Articles | Volume 28, issue 1
https://doi.org/10.5194/hess-28-21-2024
© 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-28-21-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2024
Research article |
| 02 Jan 2024
| 02 Jan 2024
A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+
Salam A. Abbas
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80521, USA
Ryan T. Bailey
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80521, USA
Jeremy T. White
INTERA, Inc., Perth, Australia
Jeffrey G. Arnold
Grassland Soil and Water Research Laboratory, USDA–ARS, Temple, TX 76502, USA
Michael J. White
Grassland Soil and Water Research Laboratory, USDA–ARS, Temple, TX 76502, USA
Natalja Čerkasova
Blackland Research & Extension Center, Texas A&M AgriLife, Temple, TX 76502, USA
Jungang Gao
Blackland Research & Extension Center, Texas A&M AgriLife, Temple, TX 76502, USA
Related authors
Ryan Bailey, Salam Abbas, Jeffrey Arnold, and Michael White
EGUsphere, https://doi.org/10.5194/egusphere-2025-300, https://doi.org/10.5194/egusphere-2025-300, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Water managers often make use of computer models to assess a region’s water supply under future conditions and management scenarios. This article introduces a new computer model that combines a land surface model (SWAT+) and a groundwater model (MODFLOW) and shows how it can be applied to managed, irrigated watersheds. This new model can be used for regions that rely on both surface water and groundwater for drinking water, agriculture, and industry.
Ryan Bailey, Salam Abbas, Jeffrey Arnold, and Michael White
EGUsphere, https://doi.org/10.5194/egusphere-2025-300, https://doi.org/10.5194/egusphere-2025-300, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Water managers often make use of computer models to assess a region’s water supply under future conditions and management scenarios. This article introduces a new computer model that combines a land surface model (SWAT+) and a groundwater model (MODFLOW) and shows how it can be applied to managed, irrigated watersheds. This new model can be used for regions that rely on both surface water and groundwater for drinking water, agriculture, and industry.
Raoul A. Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim J. Peterson, Jānis Bikše, Antoine Di Ciacca, Xinyue Wang, Yang Zheng, Maximilian Nölscher, Julian Koch, Raphael Schneider, Nikolas Benavides Höglund, Sivarama Krishna Reddy Chidepudi, Abel Henriot, Nicolas Massei, Abderrahim Jardani, Max Gustav Rudolph, Amir Rouhani, J. Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, and Rojin Meysami
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024, https://doi.org/10.5194/hess-28-5193-2024, 2024
Short summary
Short summary
We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.
Natalja Čerkasova, Jovita Mėžinė, Rasa Idzelytė, Jūratė Lesutienė, Ali Ertürk, and Georg Umgiesser
Ocean Sci., 20, 1123–1147, https://doi.org/10.5194/os-20-1123-2024, https://doi.org/10.5194/os-20-1123-2024, 2024
Short summary
Short summary
This study advances the understanding of climate projection variability in the Nemunas River, Curonian Lagoon, and southeastern Baltic Sea continuum by analyzing a subset of climate models with a focus on a coupled ocean and drainage basin model. This study investigates the variability and trends in environmental parameters, such as water fluxes, timing, nutrient load, water temperature, ice cover, and saltwater intrusions in Representative Concentration Pathway 4.5 and 8.5 scenarios.
Brigitta Szabó, Piroska Kassai, Svajunas Plunge, Attila Nemes, Péter Braun, Michael Strauch, Felix Witing, János Mészáros, and Natalja Čerkasova
SOIL, 10, 587–617, https://doi.org/10.5194/soil-10-587-2024, https://doi.org/10.5194/soil-10-587-2024, 2024
Short summary
Short summary
This research introduces methods and tools for obtaining soil input data in European case studies for environmental models like SWAT+. With various available soil datasets and prediction methods, determining the most suitable is challenging. The study aims to (i) catalogue open-access datasets and prediction methods for Europe, (ii) demonstrate and quantify differences between prediction approaches, and (iii) offer a comprehensive workflow with open-source R codes for deriving missing soil data.
