Deep learning for monthly rainfallârunoff modelling: a large-sample comparison with conceptual models across Australia
Stephanie R Jean-Michel
Abstract. A deep learning model designed for time series predictions, the long short-term memory (LSTM) architecture, is regularly producing reliable results in local and regional rainfallârunoff applications around the world. Recent large-sample hydrology studies in North America and Europe have shown the LSTM model to successfully match conceptual model performance at a daily time step over hundreds of catchments. Here we investigate how these models perform in producing monthly runoff predictions in the relatively dry and variable conditions of the Australian continent. The monthly timeÂ step matches historic data availability and is also important for future water resources planning; however, it provides significantly smaller training datasets than daily time series. In this study, a continental-scale comparison of monthly deep learning (LSTM) predictions to conceptual rainfallârunoff (WAPABA model) predictions is performed on almost 500 catchments across Australia with performance results aggregated over a variety of catchment sizes, flow conditions, and hydrological record lengths. The study period covers a wet phase followed by a prolonged drought, introducing challenges for making predictions outside of known conditions â challenges that will intensify as climate change progresses. The results show that LSTM models matched or exceeded WAPABA prediction performance for more than two-thirds of the study catchments, the largest performance gains of LSTM versus WAPABA occurred in large catchments, the LSTMs struggled less to generalise than the WAPABA models (e.g. making predictions under new conditions), and catchments with few training observations due to the monthly timeÂ step did not demonstrate a clear benefit with either WAPABA or LSTM.
https://www.hydrology-and-earth-system-sciences.net/favicon_copernicus_16x16_.ico
Articles | Volume 28, issue 5

https://doi.org/10.5194/hess-28-1191-2024

© Author(s) 2024. This work is distributed under

the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-28-1191-2024

© Author(s) 2024. This work is distributed under

the Creative Commons Attribution 4.0 License.

Research article

13 Mar 2024

Research article | | 13 Mar 2024

Deep learning for monthly rainfallârunoff modelling: a large-sample comparison with conceptual models across Australia

Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch

Stephanie R. Clark

CORRESPONDING AUTHOR

CSIRO, Environment, Sydney, NSW, Australia

Julien Lerat

CSIRO, Environment, Canberra, ACT, Australia

Jean-Michel Perraud

CSIRO, Environment, Canberra, ACT, Australia

Peter Fitch

CSIRO, Environment, Canberra, ACT, Australia

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches

On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow

Dipti Tiwari, MÃ©lanie Trudel, and Robert Leconte

Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024,https://doi.org/10.5194/hess-28-1127-2024, 2024

Short summary

Short summary

Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.

Toward interpretable LSTM-based modeling of hydrological systems

Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon

Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024,https://doi.org/10.5194/hess-28-945-2024, 2024

Short summary

Short summary

Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but 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.

Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model

Louise Mimeau, Annika KÃ¼nne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal

Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024,https://doi.org/10.5194/hess-28-851-2024, 2024

Short summary

Short summary

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 are 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.

What controls the tail behaviour of flood series: rainfall or runoff generation?

Elena Macdonald, Bruno Merz, BjÃ¶rn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn

Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024,https://doi.org/10.5194/hess-28-833-2024, 2024

Short summary

Short summary

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.

Seasonal prediction of end-of-dry-season watershed behavior in a highly interconnected alluvial watershed in northern California

Claire Kouba and Thomas Harter

Hydrol. Earth Syst. Sci., 28, 691–718, https://doi.org/10.5194/hess-28-691-2024,https://doi.org/10.5194/hess-28-691-2024, 2024

Short summary

Short summary

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, 5–6 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.

Glaciers determine the sensitivity of hydrological processes to perturbed climate in a large mountainous basin on the Tibetan Plateau

Yi Nan and Fuqiang Tian

Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024,https://doi.org/10.5194/hess-28-669-2024, 2024

Short summary

Short summary

This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze 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.

Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records

Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, and Emily S. Bernhardt

Hydrol. Earth Syst. Sci., 28, 545–573, https://doi.org/10.5194/hess-28-545-2024,https://doi.org/10.5194/hess-28-545-2024, 2024

Short summary

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.

On the need for physical constraints in deep learning rainfallârunoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration

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

Short summary

Short summary

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.

Evaluation of hydrological models on small mountainous catchments: impact of the meteorological forcings

Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-AndrÃ© 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

Short summary

Short summary

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.

Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool

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

Short summary

Short summary

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.

A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+

Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Äerkasova, 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

Short summary

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.

On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions

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

Short summary

Short summary

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.

Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations

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

Short summary

Short summary

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.

Recent ground thermo-hydrological changes in a southern Tibetan endorheic catchment and implications for lake level changes

LÃ©o 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

Short summary

Short summary

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.

Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling

Diego Araya, Pablo A. Mendoza, Eduardo MuÃ±oz-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

Short summary

Short summary

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.

Modelling flood frequency and magnitude in a glacially conditioned, heterogeneous landscape: testing the importance of land cover and land use

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

Short summary

Short summary

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.

Direct integration of reservoirs' operations in a hydrological model for streamflow estimation: coupling a CLSTM model with MOHID-Land

Ana Ramos Oliveira, Tiago Brito Ramos, LÃ­gia 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

Short summary

Short summary

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.

