Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2951-2021
© Author(s) 2021. 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-25-2951-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 May 2021
Research article |
| 31 May 2021
| 31 May 2021
Machine-learning methods for stream water temperature prediction
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Katharina Lebiedzinski
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Mathew Herrnegger
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Karsten Schulz
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
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Hatice Türk, Christine Stumpp, Markus Hrachowitz, Karsten Schulz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-359, https://doi.org/10.5194/hess-2024-359, 2024
Preprint under review for HESS
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Using advances in transit time estimation and tracer data, we tested if fast-flow transit times are controlled solely by soil moisture or are also controlled by precipitation intensity. We used soil moisture-dependent and precipitation intensity-conditional transfer functions. We showed that significant portion of event water bypasses the soil matrix through fast flow paths (overland flow, tile drains, preferential flow paths) in dry soil conditions for both low and high-intensity precipitation.
Christian Voigt, Karsten Schulz, Franziska Koch, Karl-Friedrich Wetzel, Ludger Timmen, Till Rehm, Hartmut Pflug, Nico Stolarczuk, Christoph Förste, and Frank Flechtner
Hydrol. Earth Syst. Sci., 25, 5047–5064, https://doi.org/10.5194/hess-25-5047-2021, https://doi.org/10.5194/hess-25-5047-2021, 2021
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A continuously operating superconducting gravimeter at the Zugspitze summit is introduced to support hydrological studies of the Partnach spring catchment known as the Zugspitze research catchment. The observed gravity residuals reflect total water storage variations at the observation site. Hydro-gravimetric analysis show a high correlation between gravity and the snow water equivalent, with a gravimetric footprint of up to 4 km radius enabling integral insights into this high alpine catchment.
Christoph Klingler, Karsten Schulz, and Mathew Herrnegger
Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, https://doi.org/10.5194/essd-13-4529-2021, 2021
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LamaH-CE is a large-sample catchment hydrology dataset for Central Europe. The dataset contains hydrometeorological time series (daily and hourly resolution) and various attributes for 859 gauged basins. Sticking closely to the CAMELS datasets, LamaH includes additional basin delineations and attributes for describing a large interconnected river network. LamaH further contains outputs of a conceptual hydrological baseline model for plausibility checking of the inputs and for benchmarking.
Josef Fürst, Hans Peter Nachtnebel, Josef Gasch, Reinhard Nolz, Michael Paul Stockinger, Christine Stumpp, and Karsten Schulz
Earth Syst. Sci. Data, 13, 4019–4034, https://doi.org/10.5194/essd-13-4019-2021, https://doi.org/10.5194/essd-13-4019-2021, 2021
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Rosalia is a 222 ha forested research watershed in eastern Austria to study water, energy and solute transport processes. The paper describes the site, monitoring network, instrumentation and the datasets: high-resolution (10 min interval) time series starting in 2015 of four discharge gauging stations, seven rain gauges, and observations of air and water temperature, relative humidity, and conductivity, as well as soil water content and temperature, at different depths at four profiles.
Michael Weber, Franziska Koch, Matthias Bernhardt, and Karsten Schulz
Hydrol. Earth Syst. Sci., 25, 2869–2894, https://doi.org/10.5194/hess-25-2869-2021, https://doi.org/10.5194/hess-25-2869-2021, 2021
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We compared a suite of globally available meteorological and DEM data with in situ data for physically based snow hydrological modelling in a small high-alpine catchment. Although global meteorological data were less suited to describe the snowpack properly, transferred station data from a similar location in the vicinity and substituting single variables with global products performed well. In addition, using 30 m global DEM products as model input was useful in such complex terrain.
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020, https://doi.org/10.5194/hess-24-4463-2020, 2020
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The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
Benjamin Müller, Matthias Bernhardt, and Karsten Schulz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-563, https://doi.org/10.5194/hess-2019-563, 2019
Manuscript not accepted for further review
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Time series of thermal remote sensing images include more information than usually used. Land surface related processes are combined into a single image. Activity of these processes change from image to image. Thus, information on land surface characteristics is to be found
somewhere in betweenthe images. We provide an algorithm to test the presence of such characteristics within a set of images. The algorithm can be used for process understanding, model evaluation, data assimilation, etc.
Christoph Schürz, Brigitta Hollosi, Christoph Matulla, Alexander Pressl, Thomas Ertl, Karsten Schulz, and Bano Mehdi
Hydrol. Earth Syst. Sci., 23, 1211–1244, https://doi.org/10.5194/hess-23-1211-2019, https://doi.org/10.5194/hess-23-1211-2019, 2019
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For two Austrian catchments we simulated discharge and nitrate-nitrogen (NO3-N) considering future changes of climate, land use, and point source emissions together with the impact of different setups and parametrizations of the implemented eco-hydrological model. In a comprehensive analysis we identified the dominant sources of uncertainty for the simulation of discharge and NO3-N and further examined how specific properties of the model inputs control the future simulation results.
Abolanle E. Odusanya, Bano Mehdi, Christoph Schürz, Adebayo O. Oke, Olufiropo S. Awokola, Julius A. Awomeso, Joseph O. Adejuwon, and Karsten Schulz
Hydrol. Earth Syst. Sci., 23, 1113–1144, https://doi.org/10.5194/hess-23-1113-2019, https://doi.org/10.5194/hess-23-1113-2019, 2019
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The main objective was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data for the data-sparse Ogun River Basin (20 292 km2) located in southwestern Nigeria. The SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool.
Maik Renner, Claire Brenner, Kaniska Mallick, Hans-Dieter Wizemann, Luigi Conte, Ivonne Trebs, Jianhui Wei, Volker Wulfmeyer, Karsten Schulz, and Axel Kleidon
Hydrol. Earth Syst. Sci., 23, 515–535, https://doi.org/10.5194/hess-23-515-2019, https://doi.org/10.5194/hess-23-515-2019, 2019
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We estimate the phase lag of surface states and heat fluxes to incoming solar radiation at the sub-daily timescale. While evapotranspiration reveals a minor phase lag, the vapor pressure deficit used as input by Penman–Monteith approaches shows a large phase lag. The surface-to-air temperature gradient used by energy balance residual approaches shows a small phase shift in agreement with the sensible heat flux and thus explains the better correlation of these models at the sub-daily timescale.
Lu Gao, Jianhui Wei, Lingxiao Wang, Matthias Bernhardt, Karsten Schulz, and Xingwei Chen
Earth Syst. Sci. Data, 10, 2097–2114, https://doi.org/10.5194/essd-10-2097-2018, https://doi.org/10.5194/essd-10-2097-2018, 2018
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High-resolution temperature data sets are important for the Chinese Tian Shan, which has a complex ecological environment system. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for this area from 1979 to 2016 based on a robust statistical downscaling framework. The strongest advantage of this method is its independence of local meteorological stations due to a model internal, vertical lapse rate scheme. This method was validated for other mountains.
Frederik Kratzert, Daniel Klotz, Claire Brenner, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, https://doi.org/10.5194/hess-22-6005-2018, 2018
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In this paper, we propose a novel data-driven approach for
rainfall–runoff modelling, using the long short-term memory (LSTM) network, a special type of recurrent neural network. We show in three different experiments that this network is able to learn to predict the discharge purely from meteorological input parameters (such as precipitation or temperature) as accurately as (or better than) the well-established Sacramento Soil Moisture Accounting model, coupled with the Snow-17 snow model.
Stefan Härer, Matthias Bernhardt, Matthias Siebers, and Karsten Schulz
The Cryosphere, 12, 1629–1642, https://doi.org/10.5194/tc-12-1629-2018, https://doi.org/10.5194/tc-12-1629-2018, 2018
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The paper presents an approach which can be used to process satellite-based snow cover maps with a higher-than-today accuracy at the local scale. Many of the current satellite-based snow maps are using the NDSI with a threshold as a tool for deciding if there is snow on the ground or not. The presented study has shown that, firstly, using the standard threshold of 0.4 can result in significant derivations at the local scale and that, secondly, the deviations become smaller for coarser scales.
Karsten Schulz, Reinhard Burgholzer, Daniel Klotz, Johannes Wesemann, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 22, 2607–2613, https://doi.org/10.5194/hess-22-2607-2018, https://doi.org/10.5194/hess-22-2607-2018, 2018
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The unit hydrograph has been one of the most widely employed modelling techniques to predict rainfall-runoff behaviour of hydrological catchments. We developed a lecture theatre experiment including some student involvement to illustrate the principles behind this modelling technique. The experiment only uses very simple and cheap material including a set of plastic balls (representing rainfall), magnetic stripes (tacking the balls to the white board) and sieves (for ball/water gauging).
Benjamin Müller, Matthias Bernhardt, Conrad Jackisch, and Karsten Schulz
Hydrol. Earth Syst. Sci., 20, 3765–3775, https://doi.org/10.5194/hess-20-3765-2016, https://doi.org/10.5194/hess-20-3765-2016, 2016
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A technology for the spatial derivation of soil texture classes is presented. Information about soil texture is key for predicting the local and regional hydrological cycle. It is needed for the calculation of soil water movement, the share of surface runoff, the evapotranspiration rate and others. Nevertheless, the derivation of soil texture classes is expensive and time-consuming. The presented technique uses soil samples and remotely sensed data for estimating their spatial distribution.
S. Härer, M. Bernhardt, and K. Schulz
Geosci. Model Dev., 9, 307–321, https://doi.org/10.5194/gmd-9-307-2016, https://doi.org/10.5194/gmd-9-307-2016, 2016
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This paper describes a new method to produce spatially and temporally calibrated NDSI-based satellite snow cover maps utilizing simultaneously captured terrestrial photographs as in situ information. First results confirm a high quality of the produced satellite snow cover maps and emphasize the need for calibration of the NDSI threshold value to ensure a high accuracy and reproduciblity. The software "PRACTISE V.2.1" was developed to automatically process the photographs and satellite images.
M. Herrnegger, H. P. Nachtnebel, and K. Schulz
Hydrol. Earth Syst. Sci., 19, 4619–4639, https://doi.org/10.5194/hess-19-4619-2015, https://doi.org/10.5194/hess-19-4619-2015, 2015
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Especially in alpine catchments, areal rainfall estimates often exhibit large errors. Runoff measurements are, on the other hand, one of the most robust observations within the hydrological cycle. We therefore calculate mean catchment rainfall by inverting an HBV-type rainfall-runoff model, using runoff observations as input. The inverse model may e.g. be used to analyse rainfall conditions of extreme flood events or estimation of snowmelt contribution.
