Articles | Volume 21, issue 8
https://doi.org/10.5194/hess-21-4073-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-4073-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
Laboratorio de Hidrobiología, ETSI Montes, Forestal y Medio Natural, Universidad Politécnica de Madrid, Camino de las Moreras s/n, Madrid 28040, Spain
Rafael Muñoz-Mas
Institut d'Investigació per a la Gestió Integrada de Zones Costaneres (IGIC), Universitat Politècnica de València, C/ Paranimf 1, Grau de Gandia, Valencia 46730, Spain
Joaquín Solana-Gutiérrez
Laboratorio de Hidrobiología, ETSI Montes, Forestal y Medio Natural, Universidad Politécnica de Madrid, Camino de las Moreras s/n, Madrid 28040, Spain
Diego García de Jalón
Laboratorio de Hidrobiología, ETSI Montes, Forestal y Medio Natural, Universidad Politécnica de Madrid, Camino de las Moreras s/n, Madrid 28040, Spain
Carlos Alonso
Laboratorio de Hidrobiología, ETSI Montes, Forestal y Medio Natural, Universidad Politécnica de Madrid, Camino de las Moreras s/n, Madrid 28040, Spain
Francisco Martínez-Capel
Institut d'Investigació per a la Gestió Integrada de Zones Costaneres (IGIC), Universitat Politècnica de València, C/ Paranimf 1, Grau de Gandia, Valencia 46730, Spain
Javier Pórtoles
Fundación para la Investigación del Clima, C/ Tremps 11. Madrid 28040, Spain
Robert Monjo
Fundación para la Investigación del Clima, C/ Tremps 11. Madrid 28040, Spain
Jaime Ribalaygua
Fundación para la Investigación del Clima, C/ Tremps 11. Madrid 28040, Spain
Related subject area
Subject: Ecohydrology | Techniques and Approaches: Modelling approaches
Regional patterns and drivers of modelled water flows along environmental, functional, and stand structure gradients in Spanish forests
Machine learning and global vegetation: random forests for downscaling and gap filling
Unraveling phenological and stomatal responses to flash drought and implications for water and carbon budgets
Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe
Technical note: Seamless extraction and analysis of river networks in R
Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California
Improving regional climate simulations based on a hybrid data assimilation and machine learning method
A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest
Does non-stationarity induced by multiyear drought invalidate the paired-catchment method?
Is the reputation of Eucalyptus plantations for using more water than Pinus plantations justified?
Attributing trend in naturalized streamflow to temporally explicit vegetation change and climate variation in the Yellow River basin of China
Impacts of different types of El Niño events on water quality over the Corn Belt, United States
Leveraging sap flow data in a catchment-scale hybrid model to improve soil moisture and transpiration estimates
Coupled modelling of hydrological processes and grassland production in two contrasting climates
Does maximization of net carbon profit enable the prediction of vegetation behaviour in savanna sites along a precipitation gradient?
Modelling the artificial forest (Robinia pseudoacacia L.) root–soil water interactions in the Loess Plateau, China
A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration
Vegetation greening weakened the capacity of water supply to China's South-to-North Water Diversion Project
Structural changes to forests during regeneration affect water flux partitioning, water ages and hydrological connectivity: Insights from tracer-aided ecohydrological modelling
How does water yield respond to mountain pine beetle infestation in a semiarid forest?
