Articles | Volume 23, issue 3
https://doi.org/10.5194/hess-23-1211-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-1211-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Mar 2019
Research article |
| 04 Mar 2019
| 04 Mar 2019
A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions
Christoph Schürz
CORRESPONDING AUTHOR
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
Brigitta Hollosi
Department of Climate Research, Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, Austria
Christoph Matulla
Department of Climate Research, Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, Austria
Alexander Pressl
Institute of Sanitary Engineering and Water Pollution Control, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
Thomas Ertl
Institute of Sanitary Engineering and Water Pollution Control, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
Karsten Schulz
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
Bano Mehdi
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
Division of Agronomy, Department of Crop Sciences, University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
Related authors
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020, https://doi.org/10.5194/hess-24-4463-2020, 2020
Short summary
Short summary
The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
Abolanle E. Odusanya, Bano Mehdi, Christoph Schürz, Adebayo O. Oke, Olufiropo S. Awokola, Julius A. Awomeso, Joseph O. Adejuwon, and Karsten Schulz
Hydrol. Earth Syst. Sci., 23, 1113–1144, https://doi.org/10.5194/hess-23-1113-2019, https://doi.org/10.5194/hess-23-1113-2019, 2019
Short summary
Short summary
The main objective was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data for the data-sparse Ogun River Basin (20 292 km2) located in southwestern Nigeria. The SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool.
Christian Voigt, Karsten Schulz, Franziska Koch, Karl-Friedrich Wetzel, Ludger Timmen, Till Rehm, Hartmut Pflug, Nico Stolarczuk, Christoph Förste, and Frank Flechtner
Hydrol. Earth Syst. Sci., 25, 5047–5064, https://doi.org/10.5194/hess-25-5047-2021, https://doi.org/10.5194/hess-25-5047-2021, 2021
Short summary
Short summary
A continuously operating superconducting gravimeter at the Zugspitze summit is introduced to support hydrological studies of the Partnach spring catchment known as the Zugspitze research catchment. The observed gravity residuals reflect total water storage variations at the observation site. Hydro-gravimetric analysis show a high correlation between gravity and the snow water equivalent, with a gravimetric footprint of up to 4 km radius enabling integral insights into this high alpine catchment.
Christoph Klingler, Karsten Schulz, and Mathew Herrnegger
Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, https://doi.org/10.5194/essd-13-4529-2021, 2021
Short summary
Short summary
LamaH-CE is a large-sample catchment hydrology dataset for Central Europe. The dataset contains hydrometeorological time series (daily and hourly resolution) and various attributes for 859 gauged basins. Sticking closely to the CAMELS datasets, LamaH includes additional basin delineations and attributes for describing a large interconnected river network. LamaH further contains outputs of a conceptual hydrological baseline model for plausibility checking of the inputs and for benchmarking.
Josef Fürst, Hans Peter Nachtnebel, Josef Gasch, Reinhard Nolz, Michael Paul Stockinger, Christine Stumpp, and Karsten Schulz
Earth Syst. Sci. Data, 13, 4019–4034, https://doi.org/10.5194/essd-13-4019-2021, https://doi.org/10.5194/essd-13-4019-2021, 2021
Short summary
Short summary
Rosalia is a 222 ha forested research watershed in eastern Austria to study water, energy and solute transport processes. The paper describes the site, monitoring network, instrumentation and the datasets: high-resolution (10 min interval) time series starting in 2015 of four discharge gauging stations, seven rain gauges, and observations of air and water temperature, relative humidity, and conductivity, as well as soil water content and temperature, at different depths at four profiles.
Moritz Feigl, Katharina Lebiedzinski, Mathew Herrnegger, and Karsten Schulz
Hydrol. Earth Syst. Sci., 25, 2951–2977, https://doi.org/10.5194/hess-25-2951-2021, https://doi.org/10.5194/hess-25-2951-2021, 2021
Short summary
Short summary
In this study we developed machine learning approaches for daily river water temperature prediction, using different data preprocessing methods, six model types, a range of different data inputs and 10 study catchments. By comparing to current state-of-the-art models, we could show a significant improvement of prediction performance of the tested approaches. Furthermore, we could gain insight into the relationships between model types, input data and predicted stream water temperature.
Michael Weber, Franziska Koch, Matthias Bernhardt, and Karsten Schulz
Hydrol. Earth Syst. Sci., 25, 2869–2894, https://doi.org/10.5194/hess-25-2869-2021, https://doi.org/10.5194/hess-25-2869-2021, 2021
Short summary
Short summary
We compared a suite of globally available meteorological and DEM data with in situ data for physically based snow hydrological modelling in a small high-alpine catchment. Although global meteorological data were less suited to describe the snowpack properly, transferred station data from a similar location in the vicinity and substituting single variables with global products performed well. In addition, using 30 m global DEM products as model input was useful in such complex terrain.
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020, https://doi.org/10.5194/hess-24-4463-2020, 2020
Short summary
Short summary
The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
Benjamin Müller, Matthias Bernhardt, and Karsten Schulz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-563, https://doi.org/10.5194/hess-2019-563, 2019
Manuscript not accepted for further review
Short summary
Short summary
Time series of thermal remote sensing images include more information than usually used. Land surface related processes are combined into a single image. Activity of these processes change from image to image. Thus, information on land surface characteristics is to be found
somewhere in betweenthe images. We provide an algorithm to test the presence of such characteristics within a set of images. The algorithm can be used for process understanding, model evaluation, data assimilation, etc.
Abolanle E. Odusanya, Bano Mehdi, Christoph Schürz, Adebayo O. Oke, Olufiropo S. Awokola, Julius A. Awomeso, Joseph O. Adejuwon, and Karsten Schulz
Hydrol. Earth Syst. Sci., 23, 1113–1144, https://doi.org/10.5194/hess-23-1113-2019, https://doi.org/10.5194/hess-23-1113-2019, 2019
Short summary
Short summary
The main objective was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data for the data-sparse Ogun River Basin (20 292 km2) located in southwestern Nigeria. The SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool.
