Research article 14 Aug 2017
Research article | 14 Aug 2017
Waning habitats due to climate change: the effects of changes in streamflow and temperature at the rear edge of the distribution of a cold-water fish
José María Santiago et al.
Related subject area
Subject: Ecohydrology | Techniques and Approaches: Modelling approaches
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Novel Keeling-plot-based methods to estimate the isotopic composition of ambient water vapor
Disentangling temporal and population variability in plant root water uptake from stable isotopic analysis: when rooting depth matters in labeling studies
Calibration of hydrological models for ecologically relevant streamflow predictions: a trade-off between fitting well to data and estimating consistent parameter sets?
Spatial variability of mean daily estimates of actual evaporation from remotely sensed imagery and surface reference data
Quantification of soil water balance components based on continuous soil moisture measurement and the Richards equation in an irrigated agricultural field of a desert oasis
Mapping the suitability of groundwater-dependent vegetation in a semi-arid Mediterranean area
Modeling boreal forest evapotranspiration and water balance at stand and catchment scales: a spatial approach
The 18O ecohydrology of a grassland ecosystem – predictions and observations
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Evaluation of ORCHIDEE-MICT-simulated soil moisture over China and impacts of different atmospheric forcing data
Testing an optimality-based model of rooting zone water storage capacity in temperate forests
A regional-scale ecological risk framework for environmental flow evaluations
Climate-driven disturbances in the San Juan River sub-basin of the Colorado River
Dominant effect of increasing forest biomass on evapotranspiration: interpretations of movement in Budyko space
Modeling the potential impacts of climate change on the water table level of selected forested wetlands in the southeastern United States
Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI
Importance of considering riparian vegetation requirements for the long-term efficiency of environmental flows in aquatic microhabitats
Cosmic-ray neutron transport at a forest field site: the sensitivity to various environmental conditions with focus on biomass and canopy interception
Estimation of surface energy fluxes in the Arctic tundra using the remote sensing thermal-based Two-Source Energy Balance model
Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements
Attributing regional trends of evapotranspiration and gross primary productivity with remote sensing: a case study in the North China Plain
A Budyko framework for estimating how spatial heterogeneity and lateral moisture redistribution affect average evapotranspiration rates as seen from the atmosphere
Regionalization of monthly rainfall erosivity patterns in Switzerland
Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin
Technical note: Fourier approach for estimating the thermal attributes of streams
Dominant controls of transpiration along a hillslope transect inferred from ecohydrological measurements and thermodynamic limits
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The effect of assimilating satellite-derived soil moisture data in SiBCASA on simulated carbon fluxes in Boreal Eurasia
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Effect of parameter choice in root water uptake models – the arrangement of root hydraulic properties within the root architecture affects dynamics and efficiency of root water uptake
The influence of methodological procedures on hydrological classification performance
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Development of a zoning-based environmental–ecological coupled model for lakes: a case study of Baiyangdian Lake in northern China
Does consideration of water routing affect simulated water and carbon dynamics in terrestrial ecosystems?
Climate and topographic controls on simulated pasture production in a semiarid Mediterranean watershed with scattered tree cover
Portfolio optimisation for hydropower producers that balances riverine ecosystem protection and producer needs
Eco-environmentally friendly operational regulation: an effective strategy to diminish the TDG supersaturation of reservoirs
Generalized combination equations for canopy evaporation under dry and wet conditions
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Songyan Yu, Hong Xuan Do, Albert I. J. M. van Dijk, Nick R. Bond, Peirong Lin, and Mark J. Kennard
Hydrol. Earth Syst. Sci., 24, 5279–5295, https://doi.org/10.5194/hess-24-5279-2020, https://doi.org/10.5194/hess-24-5279-2020, 2020
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There is a growing interest globally in the spatial distribution and temporal dynamics of intermittently flowing streams and rivers. We developed an approach to quantify catchment-wide flow intermittency over long time frames. Modelled patterns of flow intermittency in eastern Australia revealed highly dynamic behaviour in space and time. The developed approach is transferable to other parts of the world and can inform hydro-ecological understanding and management of intermittent streams.
Natasha MacBean, Russell L. Scott, Joel A. Biederman, Catherine Ottlé, Nicolas Vuichard, Agnès Ducharne, Thomas Kolb, Sabina Dore, Marcy Litvak, and David J. P. Moore
Hydrol. Earth Syst. Sci., 24, 5203–5230, https://doi.org/10.5194/hess-24-5203-2020, https://doi.org/10.5194/hess-24-5203-2020, 2020
Xiangyu Luan and Giulia Vico
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-549, https://doi.org/10.5194/hess-2020-549, 2020
Revised manuscript accepted for HESS
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Crop yield is reduced by heat and water stress, in particular when they co-occur. We quantify the joint effects of the (unpredictable) air temperature and water availability on crop heat stress via a mechanistic model. Larger but more infrequent rainfalls increased crop canopy temperatures. Keeping crops well watered via irrigation could reduce canopy temperature, but not enough to exclude heat damage. Thus, irrigation is only a partial solution to adapt to warmer and drier climates.
Yusen Yuan, Taisheng Du, Honglang Wang, and Lixin Wang
Hydrol. Earth Syst. Sci., 24, 4491–4501, https://doi.org/10.5194/hess-24-4491-2020, https://doi.org/10.5194/hess-24-4491-2020, 2020
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The isotopic composition of ambient water vapor is an important source of atmospheric water vapor and has not been able to be estimated to date using the Keeling plot approach. Here we proposed two new methods to estimate the isotopic composition of ambient water vapor: one using the intersection point method and another relying on the intermediate value theorem.
Valentin Couvreur, Youri Rothfuss, Félicien Meunier, Thierry Bariac, Philippe Biron, Jean-Louis Durand, Patricia Richard, and Mathieu Javaux
Hydrol. Earth Syst. Sci., 24, 3057–3075, https://doi.org/10.5194/hess-24-3057-2020, https://doi.org/10.5194/hess-24-3057-2020, 2020
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Isotopic labeling of soil water is a broadly used tool for tracing the origin of water extracted by plants and computing root water uptake (RWU) profiles with multisource mixing models. In this study, we show how a method such as this may misconstrue time series of xylem water isotopic composition as the temporal dynamics of RWU by simulating data collected during a tall fescue rhizotron experiment with an isotope-enabled physical soil–root model accounting for variability in root traits.
Thibault Hallouin, Michael Bruen, and Fiachra E. O'Loughlin
Hydrol. Earth Syst. Sci., 24, 1031–1054, https://doi.org/10.5194/hess-24-1031-2020, https://doi.org/10.5194/hess-24-1031-2020, 2020
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A hydrological model was used to compare different parameterisation strategies in view of predicting ecologically relevant streamflow indices in 33 Irish catchments. Compared for 14 different periods, a strategy fitting simulated and observed streamflow indices yielded better performance than fitting simulated and observed streamflow, but it also yielded a less consistent ensemble of parameter sets, suggesting that these indices may not be hydrologically relevant for model parameterisation.
Robert N. Armstrong, John W. Pomeroy, and Lawrence W. Martz
Hydrol. Earth Syst. Sci., 23, 4891–4907, https://doi.org/10.5194/hess-23-4891-2019, https://doi.org/10.5194/hess-23-4891-2019, 2019
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Digital and thermal images taken near midday were used to scale daily point observations of key factors driving actual-evaporation estimates across a complex Canadian Prairie landscape. Point estimates of actual evaporation agreed well with observed values via eddy covariance. Impacts of spatial variations on areal estimates were minor, and no covariance was found between model parameters driving the energy term. The methods can be applied further to improve land surface parameterisations.
Zhongkai Li, Hu Liu, Wenzhi Zhao, Qiyue Yang, Rong Yang, and Jintao Liu
Hydrol. Earth Syst. Sci., 23, 4685–4706, https://doi.org/10.5194/hess-23-4685-2019, https://doi.org/10.5194/hess-23-4685-2019, 2019
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A database of soil moisture measurements from the middle Heihe River basin of China was used to test the potential of a soil moisture database in estimating the soil water balance components (SWBCs). We determined SWBCs using a method that combined the soil water balance method and the inverse Richards equation. This work confirmed that relatively reasonable estimations of the SWBCs in coarse-textured sandy soils can be derived using soil moisture measurements.
