Articles | Volume 21, issue 8
Research article 29 Aug 2017
Research article | 29 Aug 2017
The critical role of uncertainty in projections of hydrological extremes
Hadush K. Meresa and Renata J. Romanowicz
No articles found.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Ksenija Cindrić Kalin, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Silvana Stevkova, Lena M. Tallaksen, Iryna Trofimova, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci. Discuss.,
Preprint under review for NHESSShort summary
Recent drought events caused enormous damages in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts, and how drought is perceived by relevant stakeholders. Over 700 participant from 28 European countries provided insights to drought hazard and impact perception, and current management strategies. The study concludes with an urgent need to collectively combat drought risk via an European macro-level drought governance approach.
Harm-Jan F. Benninga, Martijn J. Booij, Renata J. Romanowicz, and Tom H. M. Rientjes
Hydrol. Earth Syst. Sci., 21, 5273–5291,Short summary
Accurate flood and low-streamflow forecasting are important. The paper presents a methodology to evaluate ensemble streamflow-forecasting systems for different lead times; low, medium and high streamflow; and related runoff-generating processes. We applied the methodology to a study forecasting system of the Biała Tarnowska River in Poland. The results provide valuable information about the forecasting system: in which conditions it can be used and how the system can be improved effectively.
Gregor Laaha, Tobias Gauster, Lena M. Tallaksen, Jean-Philippe Vidal, Kerstin Stahl, Christel Prudhomme, Benedikt Heudorfer, Radek Vlnas, Monica Ionita, Henny A. J. Van Lanen, Mary-Jeanne Adler, Laurie Caillouet, Claire Delus, Miriam Fendekova, Sebastien Gailliez, Jamie Hannaford, Daniel Kingston, Anne F. Van Loon, Luis Mediero, Marzena Osuch, Renata Romanowicz, Eric Sauquet, James H. Stagge, and Wai K. Wong
Hydrol. Earth Syst. Sci., 21, 3001–3024,Short summary
In 2015 large parts of Europe were affected by a drought. In terms of low flow magnitude, a region around the Czech Republic was most affected, with return periods > 100 yr. In terms of deficit volumes, the drought was particularly severe around S. Germany where the event lasted notably long. Meteorological and hydrological events developed differently in space and time. For an assessment of drought impacts on water resources, hydrological data are required in addition to meteorological indices.
Marzena Osuch, Renata J. Romanowicz, Deborah Lawrence, and Wai K. Wong
Hydrol. Earth Syst. Sci., 20, 1947–1969,Short summary
Possible future climate change effects on dryness conditions in Poland are estimated for six climate projections using the standardized precipitation index. The time series of precipitation represent six different climate model runs under the A1B SRES scenario for the period 1971–2099. Monthly precipitation values were used to estimate the standardized precipitation index for multiple timescales (1, 3, 6, 12 and 24 months) for a spatial resolution of 25 km for the whole country.
A. Kiczko, R. J. Romanowicz, M. Osuch, and E. Karamuz
Nat. Hazards Earth Syst. Sci., 13, 3443–3455,
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysisSequential data assimilation for real-time probabilistic flood inundation mappingKey challenges facing the application of the conductivity mass balance method: a case study of the Mississippi River basinCoupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological modelA systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and UgandaTechnical note: Uncertainty in multi-source partitioning using large tracer data setsAssessment of climate change impact and difference on the river runoff in four basins in China under 1.5 and 2.0 °C global warmingA likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelationTechnical note: Analytical sensitivity analysis and uncertainty estimation of baseflow index calculated by a two-component hydrograph separation method with conductivity as a tracerUnderstanding the water cycle over the upper Tarim Basin: retrospecting the estimated discharge bias to atmospheric variables and model structureThe effect of input data resolution and complexity on the uncertainty of hydrological predictions in a humid vegetated watershedParameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approachesTechnical note: Pitfalls in using log-transformed flows within the KGE criterionImprovement of model evaluation by incorporating prediction and measurement uncertaintyTransferability of climate simulation uncertainty to hydrological impactsIntercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)Mapping (dis)agreement in hydrologic projectionsConsistency assessment of rating curve data in various locations using Bidirectional Reach (BReach)Residual uncertainty estimation using instance-based learning with applications to hydrologic forecastingCharacterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understandingEffects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scalesExtending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological modelQuantifying uncertainty on sediment loads using bootstrap confidence intervalsEvent-scale power law recession analysis: quantifying methodological uncertaintyDisentangling