Maik Renner, Claire Brenner, Kaniska Mallick, Hans-Dieter Wizemann, Luigi Conte, Ivonne Trebs, Jianhui Wei, Volker Wulfmeyer, Karsten Schulz, and Axel Kleidon
Hydrol. Earth Syst. Sci., 23, 515–535, https://doi.org/10.5194/hess-23-515-2019, https://doi.org/10.5194/hess-23-515-2019, 2019
Short summary
Short summary
We estimate the phase lag of surface states and heat fluxes to incoming solar radiation at the sub-daily timescale. While evapotranspiration reveals a minor phase lag, the vapor pressure deficit used as input by Penman–Monteith approaches shows a large phase lag. The surface-to-air temperature gradient used by energy balance residual approaches shows a small phase shift in agreement with the sensible heat flux and thus explains the better correlation of these models at the sub-daily timescale.
Lu Gao, Jianhui Wei, Lingxiao Wang, Matthias Bernhardt, Karsten Schulz, and Xingwei Chen
Earth Syst. Sci. Data, 10, 2097–2114, https://doi.org/10.5194/essd-10-2097-2018, https://doi.org/10.5194/essd-10-2097-2018, 2018
Short summary
Short summary
High-resolution temperature data sets are important for the Chinese Tian Shan, which has a complex ecological environment system. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for this area from 1979 to 2016 based on a robust statistical downscaling framework. The strongest advantage of this method is its independence of local meteorological stations due to a model internal, vertical lapse rate scheme. This method was validated for other mountains.
Frederik Kratzert, Daniel Klotz, Claire Brenner, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, https://doi.org/10.5194/hess-22-6005-2018, 2018
Short summary
Short summary
In this paper, we propose a novel data-driven approach for
rainfall–runoff modelling, using the long short-term memory (LSTM) network, a special type of recurrent neural network. We show in three different experiments that this network is able to learn to predict the discharge purely from meteorological input parameters (such as precipitation or temperature) as accurately as (or better than) the well-established Sacramento Soil Moisture Accounting model, coupled with the Snow-17 snow model.
Stefan Härer, Matthias Bernhardt, Matthias Siebers, and Karsten Schulz
The Cryosphere, 12, 1629–1642, https://doi.org/10.5194/tc-12-1629-2018, https://doi.org/10.5194/tc-12-1629-2018, 2018
Short summary
Short summary
The paper presents an approach which can be used to process satellite-based snow cover maps with a higher-than-today accuracy at the local scale. Many of the current satellite-based snow maps are using the NDSI with a threshold as a tool for deciding if there is snow on the ground or not. The presented study has shown that, firstly, using the standard threshold of 0.4 can result in significant derivations at the local scale and that, secondly, the deviations become smaller for coarser scales.
Karsten Schulz, Reinhard Burgholzer, Daniel Klotz, Johannes Wesemann, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 22, 2607–2613, https://doi.org/10.5194/hess-22-2607-2018, https://doi.org/10.5194/hess-22-2607-2018, 2018
Short summary
Short summary
The unit hydrograph has been one of the most widely employed modelling techniques to predict rainfall-runoff behaviour of hydrological catchments. We developed a lecture theatre experiment including some student involvement to illustrate the principles behind this modelling technique. The experiment only uses very simple and cheap material including a set of plastic balls (representing rainfall), magnetic stripes (tacking the balls to the white board) and sieves (for ball/water gauging).
Matthias Schlögl and Christoph Matulla
Nat. Hazards Earth Syst. Sci., 18, 1121–1132, https://doi.org/10.5194/nhess-18-1121-2018, https://doi.org/10.5194/nhess-18-1121-2018, 2018
Short summary
Short summary
Reliable information on the extent of climate change and its projected future impacts on transport infrastructure is of prime importance for the smooth functioning of societies. Rainfall events which may trigger landslides are analysed until the end of this century and compared to present-day conditions. Results indicate overall increases of landslide activity, especially in areas with structured terrain. Derived findings support proactive adaptation to rainfall-induced landslide exposure.
Benjamin Müller, Matthias Bernhardt, Conrad Jackisch, and Karsten Schulz
Hydrol. Earth Syst. Sci., 20, 3765–3775, https://doi.org/10.5194/hess-20-3765-2016, https://doi.org/10.5194/hess-20-3765-2016, 2016
Short summary
Short summary
A technology for the spatial derivation of soil texture classes is presented. Information about soil texture is key for predicting the local and regional hydrological cycle. It is needed for the calculation of soil water movement, the share of surface runoff, the evapotranspiration rate and others. Nevertheless, the derivation of soil texture classes is expensive and time-consuming. The presented technique uses soil samples and remotely sensed data for estimating their spatial distribution.
S. Härer, M. Bernhardt, and K. Schulz
Geosci. Model Dev., 9, 307–321, https://doi.org/10.5194/gmd-9-307-2016, https://doi.org/10.5194/gmd-9-307-2016, 2016
Short summary
Short summary
This paper describes a new method to produce spatially and temporally calibrated NDSI-based satellite snow cover maps utilizing simultaneously captured terrestrial photographs as in situ information. First results confirm a high quality of the produced satellite snow cover maps and emphasize the need for calibration of the NDSI threshold value to ensure a high accuracy and reproduciblity. The software "PRACTISE V.2.1" was developed to automatically process the photographs and satellite images.
M. Herrnegger, H. P. Nachtnebel, and K. Schulz
Hydrol. Earth Syst. Sci., 19, 4619–4639, https://doi.org/10.5194/hess-19-4619-2015, https://doi.org/10.5194/hess-19-4619-2015, 2015
Short summary
Short summary
Especially in alpine catchments, areal rainfall estimates often exhibit large errors. Runoff measurements are, on the other hand, one of the most robust observations within the hydrological cycle. We therefore calculate mean catchment rainfall by inverting an HBV-type rainfall-runoff model, using runoff observations as input. The inverse model may e.g. be used to analyse rainfall conditions of extreme flood events or estimation of snowmelt contribution.
