Articles | Volume 22, issue 11
https://doi.org/10.5194/hess-22-5639-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-22-5639-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Opinion article
| 
01 Nov 2018
Opinion article |
| 01 Nov 2018
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
Civil and Environmental Engineering, Pennsylvania State University,
University Park, PA 16802, USA
Eric Laloy
Institute for Environment, Health and Safety,
Belgian Nuclear Research Centre, Mol, Belgium
Amin Elshorbagy
Dept. of Civil,
Geological, and Environmental Engineering, University of Saskatchewan,
Saskatoon, Canada
Adrian Albert
National Energy Research Supercomputing Center, Lawrence Berkeley
National Laboratory, Berkeley, CA 94720, USA
Jerad Bales
Consortium of
Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI),
Cambridge, MA, USA
Fi-John Chang
Department of Bioenvironmental Systems
Engineering, National Taiwan University, Taipei, 10617, Taiwan
Sangram Ganguly
NASA Ames Research Center/BAER Institute, Moffett Field, CA 94035,
USA
Kuo-Lin Hsu
Civil and Environmental Engineering, University of California,
Irvine, Irvine, CA 92697, USA
Daniel Kifer
Computer Science and Engineering,
Pennsylvania State University, University Park, PA 16802, USA
Zheng Fang
Civil Engineering, University of Texas at Arlington, Arlington, TX
76013, USA
Kuai Fang
Civil and Environmental Engineering, Pennsylvania State University,
University Park, PA 16802, USA
Dongfeng Li
Civil Engineering, University of Texas at Arlington, Arlington, TX
76013, USA
Xiaodong Li
State Key Laboratory of Hydraulics and Mountain River
Engineering, Sichuan University, Sichuan, China
Wen-Ping Tsai
Civil and Environmental Engineering, Pennsylvania State University,
University Park, PA 16802, USA
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Hybrid models (we call δ models) that embrace the fundamental learning capability of AI but can explain all the physical processes can be powerful. In this paper we assess how they perform when applied in 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. δ models could be ideal candidates for global hydrologic assessments.
W. J. Riley and C. Shen
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Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
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Hydrol. Earth Syst. Sci., 27, 1201–1219, https://doi.org/10.5194/hess-27-1201-2023, https://doi.org/10.5194/hess-27-1201-2023, 2023
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Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-377, https://doi.org/10.5194/hess-2022-377, 2022
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H. Tang, S. Ganguly, G. Zhang, M. A. Hofton, R. F. Nelson, and R. Dubayah
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Y.-T. Shih, T.-Y. Lee, J.-C. Huang, S.-J. Kao, K.-K. Liu, and F.-J. Chang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-449-2015, https://doi.org/10.5194/hessd-12-449-2015, 2015
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R. Sultana, K.-L. Hsu, J. Li, and S. Sorooshian
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W. J. Riley and C. Shen
Hydrol. Earth Syst. Sci., 18, 2463–2483, https://doi.org/10.5194/hess-18-2463-2014, https://doi.org/10.5194/hess-18-2463-2014, 2014
F.-J. Chang and W. Sun
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-6153-2013, https://doi.org/10.5194/hessd-10-6153-2013, 2013
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Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Revisiting the hydrological basis of the Budyko framework with the principle of hydrologically similar groups
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Assessing the influence of water sampling strategy on the performance of tracer-aided hydrological modeling in a mountainous basin on the Tibetan Plateau
The suitability of differentiable, learnable hydrologic models for ungauged regions and climate change impact assessment
Flood forecasting with machine learning models in an operational framework
Precipitation fate and transport in a Mediterranean catchment through models calibrated on plant and stream water isotope data
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A conceptual-model-based sediment connectivity assessment for patchy agricultural catchments
The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)
Spatial extrapolation of stream thermal peaks using heterogeneous time series at a national scale
Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient
Deep learning rainfall–runoff predictions of extreme events
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Yuchan Chen, Xiuzhi Chen, Meimei Xue, Chuanxun Yang, Wei Zheng, Jun Cao, Wenting Yan, and Wenping Yuan
Hydrol. Earth Syst. Sci., 27, 1929–1943, https://doi.org/10.5194/hess-27-1929-2023, https://doi.org/10.5194/hess-27-1929-2023, 2023
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This study addresses the quantification and estimation of the watershed-characteristic-related parameter (Pw) in the Budyko framework with the principle of hydrologically similar groups. The results show that Pw is closely related to soil moisture and fractional vegetation cover, and the relationship varies across specific hydrologic similarity groups. The overall satisfactory performance of the Pw estimation model improves the applicability of the Budyko framework for global runoff estimation.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, Christoph Mayer, and Axel Bronstert
Hydrol. Earth Syst. Sci., 27, 1841–1863, https://doi.org/10.5194/hess-27-1841-2023, https://doi.org/10.5194/hess-27-1841-2023, 2023
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We present a suitable method to reconstruct sediment export from decadal records of hydroclimatic predictors (discharge, precipitation, temperature) and shorter suspended sediment measurements. This lets us fill the knowledge gap on how sediment export from glacierized high-alpine areas has responded to climate change. We find positive trends in sediment export from the two investigated nested catchments with step-like increases around 1981 which are linked to crucial changes in glacier melt.
Samantha Petch, Bo Dong, Tristan Quaife, Robert P. King, and Keith Haines
Hydrol. Earth Syst. Sci., 27, 1723–1744, https://doi.org/10.5194/hess-27-1723-2023, https://doi.org/10.5194/hess-27-1723-2023, 2023
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Gravitational measurements of water storage from GRACE (Gravity Recovery and Climate Experiment) can improve understanding of the water budget. We produce flux estimates over large river catchments based on observations that close the monthly water budget and ensure consistency with GRACE on short and long timescales. We use energy data to provide additional constraints and balance the long-term energy budget. These flux estimates are important for evaluating climate models.
Ting Su, Chiyuan Miao, Qingyun Duan, Jiaojiao Gou, Xiaoying Guo, and Xi Zhao
Hydrol. Earth Syst. Sci., 27, 1477–1492, https://doi.org/10.5194/hess-27-1477-2023, https://doi.org/10.5194/hess-27-1477-2023, 2023
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The Three-River Source Region (TRSR) plays an extremely important role in water resources security and ecological and environmental protection in China and even all of Southeast Asia. This study used the variable infiltration capacity (VIC) land surface hydrologic model linked with the degree-day factor algorithm to simulate the runoff change in the TRSR. These results will help to guide current and future regulation and management of water resources in the TRSR.
Andreas Hartmann, Jean-Lionel Payeur-Poirier, and Luisa Hopp
Hydrol. Earth Syst. Sci., 27, 1325–1341, https://doi.org/10.5194/hess-27-1325-2023, https://doi.org/10.5194/hess-27-1325-2023, 2023
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We advance our understanding of including information derived from environmental tracers into hydrological modeling. We present a simple approach that integrates streamflow observations and tracer-derived streamflow contributions for model parameter estimation. We consider multiple observed streamflow components and their variation over time to quantify the impact of their inclusion for streamflow prediction at the catchment scale.
Dharmaveer Singh, Manu Vardhan, Rakesh Sahu, Debrupa Chatterjee, Pankaj Chauhan, and Shiyin Liu
Hydrol. Earth Syst. Sci., 27, 1047–1075, https://doi.org/10.5194/hess-27-1047-2023, https://doi.org/10.5194/hess-27-1047-2023, 2023
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This study examines, for the first time, the potential of various machine learning models in streamflow prediction over the Sutlej River basin (rainfall-dominated zone) in western Himalaya during the period 2041–2070 (2050s) and 2071–2100 (2080s) and its relationship to climate variability. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between the 2050s and 2080s by 0.79 to 1.43 % for SSP585 and by 0.87 to 1.10 % for SSP245.
Monica Coppo Frias, Suxia Liu, Xingguo Mo, Karina Nielsen, Heidi Ranndal, Liguang Jiang, Jun Ma, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 27, 1011–1032, https://doi.org/10.5194/hess-27-1011-2023, https://doi.org/10.5194/hess-27-1011-2023, 2023
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This paper uses remote sensing data from ICESat-2 to calibrate a 1D hydraulic model. With the model, we can make estimations of discharge and water surface elevation, which are important indicators in flooding risk assessment. ICESat-2 data give an added value, thanks to the 0.7 m resolution, which allows the measurement of narrow river streams. In addition, ICESat-2 provides measurements on the river dry portion geometry that can be included in the model.