Daily soil temperature modeling improved by integrating observed snow cover and estimated soil moisture in the USA Great Plains
Plant hydraulic transport controls transpiration sensitivity to soil water stress
Drought onset and propagation into soil moisture and grassland vegetation responses during the 2012–2019 major drought in Southern California
Quantifying the effects of urban green space on water partitioning and ages using an isotope-based ecohydrological model
Low and contrasting impacts of vegetation CO2 fertilization on global terrestrial runoff over 1982–2010: accounting for aboveground and belowground vegetation–CO2 effects
Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion
Quantifying the effects of land use and model scale on water partitioning and water ages using tracer-aided ecohydrological models
Quantification of ecohydrological sensitivities and their influencing factors at the seasonal scale
Canopy temperature and heat stress are increased by compound high air temperature and water stress and reduced by irrigation – a modeling analysis
Evaluating a landscape-scale daily water balance model to support spatially continuous representation of flow intermittency throughout stream networks
Testing water fluxes and storage from two hydrology configurations within the ORCHIDEE land surface model across US semi-arid sites
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
A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions
Dynamic responses of DOC and DIC transport to different flow regimes in a subtropical small mountainous river
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
Jesús Sánchez-Dávila, Miquel De Cáceres, Jordi Vayreda, and Javier Retana
Hydrol. Earth Syst. Sci., 28, 3037–3050, https://doi.org/10.5194/hess-28-3037-2024, https://doi.org/10.5194/hess-28-3037-2024, 2024
Short summary
Short summary
Forest blue water is determined by the climate, functional traits, and stand structure variables. The leaf area index (LAI) is the main driver of the trade-off between the blue and green water. Blue water is concentrated in the autumn–winter season, and deciduous trees can increase the relative blue water. The leaf phenology and seasonal distribution are determinants for the relative blue water.
Barry van Jaarsveld, Sandra M. Hauswirth, and Niko Wanders
Hydrol. Earth Syst. Sci., 28, 2357–2374, https://doi.org/10.5194/hess-28-2357-2024, https://doi.org/10.5194/hess-28-2357-2024, 2024
Short summary
Short summary
Drought often manifests itself in vegetation; however, obtaining high-resolution remote-sensing products that are spatially and temporally consistent is difficult. In this study, we show that machine learning (ML) can fill data gaps in existing products. We also demonstrate that ML can be used as a downscaling tool. By relying on ML for gap filling and downscaling, we can obtain a more holistic view of the impacts of drought on vegetation.
Nicholas K. Corak, Jason A. Otkin, Trent W. Ford, and Lauren E. L. Lowman
Hydrol. Earth Syst. Sci., 28, 1827–1851, https://doi.org/10.5194/hess-28-1827-2024, https://doi.org/10.5194/hess-28-1827-2024, 2024
Short summary
Short summary
We simulate how dynamic vegetation interacts with the atmosphere during extreme drought events known as flash droughts. We find that plants nearly halt water and carbon exchanges and limit their growth during flash drought. This work has implications for how to account for changes in vegetation state during extreme drought events when making predictions under future climate scenarios.
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
Short summary
Short summary
We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.
Luca Carraro
Hydrol. Earth Syst. Sci., 27, 3733–3742, https://doi.org/10.5194/hess-27-3733-2023, https://doi.org/10.5194/hess-27-3733-2023, 2023
Short summary
Short summary
Mathematical models are key to the study of environmental processes in rivers. Such models often require information on river morphology from geographic information system (GIS) software, which hinders the use of replicable workflows. Here I present rivnet, an R package for simple, robust, GIS-free extraction and analysis of river networks. The package is designed so as to require minimal user input and is oriented towards ecohydrological, ecological and biogeochemical modeling.
Stephen K. Adams, Brian P. Bledsoe, and Eric D. Stein
Hydrol. Earth Syst. Sci., 27, 3021–3039, https://doi.org/10.5194/hess-27-3021-2023, https://doi.org/10.5194/hess-27-3021-2023, 2023
Short summary
Short summary
Managing streams for environmental flows involves prioritizing healthy stream ecosystems while distributing water resources. Classifying streams of similar types is a useful step in developing environmental flows. Environmental flows are often developed on data-poor streams that must be modeled. This paper has developed a new method of classification that prioritizes model accuracy. The new method advances environmental streamflow management and modeling of data-poor watersheds.
Xinlei He, Yanping Li, Shaomin Liu, Tongren Xu, Fei Chen, Zhenhua Li, Zhe Zhang, Rui Liu, Lisheng Song, Ziwei Xu, Zhixing Peng, and Chen Zheng
Hydrol. Earth Syst. Sci., 27, 1583–1606, https://doi.org/10.5194/hess-27-1583-2023, https://doi.org/10.5194/hess-27-1583-2023, 2023
Short summary
Short summary
This study highlights the role of integrating vegetation and multi-source soil moisture observations in regional climate models via a hybrid data assimilation and machine learning method. In particular, we show that this approach can improve land surface fluxes, near-surface atmospheric conditions, and land–atmosphere interactions by implementing detailed land characterization information in basins with complex underlying surfaces.