timing and amplitude errors in streamflow simulationsReliability of lumped hydrological modeling in a semi-arid mountainous catchment facing water-use changesUsing dry and wet year hydroclimatic extremes to guide future hydrologic projectionsUncertainty contributions to low-flow projections in AustriaAccounting for dependencies in regionalized signatures for predictions in ungauged catchmentsClimate change and its impacts on river discharge in two climate regions in ChinaUncertainty in hydrological signaturesClimate model uncertainty versus conceptual geological uncertainty in hydrological modelingEstimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchmentsTransferring global uncertainty estimates from gauged to ungauged catchmentsSpatial sensitivity analysis of snow cover data in a distributed rainfall-runoff modelUncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing dataThe skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological modelsFlow pathways and nutrient transport mechanisms drive hydrochemical sensitivity to climate change across catchments with different geology and topographyThe importance of hydrological uncertainty assessment methods in climate change impact studiesRegional water balance modelling using flow-duration curves with observational uncertaintiesClimate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structuresFrom maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed modelsBridging the gap between GLUE and formal statistical approaches: approximate Bayesian computationConsidering rating curve uncertainty in water level predictionsTechnical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed modelsThe impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchmentsAn ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resourcesLocal sensitivity analysis for compositional data with application to soil texture in hydrologic modellingAdaptive correction of deterministic models to produce probabilistic forecastsBayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff modelsHydrological education and training needs in sub-Saharan Africa: requirements, constraints and progress
Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 25, 4995–5011,Short summary
In this study, daily observations are assimilated into a hydrodynamic model to update the performance of modeling and improve the flood inundation mapping skill. Results demonstrate that integrating data assimilation with a hydrodynamic model improves the performance of flood simulation and provides more reliable inundation maps. A flowchart provides the overall steps for applying this framework in practice and forecasting probabilistic flood maps before the onset of upcoming floods.
Hang Lyu, Chenxi Xia, Jinghan Zhang, and Bo Li
Hydrol. Earth Syst. Sci., 24, 6075–6090,Short summary
Baseflow separation plays a critical role in science-based management of water resources. This study addressed key challenges hindering the application of the generally accepted conductivity mass balance (CMB). Monitoring data for over 200 stream sites of the Mississippi River basin were collected to answer the following questions. What are the characteristics of a watershed that determine the method suitability? What length of monitoring data is needed? How can the parameters be more accurate?
Aynom T. Teweldebrhan, Thomas V. Schuler, John F. Burkhart, and Morten Hjorth-Jensen
Hydrol. Earth Syst. Sci., 24, 4641–4658,
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489,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.
Alicia Correa, Diego Ochoa-Tocachi, and Christian Birkel
Hydrol. Earth Syst. Sci., 23, 5059–5068,Short summary
The applications and availability of large tracer data sets have vastly increased in recent years leading to research into the contributions of multiple sources to a mixture. We introduce a method based on Taylor series approximation to estimate the uncertainties of such sources' contributions. The method is illustrated with examples of hydrology (14 tracers) and a MATLAB code is provided for reproducibility. This method can be generalized to any number of tracers across a range of disciplines.
Hongmei Xu, Lüliu Liu, Yong Wang, Sheng Wang, Ying Hao, Jingjin Ma, and Tong Jiang
Hydrol. Earth Syst. Sci., 23, 4219–4231,Short summary
1.5 and 2 °C have become targets in the discussion of climate change impacts. However, climate research is also challenged to provide more robust information on the impact of climate change at local and regional scales to assist the development of sound scientific adaptation and mitigation measures. This study assessed the impacts and differences of 1.5 and 2.0 °C global warming on basin-scale river runoff by examining four river basins covering a wide hydroclimatic setting in China.
Lorenz Ammann, Fabrizio Fenicia, and Peter Reichert
Hydrol. Earth Syst. Sci., 23, 2147–2172,Short summary
The uncertainty of hydrological models can be substantial, and its quantification and realistic description are often difficult. We propose a new flexible probabilistic framework to describe and quantify this uncertainty. It is show that the correlation of the errors can be non-stationary, and that accounting for temporal changes in correlation can lead to strongly improved probabilistic predictions. This is a promising avenue for improving uncertainty estimation in hydrological modelling.