Cécile Coulon, Jeremy T. White, Alexandre Pryet, Laura Gatel, and Jean-Michel Lemieux
Hydrol. Earth Syst. Sci., 28, 303–319, https://doi.org/10.5194/hess-28-303-2024, https://doi.org/10.5194/hess-28-303-2024, 2024
Short summary
Short summary
In coastal areas, groundwater managers require information on the risk of well salinization associated with various pumping scenarios. We developed a modeling approach to identify the optimal tradeoff between groundwater pumping and probability of salinization, considering model parameter and historical observation uncertainty as well as uncertainty in sea level and recharge projections. The workflow can be implemented in a wide range of coastal settings.
Rasa Idzelytė, Natalja Čerkasova, Jovita Mėžinė, Toma Dabulevičienė, Artūras Razinkovas-Baziukas, Ali Ertürk, and Georg Umgiesser
Ocean Sci., 19, 1047–1066, https://doi.org/10.5194/os-19-1047-2023, https://doi.org/10.5194/os-19-1047-2023, 2023
Short summary
Short summary
This work is focused on the impacts of climate change on a complex water flow system in the southeastern (SE) Baltic Sea, covering the Nemunas river basin and Curonian Lagoon. The results show that lagoon and sea will receive more water coming from the Nemunas. This will lead to a decreased frequency of saltwater inflow to the lagoon, and water will take less time to renew. Water temperatures in the entire lagoon and the SE Baltic Sea will increase steadily, and salinity values will decrease.
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
Short summary
Short summary
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.
Estifanos Addisu Yimer, Ryan T. Bailey, Lise Leda Piepers, Jiri Nossent, and Ann van Griensven
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-169, https://doi.org/10.5194/hess-2022-169, 2022
Manuscript not accepted for further review
Short summary
Short summary
A recently developed groundwater module (gwflow) coupled with the soil water assessment tool (SWAT+) is used to simulate the streamflow of the Dijle catchment, Belgium. The standalone model (SWAT+) resulted in unsatisfactory streamflow simulations while SWAT+gwflow produced streamflow that considerably mimics the measured river discharge. Furthermore, modifications to the gwflow module are made to account for the vital hydrological process (groundwater-soil profile interactions).
Wei Liu, Seonggyu Park, Ryan T. Bailey, Eugenio Molina-Navarro, Hans Estrup Andersen, Hans Thodsen, Anders Nielsen, Erik Jeppesen, Jacob Skødt Jensen, Jacob Birk Jensen, and Dennis Trolle
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-232, https://doi.org/10.5194/hess-2019-232, 2019
Manuscript not accepted for further review
Short summary
Short summary
We compared the performance of SWAT and SWAT-MODFLOW and assessed the simulated streamflow signals in response to a range of groundwater abstraction scenarios for irrigation and drinking water. The SWAT-MODFLOW complex was further developed to enable the application of the Drain Package and an auto-irrigation routine. A PEST-based approach was developed to calibrate the coupled SWAT-MODFLOW. The SWAT-MODFLOW model produced more realistic results on groundwater abstraction effects on streamflow.
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric–hydrological model
Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments
A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Simulating the Tone River eastward diversion project in Japan carried out 4 centuries ago
Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers
Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study
Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation?
State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models
Improving the hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations
Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments
The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa
Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model
Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Estimating response times, flow velocities, and roughness coefficients of Canadian Prairie basins
Learning landscape features from streamflow with autoencoders
On the use of streamflow transformations for hydrological model calibration
Simulation-based inference for parameter estimation of complex watershed simulators
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
CONCN: A high-resolution, integrated surface water-groundwater ParFlow modeling platform of continental China
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Karst aquifer discharge response to rainfall interpreted as anomalous transport
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Large-sample hydrology – a few camels or a whole caravan?
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
Projections of streamflow intermittence under climate change in European drying river networks
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis
Extended range forecasting of stream water temperature with deep learning models
Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China
On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?
Assessing the adequacy of traditional hydrological models for climate change impact studies: A case for long-short-term memory (LSTM) neural networks
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes
Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
To what extent do flood-inducing storm events change future flood hazards?