Marissa Kivi, Noemi Vergopolan, and Hamze Dokoohaki
Hydrol. Earth Syst. Sci., 27, 1173–1199, https://doi.org/10.5194/hess-27-1173-2023, https://doi.org/10.5194/hess-27-1173-2023, 2023
Short summary
Short summary
This study attempts to provide a framework for direct integration of soil moisture observations collected from soil sensors and satellite imagery into process-based crop models for improving the representation of agricultural systems. The performance of this framework was evaluated across 19 sites times years for crop yield, normalized difference vegetation index (NDVI), soil moisture, tile flow drainage, and nitrate leaching.
Yunfan Zhang, Lei Cheng, Lu Zhang, Shujing Qin, Liu Liu, Pan Liu, and Yanghe Liu
Hydrol. Earth Syst. Sci., 26, 6379–6397, https://doi.org/10.5194/hess-26-6379-2022, https://doi.org/10.5194/hess-26-6379-2022, 2022
Short summary
Short summary
Multiyear drought has been demonstrated to cause non-stationary rainfall–runoff relationship. But whether changes can invalidate the most fundamental method (i.e., paired-catchment method (PCM)) for separating vegetation change impacts is still unknown. Using paired-catchment data with 10-year drought, PCM is shown to still be reliable even in catchments with non-stationarity. A new framework is further proposed to separate impacts of two non-stationary drivers, using paired-catchment data.
Don A. White, Shiqi Ren, Daniel S. Mendham, Francisco Balocchi-Contreras, Richard P. Silberstein, Dean Meason, Andrés Iroumé, and Pablo Ramirez de Arellano
Hydrol. Earth Syst. Sci., 26, 5357–5371, https://doi.org/10.5194/hess-26-5357-2022, https://doi.org/10.5194/hess-26-5357-2022, 2022
Short summary
Short summary
Of all the planting options for wood production and carbon storage, Eucalyptus species provoke the greatest concern about their effect on water resources. We compared Eucalyptus and Pinus species (the two most widely planted genera) by fitting a simple model to the published estimates of their annual water use. There was no significant difference between the two genera. This has important implications for the global debate around Eucalyptus and is an option for carbon forests.
Zhihui Wang, Qiuhong Tang, Daoxi Wang, Peiqing Xiao, Runliang Xia, Pengcheng Sun, and Feng Feng
Hydrol. Earth Syst. Sci., 26, 5291–5314, https://doi.org/10.5194/hess-26-5291-2022, https://doi.org/10.5194/hess-26-5291-2022, 2022
Short summary
Short summary
Variable infiltration capacity simulation considering dynamic vegetation types and structural parameters is able to better capture the effect of temporally explicit vegetation change and climate variation in hydrological regimes. Vegetation greening including interannual LAI and intra-annual LAI temporal pattern change induced by large-scale ecological restoration and non-vegetation underlying surface change played dominant roles in the natural streamflow reduction of the Yellow River basin.
Pan Chen, Wenhong Li, and Keqi He
Hydrol. Earth Syst. Sci., 26, 4875–4892, https://doi.org/10.5194/hess-26-4875-2022, https://doi.org/10.5194/hess-26-4875-2022, 2022
Short summary
Short summary
The study assessed changes in total nitrogen (TN) and total phosphorus (TP) loads in response to eastern Pacific (EP) and central Pacific (CP) El Niño events over the Corn Belt, USA, using the SWAT model. Results showed that EP (CP) El Niño events improved (exacerbated) water quality in the region. Furthermore, EP El Niño had a much broader and longer impact on water quality at the outlets, but CP El Niño could lead to similar increases in TN/TP loads as EP El Niño at the specific watersheds.