B. Müller, M. Bernhardt, and K. Schulz
Hydrol. Earth Syst. Sci., 18, 5345–5359, https://doi.org/10.5194/hess-18-5345-2014, https://doi.org/10.5194/hess-18-5345-2014, 2014
Short summary
Short summary
We present a method to define hydrological landscape units by a time series of thermal infrared satellite data. Land surface temperature is calculated for 28 images in 12 years for a catchment in Luxembourg. Pattern measures show spatio-temporal persistency; principle component analysis extracts relevant patterns. Functional units represent similar behaving entities based on a representative set of images. Resulting classification and patterns are discussed regarding potential applications.
E. Zehe, U. Ehret, L. Pfister, T. Blume, B. Schröder, M. Westhoff, C. Jackisch, S. J. Schymanski, M. Weiler, K. Schulz, N. Allroggen, J. Tronicke, L. van Schaik, P. Dietrich, U. Scherer, J. Eccard, V. Wulfmeyer, and A. Kleidon
Hydrol. Earth Syst. Sci., 18, 4635–4655, https://doi.org/10.5194/hess-18-4635-2014, https://doi.org/10.5194/hess-18-4635-2014, 2014
S. Härer, M. Bernhardt, J. G. Corripio, and K. Schulz
Geosci. Model Dev., 6, 837–848, https://doi.org/10.5194/gmd-6-837-2013, https://doi.org/10.5194/gmd-6-837-2013, 2013
Related subject area
Subject: Ecohydrology | Techniques and Approaches: Modelling approaches
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
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
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
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
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.
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.
José María Santiago, Rafael Muñoz-Mas, Joaquín Solana-Gutiérrez, Diego García de Jalón, Carlos Alonso, Francisco Martínez-Capel, Javier Pórtoles, Robert Monjo, and Jaime Ribalaygua
Hydrol. Earth Syst. Sci., 21, 4073–4101, https://doi.org/10.5194/hess-21-4073-2017, https://doi.org/10.5194/hess-21-4073-2017, 2017
Short summary
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.
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Abbaspour, K. C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J.,
Zobrist, J., and Srinivasan, R.: Modelling hydrology and water quality in the
pre-alpine/alpine Thur watershed using SWAT, J. Hydrol., 333, 413–430,
https://doi.org/10.1016/j.jhydrol.2006.09.014, 2007. a
Amt d. Stmk LReg: Regionale Bevölkerungsprognose Steiermark 2015/16 – Bundesland,
Bezirke und Gemeinden, Tech. rep., Graz, Austria, available at:
http://docplayer.org/32447223-Regionale-bevoelkerungsprognose-steiermark
(last access: 30 April 2018), 2016. a
Anderson, B., Borgonovo, E., Galeotti, M., and Roson, R.: Uncertainty in climate
change modeling: can global sensitivity analysis be of help?, Risk Anal., 34,
271–293, https://doi.org/10.1111/risa.12117, 2014. a
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area
hydrologic modeling and assessment part I: model development, J. Am. Water
Resour. Assoc., 34, 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998. a, b
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J.,
Srinivasan, R., Santhi, C., Harmel, R. D., Griensven, A. V., VanLiew, M. W.,
Kannan, N., and Jha, M. K.: Swat: Model Use, Calibration, and Validation,
T. Asabe, 55, 1491–1508, 2012. a
Beven, K.: The limits of splitting: Hydrology, Sci. Total Environ., 183, 89–97,
https://doi.org/10.1016/0048-9697(95)04964-9, 1996. a
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36,
https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. a, b
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty
estimation in mechanistic modelling of complex environmental systems using the
GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. a
BGBl. 1996/210: Verordnung des Bundesministers für Land- und Forstwirtschaft
über die Begrenzung von Abwasseremissionen aus Abwasserreinigungsanlagen
für Siedlungsgebiete (1. AEV für kommunales Abwasser), Bundeskanzleramt
d. Republik Österreich, Vienna, Austria, 1996. a
BGBl. II 2006/96: Qualitätszielverordnung Chemie Oberflächen-gewässer
(QZV Chemie OG), Bundeskanzleramt d. Republik Österreich, Vienna, Austria, 2006. a
BGBl. II 2010/99: Qualitätszielverordnung Ökologie Oberflächen-gewässer
(QZV Ökologie OG), Bundeskanzleramt d. Republik Österreich, Vienna, Austria, 2010. a
BGBl. II Nr. 10/1999: Verordnung des Bundesministers für Land- und
Forstwirtschaft über die Begrenzung von Abwasseremissionen aus Gerbereien,
Lederfabriken und Pelzzurichtereien (AEV Gerberei), Bundeskanzleramt d. Republik
Österreich, Vienna, Austria, 1999. a
BGBl. II Nr. 12/1999: Verordnung des Bundesministers für Land- und
Forstwirtschaft über die Begrenzung von Abwasseremissionen aus der
Schlachtung und Fleischverarbeitung (AEV Fleischwirtschaft), Bundeskanzleramt
d. Republik Österreich, Vienna, Austria, 1999. a
Bieger, K., Hörmann, G., and Fohrer, N.: The impact of land use change in
the Xiangxi Catchment (China) on water balance and sediment transport, Reg.