Inês Gomes Marques, João Nascimento, Rita M. Cardoso, Filipe Miguéns, Maria Teresa Condesso de Melo, Pedro M. M. Soares, Célia M. Gouveia, and Cathy Kurz Besson
Hydrol. Earth Syst. Sci., 23, 3525–3552, https://doi.org/10.5194/hess-23-3525-2019, https://doi.org/10.5194/hess-23-3525-2019, 2019
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Mediterranean cork woodlands are very particular agroforestry systems present in a confined area of the Mediterranean Basin. They are of great importance due to their high socioeconomic value; however, a decrease in water availability has put this system in danger. In this paper we build a model that explains this system's tree-species distribution in southern Portugal from environmental variables. This could help predict their future distribution under changing climatic conditions.
Samuli Launiainen, Mingfu Guan, Aura Salmivaara, and Antti-Jussi Kieloaho
Hydrol. Earth Syst. Sci., 23, 3457–3480, https://doi.org/10.5194/hess-23-3457-2019, https://doi.org/10.5194/hess-23-3457-2019, 2019
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Boreal forest evapotranspiration and water cycle is modeled at stand and catchment scale using physiological and physical principles, open GIS data and daily weather data. The approach can predict daily evapotranspiration well across Nordic coniferous-dominated stands and successfully reproduces daily streamflow and annual evapotranspiration across boreal headwater catchments in Finland. The model is modular and simple and designed for practical applications over large areas using open data.
Regina T. Hirl, Hans Schnyder, Ulrike Ostler, Rudi Schäufele, Inga Schleip, Sylvia H. Vetter, Karl Auerswald, Juan C. Baca Cabrera, Lisa Wingate, Margaret M. Barbour, and Jérôme Ogée
Hydrol. Earth Syst. Sci., 23, 2581–2600, https://doi.org/10.5194/hess-23-2581-2019, https://doi.org/10.5194/hess-23-2581-2019, 2019
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We evaluated the system-scale understanding of the propagation of the oxygen isotope signal (δ18O) of rain through soil and xylem to leaf water in a temperate drought-prone grassland. Biweekly δ18O observations of the water pools made during seven growing seasons were accurately reproduced by the 18O-enabled process-based model MuSICA. While water uptake occurred from shallow soil depths throughout dry and wet periods, leaf water 18O enrichment responded to both soil and atmospheric moisture.
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.
Yu-Ting Shih, Pei-Hao Chen, Li-Chin Lee, Chien-Sen Liao, Shih-Hao Jien, Fuh-Kwo Shiah, Tsung-Yu Lee, Thomas Hein, Franz Zehetner, Chung-Te Chang, and Jr-Chuan Huang
Hydrol. Earth Syst. Sci., 22, 6579–6590, https://doi.org/10.5194/hess-22-6579-2018, https://doi.org/10.5194/hess-22-6579-2018, 2018
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DOC and DIC export in Taiwan shows that the annual DOC and DIC fluxes were 2.7–4.8 and 48.4–54.3 ton C km2 yr1, respectively, which were approximately 2 and 20 times higher than the global means of 1.4 and 2.6 ton C km2 yr1, respectively.
Zun Yin, Catherine Ottlé, Philippe Ciais, Matthieu Guimberteau, Xuhui Wang, Dan Zhu, Fabienne Maignan, Shushi Peng, Shilong Piao, Jan Polcher, Feng Zhou, Hyungjun Kim, and other China-Trend-Stream project members
Hydrol. Earth Syst. Sci., 22, 5463–5484, https://doi.org/10.5194/hess-22-5463-2018, https://doi.org/10.5194/hess-22-5463-2018, 2018
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Simulations in China were performed in ORCHIDEE driven by different forcing datasets: GSWP3, PGF, CRU-NCEP, and WFDEI. Simulated soil moisture was compared to several datasets to evaluate the ability of ORCHIDEE in reproducing soil moisture dynamics. Results showed that ORCHIDEE soil moisture coincided well with other datasets in wet areas and in non-irrigated areas. It suggested that the ORCHIDEE-MICT was suitable for further hydrological studies in China.
Matthias J. R. Speich, Heike Lischke, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 22, 4097–4124, https://doi.org/10.5194/hess-22-4097-2018, https://doi.org/10.5194/hess-22-4097-2018, 2018
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To simulate the water balance of, e.g., a forest plot, it is important to estimate the maximum volume of water available to plants. This depends on soil properties and the average depth of roots. Rooting depth has proven challenging to estimate. Here, we applied a model assuming that plants dimension their roots to optimize their carbon budget. We compared its results with values obtained by calibrating a dynamic water balance model. In most cases, there is good agreement between both methods.
Gordon C. O'Brien, Chris Dickens, Eleanor Hines, Victor Wepener, Retha Stassen, Leo Quayle, Kelly Fouchy, James MacKenzie, P. Mark Graham, and Wayne G. Landis
Hydrol. Earth Syst. Sci., 22, 957–975, https://doi.org/10.5194/hess-22-957-2018, https://doi.org/10.5194/hess-22-957-2018, 2018
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In global water resource allocation, robust tools are required to establish environmental flows. In addition, tools should characterize past, present and future consequences of altered flows and non-flow variables to social and ecological management objectives. PROBFLO is a risk assessment method designed to meet best practice principles for regional-scale holistic E-flow assessments. The approach has been developed in Africa and applied across the continent.
Katrina E. Bennett, Theodore J. Bohn, Kurt Solander, Nathan G. McDowell, Chonggang Xu, Enrique Vivoni, and Richard S. Middleton
Hydrol. Earth Syst. Sci., 22, 709–725, https://doi.org/10.5194/hess-22-709-2018, https://doi.org/10.5194/hess-22-709-2018, 2018
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We applied the Variable Infiltration Capacity hydrologic model to examine scenarios of change under climate and landscape disturbances in the San Juan River basin, a major sub-watershed of the Colorado River basin. Climate change coupled with landscape disturbance leads to reduced streamflow in the San Juan River basin. Disturbances are expected to be widespread in this region. Therefore, accounting for these changes within the context of climate change is imperative for water resource planning.
Fernando Jaramillo, Neil Cory, Berit Arheimer, Hjalmar Laudon, Ype van der Velde, Thomas B. Hasper, Claudia Teutschbein, and Johan Uddling
Hydrol. Earth Syst. Sci., 22, 567–580, https://doi.org/10.5194/hess-22-567-2018, https://doi.org/10.5194/hess-22-567-2018, 2018
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Which is the dominant effect on evapotranspiration in northern forests, an increase by recent forests expansion or a decrease by the water use response due to increasing CO2 concentrations? We determined the dominant effect during the period 1961–2012 in 65 Swedish basins. We used the Budyko framework to study the hydroclimatic movements in Budyko space. Our findings suggest that forest expansion is the dominant driver of long-term and large-scale evapotranspiration changes.
Jie Zhu, Ge Sun, Wenhong Li, Yu Zhang, Guofang Miao, Asko Noormets, Steve G. McNulty, John S. King, Mukesh Kumar, and Xuan Wang
Hydrol. Earth Syst. Sci., 21, 6289–6305, https://doi.org/10.5194/hess-21-6289-2017, https://doi.org/10.5194/hess-21-6289-2017, 2017
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Forested wetlands provide myriad ecosystem services threatened by climate change. This study develops empirical hydrologic models by synthesizing hydrometeorological data across the southeastern US. We used global climate projections to model hydrological changes for five wetlands. We found all wetlands are predicted to become drier by the end of this century. This study suggests that climate change may substantially affect wetland biogeochemical cycles and other functions in the future.
Guiomar Ruiz-Pérez, Julian Koch, Salvatore Manfreda, Kelly Caylor, and Félix Francés
Hydrol. Earth Syst. Sci., 21, 6235–6251, https://doi.org/10.5194/hess-21-6235-2017, https://doi.org/10.5194/hess-21-6235-2017, 2017
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Plants are shaping the landscape and controlling the hydrological cycle, particularly in arid and semi-arid ecosystems. Remote sensing data appears as an appealing source of information for vegetation monitoring, in particular in areas with a limited amount of available field data. Here, we present an example of how remote sensing data can be exploited in a data-scarce basin. We propose a mathematical methodology that can be used as a springboard for future applications.