B. Müller, M. Bernhardt, and K. Schulz
Hydrol. Earth Syst. Sci., 18, 5345–5359, https://doi.org/10.5194/hess-18-5345-2014, https://doi.org/10.5194/hess-18-5345-2014, 2014
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We present a method to define hydrological landscape units by a time series of thermal infrared satellite data. Land surface temperature is calculated for 28 images in 12 years for a catchment in Luxembourg. Pattern measures show spatio-temporal persistency; principle component analysis extracts relevant patterns. Functional units represent similar behaving entities based on a representative set of images. Resulting classification and patterns are discussed regarding potential applications.
E. Zehe, U. Ehret, L. Pfister, T. Blume, B. Schröder, M. Westhoff, C. Jackisch, S. J. Schymanski, M. Weiler, K. Schulz, N. Allroggen, J. Tronicke, L. van Schaik, P. Dietrich, U. Scherer, J. Eccard, V. Wulfmeyer, and A. Kleidon
Hydrol. Earth Syst. Sci., 18, 4635–4655, https://doi.org/10.5194/hess-18-4635-2014, https://doi.org/10.5194/hess-18-4635-2014, 2014
S. Härer, M. Bernhardt, J. G. Corripio, and K. Schulz
Geosci. Model Dev., 6, 837–848, https://doi.org/10.5194/gmd-6-837-2013, https://doi.org/10.5194/gmd-6-837-2013, 2013
Related subject area
Subject: Rivers and Lakes | Techniques and Approaches: Modelling approaches
On the cause of large daily river flow fluctuations in the Mekong River
A hybrid data-driven approach to analyze the drivers of lake level dynamics
Estimating velocity distribution and flood discharge at river bridges using entropy theory – insights from computational fluid dynamics flow fields
Assessing the different components of the water balance of Lake Titicaca
Late-Quaternary hydrological evolution of Fuente de Piedra playa-lake (southern Iberia) controlled by neotectonics and climate changes
Isotopic evaluation of the National Water Model reveals missing agricultural irrigation contributions to streamflow across the western United States
Timing of spring events changes under modelled future climate scenarios in a mesotrophic lake
Effects of high-quality elevation data and explanatory variables on the accuracy of flood inundation mapping via Height Above Nearest Drainage
Apparent Friction Coefficient Used for Flow Calculation in Straight Compound Channels With Trees On Floodplains
Assessing national exposure and impact to glacial lake outburst floods considering uncertainty under data sparsity
Understanding the compound flood risk along the coast of the contiguous United States
Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States
Sources of skill in lake temperature, discharge and ice-off seasonal forecasting tools
Past and future climate change effects on the thermal regime and oxygen solubility of four peri-alpine lakes
Exploring tracer information in a small stream to improve parameter identifiability and enhance the process interpretation in transient storage models
How do inorganic nitrogen processing pathways change quantitatively at daily, seasonal, and multiannual scales in a large agricultural stream?
Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network
Spatially referenced Bayesian state-space model of total phosphorus in western Lake Erie
Future water temperature of rivers in Switzerland under climate change investigated with physics-based models
Physical controls and a priori estimation of raising land surface elevation across the southwestern Bangladesh delta using tidal river management
Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling
Synthesizing the impacts of baseflow contribution on concentration–discharge (C–Q) relationships across Australia using a Bayesian hierarchical model
Calibrating 1D hydrodynamic river models in the absence of cross-section geometry using satellite observations of water surface elevation and river width
A global algorithm for identifying changing streamflow regimes: application to Canadian natural streams (1966–2010)
Streamflow drought: implication of drought definitions and its application for drought forecasting
Quantifying floodwater impacts on a lake water budget via volume-dependent transient stable isotope mass balance
River runoff in Switzerland in a changing climate – changes in moderate extremes and their seasonality
River runoff in Switzerland in a changing climate – runoff regime changes and their time of emergence
Bathymetry and latitude modify lake warming under ice
Lake thermal structure drives interannual variability in summer anoxia dynamics in a eutrophic lake over 37 years
Reservoir evaporation in a Mediterranean climate: comparing direct methods in Alqueva Reservoir, Portugal
Diverging hydrological drought traits over Europe with global warming
Anthropogenic influence on the Rhine water temperatures
A new form of the Saint-Venant equations for variable topography
Simulations of future changes in thermal structure of Lake Erken: proof of concept for ISIMIP2b lake sector local simulation strategy
Assessment of the geomorphic effectiveness of controlled floods in a braided river using a reduced-complexity numerical model
Worldwide lake level trends and responses to background climate variation
Modeling inorganic carbon dynamics in the Seine River continuum in France
A data-based predictive model for spatiotemporal variability in stream water quality
Flooding in the Mekong Delta: the impact of dyke systems on downstream hydrodynamics
Reconstruction of the 1941 GLOF process chain at Lake Palcacocha (Cordillera Blanca, Peru)
Historical modelling of changes in Lake Erken thermal conditions
Improving lake mixing process simulations in the Community Land Model by using K profile parameterization
Upgraded global mapping information for earth system modelling: an application to surface water depth at the ECMWF
Sediment transport modelling in riverine environments: on the importance of grain-size distribution, sediment density, and suspended sediment concentrations at the upstream boundary
Replication of ecologically relevant hydrological indicators following a modified covariance approach to hydrological model parameterization
Lidar-based approaches for estimating solar insolation in heavily forested streams
Numerical study on the response of the largest lake in China to climate change
Unraveling the hydrological budget of isolated and seasonally contrasted subtropical lakes
Future projections of temperature and mixing regime of European temperate lakes
Khosro Morovati, Keer Zhang, Lidi Shi, Yadu Pokhrel, Maozhou Wu, Paradis Someth, Sarann Ly, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 5133–5147, https://doi.org/10.5194/hess-28-5133-2024, https://doi.org/10.5194/hess-28-5133-2024, 2024
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This study examines large daily river flow fluctuations in the dammed Mekong River, developing integrated 3D hydrodynamic and response time models alongside a hydrological model with an embedded reservoir module. This approach allows estimation of travel times between hydrological stations and contributions of subbasins and upstream regions. Findings show a power correlation between upstream discharge and travel time, and significant fluctuations occurred even before dam construction.
Márk Somogyvári, Dieter Scherer, Frederik Bart, Ute Fehrenbach, Akpona Okujeni, and Tobias Krueger
Hydrol. Earth Syst. Sci., 28, 4331–4348, https://doi.org/10.5194/hess-28-4331-2024, https://doi.org/10.5194/hess-28-4331-2024, 2024
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We study the drivers behind the changes in lake levels, creating a series of models from least to most complex. In this study, we have shown that the decreasing levels of Groß Glienicker Lake in Germany are not simply the result of changes in climate but are affected by other processes. In our example, reduced inflow from a growing forest, regionally sinking groundwater levels and the modifications in the local rainwater infrastructure together resulted in an increasing lake level loss.
Farhad Bahmanpouri, Tommaso Lazzarin, Silvia Barbetta, Tommaso Moramarco, and Daniele P. Viero
Hydrol. Earth Syst. Sci., 28, 3717–3737, https://doi.org/10.5194/hess-28-3717-2024, https://doi.org/10.5194/hess-28-3717-2024, 2024
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The entropy model is a reliable tool to estimate flood discharge in rivers using observed level and surface velocity. Often, level and velocity sensors are placed on bridges, which may disturb the flow. Using accurate numerical models, we explored the entropy model reliability nearby a multi-arch bridge. We found that it is better to place sensors and to estimate the discharge upstream of bridges; downstream, the entropy model needs the river-wide distribution of surface velocity as input data.
Nilo Lima-Quispe, Denis Ruelland, Antoine Rabatel, Waldo Lavado-Casimiro, and Thomas Condom
EGUsphere, https://doi.org/10.5194/egusphere-2024-2370, https://doi.org/10.5194/egusphere-2024-2370, 2024
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This study estimated the water balance of Lake Titicaca using an integrated modeling framework that considers natural hydrological processes and net irrigation consumption. The proposed approach was implemented at a daily scale for a period of 35 years. This framework is able to simulate lake water levels with good accuracy over a wide range of hydroclimatic conditions. The findings demonstrate that a simple representation of hydrological processes is suitable for use in poorly-gauged regions.
Alejandro Jiménez Bonilla, Lucía Martegani, Miguel Rodríguez-Rodríguez, Fernando Gázquez, Manuel Díaz-Azpíroz, Sergio Martos, Klaus Reicherter, and Inmaculada Expósito
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-142, https://doi.org/10.5194/hess-2024-142, 2024
Revised manuscript accepted for HESS
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We accomplished an interdisciplinary study to study the Fuente de Piedra (FdP) playa-lake evolution in southern Spain. We made water balances during the FdP lifespan . Our results indicate that the FdP playa-lake level moved and tilted towards SW caused by active faults.
Annie L. Putman, Patrick C. Longley, Morgan C. McDonnell, James Reddy, Michelle Katoski, Olivia L. Miller, and J. Renée Brooks
Hydrol. Earth Syst. Sci., 28, 2895–2918, https://doi.org/10.5194/hess-28-2895-2024, https://doi.org/10.5194/hess-28-2895-2024, 2024
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Accuracy of streamflow estimates where water management and use are prevalent, such as the western US, reflect hydrologic modeling decisions. To evaluate process inclusion decisions, we equipped a hydrologic model with tracers and compared estimates to observations. The tracer-equipped model performed well, and differences between the model and observations suggest that the inclusion of water from irrigation may improve model performance in this region.
Jorrit P. Mesman, Inmaculada C. Jiménez-Navarro, Ana I. Ayala, Javier Senent-Aparicio, Dennis Trolle, and Don C. Pierson
Hydrol. Earth Syst. Sci., 28, 1791–1802, https://doi.org/10.5194/hess-28-1791-2024, https://doi.org/10.5194/hess-28-1791-2024, 2024
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Spring events in lakes are key processes for ecosystem functioning. We used a coupled catchment–lake model to investigate future changes in the timing of spring discharge, ice-off, spring phytoplankton peak, and onset of stratification in a mesotrophic lake. We found a clear trend towards earlier occurrence under climate warming but also that relative shifts in the timing occurred, such as onset of stratification advancing more slowly than the other events.
