Articles | Volume 28, issue 3
https://doi.org/10.5194/hess-28-545-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-545-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Feb 2024
Research article |
| 08 Feb 2024
| 08 Feb 2024
Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records
Michael J. Vlah
CORRESPONDING AUTHOR
Department of Biology, Duke University, Durham, USA
Matthew R. V. Ross
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, USA
Spencer Rhea
Department of Biology, Duke University, Durham, USA
Emily S. Bernhardt
Department of Biology, Duke University, Durham, USA
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration
Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings
Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool
A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+
On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions
Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations
Recent ground thermo-hydrological changes in a southern Tibetan endorheic catchment and implications for lake level changes
Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling
Modelling flood frequency and magnitude in a glacially conditioned, heterogeneous landscape: testing the importance of land cover and land use
Direct integration of reservoirs' operations in a hydrological model for streamflow estimation: coupling a CLSTM model with MOHID-Land
Towards Interpretable LSTM-based Modelling of Hydrological Systems
Modelling the regional sensitivity of snowmelt, soil moisture, and streamflow generation to climate over the Canadian Prairies using a basin classification approach
To what extent does river routing matter in hydrological modeling?
Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins
An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume
airGRteaching: an open-source tool for teaching hydrological modeling with R
Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models
Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation
Changes in Mediterranean flood processes and seasonality
On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow
Glaciers determine the sensitivity of hydrological processes to perturbed climate in a large mountainous basin on the Tibetan Plateau
Technical Note: Testing the Connection Between Hillslope Scale Runoff Fluctuations and Streamflow Hydrographs at the Outlet of Large River Basins
What controls the tail behaviour of flood series: Rainfall or runoff generation?
Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model
Can the combining of wetlands with reservoir operation reduce the risk of future floods and droughts?
Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado
When best is the enemy of good – critical evaluation of performance criteria in hydrological models
The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
Producing reliable hydrologic scenarios from raw climate model outputs without resorting to meteorological observations
Using normalised difference infrared index patterns to constrain semi-distributed rainfall–runoff models in tropical nested catchments
Deep learning for monthly rainfall-runoff modelling: a comparison with classical rainfall-runoff modelling across Australia
Revisiting the hydrological basis of the Budyko framework with the principle of hydrologically similar groups
Reconstructing five decades of sediment export from two glacierized high-alpine catchments in Tyrol, Austria, using nonparametric regression
Water and energy budgets over hydrological basins on short and long timescales
Hydrological response to climate change and human activities in the Three-River Source Region
Incorporating experimentally derived streamflow contributions into model parameterization to improve discharge prediction
Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data
River hydraulic modeling with ICESat-2 land and water surface elevation
Seasonal prediction of end-of-dry season watershed behavior in a highly interconnected alluvial watershed, northern California
Hydrological modeling using the Soil and Water Assessment Tool in urban and peri-urban environments: the case of Kifisos experimental subbasin (Athens, Greece)
Empirical stream thermal sensitivities cluster on the landscape according to geology and climate
Monetizing the role of water in sustaining watershed ecosystem services using a fully integrated subsurface–surface water model
Technical note: How physically based is hydrograph separation by recursive digital filtering?
A comprehensive open-source course for teaching applied hydrological modelling in Central Asia
Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment
Technical note: Extending the SWAT model to transport chemicals through tile and groundwater flow
Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China
Disentangling scatter in long-term concentration–discharge relationships: the role of event types
Simulating the hydrological impacts of land use conversion from annual crop to perennial forage in the Canadian Prairies using the Cold Regions Hydrological Modelling platform
How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models?
Sungwook Wi and Scott Steinschneider
Hydrol. Earth Syst. Sci., 28, 479–503, https://doi.org/10.5194/hess-28-479-2024, https://doi.org/10.5194/hess-28-479-2024, 2024
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We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
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How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao
Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, https://doi.org/10.5194/hess-28-21-2024, 2024
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Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, and Yuxuan Wang
Hydrol. Earth Syst. Sci., 27, 4529–4550, https://doi.org/10.5194/hess-27-4529-2023, https://doi.org/10.5194/hess-27-4529-2023, 2023
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Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Léo C. P. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, Joel Fiddes, Yanbin Lei, Philip Kraaijenbrink, Tamara Mathys, Moritz Langer, Simon Allen, and Walter W. Immerzeel
Hydrol. Earth Syst. Sci., 27, 4409–4436, https://doi.org/10.5194/hess-27-4409-2023, https://doi.org/10.5194/hess-27-4409-2023, 2023
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Across the Tibetan Plateau, many large lakes have been changing level during the last decades as a response to climate change. In high-mountain environments, water fluxes from the land to the lakes are linked to the ground temperature of the land and to the energy fluxes between the ground and the atmosphere, which are modified by climate change. With a numerical model, we test how these water and energy fluxes have changed over the last decades and how they influence the lake level variations.