Evgenia Koltsida, Nikos Mamassis, and Andreas Kallioras
Hydrol. Earth Syst. Sci., 27, 917–931, https://doi.org/10.5194/hess-27-917-2023, https://doi.org/10.5194/hess-27-917-2023, 2023
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Daily and hourly rainfall observations were inputted to a Soil and Water Assessment Tool (SWAT) hydrological model to investigate the impacts of rainfall temporal resolution on a discharge simulation. Results indicated that groundwater flow parameters were more sensitive to daily time intervals, and channel routing parameters were more influential for hourly time intervals. This study suggests that the SWAT model appears to be a reliable tool to predict discharge in a mixed-land-use basin.
Klaus Eckhardt
Hydrol. Earth Syst. Sci., 27, 495–499, https://doi.org/10.5194/hess-27-495-2023, https://doi.org/10.5194/hess-27-495-2023, 2023
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An important hydrological issue is to identify components of streamflow that react to precipitation with different degrees of attenuation and delay. From the multitude of methods that have been developed for this so-called hydrograph separation, a specific, frequently used one is singled out here. It is shown to be derived from plausible physical principles. This increases confidence in its results.
Beatrice Sabine Marti, Aidar Zhumabaev, and Tobias Siegfried
Hydrol. Earth Syst. Sci., 27, 319–330, https://doi.org/10.5194/hess-27-319-2023, https://doi.org/10.5194/hess-27-319-2023, 2023
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Numerical modelling is often used for climate impact studies in water resources management. It is, however, not yet highly accessible to many students of hydrology in Central Asia. One big hurdle for new learners is the preparation of relevant data prior to the actual modelling. We present a robust, open-source workflow and comprehensive teaching material that can be used by teachers and by students for self study.
Aniket Gupta, Alix Reverdy, Jean-Martial Cohard, Basile Hector, Marc Descloitres, Jean-Pierre Vandervaere, Catherine Coulaud, Romain Biron, Lucie Liger, Reed Maxwell, Jean-Gabriel Valay, and Didier Voisin
Hydrol. Earth Syst. Sci., 27, 191–212, https://doi.org/10.5194/hess-27-191-2023, https://doi.org/10.5194/hess-27-191-2023, 2023
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Patchy snow cover during spring impacts mountainous ecosystems on a large range of spatio-temporal scales. A hydrological model simulated such snow patchiness at 10 m resolution. Slope and orientation controls precipitation, radiation, and wind generate differences in snowmelt, subsurface storage, streamflow, and evapotranspiration. The snow patchiness increases the duration of the snowmelt to stream and subsurface storage, which sustains the plants and streamflow later in the summer.
Hendrik Rathjens, Jens Kiesel, Michael Winchell, Jeffrey Arnold, and Robin Sur
Hydrol. Earth Syst. Sci., 27, 159–167, https://doi.org/10.5194/hess-27-159-2023, https://doi.org/10.5194/hess-27-159-2023, 2023
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The SWAT model can simulate the transport of water-soluble chemicals through the landscape but neglects the transport through groundwater or agricultural tile drains. These transport pathways are, however, important to assess the amount of chemicals in streams. We added this capability to the model, which significantly improved the simulation. The representation of all transport pathways in the model enables watershed managers to develop robust strategies for reducing chemicals in streams.
Wencong Yang, Hanbo Yang, Changming Li, Taihua Wang, Ziwei Liu, Qingfang Hu, and Dawen Yang
Hydrol. Earth Syst. Sci., 26, 6427–6441, https://doi.org/10.5194/hess-26-6427-2022, https://doi.org/10.5194/hess-26-6427-2022, 2022
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We produced a daily 0.1° dataset of precipitation, soil moisture, and snow water equivalent in 1981–2017 across China via reconstructions. The dataset used global background data and local on-site data as forcing input and satellite-based data as reconstruction benchmarks. This long-term high-resolution national hydrological dataset is valuable for national investigations of hydrological processes.
Felipe A. Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova
Hydrol. Earth Syst. Sci., 26, 6227–6245, https://doi.org/10.5194/hess-26-6227-2022, https://doi.org/10.5194/hess-26-6227-2022, 2022
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Nitrate contamination of rivers from agricultural sources is a challenge for water quality management. During runoff events, different transport paths within the catchment might be activated, generating a variety of responses in nitrate concentration in stream water. Using nitrate samples from 184 German catchments and a runoff event classification, we show that hydrologic connectivity during runoff events is a key control of nitrate transport from catchments to streams in our study domain.
Marcos R. C. Cordeiro, Kang Liang, Henry F. Wilson, Jason Vanrobaeys, David A. Lobb, Xing Fang, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 26, 5917–5931, https://doi.org/10.5194/hess-26-5917-2022, https://doi.org/10.5194/hess-26-5917-2022, 2022
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This study addresses the issue of increasing interest in the hydrological impacts of converting cropland to perennial forage cover in the Canadian Prairies. By developing customized models using the Cold Regions Hydrological Modelling (CRHM) platform, this long-term (1992–2013) modelling study is expected to provide stakeholders with science-based information regarding the hydrological impacts of land use conversion from annual crop to perennial forage cover in the Canadian Prairies.
Reyhaneh Hashemi, Pierre Brigode, Pierre-André Garambois, and Pierre Javelle
Hydrol. Earth Syst. Sci., 26, 5793–5816, https://doi.org/10.5194/hess-26-5793-2022, https://doi.org/10.5194/hess-26-5793-2022, 2022
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Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments. Data-driven long short-term memory (LSTM) models appear very promising to the hydrology community in this respect. Here, we have sought to benefit from traditional practices in hydrology to improve the effectiveness of LSTM models. We discovered that one LSTM parameter has a hydrologic interpretation and that there is a need to increase the data and to tune two parameters, thereby improving predictions.
Guillaume Cinkus, Naomi Mazzilli, Hervé Jourde, Andreas Wunsch, Tanja Liesch, Nataša Ravbar, Zhao Chen, and Nico Goldscheider
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-380, https://doi.org/10.5194/hess-2022-380, 2022
Revised manuscript accepted for HESS
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The Kling-Gupta Efficiency (KGE) is a performance criterion extensively used to evaluate hydrological models. We conduct a critical study on the KGE and its variant to examine counterbalancing errors. Results show that, assessing a simulation, concurrent over- and underestimation of discharge can lead to an overall higher criterion score without being associated to an increase in model relevance. We suggest to carefully choose performance criteria and to use scaling factors.
Mu Xiao, Giuseppe Mascaro, Zhaocheng Wang, Kristen M. Whitney, and Enrique R. Vivoni
Hydrol. Earth Syst. Sci., 26, 5627–5646, https://doi.org/10.5194/hess-26-5627-2022, https://doi.org/10.5194/hess-26-5627-2022, 2022
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As the major water resource in the southwestern United States, the Colorado River is experiencing decreases in naturalized streamflow and is predicted to face severe challenges under future climate scenarios. Here, we demonstrate the value of Earth observing satellites to improve and build confidence in the spatiotemporal simulations from regional hydrologic models for assessing the sensitivity of the Colorado River to climate change and supporting regional water managers.
Christopher Spence, Zhihua He, Kevin R. Shook, John W. Pomeroy, Colin J. Whitfield, and Jared D. Wolfe
Hydrol. Earth Syst. Sci., 26, 5555–5575, https://doi.org/10.5194/hess-26-5555-2022, https://doi.org/10.5194/hess-26-5555-2022, 2022
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We learnt how streamflow from small creeks could be altered by wetland removal in the Canadian Prairies, where this practice is pervasive. Every creek basin in the region was placed into one of seven groups. We selected one of these groups and used its traits to simulate streamflow. The model worked well enough so that we could trust the results even if we removed the wetlands. Wetland removal did not change low flow amounts very much, but it doubled high flow and tripled average flow.
Rosanna A. Lane, Gemma Coxon, Jim Freer, Jan Seibert, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 26, 5535–5554, https://doi.org/10.5194/hess-26-5535-2022, https://doi.org/10.5194/hess-26-5535-2022, 2022
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This study modelled the impact of climate change on river high flows across Great Britain (GB). Generally, results indicated an increase in the magnitude and frequency of high flows along the west coast of GB by 2050–2075. In contrast, average flows decreased across GB. All flow projections contained large uncertainties; the climate projections were the largest source of uncertainty overall but hydrological modelling uncertainties were considerable in some regions.