Fernando Aristizabal, Taher Chegini, Gregory Petrochenkov, Fernando Salas, and Jasmeet Judge
Hydrol. Earth Syst. Sci., 28, 1287–1315, https://doi.org/10.5194/hess-28-1287-2024, https://doi.org/10.5194/hess-28-1287-2024, 2024
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Floods are significant natural disasters that affect people and property. This study uses a simplified terrain index and the latest lidar-derived digital elevation maps (DEMs) to investigate flood inundation extent quality. We examined inundation quality influenced by different spatial resolutions and other variables. Results showed that lidar DEMs enhance inundation quality, but their resolution is less impactful in our context. Further studies on reservoirs and urban flooding are recommended.
Adam Kozioł, Adam Kiczko, Marcin Krukowski, Elżbieta Kubrak, Janusz Kubrak, Grzegorz Majewski, and Andrzej Brandyk
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-74, https://doi.org/10.5194/hess-2024-74, 2024
Revised manuscript accepted for HESS
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Floodplain trees play a crucial role in increasing flow resistance. Their impact extends beyond floodplains to affect the main channel. The experiments reveal the influence of floodplain trees on the discharge capacity of channels with varying roughness. We determine resistance coefficients for different roughness levels of the main channel bottom. The research contributes to a deeper understanding of open-channel flow dynamics and has practical implications for river engineering.
Huili Chen, Qiuhua Liang, Jiaheng Zhao, and Sudan Bikash Maharjan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-260, https://doi.org/10.5194/hess-2023-260, 2024
Revised manuscript accepted for HESS
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Glacial Lake Outburst Floods (GLOFs) can cause serious damage. To assess their risks, we developed an innovative framework using remote sensing, Bayesian models, flood modeling, and open-source data. This enables us to evaluate GLOFs on a national scale, despite limited data and challenges accessing high-altitude lakes. We evaluated dangerous lakes in Nepal, identifying those most at risk. This work is crucial for understanding GLOF risks and the framework can be transferred to other areas.
Dongyu Feng, Zeli Tan, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 27, 3911–3934, https://doi.org/10.5194/hess-27-3911-2023, https://doi.org/10.5194/hess-27-3911-2023, 2023
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This study assesses the flood risks concurrently induced by river flooding and coastal storm surge along the coast of the contiguous United States using statistical and numerical models. We reveal a few hotspots of such risks, the critical spatial variabilities within a river basin and over the whole US coast, and the uncertainties of the risk assessment. We highlight the importance of weighing different risk measures to avoid underestimating or exaggerating the compound flood impacts.
Erin Towler, Sydney S. Foks, Aubrey L. Dugger, Jesse E. Dickinson, Hedeff I. Essaid, David Gochis, Roland J. Viger, and Yongxin Zhang
Hydrol. Earth Syst. Sci., 27, 1809–1825, https://doi.org/10.5194/hess-27-1809-2023, https://doi.org/10.5194/hess-27-1809-2023, 2023
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Hydrologic models developed to assess water availability need to be systematically evaluated. This study evaluates the long-term performance of two high-resolution hydrologic models that simulate streamflow across the contiguous United States. Both models show similar performance overall and regionally, with better performance in minimally disturbed basins than in those impacted by human activity. At about 80 % of the sites, both models outperform the seasonal climatological benchmark.
François Clayer, Leah Jackson-Blake, Daniel Mercado-Bettín, Muhammed Shikhani, Andrew French, Tadhg Moore, James Sample, Magnus Norling, Maria-Dolores Frias, Sixto Herrera, Elvira de Eyto, Eleanor Jennings, Karsten Rinke, Leon van der Linden, and Rafael Marcé
Hydrol. Earth Syst. Sci., 27, 1361–1381, https://doi.org/10.5194/hess-27-1361-2023, https://doi.org/10.5194/hess-27-1361-2023, 2023
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We assessed the predictive skill of forecasting tools over the next season for water discharge and lake temperature. Tools were forced with seasonal weather predictions; however, most of the prediction skill originates from legacy effects and not from seasonal weather predictions. Yet, when skills from seasonal weather predictions are present, additional skill comes from interaction effects. Skilful lake seasonal predictions require better weather predictions and realistic antecedent conditions.
Olivia Desgué-Itier, Laura Melo Vieira Soares, Orlane Anneville, Damien Bouffard, Vincent Chanudet, Pierre Alain Danis, Isabelle Domaizon, Jean Guillard, Théo Mazure, Najwa Sharaf, Frédéric Soulignac, Viet Tran-Khac, Brigitte Vinçon-Leite, and Jean-Philippe Jenny
Hydrol. Earth Syst. Sci., 27, 837–859, https://doi.org/10.5194/hess-27-837-2023, https://doi.org/10.5194/hess-27-837-2023, 2023
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The long-term effects of climate change will include an increase in lake surface and deep water temperatures. Incorporating up to 6 decades of limnological monitoring into an improved 1D lake model approach allows us to predict the thermal regime and oxygen solubility in four peri-alpine lakes over the period 1850–2100. Our modeling approach includes a revised selection of forcing variables and provides a way to investigate the impacts of climate variations on lakes for centennial timescales.
Enrico Bonanno, Günter Blöschl, and Julian Klaus
Hydrol. Earth Syst. Sci., 26, 6003–6028, https://doi.org/10.5194/hess-26-6003-2022, https://doi.org/10.5194/hess-26-6003-2022, 2022
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There is an unclear understanding of which processes regulate the transport of water, solutes, and pollutants in streams. This is crucial since these processes control water quality in river networks. Compared to other approaches, we obtained clearer insights into the processes controlling solute transport in the investigated reach. This work highlights the risks of using uncertain results for interpreting the processes controlling water movement in streams.
Jingshui Huang, Dietrich Borchardt, and Michael Rode
Hydrol. Earth Syst. Sci., 26, 5817–5833, https://doi.org/10.5194/hess-26-5817-2022, https://doi.org/10.5194/hess-26-5817-2022, 2022
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In this study, we set up a water quality model using a 5-year paired high-frequency water quality dataset from a large agricultural stream. The simulations were compared with the 15 min interval measurements and showed very good fits. Based on these, we quantified the N uptake pathway rates and efficiencies at daily, seasonal, and yearly scales. This study offers an overarching understanding of N processing in large agricultural streams across different temporal scales.
Leah A. Jackson-Blake, François Clayer, Sigrid Haande, James E. Sample, and S. Jannicke Moe
Hydrol. Earth Syst. Sci., 26, 3103–3124, https://doi.org/10.5194/hess-26-3103-2022, https://doi.org/10.5194/hess-26-3103-2022, 2022
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We develop a Gaussian Bayesian network (GBN) for seasonal forecasting of lake water quality and algal bloom risk in a nutrient-impacted lake in southern Norway. Bayesian networks are powerful tools for environmental modelling but are almost exclusively discrete. We demonstrate that a continuous GBN is a promising alternative approach. Predictive performance of the GBN was similar or improved compared to a discrete network, and it was substantially less time-consuming and subjective to develop.
Timothy J. Maguire, Craig A. Stow, and Casey M. Godwin
Hydrol. Earth Syst. Sci., 26, 1993–2017, https://doi.org/10.5194/hess-26-1993-2022, https://doi.org/10.5194/hess-26-1993-2022, 2022
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Water within large water bodies is constantly moving. Consequently, water movement masks causal relationships that exist between rivers and lakes. Incorporating water movement into models of nutrient concentration allows us to predict concentrations at unobserved locations and at observed locations on days not sampled. Our modeling approach does this while accommodating nutrient concentration data from multiple sources and provides a way to experimentally define the impact of rivers on lakes.
Adrien Michel, Bettina Schaefli, Nander Wever, Harry Zekollari, Michael Lehning, and Hendrik Huwald
Hydrol. Earth Syst. Sci., 26, 1063–1087, https://doi.org/10.5194/hess-26-1063-2022, https://doi.org/10.5194/hess-26-1063-2022, 2022
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This study presents an extensive study of climate change impacts on river temperature in Switzerland. Results show that, even for low-emission scenarios, water temperature increase will lead to adverse effects for both ecosystems and socio-economic sectors throughout the 21st century. For high-emission scenarios, the effect will worsen. This study also shows that water seasonal warming will be different between the Alpine regions and the lowlands. Finally, efficiency of models is assessed.
Md Feroz Islam, Paul P. Schot, Stefan C. Dekker, Jasper Griffioen, and Hans Middelkoop
Hydrol. Earth Syst. Sci., 26, 903–921, https://doi.org/10.5194/hess-26-903-2022, https://doi.org/10.5194/hess-26-903-2022, 2022
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The potential of sedimentation in the lowest parts of polders (beels) through controlled flooding with dike breach (tidal river management – TRM) to counterbalance relative sea level rise (RSLR) in 234 beels of SW Bangladesh is determined in this study, using 2D models and multiple regression. Lower beels located closer to the sea have the highest potential. Operating TRM only during the monsoon season is sufficient to raise the land surface of most beels by more than 3 times the yearly RSLR.
Sam Anderson and Valentina Radić
Hydrol. Earth Syst. Sci., 26, 795–825, https://doi.org/10.5194/hess-26-795-2022, https://doi.org/10.5194/hess-26-795-2022, 2022
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We develop and interpret a spatiotemporal deep learning model for regional streamflow prediction at more than 200 stream gauge stations in western Canada. We find the novel modelling style to work very well for daily streamflow prediction. Importantly, we interpret model learning to show that it has learned to focus on physically interpretable and physically relevant information, which is a highly desirable quality of machine-learning-based hydrological models.
Danlu Guo, Camille Minaudo, Anna Lintern, Ulrike Bende-Michl, Shuci Liu, Kefeng Zhang, and Clément Duvert
Hydrol. Earth Syst. Sci., 26, 1–16, https://doi.org/10.5194/hess-26-1-2022, https://doi.org/10.5194/hess-26-1-2022, 2022
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We investigate the impact of baseflow contribution on concentration–flow (C–Q) relationships across the Australian continent. We developed a novel Bayesian hierarchical model for six water quality variables across 157 catchments that span five climate zones. For sediments and nutrients, the C–Q slope is generally steeper for catchments with a higher median and a greater variability of baseflow contribution, highlighting the key role of variable flow pathways in particulate and solute export.