Diego Araya, Pablo A. Mendoza, Eduardo Muñoz-Castro, and James McPhee
Hydrol. Earth Syst. Sci., 27, 4385–4408, https://doi.org/10.5194/hess-27-4385-2023, https://doi.org/10.5194/hess-27-4385-2023, 2023
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Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts, which are critical for decision-making. Such systems rely on hydrology models, which contain parameters that are typically estimated using a target performance metric (i.e., objective function). This study explores the effects of this decision across mountainous basins in Chile, illustrating tradeoffs between seasonal forecast quality and the models' capability to simulate streamflow characteristics.
Pamela E. Tetford and Joseph R. Desloges
Hydrol. Earth Syst. Sci., 27, 3977–3998, https://doi.org/10.5194/hess-27-3977-2023, https://doi.org/10.5194/hess-27-3977-2023, 2023
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An efficient regional flood frequency model relates drainage area to discharge, with a major assumption of similar basin conditions. In a landscape with variable glacial deposits and land use, we characterize varying hydrological function using 28 explanatory variables. We demonstrate that (1) a heterogeneous landscape requires objective model selection criteria to optimize the fit of flow data, and (2) incorporating land use as a predictor variable improves the drainage area to discharge model.
Ana Ramos Oliveira, Tiago Brito Ramos, Lígia Pinto, and Ramiro Neves
Hydrol. Earth Syst. Sci., 27, 3875–3893, https://doi.org/10.5194/hess-27-3875-2023, https://doi.org/10.5194/hess-27-3875-2023, 2023
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This paper intends to demonstrate the adequacy of a hybrid solution to overcome the difficulties related to the incorporation of human behavior when modeling hydrological processes. Two models were implemented, one to estimate the outflow of a reservoir and the other to simulate the hydrological processes of the watershed. With both models feeding each other, results show that the proposed approach significantly improved the streamflow estimation downstream of the reservoir.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-252, https://doi.org/10.5194/hess-2023-252, 2023
Revised manuscript accepted for HESS
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Long Short-Term Memory (LSTM) is a widely used machine learning model in hydrology. However, it is difficult to extract knowledge from it. We propose HydroLSTM which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment.Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Zhihua He, Kevin Shook, Christopher Spence, John W. Pomeroy, and Colin Whitfield
Hydrol. Earth Syst. Sci., 27, 3525–3546, https://doi.org/10.5194/hess-27-3525-2023, https://doi.org/10.5194/hess-27-3525-2023, 2023
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This study evaluated the impacts of climate change on snowmelt, soil moisture, and streamflow over the Canadian Prairies. The entire prairie region was divided into seven basin types. We found strong variations of hydrological sensitivity to precipitation and temperature changes in different land covers and basins, which suggests that different water management and adaptation methods are needed to address enhanced water stress due to expected climate change in different regions of the prairies.
Nicolás Cortés-Salazar, Nicolás Vásquez, Naoki Mizukami, Pablo A. Mendoza, and Ximena Vargas
Hydrol. Earth Syst. Sci., 27, 3505–3524, https://doi.org/10.5194/hess-27-3505-2023, https://doi.org/10.5194/hess-27-3505-2023, 2023
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This paper shows how important river models can be for water resource applications that involve hydrological models and, in particular, parameter calibration. To this end, we conduct numerical experiments in a pilot basin using a combination of hydrologic model simulations obtained from a large sample of parameter sets and different routing methods. We find that routing can affect streamflow simulations, even at monthly time steps; the choice of parameters; and relevant streamflow metrics.
Dung Trung Vu, Thanh Duc Dang, Francesca Pianosi, and Stefano Galelli
Hydrol. Earth Syst. Sci., 27, 3485–3504, https://doi.org/10.5194/hess-27-3485-2023, https://doi.org/10.5194/hess-27-3485-2023, 2023
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The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.