Weifei Yang, Changlai Xiao, and Xiujuan Liang
Hydrol. Earth Syst. Sci., 23, 1103–1112,Short summary
This paper analyzed the sensitivity of the baseflow index to the parameters of the conductivity two-component hydrograph separation method. The results indicated that the baseflow index is more sensitive to the conductivity of baseflow and the separation method may be more suitable for the long time series in a small watershed. After considering the mutual offset of the measurement errors of conductivity and streamflow, the uncertainty in baseflow index was reduced by half.
Xudong Zhou, Jan Polcher, Tao Yang, Yukiko Hirabayashi, and Trung Nguyen-Quang
Hydrol. Earth Syst. Sci., 22, 6087–6108,Short summary
Model bias is commonly seen in discharge simulation by hydrological or land surface models. This study tested an approach with the Budyko hypothesis to retrospect the estimated discharge bias to different bias sources including the atmospheric variables and model structure. Results indicate that the bias is most likely caused by the forcing variables, and the forcing bias should firstly be assessed and reduced in order to perform pertinent analysis of the regional water cycle.
Linh Hoang, Rajith Mukundan, Karen E. B. Moore, Emmet M. Owens, and Tammo S. Steenhuis
Hydrol. Earth Syst. Sci., 22, 5947–5965,Short summary
The paper analyzes the effect of two input data (DEMs and the combination of soil and land use data) with different resolution and complexity on the uncertainty of model outputs (the predictions of streamflow and saturated areas) and parameter uncertainty using SWAT-HS. Results showed that DEM resolution has significant effect on the spatial pattern of saturated areas and using complex soil and land use data may not necessarily improve model performance or reduce model uncertainty.
Aynom T. Teweldebrhan, John F. Burkhart, and Thomas V. Schuler
Hydrol. Earth Syst. Sci., 22, 5021–5039,
Léonard Santos, Guillaume Thirel, and Charles Perrin
Hydrol. Earth Syst. Sci., 22, 4583–4591,Short summary
The Kling and Gupta efficiency (KGE) is a score used in hydrology to evaluate flow simulation compared to observations. In order to force the evaluation on the low flows, some authors used the log-transformed flow to calculate the KGE. In this technical note, we show that this transformation should be avoided because it produced numerical flaws that lead to difficulties in the score value interpretation.
Lei Chen, Shuang Li, Yucen Zhong, and Zhenyao Shen
Hydrol. Earth Syst. Sci., 22, 4145–4154,Short summary
In this study, the cumulative distribution function approach (CDFA) and the Monte Carlo approach (MCA) were used to develop two new approaches for model evaluation within an uncertainty framework. These proposed methods could be extended to watershed models to provide a substitution for traditional model evaluations within an uncertainty framework.
Hui-Min Wang, Jie Chen, Alex J. Cannon, Chong-Yu Xu, and Hua Chen
Hydrol. Earth Syst. Sci., 22, 3739–3759,Short summary
Facing a growing number of climate models, many selection methods were proposed to select subsets in the field of climate simulation, but the transferability of their performances to hydrological impacts remains doubtful. We investigate the transferability of climate simulation uncertainty to hydrological impacts using two selection methods, and conclude that envelope-based selection of about 10 climate simulations based on properly chosen climate variables is suggested for impact studies.
Andreas M. Jobst, Daniel G. Kingston, Nicolas J. Cullen, and Josef Schmid
Hydrol. Earth Syst. Sci., 22, 3125–3142,
Lieke A. Melsen, Nans Addor, Naoki Mizukami, Andrew J. Newman, Paul J. J. F. Torfs, Martyn P. Clark, Remko Uijlenhoet, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 22, 1775–1791,Short summary
Long-term hydrological predictions are important for water management planning, but are also prone to uncertainty. This study investigates three sources of uncertainty for long-term hydrological predictions in the US: climate models, hydrological models, and hydrological model parameters. Mapping the results revealed spatial patterns in the three sources of uncertainty: different sources of uncertainty dominate in different regions.
Katrien Van Eerdenbrugh, Stijn Van Hoey, Gemma Coxon, Jim Freer, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 21, 5315–5337,Short summary
Consistency in stage–discharge data is investigated using a methodology called Bidirectional Reach (BReach). Various measurement stations in the UK, New Zealand and Belgium are selected based on their historical ratings information and their characteristics related to data consistency. When applying a BReach analysis on them, the methodology provides results that appear consistent with the available knowledge and thus facilitates a reliable assessment of (in)consistency in stage–discharge data.