Guangxuan Li, Xi Chen, Zhicai Zhang, Lichun Wang, and Chris Soulsby
Hydrol. Earth Syst. Sci., 26, 5515–5534, https://doi.org/10.5194/hess-26-5515-2022, https://doi.org/10.5194/hess-26-5515-2022, 2022
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We developed a coupled flow–tracer model to understand the effects of passive storage on modeling hydrological function and isotope dynamics in a karst flow system. Models with passive storages show improvement in matching isotope dynamics performance, and the improved performance also strongly depends on the number and location of passive storages. Our results also suggested that the solute transport is primarily controlled by advection and hydrodynamic dispersion in the steep hillslope unit.
Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, and Sella Nevo
Hydrol. Earth Syst. Sci., 26, 5493–5513, https://doi.org/10.5194/hess-26-5493-2022, https://doi.org/10.5194/hess-26-5493-2022, 2022
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When designing flood forecasting models, it is necessary to use all available data to achieve the most accurate predictions possible. This manuscript explores two basic ways of ingesting near-real-time streamflow data into machine learning streamflow models. The point we want to make is that when working in the context of machine learning (instead of traditional hydrology models that are based on
bio-geophysics), it is not necessary to use complex statistical methods for injecting sparse data.
Xiongpeng Tang, Guobin Fu, Silong Zhang, Chao Gao, Guoqing Wang, Zhenxin Bao, Yanli Liu, Cuishan Liu, and Junliang Jin
Hydrol. Earth Syst. Sci., 26, 5315–5339, https://doi.org/10.5194/hess-26-5315-2022, https://doi.org/10.5194/hess-26-5315-2022, 2022
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In this study, we proposed a new framework that considered the uncertainties of model simulations in quantifying the contribution rate of climate change and human activities to streamflow changes. Then, the Lancang River basin was selected for the case study. The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was mainly human activities.
Bin Yi, Lu Chen, Hansong Zhang, Vijay P. Singh, Ping Jiang, Yizhuo Liu, Hexiang Guo, and Hongya Qiu
Hydrol. Earth Syst. Sci., 26, 5269–5289, https://doi.org/10.5194/hess-26-5269-2022, https://doi.org/10.5194/hess-26-5269-2022, 2022
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An improved GIS-derived distributed unit hydrograph routing method considering time-varying soil moisture was proposed for flow routing. The method considered the changes of time-varying soil moisture and rainfall intensity. The response of underlying surface to the soil moisture content was considered an important factor in this study. The SUH, DUH, TDUH and proposed routing methods (TDUH-MC) were used for flood forecasts, and the simulated results were compared and discussed.
Audrey Douinot, Jean François Iffly, Cyrille Tailliez, Claude Meisch, and Laurent Pfister
Hydrol. Earth Syst. Sci., 26, 5185–5206, https://doi.org/10.5194/hess-26-5185-2022, https://doi.org/10.5194/hess-26-5185-2022, 2022
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The objective of the paper is to highlight the seasonal and singular shift of the transfer time distributions of two catchments (≅10 km2).
Based on 2 years of rainfall and discharge observations, we compare variations in the properties of TTDs with the physiographic characteristics of catchment areas and the eco-hydrological cycle. The paper eventually aims to deduce several factors conducive to particularly rapid and concentrated water transfers, which leads to flash floods.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
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High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 26, 5085–5102, https://doi.org/10.5194/hess-26-5085-2022, https://doi.org/10.5194/hess-26-5085-2022, 2022
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Neural ODEs fuse physics-based models with deep learning: neural networks substitute terms in differential equations that represent the mechanistic structure of the system. The approach combines the flexibility of machine learning with physical constraints for inter- and extrapolation. We demonstrate that neural ODE models achieve state-of-the-art predictive performance while keeping full interpretability of model states and processes in hydrologic modelling over multiple catchments.
Jing Tian, Zhengke Pan, Shenglian Guo, Jiabo Yin, Yanlai Zhou, and Jun Wang
Hydrol. Earth Syst. Sci., 26, 4853–4874, https://doi.org/10.5194/hess-26-4853-2022, https://doi.org/10.5194/hess-26-4853-2022, 2022
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Most of the literature has focused on the runoff response to climate change, while neglecting the impacts of the potential variation in the active catchment water storage capacity (ACWSC) that plays an essential role in the transfer of climate inputs to the catchment runoff. This study aims to systematically identify the response of the ACWSC to a long-term meteorological drought and asymptotic climate change.
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
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The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
Hapu Arachchige Prasantha Hapuarachchi, Mohammed Abdul Bari, Aynul Kabir, Mohammad Mahadi Hasan, Fitsum Markos Woldemeskel, Nilantha Gamage, Patrick Daniel Sunter, Xiaoyong Sophie Zhang, David Ewen Robertson, James Clement Bennett, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 26, 4801–4821, https://doi.org/10.5194/hess-26-4801-2022, https://doi.org/10.5194/hess-26-4801-2022, 2022
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Methodology for developing an operational 7-day ensemble streamflow forecasting service for Australia is presented. The methodology is tested for 100 catchments to learn the characteristics of different NWP rainfall forecasts, the effect of post-processing, and the optimal ensemble size and bootstrapping parameters. Forecasts are generated using NWP rainfall products post-processed by the CHyPP model, the GR4H hydrologic model, and the ERRIS streamflow post-processor inbuilt in the SWIFT package
Chenhao Chai, Lei Wang, Deliang Chen, Jing Zhou, Hu Liu, Jingtian Zhang, Yuanwei Wang, Tao Chen, and Ruishun Liu
Hydrol. Earth Syst. Sci., 26, 4657–4683, https://doi.org/10.5194/hess-26-4657-2022, https://doi.org/10.5194/hess-26-4657-2022, 2022
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This work quantifies future snow changes and their impacts on hydrology in the upper Salween River (USR) under SSP126 and SSP585 using a cryosphere–hydrology model. Future warm–wet climate is not conducive to the development of snow. The rain–snow-dominated pattern of runoff will shift to a rain-dominated pattern after the 2040s under SSP585 but is unchanged under SSP126. The findings improve our understanding of cryosphere–hydrology processes and can assist water resource management in the USR.
Remko C. Nijzink and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 4575–4585, https://doi.org/10.5194/hess-26-4575-2022, https://doi.org/10.5194/hess-26-4575-2022, 2022
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Most catchments plot close to the empirical Budyko curve, which allows for the estimation of the long-term mean annual evaporation and runoff. The Budyko curve can be defined as a function of a wetness index or a dryness index. We found that differences can occur and that there is an uncertainty due to the different formulations.
Anna Msigwa, Celray James Chawanda, Hans C. Komakech, Albert Nkwasa, and Ann van Griensven
Hydrol. Earth Syst. Sci., 26, 4447–4468, https://doi.org/10.5194/hess-26-4447-2022, https://doi.org/10.5194/hess-26-4447-2022, 2022
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Studies using agro-hydrological models, like the Soil and Water Assessment Tool (SWAT), to map evapotranspiration (ET) do not account for cropping seasons. A comparison between the default SWAT+ set-up (with static land use representation) and a dynamic SWAT+ model set-up (with seasonal land use representation) is made by spatial mapping of the ET. The results show that ET with seasonal representation is closer to remote sensing estimates, giving better performance than ET with static land use.
Jerom P. M. Aerts, Rolf W. Hut, Nick C. van de Giesen, Niels Drost, Willem J. van Verseveld, Albrecht H. Weerts, and Pieter Hazenberg
Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022, https://doi.org/10.5194/hess-26-4407-2022, 2022
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In recent years gridded hydrological modelling moved into the realm of hyper-resolution modelling (<10 km). In this study, we investigate the effect of varying grid-cell sizes for the wflow_sbm hydrological model. We used a large sample of basins from the CAMELS data set to test the effect that varying grid-cell sizes has on the simulation of streamflow at the basin outlet. Results show that there is no single best grid-cell size for modelling streamflow throughout the domain.
Taher Chegini and Hong-Yi Li
Hydrol. Earth Syst. Sci., 26, 4279–4300, https://doi.org/10.5194/hess-26-4279-2022, https://doi.org/10.5194/hess-26-4279-2022, 2022
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Belowground urban stormwater networks (BUSNs) play a critical and irreplaceable role in preventing or mitigating urban floods. However, they are often not available for urban flood modeling at regional or larger scales. We develop a novel algorithm to estimate existing BUSNs using ubiquitously available aboveground data at large scales based on graph theory. The algorithm has been validated in different urban areas; thus, it is well transferable.
Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, and François Anctil
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-264, https://doi.org/10.5194/hess-2022-264, 2022
Revised manuscript accepted for HESS
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A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. Results confirm the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. The proposed approach supports the participation of end-users in interpreting the impact of climate change on water resources.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, https://doi.org/10.5194/hess-26-4147-2022, 2022
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Tracer-aided hydrological models are useful tool to reduce uncertainty of hydrological modeling in cold basins, but there is little guidance on the sampling strategy for isotope analysis, which is important for large mountainous basins. This study evaluated the reliance of the tracer-aided modeling performance on the availability of isotope data in the Yarlung Tsangpo river basin, and provides implications for collecting water isotope data for running tracer-aided hydrological models.