Francesco Fatone, Bartosz Szeląg, Przemysław Kowal, Arthur McGarity, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Nicolas Caradot
Hydrol. Earth Syst. Sci., 27, 3329–3349, https://doi.org/10.5194/hess-27-3329-2023, https://doi.org/10.5194/hess-27-3329-2023, 2023
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A novel methodology for the development of a stormwater network performance simulator including advanced risk assessment was proposed. The applied tool enables the analysis of the influence of spatial variability in catchment and stormwater network characteristics on the relation between (SWMM) model parameters and specific flood volume, as an alternative approach to mechanistic models. The proposed method can be used at the stage of catchment model development and spatial planning management.
Olivier Delaigue, Pierre Brigode, Guillaume Thirel, and Laurent Coron
Hydrol. Earth Syst. Sci., 27, 3293–3327, https://doi.org/10.5194/hess-27-3293-2023, https://doi.org/10.5194/hess-27-3293-2023, 2023
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Teaching hydrological modeling is an important, but difficult, matter. It requires appropriate tools and teaching material. In this article, we present the airGRteaching package, which is an open-source software tool relying on widely used hydrological models. This tool proposes an interface and numerous hydrological modeling exercises representing a wide range of hydrological applications. We show how this tool can be applied to simple but real-life cases.
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, https://doi.org/10.5194/hess-27-3083-2023, 2023
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This study shows that previously reported underestimations of water ages are most likely not due to the use of seasonally variable tracers. Rather, these underestimations can be largely attributed to the choices of model approaches which rely on assumptions not frequently met in catchment hydrology. We therefore strongly advocate avoiding the use of this model type in combination with seasonally variable tracers and instead adopting StorAge Selection (SAS)-based or comparable model formulations.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci., 27, 2989–3004, https://doi.org/10.5194/hess-27-2989-2023, https://doi.org/10.5194/hess-27-2989-2023, 2023
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We analyzed the uncertainty of the water transit time distribution (TTD) arising from model input (interpolated tracer data) and structure (StorAge Selection, SAS, functions). We found that uncertainty was mainly associated with temporal interpolation, choice of SAS function, nonspatial interpolation, and low-flow conditions. It is important to characterize the specific uncertainty sources and their combined effects on TTD, as this has relevant implications for both water quantity and quality.
Yves Tramblay, Patrick Arnaud, Guillaume Artigue, Michel Lang, Emmanuel Paquet, Luc Neppel, and Eric Sauquet
Hydrol. Earth Syst. Sci., 27, 2973–2987, https://doi.org/10.5194/hess-27-2973-2023, https://doi.org/10.5194/hess-27-2973-2023, 2023
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Mediterranean floods are causing major damage, and recent studies have shown that, despite the increase in intense rainfall, there has been no increase in river floods. This study reveals that the seasonality of floods changed in the Mediterranean Basin during 1959–2021. There was also an increased frequency of floods linked to short episodes of intense rain, associated with a decrease in soil moisture. These changes need to be taken into consideration to adapt flood warning systems.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-143, https://doi.org/10.5194/hess-2023-143, 2023
Revised manuscript accepted for HESS
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Calibrating hydrological models with multiple objective functions enhances model robustness. By integrating spatially distributed snow information into the calibration process, the overall performance of the model can be enhanced without compromising the model outputs. In this study, HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (SPAtial Efficiency metric) alongside NSE and RMSE, with the aim of identifying the optimal calibration strategy.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-182, https://doi.org/10.5194/hess-2023-182, 2023
Revised manuscript accepted for HESS
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This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze the hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Ricardo Mantilla, Morgan Fonley, and Nicolas Velasquez
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-187, https://doi.org/10.5194/hess-2023-187, 2023
Revised manuscript accepted for HESS
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle, however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator that a correct description of the small-scale runoff processes.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-186, https://doi.org/10.5194/hess-2023-186, 2023
Revised manuscript accepted for HESS
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
EGUsphere, https://doi.org/10.5194/egusphere-2023-1322, https://doi.org/10.5194/egusphere-2023-1322, 2023
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging and observed data on temporary rivers is scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Yanfeng Wu, Jingxuan Sun, Boting Hu, Y. Jun Xu, Alain N. Rousseau, and Guangxin Zhang
Hydrol. Earth Syst. Sci., 27, 2725–2745, https://doi.org/10.5194/hess-27-2725-2023, https://doi.org/10.5194/hess-27-2725-2023, 2023
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Reservoirs and wetlands are important regulators of watershed hydrology, which should be considered when projecting floods and droughts. We first coupled wetlands and reservoir operations into a semi-spatially-explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. We found that, overall, the risk of future floods and droughts will increase further even under the combined influence of reservoirs and wetlands.
