saudi Arabia. Using Kling-Gupta Efficiency (KGE), results show YSU (BL1) and Thompson (MP8) perform best, while Morrison-MYNN (MP10_BL6) ranks lowest. The mean temporal KGE is 0.37, and the spatial KGE is 0.26, highlighting spatial prediction challenges. Findings aid model evaluation and forecasting in arid regions.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
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This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Anjana Devanand, Jason Evans, Andy Pitman, Sujan Pal, David Gochis, and Kevin Sampson
EGUsphere, https://doi.org/10.5194/egusphere-2024-3148, https://doi.org/10.5194/egusphere-2024-3148, 2024
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Including lateral flow increases evapotranspiration near major river channels in high-resolution land surface simulations in southeast Australia, consistent with observations. The 1-km resolution model shows a widespread pattern of dry ridges that does not exist at coarser resolutions. Our results have implications for improved simulations of droughts and future water availability.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael A. Viscarra Rossel
SOIL, 10, 619–636, https://doi.org/10.5194/soil-10-619-2024, https://doi.org/10.5194/soil-10-619-2024, 2024
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Effective management of soil organic carbon (SOC) requires accurate knowledge of its distribution and factors influencing its dynamics. We identify the importance of variables in spatial SOC variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on the SOC distribution derived from a single approach.
Margarita Choulga, Francesca Moschini, Cinzia Mazzetti, Stefania Grimaldi, Juliana Disperati, Hylke Beck, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 28, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024, https://doi.org/10.5194/hess-28-2991-2024, 2024
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CEMS_SurfaceFields_2022 dataset is a new set of high-resolution maps for land type (e.g. lake, forest), soil properties and population water needs at approximately 2 and 6 km at the Equator, covering Europe and the globe (excluding Antarctica). We describe what and how new high-resolution information can be used to create the dataset. The paper suggests that the dataset can be used as input for river, weather or other models, as well as for statistical descriptions of the region of interest.
Oscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Diego G. Miralles, Hylke E. Beck, Jonatan F. Siegmund, Camila Alvarez-Garreton, Koen Verbist, René Garreaud, Juan Pablo Boisier, and Mauricio Galleguillos
Hydrol. Earth Syst. Sci., 28, 1415–1439, https://doi.org/10.5194/hess-28-1415-2024, https://doi.org/10.5194/hess-28-1415-2024, 2024
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Various drought indices exist, but there is no consensus on which index to use to assess streamflow droughts. This study addresses meteorological, soil moisture, and snow indices along with their temporal scales to assess streamflow drought across hydrologically diverse catchments. Using data from 100 Chilean catchments, findings suggest that there is not a single drought index that can be used for all catchments and that snow-influenced areas require drought indices with larger temporal scales.
Conrad Wasko, Seth Westra, Rory Nathan, Acacia Pepler, Timothy H. Raupach, Andrew Dowdy, Fiona Johnson, Michelle Ho, Kathleen L. McInnes, Doerte Jakob, Jason Evans, Gabriele Villarini, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 28, 1251–1285, https://doi.org/10.5194/hess-28-1251-2024, https://doi.org/10.5194/hess-28-1251-2024, 2024
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In response to flood risk, design flood estimation is a cornerstone of infrastructure design and emergency response planning, but design flood estimation guidance under climate change is still in its infancy. We perform the first published systematic review of the impact of climate change on design flood estimation and conduct a meta-analysis to provide quantitative estimates of possible future changes in extreme rainfall.
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 27, 2357–2373, https://doi.org/10.5194/hess-27-2357-2023, https://doi.org/10.5194/hess-27-2357-2023, 2023
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Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.
Lina Teckentrup, Martin G. De Kauwe, Gab Abramowitz, Andrew J. Pitman, Anna M. Ukkola, Sanaa Hobeichi, Bastien François, and Benjamin Smith
Earth Syst. Dynam., 14, 549–576, https://doi.org/10.5194/esd-14-549-2023, https://doi.org/10.5194/esd-14-549-2023, 2023
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Studies analyzing the impact of the future climate on ecosystems employ climate projections simulated by global circulation models. These climate projections display biases that translate into significant uncertainty in projections of the future carbon cycle. Here, we test different methods to constrain the uncertainty in simulations of the carbon cycle over Australia. We find that all methods reduce the bias in the steady-state carbon variables but that temporal properties do not improve.