Modelling the regional sensitivity of snowmelt, soil moisture, and streamflow generation to climate over the Canadian Prairies using a basin classification approach

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

Short summary

Short summary

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.

To what extent does river routing matter in hydrological modeling?

NicolÃ¡s CortÃ©s-Salazar, NicolÃ¡s VÃ¡squez, 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

Short summary

Short summary

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.

Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins

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

Short summary

Short summary

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.

An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume

Francesco Fatone, Bartosz SzelÄ g, PrzemysÅaw Kowal, Arthur McGarity, Adam Kiczko, Grzegorz WaÅek, Ewa Wojciechowska, MichaÅ 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

Short summary

Short summary

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.

airGRteaching: an open-source tool for teaching hydrological modeling withÂ R

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

Short summary

Short summary

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.

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

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

Short summary

Short summary

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.

Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation

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

Short summary

Short summary

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.

Changes in Mediterranean flood processes and seasonality

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

Short summary

Short summary

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.

Technical Note: Testing the Connection Between Hillslope Scale Runoff Fluctuations and Streamflow Hydrographs at the Outlet of Large River Basins

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

Short summary

Short summary

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.

A Network Approach for Multiscale Catchment Classification using Traits

Fabio Ciulla and Charuleka Varadharajan

EGUsphere, https://doi.org/10.5194/egusphere-2023-1675,https://doi.org/10.5194/egusphere-2023-1675, 2023

Short summary

Short summary

When studying the behavior of rivers, like their tendency to flood, it is useful to group them using the characteristics of their surrounding areas like geology, climate, land use and human influence. We developed a method that, in addition to this classification, also returns the relevant characteristics of each group and associates them to particular behaviors. In this way we better understand how rivers interact with the environment and can try to improve the predictions of future behaviors.

Can the combining of wetlands with reservoir operation reduce the risk of future floods and droughts?

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

Short summary

Short summary

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.

Advancing Understanding of Lake-Watershed Hydrology Through A Fully Coupled Numerical Model

Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu

Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-166,https://doi.org/10.5194/hess-2023-166, 2023

Revised manuscript accepted for HESS

Short summary

Short summary

We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

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

Short summary

Short summary

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.

When best is the enemy of good â critical evaluation of performance criteria in hydrological models

Guillaume Cinkus, Naomi Mazzilli, HervÃ© Jourde, Andreas Wunsch, Tanja Liesch, NataÅ¡a 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

Short summary

Short summary

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.

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

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

Short summary

Short summary

Powerful hybrid models (called Î´ 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. Î´ 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. Î´ models could be ideal candidates for global hydrologic assessment.

Producing reliable hydrologic scenarios from raw climate model outputs without resorting to meteorological observations

Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, and FranÃ§ois 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

Short summary

Short summary

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.

Afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain

Marcus Edmund Henry Buechel, Louise Slater, and Simon Dadson

Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-138,https://doi.org/10.5194/hess-2023-138, 2023

Revised manuscript accepted for HESS

Short summary

Short summary

Afforestation has been proposed internationally, but the hydrological implications of such large increases in spatial extent of woodland are not fully understood. In this study we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.

Using normalised difference infrared index patterns to constrain semi-distributed rainfallârunoff models in tropical nested catchments

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

Short summary

Short summary

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.

Revisiting the hydrological basis of the Budyko framework with the principle of hydrologically similar groups

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

Short summary

Short summary

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.

Reconstructing five decades of sediment export from two glacierized high-alpine catchments in Tyrol, Austria, using nonparametric regression

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

Short summary

Short summary

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.

Water and energy budgets over hydrological basins on short and long timescales

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

Short summary

Short summary

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.

Multi-model approach in a variable spatial framework for streamflow simulation

Cyril ThÃ©bault, Charles Perrin, Vazken AndrÃ©assian, Guillaume Thirel, SÃ©bastien Legrand, and Olivier Delaigue

EGUsphere, https://doi.org/10.5194/egusphere-2023-569,https://doi.org/10.5194/egusphere-2023-569, 2023

Short summary

Short summary

Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling frameworks tested.

Hydrological response to climate change and human activities in the Three-River Source Region

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

Short summary

Short summary

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 inï¬ltration 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.

Incorporating experimentally derived streamflow contributions into model parameterization to improve discharge prediction

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

Short summary

Short summary

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.

Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data

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

Short summary

Short summary

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.

River hydraulic modeling with ICESat-2 land and water surface elevation

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

Short summary

Short summary

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.

Hydrological modeling using the Soil and Water Assessment Tool in urban and peri-urban environments: the case of Kifisos experimental subbasin (Athens, Greece)

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

Short summary

Short summary

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.

Empirical stream thermal sensitivities cluster on the landscape according to geology and climate

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

Short summary

Short summary

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.

Monetizing the role of water in sustaining watershed ecosystem services using a fully integrated subsurface–surface water model

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

Short summary

Short summary

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.

Technical note: How physically based is hydrograph separation by recursive digital filtering?

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

Short summary

Short summary

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.

A comprehensive open-source course for teaching applied hydrological modelling in Central Asia

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

Short summary

Short summary

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.

Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment

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

Short summary

Short summary

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.

Technical note: Extending the SWAT model to transport chemicals through tile and groundwater flow

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.

Cited articles