Articles | Volume 22, issue 11
Hydrol. Earth Syst. Sci., 22, 5639–5656, 2018
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: HESS Opinions 2018
Hydrol. Earth Syst. Sci., 22, 5639–5656, 2018
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
Chaopeng Shen et al.
Related authors
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
Mohammad Ghoreishi, Amin Elshorbagy, Saman Razavi, Günter Blöschl, Murugesu Sivapalan, and Ahmed Abdelkader
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-172, https://doi.org/10.5194/hess-2022-172, 2022
Preprint under review for HESS
Short summary
Short summary
We propose a quantitative model of the willingness to cooperate at the national and Eastern Nile River Basin. Our results suggest that the 2008 food crisis may account for Sudan recovering its willingness to cooperate with Ethiopia. Long-term lack of trust among the riparian countries may have reduced basin-wide cooperation. The model can be used to explore the effects of changes in future dam operation and other management decisions on the emergence of basin cooperation.
Yi Liu, Jiang Li, Zheng N. Fang, Mojtaba Rashvand, and Tranell Griffin
Proc. IAHS, 382, 315–320, https://doi.org/10.5194/piahs-382-315-2020, https://doi.org/10.5194/piahs-382-315-2020, 2020
Short summary
Short summary
This paper shows the subsidence due to creep of the coastal compressible aquifer systems with the borehole extensometer compaction measurements and groundwater level observations. So this creep subsidence can become one important component of relative sea level rise in the coastal areas in the world in addition to tectonic subsidence and subsidence due to groundwater withdrawal. This research is because we need to project relative sea level rise in the Houston-Galveston region.
Ji Li, Daoxian Yuan, Jiao Liu, Yongjun Jiang, Yangbo Chen, Kuo Lin Hsu, and Soroosh Sorooshian
Hydrol. Earth Syst. Sci., 23, 1505–1532, https://doi.org/10.5194/hess-23-1505-2019, https://doi.org/10.5194/hess-23-1505-2019, 2019
Short summary
Short summary
There are no long-term reasonable rainfall data to build a hydrological model in karst river basins to a large extent. In this paper, the PERSIANN-CCS QPEs are employed to estimate the precipitation data as an attempt in the Liujiang karst river basin, 58 270 km2, China. An improved method is proposed to revise the results of the PERSIANN-CCS QPEs. The post-processed PERSIANN-CCS QPE with a distributed hydrological model, the Liuxihe model, has a better performance in karst flood forecasting.
Phu Nguyen, Mohammed Ombadi, Soroosh Sorooshian, Kuolin Hsu, Amir AghaKouchak, Dan Braithwaite, Hamed Ashouri, and Andrea Rose Thorstensen
Hydrol. Earth Syst. Sci., 22, 5801–5816, https://doi.org/10.5194/hess-22-5801-2018, https://doi.org/10.5194/hess-22-5801-2018, 2018
Short summary
Short summary
The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. We evaluate the products over CONUS at different spatial and temporal scales using CPC data. Daily scale is the finest temporal scale used for the evaluation over CONUS. We provide a comparison of the available products at a quasi-global scale. We highlight the strengths and limitations of the PERSIANN products.
Yanlai Zhou, Fi-John Chang, Shenglian Guo, Huanhuan Ba, and Shaokun He
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-457, https://doi.org/10.5194/hess-2017-457, 2017
Revised manuscript not accepted
Short summary
Short summary
Developing a robust recurrent ANFIS for modeling multi-step-ahead flood forecast. Fusing the LSE into GA for optimizing the parameters of recurrent ANFIS. Improving the robustness and generalization of recurrent ANFIS. An accurate and robust multi-step-ahead inflow forecast in the Three Gorges Reservoir will provide precious decision-making time for effectively managing contingencies and emergencies and greatly alleviating flood risk as well as loss of life and property.
Amin Elshorbagy, Raja Bharath, Anchit Lakhanpal, Serena Ceola, Alberto Montanari, and Karl-Erich Lindenschmidt
Hydrol. Earth Syst. Sci., 21, 2219–2232, https://doi.org/10.5194/hess-21-2219-2017, https://doi.org/10.5194/hess-21-2219-2017, 2017
Short summary
Short summary
Flood mapping is one of Canada's major national interests. This work presents a simple and effective method for large-scale flood hazard and risk mapping, applied in this study to Canada. Readily available data, such as remote sensing night-light data, topography, and stream network were used to create the maps.
Xiaomang Liu, Tiantian Yang, Koulin Hsu, Changming Liu, and Soroosh Sorooshian
Hydrol. Earth Syst. Sci., 21, 169–181, https://doi.org/10.5194/hess-21-169-2017, https://doi.org/10.5194/hess-21-169-2017, 2017
Short summary
Short summary
A long-term, global, high-resolution, satellite-based precipitation estimation database (PERSIANN-CDR) was recently released. We evaluate the streamflow simulation capability of PERSIANN-CDR over two major river basins on the Tibetan Plateau. Results show that PERSIANN-CDR is a good alternative for a sparse gauge network and has the potentials for future hydrological and climate studies. The streamflow uncertainties are due to the hydrological model parameters and the length of calibration data.