Environ. Change, 15, 485–498, https://doi.org/10.1007/s10113-013-0429-3, 2013. a, b
BMLFUW: IMW3: Integrierte Betrachtung eines Gewässerabschnitts auf Basis
kontinuierlicher und validierter Langzeitmessreihen [Integrated Monitoring of
a river section on the basis of continuous and validated long measurement
time series], Tech. rep., Bundesministerium für Land- und Forstwirtschaft,
Umwelt und Wasserwirtschaft, Sektion VII Wasser, Vienna, 2013. a
BMLFUW: Online monitoring at the Station Neumarkt/Raab at the River Raab,
Operated by the TU Wien, Institut für Gewässergüte, Resourcenmanagement
und Abfallwirtschaft, Vienna, Austria, 2015a. a
BMLFUW: Online monitoring at the Station St.Margarethen/Takern II at the River
Raab, Operated by TBS Water Consult., Vienna, Austria, 2015b. a
Borgonovo, E., Lu, X., Plischke, E., Rakovec, O., and Hill, M. C.: Making the
most out of a hydrological model data set: Sensitivity analyses to open the
model black-box, Water Resour. Res., 53, 7933–7950, https://doi.org/10.1002/2017WR020767, 2017. a
Butler, M. P., Reed, P. M., Fisher-Vanden, K., Keller, K., and Wagener, T.:
Identifying parametric controls and dependencies in integrated assessment models
using global sensitivity analysis, Environ. Model. Softw., 59, 10–29,
https://doi.org/10.1016/j.envsoft.2014.05.001, 2014. a
Canty, A. and Ripley, B. D.: boot: Bootstrap R (S-Plus) Functions, r package
version 1.3-20, available at: https://cran.r-project.org/package=boot
(last access: 20 September 2018), 2017. a
Chiew, F. H. and Vaze, J.: Hydrologic nonstationarity and extrapolating models
to predict the future: Overview of session and proceeding, in: IAHS-AISH
Proceedings and Reports, vol. 371, Copernicus GmbH, 17–21, https://doi.org/10.5194/piahs-371-17-2015, 2015. a
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H.
V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural
Errors (FUSE): A modular framework to diagnose differences between hydrological
models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007WR006735, 2008. a
Clark, M. P., Wilby, R. L., Gutmann, E. D., Vano, J. A., Gangopadhyay, S.,
Wood, A. W., Fowler, H. J., Prudhomme, C., Arnold, J. R., and Brekke, L. D.:
Characterizing Uncertainty of the Hydrologic Impacts of Climate Change, Curr.
Climate Change Rep., 2, 55–64, https://doi.org/10.1007/s40641-016-0034-x, 2016. a, b, c
Cuntz, M., Mai, J., Zink, M., Thober, S., Kumar, R., Schäfer, D., Schrön,
M., Craven, J., Rakovec, O., Spieler, D., Prykhodko, V., Dalmasso, G., Musuuza,
J., Langenberg, B., Attinger, S., and Samaniego, L.: Computationally inexpensive
identification of noninformative model parameters by sequential screening, Water
Resour. Res., 51, 6417–6441, https://doi.org/10.1002/2015WR016907, 2015. a
Dai, H. and Ye, M.: Variance-based global sensitivity analysis for multiple
scenarios and models with implementation using sparse grid collocation, J.
Hydrol., 528, 286–300, https://doi.org/10.1016/j.jhydrol.2015.06.034, 2015. a
Dai, H., Ye, M., Walker, A. P., and Chen, X.: A new process sensitivity index
to identify important system processes under process model and parametric
uncertainty, Water Resour. Res., 53, 3476–3490, https://doi.org/10.1002/2016WR019715, 2017. a, b
Dile, Y. T., Daggupati, P., George, C., Srinivasan, R., and Arnold, J.:
Introducing a new open source GIS user interface for the SWAT model, Environ.
Model. Softw., 85, 129–138, https://doi.org/10.1016/j.envsoft.2016.08.004, 2016. a
Duran-Encalada, J. A., Paucar-Caceres, A., Bandala, E. R., and Wright, G. H.:
The impact of global climate change on water quantity and quality: A system
dynamics approach to the US–Mexican transborder region, Eur. J. Operat. Res.,
256, 567–581, https://doi.org/10.1016/j.ejor.2016.06.016, 2017. a
Efron, B.: Better bootstrap confidence intervals, J. Am. Stat. Assoc., 82,
171–185, https://doi.org/10.2307/2289144, 1987. a
ESRI: ArcGIS Desktop: Release 10.1, Environmental Systems Research
Institute (ESRI), Redlands, CA, 2012. a
Euser, T., Winsemius, H. C., Hrachowitz, M., Fenicia, F., Uhlenbrook, S., and
Savenije, H. H.: A framework to assess the realism of model structures using
hydrological signatures, Hydrol. Earth Syst. Sci., 17, 1893–1912,
https://doi.org/10.5194/hess-17-1893-2013, 2013. a
Godet, M. and Roubelat, F.: Creating the future: The use and misuse of scenarios,
Long Range Plan., 29, 164–171, https://doi.org/10.1016/0024-6301(96)00004-0, 1996. a
Gupta, H. V. and Razavi, S.: Challenges and Future Outlook of Sensitivity Analysis,
in: Sensitivity Analysis in Earth Observation Modelling, chap. 20, 1st Edn.,
edited by: Petropoulos, G. P. and Srivastava, P. K., Elsevier, 397–415,
https://doi.org/10.1016/B978-0-12-803011-0.00020-3, 2017. a, b
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Status of Automatic Calibration
for Hydrologic Models: Comparison with Multilevel Expert Calibration, J. Hydrol.