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
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We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
Guillaume Cinkus, Naomi Mazzilli, Hervé Jourde, Andreas Wunsch, Tanja Liesch, Nataša Ravbar, Zhao Chen, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 27, 2397–2411, https://doi.org/10.5194/hess-27-2397-2023, https://doi.org/10.5194/hess-27-2397-2023, 2023
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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, when assessing a simulation, concurrent over- and underestimation of discharge can lead to an overall higher criterion score without an associated increase in model relevance. We suggest that one carefully choose performance criteria and use scaling factors.
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 27, 2357–2373, https://doi.org/10.5194/hess-27-2357-2023, https://doi.org/10.5194/hess-27-2357-2023, 2023
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Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.
Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, and François Anctil
Hydrol. Earth Syst. Sci., 27, 2375–2395, https://doi.org/10.5194/hess-27-2375-2023, https://doi.org/10.5194/hess-27-2375-2023, 2023
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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 that 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.
Nutchanart Sriwongsitanon, Wasana Jandang, James Williams, Thienchart Suwawong, Ekkarin Maekan, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 27, 2149–2171, https://doi.org/10.5194/hess-27-2149-2023, https://doi.org/10.5194/hess-27-2149-2023, 2023
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We developed predictive semi-distributed rainfall–runoff models for nested sub-catchments in the upper Ping basin, which yielded better or similar performance compared to calibrated lumped models. The normalised difference infrared index proves to be an effective proxy for distributed root zone moisture capacity over sub-catchments and is well correlated with the percentage of evergreen forest. In validation, soil moisture simulations appeared to be highly correlated with the soil wetness index.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-124, https://doi.org/10.5194/hess-2023-124, 2023
Revised manuscript accepted for HESS
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Are machine learning models able to produce reliable rainfall-runoff predictions and add enough benefits that justify the effort to implement these methods? This study covers a large set of Australian catchments (almost 500) comparing deep learning and traditional model results. The deep learning model matched or exceeded conceptual model performance for more than two-thirds of the study catchments, indicating the general viability of these models in a variety of catchments conditions.
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.
Claire Marie Kouba and Thomas Harter
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-41, https://doi.org/10.5194/hess-2023-41, 2023
Revised manuscript accepted for HESS
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In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, about 5 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.
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.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-428, https://doi.org/10.5194/hess-2022-428, 2023
Revised manuscript accepted for HESS
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This study used the relationship between river water and air temperature to understand processes causing stream warming and predict how streams might respond to future climate warming. We found that the air-water relationship was diverse across sites and controlled largely by geology and snowmelt. Our findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate insensitive areas of the basin that should be preserved and protected.
Tariq Aziz, Steven K. Frey, David R. Lapen, Susan Preston, Hazen A. J. Russell, Omar Khader, Andre R. Erler, and Edward A. Sudicky
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-25, https://doi.org/10.5194/hess-2023-25, 2023
Revised manuscript accepted for HESS
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The study determines the value of water towards ecosystem services production in an agricultural watershed in Ontario, Canada. It uses a computer model and an economic valuation approach to determine how subsurface and surface water affect ecosystem services supply. The results show that subsurface water plays a critical role in maintaining ecosystem services. The study informs on the sustainable use of subsurface water and introduces a new method for managing watershed ecosystem services.
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.
Cited articles
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Appelhans, T., Detsch, F., Reudenbach, C., and Woellauer, S.: mapview: Interactive Viewing of Spatial Data in R, https://CRAN.R-project.org/package=mapview (last access: 10 June 2023), 2022.
Appling, A. P., Oliver, S. K., Read, J. S., Sadler, J. M., and Zwart, J.: Machine learning for understanding inland water quantity, quality, and ecology, in: Encyclopedia of Inland Waters (Second Edition), Elsevier, Oxford, ISBN 978-0-12-822041-2, 585–606, https://doi.org/10.1016/B978-0-12-819166-8.00121-3, 2022.
Arriagada, P., Karelovic, B., and Link, O.: Automatic gap-filling of daily streamflow time series in data-scarce regions using a machine learning algorithm, J. Hydrol., 598, 126454, https://doi.org/10.1016/j.jhydrol.2021.126454, 2021.
Arsenault, R., Brissette, F., and Martel, J.-L.: The hazards of split-sample validation in hydrological model calibration, J. Hydrol., 566, 346–362, https://doi.org/10.1016/j.jhydrol.2018.09.027, 2018.