H. Tang, S. Ganguly, G. Zhang, M. A. Hofton, R. F. Nelson, and R. Dubayah
Biogeosciences, 13, 239–252, https://doi.org/10.5194/bg-13-239-2016, https://doi.org/10.5194/bg-13-239-2016, 2016
Short summary
Short summary
This paper provides a unique insight into vertical distribution of leaf area index across North American ecosystems using spaceborne lidar data. This data set of leaf area index and vertical foliage profile can help set up a baseline of canopy structure needed for evaluating climate and land use induced forest changes at continental scale in the future.
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
Revised manuscript not accepted
Short summary
Short summary
This study combines the observed riverine DIN (dissolved inorganic nitrogen) export and the controlling factors (land-use, population and discharge) to inversely estimate the effective DIN yield factors for individual land-use and per capita loading. Those estimated DIN yield factors can extrapolate all possible combinations of land-use, discharge, and population density, demonstrating the capability for scenario assessment.
R. Sultana, K.-L. Hsu, J. Li, and S. Sorooshian
Hydrol. Earth Syst. Sci., 18, 3553–3570, https://doi.org/10.5194/hess-18-3553-2014, https://doi.org/10.5194/hess-18-3553-2014, 2014
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
Revised manuscript not accepted
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone
Quantifying multi-year hydrological memory with Catchment Forgetting Curves
On constraining a lumped hydrological model with both piezometry and streamflow: results of a large sample evaluation
Influences of land use changes on the dynamics of water quantity and quality in the German lowland catchment of the Stör
Impact of spatial distribution information of rainfall in runoff simulation using deep learning method
Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco
The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses
Hydrological response of a peri-urban catchment exploiting conventional and unconventional rainfall observations: the case study of Lambro Catchment
Assessing hydrological sensitivity of grassland basins in the Canadian Prairies to climate using a basin classification-based virtual modelling approach
The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites
Storylines of UK drought based on the 2010–2012 event
Uncertainty estimation with deep learning for rainfall–runoff modeling
Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges
The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL)
Contrasting changes in hydrological processes of the Volta River basin under global warming
A retrospective on hydrological catchment modelling based on half a century with the HBV model
Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters
Rainfall–runoff relationships at event scale in western Mediterranean ephemeral streams
Combined impacts of uncertainty in precipitation and air temperature on simulated mountain system recharge from an integrated hydrologic model
Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping
Remote sensing-aided rainfall–runoff modeling in the tropics of Costa Rica
Drivers of drought-induced shifts in the water balance through a Budyko approach
Regionalization of hydrological model parameters using gradient boosting machine
Aquifer recharge in the Piedmont Alpine zone: historical trends and future scenarios
Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+
How well are we able to close the water budget at the global scale?
Bending of the concentration discharge relationship can inform about in-stream nitrate removal
Quantifying the impacts of land cover change on hydrological responses in the Mahanadi river basin in India
Identification of the contributing area to river discharge during low-flow periods
Simulating sediment discharge at water treatment plants under different land use scenarios using cascade modelling with an expert-based erosion-runoff model and a deep neural network
In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models
Can we use precipitation isotope outputs of isotopic general circulation models to improve hydrological modeling in large mountainous catchments on the Tibetan Plateau?
Small-scale topography explains patterns and dynamics of dissolved organic carbon exports from the riparian zone of a temperate, forested catchment
Revisiting parameter sensitivities in the Variable Infiltration Capacity model
Flood forecasting with machine learning models in an operational framework
Effects of spatial resolution of terrain models on modelled discharge and soil loss in Oaxaca, Mexico
Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models
Numerical daemons of hydrological models are summoned by extreme precipitation
How is Baseflow Index (BFI) impacted by water resource management practices?
Technical note: RAT – a robustness assessment test for calibrated and uncalibrated hydrological models
Reduction of vegetation-accessible water storage capacity after deforestation affects catchment travel time distributions and increases young water fractions in a headwater catchment
Combining split-sample testing and hidden Markov modelling to assess the robustness of hydrological models
Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling
Deep learning rainfall-runoff predictions of extreme events
Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling
Development and evaluation of 0.05° terrestrial water storage estimates using Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and assimilation of GRACE data
Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times
Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective
A new fractal-theory-based criterion for hydrological model calibration
The value of water isotope data on improving process understanding in a glacierized catchment on the Tibetan Plateau
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
Short summary
Short summary
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.
Alban de Lavenne, Vazken Andréassian, Louise Crochemore, Göran Lindström, and Berit Arheimer
Hydrol. Earth Syst. Sci., 26, 2715–2732, https://doi.org/10.5194/hess-26-2715-2022, https://doi.org/10.5194/hess-26-2715-2022, 2022
Short summary
Short summary
A watershed remembers the past to some extent, and this memory influences its behavior. This memory is defined by the ability to store past rainfall for several years. By releasing this water into the river or the atmosphere, it tends to forget. We describe how this memory fades over time in France and Sweden. A few watersheds show a multi-year memory. It increases with the influence of groundwater or dry conditions. After 3 or 4 years, they behave independently of the past.