Eng., 4, 135–143, https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135), 1999. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for improving
hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Guse, B., Pfannerstill, M., and Fohrer, N.: Dynamic Modelling of Land Use Change
Impacts on Nitrate Loads in Rivers, Enviro. Process., 2, 575–592, https://doi.org/10.1007/s40710-015-0099-x, 2015. a, b, c
Guse, B., Pfannerstill, M., Gafurov, A., Fohrer, N., and Gupta, H.: Demasking
the integrated information of discharge: Advancing sensitivity analysis to
consider different hydrological components and their rates of change, Water
Resour. Res., 52, 8724–8743, https://doi.org/10.1002/2016WR018894, 2016a. a
Guse, B., Pfannerstill, M., Strauch, M., Reusser, D. E., Lüdtke, S., Volk,
M., Gupta, H., and Fohrer, N.: On characterizing the temporal dominance patterns
of model parameters and processes, Hydrol. Process., 30, 2255–2270, https://doi.org/10.1002/hyp.10764, 2016b. a
Haas, M. B., Guse, B., Pfannerstill, M., and Fohrer, N.: Detection of dominant
nitrate processes in ecohydrological modeling with temporal parameter sensitivity
analysis, Ecol. Model., 314, 62–72, https://doi.org/10.1016/j.ecolmodel.2015.07.009, 2015. a
Haas, M. B., Guse, B., Pfannerstill, M., and Fohrer, N.: A joined multi-metric
calibration of river discharge and nitrate loads with different performance
measures, J. Hydrol., 536, 534–545, https://doi.org/10.1016/j.jhydrol.2016.03.001, 2016. a, b, c
Haghnegahdar, A. and Razavi, S.: Insights into sensitivity analysis of earth
and environmental systems models: On the impact of parameter perturbation scale,
Environ. Model. Softw., 95, 115–131, https://doi.org/10.1016/j.envsoft.2017.03.031, 2017. a
Haghnegahdar, A., Razavi, S., Yassin, F., and Wheater, H.: Multi-criteria
sensitivity analysis as a diagnostic tool for understanding model behavior and
characterizing model uncertainty, Hydrol. Process., 31, 4462–4476, https://doi.org/10.1002/hyp.11358, 2017. a, b
Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B., and Gruber, C.:
The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its
Validation over the Eastern Alpine Region, Weather Forecast., 26, 166–183,
https://doi.org/10.1175/2010WAF2222451.1, 2011. a, b
Hart, J. and Gremaud, P.: Robustness of the Sobol'indices to distributional
uncertainty, arXiv preprint, arXiv:1803.11249v3, 2018. a
Hartigan, J. A. and Wong, M. A.: Algorithm AS 136: A K-Means Clustering
Algorithm, Appl. Statist., 28, 100–108, https://doi.org/10.2307/2346830, 1979. a
Haslinger, K., Anders, I., and Hofstätter, M.: Regional climate modelling
over complex terrain: an evaluation study of COSMO-CLM hindcast model runs for
the Greater Alpine Region, Clim. Dynam., 40, 511–529, https://doi.org/10.1007/s00382-012-1452-7, 2013. a
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A
trend-preserving bias correction – The ISI-MIP approach, Earth Syst. Dynam.,
4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013. a
Hengl, T., De Jesus, J. M., Heuvelink, G. B., Gonzalez, M. R., Kilibarda, M.,
Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger,
B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J.
G., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global
gridded soil information based on machine learning, PLoS One, 12, 1–40,
https://doi.org/10.1371/journal.pone.0169748, 2017. a, b
Hiebl, J. and Frei, C.: Daily temperature grids for Austria since 1961 – concept,
creation and applicability, Theor. Appl. Climatol., 124, 161–178,
https://doi.org/10.1007/s00704-015-1411-4, 2016. a
Hinkley, D. V.: Bootstrap methods, J. Roy. Stat. Soc. Ser. B, 50, 321–337, 1988. a
Hofstätter, M., Ganekind, M., and Hiebl, J.: GPARD-6: A new 60-year gridded
precipitation dataset for Austria based on daily rain gauge measurements, in:
DACH 2013 – Deutsch-Österreichisch-Schweizerische Meteorologen-Tagung,
Innsbruck, Austria, 2013. a
Honti, M., Schuwirth, N., Rieckermann, J., and Stamm, C.: Can integrative
catchment management mitigate future water quality issues caused by climate
change and socio-economic development?, Hydrol. Earth Syst. Sci., 21, 1593–1609,
https://doi.org/10.5194/hess-21-1593-2017, 2017. a
Houska, T., Kraft, P., Chamorro-Chavez, A., and Breuer, L.: SPOTting model
parameters using a ready-made python package, PloS One, 10, e0145180,
https://doi.org/10.1371/journal.pone.0145180, 2015. a
Hrachowitz, M., Fovet, O., Ruiz, L., Euser, T., Gharari, S., Nijzink, R., Freer,
J., Savenije, H. H., and Gascuel-Odoux, C.: Process consistency in models: The
importance of system signatures, expert knowledge, and process complexity,
Water Resour. Res., 50, 7445–7469, https://doi.org/10.1002/2014WR015484, 2014. a
Iooss, B., Janon, A., Pujol, G., with contributions from Khalid Boumhaout,
Veiga, S. D., Delage, T., Fruth, J., Gilquin, L., Guillaume, J., Le Gratiet,
L., Lemaitre, P., Nelson, B. L., Monari, F., Oomen, R., Ramos, B., Roustant,
O., Song, E., Staum, J., Sueur, R., Touati, T., and Weber, F.: Sensitivity:
Global Sensitivity Analysis of Model Outputs, r package version 1.15.1,
available at: https://CRAN.R-project.org/package=sensitivity (last access:
6 February 2019), 2018. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L.
M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou,
E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones,
C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A.,
Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S.,
Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot,
S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and
Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for
European impact research, Reg. Environ. Change, 14, 563–578,
https://doi.org/10.1007/s10113-013-0499-2, 2014. a, b, c
Jiménez, B. E., Oki, T., Arnell, N. W., Benito, G., Cogley, J. G., Döll,
P., Jiang, T., and Mwakalila, S. S.: Freshwater Resources, in: Climate Change 2014:
Impacts, Adaptation, and Vulnerability, Part A: Global and Sectoral Aspects,
Contribution of Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Field, C., Barros, V.,
Dokken, D., Mach, K., Mastrandrea, M., Bilir, T., Chatterjee, M., Ebi, K.,
Estrada, Y., Genova, R., Girma, B., Kissel, E., Levy, A., MacCracken, S.,
Mastrandrea, P., and White, L., Cambridge University Press, Cambridge, UK and
New York, NY, USA, 229–269, 2014. a
Jones, R., Patwardhan, A., Cohen, S., Dessai, S., Lammel, A., Lempert, R.,
Mirza, M., and von Storch, H.: Foundations for Decision Making, in: Climate
Change 2014: Impacts, Adaptation, and Vulnerability, Part A: Global and
Sectoral Aspects, Contribution of Working Group II to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by: Field, C.,
Barros, V., Dokken, D., Mach, K., Mastrandrea, M., Bilir, T., Chatterjee, M.,
Ebi, K., Estrada, Y., Genova, R., Girma, B., Kissel, E., Levy, A., MacCracken,
S., Mastrandrea, P., and White, L., Cambridge University Press, Cambridge, UK
and New York, NY, USA, 195–228, https://doi.org/10.1017/CBO9781107415379.007, 2014. a
Knutti, R. and Sedláček, J.: Robustness and uncertainties in the new
CMIP5 climate model projections, Nat. Clim. Change, 3, 369–373, https://doi.org/10.1038/nclimate1716, 2013. a
LGBl. Nr. 39/2015: Verordnung des Landeshauptmannes von Steiermark vom
20. Mai 2015, mit der ein Regionalprogramm zum Schutz der Grundwasserkörper
Grazer Feld, Leibnitzer Feld und Unteres Murtal erlassen und Schongebiete
bestimmt werden (Grundwasserschutzprogramm Graz bis B), Land Steiermark, Graz, Austria, 2015. a
Mahmoud, M., Liu, Y., Hartmann, H., Stewart, S., Wagener, T., Semmens, D.,
Stewart, R., Gupta, H., Dominguez, D., Dominguez, F., Hulse, D., Letcher, R.,
Rashleigh, B., Smith, C., Street, R., Ticehurst, J., Twery, M., van Delden, H.,
Waldick, R., White, D., and Winter, L.: A formal framework for scenario
development in support of environmental decision-making, Environ. Model. Softw.,
24, 798–808, https://doi.org/10.1016/j.envsoft.2008.11.010, 2009. a, b, c, d
Mara, T. A. and Tarantola, S.: Variance-based sensitivity indices for models
with dependent inputs, Reliabil. Eng. Sys. Saf., 107, 115–121, https://doi.org/10.1016/j.ress.2011.08.008, 2012. a
Massmann, C. and Holzmann, H.: Analysing the Sub-processes of a Conceptual
Rainfall-Runoff Model Using Information About the Parameter Sensitivity and
Variance, Environ. Model. Assess., 20, 41–53, https://doi.org/10.1007/s10666-014-9414-6, 2015. a
Massmann, C., Wagener, T., and Holzmann, H.: A new approach to visualizing
time-varying sensitivity indices for environmental model diagnostics across
evaluation time-scales, Environ. Model. Softw., 51, 190–194, https://doi.org/10.1016/J.ENVSOFT.2013.09.033, 2014. a
Mehdi, B., Lehner, B., Gombault, C., Michaud, A., Beaudin, I., Sottile, M.-F.,
and Blondlot, A.: Simulated impacts of climate change and agricultural land
use change on surface water quality with and without adaptation management
strategies, Agr. Ecosyst. Environ., 213, 47–60, https://doi.org/10.1016/j.agee.2015.07.019, 2015a. a
Mehdi, B., Ludwig, R., and Lehner, B.: Evaluating the impacts of climate change
and crop land use change on streamflow, nitrates and phosphorus: A modeling
study in Bavaria, J. Hydrol.: Reg. Stud., 4, 60–90, https://doi.org/10.1016/j.ejrh.2015.04.009, 2015b. a, b, c
Mehdi, B., Schulz, K., Ludwig, R., Ferber, F., and Lehner, B.: Evaluating the
Importance of Non-Unique Behavioural Parameter Sets on Surface Water Quality
Variables under Climate Change Conditions in a Mesoscale Agricultural Watershed,
Water Resour. Manage., 32, 619–639, https://doi.org/10.1007/s11269-017-1830-3, 2018. a, b
Milly, P. C. D. and Dunne, K. A.: On the hydrologic adjustment of climate-model
projections: The potential pitfall of potential evapotranspiration, Earth
Interact., 15, 1–14, https://doi.org/10.1175/2010EI363.1, 2011. a
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z.
W., Lettenmaier, D. P., and Stouffer, R. J.: Climate change. Stationarity is
dead: whither water management?, Science, 319, 573–574, https://doi.org/10.1126/science.1151915, 2008. a, b
Momm, H. G., Bingner, R. L., Emilaire, R., Garbrecht, J., Wells, R. R., and
Kuhnle, R. A.: Automated watershed subdivision for simulations using
multi-objective optimization, Hydrolog. Sci. J., 62, 1564–1582, https://doi.org/10.1080/02626667.2017.1346794, 2017. a, b
Moriasi, D., Arnold, J., Van Liew, M., Binger, R., Harmel, R., and Veith, T.:
Model evaluation guidelines for systematic quantification of accuracy in
watershed simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007. a
Morris, M. D.: Factorial sampling plans for preliminary computational experiments,
Technometrics, 33, 161–174, 1991. a
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K.,
Van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G.
A., Mitchell, J. F., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J.,
Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of
scenarios for climate change research and assessment, Nature, 463, 747–756,
https://doi.org/10.1038/nature08823, 2010. a
Muerth, M. J., Gauvin St-Denis, B., Ricard, S., Velázquez, J. A., Schmid,
J., Minville, M., Caya, D., Chaumont, D., Ludwig, R., and Turcotte, R.: On the
need for bias correction in regional climate scenarios to assess climate change
impacts on river runoff, Hydrol. Earth Syst. Sci., 17, 1189–1204, https://doi.org/10.5194/hess-17-1189-2013, 2013. a
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. a
Nossent, J., Elsen, P., and Bauwens, W.: Sobol' sensitivity analysis of a
complex environmental model, Environ. Model. Softw., 26, 1515–1525,
https://doi.org/10.1016/j.envsoft.2011.08.010, 2011. a
ÖWAV: ÖWAV-Regelblatt 25: Abwasserentsorgung in dünn besiedelten
Gebieten, 2. vollständig überarbeitete Auflage, Österreichischer
Wasser- und Abwasserwirtschaftsverband (ÖWAV), Vienna, Austria, 2010. a
Pfannerstill, M., Guse, B., and Fohrer, N.: Smart low flow signature metrics
for an improved overall performance evaluation of hydrological models, J.