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
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A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
Jiawei Hou, Albert I. J. M. van Dijk, Hylke E. Beck, Luigi J. Renzullo, and Yoshihide Wada
Hydrol. Earth Syst. Sci., 26, 3785–3803, https://doi.org/10.5194/hess-26-3785-2022, https://doi.org/10.5194/hess-26-3785-2022, 2022
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We used satellite imagery to measure monthly reservoir water volumes for 6695 reservoirs worldwide for 1984–2015. We investigated how changing precipitation, streamflow, evaporation, and human activity affected reservoir water storage. Almost half of the reservoirs showed significant increasing or decreasing trends over the past three decades. These changes are caused, first and foremost, by changes in precipitation rather than by changes in net evaporation or dam release patterns.
Jon Cranko Page, Martin G. De Kauwe, Gab Abramowitz, Jamie Cleverly, Nina Hinko-Najera, Mark J. Hovenden, Yao Liu, Andy J. Pitman, and Kiona Ogle
Biogeosciences, 19, 1913–1932, https://doi.org/10.5194/bg-19-1913-2022, https://doi.org/10.5194/bg-19-1913-2022, 2022
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Although vegetation responds to climate at a wide range of timescales, models of the land carbon sink often ignore responses that do not occur instantly. In this study, we explore the timescales at which Australian ecosystems respond to climate. We identified that carbon and water fluxes can be modelled more accurately if we include environmental drivers from up to a year in the past. The importance of antecedent conditions is related to ecosystem aridity but is also influenced by other factors.
Anna M. Ukkola, Gab Abramowitz, and Martin G. De Kauwe
Earth Syst. Sci. Data, 14, 449–461, https://doi.org/10.5194/essd-14-449-2022, https://doi.org/10.5194/essd-14-449-2022, 2022
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Flux towers provide measurements of water, energy, and carbon fluxes. Flux tower data are invaluable in improving and evaluating land models but are not suited to modelling applications as published. Here we present flux tower data tailored for land modelling, encompassing 170 sites globally. Our dataset resolves several key limitations hindering the use of flux tower data in land modelling, including incomplete forcing variable, data format, and low data quality.
Oscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Pablo A. Mendoza, Ian McNamara, Hylke E. Beck, Joschka Thurner, Alexandra Nauditt, Lars Ribbe, and Nguyen Xuan Thinh
Hydrol. Earth Syst. Sci., 25, 5805–5837, https://doi.org/10.5194/hess-25-5805-2021, https://doi.org/10.5194/hess-25-5805-2021, 2021
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Most rivers worldwide are ungauged, which hinders the sustainable management of water resources. Regionalisation methods use information from gauged rivers to estimate streamflow over ungauged ones. Through hydrological modelling, we assessed how the selection of precipitation products affects the performance of three regionalisation methods. We found that a precipitation product that provides the best results in hydrological modelling does not necessarily perform the best for regionalisation.
Peter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, and Hylke E. Beck
Geosci. Model Dev., 14, 4865–4890, https://doi.org/10.5194/gmd-14-4865-2021, https://doi.org/10.5194/gmd-14-4865-2021, 2021
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We present a cascade of models to compute high-resolution river flooding. This takes meteorological inputs, e.g., rainfall and temperature from observations or climate models, and takes them through a series of modeling steps. This is relevant to evaluating current day and future flood risk and impacts. The model framework uses global data sets, allowing it to be applied anywhere in the world.