Rui Rivaes, Isabel Boavida, José M. Santos, António N. Pinheiro, and Teresa Ferreira
Hydrol. Earth Syst. Sci., 21, 5763–5780, https://doi.org/10.5194/hess-21-5763-2017, https://doi.org/10.5194/hess-21-5763-2017, 2017
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We analyzed the influence of considering riparian requirements for the long-term efficiency of environmental flows. After a decade, environmental flows disregarding riparian requirements promoted riparian degradation and consequently the change in the hydraulic characteristics of the river channel and the modification of the available habitat area for fish species. Environmental flows regarding riparian vegetation requirements were able to sustain the fish habitat close to the natural condition.
Mie Andreasen, Karsten H. Jensen, Darin Desilets, Marek Zreda, Heye R. Bogena, and Majken C. Looms
Hydrol. Earth Syst. Sci., 21, 1875–1894, https://doi.org/10.5194/hess-21-1875-2017, https://doi.org/10.5194/hess-21-1875-2017, 2017
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The cosmic-ray method holds a potential for quantifying canopy interception and biomass. We use measurements and modeling of thermal and epithermal neutron intensity in a forest to examine this potential. Canopy interception is a variable important to forest hydrology, yet difficult to monitor remotely. Forest growth impacts the carbon-cycle and can be used to mitigate climate changes by carbon sequestration in biomass. An efficient method to monitor tree growth is therefore of high relevance.
Jordi Cristóbal, Anupma Prakash, Martha C. Anderson, William P. Kustas, Eugénie S. Euskirchen, and Douglas L. Kane
Hydrol. Earth Syst. Sci., 21, 1339–1358, https://doi.org/10.5194/hess-21-1339-2017, https://doi.org/10.5194/hess-21-1339-2017, 2017
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Quantifying trends in surface energy fluxes is crucial for forecasting ecological responses in Arctic regions.
An extensive evaluation using a thermal-based remote sensing model and ground measurements was performed in Alaska's Arctic tundra for 5 years. Results showed an accurate temporal trend of surface energy fluxes in concert with vegetation dynamics. This work builds toward a regional implementation over Arctic ecosystems to assess response of surface energy fluxes to climate change.
Chunwei Liu, Ge Sun, Steven G. McNulty, Asko Noormets, and Yuan Fang
Hydrol. Earth Syst. Sci., 21, 311–322, https://doi.org/10.5194/hess-21-311-2017, https://doi.org/10.5194/hess-21-311-2017, 2017
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The paper aimed at deriving Kc (AET/PET) for multiple vegetation types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We established multiple linear equations for different land covers and seasons to model the dynamics of Kc as function of LAI, site latitude, and precipitation. Our study extended the applications of the traditional Kc method for estimating crop water use to estimating AET rates for natural ecosystems.
Xingguo Mo, Xuejuan Chen, Shi Hu, Suxia Liu, and Jun Xia
Hydrol. Earth Syst. Sci., 21, 295–310, https://doi.org/10.5194/hess-21-295-2017, https://doi.org/10.5194/hess-21-295-2017, 2017
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Attributing changes in ET and GPP is crucial to impact and adaptation assessment of climate change over the NCP. Simulations with the VIP ecohydrological model illustrated relative contributions of climatic change, CO2 fertilization, and management to ET and GPP. Global radiation was the cause of GPP decline in summer, while air warming intensified the water cycle and advanced plant productivity in spring. Agronomical improvement was the main driver of crop productivity enhancement.
Elham Rouholahnejad Freund and James W. Kirchner
Hydrol. Earth Syst. Sci., 21, 217–233, https://doi.org/10.5194/hess-21-217-2017, https://doi.org/10.5194/hess-21-217-2017, 2017
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Our analysis shows that averaging over sub-grid heterogeneity in precipitation and potential evapotranspiration (ET), as typical earth system models do, overestimates the average of the spatially variable ET. We also show when aridity index increases with altitude, lateral redistribution would transfer water from more humid uplands to more arid lowlands, resulting in a net increase in ET. Therefore, the Earth system models that neglect lateral transfer underestimate ET in those regions.
Simon Schmidt, Christine Alewell, Panos Panagos, and Katrin Meusburger
Hydrol. Earth Syst. Sci., 20, 4359–4373, https://doi.org/10.5194/hess-20-4359-2016, https://doi.org/10.5194/hess-20-4359-2016, 2016
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We present novel research on the seasonal dynamics of the impact of rainfall (R-factor) on the mobilization of topsoil as soil erosion by water for Switzerland. A modeling approach was chosen that enables the dynamical mapping of the R-factor. Based on the maps and modeling results, we could investigate the spatial and temporal distribution of that factor, which is high for Switzerland. With these results, agronomists can introduce selective erosion control measures.
Kaniska Mallick, Ivonne Trebs, Eva Boegh, Laura Giustarini, Martin Schlerf, Darren T. Drewry, Lucien Hoffmann, Celso von Randow, Bart Kruijt, Alessandro Araùjo, Scott Saleska, James R. Ehleringer, Tomas F. Domingues, Jean Pierre H. B. Ometto, Antonio D. Nobre, Osvaldo Luiz Leal de Moraes, Matthew Hayek, J. William Munger, and Steven C. Wofsy
Hydrol. Earth Syst. Sci., 20, 4237–4264, https://doi.org/10.5194/hess-20-4237-2016, https://doi.org/10.5194/hess-20-4237-2016, 2016
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While quantifying vegetation water use over multiple plant function types in the Amazon Basin, we found substantial biophysical control during drought as well as a water-stress period and dominant climatic control during a water surplus period. This work has direct implication in understanding the resilience of the Amazon forest in the spectre of frequent drought menace as well as the role of drought-induced plant biophysical functioning in modulating the water-carbon coupling in this ecosystem.
Masahiro Ryo, Marie Leys, and Christopher T. Robinson
Hydrol. Earth Syst. Sci., 20, 3411–3418, https://doi.org/10.5194/hess-20-3411-2016, https://doi.org/10.5194/hess-20-3411-2016, 2016
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We developed an analytical method to estimate thermal attributes (seasonal and diel periodicities as well as irregularities) in stream temperature at data-poor sites. We extrapolated the thermal attributes of a glacier-fed stream in the Swiss Alps using 2 years of hourly recorded temperature to the data-poor sites. The R scripts used in this study are available in the Supplement.
Maik Renner, Sibylle K. Hassler, Theresa Blume, Markus Weiler, Anke Hildebrandt, Marcus Guderle, Stanislaus J. Schymanski, and Axel Kleidon
Hydrol. Earth Syst. Sci., 20, 2063–2083, https://doi.org/10.5194/hess-20-2063-2016, https://doi.org/10.5194/hess-20-2063-2016, 2016
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We estimated forest transpiration (European beech) along a steep valley cross section. Atmospheric demand, obtained by the thermodynamic limit of maximum power, is the dominant control of transpiration at all sites.
To our surprise we find that transpiration is rather similar across sites with different aspect (north vs. south) and different stand structure due to systematically varying sap velocities. Such a compensation effect is highly relevant for modeling and upscaling of transpiration.
Congsheng Fu, Guiling Wang, Michael L. Goulden, Russell L. Scott, Kenneth Bible, and Zoe G. Cardon
Hydrol. Earth Syst. Sci., 20, 2001–2018, https://doi.org/10.5194/hess-20-2001-2016, https://doi.org/10.5194/hess-20-2001-2016, 2016
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Hydraulic redistribution (HR) of plant root has important hydrological impact (on evapotranspiration, Bowen ratio, and soil moisture) in ecosystems that have a pronounced dry season but are not overall so dry that sparse vegetation and very low soil moisture limit HR.
Shanlei Sun, Ge Sun, Erika Cohen, Steven G. McNulty, Peter V. Caldwell, Kai Duan, and Yang Zhang
Hydrol. Earth Syst. Sci., 20, 935–952, https://doi.org/10.5194/hess-20-935-2016, https://doi.org/10.5194/hess-20-935-2016, 2016
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This study links an ecohydrological model with WRF (Weather Research and Forecasting Model) dynamically downscaled climate projections of the HadCM3 model under the IPCC SRES A2 emission scenario. Water yield and ecosystem productivity response to climate change were highly variable with an increasing trend across the 82 773 watersheds. Results are useful for policy-makers and land managers in formulating appropriate watershed-specific strategies for sustaining water and carbon sources.