Hydrol., 510, 447–458, https://doi.org/10.1016/j.jhydrol.2013.12.044, 2014. a, b
Pfannerstill, M., Bieger, K., Guse, B., Bosch, D. D., Fohrer, N., and Arnold,
J. G.: How to Constrain Multi-Objective Calibrations of the SWAT Model Using
Water Balance Components, J. Am. Water Resour. Assoc., 53, 532–546,
https://doi.org/10.1111/1752-1688.12524, 2017. a
Pianosi, F. and Wagener, T.: Distribution-based sensitivity analysis from a
generic input-output sample, Environ. Model. Softw., 108, 197–207,
https://doi.org/10.1016/j.envsoft.2018.07.019, 2018. a, b
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B.,
and Wagener, T.: Sensitivity analysis of environmental models: A systematic
review with practical workflow, Environ. Model. Softw., 79, 214–232,
https://doi.org/10.1016/j.envsoft.2016.02.008, 2016. a, b
Pignotti, G., Rathjens, H., Cibin, R., Chaubey, I., and Crawford, M.:
Comparative analysis of HRU and grid-based SWAT models, Water, 9, 272, https://doi.org/10.3390/w9040272, 2017. a
Rakovec, O., Hill, M. C., Clark, M. P., Weerts, A. H., Teuling, A. J., and
Uijlenhoet, R.: Distributed Evaluation of Local Sensitivity Analysis (DELSA),
with application to hydrologic models, Water Resour. Res, 50, 409–426,
https://doi.org/10.1002/2013WR014063, 2014. a
Razavi, S. and Gupta, H. V.: What do we mean by sensitivity analysis? The need
for comprehensive characterization of “global” sensitivity in Earth and
Environmental systems models, Water Resour. Res., 51, 3070–3092, https://doi.org/10.1002/2014WR016527, 2015. a
R Core Team: R: A language and environment for statistical computing, available
at: https://www.r-project.org/, last access: 6 March 2017. a
Reusser, D.: fast: Implementation of the Fourier Amplitude Sensitivity Test (FAST),
r package version 0.64, available at: https://CRAN.R-project.org/package=fast
(last access: 6 March 2017), 2015. a
Riahi, K., Grübler, A., and Nakicenovic, N.: Scenarios of long-term
socio-economic and environmental development under climate stabilization,
Technol. Forecast. Social Change, 74, 887–935, https://doi.org/10.1016/j.techfore.2006.05.026, 2007. a
Roderick, M. L., Sun, F., Lim, W. H., and Farquhar, G. D.: A general framework
for understanding the response of the water cycle to global warming over land
and ocean, Hydrol. Earth Syst. Sci., 18, 1575–1589, https://doi.org/10.5194/hess-18-1575-2014, 2014. a
Rosolem, R., Gupta, H. V., Shuttleworth, W. J., Zeng, X., and De Gonçalves,
L. G. G.: A fully multiple-criteria implementation of the Sobol method for
parameter sensitivity analysis, J. Geophys. Res.-Atmos., 117, D07103, https://doi.org/10.1029/2011JD016355, 2012. a
Rounsevell, M. D. and Metzger, M. J.: Developing qualitative scenario storylines
for environmental change assessment, Wiley Interdisciplin. Rev.: Clim. Change,
1, 606–619, https://doi.org/10.1002/wcc.63, 2010. a
Ruzicka, K., Gabriel, O., Bletterie, U., Winkler, S., and Zessner, M.: Cause
and effect relationship between foam formation and treated wastewater effluents
in a transboundary river, Phys. Chem. Earth, 34, 565–573, https://doi.org/10.1016/j.pce.2009.01.002, 2009. a
Saltelli, A. and Annoni, P.: How to avoid a perfunctory sensitivity analysis,
Environ. Model. Softw., 25, 1508–1517, https://doi.org/10.1016/j.envsoft.2010.04.012, 2010. a, b
Saltelli, A. and Tarantola, S.: On the Relative Importance of Input Factors in
Mathematical Models, J. Am. Stat. Assoc., 97, 702–709, https://doi.org/10.1198/016214502388618447, 2002. a, b, c
Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M.: Sensitivity analysis
in practice: A guide to assessing scientific models, in: vol. 91, 1st Edn.,
John Wiley & Sons Ltd, Chichester, West Sussex, UK, 2004. a
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D.,
Saisana, M., and Tarantola, S.: Global Sensitivity Analysis. The Primer,
John Wiley & Sons, Ltd, Chichester, UK, https://doi.org/10.1002/9780470725184, 2008. a, b
Sarrazin, F., Pianosi, F., and Wagener, T.: Global Sensitivity Analysis of
environmental models: Convergence and validation, Environ. Model. Softw., 79,
135–152, https://doi.org/10.1016/j.envsoft.2016.02.005, 2016. a, b, c, d
Savage, J. T. S., Pianosi, F., Bates, P., Freer, J., and Wagener, T.:
Quantifying the importance of spatial resolution and other factors through
global sensitivity analysis of a flood inundation model, Water Resour. Res.,
52, 9146–9163, https://doi.org/10.1002/2015WR018198, 2016. a
Schönhart, M., Trautvetter, H., Parajka, J., Blaschke, A. P., Hepp, G.,
Kirchner, M., Mitter, H., Schmid, E., Strenn, B., and Zessner, M.: Modelled
impacts of policies and climate change on land use and water quality in Austria,
Land Use Policy, 76, 500–514, https://doi.org/10.1016/j.landusepol.2018.02.031, 2018. a
Schulz, K., Beven, K., and Huwe, B.: Equifinality and the problem of robust
calibration in nitrogen budget simulations, Soil Sci. Soc. Am. J., 63, 1934–1941,
https://doi.org/10.2136/sssaj1999.6361934x, 1999. a, b
Shaw, S. B. and Riha, S. J.: Assessing temperature-based PET equations under a
changing climate in temperate, deciduous forests, Hydrol. Process., 25,
1466–1478, https://doi.org/10.1002/hyp.7913, 2011. a
Sheffield, J., Wood, E. F., and Roderick, M. L.: Little change in global drought
over the past 60 years, Nature, 491, 435–438, https://doi.org/10.1038/nature11575, 2012. a
Sheikholeslami, R., Razavi, S., Gupta, H. V., Becker, W., and Haghnegahdar, A.:
Global sensitivity analysis for high-dimensional problems: How to objectively
group factors and measure robustness and convergence while reducing computational
cost, Environ. Model. Softw., 111, 282–299, https://doi.org/10.1016/j.envsoft.2018.09.002, 2019. a
Smith, S. J. and Wigley, T. M.: Multi-gas forcing stabilization with minicam,
Energy J., 27, 373–391, https://doi.org/10.5547/ISSN0195-6574-EJ-VolSI2006-NoSI3-19, 2006. a
Sobol, I. M.: Sensitivity analysis for nonlinear mathematical models, Math.