Omar Wani, Joost V. L. Beckers, Albrecht H. Weerts, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 21, 4021–4036,Short summary
We generate uncertainty intervals for hydrologic model predictions using a simple instance-based learning scheme. Errors made by the model in some specific hydrometeorological conditions in the past are used to predict the probability distribution of its errors during forecasting. We test it for two different case studies in England. We find that this technique, even though conceptually simple and easy to implement, performs as well as some other sophisticated uncertainty estimation methods.
Christa Kelleher, Brian McGlynn, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 21, 3325–3352,Short summary
Models are tools for understanding how watersheds function and may respond to land cover and climate change. Before we can use models towards these purposes, we need to ensure that a model adequately represents watershed-wide observations. In this paper, we propose a new way to evaluate whether model simulations match observations, using a variety of information sources. We show how this information can reduce uncertainty in inputs to models, reducing uncertainty in hydrologic predictions.
Gabriele Baroni, Matthias Zink, Rohini Kumar, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 2301–2320,Short summary
Three methods are used to characterize the uncertainty in soil properties. The effect on simulated states and fluxes is quantified using a distributed hydrological model. Different impacts are identified as function of the perturbation method, of the model outputs and of the spatio-temporal resolution. The study underlines the importance of a proper characterization of the uncertainty in soil properties for a correct assessment of their role and further improvements in the model application.
Ji Li, Yangbo Chen, Huanyu Wang, Jianming Qin, Jie Li, and Sen Chiao
Hydrol. Earth Syst. Sci., 21, 1279–1294,Short summary
Quantitative precipitation forecast produced by the WRF model has a similar pattern to that estimated by rain gauges in a southern China large watershed, hydrological model parameters should be optimized with QPF produced by WRF, and simulating floods by coupling the WRF QPF with a distributed hydrological model provides a good reference for large watershed flood warning and could benefit the flood management communities due to its longer lead time.
Johanna I. F. Slaets, Hans-Peter Piepho, Petra Schmitter, Thomas Hilger, and Georg Cadisch
Hydrol. Earth Syst. Sci., 21, 571–588,Short summary
Determining measures of uncertainty on loads is not trivial, as a load is a product of concentration and discharge per time point, summed up over time. A bootstrap approach enables the calculation of confidence intervals on constituent loads. Ignoring the uncertainty on the discharge will typically underestimate the width of 95 % confidence intervals by around 10 %. Furthermore, confidence intervals are asymmetric, with the largest uncertainty on the upper limit.
David N. Dralle, Nathaniel J. Karst, Kyriakos Charalampous, Andrew Veenstra, and Sally E. Thompson
Hydrol. Earth Syst. Sci., 21, 65–81,Short summary
The streamflow recession is the period following rainfall during which flow declines. This paper examines a common method of recession analysis and identifies sensitivity of the technique's results to necessary, yet subjective, methodological choices. The results have implications for hydrology, sediment and solute transport, and geomorphology, as well as for testing numerous hydrologic theories which predict the mathematical form of the recession.
Simon Paul Seibert, Uwe Ehret, and Erwin Zehe
Hydrol. Earth Syst. Sci., 20, 3745–3763,Short summary
While the assessment of "vertical" (magnitude) errors of streamflow simulations is standard practice, "horizontal" (timing) errors are rarely considered. To assess their role, we propose a method to quantify both errors simultaneously which closely resembles visual hydrograph comparison. Our results reveal differences in time–magnitude error statistics for different flow conditions. The proposed method thus offers novel perspectives for model diagnostics and evaluation.
Paul Hublart, Denis Ruelland, Inaki García de Cortázar-Atauri, Simon Gascoin, Stef Lhermitte, and Antonio Ibacache
Hydrol. Earth Syst. Sci., 20, 3691–3717,Short summary
Our paper explores the reliability of conceptual catchment models in the dry Andes. First, we show that explicitly accounting for irrigation water use improves streamflow predictions during dry years. Second, we show that sublimation losses can be easily incorporated into temperature-based melt models without increasing model complexity too much. Our work also highlights areas requiring additional research, including the need for a better conceptualization of runoff generation processes.
Stephen Oni, Martyn Futter, Jose Ledesma, Claudia Teutschbein, Jim Buttle, and Hjalmar Laudon
Hydrol. Earth Syst. Sci., 20, 2811–2825,Short summary
This paper presents an important framework to improve hydrologic projections in cold regions. Hydrologic modelling/projections are often based on model calibration to long-term data. Here we used dry and wet years as a proxy to quantify uncertainty in projecting hydrologic extremes. We showed that projections based on long-term data could underestimate runoff by up to 35% in boreal regions. We believe the hydrologic modelling community will benefit from new insights derived from this study.