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Assessing the value of high-resolution rainfall and streamflow data for hydrological modeling: An analysis based on 63 catchments in southeast China
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025, https://doi.org/10.5194/hess-29-1277-2025, 2025
Short summary
Short summary
Data-driven techniques have shown the potential to outperform process-based models in rainfall–runoff simulations. Hybrid models, combining both approaches, aim to enhance accuracy and maintain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions, we test their generalization capabilities for extreme hydrological events.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
Hydrol. Earth Syst. Sci., 29, 1061–1082, https://doi.org/10.5194/hess-29-1061-2025, https://doi.org/10.5194/hess-29-1061-2025, 2025
Short summary
Short summary
This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1033–1060, https://doi.org/10.5194/hess-29-1033-2025, https://doi.org/10.5194/hess-29-1033-2025, 2025
Short summary
Short summary
Owing to differences in the existing published results, we conducted a detailed analysis of the runoff components and future trends in the Yarlung Tsangpo River basin and found that the contributions of snowmelt and glacier melt runoff to streamflow (both ~5 %) are limited and much lower than previous results. The streamflow in this area will continuously increase in the future, but the overestimated contribution of glacier melt could lead to an underestimation of this increasing trend.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 29, 903–924, https://doi.org/10.5194/hess-29-903-2025, https://doi.org/10.5194/hess-29-903-2025, 2025
Short summary
Short summary
This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling changes in precipitation–runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation–runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci., 29, 785–798, https://doi.org/10.5194/hess-29-785-2025, https://doi.org/10.5194/hess-29-785-2025, 2025
Short summary
Short summary
Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine learning (ML) models are increasingly being applied for flood forecasting. Such models are typically trained on large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets that maximise the spatio-temporal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Joško Trošelj and Naota Hanasaki
Hydrol. Earth Syst. Sci., 29, 753–766, https://doi.org/10.5194/hess-29-753-2025, https://doi.org/10.5194/hess-29-753-2025, 2025
Short summary
Short summary
This study presents the first distributed hydrological simulation which confirms claims raised by historians that the eastward diversion project of the Tone River in Japan was conducted 4 centuries ago to increase low flows and subsequent travelling possibilities surrounding the capital, Edo (Tokyo), using inland navigation. We showed that great steps forward can be made for improving quality of life with small human engineering waterworks and small interventions in the regime of natural flows.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 29, 683–700, https://doi.org/10.5194/hess-29-683-2025, https://doi.org/10.5194/hess-29-683-2025, 2025
Short summary
Short summary
This work investigates how hydrological models are transferred to a period in which climate conditions are different to the ones of the period in which they were set up. The robustness assessment test built to detect dependencies between model error and climatic drivers was applied to three hydrological models in 352 catchments in Denmark, France and Sweden. Potential issues are seen in a significant number of catchments for the models, even though the catchments differ for each model.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci., 29, 627–653, https://doi.org/10.5194/hess-29-627-2025, https://doi.org/10.5194/hess-29-627-2025, 2025
Short summary
Short summary
Water budget non-closure is a widespread phenomenon among multisource datasets which undermines the robustness of hydrological inferences. This study proposes a Multisource Dataset Correction Framework grounded in Physical Hydrological Process Modelling to enhance water budget closure, termed PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
Short summary
Short summary
Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht H. Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci., 29, 335–360, https://doi.org/10.5194/hess-29-335-2025, https://doi.org/10.5194/hess-29-335-2025, 2025
Short summary
Short summary
Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping to better prepare for and respond to floods.
Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
Short summary
Short summary
To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Haley A. Canham, Belize Lane, Colin B. Phillips, and Brendan P. Murphy
Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025, https://doi.org/10.5194/hess-29-27-2025, 2025
Short summary
Short summary
The influence of watershed disturbances has proved challenging to disentangle from natural streamflow variability. This study evaluates the influence of time-varying hydrologic controls on rainfall–runoff in undisturbed and wildfire-disturbed watersheds using a novel time-series event separation method. Across watersheds, water year type and season influenced rainfall–runoff patterns. Accounting for these controls enabled clearer isolation of wildfire effects.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024, https://doi.org/10.5194/hess-28-5511-2024, 2024
Short summary
Short summary
Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil evaporation). In western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented using the leaf area index (LAI). In this study, we evaluate the importance of the LAI for ET calculation. We take a close look at this interaction and highlight its relevance. Our work contributes to the understanding of terrestrial water cycle processes .