Sanaa Hobeichi, Gab Abramowitz, and Jason P. Evans
Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, https://doi.org/10.5194/hess-25-3855-2021, 2021
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Evapotranspiration (ET) links the water, energy and carbon cycle on land. Reliable ET estimates are key to understand droughts and flooding. We develop a new ET dataset, DOLCE V3, by merging multiple global ET datasets, and we show that it matches ET observations better and hence is more reliable than its parent datasets. Next, we use DOLCE V3 to examine recent changes in ET and find that ET has increased over most of the land, decreased in some regions, and has not changed in some other regions
Yuting Yang, Tim R. McVicar, Dawen Yang, Yongqiang Zhang, Shilong Piao, Shushi Peng, and Hylke E. Beck
Hydrol. Earth Syst. Sci., 25, 3411–3427, https://doi.org/10.5194/hess-25-3411-2021, https://doi.org/10.5194/hess-25-3411-2021, 2021
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This study developed an analytical ecohydrological model that considers three aspects of vegetation response to eCO2 (i.e., stomatal response, LAI response, and rooting depth response) to detect the impact of eCO2 on continental runoff over the past 3 decades globally. Our findings suggest a minor role of eCO2 on the global runoff changes, yet highlight the negative runoff–eCO2 response in semiarid and arid regions which may further threaten the limited water resource there.
Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson
Geosci. Model Dev., 14, 3539–3551, https://doi.org/10.5194/gmd-14-3539-2021, https://doi.org/10.5194/gmd-14-3539-2021, 2021
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We present a novel stochastic approach based on Markov chains to estimate climate model weights of multi-model ensemble means. This approach showed improved performance (better correlation with observations) over existing alternatives during cross-validation and model-as-truth tests. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods to find optimal model weights for constructing ensemble means.
Noemi Vergopolan, Sitian Xiong, Lyndon Estes, Niko Wanders, Nathaniel W. Chaney, Eric F. Wood, Megan Konar, Kelly Caylor, Hylke E. Beck, Nicolas Gatti, Tom Evans, and Justin Sheffield
Hydrol. Earth Syst. Sci., 25, 1827–1847, https://doi.org/10.5194/hess-25-1827-2021, https://doi.org/10.5194/hess-25-1827-2021, 2021
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Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
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We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Cited articles
Abramowitz, G. and Bishop, C. H.: Climate Model Dependence and the Ensemble
Dependence Transformation of CMIP Projections, J. Climate, 28, 2332–2348,
https://doi.org/10.1175/JCLI-D-14-00364.1, 2015.
Aires, F.: Combining Datasets of Satellite-Retrieved Products. Part I:
Methodology and Water Budget Closure, J. Hydrometeorol., 15, 1677–1691,
https://doi.org/10.1175/JHM-D-13-0148.1, 2014.
Bai, Y., Xu, H., and Ling, H.: Drought-flood variation and its correlation
with runoff in three headstreams of Tarim River, Xinjiang, China, Environ.
Earth Sci., 71, 1297–1309, https://doi.org/10.1007/s12665-013-2534-5, 2014.
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.
Balsamo, G., Pappenberger, F., Dutra, E., Viterbo, P., and van den Hurk, B.:
A revised land hydrology in the ECMWF model: A step towards daily water flux
prediction in a fully-closed water cycle, Hydrol. Process., 25,
1046–1054, https://doi.org/10.1002/hyp.7808, 2011.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Miralles, D. G., Mcvicar, T.
R., Schellekens, J., and Bruijnzeel, L. A.: Global-scale regionalization of
hydrologic model parameters, Water Resour. Res., 52, 3599–3622,
https://doi.org/10.1002/2015WR018247, 2016.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth,
R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art
hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903,
https://doi.org/10.5194/hess-21-2881-2017, 2017a.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles,
D. G., Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25∘ global
gridded precipitation (1979–2015) by merging gauge, satellite, and
reanalysis data, Hydrol. Earth Syst. Sci., 21, 589–615,
https://doi.org/10.5194/hess-21-589-2017, 2017b.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H.,
Menard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N.,
Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C.
S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES),
model description –Part 1: Energy and water fluxe, Geosci. Model Dev.
Discuss., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Beven, K. J.: Changing ideas in hydrology: The case of physically-based
models, J. Hydrol., 105, 157–172, 1989.
Bontemps, S., Defourny, P., Bogaert, E. V., Arino, O., Kalogirou, V., and
Perez, J. R.: GLOBCOVER 2009 – Products description and validation report, UCLouvain & ESA Team,
available at: http://due.esrin.esa.int/files/GLOBCOVER2009_Validation_Report_2.2.pdf (last access: 3 October 2017), 2011.
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions,
Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015WR017173, 2015.