Juraj Parajka, Alfred Paul Blaschke, Günter Blöschl, Klaus Haslinger, Gerold Hepp, Gregor Laaha, Wolfgang Schöner, Helene Trautvetter, Alberto Viglione, and Matthias Zessner
Hydrol. Earth Syst. Sci., 20, 2085–2101,Short summary
Streamflow estimation during low-flow conditions is important for estimation of environmental flows, effluent water quality, hydropower operations, etc. However, it is not clear how the uncertainties in assumptions used in the projections translate into uncertainty of estimated future low flows. The objective of the study is to explore the relative role of hydrologic model calibration and climate scenarios in the uncertainty of low-flow projections in Austria.
Susana Almeida, Nataliya Le Vine, Neil McIntyre, Thorsten Wagener, and Wouter Buytaert
Hydrol. Earth Syst. Sci., 20, 887–901,Short summary
The absence of flow data to calibrate hydrologic models may reduce the ability of such models to reliably inform water resources management. To address this limitation, it is common to condition hydrological model parameters on regionalized signatures. In this study, we justify the inclusion of larger sets of signatures in the regionalization procedure if their error correlations are formally accounted for and thus enable a more complete use of all available information.
H. Xu and Y. Luo
Hydrol. Earth Syst. Sci., 19, 4609–4618,Short summary
This study quantified the climate impact on river discharge in the River Huangfuchuan in semi-arid northern China and the River Xiangxi in humid southern China. Climate projections showed trends toward warmer and wetter conditions, particularly for the River Huangfuchuan. The main projected hydrologic impact was a more pronounced increase in annual discharge in both catchments. Peak flows are projected to appear earlier than usual in the River Huangfuchuan and later than usual in River Xiangxi.
I. K. Westerberg and H. K. McMillan
Hydrol. Earth Syst. Sci., 19, 3951–3968,Short summary
This study investigated the effect of uncertainties in data and calculation methods on hydrological signatures. We present a widely applicable method to evaluate signature uncertainty and show results for two example catchments. The uncertainties were often large (i.e. typical intervals of ±10–40% relative uncertainty) and highly variable between signatures. It is therefore important to consider uncertainty when signatures are used for hydrological and ecohydrological analyses and modelling.
T. O. Sonnenborg, D. Seifert, and J. C. Refsgaard
Hydrol. Earth Syst. Sci., 19, 3891–3901,Short summary
The impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Geology is the dominating uncertainty source for travel time and capture zones, while climate dominates for hydraulic heads and steam flow.
N. Dogulu, P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha
Hydrol. Earth Syst. Sci., 19, 3181–3201,
F. Bourgin, V. Andréassian, C. Perrin, and L. Oudin
Hydrol. Earth Syst. Sci., 19, 2535–2546,
T. Berezowski, J. Nossent, J. Chormański, and O. Batelaan
Hydrol. Earth Syst. Sci., 19, 1887–1904,
F. Silvestro, S. Gabellani, R. Rudari, F. Delogu, P. Laiolo, and G. Boni
Hydrol. Earth Syst. Sci., 19, 1727–1751,
M. C. Demirel, M. J. Booij, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 19, 275–291,Short summary
This paper investigates the skill of 90-day low-flow forecasts using three models. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
J. Crossman, M. N. Futter, P. G. Whitehead, E. Stainsby, H. M. Baulch, L. Jin, S. K. Oni, R. L. Wilby, and P. J. Dillon
Hydrol. Earth Syst. Sci., 18, 5125–5148,Short summary
We projected potential hydrochemical responses in four neighbouring catchments to a range of future climates. The highly variable responses in streamflow and total phosphorus (TP) were governed by geology and flow pathways, where larger catchment responses were proportional to greater soil clay content. This suggests clay content might be used as an indicator of catchment sensitivity to climate change, and highlights the need for catchment-specific management plans.