Model. Comput. Exp., 4, 407–414, https://doi.org/10.18287/0134-2452-2015-39-4-459-461, 1993. a, b
Statistik Austria: STATCube – Statistical Data base of the Statistik Austria:
Agricultural and forestry holdings with arable land and their cultivated land
area (not openly accessible), available at: http://statcube.at/statistik.at/ext/statcube,
last access: 14 June 2017. a
Strauch, M., Schweppe, R., and Schürz, C.: TopHRU: Threshold optimization
for HRUs in SWAT, https://doi.org/10.5281/zenodo.154379, 2016. a
Tang, Y., Reed, P., Wagener, T., and van Werkhoven, K.: Comparing sensitivity
analysis methods to advance lumped watershed model identification and evaluation,
Hydrol. Earth Syst. Sci., 11, 793–817, https://doi.org/10.5194/hess-11-793-2007, 2007. a
Tarantola, S. and Mara, T. A.: Variance-based sensitivity indices of computer
models with dependent inputs: The Fourier Amplitude Sensitivity Test, Int. J.
Uncertain. Quant., 7, 511–523, https://doi.org/10.1615/Int.J.UncertaintyQuantification.2017020291, 2017. a
Teshager, A. D., Gassman, P. W., Schoof, J. T., and Secchi, S.: Assessment of
impacts of agricultural and climate change scenarios on watershed water quantity
and quality, and crop production, Hydrol. Earth Syst. Sci., 20, 3325–3342,
https://doi.org/10.5194/hess-20-3325-2016, 2016. a, b
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model
simulations for hydrological climate-change impact studies: Review and
evaluation of different methods, J. Hydrol., 456–457, 12–29, https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012. a
Teutschbein, C. and Seibert, J.: Is bias correction of regional climate model (RCM)
simulations possible for non-stationary conditions, Hydrol. Earth Syst. Sci.,
17, 5061–5077, https://doi.org/10.5194/hess-17-5061-2013, 2013. a, b
Tóth, B., Weynants, M., Nemes, A., Makó, A., Bilas, G., and Tóth,
G.: New generation of hydraulic pedotransfer functions for Europe, Eur. J. Soil
Sci., 66, 226–238, https://doi.org/10.1111/ejss.12192, 2015. a, b
Tripathi, M. P., Raghuwanshi, N. S., and Rao, G. P.: Effect of watershed
subdivision on simulation of water balance components, Hydrol. Process., 20,
1137–1156, https://doi.org/10.1002/hyp.5927, 2006. a
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J. F., Masui, T., Meinshausen,
M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The representative
concentration pathways: An overview, Climatic Change, 109, 5–31,
https://doi.org/10.1007/s10584-011-0148-z, 2011. a
van Vuuren, D. P., Kok, M. T., Girod, B., Lucas, P. L., and de Vries, B.:
Scenarios in Global Environmental Assessments: Key characteristics and lessons
for future use, Global Environ.Change, 22, 884–895, https://doi.org/10.1016/j.gloenvcha.2012.06.001, 2012. a
Wagner, P. D., Bhallamudi, S. M., Narasimhan, B., Kumar, S., Fohrer, N., and
Fiener, P.: Comparing the effects of dynamic versus static representations of
land use change in hydrologic impact assessments, Environ. Model. Softw.,
https://doi.org/10.1016/j.envsoft.2017.06.023, in press, 2017. a
Wilby, R. L.: Uncertainty in water resource model parameters used for climate
change impact assessment, Hydrol. Process., 19, 3201–3219, https://doi.org/10.1002/hyp.5819, 2005. a
Wilby, R. L., Wigley, T. M. L., Conway, D., Jones, P. D., Hewitson, B. C., Main,
J., and Wilks, D. S.: Statistical downscaling of general circulation model
output: A comparison of methods, Water Resour. Res., 34, 2995–3008, https://doi.org/10.1029/98wr02577, 1998. a
Willmott, C. J., Robeson, S. M., and Matsuura, K.: A refined index of model
performance, Int. J. Climatol., 32, 2088–2094, https://doi.org/10.1002/joc.2419, 2012.
a
Winchell, M., Srinivasan, R., Di Luzio, M., and Arnold, J. G.: ArcSWAT 2012.10.19
Interface for SWAT2012, available at: http://swat.tamu.edu/software/arcswat/
(last access: 27 January 2017), 2015. a
Wise, M., Calvin, K., Thomson, A., Clarke, L., Bond-Lamberty, B., Sands, R.,
Smith, S. J., Janetos, A., and Edmonds, J.: Implications of Limiting
CO2 Concentrations for Land Use and Energy, Science, 324, 1183–1186,
https://doi.org/10.1126/science.1168475, 2009. a
Wood, A. W., Leung, L. R., Sridhar, V., and Lettenmaier, D. P.: Hydrologic
implications of dynamical and statistical approaches to downscaling climate
model outputs, Climatic Change, 62, 189–216, https://doi.org/10.1023/B:CLIM.0000013685.99609.9e, 2004. a
Yates, D. N., Miller, K. A., Wilby, R. L., and Kaatz, L.: Decision-centric
adaptation appraisal for water management across Colorado's Continental Divide,
Clim. Risk Manage., 10, 35–50, https://doi.org/10.1016/j.crm.2015.06.001, 2015. a
Zadeh, F. K., Nossent, J., Sarrazin, F., Pianosi, F., van Griensven, A., Wagener,
T., and Bauwens, W.: Comparison of variance-based and moment-independent global
sensitivity analysis approaches by application to the SWAT model, Environ. Model.
Softw., 91, 210–222, https://doi.org/10.1016/j.envsoft.2017.02.001, 2017. a
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. a
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.
For two Austrian catchments we simulated discharge and nitrate-nitrogen (NO3-N) considering...