M. K. van der Molen, R. A. M. de Jeu, W. Wagner, I. R. van der Velde, P. Kolari, J. Kurbatova, A. Varlagin, T. C. Maximov, A. V. Kononov, T. Ohta, A. Kotani, M. C. Krol, and W. Peters
Hydrol. Earth Syst. Sci., 20, 605–624, https://doi.org/10.5194/hess-20-605-2016, https://doi.org/10.5194/hess-20-605-2016, 2016
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Boreal Eurasia contains extensive forests, which play an important role in the terrestrial carbon cycle. Droughts can modify this cycle considerably, although very few ground-based observations are available in the region. We test whether satellite-observed soil moisture may be used to improve carbon cycle models in this region. This paper explains when and where this works best. The interpretation of satellite soil moisture is best in summer conditions, and is hampered by snow, ice and ponding.
E. S. Garcia and C. L. Tague
Hydrol. Earth Syst. Sci., 19, 4845–4858, https://doi.org/10.5194/hess-19-4845-2015, https://doi.org/10.5194/hess-19-4845-2015, 2015
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In forests of the western United States, annual evapotranspiration (ET) varies with precipitation and temperature; geologically mediated drainage and storage properties may influence the relationship between climate and ET. A process-based model is used to evaluate how water storage capacity influences model estimates of ET-climate relationships for three snow-dominated basins. Results show that uncertainty in subsurface properties can strongly influence model estimates of watershed-scale ET.
P. Fox, P. H. Hutton, D. J. Howes, A. J. Draper, and L. Sears
Hydrol. Earth Syst. Sci., 19, 4257–4274, https://doi.org/10.5194/hess-19-4257-2015, https://doi.org/10.5194/hess-19-4257-2015, 2015
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The development of California was facilitated by redistributing water from the natural landscape to other uses. This development was accompanied by declines in native aquatic species, which have been attributed to reductions in Delta outflow. By reconstructing the natural landscape and using water balances to estimate natural Delta outflow, this flow is shown to be consistent with current outflow on a long-term annual average basis.
S. Acharya, D. A. Kaplan, S. Casey, M. J. Cohen, and J. W. Jawitz
Hydrol. Earth Syst. Sci., 19, 2133–2144, https://doi.org/10.5194/hess-19-2133-2015, https://doi.org/10.5194/hess-19-2133-2015, 2015
R. G. Knox, M. Longo, A. L. S. Swann, K. Zhang, N. M. Levine, P. R. Moorcroft, and R. L. Bras
Hydrol. Earth Syst. Sci., 19, 241–273, https://doi.org/10.5194/hess-19-241-2015, https://doi.org/10.5194/hess-19-241-2015, 2015
C. Velluet, J. Demarty, B. Cappelaere, I. Braud, H. B.-A. Issoufou, N. Boulain, D. Ramier, I. Mainassara, G. Charvet, M. Boucher, J.-P. Chazarin, M. Oï, H. Yahou, B. Maidaji, F. Arpin-Pont, N. Benarrosh, A. Mahamane, Y. Nazoumou, G. Favreau, and J. Seghieri
Hydrol. Earth Syst. Sci., 18, 5001–5024, https://doi.org/10.5194/hess-18-5001-2014, https://doi.org/10.5194/hess-18-5001-2014, 2014
Short summary
Short summary
Long-term average water and energy cycles are described for two main land cover types in the cultivated Sahel (millet crop and fallow bush). Mean seasonal cycles and annual budgets for all component variables were estimated from detailed field and model analysis. Evapotranspiration totals over 80% of rainfall for both covers, but with different time distribution and soil/plant contributions. The remainder is shared between runoff and deep drainage for the crop, but is only runoff for the fallow.
D. L. Ficklin, B. L. Barnhart, J. H. Knouft, I. T. Stewart, E. P. Maurer, S. L. Letsinger, and G. W. Whittaker
Hydrol. Earth Syst. Sci., 18, 4897–4912, https://doi.org/10.5194/hess-18-4897-2014, https://doi.org/10.5194/hess-18-4897-2014, 2014
Short summary
Short summary
We use a hydrologic model coupled with a stream temperature model and downscaled general circulation model outputs to explore changes in stream temperature in the Columbia River basin for the late 21st century. On average, stream temperatures are projected to increase 3.5 °C for the spring, 5.2 °C for the summer, 2.7 °C for the fall, and 1.6 °C for the winter. Our results capture the important, and often ignored, influence of hydrological processes on changes in stream temperature.
M. Bechmann, C. Schneider, A. Carminati, D. Vetterlein, S. Attinger, and A. Hildebrandt
Hydrol. Earth Syst. Sci., 18, 4189–4206, https://doi.org/10.5194/hess-18-4189-2014, https://doi.org/10.5194/hess-18-4189-2014, 2014
F. J. Peñas, J. Barquín, T. H. Snelder, D. J. Booker, and C. Álvarez
Hydrol. Earth Syst. Sci., 18, 3393–3409, https://doi.org/10.5194/hess-18-3393-2014, https://doi.org/10.5194/hess-18-3393-2014, 2014
M. Bechtold, B. Tiemeyer, A. Laggner, T. Leppelt, E. Frahm, and S. Belting
Hydrol. Earth Syst. Sci., 18, 3319–3339, https://doi.org/10.5194/hess-18-3319-2014, https://doi.org/10.5194/hess-18-3319-2014, 2014
Z. M. Subin, P. C. D. Milly, B. N. Sulman, S. Malyshev, and E. Shevliakova
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-8443-2014, https://doi.org/10.5194/hessd-11-8443-2014, 2014
Preprint withdrawn
Y. W. Zhao, M. J. Xu, F. Xu, S. R. Wu, and X. A. Yin
Hydrol. Earth Syst. Sci., 18, 2113–2126, https://doi.org/10.5194/hess-18-2113-2014, https://doi.org/10.5194/hess-18-2113-2014, 2014
G. Tang, T. Hwang, and S. M. Pradhanang
Hydrol. Earth Syst. Sci., 18, 1423–1437, https://doi.org/10.5194/hess-18-1423-2014, https://doi.org/10.5194/hess-18-1423-2014, 2014
J. Lozano-Parra, M. P. Maneta, and S. Schnabel
Hydrol. Earth Syst. Sci., 18, 1439–1456, https://doi.org/10.5194/hess-18-1439-2014, https://doi.org/10.5194/hess-18-1439-2014, 2014
X. A. Yin, Z. F. Yang, and C. L. Liu
Hydrol. Earth Syst. Sci., 18, 1359–1368, https://doi.org/10.5194/hess-18-1359-2014, https://doi.org/10.5194/hess-18-1359-2014, 2014
J. Feng, R. Li, R. Liang, and X. Shen
Hydrol. Earth Syst. Sci., 18, 1213–1223, https://doi.org/10.5194/hess-18-1213-2014, https://doi.org/10.5194/hess-18-1213-2014, 2014
J. P. Lhomme and C. Montes
Hydrol. Earth Syst. Sci., 18, 1137–1149, https://doi.org/10.5194/hess-18-1137-2014, https://doi.org/10.5194/hess-18-1137-2014, 2014
S. Yoshikawa, A. Yanagawa, Y. Iwasaki, P. Sui, S. Koirala, K. Hirano, A. Khajuria, R. Mahendran, Y. Hirabayashi, C. Yoshimura, and S. Kanae
Hydrol. Earth Syst. Sci., 18, 621–630, https://doi.org/10.5194/hess-18-621-2014, https://doi.org/10.5194/hess-18-621-2014, 2014
Cited articles
Ahmed, S. and Tsanis, I.: Hydrologic and Hydraulic Impact of Climate Change on Lake Ontario Tributary, Am. J. Water Resour., 4, 1–15, https://doi.org/10.12691/ajwr-4-1-1, 2016.
Allen, K. R.: Comparison of the Growth Rate of Brown Trout (Salmo trutta) in a New Zealand Stream with Experimental Fish in Britain, J. Anim. Ecol., 54, 487–495, https://doi.org/10.2307/4493, 1985.
Almodóvar, A., Nicola, G. G., Ayllón, D., and Elvira, B.: Global warming threatens the persistence of Mediterranean brown trout, Glob. Change Biol., 18, 1549–1560, https://doi.org/10.1111/j.1365-2486.2011.02608.x, 2011.
Angilletta Jr., M. J.: Thermal Adaptation: A Theoretical and Empirical Synthesis, Oxford University Press, New York, USA, 2009.
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.