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Short summary
This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024, https://doi.org/10.5194/hess-28-5353-2024, 2024
Short summary
Short summary
Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological model, integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024, https://doi.org/10.5194/hess-28-5331-2024, 2024
Short summary
Short summary
Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. We investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analyses indicate that adding two vegetation parameters is enough to improve the representation of evaporation and that the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2024-3355, https://doi.org/10.5194/egusphere-2024-3355, 2024
Short summary
Short summary
Long Short-Term Memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modeling. However, most studies focus on daily-scale predictions, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use input of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
Short summary
Short summary
We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
Short summary
Short summary
Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
Short summary
Short summary
The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
Short summary
Short summary
We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
Short summary
Short summary
Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
Short summary
Short summary
We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-292, https://doi.org/10.5194/hess-2024-292, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We developed the first high-resolution, integrated surface water-groundwater hydrologic model of the entire continental China using ParFlow. The model shows good performance of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
Short summary
Short summary
This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279, https://doi.org/10.5194/hess-2024-279, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Hydrologic models are needed to provide simulations of water availability, floods and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models, and thus cannot easily be used to select an appropriate model for a specific place.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
Short summary
Short summary
A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
Short summary
Short summary
Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
Short summary
Short summary
We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
Short summary
Short summary
Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-272, https://doi.org/10.5194/hess-2024-272, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Our study projects how climate change will affect drying of river segments and stream networks in Europe, using advanced modeling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent, intense and start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists in evaluating the ecological health of river ecosystem.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
Short summary
Short summary
Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Fabián Lema, Pablo A. Mendoza, Nicolás A. Vásquez, Naoki Mizukami, Mauricio Zambrano-Bigiarini, and Ximena Vargas
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-221, https://doi.org/10.5194/hess-2024-221, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what it truly reflects in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and time scales for accurate drought characterization and monitoring.
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2591, https://doi.org/10.5194/egusphere-2024-2591, 2024
Short summary
Short summary
We generate operational forecasts of daily maximum stream water temperature for the next month at 54 stations in Switzerland with our best performing data-driven model. The average forecast error is 0.38 °C for 1 day ahead and increases to 0.90 °C for 1 month ahead given the uncertainty in the meteorological variables influencing water temperature. Here we compare the skill of several models, how well they can forecast at new and ungauged stations, and the importance of different model inputs.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
Short summary
Short summary
An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
Short summary
Short summary
The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Jean-Luc Martel, François Brissette, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Gabriel Rondeau-Genesse, and Louis-Philippe Caron
EGUsphere, https://doi.org/10.5194/egusphere-2024-2133, https://doi.org/10.5194/egusphere-2024-2133, 2024
Short summary
Short summary
This study compares Long Short-Term Memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrolgical models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
Short summary
Short summary
The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
Short summary
Short summary
By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
Short summary
Short summary
Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
Short summary
Short summary
Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
Short summary
Short summary
Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1438, https://doi.org/10.5194/egusphere-2024-1438, 2024
Short summary
Short summary
Common intuition holds that higher input data resolution leads to better results. To assess the benefits of high-resolution data, we conducted simulation experiments using data with various temporal resolutions across multiple catchments, and found that higher resolution data does not always improve model performance, challenging the necessity of pursuing such data. In catchments with small areas or significant flow variability, high-resolution data is more valuable.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
Short summary
Short summary
Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
Short summary
Short summary
Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
Short summary
Short summary
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
Short summary
Short summary
A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
Short summary
Short summary
Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
Short summary
Short summary
Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Cited articles
Abbas, S., Xuan, Y., and Bailey, R.: Assessing Climate Change Impact on Water Resources in Water Demand Scenarios Using SWAT-MODFLOW-WEAP, Hydrology, 9, 164, https://doi.org/10.3390/hydrology9100164, 2022.
Arnold, J., Srinivasan, R., Muttiah, R., and Williams, J.: Large area hydrologic modeling and assessment part I: Model development, J. Am. Water Resour. Assoc., 34, 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998.
Arnold, J., Kiniry, J., Srinivasan, R., Williams, J., Haney, E., and Neitsch, S.: Soil & Water Assessment Tool: Input/output documentation, version 2012, 2013 TR-439, Texas Water Resources Institute, https://swat.tamu.edu/media/69296/swat-io-documentation-2012.pdf (last access: 16 June 2023), 2013.
Arnold, J., White, M., Allen, P., Gassman, P., and Bieger, K.: Conceptual Framework of connectivity for a national agroecosystem model based on transport processes and management practices. J. Am. Water Resour. Assoc., 57, 154–169, https://doi.org/10.1111/1752-1688.12890, 2020.