Antoine Pelletier and Vazken Andréassian
Hydrol. Earth Syst. Sci., 26, 2733–2758, https://doi.org/10.5194/hess-26-2733-2022, https://doi.org/10.5194/hess-26-2733-2022, 2022
Short summary
Short summary
A large part of the water cycle takes place underground. In many places, the soil stores water during the wet periods and can release it all year long, which is particularly visible when the river level is low. Modelling tools that are used to simulate and forecast the behaviour of the river struggle to represent this. We improved an existing model to take underground water into account using measurements of the soil water content. Results allow us make recommendations for model users.
Chaogui Lei, Paul D. Wagner, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 26, 2561–2582, https://doi.org/10.5194/hess-26-2561-2022, https://doi.org/10.5194/hess-26-2561-2022, 2022
Short summary
Short summary
We presented an integrated approach to hydrologic modeling and partial least squares regression quantifying land use change impacts on water and nutrient balance over 3 decades. Results highlight that most variations (70 %–80 %) in water quantity and quality variables are explained by changes in land use class-specific areas and landscape metrics. Arable land influences water quantity and quality the most. The study provides insights on water resources management in rural lowland catchments.
Yang Wang and Hassan A. Karimi
Hydrol. Earth Syst. Sci., 26, 2387–2403, https://doi.org/10.5194/hess-26-2387-2022, https://doi.org/10.5194/hess-26-2387-2022, 2022
Short summary
Short summary
We found that rainfall data with spatial information can improve the model's performance, especially when simulating the future multi-day discharges. We did not observe that regional LSTM as a regional model achieved better results than LSTM as individual model. This conclusion applies to both one-day and multi-day simulations. However, we found that using spatially distributed rainfall data can reduce the difference between individual LSTM and regional LSTM.
Wanshu Nie, Sujay V. Kumar, Kristi R. Arsenault, Christa D. Peters-Lidard, Iliana E. Mladenova, Karim Bergaoui, Abheera Hazra, Benjamin F. Zaitchik, Sarith P. Mahanama, Rachael McDonnell, David M. Mocko, and Mahdi Navari
Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, https://doi.org/10.5194/hess-26-2365-2022, 2022
Short summary
Short summary
The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
Pin Shuai, Xingyuan Chen, Utkarsh Mital, Ethan T. Coon, and Dipankar Dwivedi
Hydrol. Earth Syst. Sci., 26, 2245–2276, https://doi.org/10.5194/hess-26-2245-2022, https://doi.org/10.5194/hess-26-2245-2022, 2022
Short summary
Short summary
Using an integrated watershed model, we compared simulated watershed hydrologic variables driven by three publicly available gridded meteorological forcings (GMFs) at various spatial and temporal resolutions. Our results demonstrated that spatially distributed variables are sensitive to the spatial resolution of the GMF. The temporal resolution of the GMF impacts the dynamics of watershed responses. The choice of GMF depends on the quantity of interest and its spatial and temporal scales.
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
Short summary
Short summary
Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
Christopher Spence, Zhihua He, Kevin R. Shook, Balew A. Mekonnen, John W. Pomeroy, Colin J. Whitfield, and Jared D. Wolfe
Hydrol. Earth Syst. Sci., 26, 1801–1819, https://doi.org/10.5194/hess-26-1801-2022, https://doi.org/10.5194/hess-26-1801-2022, 2022
Short summary
Short summary
We determined how snow and flow in small creeks change with temperature and precipitation in the Canadian Prairie, a region where water resources are often under stress. We tried something new. Every watershed in the region was placed in one of seven groups based on their landscape traits. We selected one of these groups and used its traits to build a model of snow and streamflow. It worked well, and by the 2040s there may be 20 %–40 % less snow and 30 % less streamflow than the 1980s.
Rui Tong, Juraj Parajka, Borbála Széles, Isabella Greimeister-Pfeil, Mariette Vreugdenhil, Jürgen Komma, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 26, 1779–1799, https://doi.org/10.5194/hess-26-1779-2022, https://doi.org/10.5194/hess-26-1779-2022, 2022
Short summary
Short summary
The role and impact of using additional data (other than runoff) for the prediction of daily hydrographs in ungauged basins are not well understood. In this study, we assessed the model performance in terms of runoff, soil moisture, and snow cover predictions with the existing regionalization approaches. Results show that the best transfer methods are the similarity and the kriging approaches. The performance of the transfer methods differs between lowland and alpine catchments.