Ayllón, D., Railsback, S. F., Vincenzi, S., Groeneveld, J., Almodóvar, A., and Grimm, V.: InSTREAM-Gen: Modelling eco-evolutionary dynamics of trout populations under anthropogenic environmental change, Ecol. Model., 326, 36–53, https://doi.org/10.1016/j.ecolmodel.2015.07.026, 2016.
Beer, W. N. and Anderson, J. J.: Sensitivity of salmonid freshwater life history in western US streams to future climate conditions, Glob. Change Biol., 19, 2547–2556, https://doi.org/10.1111/gcb.12242, 2013.
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P., Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B. D., and Andreassian, V.: Characterising performance of environmental models, Environ. Modell. Softw., 40, 1–20, https://doi.org/10.1016/j.envsoft.2012.09.011, 2013.
Beven, K.: I believe in climate change but how precautionary do we need to be in planning for the future?, Hydrol. Process., 25, 1517–1520, https://doi.org/10.1002/hyp.7939, 2011.
Beven, K. and Westerberg, I.: On red herrings and real herrings: disinformation and information in hydrological inference, Hydrol. Process., 25, 1676–1680, https://doi.org/10.1002/hyp.7963, 2011.
Bogan, T., Mohseni, O., and Stefan, H. G.: Stream temperature-equilibrium temperature relationship, Water Resour. Res., 39, 1245, https://doi.org/10.1029/2003WR002034, 2003.
Borra, S. and Di Ciaccio, A.: Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods, Comput. Stat. Data An., 54, 2976–2989, https://doi.org/10.1016/j.csda.2010.03.004, 2010.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brewitt, K. S. and Danner, E. M.: Spatio-temporal temperature variation influences juvenile steelhead (Oncorhynchus mykiss) use of thermal refuges, Ecosphere, 5, 92, https://doi.org/10.1890/ES14-00036.1, 2014.
Bustillo, V., Moatar, F., Ducharne, A., Thiéry, D., and Poirel, A.: A multimodel comparison for assessing water temperatures under changing climate conditions via the equilibrium temperature concept: case study of the Middle Loire River, France, Hydrol. Process., 28, 1507–1524, https://doi.org/10.1002/hyp.9683, 2013.
Caiola, N., Ibáñez, C., Verdú, J., and Munné, A.: Effects of flow regulation on the establishment of alien fish species: A community structure approach to biological validation of environmental flows, Ecol. Indic., 45, 598–604, https://doi.org/10.1016/j.ecolind.2014.05.012, 2014.
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.
Ceballos-Barbancho, A., Morán-Tejeda, E., Luego-Ugidos, M. A., and Llorente-Pinto, J. M.: Water resources and environmental change in a Mediterranean environment: The south-west sector of the Duero river basin (Spain), J. Hydrol., 351, 126–138, https://doi.org/10.1016/j.jhydrol.2007.12.004, 2008.
Chen, D., Hu, M., Guo, Y., and Dahlgren, R.A.: Changes in river water temperature between 1980–2012 in Yongan watershed, eastern China: magnitude, drivers and models, J. Hydrol., 533, 191–199, https://doi.org/10.1016/j.jhydrol.2015.12.005, 2016.
Chessman, B. C.: Climatic changes and 13-year trends in stream macroinvertebrate assemblages in New South Wales, Australia, Glob. Change Biol., 15, 2791–2802, https://doi.org/10.1111/j.1365-2486.2008.01840.x, 2009.
Chilton, J.: Groundwater, in: Water quality assessments: A guide to the use of biota, sediments and water in environmental monitoring, 2nd ed., edited by: Chapman, D. E. and Spon, F. N., London, UK, 413–510, 1996.
Colchen, T., Teletchea, F., Fontaine, P., and Pasquet, A.: Temperature modifies activity, inter-individual relationships and group structure in fish, Curr. Zool., 63, 175–183, https://doi.org/10.1093/cz/zow048, 2017.
Comte, L., Buisson, L., Daufresne, M., and Grenouillet, G.: Climate-induced changes in the distribution of freshwater fish: observed and predicted trends, Freshwater Biol., 58, 625–639, https://doi.org/10.1111/fwb.12081, 2013.
Daigle, A., Jeong, D. I., and Lapointe, M. F.: Climate change and resilience of tributary thermal refugia for salmonids in eastern Canadian rivers, Hydrolog. Sci. J., 60, 1044–1063, https://doi.org/10.1080/02626667.2014.898121, 2014.
De'ath, G. and Fabricius, K. E.: Classification and regression trees: a powerful yet simple technique for ecological data analysis, Ecology, 81, 3178–3192, https://doi.org/10.2307/177409, 2000.
DeWeber, J. T. and Wagner, T.: Predicting Brook trout occurrence in stream reaches throughout their native range in the Eastern United States, T. Am. Fish. Soc., 144, 11–24, https://doi.org/10.1080/00028487.2014.963256, 2015.
Eby, L. A., Helmy, O., Holsinger, L. M., and Young, M. K.: Evidence of climate-induced range contractions in bull trout Salvelinus confluentus in a Rocky Mountain watershed, U.S.A., PLoS ONE, 9, e98812, https://doi.org/10.1371/journal.pone.0098812, 2014.
Edinger, J. E., Duttweiler, D. W., and Geyer, J. C.: The response of water temperatures to meteorological conditions, Water Resour. Res., 4, 1137–1143, https://doi.org/10.1029/WR004i005p01137, 1968.
Elith, J. and Leathwick, J. R.: Species distribution models: Ecological explanation and prediction across space and time, Annu. Rev. Ecol. Evol. S., 40, 677–697, https://doi.org/10.1146/annurev.ecolsys.110308.120159, 2009.
Elliott, J. M.: Some aspects of thermal stress on freshwater teleosts, in: Stress and Fish, edited by: Pickering, A. D., Academic Press, London, UK, 209–245, 1981.
Elliott, J. M.: Pools as refugia for brown trout during two summer droughts: trout responses to thermal and oxygen stress, J. Fish Biol., 56, 938–948, https://doi.org/10.1111/j.1095-8649.2000.tb00883.x, 2000.
Elliott, J. M. and Allonby, J. D.: An experimental study of ontogenetic and seasonal changes in the temperature preferences of unfed and fed brown trout, Salmo trutta, Freshwater Biol., 58, 1840–1848, https://doi.org/10.1111/fwb.12173, 2013.
Elliott, J. M. and Elliott, J. A.: Temperature requirements of Atlantic salmon Salmo salar, Brown trout Salmo trutta and Arctic charr Salvelinus alpinus: predicting the effects of climate change, J. Fish Biol., 77, 1793–1817, https://doi.org/10.1111/j.1095-8649.2010.02762.x, 2010.
Elliott, J. M., Hurley, M. A., and Fryer, J.: A new, improved growth model for brown trout, Salmo trutta, Funct. Ecol., 9, 290–298, https://doi.org/10.2307/2390576, 1995.
European Environment Agency: CLC2006 technical guidelines. Technical report No. 17/2007, Publications Office, Luxembourg, https://doi.org/10.2800/12134, 2007.
Fey, S. B. and Herren, C. M.: Temperature-mediated biotic interactions influence enemy release of non-native species in warming environments, Ecology, 95, 2246–2256, https://doi.org/10.1890/13-1799.1, 2014.
Fielding, A. H.: An introduction to machine learning methods. In: Machine Learning Methods for Ecological Applications, edited by: Fielding, A. H., Kluwer, Boston, USA, 1–35, 1999.
Filipe, A. F., Markovic, D., Pletterbauer, F., Tisseuil, C., De Wever, A., Schmutz, S., Bonada, N., and Freyhof, J: Forecasting fish distribution along stream networks: brown trout (Salmo trutta) in Europe, Divers. Distrib., 19, 1059–1071, https://doi.org/10.1111/ddi.12086, 2013.
Forseth, T. and Jonsson, B.: The growth and food ration of piscivorous brown trout (Salmo trutta), Funct. Ecol., 8, 171–177, https://doi.org/10.2307/2389900, 1994.
Forseth, T., Larsson, S., Jensen, A. J., Jonsson, B., Näslund, I., and Berglund, I.: Thermal growth performance of juvenile brown trout Salmo trutta?: no support for thermal adaptation hypotheses, J. Fish Biol., 74, 133–149, https://doi.org/10.1111/j.1095-8649.2008.02119.x, 2009.
Fukuda, S., De Baets, B., Waegeman, W., Verwaeren, J., and Mouton, A. M.: Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models, Environ. Modell. Softw., 47, 1–6, https://doi.org/10.1016/j.envsoft.2013.04.005, 2013.