Liguang Jiang, Silja Westphal Christensen, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 25, 6359–6379, https://doi.org/10.5194/hess-25-6359-2021, https://doi.org/10.5194/hess-25-6359-2021, 2021
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River roughness and geometry are essential to hydraulic river models. However, measurements of these quantities are not available in most rivers globally. Nevertheless, simultaneous calibration of channel geometric parameters and roughness is difficult as they compensate for each other. This study introduces an alternative approach of parameterization and calibration that reduces parameter correlations by combining cross-section geometry and roughness into a conveyance parameter.
Masoud Zaerpour, Shadi Hatami, Javad Sadri, and Ali Nazemi
Hydrol. Earth Syst. Sci., 25, 5193–5217, https://doi.org/10.5194/hess-25-5193-2021, https://doi.org/10.5194/hess-25-5193-2021, 2021
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Streamflow regimes are changing globally particularly in cold regions. We develop a novel algorithm for detecting shifting streamflow regimes using changes in first and second moments of ensemble streamflow features. This algorithm is generic and can be used globally. To showcase its application, we assess alterations in Canadian natural streams from 1966 to 2010 to provide the first temporally consistent, pan-Canadian assessment of change in natural streamflow regimes, coast to coast to coast.
Samuel J. Sutanto and Henny A. J. Van Lanen
Hydrol. Earth Syst. Sci., 25, 3991–4023, https://doi.org/10.5194/hess-25-3991-2021, https://doi.org/10.5194/hess-25-3991-2021, 2021
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This paper provides a comprehensive overview of the differences within streamflow droughts derived using different identification approaches, namely the variable threshold, fixed threshold, and the Standardized Streamflow Index, including an analysis of both historical drought and implications for forecasting. Our results clearly show that streamflow droughts derived from different approaches deviate from each other in terms of drought occurrence, timing, duration, and deficit volume.
Janie Masse-Dufresne, Florent Barbecot, Paul Baudron, and John Gibson
Hydrol. Earth Syst. Sci., 25, 3731–3757, https://doi.org/10.5194/hess-25-3731-2021, https://doi.org/10.5194/hess-25-3731-2021, 2021
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A volume-dependent transient isotopic mass balance model was developed for an artificial lake in Canada, in a context where direct measurements of surface water fluxes are difficult. It revealed that floodwater inputs affected the dynamics of the lake in spring but also significantly influenced the long-term water balance due to temporary subsurface storage of floodwater. Such models are paramount for understanding the vulnerability of lakes to changes in groundwater quantity and quality.
Regula Muelchi, Ole Rössler, Jan Schwanbeck, Rolf Weingartner, and Olivia Martius
Hydrol. Earth Syst. Sci., 25, 3577–3594, https://doi.org/10.5194/hess-25-3577-2021, https://doi.org/10.5194/hess-25-3577-2021, 2021
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This study analyses changes in magnitude, frequency, and seasonality of moderate low and high flows for 93 catchments in Switzerland. In lower-lying catchments (below 1500 m a.s.l.), moderate low-flow magnitude (frequency) will decrease (increase). In Alpine catchments (above 1500 m a.s.l.), moderate low-flow magnitude (frequency) will increase (decrease). Moderate high flows tend to occur more frequent, and their magnitude increases in most catchments except some Alpine catchments.
Regula Muelchi, Ole Rössler, Jan Schwanbeck, Rolf Weingartner, and Olivia Martius
Hydrol. Earth Syst. Sci., 25, 3071–3086, https://doi.org/10.5194/hess-25-3071-2021, https://doi.org/10.5194/hess-25-3071-2021, 2021
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Runoff regimes in Switzerland will change significantly under climate change. Projected changes are strongly elevation dependent with earlier time of emergence and stronger changes in high-elevation catchments where snowmelt and glacier melt play an important role. The magnitude of change and the climate model agreement on the sign increase with increasing global mean temperatures and stronger emission scenarios. This amplification highlights the importance of climate change mitigation.
Cintia L. Ramón, Hugo N. Ulloa, Tomy Doda, Kraig B. Winters, and Damien Bouffard
Hydrol. Earth Syst. Sci., 25, 1813–1825, https://doi.org/10.5194/hess-25-1813-2021, https://doi.org/10.5194/hess-25-1813-2021, 2021
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When solar radiation penetrates the frozen surface of lakes, shallower zones underneath warm faster than deep interior waters. This numerical study shows that the transport of excess heat to the lake interior depends on the lake circulation, affected by Earth's rotation, and controls the lake warming rates and the spatial distribution of the heat flux across the ice–water interface. This work contributes to the understanding of the circulation and thermal structure patterns of ice-covered lakes.
Robert Ladwig, Paul C. Hanson, Hilary A. Dugan, Cayelan C. Carey, Yu Zhang, Lele Shu, Christopher J. Duffy, and Kelly M. Cobourn
Hydrol. Earth Syst. Sci., 25, 1009–1032, https://doi.org/10.5194/hess-25-1009-2021, https://doi.org/10.5194/hess-25-1009-2021, 2021
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Using a modeling framework applied to 37 years of dissolved oxygen time series data from Lake Mendota, we identified the timing and intensity of thermal energy stored in the lake water column, the lake's resilience to mixing, and surface primary production as the most important drivers of interannual dynamics of low oxygen concentrations at the lake bottom. Due to climate change, we expect an increase in the spatial and temporal extent of low oxygen concentrations in Lake Mendota.
Carlos Miranda Rodrigues, Madalena Moreira, Rita Cabral Guimarães, and Miguel Potes
Hydrol. Earth Syst. Sci., 24, 5973–5984, https://doi.org/10.5194/hess-24-5973-2020, https://doi.org/10.5194/hess-24-5973-2020, 2020
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In Mediterranean environments, evaporation is a key component of reservoir water budgets. Prediction of surface evaporation becomes crucial for adequate reservoir water management. This study provides an applicable method for calculating evaporation based on pan measurements applied at Alqueva Reservoir (southern Portugal), one of the largest artificial lakes in Europe. Moreover, the methodology presented here could be applied to other Mediterranean reservoirs.
Carmelo Cammalleri, Gustavo Naumann, Lorenzo Mentaschi, Bernard Bisselink, Emiliano Gelati, Ad De Roo, and Luc Feyen
Hydrol. Earth Syst. Sci., 24, 5919–5935, https://doi.org/10.5194/hess-24-5919-2020, https://doi.org/10.5194/hess-24-5919-2020, 2020
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Climate change is anticipated to alter the demand and supply of water at the earth's surface. This study shows how hydrological droughts will change across Europe with increasing global warming levels, showing that at 3 K global warming an additional 11 million people and 4.5 ×106 ha of agricultural land will be exposed to droughts every year, on average. These effects are mostly located in the Mediterranean and Atlantic regions of Europe.
Alex Zavarsky and Lars Duester
Hydrol. Earth Syst. Sci., 24, 5027–5041, https://doi.org/10.5194/hess-24-5027-2020, https://doi.org/10.5194/hess-24-5027-2020, 2020
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River water temperature is an important parameter for water quality and an important variable for physical, chemical and biological processes. River water is also used as a cooling agent by power plants and production facilities. We study long-term trends in river water temperature and correlate them to meteorological influences and power production or economic indices.
Cheng-Wei Yu, Ben R. Hodges, and Frank Liu
Hydrol. Earth Syst. Sci., 24, 4001–4024, https://doi.org/10.5194/hess-24-4001-2020, https://doi.org/10.5194/hess-24-4001-2020, 2020
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This study investigates the effects of bottom slope discontinuity on the stability of numerical solutions for the Saint-Venant equations. A new reference slope concept is proposed to ensure smooth source terms and eliminate potential numerical oscillations. It is shown that a simple algebraic transformation of channel geometry provides a smooth reference slope while preserving the correct cross-sectional flow area and the piezometric pressure gradient that drives the flow.
Ana I. Ayala, Simone Moras, and Donald C. Pierson
Hydrol. Earth Syst. Sci., 24, 3311–3330, https://doi.org/10.5194/hess-24-3311-2020, https://doi.org/10.5194/hess-24-3311-2020, 2020
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The impacts of different levels of global warming on the thermal structure of Lake Erken are assessed. We used the General Ocean Turbulence Model (GOTM) to simulate water temperature driven by meteorological scenarios supplied by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) and tested its ability at different frequencies. Then, daily ISIMIP meteorological scenarios were disaggregated and assessed for the effects of climate change on lake thermal structure.
Luca Ziliani, Nicola Surian, Gianluca Botter, and Luca Mao
Hydrol. Earth Syst. Sci., 24, 3229–3250, https://doi.org/10.5194/hess-24-3229-2020, https://doi.org/10.5194/hess-24-3229-2020, 2020
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Although geomorphic recovery is a key issue in many rivers worldwide, controlled floods have been rarely designed using geomorphological criteria. An integrated approach is used to assess the effects of different controlled-flood scenarios in a strongly regulated river. None of the controlled-flood strategies provide significant morphological benefits. Nevertheless, this study represents a significant contribution for the management and restoration of highly disturbed rivers.
Benjamin M. Kraemer, Anton Seimon, Rita Adrian, and Peter B. McIntyre
Hydrol. Earth Syst. Sci., 24, 2593–2608, https://doi.org/10.5194/hess-24-2593-2020, https://doi.org/10.5194/hess-24-2593-2020, 2020
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Lake levels go up and down due to natural variability in the climate. But the effects of natural variability on lake levels can sometimes be confused for the influence of humans. Here we used long-term data from 200 globally distributed lakes and an advanced statistical approach to show that the effects of natural variability on lake levels can be disentangled from other effects leading to better estimates of long-term changes that may be partially caused by humans.