Bahremand, A. and De Smedt, F.: Predictive Analysis and Simulation Uncertainty of a Distributed Hydrological Model, Water Resour. Manage., 24, 2869–2880, https://doi.org/10.1007/s11269-010-9584-1, 2010.
Bailey, R. and Alderfer, C.: Groundwater Data in Unconfined Aquifers – conterminous United States, figshare [data set], https://doi.org/10.6084/m9.figshare.c.5918738.v2, 2022.
Bailey, R., Wible, T., Arabi, M., Records, R., and Ditty, J.: Assessing regional-scale spatio-temporal patterns of groundwater-surface water interactions using a coupled SWAT-MODFLOW model, Hydrol. Process., 30, 4420–4433, https://doi.org/10.1002/hyp.10933, 2016.
Bailey, R., Bieger, K., Arnold, J., and Bosch, D.: A new physically-based spatially-distributed groundwater flow module for SWAT+, Hydrology, 7, 75, https://doi.org/10.3390/hydrology7040075, 2020.
Bailey, R., Abbas, S., Arnold, J., White, M., Gao, J., and Čerkasova, N.: Augmenting the national agroecosystem model with physically based spatially distributed groundwater modeling, Environ. Model. Softw., 160, 105589, https://doi.org/10.1016/j.envsoft.2022.105589, 2023.
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P., Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B. D., and Andreassian, V.: Characterising performance of environmental models, Environ. Model. Softw., 40, 1–20, https://doi.org/10.1016/j.envsoft.2012.09.011, 2013.
Bhatta, B., Shrestha, S., Shrestha, P., and Talchabhadel, R.: Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin, Catena, 181, 104082, https://doi.org/10.1016/j.catena.2019.104082, 2019.
Bieger, K., Hörmann, G., and Fohrer, N.: Detailed spatial analysis of SWAT-simulated surface runoff and sediment yield in a mountainous watershed in China, Hydrolog. Sci. J., 60, 784–800, https://doi.org/10.1080/02626667.2014.965172, 2015.
Bieger, K., Arnold, J. G., Rathjens, H., White, M. J., Bosch, D. D., Allen, P. M., Volk, M., and Srinivasan, R.: Introduction to SWAT+, a completely restructured version of the soil and water assessment tool, J. Am. Water Resour. Assoc., 53, 115–130, https://doi.org/10.1111/1752-1688.12482, 2017.
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q. J. Roy. Meteorol. Soc., 140, 1521–1535, https://doi.org/10.1002/qj.2236, 2014.
Brunner, P., Therrien, R., Renard, P., Simmons, C., and Franssen, H.: Advances in understanding river-groundwater interactions, Rev. Geophys., 55, 818–854, https://doi.org/10.1002/2017RG000556, 2017.
Campolongo, F., Cariboni, J., and Saltelli, A.: An effective screening design for sensitivity analysis of large models, Environ. Model. Softw., 22, 1509–1518, https://doi.org/10.1016/j.envsoft.2006.10.004, 2007.
Čerkasova, N., Umgiesser, G., and Ertürk, A.: Modelling framework for flow, sediments and nutrient loads in a large transboundary river watershed: A climate change impact assessment of the Nemunas river watershed, J. Hydrol., 598, 126422, https://doi.org/10.1016/j.jhydrol.2021.126422, 2021.
Chen, Y. and Oliver, D.: Ensemble randomized maximum likelihood method as an iterative ensemble smoother, Math. Geosci., 44, 1–26, https://doi.org/10.1007/s11004-011-9376-z, 2012.
Chen, Y. and Oliver, D.: Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification, Comput. Geosci., 17, 689–703, https://doi.org/10.1007/s10596-013-9351-5, 2013.
Crestani, E., Camporese, M., Baú, D., and Salandin, P.: Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation, Hydrol. Earth Sys.t Sci., 17, 1517–1531, https://doi.org/10.5194/hess-17-1517-2013, 2013.
Crook, N., Binley, A., Knight, R., Robinson, D., Zarnetske, J., and Haggerty, R.: Electrical resistivity imaging of the architecture of substream sediments, Water Resour. Res., 44, W00D13, https://doi.org/10.1029/2008WR006968, 2008.
Devak, M. and Dhanya, C.: Sensitivity analysis of hydrological models: Review and way forward, J. Water Clim., 8, 557–575, https://doi.org/10.2166/wcc.2017.149, 2017.