Garner, G., Van Loon, A. F., Prudhomme, C., and Hannah, D. M.: Hydroclimatology of extreme river flows, Freshwater Biol., 60, 2461–2476, https://doi.org/10.1111/fwb.12667, 2015.
Gordon, N. D., McMahon, T. A., Finlayson, B. L., Gippel, C. J., and Nathan, R. J.: Stream hydrology. An introduction for ecologists, 2nd ed., John Wiley and Sons, Chichester, UK, 2004.
Gortázar, J., García de Jalón, D., Alonso-González, C., Vizcaíno, P., Baeza, D., and Marchamalo, M.: Spawning period of a southern brown trout population in a highly unpredictable stream, Ecol. Freshw. Fish, 16, 515–527, https://doi.org/10.1111/j.1600-0633.2007.00246.x, 2007.
Goyer, K., Bertolo, A., Pépino, M., and Magnan, P.: Effects of lake warming on behavioural thermoregulatory tactics in a cold-water stenothermic fish, PLoS ONE, 9, e92514, https://doi.org/10.1371/journal.pone.0092514, 2014.
Grande, M. and Andersen, S.: Critical Thermal Maxima for Young Salmonids, J. Freshwater Ecol., 6, 275–279, https://doi.org/10.1080/02705060.1991.9665304, 1991.
Haines, A. T., Finlayson, B. L., and McMahon, T. A.: A global classification of river regimes, Appl. Geogr., 8, 255–272, https://doi.org/10.1016/0143-6228(88)90035-5, 1988.
Hampe, A. and Petit, R. J.: Conserving biodiversity under climate change: the rear edge mattersm Ecol. Lett., 8, 461–467, https://doi.org/10.1111/j.1461-0248.2005.00739.x, 2005.
Hari, R. E., Livingstone, D. M., Siber, R., Burkhardt-Holm, P., and Guettinger, H.: Consequences of climatic change for water temperature and brown trout populations in Alpine rivers and streams, Glob. Change Biol., 12, 10–26, https://doi.org/10.1111/j.1365-2486.2005.001051.x, 2006.
Hein, C. L., Ohlund, G., and Englund, G.: Fish introductions reveal the temperature dependence of species interactions, P. Roy. Soc. B-Biol. Sci., 281, 20132641, https://doi.org/10.1098/rspb.2013.2641, 2013.
Hettiarachchi, P., Hall, M. J., and Minns, A. W.: The extrapolation of artificial neural networks for the modelling of rainfall–runoff relationships, J. Hydroinform., 7, 291–296, 2005.
IGME: Mapa de Litologías de España 1 : 1 000 000, Madrid, Spain, available at: http://mapas.igme.es/gis/rest/services/Cartografia_Geologica/IGME_Litologias_1M/MapServer (last access: 15 February 2016), 2015.
IPCC: Climate Change 2013. The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M. M. B., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK, 2013.
Isaak, D. J., Young, M. K., Luce, C. H., Hostetler S. W., Wenger, S. J., Peterson, E. E., Ver Hoef, J. M., Groce, M. C., Horan, D. L., and Nagel, D. E.: Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity, P. Natl. Acad. Sci. USA, 113, 4374–4379, https://doi.org/10.1073/pnas.1522429113, 2016.
Jeffries, K. M., Hinch, S. G., Martins, E. G., Clark, T. D., Lotto, A. G., Patterson, D. A., Cooke, S. J., Farrell, A. P., and Miller, K. M.: Sex and proximity to reproductive maturity influence the survival, final maturation, and blood physiology of Pacific salmon when exposed to high temperature during a simulated migration, Physiol. Biochem. Zool., 85, 62–73, https://doi.org/10.1086/663770, 2012.
Jonsson, B. and Jonsson, N.: A review of the likely effects of climate change on anadromous Atlantic salmon Salmo salar and brown trout Salmo trutta, with particular reference to water temperature and flow, J. Fish Biol., 75, 2381–2447, https://doi.org/10.1111/j.1095-8649.2009.02380.x, 2009.
Junker, J., Heimann, F. U. M., Hauer, C., Turowski, J. M., Rickenmann, D., Zappa, M., and Peter, A.: Assessing the impact of climate change on brown trout (Salmo trutta fario) recruitment, Hydrobiologia, 751, 1–21, https://doi.org/10.1007/s10750-014-2073-4, 2015.
Juston, J. M., Kauffeldt, A., Montano, B. Q., Seibert, J., Beven, K. J., and Westerberg, I. K.: Smiling in the rain: Seven reasons to be positive about uncertainty in hydrological modelling, Hydrol. Process., 27, 1117–1122, https://doi.org/10.1002/hyp.9625, 2013.
Kaufman, L. and Rousseeuw, P. J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, Hoboken, New Jersey, USA, https://doi.org/10.1002/9780470316801, 2005.
Kaushal, S. S., Likens, G. E., Jaworski, N. A., Pace, M. L., Sides, A. M., Seekell, D., Belt, K. T., Secor, D. H., and Wingate, R.: Rising stream and river temperatures in the United States, Front. Ecol. Environ., 8, 461–466, https://doi.org/10.1890/090037, 2010.
Kittler, J.: Feature set search algorithms, in: Pattern Recognition and Signal Processing, edited by: Chen, C. H., Sijthoff and Noordhoff, Alphen aan den Rijn, the Netherlands, 41–60, 1978.
Kottelat, M. and Freyhof, J.: Handbook of European freshwater fishes, Kottelat, Cornol, Switzerland and Freyhof, Berlin, Germany, 2007.
Kuhn, M., Weston, S., Keefer, C., and Coulter, N.: Cubist: Rule- and Instance-Based Regression Modeling – C code for Cubist by Ross Quinlan, R package, version 0.0.18, CRAN R-Project package, version 0.0.18, CRAN R-Project, available at: https://cran.r-project.org/package=Cubist (last access: 15 August 2015), 2014.
Kurylyk, B. L., Bourque, C. P.-A., and MacQuarrie, K. T. B.: Potential surface temperature and shallow groundwater temperature response to climate change: an example from a small forested catchment in east-central New Brunswick (Canada), Hydrol. Earth Syst. Sci., 17, 2701–2716, https://doi.org/10.5194/hess-17-2701-2013, 2013.
Kurylyk, B. L., MacQuarrie, K. T. B., Caissie, D., and McKenzie, J. M.: Shallow groundwater thermal sensitivity to climate change and land cover disturbances: derivation of analytical expressions and implications for stream temperature modeling, Hydrol. Earth Syst. Sci., 19, 2469–2489, https://doi.org/10.5194/hess-19-2469-2015, 2015.
Lahnsteiner, F. and Leitner, S.: Effect of temperature on gametogenesis and gamete quality in Brown trout, Salmo trutta, J. Exp. Zool. Part A, 319, 138–148, https://doi.org/10.1002/jez.1779, 2013.
Larios-López, J. E., Tierno de Figueroa, J. M., Galiana-García, M., Gortázar, J., and Alonso, C.: Extended spawning in brown trout (Salmo trutta) populations from the Southern Iberian Peninsula: the role of climate variability, J. Limnol., 74, 394–402, https://doi.org/10.4081/jlimnol.2015.1089, 2015.
Lassalle, G. and Rochard, E.: Impact of twenty-first century climate change on diadromous fish spread over Europe, North Africa and the Middle East, Glob. Change Biol., 15, 1072–1089, https://doi.org/10.1111/j.1365-2486.2008.01794.x, 2009.
Leppi, J. C., DeLuca, T. H., Harrar, S. W.. and Running, S. W.: Impacts of climate change on August stream discharge in the Central-Rocky Mountains, Climatic Change, 112, 997–1014, https://doi.org/10.1007/s10584-011-0235-1, 2012.
Liu, R. and Singh, K.: Moving blocks jackknife and bootstrap capture weak dependence, in: Exploring the Limits of Bootstrap, edited by: LePage, R. and Billard, L., John Wiley and Sons, New York, USA, 225–248, 1992.
Lobón-Cerviá, J. and Rincón, P. A.: Field assessment of the influence of temperature on growth rate in a brown trout population, T. Am. Fish. Soc., 127, 718–728, https://doi.org/10.1577/1548-8659(1998)127<0718:FAOTIO>2.0.CO;2, 1998.