Wilson C. H. Chan, Theodore G. Shepherd, Katie Facer-Childs, Geoff Darch, and Nigel W. Arnell
Hydrol. Earth Syst. Sci., 26, 1755–1777, https://doi.org/10.5194/hess-26-1755-2022, https://doi.org/10.5194/hess-26-1755-2022, 2022
Short summary
Short summary
We select the 2010–2012 UK drought and investigate an alternative unfolding of the drought from changes to its attributes. We created storylines of drier preconditions, alternative seasonal contributions, a third dry winter, and climate change. Storylines of the 2010–2012 drought show alternative situations that could have resulted in worse conditions than observed. Event-based storylines exploring plausible situations are used that may lead to high impacts and help stress test existing systems.
Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, and Grey Nearing
Hydrol. Earth Syst. Sci., 26, 1673–1693, https://doi.org/10.5194/hess-26-1673-2022, https://doi.org/10.5194/hess-26-1673-2022, 2022
Short summary
Short summary
This contribution evaluates distributional runoff predictions from deep-learning-based approaches. We propose a benchmarking setup and establish four strong baselines. The results show that accurate, precise, and reliable uncertainty estimation can be achieved with deep learning.
Elisa Ragno, Markus Hrachowitz, and Oswaldo Morales-Nápoles
Hydrol. Earth Syst. Sci., 26, 1695–1711, https://doi.org/10.5194/hess-26-1695-2022, https://doi.org/10.5194/hess-26-1695-2022, 2022
Short summary
Short summary
We explore the ability of non-parametric Bayesian networks to reproduce maximum daily discharge in a given month in a catchment when the remaining hydro-meteorological and catchment attributes are known. We show that a saturated network evaluated in an individual catchment can reproduce statistical characteristics of discharge in about ~ 40 % of the cases, while challenges remain when a saturated network considering all the catchments together is evaluated.
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. Discuss., https://doi.org/10.5194/hess-2022-113, https://doi.org/10.5194/hess-2022-113, 2022
Revised manuscript accepted for HESS
Short summary
Short summary
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 models setup 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.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
Short summary
Short summary
Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Jan Seibert and Sten Bergström
Hydrol. Earth Syst. Sci., 26, 1371–1388, https://doi.org/10.5194/hess-26-1371-2022, https://doi.org/10.5194/hess-26-1371-2022, 2022
Short summary
Short summary
Hydrological catchment models are commonly used as the basis for water resource management planning. The HBV model, which is a typical example of such a model, was first applied about 50 years ago in Sweden. We describe and reflect on the model development and applications. The aim is to provide an understanding of the background of model development and a basis for addressing the balance between model complexity and data availability that will continue to face hydrologists in the future.
Laurène J. E. Bouaziz, Emma E. Aalbers, Albrecht H. Weerts, Mark Hegnauer, Hendrik Buiteveld, Rita Lammersen, Jasper Stam, Eric Sprokkereef, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 26, 1295–1318, https://doi.org/10.5194/hess-26-1295-2022, https://doi.org/10.5194/hess-26-1295-2022, 2022
Short summary
Short summary
Assuming stationarity of hydrological systems is no longer appropriate when considering land use and climate change. We tested the sensitivity of hydrological predictions to changes in model parameters that reflect ecosystem adaptation to climate and potential land use change. We estimated a 34 % increase in the root zone storage parameter under +2 K global warming, resulting in up to 15 % less streamflow in autumn, due to 14 % higher summer evaporation, compared to a stationary system.
Roberto Serrano-Notivoli, Alberto Martínez-Salvador, Rafael García-Lorenzo, David Espín-Sánchez, and Carmelo Conesa-García
Hydrol. Earth Syst. Sci., 26, 1243–1260, https://doi.org/10.5194/hess-26-1243-2022, https://doi.org/10.5194/hess-26-1243-2022, 2022
Short summary
Short summary
Ephemeral streams in the western Mediterranean area are driven by the duration, magnitude, and intensity of rainfall events (REs). A detailed statistical analysis showed that the average RE (1.2 d and 1.5 mm) is not enough to generate new flow, which is only guaranteed by events occurring in return periods from 2 to > 50 years. REs explain near to 75 % of new flow, meaning that terrain and lithological characteristics play a fundamental role.
Adam P. Schreiner-McGraw and Hoori Ajami
Hydrol. Earth Syst. Sci., 26, 1145–1164, https://doi.org/10.5194/hess-26-1145-2022, https://doi.org/10.5194/hess-26-1145-2022, 2022
Short summary
Short summary
We assess the impact of uncertainty in measurements of precipitation and air temperature on simulated groundwater processes in a mountainous watershed. We illustrate the role of topography in controlling how uncertainty in the input datasets propagates through the soil and into the groundwater. While the focus of previous investigations has been on the impact of precipitation uncertainty, we show that air temperature uncertainty is equally important in controlling the groundwater recharge.