Dieter, C., Maupin, M., Caldwell, R., Harris, M., Ivahnenko, T., Lovelace, J., Barber, N., and Linsey, K.: Water availability and use science program: Estimated use of water in the United States in 2015 (Circular 1441), US Geological Survey, https://doi.org/10.3133/cir1441, 2018.
Doherty, J.: PEST model-independent parameter estimation user manual, Watermark Numerical Computing, Brisbane, Australia, 3338–3349, https://www.epa.gov/sites/default/files/documents/PESTMAN.PDF (last access: 16 June 2023), 2004.
Doherty, J.: PEST, Model-independent Parameter Estimation: User Manual, 7th Edn., Watermark Numerical Computing, Brisbane, Australia, 3338–3349, https://pesthomepage.org/documentation (last access: 16 June 2023), 2020.
Doherty, J. and Hunt, R.: Two statistics for evaluating parameter identifiability and error reduction, J. Hydrol., 366, 119–127, https://doi.org/10.1016/j.jhydrol.2008.12.018, 2009.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994.
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarboton, D.: An overview of current applications, challenges, and future trends in distributed process-based models in hydrology, J. Hydrol., 537, 45–60, https://doi.org/10.1016/j.jhydrol.2016.03.026, 2016.
Fox, G.: Estimating streambed conductivity: guidelines for stream-aquifer analysis tests, T. ASABE, 50, 107–113, https://doi.org/10.13031/2013.22416, 2007.
Fox, G. and Durnford, D.: Unsaturated hyporheic zone flow in stream/aquifer conjunctive systems, Adv. Water Resour., 26, 989–1000, https://doi.org/10.1016/S0309-1708(03)00087-3, 2003.
Gesch, D., Evans, G., Oimoen, M., and Arundel, S.: The national elevation dataset, American Society for Photogrammetry and Remote Sensing, 83–110, https://pubs.usgs.gov/publication/70201572 (last access: 16 June 2023), 2018.
Ghaffari, G., Keesstra, S., Ghodousi, J., and Ahmadi, H.: SWAT-simulated hydrological impact of land-use change in the Zanjanrood basin, Northwest Iran, Hydrol. Process., 24, 892–903, https://doi.org/10.1002/hyp.7530, 2010.
Helton, J.: Uncertainty and sensitivity analysis techniques for use in performance assessment for Radioactive Waste Disposal, Reliab. Eng. Syst. Safe., 42, 327–367, https://doi.org/10.1016/0951-8320(93)90097-i, 1993.
Herzog, A., Hector, B., Cohard, J., Vouillamoz, J., Lawson, F. M., Peugeot, C., and De Graaf, I.: A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone, Vadose Zone J., 20, e20163, https://doi.org/10.1002/vzj2.20163, 2021.
Horton, J., San Juan, C., and Stoeser, D.: The state geologic map compilation (SGMC) geodatabase of the conterminous United States, ver. 1.1, August 2017 (Data Series 1052), USGS, https://doi.org/10.3133/ds1052, 2017.
Izady, A., Joodavi, A., Ansarian, M., Shafiei, M., Majidi, M., Davary, K., Ziaei, A. N., Ansari, H., Nikoo, M. R., Al-Maktoumi, A., Chen, M., and Abdalla, O.: A scenario-based coupled SWAT-MODFLOW decision support system for Advanced Water Resource Management, J. Hydroinform., 24, 56–77, https://doi.org/10.2166/hydro.2021.081, 2022.
Jiang, S., Jomaa, S., and Rode, M.: Modelling inorganic nitrogen leaching in nested mesoscale catchments in central Germany. Ecohydrology, 7, 1345–1362, https://doi.org/10.1002/eco.1462, 2014.
Kalbus, E., Schmidt, C., Molson, J. W., Reinstorf, F., and Schirmer, M.: Influence of aquifer and streambed heterogeneity on the distribution of groundwater discharge, Hydrol. Earth Syst. Sci., 13, 69–77., https://doi.org/10.5194/hess-13-69-2009, 2009.
Koo, H., Chen, M., Jakeman, A., and Zhang, F.: A global sensitivity analysis approach for identifying critical sources of uncertainty in non-identifiable, spatially distributed environmental models: A holistic analysis applied to SWAT for input datasets and model parameters, Environ. Model. Softw., 127, 104676, https://doi.org/10.1016/j.envsoft.2020.104676, 2020.