Audrey Marescaux, Vincent Thieu, Nathalie Gypens, Marie Silvestre, and Josette Garnier
Hydrol. Earth Syst. Sci., 24, 2379–2398, https://doi.org/10.5194/hess-24-2379-2020, https://doi.org/10.5194/hess-24-2379-2020, 2020
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Rivers have been recognized as an active part of the carbon cycle where transformations are associated with CO2 outgassing. To understand it, we propose a modeling approach with the biogeochemical model, pyNuts-Riverstrahler. We implemented it on the human-impacted Seine River. Sources of carbon to the river were characterized by field measurements in groundwater and in wastewater. Outgassing was the most important in streams, and peaks were simulated downstream of wastewater treatment effluent.
Danlu Guo, Anna Lintern, J. Angus Webb, Dongryeol Ryu, Ulrike Bende-Michl, Shuci Liu, and Andrew William Western
Hydrol. Earth Syst. Sci., 24, 827–847, https://doi.org/10.5194/hess-24-827-2020, https://doi.org/10.5194/hess-24-827-2020, 2020
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This study developed predictive models to represent the spatial and temporal variation of stream water quality across Victoria, Australia. The model structures were informed by a data-driven approach, which identified the key controls of water quality variations from long-term records. These models are helpful to identify likely future changes in water quality and, in turn, provide critical information for developing management strategies to improve stream water quality.
Vo Quoc Thanh, Dano Roelvink, Mick van der Wegen, Johan Reyns, Herman Kernkamp, Giap Van Vinh, and Vo Thi Phuong Linh
Hydrol. Earth Syst. Sci., 24, 189–212, https://doi.org/10.5194/hess-24-189-2020, https://doi.org/10.5194/hess-24-189-2020, 2020
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The Vietnamese Mekong Delta (VMD) is a rice bowl of not only Vietnam, but also the world; agriculture is the main source of livelihood in the delta. The VMD is facing threats related to water management and hydraulic structures. Dykes are built to protect agricultural crops in the floodplains and may influence water regimes downstream in the VMD. If the VMD floodplains are completely protected by dykes, yearly mean water levels could increase by 3 cm (at Can Tho) and 1.5 cm (at My Thuan).
Martin Mergili, Shiva P. Pudasaini, Adam Emmer, Jan-Thomas Fischer, Alejo Cochachin, and Holger Frey
Hydrol. Earth Syst. Sci., 24, 93–114, https://doi.org/10.5194/hess-24-93-2020, https://doi.org/10.5194/hess-24-93-2020, 2020
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In 1941, the glacial lagoon Lake Palcacocha in the Cordillera Blanca (Peru) drained suddenly. The resulting outburst flood/debris flow consumed another lake and had a disastrous impact on the town of Huaraz 23 km downstream. We reconstuct this event through a numerical model to learn about the possibility of prediction of similar processes in the future. Remaining challenges consist of the complex process interactions and the lack of experience due to the rare occurrence of such process chains.
Simone Moras, Ana I. Ayala, and Don C. Pierson
Hydrol. Earth Syst. Sci., 23, 5001–5016, https://doi.org/10.5194/hess-23-5001-2019, https://doi.org/10.5194/hess-23-5001-2019, 2019
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We used a hydrodynamic model to reconstruct daily historical water temperature of Lake Erken (Sweden) between 1961 and 2017 to demonstrate the ongoing effect of climate change on lake thermal conditions. The results show that the lake has warmed most rapidly in the last 30 years and that it is now subject to a longer and more stable stratification. The methods used here to reconstruct historical water temperature records can be easily extended to other lakes.
Qunhui Zhang, Jiming Jin, Xiaochun Wang, Phaedra Budy, Nick Barrett, and Sarah E. Null
Hydrol. Earth Syst. Sci., 23, 4969–4982, https://doi.org/10.5194/hess-23-4969-2019, https://doi.org/10.5194/hess-23-4969-2019, 2019
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We improved lake mixing process simulations by applying a vertical mixing scheme, K profile parameterization (KPP), in the Community Land Model (CLM) version 4.5, developed by the National Center for Atmospheric Research. The current vertical mixing scheme in CLM requires an arbitrarily enlarged eddy diffusivity to enhance water mixing. The coupled CLM-KPP considers a boundary layer for eddy development. The improved lake model provides an important tool for lake hydrology and ecosystem studies.
Margarita Choulga, Ekaterina Kourzeneva, Gianpaolo Balsamo, Souhail Boussetta, and Nils Wedi
Hydrol. Earth Syst. Sci., 23, 4051–4076, https://doi.org/10.5194/hess-23-4051-2019, https://doi.org/10.5194/hess-23-4051-2019, 2019
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Lakes influence weather and climate of regions, especially if several of them are located close by. Just by using upgraded lake depths, based on new or more recent measurements and geological methods of depth estimation, errors of lake surface water forecasts produced by the European Centre for Medium-Range Weather Forecasts became 12–20 % lower compared with observations for 27 lakes collected by the Finnish Environment Institute. For ice-off date forecasts errors changed insignificantly.
Jérémy Lepesqueur, Renaud Hostache, Núria Martínez-Carreras, Emmanuelle Montargès-Pelletier, and Christophe Hissler
Hydrol. Earth Syst. Sci., 23, 3901–3915, https://doi.org/10.5194/hess-23-3901-2019, https://doi.org/10.5194/hess-23-3901-2019, 2019
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This article evaluates the influence of sediment representation in a sediment transport model. A short-term simulation is used to assess how far changing the sediment characteristics in the modelling experiment changes riverbed evolution and sediment redistribution during a small flood event. The study shows in particular that representing sediment with extended grain-size and grain-density distributions allows for improving model accuracy and performances.
Annie Visser-Quinn, Lindsay Beevers, and Sandhya Patidar
Hydrol. Earth Syst. Sci., 23, 3279–3303, https://doi.org/10.5194/hess-23-3279-2019, https://doi.org/10.5194/hess-23-3279-2019, 2019
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The ecological impact of changes in river flow may be explored through the simulation of ecologically relevant flow indicators. Traditional approaches to model parameterization are not well-suited for this. To this end, this paper considers the ability of a
modified covariance approach, applied to five hydrologically diverse catchments. An overall improvement in consistency is observed, whilst timing and rate of change represent the best and worst replicated indicators respectively.
Jeffrey J. Richardson, Christian E. Torgersen, and L. Monika Moskal
Hydrol. Earth Syst. Sci., 23, 2813–2822, https://doi.org/10.5194/hess-23-2813-2019, https://doi.org/10.5194/hess-23-2813-2019, 2019
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High stream temperatures can be detrimental to the survival of aquatic species such as endangered salmon. Stream temperatures can be reduced by shade provided by trees in riparian areas. Two lidar-based methods were effective at assessing stream shading. These methods can be used in place of expensive field measurements.
Dongsheng Su, Xiuqing Hu, Lijuan Wen, Shihua Lyu, Xiaoqing Gao, Lin Zhao, Zhaoguo Li, Juan Du, and Georgiy Kirillin
Hydrol. Earth Syst. Sci., 23, 2093–2109, https://doi.org/10.5194/hess-23-2093-2019, https://doi.org/10.5194/hess-23-2093-2019, 2019
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In this study, freshwater lake model simulation results, verified by satellite and buoy observation data, were used to quantify recent climate change effects on the thermal regime of the largest lake in China. Results indicate that the FLake model can reproduce the lake thermal pattern nicely. The lake surface is warming, while the lake bottom has no significant trend. Climate change also caused an earlier ice-off and later ice-on, leading to an obvious change in the energy balance of the lake.
Chloé Poulin, Bruno Hamelin, Christine Vallet-Coulomb, Guinbe Amngar, Bichara Loukman, Jean-François Cretaux, Jean-Claude Doumnang, Abdallah Mahamat Nour, Guillemette Menot, Florence Sylvestre, and Pierre Deschamps
Hydrol. Earth Syst. Sci., 23, 1705–1724, https://doi.org/10.5194/hess-23-1705-2019, https://doi.org/10.5194/hess-23-1705-2019, 2019
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This study investigates the water budget of two intertropical lake systems in the absence of long-term hydrological monitoring. By coupling dry season isotopic data with satellite imagery, we were able to provide quantitative constrains on the hydrological balance and show that these two lake systems can be considered miniature analogs of Lake Chad, making them important targets in the future setup of any large-scale program on the hydro-climatic evolution in the Sahel region.
Tom Shatwell, Wim Thiery, and Georgiy Kirillin
Hydrol. Earth Syst. Sci., 23, 1533–1551, https://doi.org/10.5194/hess-23-1533-2019, https://doi.org/10.5194/hess-23-1533-2019, 2019
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We used models to project future temperature and mixing in temperate lakes. Lakes will probably warm faster in winter than in summer, making ice less frequent and altering mixing. We found that the layers that form seasonally in lakes (ice, stratification) and water clarity affect how lakes accumulate heat. Seasonal changes in climate were thus important. This helps us better understand how different lake types respond to warming and which physical changes to expect in the future.