Antonio Annis, Fernando Nardi, and Fabio Castelli
Hydrol. Earth Syst. Sci., 26, 1019–1041, https://doi.org/10.5194/hess-26-1019-2022, https://doi.org/10.5194/hess-26-1019-2022, 2022
Short summary
Short summary
In this work, we proposed a multi-source data assimilation framework for near-real-time flood mapping. We used a quasi-2D hydraulic model to update model states by injecting both stage gauge observations and satellite-derived flood extents. Results showed improvements in terms of water level prediction and reduction of flood extent uncertainty when assimilating both stage gauges and satellite images with respect to the disjoint assimilation of both observations.
Saúl Arciniega-Esparza, Christian Birkel, Andrés Chavarría-Palma, Berit Arheimer, and José Agustín Breña-Naranjo
Hydrol. Earth Syst. Sci., 26, 975–999, https://doi.org/10.5194/hess-26-975-2022, https://doi.org/10.5194/hess-26-975-2022, 2022
Short summary
Short summary
In the humid tropics, a notoriously data-scarce region, we need to find alternatives in order to reasonably apply hydrological models. Here, we tested remotely sensed rainfall data in order to drive a model for Costa Rica, and we evaluated the simulations against evapotranspiration satellite products. We found that our model was able to reasonably simulate the water balance and streamflow dynamics of over 600 catchments where the satellite data helped to reduce the model uncertainties.
Tessa Maurer, Francesco Avanzi, Steven D. Glaser, and Roger C. Bales
Hydrol. Earth Syst. Sci., 26, 589–607, https://doi.org/10.5194/hess-26-589-2022, https://doi.org/10.5194/hess-26-589-2022, 2022
Short summary
Short summary
Predicting how much water will end up in rivers is more difficult during droughts because the relationship between precipitation and streamflow can change in unexpected ways. We differentiate between changes that are predictable based on the weather patterns and those harder to predict because they depend on the land and vegetation of a particular region. This work helps clarify why models are less accurate during droughts and helps predict how much water will be available for human use.
Zhihong Song, Jun Xia, Gangsheng Wang, Dunxian She, Chen Hu, and Si Hong
Hydrol. Earth Syst. Sci., 26, 505–524, https://doi.org/10.5194/hess-26-505-2022, https://doi.org/10.5194/hess-26-505-2022, 2022
Short summary
Short summary
We performed a machine learning approach to regionalize the parameters of a China-wide hydrological model by linking six model parameters with 10 physical attributes (terrain and soil properties). The results show the superiority of machine-learning-based regionalization approach compared with the traditional linear regression method in ungauged regions. We also obtained the relative importance of attributes against model parameters.
Elisa Brussolo, Elisa Palazzi, Jost von Hardenberg, Giulio Masetti, Gianna Vivaldo, Maurizio Previati, Davide Canone, Davide Gisolo, Ivan Bevilacqua, Antonello Provenzale, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 26, 407–427, https://doi.org/10.5194/hess-26-407-2022, https://doi.org/10.5194/hess-26-407-2022, 2022
Short summary
Short summary
In this study, we evaluate the past, present and future quantity of groundwater potentially available for drinking purposes in the metropolitan area of Turin, north-western Italy. In order to effectively manage water resources, a knowledge of the water cycle components is necessary, including precipitation, evapotranspiration and subsurface reservoirs. All these components have been carefully evaluated in this paper, using observational datasets and modelling approaches.
Albert Nkwasa, Celray James Chawanda, Jonas Jägermeyr, and Ann van Griensven
Hydrol. Earth Syst. Sci., 26, 71–89, https://doi.org/10.5194/hess-26-71-2022, https://doi.org/10.5194/hess-26-71-2022, 2022
Short summary
Short summary
We present an approach on how to incorporate crop phenology in a regional hydrological model using decision tables and global datasets of rainfed and irrigated cropland with the associated cropping calendar and management practices. Results indicate improved temporal patterns of leaf area index (LAI) and evapotranspiration (ET) simulations in comparison with remote sensing data. In addition, the improvement of the cropping season also helps to improve soil erosion estimates in cultivated areas.
Fanny Lehmann, Bramha Dutt Vishwakarma, and Jonathan Bamber
Hydrol. Earth Syst. Sci., 26, 35–54, https://doi.org/10.5194/hess-26-35-2022, https://doi.org/10.5194/hess-26-35-2022, 2022
Short summary
Short summary
Many data sources are available to evaluate components of the water cycle (precipitation, evapotranspiration, runoff, and terrestrial water storage). Despite this variety, it remains unclear how different combinations of datasets satisfy the conservation of mass. We conducted the most comprehensive analysis of water budget closure on a global scale to date. Our results can serve as a basis to select appropriate datasets for regional hydrological studies.
Joni Dehaspe, Fanny Sarrazin, Rohini Kumar, Jan H. Fleckenstein, and Andreas Musolff
Hydrol. Earth Syst. Sci., 25, 6437–6463, https://doi.org/10.5194/hess-25-6437-2021, https://doi.org/10.5194/hess-25-6437-2021, 2021
Short summary
Short summary
Increased nitrate concentrations in surface waters can compromise river ecosystem health. As riverine nitrate uptake is hard to measure, we explore how low-frequency nitrate concentration and discharge observations (that are widely available) can help to identify (in)efficient uptake in river networks. We find that channel geometry and water velocity rather than the biological uptake capacity dominate the nitrate-discharge pattern at the outlet. The former can be used to predict uptake.