Leta, O., Nossent, J., Velez, C., Shrestha, N., Van Griensven, A., and Bauwens, W.: Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium), Environ. Model. Softw., 68, 129–146, https://doi.org/10.1016/j.envsoft.2015.02.010, 2015.
Liu, H., Jia, Y., Niu, C., Su, H., Wang, J., Du, J., Khaki, M., Hu, P., and Liu, J.: Development and validation of a physically-based, national-scale hydrological model in China, J. Hydrol., 590, 125431, https://doi.org/10.1016/j.jhydrol.2020.125431, 2020.
Moore, R. and Dewald, T.: The road to nhdp lus – advancements in digital stream networks and associated catchments, J. Am. Water Resour. Assoc., 52, 890–900, https://doi.org/10.1111/1752-1688.12389, 2016.
Morris, M.: Factorial sampling plans for preliminary computational experiments, Technometrics, 33, 161–174, https://doi.org/10.2307/1269043, 1991.
Neitsch, S., Arnold, J., Kiniry, J., and Williams, J.: Soil and Water Assessment Tool Theoretical Documentation version 2009, Texas Water Resources Institute, https://swat.tamu.edu/media/99192/swat2009-theory.pdf (last access: 16 June 2023), 2011.
Nossent, J., Elsen, P., and Bauwens, W.: Sobol' sensitivity analysis of a complex environmental model, Environ. Model. Softw., 26, 1515–1525, https://doi.org/10.1016/j.envsoft.2011.08.010, 2011.
Olaya-Abril, A., Parras-Alcántara, L., Lozano-García, B., and Obregón-Romero, R.: Soil Organic Carbon Distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis, Sci. Total Environ., 592, 134–143, https://doi.org/10.1016/j.scitotenv.2017.03.021, 2017.
Partington, D., Therrien, R., Simmons, C., and Brunner, P.: Blueprint for a coupled model of sedimentology, hydrology, and hydrogeology in streambeds, Rev. Geophys., 55, 287–309, https://doi.org/10.1002/2016RG000530, 2017.
Pianosi, F., Iwema, J., Rosolem, R., and Wagener, T.: A multimethod global sensitivity analysis approach to support the calibration and evaluation of Land Surface Models, Sens. Anal. Earth Obs. Model., 2017, 125–144, https://doi.org/10.1016/b978-0-12-803011-0.00007-0, 2017.
Plischke, E., Borgonovo, E., and Smith, C.: Global sensitivity measures from given data. European Eur, J. Oper. Res., 226, 536–550, https://doi.org/10.1016/j.ejor.2012.11.047, 2013.
Pokhrel, Y., Felfelani, F., Satoh, Y., Boulange, J., Burek, P., Gädeke, A., Gerten, D., Gosling, S. N., Grillakis, M., Gudmundsson, L., Hanasaki, N., Kim, H., Koutroulis, A., Liu, J., Papadimitriou, L., Schewe, J., Müller Schmied, H., Stacke, T., Telteu, C.-E., Thiery, W., Veldkamp, T., Zhao, F., and Wada, Y.: Global terrestrial water storage and drought severity under climate change, Nat. Clim. Change, 11, 226–233, https://doi.org/10.1038/s41558-020-00972-w, 2021.
Qiu, J., Yang, Q., Zhang, X., Huang, M., Adam, J. C., and Malek, K.: Implications of water management representations for watershed hydrologic modeling in the Yakima River Basin, Hydrol Earth Syst. Sci., 23, 35–49, https://doi.org/10.5194/hess-23-35-2019, 2019.
Rode, M., Suhr, U., and Wriedt, G.: Multi-objective calibration of a river water quality model—Information content of calibration data, Ecol. Model., 204, 129–142, https://doi.org/10.1016/j.ecolmodel.2006.12.037, 2007.
Ryken, A., Bearup, L., Jefferson, J., Constantine, P., and Maxwell, R.: Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction, Adv. Water Resour., 135, 103473, https://doi.org/10.1016/j.advwatres.2019.103473, 2020.
Santos, L., Andersson, J., and Arheimer, B.: Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set, Hydrolog. Sci. J., 67, 342–357, https://doi.org/10.1080/02626667.2022.2035388, 2022.
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.: Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Syst., 9, 65–88, https://doi.org/10.1002/2016ms000686, 2017.