Cited articles
Abba, S. I., Hadi, S. J., and Abdullahi, J.: River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques, in: Procedia Computer Science, Elsevier B.V., Budapest, Hungary, 75–82, https://doi.org/10.1016/j.procs.2017.11.212, 2017. a, b
Ahmadi-Nedushan, B., St-Hilaire, A., Ouarda, T. B. M. J., Bilodeau, L.,
Robichaud, É., Thiémonge, N., and Bobée, B.: Predicting
river water temperatures using stochastic models: case study of the Moisie
River (Québec, Canada), Hydrol. Process., 21, 21–34,
https://doi.org/10.1002/hyp.6353, 2007. a
Akaike, H.: Information theory as an extension of the likelihood principle., in: Second Akademiai International Symposium on Information Theory, edited by: Petrov, B. N. and Csaki, F., Kiado, Budapest, 267–281, 1973. a
Allaire, J. J. and Tang, Y.: tensorflow: R Interface to “TensorFlow”, available at: https://github.com/rstudio/tensorflow (last access: 13 Jauary 2021), 2020. a
Álvarez, D. and Nicieza, A. G.: Compensatory response “defends” energy levels but not growth trajectories in brown trout, Salmo trutta L.,
P. Roy. Soc. B-Biol. Sci., 272, 601–607,
https://doi.org/10.1098/rspb.2004.2991, 2005. a
Arismendi, I., Safeeq, M., Dunham, J. B., and Johnson, S. L.: Can air
temperature be used to project influences of climate change on stream
temperature?, Environ. Res. Lett., 9, 084015, https://doi.org/10.1088/1748-9326/9/8/084015, 2014. a, b
Baldi, P. and Sadowski, P.: The dropout learning algorithm, Artif. Intell., 210, 78–122, https://doi.org/10.1016/j.artint.2014.02.004, 2014. a
Beaufort, A., Moatar, F., Curie, F., Ducharne, A., Bustillo, V., and
Thiéry, D.: River Temperature Modelling by Strahler Order at the
Regional Scale in the Loire River Basin, France,
River Res. Appl., 32, 597–609, https://doi.org/10.1002/rra.2888, 2016. a, b
Bélanger, M., El-Jabi, N., Caissie, D., Ashkar, F., and Ribi, J. M.:
Water temperature prediction using neural networks and multiple linear
regression, Revue des Sciences de l'Eau, 18, 403–421,
https://doi.org/10.7202/705565ar, 2005. a, b
Bengio, Y., Courville, A., and Vincent, P.: Representation learning: A review and new perspectives, IEEE T. Pattern Anal., 35, 1798–1828, https://doi.org/10.1109/TPAMI.2013.50, 2013. a
Bentéjac, C., Csörgő, A., and Martínez-Muñoz, G.: A
comparative analysis of gradient boosting algorithms, Artif. Intell. Rev., 54, 1937–1967, https://doi.org/10.1007/s10462-020-09896-5, 2021. a, b
Benyahya, L., Caissie, D., St-Hilaire, A., Ouarda, T. B., and Bobée, B.: A Review of Statistical Water Temperature Models,
Can. Water Resour. J., 32, 179–192, https://doi.org/10.4296/cwrj3203179, 2007. a, b
BMLFUW: Hydrological Atlas of Austria, 3rd Edn., Bundesmin-isterium für Land- und Forstwirtschaft, Umwelt und Wasser-wirtschaft, Vienna, Austria, ISBN 3-85437-250-7, 2007. a
Boisneau, C., Moatar, F., Bodin, M., and Boisneau, P.: Does global warming
impact on migration patterns and recruitment of Allis shad (Alosa alosa L.)
young of the year in the Loire River, France?, in: Fish and Diadromy in
Europe (ecology, management, conservation), Springer, Dordrecht, the Netherlands, 179–186, https://doi.org/10.1007/978-1-4020-8548-2_14, 2008. a
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140,
https://doi.org/10.1007/bf00058655, 1996. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a, b, c
Brinckmann, S., Krähenmann, S., and Bissolli, P.: High-resolution daily gridded data sets of air temperature and wind speed for Europe, Earth Syst. Sci. Data, 8, 491–516, https://doi.org/10.5194/essd-8-491-2016, 2016. a
Caissie, D.: The thermal regime of rivers: A review, Freshwater Biol., 51, 1389–1406, https://doi.org/10.1111/j.1365-2427.2006.01597.x, 2006. a
Caissie, D. and Luce, C. H.: Quantifying streambed advection and conduction
heat fluxes, Water Resour. Res., 53, 1595–1624,
https://doi.org/10.1002/2016WR019813, 2017. a
Caldwell, R. J., Gangopadhyay, S., Bountry, J., Lai, Y., and Elsner, M. M.:
Statistical modeling of daily and subdaily stream temperatures: Application
to the Methow River Basin, Washington, Water Resour. Res., 49,
4346–4361, https://doi.org/10.1002/wrcr.20353, 2013. a, b, c
Central Hydrographical Bureau: eHYD, available at: https://www.ehyd.gv.at/, last access: 26 May 2021. a
Chen, T. and Guestrin, C.: XGBoost: A scalable tree boosting system, in:
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, San Francisco, California, USA, 13–17 August 2016, 785–794, https://doi.org/10.1145/2939672.2939785, 2016. a, b
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K.,
Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., and Li,
Y.: xgboost: Extreme Gradient Boosting, available at:
https://cran.r-project.org/package=xgboost (last access: 13 January 2021), 2020. a
Chenard, J.-F. and Caissie, D.: Stream temperature modelling using artificial neural networks: application on Catamaran Brook, New Brunswick, Canada, Hydrol. Process., 22, 3361–3372, https://doi.org/10.1002/hyp.6928, 2008. a
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning phrase representations using RNN
encoder-decoder for statistical machine translation, in: Proceedings
of the EMNLP 2014 – 2014 Conference on Empirical Methods in Natural Language Processing, 25–29 October 2014, Doha, Qatar, 1724–1734, https://doi.org/10.3115/v1/d14-1179, 2014. a, b
Claesen, M. and De Moor, B.: Hyperparameter Search in Machine Learning,
arXiv [preprint], arXiv:1502.02127 (last access: 4 December 2020), 2015. a
Crank, J. and Nicolson, P.: A practical method for numerical evaluation of
solutions of partial differential equations of the heat-conduction type,
Math. Proc. Cambridge, 43, 50–67, https://doi.org/10.1017/S0305004100023197, 1947. a
Crisp, D. and Howson, G.: Effect of air temperature upon mean water
temperature in streams in the north Pennines and English Lake District,
Freshwater Biol., 12, 359–367, https://doi.org/10.1111/j.1365-2427.1982.tb00629.x,
1982. a, b
Dallas, H.: Water temperature and riverine ecosystems: An overview of
knowledge and approaches for assessing biotic responses, with special
reference to South Africa, Water Sa, 34, 393–404, https://doi.org/10.4314/wsa.v34i3.180634, 2008. a
DeWeber, J. T. and Wagner, T.: A regional neural network ensemble for
predicting mean daily river water temperature, J. Hydrol., 517,
187–200, https://doi.org/10.1016/j.jhydrol.2014.05.035, 2014. a
Dugdale, S. J., Hannah, D. M., and Malcolm, I. A.: River temperature
modelling: A review of process-based approaches and future directions,
Earth-Sci. Rev., 175, 97–113, https://doi.org/10.1016/j.earscirev.2017.10.009,
2017. a, b, c, d
Dunn, O. J.: Multiple Comparisons Using Rank Sums, Technometrics, 6,
241–252, https://doi.org/10.1080/00401706.1964.10490181, 1964. a
Feigl, M.: MoritzFeigl/wateRtemp: HESS submission (Version v0.2.0), Zenodo, https://doi.org/10.5281/zenodo.4438575, 2021a. a, b
Feigl, M.: MoritzFeigl/ML_methods_for_stream_water_temperature_
prediction: HESS paper (Version v1.0), Zenodo, https://doi.org/10.5281/zenodo.4438582, 2021b. a
Fernández-Delgado, M., Cernadas, E., Barro, S., and Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems?, J. Mach. Learn. Res., 15, 3133–3181, 2014. a
Freund, Y. and Schapire, R. E.: A decision-theoretic generalization of on-line learning and an application to boosting, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 23–37, https://doi.org/10.1007/3-540-59119-2_166, 1995. a
Friberg, N., DybkjÆr, J. B., Olafsson, J. S., Gislason, G. M., Larsen,
S. E., and Lauridsen, T. L.: Relationships between structure and function in
streams contrasting in temperature, Freshwater Biol., 54, 2051–2068,
https://doi.org/10.1111/j.1365-2427.2009.02234.x, 2009. a
Friedman, J. H.: Greedy function approximation: A gradient boosting machine, Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001. a
Friedman, J. H.: Stochastic gradient boosting, Comput. Stat. Data An., 38, 367–378, https://doi.org/10.1016/S0167-9473(01)00065-2, 2002. a
Gauch, M., Tang, R., Mai, J., Tolson, B., Gharari, S., and Lin, J.: Machine
Learning for Streamflow Prediction: Current Status and Future Prospects, 9–13 December 2019, San Francisco, USA,
AGU Fall Meeting Abstracts, 2019, H33L–2127, 2019. a
Graf, R., Zhu, S., and Sivakumar, B.: Forecasting river water temperature time series using a wavelet-neural network hybrid modelling approach, J. Hydrol., 578, 124115, https://doi.org/10.1016/j.jhydrol.2019.124115, 2019. a
Hadzima-Nyarko, M., Rabi, A., and Šperac, M.: Implementation of
Artificial Neural Networks in Modeling the Water-Air Temperature Relationship
of the River Drava, Water Resour. Manag., 28, 1379–1394,
https://doi.org/10.1007/s11269-014-0557-7, 2014. a, b, c
Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B., and Gruber, C.:
The integrated nowcasting through comprehensive analysis (INCA) system and
its validation over the Eastern Alpine region, Weather Forecast., 26,
166–183, https://doi.org/10.1175/2010WAF2222451.1, 2011. a
Haiden, T., Kann, A., and Pistotnik, G.: Nowcasting with INCA During