Ralf Loritz, Maoya Bassiouni, Anke Hildebrandt, Sibylle K. Hassler, and Erwin Zehe
Hydrol. Earth Syst. Sci., 26, 4757–4771, https://doi.org/10.5194/hess-26-4757-2022, https://doi.org/10.5194/hess-26-4757-2022, 2022
Short summary
Short summary
In this study, we combine a deep-learning approach that predicts sap flow with a hydrological model to improve soil moisture and transpiration estimates at the catchment scale. Our results highlight that hybrid-model approaches, combining machine learning with physically based models, are a promising way to improve our ability to make hydrological predictions.
Nicholas Jarvis, Jannis Groh, Elisabet Lewan, Katharina H. E. Meurer, Walter Durka, Cornelia Baessler, Thomas Pütz, Elvin Rufullayev, and Harry Vereecken
Hydrol. Earth Syst. Sci., 26, 2277–2299, https://doi.org/10.5194/hess-26-2277-2022, https://doi.org/10.5194/hess-26-2277-2022, 2022
Short summary
Short summary
We apply an eco-hydrological model to data on soil water balance and grassland growth obtained at two sites with contrasting climates. Our results show that the grassland in the drier climate had adapted by developing deeper roots, which maintained water supply to the plants in the face of severe drought. Our study emphasizes the importance of considering such plastic responses of plant traits to environmental stress in the modelling of soil water balance and plant growth under climate change.
Remko C. Nijzink, Jason Beringer, Lindsay B. Hutley, and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 525–550, https://doi.org/10.5194/hess-26-525-2022, https://doi.org/10.5194/hess-26-525-2022, 2022
Short summary
Short summary
Most models that simulate water and carbon exchanges with the atmosphere rely on information about vegetation, but optimality models predict vegetation properties based on general principles. Here, we use the Vegetation Optimality Model (VOM) to predict vegetation behaviour at five savanna sites. The VOM overpredicted vegetation cover and carbon uptake during the wet seasons but also performed similarly to conventional models, showing that vegetation optimality is a promising approach.
Hongyu Li, Yi Luo, Lin Sun, Xiangdong Li, Changkun Ma, Xiaolei Wang, Ting Jiang, and Haoyang Zhu
Hydrol. Earth Syst. Sci., 26, 17–34, https://doi.org/10.5194/hess-26-17-2022, https://doi.org/10.5194/hess-26-17-2022, 2022
Short summary
Short summary
Drying soil layers (DSLs) have been extensively reported in artificial forestland in the Loess Plateau, China, which has limited water resources and deep loess. To address this issue relating to plant root–soil water interactions, this study developed a root growth model that simulates both the dynamic rooting depth and fine-root distribution. Evaluation vs. field data proved a positive performance. Long-term simulation reproduced the evolution process of the DSLs and revealed their mechanisms.
Jiancong Chen, Baptiste Dafflon, Anh Phuong Tran, Nicola Falco, and Susan S. Hubbard
Hydrol. Earth Syst. Sci., 25, 6041–6066, https://doi.org/10.5194/hess-25-6041-2021, https://doi.org/10.5194/hess-25-6041-2021, 2021
Short summary
Short summary
The novel hybrid predictive modeling (HPM) approach uses a long short-term memory recurrent neural network to estimate evapotranspiration (ET) and ecosystem respiration (Reco) with only meteorological and remote-sensing inputs. We developed four use cases to demonstrate the applicability of HPM. The results indicate HPM is capable of providing ET and Reco estimations in challenging mountainous systems and enhances our understanding of watershed dynamics at sparsely monitored watersheds.
Jiehao Zhang, Yulong Zhang, Ge Sun, Conghe Song, Matthew P. Dannenberg, Jiangfeng Li, Ning Liu, Kerong Zhang, Quanfa Zhang, and Lu Hao
Hydrol. Earth Syst. Sci., 25, 5623–5640, https://doi.org/10.5194/hess-25-5623-2021, https://doi.org/10.5194/hess-25-5623-2021, 2021
Short summary
Short summary
To quantify how vegetation greening impacts the capacity of water supply, we built a hybrid model and conducted a case study using the upper Han River basin (UHRB) that serves as the water source area to the world’s largest water diversion project. Vegetation greening in the UHRB during 2001–2018 induced annual water yield (WY) greatly decreased. Vegetation greening also increased the possibility of drought and reduced a quarter of WY on average during drought periods.