Lobón-Cerviá, J. and Mortensen, E.: Population size in stream-living juveniles of lake-migratory brown trout Salmo trutta L.: the importance of stream discharge and temperature, Ecol. Freshw. Fish, 14, 394–401, https://doi.org/10.1111/j.1600-0633.2005.00111.x, 2005.
Lobón-Cerviá, J. and Rincón, P. A.: Environmental determinants of recruitment and their influence on the population dynamics of stream-living brown trout Salmo trutta, Oikos, 105, 641–646, https://doi.org/10.1111/j.0030-1299.2004.12989.x, 2004.
Loinaz, M. C., Davidsen, H. K., Butts, M., and Bauer-Gottwein, P.: Integrated flow and temperature modeling at the catchment scale, J. Hydrol., 495, 238–251, https://doi.org/10.1016/j.jhydrol.2013.04.039, 2013.
Lorenzo-Lacruz, J., Vicente-Serrano, S. M., López-Moreno, J. I., Morán-Tejeda, E., and Zabalza, J.: Recent trends in Iberian streamflows (1945–2005), J. Hydrol., 414–415, 463–475, https://doi.org/10.1016/j.jhydrol.2011.11.023, 2012.
Luce, C. H. and Holden, Z. A.: Declining annual streamflow distributions in the Pacific Northwest United States, 1948–2006, Geophys. Res. Lett., 36, L16401, https://doi.org/10.1029/2009GL039407, 2009.
Maechler, M.: Cluster analysis extended, Rousseeuw et al., R package, version 1.14.4, CRAN R-Project, available at: https://cran.r-project.org/package=cluster (last access: 15 August 2015), 2013.
Magnuson, J. J. and Destasio, B. T.: Thermal niche of fishes and global warming, in: Global Warming: Implications for Freshwater and Marine Fish, edited by: Wood, C. M. and McDonald, D. G., Cambridge University Press, Cambridge, UK, 377–407, 1997.
Magnuson, J. J., Crowder, L. B., and Medvick, P. A.: Temperature as an Ecological Resource, Am. Zool., 19, 331–343, https://doi.org/10.1093/icb/19.1.331, 1979.
McCuen, R. H.: Hydrologic analysis and design, 2nd ed., Prentice Hall, New Jersey, USA, 1998.
McMillan, H., Krueger, K., and Freer, J.: Benchmarking observational uncertainties for hydrology: Rainfall, river discharge and water quality, Hydrol. Process., 26, 4078–4111, https://doi.org/10.1002/hyp.9384, 2012.
Meshcheryakova, O. V., Churova, M. V., Veselov, A. E., and Nemova, N. N.: Activities of cytochrome c oxidase and mitochondrial lactate dehydrogenase isozymes and Cox1, Cox2, Cox4, and Cox6 gene subunit expression in cold adaptation of Salmo trutta L., Russ. J. Bioorganic Chem., 42, 162–169, https://doi.org/10.1134/S1068162016010106, 2016.
Milly, P. C. D., Dunne, K. A., and Vecchia, A. V.: Global pattern of trends in streamflow and water availability in a changing climate, Nature, 438, 347–350, https://doi.org/10.1038/nature04312, 2005.
Mohseni, O. and Stefan, H.: Stream temperature/air temperature relationship: a physical interpretation, J. Hydrol., 218, 128–141, https://doi.org/10.1016/S0022-1694(99)00034-7, 1999.
Mohseni, O., Stefan, H. G., and Eriksson, T. R.: A nonlinear regression model for weekly stream temperatures, Water Resour. Res., 34, 2685–2692, https://doi.org/10.1029/98WR01877, 1998.
Monjo, R., Caselles, V., and Chust, G.: Probabilistic correction of RCM precipitation in the Basque Country (Northern Spain), Theor. Appl. Climatol., 117, 317–329, https://doi.org/10.1007/s00704-013-1008-8, 2014.
Morán-Tejeda, E., Lorenzo-Lacruz, J., López-Moreno, J. I., Rahman, K., and Beniston, M.: Streamflow timing of mountain rivers in Spain: Recent changes and future projections, J. Hydrol., 517, 1114–1127, https://doi.org/10.1016/j.jhydrol.2014.06.053, 2014.
Muñoz-Mas, R., López-Nicolás, A., Martínez-Capel, F., and Pulido-Velázquez, M.: Shifts in the suitable habitat available for brown trout (Salmo trutta L.) under short-term climate change scenarios, Sci. Total Environ., 544, 686–700, https://doi.org/10.1016/j.scitotenv.2015.11.147, 2016.
Nakicenovic, N., Alcamo, J., Davis, G., de Vries, H. J. M., Fenhann, J., Gaffin, S., Gregory, K., Grubler, A., Jung, T. Y., Kram, T., La Rovere, E. L., Michaelis, L., Mori, S., Morita, T., Papper, W., Pitcher, H., Price, L., Riahi, K., Roehrl, A., Rogner, H.-H., Sankovski, A., Schlesinger, M., Shukla, P., Smith, S., Swart, R., van Rooijen, S., Victor, N., and Dadi, Z.: Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 2000.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
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.
Ojanguren, A. F., Reyes-Gavilán, F. G. and Braña, F.: Thermal sensitivity of growth, food intake and activity of juvenile brown trout, J. Therm. Biol., 26, 165–170, https://doi.org/10.1016/S0306-4565(00)00038-3, 2001.
Orr, H. G., Simpson, G. L., des Clers, S., Watts, G., Hughes, M., Hannaford, J., Dunbar, M. J., Laizé, C. L. R., Wilby, R. L., Battarbee, R. W., and Evans, R.: Detecting changing river temperatures in England and Wales, Hydrol. Process., 29, 752–766, https://doi.org/10.1002/hyp.10181, 2015.
Pappenberger, F. and Beven, K. J.: Ignorance is bliss: 7 reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, https://doi.org/10.1029/2005WR004820, 2006.
Pépino, M., Goyer, K., and Magnan, P.: Heat transfer in fish: are short excursions between habitats a thermoregulatory behaviour to exploit resources in an unfavourable thermal environment?, J. Exp. Biol., 218, 3461–3467, https://doi.org/10.1242/jeb.126466, 2015.
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: Prediction of River Water Temperature, Hydrol. Process., 30, 3901–3917, https://doi.org/10.1002/hyp.10913, 2016
Pohlert, T.: Non-parametric trend tests and change-point detection, R package, version 0.1.0, CRAN R-Project, available at: https://cran.r-project.org/package=trend, last access: 10 June 2016.
Pourmokhtarian, A., Driscoll, C. T., Campbell, J. L., Hayhoe, K., and Stoner, A. M.: The effects of climate downscaling technique and observational dataset on modeled ecological responses, Ecol. Appl., 26, 1321–1337, https://doi.org/10.1890/15-0745, 2016.
Quinlan, J. R.: Learning with continuous classes, in: The 5th Australian Joint Conference on Artificial Intelligence, 16–18 November 1992, Hobart, Australia, 343–348, 1992.
Quinlan, J. R.: An overview of Cubist, available at: https://www.rulequest.com/cubist-win.html, last access: 8 June 2017.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, available at: http://www.R-project.org (last access: 20 May 2016), 2015.
Reynolds, W. W. and Casterlin, M. E.: Thermoregulatory behavior of brown trout, Salmo trutta, Hydrobiologia, 62, 79–80, https://doi.org/10.1007/BF00012567, 1979.
Ribalaygua, J., Torres, L., Pórtoles, J., Monjo, R., Gaitán, E., and Pino, M. R.: Description and validation of a two-step analogue/regression downscaling method, Theor. Appl. Climatol., 114, 253–269, https://doi.org/10.1007/s00704-013-0836-x, 2013.
Rojas, R., Feyen, L., Bianchi, A., and Dosio, A.: Assessment of future flood hazard in Europe using a large ensemble of bias-corrected regional climate simulations, J. Geophys. Res.-Atmos., 117, D17109, https://doi.org/10.1029/2012JD017461, 2012.
Ruiz-Navarro, A., Gillingham, P. K., and Britton, J. R.: Predicting shifts in the climate space of freshwater fishes in Great Britain due to climate change, Biol. Conserv., 203, 33–42, https://doi.org/10.1016/j.biocon.2016.08.021, 2016.
Sánchez-Hernández, J. and Nunn, A.D.: Environmental changes in a Mediterranean river: implications for the fish assemblage, Ecohydrology, 9, 1439–1451, https://doi.org/10.1002/eco.1737, 2016.