Arsov, N. and Mirceva, G.: Network Embedding: An Overview, arXiv [preprint], https://doi.org/10.48550/ARXIV.1911.11726, 26 November 2019.
Aschonitis, V. G., Papamichail, D., Demertzi, K., Colombani, N., Mastrocicco, M., Ghirardini, A., Castaldelli, G., and Fano, E.-A.: High resolution global grids of revised Priestley-Taylor and Hargreaves-Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation, links to ESRI-grid files, PANGAEA, https://doi.org/10.1594/PANGAEA.868808, 2017.
Benson, M. A. and Dalrymple, T.: General field and office procedures for indirect discharge measurements, US Govt. Print. Off., https://doi.org/10.3133/twri03A1, 1967.
Bergstra, J. and Bengio, Y.: Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281–305, 2012.
Bukaveckas, P., Likens, G., Winter, T., and Buso, D.: A comparison of methods for deriving solute flux rates using long-term data from streams in the Mirror Lake watershed, Water Air Soil Pollut., 105, 277–293, https://doi.org/10.1007/978-94-017-0906-4_26, 1998.
Caruana, R.: Multitask learning, Springer, https://doi.org/10.1007/978-1-4615-5529-2_5, 1998.
Chokmani, K. and Ouarda, T. B.: Physiographical space-based kriging for regional flood frequency estimation at ungauged sites, Water Resour. Res., 40, https://doi.org/10.1029/2003WR002983, 2004.
DeCicco, L. A., Lorenz, D., Hirsch, R. M., Watkins, W., and Johnson, M.: dataRetrieval: R packages for discovering and retrieving water data U.S. Federal Hydrologic Web Services, https://doi.org/10.5066/P9X4L3GE, 2022.
Durand, M., Gleason, C. J., Pavelsky, T. M., Prata de Moraes Frasson, R., Turmon, M., David, C. H., Altenau, E. H., Tebaldi, N., Larnier, K., Monnier, J., Malaterre, P. O., Oubanas, H., Allen, G. H., Astifan, B., Brinkerhoff, C., Bates, P. D., Bjerklie, D., Coss, S., Dudley, R., Fenoglio, L., Garambois, P.-A., Getirana, A., Lin, P., Margulis, S. A., Matte, P., Minear, J. T., Muhebwa, A., Pan, M., Peters, D., Riggs, R., Sikder, M. S., Simmons, T., Stuurman, C., Taneja, J., Tarpanelli, A., Schulze, K., Tourian, M. J., and Wang, J.: A Framework for Estimating Global River Discharge From the Surface Water and Ocean Topography Satellite Mission, Water Resour. Res., 59, e2021WR031614, https://doi.org/10.1029/2021WR031614, 2023.
Friedman, J., Tibshirani, R., and Hastie, T.: Regularization Paths for Generalized Linear Models via Coordinate Descent, J. Stat. Softw., 33, 1–22, https://doi.org/10.18637/jss.v033.i01, 2010.
Galton, F.: Regression towards mediocrity in hereditary stature, The Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263, https://doi.org/10.2307/2841583, 1886.
Goeman, J., Meijer, R., and Chaturvedi, N.: L1 and L2 penalized regression models, https://cran.r-project.org/web/packages/penalized/vignettes/penalized.pdf (last access: 18 May 2023), 2012.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Graf, W. H.: Hydraulics of sediment transport, Water Resources Publication, ISBN 13 978-1-887201-57-5, 1984.
Gruber, M.: Improving efficiency by shrinkage: The James–Stein and Ridge regression estimators, Routledge, https://doi.org/10.1201/9780203751220, 2017.
Guo, D., Johnson, F., and Marshall, L.: Assessing the potential robustness of conceptual rainfall-runoff models under a changing climate, Water Resour. Res., 54, 5030–5049, https://doi.org/10.1029/2018WR022636, 2018.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hall Jr., R. O.: Metabolism of streams and rivers: Estimation, controls, and application, in: Stream ecosystems in a changing environment, Elsevier, 151–180, https://doi.org/10.1016/B978-0-12-405890-3.00004-X, 2016.
Harvey, C. L., Dixon, H., and Hannaford, J.: An appraisal of the performance of data-infilling methods for application to daily mean river flow records in the UK, Hydrol. Res., 43, 618–636, https://doi.org/10.2166/nh.2012.110, 2012.