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-245, https://doi.org/10.5194/hess-2022-245, 2022
Revised manuscript accepted for HESS
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Hybrid models (we call δ models) that embrace the fundamental learning capability of AI but can explain all the physical processes can be powerful. In this paper we assess how they perform when applied in 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. δ models could be ideal candidates for global hydrologic assessments.
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, and Yossi Matias
Hydrol. Earth Syst. Sci., 26, 4013–4032, https://doi.org/10.5194/hess-26-4013-2022, https://doi.org/10.5194/hess-26-4013-2022, 2022
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Early flood warnings are one of the most effective tools to save lives and goods. Machine learning (ML) models can improve flood prediction accuracy but their use in operational frameworks is limited. The paper presents a flood warning system, operational in India and Bangladesh, that uses ML models for forecasting river stage and flood inundation maps and discusses the models' performances. In 2021, more than 100 million flood alerts were sent to people near rivers over an area of 470 000 km2.
Matthias Sprenger, Pilar Llorens, Francesc Gallart, Paolo Benettin, Scott T. Allen, and Jérôme Latron
Hydrol. Earth Syst. Sci., 26, 4093–4107, https://doi.org/10.5194/hess-26-4093-2022, https://doi.org/10.5194/hess-26-4093-2022, 2022
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Our catchment-scale transit time modeling study shows that including stable isotope data on evapotranspiration in addition to the commonly used stream water isotopes helps constrain the model parametrization and reveals that the water taken up by plants has resided longer in the catchment storage than the water leaving the catchment as stream discharge. This finding is important for our understanding of how water is stored and released, which impacts the water availability for plants and humans.
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
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This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Bruno Majone, Diego Avesani, Patrick Zulian, Aldo Fiori, and Alberto Bellin
Hydrol. Earth Syst. Sci., 26, 3863–3883, https://doi.org/10.5194/hess-26-3863-2022, https://doi.org/10.5194/hess-26-3863-2022, 2022
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In this work, we introduce a methodology for devising reliable future high streamflow scenarios from climate change simulations. The calibration of a hydrological model is carried out to maximize the probability that the modeled and observed high flow extremes belong to the same statistical population. Application to the Adige River catchment (southeastern Alps, Italy) showed that this procedure produces reliable quantiles of the annual maximum streamflow for use in assessment studies.
Pedro V. G. Batista, Peter Fiener, Simon Scheper, and Christine Alewell
Hydrol. Earth Syst. Sci., 26, 3753–3770, https://doi.org/10.5194/hess-26-3753-2022, https://doi.org/10.5194/hess-26-3753-2022, 2022
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Patchy agricultural landscapes have a large number of small fields, which are separated by linear features such as roads and field borders. When eroded sediments are transported out of the agricultural fields by surface runoff, these features can influence sediment connectivity. By use of measured data and a simulation model, we demonstrate how a dense road network (and its drainage system) facilitates sediment transport from fields to water courses in a patchy Swiss agricultural catchment.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
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Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Aurélien Beaufort, Jacob S. Diamond, Eric Sauquet, and Florentina Moatar
Hydrol. Earth Syst. Sci., 26, 3477–3495, https://doi.org/10.5194/hess-26-3477-2022, https://doi.org/10.5194/hess-26-3477-2022, 2022
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We developed one of the largest stream temperature databases to calculate a simple, ecologically relevant metric – the thermal peak – that captures the magnitude of summer thermal extremes. Using statistical models, we extrapolated the thermal peak to nearly every stream in France, finding the hottest thermal peaks along large rivers without forested riparian zones and groundwater inputs. Air temperature was a poor proxy for the thermal peak, highlighting the need to grow monitoring networks.
Ulises M. Sepúlveda, Pablo A. Mendoza, Naoki Mizukami, and Andrew J. Newman
Hydrol. Earth Syst. Sci., 26, 3419–3445, https://doi.org/10.5194/hess-26-3419-2022, https://doi.org/10.5194/hess-26-3419-2022, 2022
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This paper characterizes parameter sensitivities across more than 5500 grid cells for a commonly used macroscale hydrological model, including a suite of eight performance metrics and 43 soil, vegetation and snow parameters. The results show that the model is highly overparameterized and, more importantly, help to provide guidance on the most relevant parameters for specific target processes across diverse climatic types.
Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, https://doi.org/10.5194/hess-26-3377-2022, 2022
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The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that deep learning models may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis. The deep learning models remained relatively accurate in predicting extreme events compared with traditional models, even when extreme events were not included in the training set.
Sebastian A. Krogh, Lucia Scaff, James W. Kirchner, Beatrice Gordon, Gary Sterle, and Adrian Harpold
Hydrol. Earth Syst. Sci., 26, 3393–3417, https://doi.org/10.5194/hess-26-3393-2022, https://doi.org/10.5194/hess-26-3393-2022, 2022
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We present a new way to detect snowmelt using daily cycles in streamflow driven by solar radiation. Results show that warmer sites have earlier and more intermittent snowmelt than colder sites, and the timing of early snowmelt events is strongly correlated with the timing of streamflow volume. A space-for-time substitution shows greater sensitivity of streamflow timing to climate change in colder rather than in warmer places, which is then contrasted with land surface simulations.
Wouter J. M. Knoben and Diana Spieler
Hydrol. Earth Syst. Sci., 26, 3299–3314, https://doi.org/10.5194/hess-26-3299-2022, https://doi.org/10.5194/hess-26-3299-2022, 2022
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This paper introduces educational materials that can be used to teach students about model structure uncertainty in hydrological modelling. There are many different hydrological models and differences between these models impact their usefulness in different places. Such models are often used to support decision making about water resources and to perform hydrological science, and it is thus important for students to understand that model choice matters.
Leonie Kiewiet, Ernesto Trujillo, Andrew Hedrick, Scott Havens, Katherine Hale, Mark Seyfried, Stephanie Kampf, and Sarah E. Godsey
Hydrol. Earth Syst. Sci., 26, 2779–2796, https://doi.org/10.5194/hess-26-2779-2022, https://doi.org/10.5194/hess-26-2779-2022, 2022
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Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how variations in rainfall and snowmelt affect discharge. We found that annual discharge and stream cessation depended on the magnitude and timing of rainfall and snowmelt and on the snowpack melt-out date. This highlights the importance of precipitation timing and emphasizes the need for spatiotemporally distributed simulations of snowpack and rainfall dynamics.
Cited articles
Abramowitz, G., Gupta, H., Pitman, A., Wang, Y., Leuning, R., Cleugh, H.,
Hsu, K., Abramowitz, G., Gupta, H., Pitman, A., Wang, Y., Leuning, R.,
Cleugh, H., and Hsu, K.: Neural Error Regression Diagnosis (NERD): A Tool for
Model Bias Identification and Prognostic Data Assimilation, J.
Hydrometeorol., 7, 160–177, https://doi.org/10.1175/JHM479.1, 2006.
Abramowitz, G., Pitman, A., Gupta, H., Kowalczyk, E., Wang, Y., Abramowitz,
G., Pitman, A., Gupta, H., Kowalczyk, E., and Wang, Y.: Systematic Bias in
Land Surface Models, J. Hydrometeorol., 8, 989–1001, https://doi.org/10.1175/JHM628.1, 2007.
Ajami, H., Khan, U., Tuteja, N. K., and Sharma, A.: Development of a
computationally efficient semi-distributed hydrologic modeling application
for soil moisture, lateral flow and runoff simulation, Environ. Model.
Softw., 85, 319–331, https://doi.org/10.1016/J.ENVSOFT.2016.09.002, 2016.
Albert, A., Strano, E., Kaur, J., and Gonzalez, M.: Modeling urbanization
patterns with generative adversarial networks, arXiv:1801.02710,
available at: http://arxiv.org/abs/1801.02710, last access: 24 March 2018.
Allamano, P., Croci, A., and Laio, F.: Toward the camera rain gauge, Water
Resour. Res., 51, 1744–1757, https://doi.org/10.1002/2014WR016298, 2015.
Angermueller, C., Pärnamaa, T., Parts, L., and Stegle, O.: Deep learning
for computational biology, Mol. Syst. Biol., 12, 878,
https://doi.org/10.15252/MSB.20156651, 2016.
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.
Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal,
M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., and
Lacoste-Julien, S.: A Closer Look at Memorization in Deep Networks, in
Proceedings of the 34 th International Conference on Machine Learning,
Sydney, Australia, PMLR 70, available at:
https://arxiv.org/abs/1706.05394, last access: 19 November 2017.
Aspen: Internet of Water: Sharing and Integrating Water Data for
Sustainability, A Rep. from Aspen Inst. Dialogue Ser. Water Data,
available at:
https://www.aspeninstitute.org/publications/internet-of-water/ (last access: 27
August 2018), 2017.
Aspray, W.: Women and underrepresented minorities in computing: a historical and social study, Springer, Basel, Switzerland, 2016.
Assem, H., Ghariba, S., Makrai, G., Johnston, P., Gill, L., and Pilla, F.:
Urban Water Flow and Water Level Prediction Based on Deep Learning, in ECML
PKDD 2017: Machine Learning and Knowledge Discovery in Databases,
317–329, Springer, Cham., 2017.
Baldi, P., Sadowski, P., and Whiteson, D.: Enhanced Higgs Boson to
Banino, A., Barry, C., Uria, B., Blundell, C., Lillicrap, T., Mirowski, P.,
Pritzel, A., Chadwick, M. J., Degris, T., Modayil, J., Wayne, G., Soyer, H.,
Viola, F., Zhang, B., Goroshin, R., Rabinowitz, N., Pascanu, R., Beattie, C.,
Petersen, S., Sadik, A., Gaffney, S., King, H., Kavukcuoglu, K., Hassabis,
D., Hadsell, R., and Kumaran, D.: Vector-based navigation using grid-like
representations in artificial agents, Nature, 557, 429–433, https://doi.org/10.1038/s41586-018-0102-6, 2018.
Baughman, A. K., Chuang, W., Dixon, K. R., Benz, Z., and Basilico, J.: DeepQA
Jeopardy! Gamification: A Machine-Learning Perspective, IEEE Trans. Comput.
Intell. AI Games, 6, 55–66, https://doi.org/10.1109/TCIAIG.2013.2285651, 2014.
Beven, K.: Changing ideas in hydrology – The case of physically-based
models, J. Hydrol., 105, 157–172, https://doi.org/10.1016/0022-1694(89)90101-7,
1989.
Beven, K.: Topmodel?: A Critique, Hydrol. Process., 11,
1069–1085, 1997.
Blöschl, G.: Hydrologic synthesis: Across processes, places, and scales,
Water Resour. Res., 42, W03S02, https://doi.org/10.1029/2005WR004319, 2006.
Chaney, N. W., Herman, J. D., Ek, M. B., and Wood, E. F.: Deriving Global
Parameter Estimates for the Noah Land Surface Model using FLUXNET and Machine
Learning, J. Geophys. Res. Atmos., 121, 13218–13235, https://doi.org/10.1002/2016JD024821, 2016.
Chang, L.-C., Shen, H.-Y., and Chang, F.-J.: Regional flood inundation nowcast
using hybrid SOM and dynamic neural networks, J. Hydrol., 519, 476–489,
https://doi.org/10.1016/J.JHYDROL.2014.07.036, 2014.
Chen, I.-T., Chang, L.-C., and Chang, F.-J.: Exploring the spatio-temporal
interrelation between groundwater and surface water by using the
self-organizing maps, J. Hydrol., 556, 131–142,
https://doi.org/10.1016/J.JHYDROL.2017.10.015, 2018.
CIF: Results, Int. Time Ser. Forecast. Compet. – Comput. Intell. Forecast., available at:
http://irafm.osu.cz/cif/main.php?c=Static&page=results (last access: 24
March 2018), 2016.
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, 2015.
CoCoRaHS: Community Collaborative Rain, Hail and Snow Network (CoCoRaHS),
“Volunteers Work. together to Meas. Precip. across nations”,
available at: https://www.cocorahs.org/, last access: 23 August 2018.
Columbus, L.: Roundup Of Machine Learning Forecasts And Market Estimates,
2018, Forbes Contrib., available at:
https://www.forbes.com/sites/louiscolumbus/2018/02/18/roundup-of-machine-learning-forecasts-and-market-estimates-2018/#7d3c5bd62225,
last access: 30 July 2018.
CrowdHydrology: CrowdHydrology, available at:
http://crowdhydrology.geology.buffalo.edu/, last access: 23 August 2018.
CUAHSI: Consortium of Universities Allied for Water Research, Inc (CUASI)
Cyberseminars, available at: https://www.cuahsi.org/education/cyberseminars/, last access: 8 October 2018a.
CUAHSI: Data Driven Education, available at:
https://www.cuahsi.org/education/data-driven-education/, last access: 26
September 2018b.
CUAHSI: HydroClient, available at: http://data.cuahsi.org/, last access: 19 August 2018c.
Delle Monache, L., Nipen, T., Liu, Y., Roux, G., and Stull, R.: Kalman Filter
and Analog Schemes to Postprocess Numerical Weather Predictions, Mon. Weather
Rev., 139, 3554–3570, https://doi.org/10.1175/2011MWR3653.1, 2011.
Delle Monache, L., Eckel, F. A., Rife, D. L., Nagarajan, B., and Searight, K.:
Probabilistic Weather Prediction with an Analog Ensemble, Mon. Weather Rev.,
141, 3498–3516, https://doi.org/10.1175/MWR-D-12-00281.1, 2013.
Dignan, L.: Google Research becomes Google AI to reflect AI-first ambitions,
zdnet.com, available at:
https://www.zdnet.com/article/google-research-becomes-google-ai-to-reflect-ai-first-ambitions/, last access: 20 August 2018.
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., and Vapnik, V.: Support
vector regression machines, Proc. 9th Int. Conf. Neural Inf. Process. Syst.,
155–161, available at:
https://dl.acm.org/citation.cfm?id=2999003 (last access: 5 January 2018), 1996.
Endo, A., Burnett, K., Orencio, P., Kumazawa, T., Wada, C., Ishii, A.,
Tsurita, I., and Taniguchi, M.: Methods of the Water-Energy-Food Nexus, Water,
7, 5806–5830, https://doi.org/10.3390/w7105806, 2015.
Entekhabi, D.: The Soil Moisture Active Passive (SMAP) mission, Proc. IEEE,
98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010.
Evans, H., Gervet, E., Kuchembuck, R., and Hu, M.: Will You Embrace AI Fast Enough?
ATKearney Operations & Performance Transformation report, available at:
https://www.atkearney.com/operations-performance-transformation/article?/a/will-you-embrace-ai-fast-enough, last access 10 August 2018.
Faghmous, J. H. and Kumar, V.: A Big Data Guide to Understanding Climate
Change: The Case for Theory-Guided Data Science, Big Data, 2, 155–163,
https://doi.org/10.1089/big.2014.0026, 2014.
Fang, K. and Shen, C.: Full-flow-regime storage-streamflow correlation
patterns provide insights into hydrologic functioning over the continental
US, Water Resour. Res., 53, 8064–8083, https://doi.org/10.1002/2016WR020283, 2017.
Fang, K., Shen, C., Kifer, D., and Yang, X.: Prolongation of SMAP to
Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning
Neural Network, Geophys. Res. Lett., 44, 11030–11039, https://doi.org/10.1002/2017GL075619, 2017.
Gatys, L. A., Ecker, A. S., and Bethge, M.: Image Style Transfer Using
Convolutional Neural Networks, 2016 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 2414–2423, 2016.
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could
Machine Learning Break the Convection Parameterization Deadlock?, Geophys.
Res. Lett., 45, 5742–5751, https://doi.org/10.1029/2018GL078202, 2018.
Gershgorn, D.: The data that transformed AI research-and possibly the world,
Quartz, available at:
https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world/,
last access: 8 October 2018, 2017.
Gleeson, T., Moosdorf, N., Hartmann, J., and van Beek, L. P. H.: A glimpse
beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability
and porosity, Geophys. Res. Lett., 41, 3891–3898,
https://doi.org/10.1002/2014GL059856, 2014.
Goh, G. B., Hodas, N. O., and Vishnu, A.: Deep learning for computational
chemistry, J. Comput. Chem., 38, 1291–1307, https://doi.org/10.1002/jcc.24764, 2017.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D.,
Ozair, S., Courville, A., and Bengio, Y.: Generative Adversarial Networks, in
Proceedings of the 27th International Conference on Neural Information
Processing Systems (NIPS'14), available at:
http://arxiv.org/abs/1406.2661 (last access: 25 February 2017), 2014.