Santiago, J. M.: Thermal ecology of Brown trout and the climate change challenge, in: Tilapia and Trout: Harvesting, Prevalence and Benefits, edited by: Richardson, B., Nova Science Publishers, New York, USA, 79–119, 2017.
Santiago, J. M., García de Jalón, D., Alonso, C., and Solana, J.: Comportamiento térmico de dos tramos fluviales de cabecera del sistema central: impacto del embalse de Torrecaballeros (Segovia), in: III Jornadas del Ingeniería del Agua, Valencia, Vol. 1, Marcombo, Barcelona, Spain, 153–160, 2013.
Santiago, J. M., García de Jalón, D., Alonso, C., Solana, J., Ribalaygua, J., Pórtoles, J., and Monjo, R.: Brown trout thermal niche and climate change: expected changes in the distribution of cold-water fish in central Spain, Ecohydrology, 9, 514–528, https://doi.org/10.1002/eco.1653, 2016.
Santiago, J. M., Alonso, C., and García de Jalón, D.: Daily mean stream temperatures in Central Spain, PANGAEA, https://doi.org/10.1594/PANGAEA.879494, 2017.
Schoups, G., van de Giesen, N. C., and Savenije, H. H. G.: Model complexity control for hydrologic prediction, Water Resour. Res., 44, W00B03, https://doi.org/10.1029/2008WR006836, 2008.
Shortridge, J. E., Guikema, S. D., and Zaitchik, B. F.: Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds, Hydrol. Earth Syst. Sci., 20, 2611–2628, https://doi.org/10.5194/hess-20-2611-2016, 2016.
Snyder, C. D., Hitt, N. P., and Young, J. A.: Accounting for groundwater in stream fish thermal habitat responses to climate change, Ecol. Appl., 25, 1397–1419, https://doi.org/10.1890/14-1354.1, 2015.
Solomatine, D. P. and Dulal, K. N.: Model trees as an alternative to neural networks in rainfall – runoff modelling, Hydrolog. Sci. J., 48, 399–411, https://doi.org/10.1623/hysj.48.3.399.45291, 2003.
Stamp, J., Hamilton, A., Craddock, M., Parker, L., Roy, A. H., Isaak, D. J., Holden, Z., Passmore, M., and Bierwagen, B. G.: Best practices for continuous monitoring of temperature and flow in wadeable streams. EPA/600/R-13/170F, U.S. Environmental Protection Agency, Washington, DC, USA, 2014.
Stewart, J. S., Westenbroek, S. M., Mitro, M. G., Lyons, J. D., Kammel, L. E., and Buchwald, C. A.: A model for evaluating stream temperature response to climate change in Wisconsin, Reston, Virginia, USA, U.S. Geological Survey Scientific Investigations Report 2014–5186, https://doi.org/10.3133/sir20145186, 2015.
Taghi Sattari, M., Pal, M., Apaydin, H., and Ozturk, F.: M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey, Water Resour., 40, 233–242, https://doi.org/10.1134/S0097807813030123, 2013.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: A summary of the CMIP5 experiment design, available at: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf (last access: 1 February 2016), 2009.
Thodsen, H.: The influence of climate change on stream flow in Danish rivers, J. Hydrol., 333, 226–238, https://doi.org/10.1016/j.jhydrol.2006.08.012, 2007.
Thuiller, W., Lavorel, S., Sykes, M. T., and Araujo, M. B.: Using niche-based modelling to assess the impact of climate change on tree functional diversity in Europe, Divers. Distrib., 12, 49–60, https://doi.org/10.1111/j.1366-9516.2006.00216.x, 2006.
Uppala, S. M., KÅllberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V. D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. V. D., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Q. J. Roy. Meteor. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176, 2005.
van Vliet, M. T. H., Ludwig, F., Zwolsman, J. J. G., Weedon, G. P., and Kabat, P.: Global river temperatures and sensitivity to atmospheric warming and changes in river flow, Water Resour. Res., 47, W02544, https://doi.org/10.1029/2010WR009198, 2011.
van Vliet, M. T. H., Yearsley, J. R., Franssen, W. H. P., Ludwig, F., Haddeland, I., Lettenmaier, D. P., and Kabat, P.: Coupled daily streamflow and water temperature modelling in large river basins, Hydrol. Earth Syst. Sci., 16, 4303–4321, https://doi.org/10.5194/hess-16-4303-2012, 2012.
van Vliet, M. T. H., Franssen, W. H. P., Yearsley, J. R., Ludwig, F., Haddeland, I., Lettenmaier, D. P., and Kabat, P.: Global river discharge and water temperature under climate change, Glob. Environ. Change, 23, 450–464, https://doi.org/10.1016/j.gloenvcha.2012.11.002, 2013.
Verberk, W. C. E. P., Durance, I., Vaughan, I. P., and Ormerod, S. J.: Field and laboratory studies reveal interacting effects of stream oxygenation and warming on aquatic ectotherms, Glob. Change Biol., 22, 1769–1778, https://doi.org/10.1111/gcb.13240, 2016.
Viganò, G., Confortola, G., Fornaroli, R., Cabrini, R., Canobbio, S., Mezzanotte, V., and Bocchiola, D.: Effects of future climate change on a river habitat in an Italian alpine catchment, J. Hydrol. Eng., 21, 04015063, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001293, 2015.
Vornanen, M., Haverinen, J., and Egginton, S.: Acute heat tolerance of cardiac excitation in the brown trout (Salmo trutta fario), J. Exp. Biol., 217, 299–309, https://doi.org/10.1242/jeb.091272, 2014.
Warren, D. R., Robinson, J. M., Josephson, D. C., Sheldon, D. R., and Kraft, C. E.: Elevated summer temperatures delay spawning and reduce redd construction for resident brook trout (Salvelinus fontinalis), Glob. Change Biol., 18, 1804–1811, https://doi.org/10.1111/j.1365-2486.2012.02670.x, 2012.
Webb, B. W., Hannah, D. M., Moore, R. D., Brown, L. E., and Nobilis, F.: Recent advances in stream and river temperature research, Hydrol. Process., 22, 902–918, https://doi.org/10.1002/hyp.6994, 2008.
Wenger, S. J. and Olden, J. D.: Assessing transferability of ecological models: an underappreciated aspect of statistical validation, Methods Ecol. Evol., 3, 260–267, https://doi.org/10.1111/j.2041-210X.2011.00170.x, 2012.
Wenger, S. J., Isaak, D. J., Luce, C. H., Neville, H. M., Fausch, K. D., Dunham, J. B., Dauwalter, D. C., Young, M. K., Elsner, M. M., Rieman, B. E., Hamlet, A. F., and Williams, J. E.: Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change, P. Natl. Acad. Sci. USA, 108, 14175–14180, https://doi.org/10.1073/pnas.1103097108, 2011.
White, C. R., Alton, L. A., and Frappell, P. B.: Metabolic cold adaptation in fishes occurs at the level of whole animal, mitochondria and enzyme, P. Roy. Soc. B-Biol. Sci., 279, 1740–1747, https://doi.org/10.1098/rspb.2011.2060, 2012.
Williams, J. E., Isaak, D. J., Imhof, J., Hendrickson, D. A., and McMillan, J. R.: Cold-water fishes and climate change in North America, in: Reference Module in Earth Systems and Environmental Sciences 2015, https://doi.org/10.1016/B978-0-12-409548-9.09505-1, 2015.
Zhuo, L., Dai, Q., and Han, D.: Meta-analysis of flow modeling performances – to build a matching system between catchment complexity and model types, Hydrol. Process., 29, 2463–2477, https://doi.org/10.1002/hyp.10371, 2015.
Zorita, E. and Von Storch, H.: The analog method as a simple statistical downscaling technique: comparison with more complicated methods, J. Climate, 12, 2474–2489, https://doi.org/10.1175/1520-0442(1999)012<2474:TAMAAS>2.0.CO;2, 1999.
Short summary
High-time-resolution models for streamflow and stream temperature are used in this study to predict future brown trout habitat loss. Flow reductions falling down to 51 % of current values and water temperature increases growing up to 4 ºC are predicted. Streamflow and temperature will act synergistically affecting fish. We found that the thermal response of rivers is influenced by basin geology and, consequently, geology will be also an influent factor in the cold-water fish distribution shift.
High-time-resolution models for streamflow and stream temperature are used in this study to...