Hirsch, R. M.: A comparison of four streamflow record extension techniques, Water Resour. Res., 18, 1081–1088, https://doi.org/10.1029/WR018i004p01081, 1982.
Hirsch, R. M. and Costa, J. E.: US stream flow measurement and data dissemination improve, Eos, Transactions American Geophysical Union, 85, 197–203, https://doi.org/10.1029/2004EO200002, 2004.
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.
Horner, I., Renard, B., Le Coz, J., Branger, F., McMillan, H., and Pierrefeu, G.: Impact of stage measurement errors on streamflow uncertainty, Water Resour. Res., 54, 1952–1976, https://doi.org/10.1002/2017WR022039, 2018.
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.
Isaacson, K. and Coonrod, J.: USGS streamflow data and modeling sand-bed rivers, J. Hydraul. Eng., 137, 847–851, https://doi.org/10.1061/(ASCE)HY.1943-7900.0000362, 2011.
Johnson, S. L., Rothacher, J. S., and Wondzell, S. M.: Stream discharge in gaged watersheds at the HJ Andrews Experimental Forest, 1949 to present, Environmental Data Initiative [data set], https://doi.org/10.6073/PASTA/0066D6B04E736AF5F234D95D97EE84F3, 2020.
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 2014.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., and Klambauer, G.: NeuralHydrology–interpreting LSTMs in hydrology, Explainable AI: Interpreting, explaining and visualizing deep learning, Springer, 347–362, https://doi.org/10.1007/978-3-030-28954-6_19, 2019a.
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning, Water Resour. Res., 55, 11344–11354, https://doi.org/10.1029/2019WR026065, 2019b.
Kratzert, F., Gauch, M., Nearing, G., and Klotz, D.: NeuralHydrology – A Python library for Deep Learning research in hydrology, Journal of Open Source Software, 7, 4050, https://doi.org/10.21105/joss.04050, 2022.
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., and Matias, Y.: Caravan – A global community dataset for large-sample hydrology, Sci. Data, 10, 61, https://doi.org/10.1038/s41597-023-01975-w, 2023.
Lunch, C., Laney, C., Mietkiewicz, N., Sokol, E., Cawley, K., and NEON (National Ecological Observatory Network): neonUtilities: Utilities for Working with NEON Data (2.2.1), https://CRAN.R-project.org/package=neonUtilities (last access: 22 May 2023), 2022.
Manning, R.: On the flow of water in open channels and pipes, Transactions of the Institution of Civil Engineers of Ireland, 20, 161–207, 1891.
Moore, S. A., Jamieson, E. C., Rainville, F., Rennie, C. D., and Mueller, D. S.: Monte Carlo approach for uncertainty analysis of acoustic Doppler current profiler discharge measurement by moving boat, J. Hydraul. Eng., 143, 04016088, https://doi.org/10.1061/(ASCE)HY.1943-7900.0001249, 2017.
Moriasi, D., Gitau, M., Pai, N., and Daggupati, P.: Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria, Transactions of the ASABE (American Society of Agricultural and Biological Engineers), 58, 1763–1785, https://doi.org/10.13031/trans.58.10715, 2015.
Muggeo, V. M. R.: segmented: an R Package to Fit Regression Models with Broken-Line Relationships, R News, 8, 20–25, https://cran.r-project.org/doc/Rnews/, 2008.
Nalley, D., Adamowski, J., Khalil, B., and Biswas, A.: A comparison of conventional and wavelet transform based methods for streamflow record extension, J. Hydrol., 582, 124503, https://doi.org/10.1016/j.jhydrol.2019.124503, 2020.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I–A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
NEON (National Ecological Observatory Network): Continuous discharge (DP4.00130.001), RELEASE-2023 [data set], https://doi.org/10.48443/H2ZE-2F12, 2023a.
NEON (National Ecological Observatory Network): Discharge field collection (DP1.20048.001), PROVISIONAL, figshare [data set], https://doi.org/10.6084/m9.figshare.22344589, 2023b.
NEON (National Ecological Observatory Network): Discharge field collection (DP1.20048.001), RELEASE-2023 [data set], https://doi.org/10.48443/TYS0-ZE83, 2023c.
Newman, A., Sampson, K., Clark, M., Bock, A., Viger, R., and Blodgett, D.: A large-sample watershed-scale hydrometeorological dataset for the contiguous USA, UCAR/NCAR: Boulder, CO, USA, GDEX [data set], https://doi.org/10.5065/D6MW2F4D, 2014.