Shaini Naha, Miguel Angel Rico-Ramirez, and Rafael Rosolem
Hydrol. Earth Syst. Sci., 25, 6339–6357, https://doi.org/10.5194/hess-25-6339-2021, https://doi.org/10.5194/hess-25-6339-2021, 2021
Short summary
Short summary
Rapid growth in population in developing countries leads to an increase in food demand, and as a consequence, percentages of land are being converted to cropland which alters river flow processes. This study describes how the hydrology of a flood-prone river basin in India would respond to the current and future changes in land cover. Our findings indicate that the recurrent flood events occurring in the basin might be influenced by these changes in land cover at the catchment scale.
Maxime Gillet, Corinne Le Gal La Salle, Pierre Alain Ayral, Somar Khaska, Philippe Martin, and Patrick Verdoux
Hydrol. Earth Syst. Sci., 25, 6261–6281, https://doi.org/10.5194/hess-25-6261-2021, https://doi.org/10.5194/hess-25-6261-2021, 2021
Short summary
Short summary
This paper aims at identifying the key reservoirs sustaining river low flow during dry summer. The reservoirs are discriminated based on the geological nature of the formations and the geochemical signature of groundwater. Results show the increasing importance to low-flow support of a specific reservoir, showing only a limited outcrop area and becoming preponderant in the heart of the dry season. This finding will contribute to improving the protective measures for preserving low flows.
Edouard Patault, Valentin Landemaine, Jérôme Ledun, Arnaud Soulignac, Matthieu Fournier, Jean-François Ouvry, Olivier Cerdan, and Benoit Laignel
Hydrol. Earth Syst. Sci., 25, 6223–6238, https://doi.org/10.5194/hess-25-6223-2021, https://doi.org/10.5194/hess-25-6223-2021, 2021
Short summary
Short summary
The goal of this study was to assess the sediment discharge variability at a water treatment plant (Normandy, France) according to multiple realistic land use scenarios. We developed a new cascade modelling approach and simulations suggested that coupling eco-engineering and best farming practices can significantly reduce the sediment discharge (up to 80 %).
Ather Abbas, Sangsoo Baek, Norbert Silvera, Bounsamay Soulileuth, Yakov Pachepsky, Olivier Ribolzi, Laurie Boithias, and Kyung Hwa Cho
Hydrol. Earth Syst. Sci., 25, 6185–6202, https://doi.org/10.5194/hess-25-6185-2021, https://doi.org/10.5194/hess-25-6185-2021, 2021
Short summary
Short summary
Correct estimation of fecal indicator bacteria in surface waters is critical for public health. Process-driven models and recently data-driven models have been applied for water quality modeling; however, a systematic comparison for simulation of E. coli is missing in the literature. We compared performance of process-driven (HSPF) and data-driven (LSTM) models for E. coli simulation. We show that LSTM can be an alternative to process-driven models for estimation of E. coli in surface waters.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 25, 6151–6172, https://doi.org/10.5194/hess-25-6151-2021, https://doi.org/10.5194/hess-25-6151-2021, 2021
Short summary
Short summary
Hydrological modeling has large problems of uncertainty in cold regions. Tracer-aided hydrological models are increasingly used to reduce uncertainty and refine the parameterizations of hydrological processes, with limited application in large basins due to the unavailability of spatially distributed precipitation isotopes. This study explored the utility of isotopic general circulation models in driving a tracer-aided hydrological model in a large basin on the Tibetan Plateau.
Benedikt J. Werner, Oliver J. Lechtenfeld, Andreas Musolff, Gerrit H. de Rooij, Jie Yang, Ralf Gründling, Ulrike Werban, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 25, 6067–6086, https://doi.org/10.5194/hess-25-6067-2021, https://doi.org/10.5194/hess-25-6067-2021, 2021
Short summary
Short summary
Export of dissolved organic carbon (DOC) from riparian zones (RZs) is an important yet poorly understood component of the catchment carbon budget. This study chemically and spatially classifies DOC source zones within a RZ of a small catchment to assess DOC export patterns. Results highlight that DOC export from only a small fraction of the RZ with distinct DOC composition dominates overall DOC export. The application of a spatial, topographic proxy can be used to improve DOC export models.
Ulises Sepúlveda, Pablo A. Mendoza, Naoki Mizukami, and Andrew J. Newman
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-550, https://doi.org/10.5194/hess-2021-550, 2021
Revised manuscript accepted for HESS
Short summary
Short summary
This paper characterizes parameter sensitivities across > 5,500 grid cells for a commonly used macro-scale 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.