Aaron J. Neill, Christian Birkel, Marco P. Maneta, Doerthe Tetzlaff, and Chris Soulsby
Hydrol. Earth Syst. Sci., 25, 4861–4886, https://doi.org/10.5194/hess-25-4861-2021, https://doi.org/10.5194/hess-25-4861-2021, 2021
Short summary
Short summary
Structural changes (cover and height of vegetation plus tree canopy characteristics) to forests during regeneration on degraded land affect how water is partitioned between streamflow, groundwater recharge and evapotranspiration. Partitioning most strongly deviates from baseline conditions during earlier stages of regeneration with dense forest, while recovery may be possible as the forest matures and opens out. This has consequences for informing sustainable landscape restoration strategies.
Jianning Ren, Jennifer C. Adam, Jeffrey A. Hicke, Erin J. Hanan, Christina L. Tague, Mingliang Liu, Crystal A. Kolden, and John T. Abatzoglou
Hydrol. Earth Syst. Sci., 25, 4681–4699, https://doi.org/10.5194/hess-25-4681-2021, https://doi.org/10.5194/hess-25-4681-2021, 2021
Short summary
Short summary
Mountain pine beetle outbreaks have caused widespread tree mortality. While some research shows that water yield increases after trees are killed, many others document no change or a decrease. The climatic and environmental mechanisms driving hydrologic response to tree mortality are not well understood. We demonstrated that the direction of hydrologic response is a function of multiple factors, so previous studies do not necessarily conflict with each other; they represent different conditions.
Haidong Zhao, Gretchen F. Sassenrath, Mary Beth Kirkham, Nenghan Wan, and Xiaomao Lin
Hydrol. Earth Syst. Sci., 25, 4357–4372, https://doi.org/10.5194/hess-25-4357-2021, https://doi.org/10.5194/hess-25-4357-2021, 2021
Short summary
Short summary
This study was done to develop an improved soil temperature model for the USA Great Plains by using common weather station variables as inputs. After incorporating knowledge of estimated soil moisture and observed daily snow depth, the improved model showed a near 50 % gain in performance compared to the original model. We conclude that our improved model can better estimate soil temperature at the surface soil layer where most hydrological and biological processes occur.
Brandon P. Sloan, Sally E. Thompson, and Xue Feng
Hydrol. Earth Syst. Sci., 25, 4259–4274, https://doi.org/10.5194/hess-25-4259-2021, https://doi.org/10.5194/hess-25-4259-2021, 2021
Short summary
Short summary
Plants affect the global water and carbon cycles by modifying their water use and carbon intake in response to soil moisture. Global climate models represent this response with either simple empirical models or complex physical models. We reveal that the latter improves predictions in plants with large flow resistance; however, adding dependence on atmospheric moisture demand to the former matches performance of the latter, leading to a new tool for improving carbon and water cycle predictions.
Maria Magdalena Warter, Michael Bliss Singer, Mark O. Cuthbert, Dar Roberts, Kelly K. Caylor, Romy Sabathier, and John Stella
Hydrol. Earth Syst. Sci., 25, 3713–3729, https://doi.org/10.5194/hess-25-3713-2021, https://doi.org/10.5194/hess-25-3713-2021, 2021
Short summary
Short summary
Intensified drying of soil and grassland vegetation is raising the impact of fire severity and extent in Southern California. While browned grassland is a common sight during the dry season, this study has shown that there is a pronounced shift in the timing of senescence, due to changing climate conditions favoring milder winter temperatures and increased precipitation variability. Vegetation may be limited in its ability to adapt to these shifts, as drought periods become more frequent.
Mikael Gillefalk, Dörthe Tetzlaff, Reinhard Hinkelmann, Lena-Marie Kuhlemann, Aaron Smith, Fred Meier, Marco P. Maneta, and Chris Soulsby
Hydrol. Earth Syst. Sci., 25, 3635–3652, https://doi.org/10.5194/hess-25-3635-2021, https://doi.org/10.5194/hess-25-3635-2021, 2021
Short summary
Short summary
We used a tracer-aided ecohydrological model to quantify water flux–storage–age interactions for three urban vegetation types: trees, shrub and grass. The model results showed that evapotranspiration increased in the order shrub < grass < trees during one growing season. Additionally, we could show how
infiltration hotspotscreated by runoff from sealed onto vegetated surfaces can enhance both evapotranspiration and groundwater recharge.