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015.
Newman, A. J., Mizukami, N., Clark, M. P., Wood, A. W., Nijssen, B., and Nearing, G.: Benchmarking of a physically based hydrologic model, J. Hydrometeorol., 18, 2215–2225, https://doi.org/10.1175/JHM-D-16-0284.1, 2017.
Newman, A., Sampson, K., Clark, M., Bock, A., Viger, R., Blodgett, D., Addor, N., and Mizukami, N.: CAMELS: Catchment Attributes and MEteorology for Large-sample Studies (1.2) [data set], https://gdex.ucar.edu/dataset/camels.html (last access: 4 December 2023), 2022.
Odum, H. T.: Primary production in flowing waters 1, Limnol. Oceanogr., 1, 102–117, https://doi.org/10.4319/lo.1956.1.2.0102, 1956.
Pantelakis, D., Doulgeris, C., Hatzigiannakis, E., and Arampatzis, G.: Evaluation of discharge measurements methods in a natural river of low or middle flow using an electromagnetic flow meter, River Res. Appl., 38, 1003–1013, https://doi.org/10.1002/rra.3966, 2022.
Philip, E. and McLaughlin, J.: Evaluation of stream gauge density and implementing the concept of virtual gauges in Northern Ontario for watershed modeling, Journal of Water Management Modeling, 26, C438, https://doi.org/10.14796/JWMM.C438, 2018.
Pool, S. and Seibert, J.: Gauging ungauged catchments–Active learning for the timing of point discharge observations in combination with continuous water level measurements, J. Hydrol., 598, 126448, https://doi.org/10.1016/j.jhydrol.2021.126448, 2021.
Razavi, T. and Coulibaly, P.: Streamflow prediction in ungauged basins: review of regionalization methods, J. Hydrol. Eng., 18, 958–975, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000690, 2013.
Read, J. S., Jia, X., Willard, J., Appling, A. P., Zwart, J. A., Oliver, S. K., Karpatne, A., Hansen, G. J. A., Hanson, P. C., Watkins, W., Steinbach, M., and Kumar, V.: Process-Guided Deep Learning Predictions of Lake Water Temperature, Water Resour. Res., 55, 9173–9190, https://doi.org/10.1029/2019WR024922, 2019.
Regan, R. S., Juracek, K. E., Hay, L. E., Markstrom, S., Viger, R. J., Driscoll, J. M., LaFontaine, J., and Norton, P. A.: The US Geological Survey National Hydrologic Model infrastructure: Rationale, description, and application of a watershed-scale model for the conterminous United States, Environ. Model. Softw., 111, 192–203, https://doi.org/10.1016/j.envsoft.2018.09.023, 2019.
Rhea, S.: NEON Continuous Discharge Evaluation, HydroShare [data set], https://doi.org/10.4211/hs.03c52d47d66e40f4854da8397c7d9668, 2023.
Rhea, S., Vlah, M., Slaughter, W., and Gubbins, N.: macrosheds: Tools for interfacing with the MacroSheds dataset (1.0.2), GitHub, https://github.com/MacroSHEDS/macrosheds (last access: 21 December 2023), 2023a.
Rhea, S., Gubbins, N., DelVecchia, A. G., Ross, M. R., and Bernhardt, E. S.: User-focused evaluation of National Ecological Observatory Network streamflow estimates, Sci. Data, 10, 89, https://doi.org/10.1038/s41597-023-02026-0, 2023b.
Sadler, J. M., Appling, A. P., Read, J. S., Oliver, S. K., Jia, X., Zwart, J. A., and Kumar, V.: Multi-task deep learning of daily streamflow and water temperature, Water Resour. Res., 58, e2021WR030138, https://doi.org/10.1029/2021WR030138, 2022.
Sauer, V. B.: Standards for the analysis and processing of surface-water data and information using electronic methods, US Geological Survey, https://doi.org/10.3133/wri20014044, 2002.
Seibert, J. and Beven, K. J.: Gauging the ungauged basin: how many discharge measurements are needed?, Hydrol. Earth Syst. Sci., 13, 883–892, https://doi.org/10.5194/hess-13-883-2009, 2009.
Seibert, J., Vis, M. J. P., Lewis, E., and van Meerveld, H. J.: Upper and lower benchmarks in hydrological modelling, Hydrol. Process., 32, 1120–1125, https://doi.org/10.1002/hyp.11476, 2018.