Bishop, C. H. and Abramowitz, G.: Climate model dependence and the replicate
Earth paradigm, Clim. Dynam., 41, 885–900, https://doi.org/10.1007/s00382-012-1610-y,
2013.
Burek, P., van der Knijff, J., and de Roo, A.: LISFLOOD, distributed water
balance and flood simulation model revised user manual, Joint Research
Centre of the European Commission, Publications Office of the European Union, Luxembourg, 2013.
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M.
J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O.,
Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land
Environment Simulator (JULES), model description – Part 2: Carbon fluxes and
vegetation dynamics, Geosci. Model Dev., 4, 701–722,
https://doi.org/10.5194/gmd-4-701-2011, 2011.
Dai, A.: Historical and Future Changes in Streamflow and Continental Runoff:
A Review, in: Terrestrial Water Cycle and Climate Change: Natural and
Human-Induced Impacts, edited by: Tang, Q. and Oki, T., John Wiley & Sons,
Inc., 221, 17–37, https://doi.org/10.1002/9781118971772.ch2, 2016.
Decharme, B., Boone, A., Delire, C., and Noilhan, J.: Local evaluation of the
Interaction between Soil Biosphere Atmosphere soil multilayer diffusion
scheme using four pedotransfer functions, J. Geophys. Res.-Atmos., 116,
1–29, https://doi.org/10.1029/2011JD016002, 2011.
Decharme, B., Martin, E., and Faroux, S.: Reconciling soil thermal and
hydrological lower boundary conditions in land surface models, J. Geophys.
Res.-Atmos., 118, 7819–7834, https://doi.org/10.1002/jgrd.50631, 2013.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,
Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration
and performance of the data assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011.
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.
Dingman, S. L.: Physical Hydrology, 575 pp., Prentice-Hall, Old Tappan, N.
J., 1994.
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, 1381–1397,
https://doi.org/10.1175/BAMS-87-10-1381, 2006.
Earthdata: MEaSUREs project, available at: https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects
(last access: 31 May 2018), 2017.
Esri Education Team: World Climate Zones – Simplified [Esri shapefile], Scale Not Given, Using: ArcGIS [GIS software], National Geographic,
available at: http://services.arcgis.com/BG6nSlhZSAWtExvp/arcgis/rest/services/WorldClimateZonesSimp/FeatureServer (last access:
14 February 2016), “MappingOurWorld”, 2014.
Falcone, J. A., Carlisle, D. M., Wolock, D. M., and Meador, M. R.: GAGES: A
stream gage database for evaluating natural and altered flow conditions in
the conterminous United States, Ecology, 91, 621, https://doi.org/10.1890/09-0889.1,
2010.
Fekete, B. M., Vörösmarty, C. J., and Grabs, W.: High-resolution
fields of global runoff combining observed river discharge and simulated
water balances, Global Biogeochem. Cy., 16, 15-1–15-10,
https://doi.org/10.1029/1999GB001254, 2002.
Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F., and
Alcamo, J.: Domestic and industrial water uses of the past 60 years as a
mirror of socio-economic development: A global simulation study, Global
Environ. Chang., 23, 144–156, https://doi.org/10.1016/j.gloenvcha.2012.10.018, 2013.
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.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Hobeichi, S.: Linear Optimal Runoff Aggregate (LORA) v1.0, NCI National
Research Data Collection, https://doi.org/10.25914/5b612e993d8ea, 2018.
Hobeichi, S., Abramowitz, G., Evans, J., and Ukkola, A.: Derived Optimal
Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET
estimate, Hydrol. Earth Syst. Sci., 22, 1317–1336,
https://doi.org/10.5194/hess-22-1317-2018, 2018.
Huntzinger, D. N., Schwalm, C. R., Wei, Y., Cook, R. B., Michalak, A. M., Schaefer, K., Jacobson, A. R., Arain, M. A., Ciais, P.,
Fisher, J. B., Hayes, D. J., Huang, M., Huang, S., Ito, A., Jain, A. K., Lei, H., Lu, C., Maignan, F., Mao, J.,
Parazoo, N., Peng, C., Peng, S., Poulter, B., Ricciuto, D. M., Tian, H., Shi, X., Wang, W., Zeng, N., Zhao, F.,
Zhu, Q., Yang, J., and Tao, B.: NACP MsTMIP:
Global 0.5-deg Terrestrial Biosphere Model Outputs (version 1) in Standard
Format, ORNL DAAC, Oak Ridge, Tennessee, USA, doi:10.3334/ORNLDAAC/1225, 2016.