SNOW-V10, Pure Appl. Geophys., 171, 231–242,
https://doi.org/10.1007/s00024-012-0547-8, 2014. a
Hannah, D. M. and Garner, G.: River water temperature in the United Kingdom, Prog. Phys. Geog., 39, 68–92,
https://doi.org/10.1177/0309133314550669, 2015. a
Hannah, D. M., Webb, B. W., and Nobilis, F.: River and stream temperature:
dynamics, processes, models and implications, Hydrol. Process., 22,
899–901, https://doi.org/10.1002/hyp.6997, 2008. a
Hansen, L. K. and Salamon, P.: Neural Network Ensembles, IEEE T. Pattern Anal., 12, 993–1001,
https://doi.org/10.1109/34.58871, 1990. a
Harvey, R., Lye, L., Khan, A., and Paterson, R.: The influence of air
temperature on water temperature and the concentration of dissolved oxygen in
Newfoundland Rivers, Can. Water Resour. J., 36, 171–192,
https://doi.org/10.4296/cwrj3602849, 2011. a
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first
high-resolution meteorological forcing dataset for land process studies over
China, Scientific Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020. a
Heddam, S., Ptak, M., and Zhu, S.: Modelling of daily lake surface water
temperature from air temperature: Extremely randomized trees (ERT) versus
Air2Water, MARS, M5Tree, RF and MLPNN, J. Hydrol., 588, 125130,
https://doi.org/10.1016/j.jhydrol.2020.125130, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee,
D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M.,
Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E.,
Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti,
G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut,
J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hiebl, J. and Frei, C.: Daily temperature grids for Austria since
1961 – concept, creation and applicability,
Theor. Appl. Climatol., 124, 161–178, https://doi.org/10.1007/s00704-015-1411-4, 2016. a
Hiebl, J. and Frei, C.: Daily precipitation grids for Austria since
1961 – development and evaluation of a spatial dataset for hydroclimatic
monitoring and modelling, Theor. Appl. Climatol., 132,
327–345, https://doi.org/10.1007/s00704-017-2093-x, 2018. a
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and
Salakhutdinov, R. R.: Improving neural networks by preventing co-adaptation
of feature detectors, arXiv [preprint], arXiv:1207.0580 (last access:
7 August 2020), 2012. a, b
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory,
Neural Comput., 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997. a, b, c
Hsu, C.-W., Chang, C.-C., and Lin, C.-J.: A Practical Guide to Support Vector Classification, Tech. Rep., Taipei, 2003. a
Ibrahem Ahmed Osman, A., Najah Ahmed, A., Chow, M. F., Feng Huang, Y., and El-Shafie, A.: (2021). Extreme gradient boosting (xgboost) model to predict the groundwater levels in Selangor Malaysia, Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2020.11.011, in press, 2021. a
Imholt, C., Gibbins, C. N., Malcolm, I. A., Langan, S., and Soulsby, C.:
Influence of riparian cover on stream temperatures and the growth of the
mayfly Baetis rhodani in an upland stream, Aquat. Ecol., 44, 669–678,
https://doi.org/10.1007/s10452-009-9305-0, 2010. a
Jackson, F. L., Fryer, R. J., Hannah, D. M., Millar, C. P., and Malcolm, I. A.: A spatio-temporal statistical model of maximum daily river temperatures to
inform the management of Scotland's Atlantic salmon rivers under climate
change, Sci. Total Environ., 612, 1543–1558,
https://doi.org/10.1016/j.scitotenv.2017.09.010, 2018. a, b, c
Johnson, M. F., Wilby, R. L., and Toone, J. A.: Inferring air-water
temperature relationships from river and catchment properties, Hydrol. Process., 28, 2912–2928, https://doi.org/10.1002/hyp.9842, 2014. a
Jones, D. R., Schonlau, M., and Welch, W. J.: Efficient Global Optimization of Expensive Black-Box Functions, J. Global Optim., 13, 455–492, https://doi.org/10.1023/A:1008306431147, 1998. a
Joslyn, K.: Water quality factor prediction using supervised machine learning
REU Final Reports, 6, available at:
https://archives.pdx.edu/ds/psu/26231 (last access: 26 May 2021), 2018. a
Kȩdra, M.: Regional Response to Global Warming: Water Temperature Trends in Semi-Natural Mountain River Systems, Water, 12, 283,
https://doi.org/10.3390/w12010283, 2020. a
Kennedy, J. and Eberhart, R.: Particle swarm optimization, in: Proceedings of ICNN'95 – International Conference on Neural Networks, Perth, Australia, 27 November–1 December 1995,
1942–1948, https://doi.org/10.1109/ICNN.1995.488968, 1995. a
Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S.: Self-normalizing neural networks, arXiv [preprint], arXiv:1706.02515,
(last access: 3 August 2020), 2017. a
Kling, H., Stanzel, P., Fuchs, M., and Nachtnebel, H.-P.: Performance of the
COSERO precipitation–runoff model under non-stationary conditions in basins
with different climates, Hydrolog. Sci. J., 60, 1374–1393,
https://doi.org/10.1080/02626667.2014.959956, 2015. a
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. a, b
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019. a, b
Kruskal, W. H. and Wallis, W. A.: Use of Ranks in One-Criterion Variance
Analysis, J. Am. Stat. Assoc., 47, 583–621,
https://doi.org/10.1080/01621459.1952.10483441, 1952. a
Kuhn, M.: caret: Classification and Regression Training, R package version 6.0-86, available at: https://CRAN.R-project.org/package=caret (last access: 13 Jaunary 2021), 2020. a
Kushner, H. J.: A new method of locating the maximum point of an arbitrary
multipeak curve in the presence of noise, J. Fluid. Eng.-T. ASME, 86, 97–106, https://doi.org/10.1115/1.3653121, 1964. a, b
Laizé, C. L., Acreman, M. C., Schneider, C., Dunbar, M. J.,
Houghton-Carr, H. A., Flörke, M., and Hannah, D. M.: Projected flow
alteration and ecological risk for pan-European rivers, River Res. Appl., 30, 299–314, https://doi.org/10.1002/rra.2645, 2014. a
Li, H., Deng, X., Kim, D.-Y., and Smith, E. P.: Modeling maximum daily
temperature using a varying coefficient regression model, Water Resour. Res., 50, 3073–3087, https://doi.org/10.1002/2013WR014243, 2014. a, b, c
Li, W., Kiaghadi, A., and Dawson, C.: High temporal resolution
rainfall–runoff modeling using long-short-term-memory (LSTM) networks,
Neural Comput. Appl., 33, 1261–1278, https://doi.org/10.1007/s00521-020-05010-6, 2020. a, b
Lu, H. and Ma, X.: Hybrid decision tree-based machine learning models for
short-term water quality prediction, Chemosphere, 249, 126169,
https://doi.org/10.1016/j.chemosphere.2020.126169, 2020. a
Mackey, A. P. and Berrie, A. D.: The prediction of water temperatures in chalk streams from air temperatures, Hydrobiologia, 210, 183–189,
https://doi.org/10.1007/BF00034676, 1991. a, b
McGlynn, B. L., McDonnell, J. J., Seibert, J., and Kendall, C.: Scale effects on headwater catchment runoff timing, flow sources, and
groundwater-streamflow relations, Water Resour. Res., 40, W07504,
https://doi.org/10.1029/2003WR002494, 2004. a
McKenna, J. E., Butryn, R. S., and McDonald, R. P.: Summer Stream Water
Temperature Models for Great Lakes Streams: New York, T. Am. Fish. Soc., 139, 1399–1414, https://doi.org/10.1577/t09-153.1, 2010. a
Močkus, J.: On Bayesian Methods for Seeking the Extremum, in:
Optimization Techniques IFIP Technical Conference, Novosibirsk, 1–7 July 1974, 400–404,
https://doi.org/10.1007/978-3-662-38527-2_55, 1975. a, b
Močkus, J.: Bayesian Approach to Global Optimization, Mathematics and Its Applications Series, Springer Netherlands, Dordrecht, The Netherlands, 270 pp., https://doi.org/10.1007/978-94-009-0909-0, 1989. a, b
Močkus, J., Tiesis, V., and Zilinskas, A.: The application of Bayesian methods for seeking the extremum, Towards global optimization, 2, 117–129, https://doi.org/10.1007/978-94-009-0909-0_8, 1978. a, b
Mohseni, O. and Stefan, H. G.: Stream temperature/air temperature
relationship: A physical interpretation, J. Hydrol., 218,
128–141, https://doi.org/10.1016/S0022-1694(99)00034-7, 1999. a, b
Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models
part I – A discussion of principles, J. Hydrol., 10, 282–290,
https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Neumann, D. W., Rajagopalan, B., and Zagona, E. A.: Regression model for daily maximum stream temperature, J. Environ. Eng., 129,
667–674, https://doi.org/10.1061/(ASCE)0733-9372(2003)129:7(667), 2003. a
Ni, L., Wang, D., Wu, J., Wang, Y., Tao, Y., Zhang, J., and Liu, J.:
Streamflow forecasting using extreme gradient boosting model coupled with
Gaussian mixture model, J. Hydrol., 586, 124901,
https://doi.org/10.1016/j.jhydrol.2020.124901, 2020. a
Nielsen, D.: Tree Boosting With XGBoost: Why does XGBoost win every machine
learning competition?, Master's Thesis, Norwegian University of Science and
Technology, Norway, 98 pp., 2016. a
Pedersen, N. L. and Sand-Jensen, K.: Temperature in lowland Danish streams:
contemporary patterns, empirical models and future scenarios, Hydrol. Process., 21, 348–358, https://doi.org/10.1002/hyp.6237, 2007. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R.
Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, Tech. Rep., available at: https://hal.inria.fr/hal-00650905v2 (last access: 4 December 2020), 2011. a
Piccolroaz, S., Calamita, E., Majone, B., Gallice, A., Siviglia, A., and
Toffolon, M.: Prediction of river water temperature: a comparison between a
new family of hybrid models and statistical approaches, Hydrol. Process., 30, 3901–3917, https://doi.org/10.1002/hyp.10913, 2016. a, b, c
Pinkus, A.: Approximation theory of the MLP model in neural networks,
Acta Numer., 8, 143–195, https://doi.org/10.1017/S0962492900002919, 1999. a
Piotrowski, A. P. and Napiorkowski, J. J.: Performance of the air2stream model that relates air and stream water temperatures depends on the calibration method, J. Hydrol., 561, 395–412,
https://doi.org/10.1016/j.jhydrol.2018.04.016, 2018. a, b, c
Piotrowski, A. P. and Napiorkowski, J. J.: Simple modifications of the
nonlinear regression stream temperature model for daily data, J. Hydrol., 572, 308–328, https://doi.org/10.1016/j.jhydrol.2019.02.035, 2019. a, b, c, d
Piotrowski, A. P., Napiorkowski, J. J., and Piotrowska, A. E.: Impact of deep learning-based dropout on shallow neural networks applied to stream
temperature modelling, Earth-Sci. Rev., 201, 103076, https://doi.org/10.1016/j.earscirev.2019.103076, 2020. a
R Core Team: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, available at:
https://www.r-project.org/ (last access: 13 January 2021), 2020. a
Rabi, A., Hadzima-Nyarko, M., and Šperac, M.: Modelling river
temperature from air temperature: case of the River Drava (Croatia),
Hydrolog. Sci. J., 60, 1490–1507,
https://doi.org/10.1080/02626667.2014.914215, 2015. a, b
Razafimaharo, C., Krähenmann, S., Höpp, S., Rauthe, M., and
Deutschländer, T.: New high-resolution gridded dataset of daily mean,
minimum, and maximum temperature and relative humidity for Central Europe
(HYRAS), Theor. Appl. Climatol., 142, 1531–1553,
https://doi.org/10.1007/s00704-020-03388-w, 2020. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Risley, J. C., Roehl Jr., E. A., and Conrads, P. A.: Estimating Water
Temperatures in Small Streams in Estimating Water Temperatures in Small
Streams in Western Oregon Using Neural Network Models, Tech. Rep., USGS
Water- Resources Investigation Report 02-4218, https://doi.org/10.3133/wri024218,
2003. a
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning representations by back-propagating errors, Nature, 323, 533–536, https://doi.org/10.1038/323533a0, 1986. a
Sahoo, G. B., Schladow, S. G., and Reuter, J. E.: Forecasting stream water
temperature using regression analysis, artificial neural network, and chaotic
non-linear dynamic models, J. Hydrol., 378, 325–342,
https://doi.org/10.1016/j.jhydrol.2009.09.037, 2009. a, b
Sand-Jensen, K. and Pedersen, N. L.: Differences in temperature, organic
carbon and oxygen consumption among lowland streams, Freshwater Biol., 50,
1927–1937, https://doi.org/10.1111/j.1365-2427.2005.01436.x, 2005. a
Schapire, R. E.: The Strength of Weak Learnability, Mach. Learn., 5,
197–227, https://doi.org/10.1023/A:1022648800760, 1990. a
Segura, C., Caldwell, P., Sun, G., Mcnulty, S., and Zhang, Y.: A model to
predict stream water temperature across the conterminous USA, Hydrol. Process., 29, 2178–2195, https://doi.org/10.1002/hyp.10357, 2015. a, b
Shank, D. B., Hoogenboom, G., and McClendon, R. W.: Dewpoint temperature
prediction using artificial neural networks,
J. Appl. Meteorol. Clim., 47, 1757–1769, https://doi.org/10.1175/2007JAMC1693.1, 2008. a
Smith, K.: The prediction of river water temperatures,
Hydrol. Sci. B., 26, 19–32, https://doi.org/10.1080/02626668109490859, 1981. a, b
Snoek, J., Larochelle, H., and Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms, arXiv [preprint], arXiv:1206.2944 (last access: 6 August 2020), 2012. a
Sohrabi, M. M., Benjankar, R., Tonina, D., Wenger, S. J., and Isaak, D. J.:
Estimation of daily stream water temperatures with a Bayesian regression
approach, Hydrol. Process., 31, 1719–1733, https://doi.org/10.1002/hyp.11139,
2017. a
Srinivas, N., Krause, A., Kakade, S. M., and Seeger, M.: Gaussian Process
Optimization in the Bandit Setting: No Regret and Experimental Design,
IEEE T. Inform. Theory, 58, 3250–3265,
https://doi.org/10.1109/TIT.2011.2182033, 2009. a
Srivastava, N., Hinton, G., Krizhevsky, A., and Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Tech. Rep., 15, 1929-−1958, available at: http://jmlr.org/papers/v15/srivastava14a.html (last access: 7 August 2020), 2014. a
Stajkowski, S., Kumar, D., Samui, P., Bonakdari, H., and Gharabaghi, B.:
Genetic-algorithm-optimized sequential model for water temperature
prediction, Sustainability-Basel, 12, 5374, https://doi.org/10.3390/su12135374,
2020. a
Stefan, H. G. and Preud'homme, E. B.: Stream temperature estimation from air
temperature, J. Am. Water Resour. As., 29, 27–45, https://doi.org/10.1111/j.1752-1688.1993.tb01502.x, 1993. a, b
Stevens, H., Ficke, J., and Smoot, G.: Techniques of water-resources
investigations of the US Geological Survey, US Government Printing
Office, Washington, 65 pp., 1975. a
Tavares, M. H., Cunha, A. H. F., Motta-Marques, D., Ruhoff, A. L., Fragoso,
C. R., Munar, A. M., and Bonnet, M. P.: Derivation of consistent, continuous
daily river temperature data series by combining remote sensing and water
temperature models, Remote Sens. Environ., 241, 111721,
https://doi.org/10.1016/j.rse.2020.111721, 2020. a, b
Temizyurek, M. and Dadaser-Celik, F.: Modelling the effects of meteorological parameters on water temperature using artificial neural networks, Water Sci. Technol., 77, 1724–1733, https://doi.org/10.2166/wst.2018.058, 2018. a, b
Thornton, M. M., Shrestha, R., Wei, Y., Thornton, P. E., Kao, S., and Wilson, B. E.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1840, 2020. a
Toffolon, M. and Piccolroaz, S.: A hybrid model for river water temperature as a function of air temperature and discharge, Environ. Res. Lett., 10, 114011, https://doi.org/10.1088/1748-9326/10/11/114011, 2015. a, b, c
Trinh, N. X., Trinh, T. Q., Phan, T. P., Thanh, T. N., and Thanh, B. N.: Water Temperature Prediction Models in Northern Coastal Area, Vietnam, Asian Review of Environmental and Earth Sciences, 6, 1–8,
https://doi.org/10.20448/journal.506.2019.61.1.8, 2019. a, b
Van Vliet, M. T., Franssen, W. H., Yearsley, J. R., Ludwig, F., Haddeland,
I., Lettenmaier, D. P., and Kabat, P.: Global river discharge and water
temperature under climate change, Global Environ. Chang., 23, 450–464,
https://doi.org/10.1016/j.gloenvcha.2012.11.002, 2013. a
Webb, B. W. and Zhang, Y.: Spatial and seasonal variability in the components of the river heat budget, Hydrol. Process., 11, 79–101,
https://doi.org/10.1002/(sici)1099-1085(199701)11:1<79::aid-hyp404>3.0.co;2-n, 1997. a
Webb, B. W., Clack, P. D., and Walling, D. E.: Water-air temperature
relationships in a Devon river system and the role of flow, Hydrol. Process., 17, 3069–3084, https://doi.org/10.1002/hyp.1280, 2003. a
Webb, B. W., Hannah, D. M., Moore, R. D., Brown, L. E., and Nobilis, F.:
Recent advances in stream and river temperature research, 22, 902–918
https://doi.org/10.1002/hyp.6994, 2008. a
Wenger, S. J., Isaak, D. J., Dunham, J. B., Fausch, K. D., Luce, C. H.,
Neville, H. M., Rieman, B. E., Young, M. K., Nagel, D. E., Horan, D. L., and
Chandler, G. L.: Role of climate and invasive species in structuring trout
distributions in the interior Columbia River Basin, USA,
Can. J. Fish. Aquat. Sci., 68, 988–1008, https://doi.org/10.1139/f2011-034, 2011. a
Werner, A. T., Schnorbus, M. A., Shrestha, R. R., Cannon, A. J., Zwiers, F. W., Dayon, G., and Anslow, F.: A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America, Scientific Data, 6, 180299, https://doi.org/10.1038/sdata.2018.299, 2019. a
White, B. W. and Rosenblatt, F.: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Am. J. Psychol., 76, 705–707, https://doi.org/10.2307/1419730, 1963. a, b
Wickham, H.: ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag, New York, USA, available at: https://ggplot2.tidyverse.org (last access: 7 August 2020), 2016. a
Willmott, C. J.: On the validation of models, Phys. Geogr., 2,
184–194, https://doi.org/10.1080/02723646.1981.10642213, 1981. a
Xiang, Z., Yan, J., and Demir, I.: A Rainfall‐Runoff Model With LSTM‐Based
Sequence‐to‐Sequence Learning, Water Resour. Res., 56, e2019WR02532,
https://doi.org/10.1029/2019WR025326, 2020. a, b
Yang, D. and Peterson, A.: River water temperature in relation to local air
temperature in the Mackenzie and Yukon basins, Arctic, 70, 47–58,
https://doi.org/10.14430/arctic4627, 2017. a, b
Yazidi, A., Goyal, R., Paes, A., Gruber, N., De, N. G., and Jockisch, A.: Are GRU Cells More Specific and LSTM Cells More Sensitive in Motive
Classification of Text?, Front. Artif. Intell., 3, 40, https://doi.org/10.3389/frai.2020.00040, 2020. a
Zentralanstalt für Meteorologie und Geodynamik: ZAMG homepage, available at: https://www.zamg.ac.at, last access: 26 May 2021. a
Zhilinskas, A. G.: Single-step Bayesian search method for an extremum of
functions of a single variable, Cybernetics, 11, 160–166,
https://doi.org/10.1007/BF01069961, 1975. a, b
Zhu, S. and Piotrowski, A. P.: River/stream water temperature forecasting
using artificial intelligence models: a systematic review, Acta Geophysica, 1–10, Springer,
https://doi.org/10.1007/s11600-020-00480-7, 2020. a
Zhu, S., Nyarko, E. K., and Hadzima-Nyarko, M.: Modelling daily water
temperature from air temperature for the Missouri River, PeerJ, 6, e4894,
https://doi.org/10.7717/peerj.4894, 2018. a, b, c, d
Zhu, S., Hadzima-Nyarko, M., Gao, A., Wang, F., Wu, J., and Wu, S.: Two hybrid data-driven models for modeling water-air temperature relationship in
rivers, Environ. Sci. Pollut. R., 26, 12622–12630,
https://doi.org/10.1007/s11356-019-04716-y, 2019a. a
Zhu, S., Heddam, S., Nyarko, E. K., Hadzima-Nyarko, M., Piccolroaz, S., and Wu, S.: Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models, Environ. Sci. Pollut. R., 26, 402–420,
https://doi.org/10.1007/s11356-018-3650-2, 2019b.
a, b
Zhu, S., Heddam, S., Wu, S., Dai, J., and Jia, B.: Extreme learning
machine-based prediction of daily water temperature for rivers,
Environ. Earth Sci., 78, 202, https://doi.org/10.1007/s12665-019-8202-7,
2019c. a
Short summary
In this study we developed machine learning approaches for daily river water temperature prediction, using different data preprocessing methods, six model types, a range of different data inputs and 10 study catchments. By comparing to current state-of-the-art models, we could show a significant improvement of prediction performance of the tested approaches. Furthermore, we could gain insight into the relationships between model types, input data and predicted stream water temperature.
In this study we developed machine learning approaches for daily river water temperature...