Goodfellow, I., Shlens, J., and Szegedy, C.: Explaining and Harnessing
Adversarial Examples, International Conference on Learning
Representations, available at: http://arxiv.org/abs/1412.6572 (last access: 25 February 2017), 2015.
Goodman, B. and Flaxman, S.: European Union regulations on algorithmic
decision-making and a “right to explanation”,
arXiv:1606.08813, available at: http://arxiv.org/abs/1606.08813 (last access: 7 February 2018), 2016.
Govindaraju, R. S.: Artificial Neural Networks in Hydrology. II: Hydrologic
Applications, J. Hydrol. Eng., 5, 124–137,
https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124), 2000.
Graves, A., Mohamed, A., and Hinton, G.: Speech recognition with deep recurrent neural networks,
in: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 6645–6649, IEEE, Vancouver, Canada, 2013.
GRDC: River Discharge Data, Glob. Runoff Data Cent., available at:
http://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html, last access: 28 July 2017.
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., and
Schmidhuber, J.: LSTM: A Search Space Odyssey,
available at:
http://arxiv.org/abs/1503.04069 (last access: 18 July 2016), 2015.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S.: Deep
learning for visual understanding: A review, Neurocomputing, 187, 27–48,
https://doi.org/10.1016/J.NEUCOM.2015.09.116, 2016.
Hall, D. K., Riggs, G. A., and Salomonson., V. V.: MODIS/Terra Snow Cover
5-Min L2 Swath 500 m, Version 5, Boulder, Colorado USA, 2006.
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image
Recognition, 2016 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 770–778, 2016.
Helber, P., Bischke, B., Dengel, A., and Borth, D.: EuroSAT: A Novel Dataset
and Deep Learning Benchmark for Land Use and Land Cover Classification,
available at: http://arxiv.org/abs/1709.00029 (last access: 2
September 2018), 2017.
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., and Darrell, T.:
Generating Visual Explanations, in: Computer Vision – ECCV 2016, 3–19, available at: https://doi.org/10.1007/978-3-319-46493-0_1,
Springer, Cham, Amsterdam, The Netherlands, 2016.
Hiroi, K. and Kawaguchi, N.: FloodEye: Real-time flash flood prediction
system for urban complex water flow, 2016 IEEE SENSORS, 1–3,
2016.
Hirschberg, J. and Manning, C. D.: Advances in natural language processing,
Science, 349, 261–266, https://doi.org/10.1126/science.aaa8685, 2015.
Ho, T. K.: Random decision forests, in Proceedings of 3rd International
Conference on Document Analysis and Recognition, IEEE
Comput. Soc. Press., 1, 278–282, 1995.
Hochreiter, S.: The Vanishing Gradient Problem During Learning Recurrent
Neural Nets and Problem Solutions, Int. J. Uncertainty, Fuzziness
Knowledge-Based Syst., 6, 107–116, https://doi.org/10.1142/S0218488598000094, 1998.
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Comput.,
9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Horsburgh, J. S., Morsy, M. M., Castronova, A. M., Goodall, J. L., Gan, T.,
Yi, H., Stealey, M. J., and Tarboton, D. G.: HydroShare: Sharing Diverse
Environmental Data Types and Models as Social Objects with Application to the
Hydrology Domain, J. Am. Water Resour. As., 52, 873–889,
https://doi.org/10.1111/1752-1688.12363, 2016.
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J.,
Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret,
U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut,
R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB) – a review, Hydrol. Sci. J.,
58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013.
Hsu, K., Gupta, H. V., and Sorooshian, S.: Artificial Neural Network Modeling
of the Rainfall-Runoff Process, Water Resour. Res., 31, 2517–2530,
https://doi.org/10.1029/95WR01955, 1995.
Hsu, K., Gao, X., Sorooshian, S., Gupta, H. V., Hsu, K., Gao, X., Sorooshian,
S., and Gupta, H. V.: Precipitation Estimation from Remotely Sensed
Information Using Artificial Neural Networks, J. Appl. Meteorol., 36,
1176–1190, https://doi.org/10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2, 1997.
Hsu, K., Gupta, H. V., Gao, X., Sorooshian, S., and Imam, B.: Self-organizing
linear output map (SOLO): An artificial neural network suitable for
hydrologic modeling and analysis, Water Resour. Res., 38, 38-1–38-17,
https://doi.org/10.1029/2001WR000795, 2002.
Huang, W., He, D., Yang, X., Zhou, Z., Kifer, D., and Giles, C. L.: Detecting
Arbitrary Oriented Text in the Wild with a Visual Attention Model, in
Proceedings of the 2016 ACM on Multimedia Conference – MM '16, 551–555,
ACM Press, New York, USA, 2016.
Indermuhle, E., Frinken, V., and Bunke, H.: Mode Detection in Online
Handwritten Documents Using BLSTM Neural Networks, 2012 International
Conference on Frontiers in Handwriting Recognition, 302–307, IEEE,
2012.
Izadinia, H., Russell, B. C., Farhadi, A., Hoffman, M. D., and Hertzmann, A.:
Deep Classifiers from Image Tags in the Wild, Proceedings of the 2015
Workshop on Community-Organized Multimodal Mining: Opportunities for Novel
Solutions, 13–18, ACM, 2015.
Jackson, T., O'Neill, P., Njoku, E., Chan, S., Bindlish, R., Colliander, A.,
Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Cosh, M., Caldwell, T.,
Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H.,
Thibeault, M., Martínez-Fernández, J., González-Zamora, Á.,
Seyfried10, M., Bosch, D., Starks, P., Goodrich, D., Prueger, J., Su, Z., van
der Velde, R., Asanuma, J., Palecki, M., Small, E., Zreda, M., Calvet, J.,
Crow, W., Kerr, Y., Yueh, S., and Entekhabi, D.: Soil Moisture Active Passive
(SMAP) Project Calibration and Validation for the L2/3_SM_P Version 3 Data Products, SMAP Proj. JPL D-93720,
available at:
https://nsidc.org/data/smap/technical-references
(last access: 27 July 2017), 2016.
Josephson, J. R. and Josephson, S. G.: Abductive inference?: computation,
philosophy, technology, Cambridge University Press, 1994.
Junk, C., Delle Monache, L., Alessandrini, S., Cervone, G., and von Bremen,
L.: Predictor-weighting strategies for probabilistic wind power forecasting
with an analog ensemble, Meteorol. Z., 24, 361–379,
https://doi.org/10.1127/metz/2015/0659, 2015.
Karpathy, A., Johnson, J., and Fei-Fei, L.: Visualizing and Understanding
Recurrent Networks, ICLR 2016 Workshop, available at:
http://arxiv.org/abs/1506.02078 (last access: 7 November 2016), 2015.
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A.,
Ganguly, A., Shekhar, S., Samatova, N., and Kumar, V.: Theory-Guided Data
Science: A New Paradigm for Scientific Discovery from Data, IEEE Trans.
Knowl. Data Eng., 29, 2318–2331, https://doi.org/10.1109/TKDE.2017.2720168, 2017.
Kawaguchi, K., Kaelbling, L. P., and Bengio, Y.: Generalization in Deep
Learning, arXiv:1710.05468, available at:
http://arxiv.org/abs/1710.05468 (last access: 12 March 2018), 2017.
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, in 3rd
International Conference for Learning Representations, San Diego, CA,
available at: http://arxiv.org/abs/1412.6980 (last access: 30 March
2018), 2014.
Kingma, D. P. and Welling, M.: Auto-Encoding Variational Bayes, in
Proceedings of the 2014 International Conference on Learning Representations
(ICLR), available at: http://arxiv.org/abs/1312.6114 (last access: 24
March 2018), 2013.
Knyazikhin, Y., Glassy, J., Privette, J. L., Tian, Y., Lotsch, A., Zhang, Y.,
Wang, Y., Morisette, J. T., P.Votava, Myneni, R. B., Nemani, R. R., and
Running, S. W.: MODIS Leaf Area Index (LAI) and Fraction of
Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product
(MOD15) Algorithm Theoretical Basis Document,
https://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf (last
access: 8 October 2018), 1999.
Koza, J. R.: Genetic Programming: on the Programming of Computers by Means of
Natural Selection, MIT Press, Cambridge, MA, USA, 1992.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.:
Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks,
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-247, in
review, 2018.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet Classification
with Deep Convolutional Neural Networks, Advances in Neural Information
Processing Systems 25, 1097–1105, available at:
https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-
(last access: 30 March 2018), 2012.