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. Discuss., https://doi.org/10.5194/hess-2021-554, https://doi.org/10.5194/hess-2021-554, 2021
Revised manuscript accepted for HESS
Short summary
Short summary
Early flood warnings are one of the most effective tools to save lives and goods. Machine learning 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 machine learning models for forecasting river stage and flood inundation maps, and discusses the models’ performances. In 2021 more than 100M flood alerts were sent to people near rivers over an area of 287,000 km2.
Sergio Naranjo, Francelino A. Rodrigues Jr., Georg Cadisch, Santiago Lopez-Ridaura, Mariela Fuentes Ponce, and Carsten Marohn
Hydrol. Earth Syst. Sci., 25, 5561–5588, https://doi.org/10.5194/hess-25-5561-2021, https://doi.org/10.5194/hess-25-5561-2021, 2021
Short summary
Short summary
We integrate a spatially explicit soil erosion model with plot- and watershed-scale characterization and high-resolution drone imagery to assess the effect of spatial resolution digital terrain models (DTMs) on discharge and soil loss. Results showed reduction in slope due to resampling down of DTM. Higher resolution translates to higher slope, denser fluvial system, and extremer values of soil loss, reducing concentration time and increasing soil loss at the outlet. The best resolution was 4 m.
Thomas Lees, Marcus Buechel, Bailey Anderson, Louise Slater, Steven Reece, Gemma Coxon, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, https://doi.org/10.5194/hess-25-5517-2021, 2021
Short summary
Short summary
We used deep learning (DL) models to simulate the amount of water moving through a river channel (discharge) based on the rainfall, temperature and potential evaporation in the previous days. We tested the DL models on catchments across Great Britain finding that the model can accurately simulate hydrological systems across a variety of catchment conditions. Ultimately, the model struggled most in areas where there is chalky bedrock and where human influence on the catchment is large.
Peter T. La Follette, Adriaan J. Teuling, Nans Addor, Martyn Clark, Koen Jansen, and Lieke A. Melsen
Hydrol. Earth Syst. Sci., 25, 5425–5446, https://doi.org/10.5194/hess-25-5425-2021, https://doi.org/10.5194/hess-25-5425-2021, 2021
Short summary
Short summary
Hydrological models are useful tools that allow us to predict distributions and movement of water. A variety of numerical methods are used by these models. We demonstrate which numerical methods yield large errors when subject to extreme precipitation. As the climate is changing such that extreme precipitation is more common, we find that some numerical methods are better suited for use in hydrological models. Also, we find that many current hydrological models use relatively inaccurate methods.
John P. Bloomfield, Mengyi Gong, Benjamin P. Marchant, Gemma Coxon, and Nans Addor
Hydrol. Earth Syst. Sci., 25, 5355–5379, https://doi.org/10.5194/hess-25-5355-2021, https://doi.org/10.5194/hess-25-5355-2021, 2021
Short summary
Short summary
Groundwater provides flow, known as baseflow, to surface streams and rivers. It is important as it sustains the flow of many rivers at times of water stress. However, it may be affected by water management practices. Statistical models have been used to show that abstraction of groundwater may influence baseflow. Consequently, it is recommended that information on groundwater abstraction is included in future assessments and predictions of baseflow.
Pierre Nicolle, Vazken Andréassian, Paul Royer-Gaspard, Charles Perrin, Guillaume Thirel, Laurent Coron, and Léonard Santos
Hydrol. Earth Syst. Sci., 25, 5013–5027, https://doi.org/10.5194/hess-25-5013-2021, https://doi.org/10.5194/hess-25-5013-2021, 2021
Short summary
Short summary
In this note, a new method (RAT) is proposed to assess the robustness of hydrological models. The RAT method is particularly interesting because it does not require multiple calibrations (it is therefore applicable to uncalibrated models), and it can be used to determine whether a hydrological model may be safely used for climate change impact studies. Success at the robustness assessment test is a necessary (but not sufficient) condition of model robustness.
Markus Hrachowitz, Michael Stockinger, Miriam Coenders-Gerrits, Ruud van der Ent, Heye Bogena, Andreas Lücke, and Christine Stumpp
Hydrol. Earth Syst. Sci., 25, 4887–4915, https://doi.org/10.5194/hess-25-4887-2021, https://doi.org/10.5194/hess-25-4887-2021, 2021
Short summary
Short summary
Deforestation affects how catchments store and release water. Here we found that deforestation in the study catchment led to a 20 % increase in mean runoff, while reducing the vegetation-accessible water storage from about 258 to 101 mm. As a consequence, fractions of young water in the stream increased by up to 25 % during wet periods. This implies that water and solutes are more rapidly routed to the stream, which can, after contamination, lead to increased contaminant peak concentrations.
Etienne Guilpart, Vahid Espanmanesh, Amaury Tilmant, and François Anctil
Hydrol. Earth Syst. Sci., 25, 4611–4629, https://doi.org/10.5194/hess-25-4611-2021, https://doi.org/10.5194/hess-25-4611-2021, 2021
Short summary
Short summary
The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. We illustrate the method on the Senegal River.