Shi, W. and Wang, Q.: An Analytical Model of Multi-layered Heat Transport to Estimate Vertical Streambed Fluxes and Sediment Thermal Properties, J. Hydrol., 625, 129963, https://doi.org/10.1016/j.jhydrol.2023.129963, 2023.
Sith, R., Watanabe, A., Nakamura, T., Yamamoto, T., and Nadaoka, K.: Assessment of water quality and evaluation of best management practices in a small agricultural watershed adjacent to Coral Reef area in Japan, Agr. Water Manage., 213, 659–673, https://doi.org/10.1016/j.agwat.2018.11.014, 2019.
Skinner, K. and Maupin, M.: Point-source nutrient loads to streams of the conterminous United States, 2012 (No. 1101), US Geological Survey, https://doi.org/10.3133/ds1101, 2019.
Soil Survey Staff: Gridded soil survey geographic (gSSURGO) database for the conterminous United States, https://data.nal.usda.gov/dataset/gridded-soil-survey-geographic-database-gssurgo (last access: 1 July 2020), 2014.
SWAT – Soil & Water Assessment Tool: gwflow module for SWAT+, https://swat.tamu.edu/software/plus/gwflow/ (last access: 22 December 2023), 2023.
Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., and Franz, K.: Mapping of 30-meter resolution tile-drained croplands using a geospatial modeling approach, Sci. Data, 7, 257, https://doi.org/10.1038/s41597-020-00596-x, 2020.
Van Leeuwen, P. and Evensen, G.: Data assimilation and inverse methods in terms of a probabilistic formulation, Mon. Weather Rev., 124, 2898–2913, https://doi.org/10.1175/1520-0493(1996)124<2898:DAAIMI>2.0.CO;2, 1996.
Wang, Y., Bian, J., Zhao, Y., Tang, J., and Jia, Z.: Assessment of future climate change impacts on nonpoint source pollution in snowmelt period for a cold area using SWAT, Sci. Rep., 8, 1–13, https://doi.org/10.1038/s41598-018-20818-y, 2018.
Wei, X., Bailey, R., and Tasdighi, A.: Using the SWAT model in intensively managed irrigated watersheds: Model modification and Application, J. Hydrol. Eng., 23, 04018044-1–04018044-17, https://doi.org/10.1061/(asce)he.1943-5584.0001696, 2018.
White, J.: A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions, Environ. Model. Softw., 109, 191–201, https://doi.org/10.1016/j.envsoft.2018.06.009, 2018.
White, J., Hunt, R., Fienen, M., and Doherty, J.: Approaches to Highly Parameterized Inversion: PEST
White, M. J., Arnold, J. G., Bieger, K., Allen, P. M., Gao, J., Čerkasova, N., Gambone, M., Park, S., Bosch, D. D., Yen, H., and Osorio, J. M.: Development of a field scale SWAT+ Modeling Framework for the contiguous U.S., J. Am. Water Resour. Assoc., 58, 1545–1560, https://doi.org/10.1111/1752-1688.13056, 2022.
Wojnar, A., Mutiti, S., and Levy, J.: Assessment of geophysical surveys as a tool to estimate riverbed hydraulic conductivity, J. Hydrol., 482, 40–56, https://doi.org/10.1016/j.jhydrol.2012.12.018, 2013.
Wolock, D. M.: Base-flow index grid for the conterminous United States (No. 2003-263), USGS, https://doi.org/10.3133/ofr03263, 2003.
Wu, B., Zheng, Y., Tian, Y., Wu, X., Yao, Y., Han, F., Liu, J., and Zheng, C.: Systematic assessment of the uncertainty in integrated surface water-groundwater modeling based on the probabilistic collocation method, Water Resour. Res., 50, 5848–5865, https://doi.org/10.1002/2014WR015366, 2014.
Yan, L. and Roy, D.: Conterminous United States crop field size quantification from multi-temporal Landsat Data, Remote Sen. Environ., 172, 67–86, https://doi.org/10.1016/j.rse.2015.10.034, 2016.
Zhang, H., Wang, B., Li Liu, D., Zhang, M., Leslie, L. M., and Yu, Q.: Using an improved SWAT model to simulate hydrological responses to land use change: A case study of a catchment in tropical Australia, J. Hydrol., 585, 124822, https://doi.org/10.1016/j.jhydrol.2020.124822, 2020.
Short summary
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.
Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds...