Krueger, D., Ballas, N., Jastrzebski, S., Arpit, D., Kanwal, M. S., Maharaj,
T., Bengio, E., Erraqabi, A., Fischer, A., Lacoste-Julien, S., and Courville,
A.: Deep nets don't learn via memorization, ICLR 2017, available at:
https://openreview.net/pdf?id=rJv6ZgHYg, 2017.
Kumar, D. and Menkovski, V.: Understanding Anatomy Classification Through
Visualization, 30th NIPS Machine learning for Health Workshop, available at:
https://arxiv.org/abs/1611.06284 (last access: 24 November 2017), 2016.
Laloy, E., Hérault, R., Lee, J., Jacques, D., and Linde, N.: Inversion
using a new low-dimensional representation of complex binary geological media
based on a deep neural network, Adv. Water Resour., 110, 387–405,
https://doi.org/10.1016/J.ADVWATRES.2017.09.029, 2017.
Laloy, E., Hérault, R., Jacques, D., and Linde, N.: Training-Image Based
Geostatistical Inversion Using a Spatial Generative Adversarial Neural
Network, Water Resour. Res., 54, 381–406, https://doi.org/10.1002/2017WR022148, 2018.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015.
Lehman, K. J.: Courting the Uncommitted: A Mixed-Methods Study of Undecided
Students in Introductory Computer Science Courses, UCLA Electron, Theses
Diss., available at: https://escholarship.org/uc/item/94k326xs (last
access: 31 July 2018), 2017.
Leviathan, Y. and Matias, Y.: Google Duplex: An AI System for Accomplishing
Real-World Tasks Over the Phone, Google AI Blog, available at:
https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html,
last access: 20 August 2018.
Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D.,
Kunkel, K., Wehner, M., and Collins, W.: Application of Deep Convolutional
Neural Networks for Detecting Extreme Weather in Climate Datasets, ACM SIGKDD
2016 Conference on Knowledge Discovery & Data Mining, available at:
http://arxiv.org/abs/1605.01156, last access: 21 October 2016.
Mahendran, A. and Vedaldi, A.: Understanding deep image representations by
inverting them, 2015 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 5188–5196, 2015.
Maier, H. R., Jain, A., Dandy, G. C., and Sudheer, K. P.: Methods used for
the development of neural networks for the prediction of water resource
variables in river systems: Current status and future directions, Environ.
Model. Softw., 25, 891–909, https://doi.org/10.1016/J.ENVSOFT.2010.02.003, 2010.
Marshall, L.: Creativity, Uncertainty, and Automated Model Building,
Groundwater, 55, 693–697, https://doi.org/10.1111/gwat.12552, 2017.
Matsuoka, D., Nakano, M., Sugiyama, D., and Uchida, S.: Detecting Precursors of Tropical Cyclone
using Deep Neural Networks, The 7th International Workshop on Climate
Informatics, CI, 2017.
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.
Mecklenburg, S., Kerr, Y., Font, J., and Hahne, A.: The Soil Moisture and
Ocean Salinity Mission – An Overview, IGARSS 2008, IEEE International
Geoscience and Remote Sensing Symposium, IV-938–IV-941, 2008.
Merwade, V. and Ruddell, B. L.: Moving university hydrology education forward
with community-based geoinformatics, data and modeling resources, Hydrol.
Earth Syst. Sci., 16, 2393–2404, https://doi.org/10.5194/hess-16-2393-2012,
2012.
Montanari, A., Young, G., Savenije, H. H. G., Hughes, D., Wagener, T., Ren,
L. L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl,
G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer,
B., Boegh, E., Schymanski, S. J., Baldassarre, G. Di, Yu, B., Hubert, P.,
Huang, Y., Schumann, A., Post, D. A., Srinivasan, V., Harman, C., Thompson,
S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and
Belyaev, V.: “Panta Rhei – Everything Flows”: Change in hydrology and
society – The IAHS Scientific Decade 2013–2022, Hydrol. Sci. J., 58,
https://doi.org/10.1080/02626667.2013.809088, 2013.
Moradkhani, H., Hsu, K., Gupta, H. V., and Sorooshian, S.: Improved
streamflow forecasting using self-organizing radial basis function artificial
neural networks, J. Hydrol., 295, 246–262,
https://doi.org/10.1016/J.JHYDROL.2004.03.027, 2004.
Mosser, L., Dubrule, O., and Blunt, M. J.: Reconstruction of
three-dimensional porous media using generative adversarial neural networks,
Phys. Rev. E, 96, 43309, https://doi.org/10.1103/PhysRevE.96.043309, 2017.
Mount, N. J., Maier, H. R., Toth, E., Elshorbagy, A., Solomatine, D., Chang,
F.-J., and Abrahart, R. J.: Data-driven modelling approaches for
socio-hydrology: opportunities and challenges within the Panta Rhei Science
Plan, Hydrol. Sci. J., 61, 1–17,
https://doi.org/10.1080/02626667.2016.1159683, 2016.
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.
Nearing, G. S., Mocko, D. M., Peters-Lidard, C. D., Kumar, S. V., and Xia, Y.: Benchmarking
NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty
Contributions, J. Hydrometeorol., 17, 745–759, https://doi.org/10.1175/JHM-D-15-0063.1,
2016.
Ororbia, A. G., Giles, C. L., and Kifer, D.: Unifying Adversarial Training
Algorithms with Flexible Deep Data Gradient Regularization, Neural
Computation, MIT Press, available at: http://arxiv.org/abs/1601.07213
(last access: 24 February 2017), 2016.
Pavelsky, T. M., Durand, M. T., Andreadis, K. M., Beighley, R. E., Paiva, R.
C. D., Allen, G. H., and Miller, Z. F.: Assessing the potential global extent
of SWOT river discharge observations, J. Hydrol., 519, 1516–1525,
https://doi.org/10.1016/j.jhydrol.2014.08.044, 2014.
Ranzato, M., Poultney, C., Chopra, S., and LeCun, Y.: Efficient learning of
sparse representations with an energy-based model, Proc. 19th Int. Conf.
Neural Inf. Process. Syst., 1137–1144, available at:
https://dl.acm.org/citation.cfm?id=2976599 (last access: 30 March
2018), 2006.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang,
Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L.:
ImageNet Large Scale Visual Recognition Challenge, arXiv:1409.0575,
available at: http://arxiv.org/abs/1409.0575 (last access: 24 March 2018),
2014.
Sadler, J. M., Goodall, J. L., Morsy, M. M., and Spencer, K.: Modeling urban
coastal flood severity from crowd-sourced flood reports using Poisson
regression and Random Forest, J. Hydrol., 559, 43–55,
https://doi.org/10.1016/J.JHYDROL.2018.01.044, 2018.
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., and Muller, K.-R.:
Evaluating the Visualization of What a Deep Neural Network Has Learned, IEEE
Trans. Neural Networks Learn. Syst., 28, 2660–2673,
https://doi.org/10.1109/TNNLS.2016.2599820, 2017.
Sax, L. J., Lehman, K. J., Jacobs, J. A., Kanny, M. A., Lim, G.,
Monje-Paulson, L., and Zimmerman, H. B.: Anatomy of an Enduring Gender Gap:
The Evolution of Women's Participation in Computer Science, J. Higher Educ.,
88, 258–293, https://doi.org/10.1080/00221546.2016.1257306, 2017.
Schaap, M. G., Leij, F. J., and van Genuchten, M. T.: Rosetta: a Computer Program for Estimating Soil
Hydraulic Parameters With Hierarchical Pedotransfer Functions, J. Hydrol., 251, 163–176, doi:10.1016/S0022-1694(01)00466-8, 2001.
Schmidhuber, J.: Deep learning in neural networks: An overview, Neural
Networks, 61, 85–117, https://doi.org/10.1016/j.neunet.2014.09.003, 2015.
Schwalm, C. R., Anderegg, W. R. L., Michalak, A. M., Fisher, J. B., Biondi,
F., Koch, G., Litvak, M., Ogle, K., Shaw, J. D., Wolf, A., Huntzinger, D. N.,
Schaefer, K., Cook, R., Wei, Y., Fang, Y., Hayes, D., Huang, M., Jain, A.,
and Tian, H.: Global patterns of drought recovery, Nature, 548, 202–205,
https://doi.org/10.1038/nature23021, 2017.
Settles, B.: Active Learning, in: Synthesis lectures on artificial
intelligence and machine learning, edited by: Brachman, R. J., Cohen, W.
W.,
and Dietterich, T. G., Norgan & Claypool, Williston, VT, USA, 2012.
Shen, C.: A trans-disciplinary review of deep learning research and its
relevance for water resources scientists, Water Resour. Res., https://doi.org/10.1029/2018WR022643, 2018.