Sebastian A. Krogh, Lucia Scaff, Gary Sterle, James Kirchner, Beatrice Gordon, and Adrian Harpold
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-437, https://doi.org/10.5194/hess-2021-437, 2021
Revised manuscript accepted for HESS
Short summary
Short summary
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 than in warmer places, which is then contrasted with land-surface simulations.
Jonathan Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shelev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, and Grey S. Nearing
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-423, https://doi.org/10.5194/hess-2021-423, 2021
Revised manuscript accepted for HESS
Short summary
Short summary
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 traditional models, even when extreme events are not included in the training set.
Herath Mudiyanselage Viraj Vidura Herath, Jayashree Chadalawada, and Vladan Babovic
Hydrol. Earth Syst. Sci., 25, 4373–4401, https://doi.org/10.5194/hess-25-4373-2021, https://doi.org/10.5194/hess-25-4373-2021, 2021
Short summary
Short summary
Existing hydrological knowledge has been integrated with genetic programming based on a machine learning algorithm (MIKA-SHA) to induce readily interpretable distributed rainfall–runoff models. At present, the model building components of two flexible modelling frameworks (FUSE and SUPERFLEX) represent the elements of hydrological knowledge. The proposed toolkit captures spatial variabilities and automatically induces semi-distributed rainfall–runoff models without any explicit user selections.
Natthachet Tangdamrongsub, Michael F. Jasinski, and Peter J. Shellito
Hydrol. Earth Syst. Sci., 25, 4185–4208, https://doi.org/10.5194/hess-25-4185-2021, https://doi.org/10.5194/hess-25-4185-2021, 2021
Short summary
Short summary
Accurate estimation of terrestrial water storage (TWS) is essential for reliable water resource assessments. TWS can be estimated from the Community Atmosphere–Biosphere Land Exchange model (CABLE), but the resolution is limited to 0.5°. We reconfigure CABLE to improve TWS spatial details from 0.5° to 0.05°. GRACE satellite data are assimilated into CABLE to improve TWS accuracy. Our workflow relies only on publicly accessible data, allowing reproduction of 0.05° TWS in any region.
Seán Donegan, Conor Murphy, Shaun Harrigan, Ciaran Broderick, Dáire Foran Quinn, Saeed Golian, Jeff Knight, Tom Matthews, Christel Prudhomme, Adam A. Scaife, Nicky Stringer, and Robert L. Wilby
Hydrol. Earth Syst. Sci., 25, 4159–4183, https://doi.org/10.5194/hess-25-4159-2021, https://doi.org/10.5194/hess-25-4159-2021, 2021
Short summary
Short summary
We benchmarked the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46 Irish catchments. We found that ESP is skilful in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. We also conditioned ESP with the winter North Atlantic Oscillation and show that improvements in forecast skill, reliability, and discrimination are possible.
Paul C. Astagneau, Guillaume Thirel, Olivier Delaigue, Joseph H. A. Guillaume, Juraj Parajka, Claudia C. Brauer, Alberto Viglione, Wouter Buytaert, and Keith J. Beven
Hydrol. Earth Syst. Sci., 25, 3937–3973, https://doi.org/10.5194/hess-25-3937-2021, https://doi.org/10.5194/hess-25-3937-2021, 2021
Short summary
Short summary
The R programming language has become an important tool for many applications in hydrology. In this study, we provide an analysis of some of the R tools providing hydrological models. In total, two aspects are uniformly investigated, namely the conceptualisation of the models and the practicality of their implementation for end-users. These comparisons aim at easing the choice of R tools for users and at improving their usability for hydrology modelling to support more transferable research.
Zhixu Bai, Yao Wu, Di Ma, and Yue-Ping Xu
Hydrol. Earth Syst. Sci., 25, 3675–3690, https://doi.org/10.5194/hess-25-3675-2021, https://doi.org/10.5194/hess-25-3675-2021, 2021
Short summary
Short summary
To test our hypothesis that the fractal dimensions of streamflow series can be used to improve the calibration of hydrological models, we designed the E–RD efficiency ratio of fractal dimensions strategy and examined its usability in the calibration of lumped models. The results reveal that, in most aspects, introducing RD into model calibration makes the simulation of streamflow components more reasonable. Also, pursuing a better RD during calibration leads to only a minor decrease in E.
Yi Nan, Lide Tian, Zhihua He, Fuqiang Tian, and Lili Shao
Hydrol. Earth Syst. Sci., 25, 3653–3673, https://doi.org/10.5194/hess-25-3653-2021, https://doi.org/10.5194/hess-25-3653-2021, 2021
Short summary
Short summary
This study integrated a water isotope module into the hydrological model THREW. The isotope-aided model was subsequently applied for process understanding in the glacierized watershed of Karuxung river on the Tibetan Plateau. The model was used to quantify the contribution of runoff component and estimate the water travel time in the catchment. Model uncertainties were significantly constrained by using additional isotopic data, improving the process understanding in the catchment.
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...
Special issue