Jiménez, C., Martens, B., Miralles, D. M., Fisher, J. B., Beck, H. E., and
Fernández-Prieto, D.: Exploring the merging of the global land evaporation
WACMOS-ET products based on local tower measurements, Hydrol. Earth Syst.
Sci., 22, 4513–4533, https://doi.org/10.5194/hess-22-4513-2018, 2018.
Kauffeldt, A., Wetterhall, F., Pappenberger, F., Salamon, P., and Thielen,
J.: Technical review of large-scale hydrological models for implementation in
operational flood forecasting schemes on continental level, Environ. Modell.
Softw., 75, 68–76, https://doi.org/10.1016/j.envsoft.2015.09.009, 2016.
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 models, J. Geophys. Res.-Atmos., 99, 14415–14428,
https://doi.org/10.1029/94JD00483, 1994.
Ling, H., Deng, X., Long, A., and Gao, H.: The multi-time-scale correlations
for drought–flood index to runoff and North Atlantic Oscillation in the
headstreams of Tarim River, Xinjiang, China, Hydrol. Res., 48, 1–12,
https://doi.org/10.2166/nh.2016.166, 2016.
Morel, P.: Why GEWEX? The agenda for a global energy and water cycle
research program, GEWEX News, 11, 7–11, 2001.
Mueller, B., Hirschi, M., Jimenez, C., Ciais, P., Dirmeyer, P. A., Dolman, A.
J., Fisher, J. B., Jung, M., Ludwig, F., Maignan, F., Miralles, D. G.,
McCabe, M. F., Reichstein, M., Sheffield, J., Wang, K., Wood, E. F., Zhang,
Y., and Seneviratne, S. I.: Benchmark products for land evapotranspiration:
LandFlux-EVAL multi-data set synthesis, Hydrol. Earth Syst. Sci., 17,
3707–3720, https://doi.org/10.5194/hess-17-3707-2013, 2013.
Munier, S., Aires, F., Schlaffer, S., Prigent, C., Papa, F., Maisongrande,
P., and Pan, M.: Combining datasets of satellite retrieved products for
basin-scale water balance study. Part II: Evaluation on the Mississippi Basin
and closure correction model, J. Geophys. Res.-Atmos., 119, 12100–12116, https://doi.org/10.1002/2014JD021953, 2014.
Pan, M., Sahoo, A. K., Troy, T. J., Vinukollu, R. K., Sheffield, J., and
Wood, A. 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.
Pechlivanidis, I. G., Jackson, B. M., Mcintyre, N. R., and Wheater, H. S.:
Catchment Scale Hydrological Modelling: A Review Of Model Types, Calibration
Approaches And Uncertainty Analysis Methods In The Context Of Recent
Developments In Technology And Applications, Global Nest J., 13, 193–214,
2011.
Peel, M. C., Chiew, F. H. S., Western, A. W., and McMahon, T. A.: Extension
of Unimpaired Monthly Streamflow Data and Regionalisation of Parameter Values
to Estimate Streamflow in Ungauged Catchments, Report to the National Land
and Water Resources Audit, Centre for Environmental Applied Hydrology, The
University of Melbourne, Australia, 2000.
Rantz, S. E.: Measurement and computation of stream flow, Volume 2:
Computation of discharge, USGPO, No. 2175, 631 pp., https://doi.org/10.3133/wsp2175,
1982.
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.
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., 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,
https://doi.org/10.1175/JCLI-D-14-00555.1, 2015.
Sahoo, A. K., Pan, M., Troy, T. J., Vinukollu, R. K., Sheffield, J., and
Wood, E. F.: Reconciling the global terrestrial water budget using satellite
remote sensing, Remote Sens. Environ., 115, 1850–1865,
https://doi.org/10.1016/j.rse.2011.03.009, 2011.
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van
Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B.,
Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher,
J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.:
A global water resources ensemble of hydrological models: the eartH2Observe
Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413,
https://doi.org/10.5194/essd-9-389-2017, 2017.