Simonite, T.: AI is the future – but where are the women, Wired,
available at:
https://www.wired.com/story/artificial-intelligence-researchers-gender-imbalance/, last access: 1 September
2018.
Simonyan, K. and Zisserman, A.: Very Deep Convolutional Networks for
Large-Scale Image Recognition, ICLR 2015, available at:
http://arxiv.org/abs/1409.1556 (last access: 25 August 2018), 2014.
Snelson, E. and Ghahramani, Z.: Sparse Gaussian Processes using
Pseudo-inputs, Adv. Neural Inf. Process. Syst., 18, 1257–1264, 2006.
Srinivasan, M.: Hydrology from space: NASA's satellites supporting water
resources applications, Water Forum III Droughts Other Extrem. Weather Events, available at:
http://www.jsg.utexas.edu/ciess/files/Srinivasanetal_TWF_Oct14_Final.pdf (last access: 12 July 2016),
2013.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov,
R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J.
Mach. Learn. Res., 15, 1929–1958, 2014.
Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C.: The German Traffic
Sign Recognition Benchmark: A multi-class classification competition, The
2011 International Joint Conference on Neural Networks, 1453–1460,
2011.
Stocker, T. F., Qin, D., Plattner, G.-K., Alexander, L. V., Allen, S. K.,
Bindoff, N. L., Bréon, F.-M., Church, J. A., Cubasch, U., Emori, S.,
Forster, P., Friedlingstein, P., Gillett, N., Gregory, J. M., Hartmann, D.
L., Jansen, E., Kirtman, B., Knutti, R., Kumar, K. K., Lemke, P., Marotzke,
J., Masson-Delmotte, V., Meehl, G. A., Mokhov, I. I., Piao, S., Ramaswamy,
V., Randall, D., Rhein, M., Rojas, M., Sabine, C., Shindell, D., Talley, L.
D., Vaughan, D. G., and Xie, S.-P.: Technical summary, Climate Change 2013,
in:
The Physical Science Basis, edited by: Stocker, T. F.,
Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y.,
Bex, V., and Midgley, P. M., Contrib. Work. Gr. I to Fifth Assess. Rep. Intergov. Panel Clim.
Chang., available at:
http://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_TS_FINAL.pdf
(last
access: 8 October 2018), 2013.
Stollenga, M. F., Byeon, W., Liwicki, M., and Schmidhuber, J.: Parallel
multi-dimensional LSTM, with application to fast biomedical volumetric image
segmentation, Proc. 28th Int. Conf. Neural Inf. Process. Syst., 2,
2998–3006, 2015.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R.: Intriguing properties of neural networks,
available at:
http://arxiv.org/abs/1312.6199 (last access: 8 June 2016), 2013.
Szegedy, C., Wei Liu, Yangqing Jia, Sermanet, P., Reed, S., Anguelov, D.,
Erhan, D., Vanhoucke, V., and Rabinovich, A.: Going deeper with convolutions,
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
1–9, , 2015.
Tao, Y., Gao, X., Hsu, K., Sorooshian, S., and Ihler, A.: A Deep Neural
Network Modeling Framework to Reduce Bias in Satellite Precipitation
Products, J. Hydrometeorol., 17, 931–945, https://doi.org/10.1175/JHM-D-15-0075.1, 2016.
Tao, Y., Gao, X., Ihler, A., Sorooshian, S., Hsu, K., Tao, Y., Gao, X.,
Ihler, A., Sorooshian, S., and Hsu, K.: Precipitation Identification with
Bispectral Satellite Information Using Deep Learning Approaches, J.
Hydrometeorol., 18, 1271–1283, https://doi.org/10.1175/JHM-D-16-0176.1, 2017.
Tao, Y., Hsu, K., Ihler, A., Gao, X., Sorooshian, S., Tao, Y., Hsu, K.,
Ihler, A., Gao, X., and Sorooshian, S.: A Two-Stage Deep Neural Network
Framework for Precipitation Estimation from Bispectral Satellite Information,
J. Hydrometeorol., 19, 393–408, https://doi.org/10.1175/JHM-D-17-0077.1, 2018.
Thompson, J. A., Pena-Yewtukhiw, E. M., and Grove, J. H.: Soil–landscape
modeling across a physiographic region: Topographic patterns and model
transportability, Geoderma, 133, 57–70,
https://doi.org/10.1016/J.GEODERMA.2006.03.037, 2006.
Tibshirani, R. and Tibshirani, R.: Regression Shrinkage and Selection Via the
Lasso, J. R. Stat. Soc. Ser. B, 58, 267–288, 1994.
Troch, P. A., Carrillo, G., Sivapalan, M., Wagener, T., and Sawicz, K.:
Climate-vegetation-soil interactions and long-term hydrologic partitioning:
signatures of catchment co-evolution, Hydrol. Earth Syst. Sci., 17,
2209–2217, https://doi.org/10.5194/hess-17-2209-2013, 2013.
Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., and Ganguly,
A. R.: DeepSD: Generating High Resolution Climate Change Projections through
Single Image Super-Resolution, 23rd ACM SIGKDD Conference on Knowledge
Discovery and Data Mining., available at:
http://arxiv.org/abs/1703.03126, last access: 2 December 2017.
Vondrick, C., Pirsiavash, H., and Torralba, A.: Anticipating Visual
Representations from Unlabeled Video, CVPR 2016, available at:
http://arxiv.org/abs/1504.08023 (last access: 20 August 2018), 2016.
Voosen, P.: The AI detectives, Science, 80, 357,
https://doi.org/10.1126/science.357.6346.22, 2017.
Wagener, T., Sivapalan, M., Troch, P. A., McGlynn, B. L., Harman, C. J.,
Gupta, H. V., Kumar, P., Rao, P. S. C., Basu, N. B., and Wilson, J. S.: The
future of hydrology: An evolving science for a changing world, Water Resour.
Res., 46, 1–10, https://doi.org/10.1029/2009WR008906, 2010.
Wagener, T., Kelleher, C., Weiler, M., McGlynn, B., Gooseff, M., Marshall,
L., Meixner, T., McGuire, K., Gregg, S., Sharma, P., and Zappe, S.: It takes
a community to raise a hydrologist: the Modular Curriculum for Hydrologic
Advancement (MOCHA), Hydrol. Earth Syst. Sci., 16, 3405–3418,
https://doi.org/10.5194/hess-16-3405-2012, 2012.
Wahr, J.: Time-variable gravity from GRACE: First results, Geophys. Res.
Lett., 31, L11501, https://doi.org/10.1029/2004GL019779, 2004.
Wahr, J., Swenson, S., and Velicogna, I.: Accuracy of GRACE mass estimates,
Geophys. Res. Lett., 33, L06401, https://doi.org/10.1029/2005GL025305, 2006.
Wang, H., Skau, E., Krim, H., and Cervone, G.: Fusing Heterogeneous Data: A
Case for Remote Sensing and Social Media, IEEE Trans. Geosci. Remote Sens., 1–13, https://doi.org/10.1109/TGRS.2018.2846199, 2018.
WaterML2: WaterML2, A global standard for hydrological time series, available
at: http://www.waterml2.org/, last access: 23 August 2018.
Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Yu, D., and Zweig, G.: Achieving Human Parity in Conversational Speech Recognition,
Microsoft Res. Tech. Rep. MSR-TR-2016-71. arXiv1610.05256, available at: http://arxiv.org/abs/1610.05256 (last access: 24 March 2018), 2016.
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., and Lipson, H.: Understanding
Neural Networks Through Deep Visualization, in Deep Learning Workshop, 31 st
International Conference on Machine Learning, Lille, France, available at:
http://arxiv.org/abs/1506.06579 (last access: 19 November 2017), 2015.
Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., and Snyder, M.: Predicting non-small cell lung cancer prognosis by fully
automated microscopic pathology image features, Nat. Commun., 7, 12474,
https://doi.org/10.1038/ncomms12474, 2016.
Zen, H. and Sak, H.: Unidirectional long short-term memory recurrent neural
network with recurrent output layer for low-latency speech synthesis, 2015
IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 4470–4474, 2015.
Zhang, D., Lindholm, G., and Ratnaweera, H.: Use long short-term memory to
enhance Internet of Things for combined sewer overflow monitoring, J.
Hydrol., 556, 409–418, https://doi.org/10.1016/J.JHYDROL.2017.11.018, 2018.
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., and Fraundorfer, F.:
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources, IEEE Geosci.
Remote Sens. Mag., 5, 8–36, https://doi.org/10.1109/MGRS.2017.2762307, 2017.
Short summary
Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.
Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and...
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