Seibert, J., Strobl, B., Etter, S., Hummer, P., and van Meerveld, H. J. (Ilja): Virtual Staff Gauges for Crowd-Based Stream Level Observations, Front. Earth Sci., 7, 70, https://doi.org/10.3389/feart.2019.00070, 2019.
Shen, H., Tolson, B. A., and Mai, J.: Time to update the split-sample approach in hydrological model calibration, Water Resour. Res., 58, e2021WR031523, https://doi.org/10.1029/2021WR031523, 2022.
Shen, J.: Discharge characteristics of triangular-notch thin-plate weirs, United States Department of the Interior, Geological Survey, 80607045, 1981.
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, https://doi.org/10.5555/2627435.2670313, 2014.
Tazioli, A.: Experimental methods for river discharge measurements: comparison among tracers and current meter, Hydrol. Sci. J., 56, 1314–1324, https://doi.org/10.1080/02626667.2011.607822, 2011.
Thornton, M. M., Shrestha, R., Wei, Y., Thornton, P. E., Kao, S.-C., and Wilson, B. E.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1, ORNL Distributed Active Archive Center [data set], https://doi.org/10.3334/ORNLDAAC/2129, 2022.
Turnipseed, D. P. and Sauer, V. B.: Discharge measurements at gaging stations, US Geological Survey, https://doi.org/10.3133/tm3A8, 2010.
U.S. Geological Survey: National Water Information System data available on the World Wide Web (USGS Water Data for the Nation), https://doi.org/10.5066/F7P55KJN, 2016.
Van Rossum, G. and Drake, F. L.: Python 3 Reference Manual, CreateSpace, Scotts Valley, CA, ISBN 1-4414-1269-7, 2009.
Vlah, M. J.: Composite discharge plots, https://macrosheds.org/data/vlah_etal_2023_composites/ (last access: 3 February 2024), 2023a.
Vlah, M.: vlahm/neon_q_sim: HESS submission (v1.1), Zenodo [code], https://doi.org/10.5281/zenodo.10067683, 2023b.
Vlah, M. J., Rhea, S., Bernhardt, E. S., Slaughter, W., Gubbins, N., DelVecchia, A. G., Thellman, A., and Ross, M. R.: MacroSheds: A synthesis of long-term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies, Limnol. Oceanogr., 8, 419–452, https://doi.org/10.1002/lol2.10325, 2023a.
Vlah, M. J., Rhea, S., Slaughter, W., Bernhardt, E. S., Gubbins, N., DelVecchia, A. G., Thellman, A., and Ross, M. R. V.: MacroSheds: a synthesis of long-term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies (1.0), Environmental Data Initiative [data set], https://doi.org/10.6073/pasta/c8d6d29703f14735bf24cd8cebe91f24, 2023b.
Vlah, M., R. V. Ross, M., Rhea, S., and Bernhardt, E. S.: Composite discharge series for all NEON river/stream sites, plus figures and all input/output data associated with Vlah, Ross, Rhea, Bernhardt. 2023. ”Virtual gauges: the surprising potential to reconstruct continuous streamflow from strategic measurements”, figshare [data set], https://doi.org/10.6084/m9.figshare.c.6488065.v1, 2023c.
Wang, C. P.: Laser doppler velocimetry, J. Quant. Spectrosc. Ra., 40, 309–319, https://doi.org/10.1016/0022-4073(88)90122-7, 1988.
White, A. F. and Blum, A. E.: Effects of climate on chemical_weathering in watersheds, Geochim. Cosmochim. Ac., 59, 1729–1747, https://doi.org/10.1016/0016-7037(95)00078-E, 1995.
Whittaker, J., Whitehead, C., and Somers, M.: The neglog transformation and quantile regression for the analysis of a large credit scoring database, J. Roy. Stat. C, 54, 863–878, https://doi.org/10.1111/j.1467-9876.2005.00520.x, 2005.
Zakwan, M., Muzzammil, M., and Alam, J.: Developing stage-discharge relations using optimization techniques, Aquademia: Water, Environment and Technology, 1, 5, https://doi.org/10.20897/awet/81286, 2017.
Short summary
Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult or impractical to install. We reconstructed flow at 27 gauges of the National Ecological Observatory Network (NEON), informing ~199 site-months of missing data in the official record and improving that accuracy of official estimates at 11 sites. This study shows that machine learning, but also routine regression methods, can be used to supplement existing gauge networks and reduce monitoring costs.
Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult...