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, 1–5, https://doi.org/10.1029/2009GL037338, 2009.
Shukla, S. and Wood, A. W.: Use of a standardized runoff index for
characterizing hydrologic drought, Geophys. Res. Lett., 35, 1–7,
https://doi.org/10.1029/2007GL032487, 2008.
Siebert, S., Döll, P., Feick, S., Hoogeveen, J., and Frenken, K.: Global map
of irrigation areas version 4.0.1, Johann Wolfgang Goethe University,
Frankfurt am Main, Germany/Food and Agriculture Organization of the United
Nations, Rome, Italy, 2007.
Sood, A. and Smakhtin, V.: Global hydrological models: a review, Hydrolog.
Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Tomy, T. and Sumam, K. S.: Determining the Adequacy of CFSR Data for
Rainfall-Runoff Modeling Using SWAT, Procedia Tech., 24, 309–316,
https://doi.org/10.1016/j.protcy.2016.05.041, 2016.
Ukkola, A. M., Prentice, I. C., Keenan, T. F., van Dijk, A. I. J. M., Viney,
N. R., Myneni, R. B., and Bi, J.: Reduced streamflow in water-stressed
climates consistent with CO2 effects on vegetation, Nat. Clim.
Change, 6, 75–78, https://doi.org/10.1038/nclimate2831, 2016.
Van Beek, L. P. H. and Bierkens, M. F. P.: The Global Hydrological Model
PCR-GLOBWB: Conceptualization, Parameterization and Verification, Department
of Physical Geography, Utrecht University, Utrecht, the Netherlands,
available at: http://vanbeek.geo.uu.nl/suppinfo/vanbeekbierkens2009.pdf
(last access: 25 April 2018), 2008.
Van Der Knijff, J. M., Younis, J., and De Roo, A. P. J.: LISFLOOD: a
GIS-based distributed model for river basin scale water balance and flood
simulation, Int. J. Geogr. Inf. Sci., 24, 189–212,
https://doi.org/10.1080/13658810802549154, 2010.
Van Dijk, A. I. J. M. and Warren, G.: The Australian Water Resources
Assessment System. Technical Report 4. Landscape Model (version 0.5)
Evaluation Against Observations. CSIRO: Water for a Healthy Country National
Research Flagship, CSIRO, Australia, 2010.
Van Dijk, A. I. J. M., Renzullo, L. J., Wada, Y., and Tregoning, P.: A global
water cycle reanalysis (2003–2012) merging satellite gravimetry and
altimetry observations with a hydrological multi-model ensemble, Hydrol.
Earth Syst. Sci., 18, 2955–2973, https://doi.org/10.5194/hess-18-2955-2014,
2014.
van Huijgevoort, M. H. J., Hazenberg, P., van Lanen, H. A. J., Teuling, A.
J., Clark, D. B., Folwell, S., Gosling, S. N., Hanasaki, N., Heinke, J.,
Koirala, S., Stacke, T., Voss, F., Sheffield, J., and Uijlenhoet, R.: Global
Multimodel Analysis of Drought in Runoff for the Second Half of the Twentieth
Century, J. Hydrometeorol., 14, 1535–1552, https://doi.org/10.1175/JHM-D-12-0186.1,
2013.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and
Viterbo, P.: Data methodology applied to ERA-Interim reanalysis data, Water
Resour. Res., 50, 7505–7514, https://doi.org/10.1002/2014WR015638, 2014.
Ye, A., Duan, Q., Yuan, X., Wood, E. F., and Schaake, J.: Hydrologic
post-processing of MOPEX streamflow simulations, J. Hydrol., 508, 147–156,
https://doi.org/10.1016/j.jhydrol.2013.10.055, 2014.
Ye, W., Bates, B. C., Viney, N. R., Sivapalan, M., and Jakeman, A. J.:
Performance of conceptual rainfall-runoff models in low-yielding ephemeral
catchments, Water Resour. Res., 33, 153–166, 1997.
Zhai, R. and Tao, F.: Contributions of climate change and human activities
to runoff change in seven typical catchments across China, Sci. Total
Environ., 605–606, 219–229, https://doi.org/10.1016/j.scitotenv.2017.06.210, 2017.
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.