Yuting Yang, Tim R. McVicar, Dawen Yang, Yongqiang Zhang, Shilong Piao, Shushi Peng, and Hylke E. Beck
Hydrol. Earth Syst. Sci., 25, 3411–3427, https://doi.org/10.5194/hess-25-3411-2021, https://doi.org/10.5194/hess-25-3411-2021, 2021
Short summary
Short summary
This study developed an analytical ecohydrological model that considers three aspects of vegetation response to eCO2 (i.e., stomatal response, LAI response, and rooting depth response) to detect the impact of eCO2 on continental runoff over the past 3 decades globally. Our findings suggest a minor role of eCO2 on the global runoff changes, yet highlight the negative runoff–eCO2 response in semiarid and arid regions which may further threaten the limited water resource there.
Yanlan Liu, Nataniel M. Holtzman, and Alexandra G. Konings
Hydrol. Earth Syst. Sci., 25, 2399–2417, https://doi.org/10.5194/hess-25-2399-2021, https://doi.org/10.5194/hess-25-2399-2021, 2021
Short summary
Short summary
The flow of water through plants varies with species-specific traits. To determine how they vary across the world, we mapped the traits that best allowed a model to match microwave satellite data. We also defined average values across a few clusters of trait behavior. These form a tractable solution for use in large-scale models. Transpiration estimates using these clusters were more accurate than if using plant functional types. We expect our maps to improve transpiration forecasts.
Aaron Smith, Doerthe Tetzlaff, Lukas Kleine, Marco Maneta, and Chris Soulsby
Hydrol. Earth Syst. Sci., 25, 2239–2259, https://doi.org/10.5194/hess-25-2239-2021, https://doi.org/10.5194/hess-25-2239-2021, 2021
Short summary
Short summary
We used a tracer-aided ecohydrological model on a mixed land use catchment in northeastern Germany to quantify water flux–storage–age interactions at four model grid resolutions. The model's ability to reproduce spatio-temporal flux–storage–age interactions decreases with increasing model grid sizes. Similarly, larger model grids showed vegetation-influenced changes in blue and green water partitioning. Simulations reveal the value of measured soil and stream isotopes for model calibration.
Yiping Hou, Mingfang Zhang, Xiaohua Wei, Shirong Liu, Qiang Li, Tijiu Cai, Wenfei Liu, Runqi Zhao, and Xiangzhuo Liu
Hydrol. Earth Syst. Sci., 25, 1447–1466, https://doi.org/10.5194/hess-25-1447-2021, https://doi.org/10.5194/hess-25-1447-2021, 2021
Short summary
Short summary
Ecohydrological sensitivity, defined as the response intensity of streamflow to vegetation change, indicates the hydrological sensitivity to vegetation change. The study revealed seasonal ecohydrological sensitivities were highly variable, depending on climate condition and watershed attributes. Dry season ecohydrological sensitivity was mostly determined by topography, soil and vegetation, while wet season ecohydrological sensitivity was mainly controlled by soil, landscape and vegetation.
Xiangyu Luan and Giulia Vico
Hydrol. Earth Syst. Sci., 25, 1411–1423, https://doi.org/10.5194/hess-25-1411-2021, https://doi.org/10.5194/hess-25-1411-2021, 2021
Short summary
Short summary
Crop yield is reduced by heat and water stress, particularly when they co-occur. We quantify the joint effects of (unpredictable) air temperature and soil water availability on crop heat stress via a mechanistic model. Larger but more infrequent precipitation increased crop canopy temperatures. Keeping crops well watered via irrigation could reduce canopy temperature but not enough to always exclude heat damage. Thus, irrigation is only a partial solution to adapt to warmer and drier climates.
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
Short summary
Short summary
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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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...