M. Honti, A. Scheidegger, and C. Stamm
Hydrol. Earth Syst. Sci., 18, 3301–3317,
I. K. Westerberg, L. Gong, K. J. Beven, J. Seibert, A. Semedo, C.-Y. Xu, and S. Halldin
Hydrol. Earth Syst. Sci., 18, 2993–3013,
G. Seiller and F. Anctil
Hydrol. Earth Syst. Sci., 18, 2033–2047,
J. D. Herman, J. B. Kollat, P. M. Reed, and T. Wagener
Hydrol. Earth Syst. Sci., 17, 5109–5125,
M. Sadegh and J. A. Vrugt
Hydrol. Earth Syst. Sci., 17, 4831–4850,
A. E. Sikorska, A. Scheidegger, K. Banasik, and J. Rieckermann
Hydrol. Earth Syst. Sci., 17, 4415–4427,
J. D. Herman, J. B. Kollat, P. M. Reed, and T. Wagener
Hydrol. Earth Syst. Sci., 17, 2893–2903,
H. E. Beck, L. A. Bruijnzeel, A. I. J. M. van Dijk, T. R. McVicar, F. N. Scatena, and J. Schellekens
Hydrol. Earth Syst. Sci., 17, 2613–2635,
J. A. Velázquez, J. Schmid, S. Ricard, M. J. Muerth, B. Gauvin St-Denis, M. Minville, D. Chaumont, D. Caya, R. Ludwig, and R. Turcotte
Hydrol. Earth Syst. Sci., 17, 565–578,
L. Loosvelt, H. Vernieuwe, V. R. N. Pauwels, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 461–478,
P. J. Smith, K. J. Beven, A. H. Weerts, and D. Leedal
Hydrol. Earth Syst. Sci., 16, 2783–2799,
A. E. Sikorska, A. Scheidegger, K. Banasik, and J. Rieckermann
Hydrol. Earth Syst. Sci., 16, 1221–1236,
D. A. Hughes
Hydrol. Earth Syst. Sci., 16, 861–871,
Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., and Klrve, B.: A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model, J. Hydrol., 524, 733–752, https://doi.org/10.1016/j.jhydrol.2015.03.027, 2015.
Addor, N., Rossler, O., Koplin, N., Huss, M., Weingartner, R., and Seibert, J.: Robust changes and sources of uncertainty in the projected hydrological regimes of Swiss catchments, Water Resour. Res., 50, 7541–7562, https://doi.org/10.1002/2014WR015549, 2014.
Alfieri, L., Burek, P., Feyen, L., and Forzieri, G.: Global warming increases the frequency of river floods in Europe, Hydrol. Earth Syst. Sci., 19, 2247–2260, https://doi.org/10.5194/hess-19-2247-2015, 2015.
Bennett, K. E., Werner, A. T., and Schnorbus, M.: Uncertainties in hydrologic and climate change impact analyses in headwater basins of British Columbia, J. Climate, 25, 5711–5730, 2012.
Benninga, H.-J.: Performance and limitations of ensemble river flow forecasts, Thesis for the degree of Master of Science in Civil Engineering and Management, University of Twente, Netherlands available at: https://www.utwente.nl/en/et/wem/education/msc-thesis/2015/benninga.pdf (last access: 20 August 2015), 2015.
Benninga, H.-J. F., Booij, M. J., Romanowicz, R. J., and Rientjes, T. H. M.: Performance of ensemble streamflow forecasts under varied hydrometeorological conditions, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-584, in review, 2016.
Bergstrom, S.: The HBV model, in: Computer Models of Watershed Hydrology, edited by: Singh, V. P., Water Resources Publications, Highland Ranch, CO, 443–476, 1995.
Beven, K.: Towards integrated environmental models of everywhere: uncertainty, data and modelling as a learning process, Hydrol. Earth Syst. Sci., 11, 460–467, https://doi.org/10.5194/hess-11-460-2007, 2007.
Beven, K. J.: A manifesto for the equifinality thesis. J. Hydrol., 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006.
Beven, K. J.: Facets of uncertainty: epistemic uncertainty, nonstationarity, likelihood, hypothesis testing, and communication, Hydrol. Sci. J., 61, 1652–1665, https://doi.org/10.1080/02626667.2015.1031761, 2016.
Beven, K. J. and Binley, A.: The future of distributed models: model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992.
Beven, K. J. and Binley, A.: GLUE 20-years on, Hydrol. Process., 28, 5897–5918, https://doi.org/10.1002/hyp.10082, 2014.
Bosshard, T., Carambia, M., Goergen, K., Kotlarski, S., Krahe, P., and Zappa, M., and Schar, C.: Quantifying uncertainty sources in an ensemble of hydrological climate- impact projections, Water Resour. Res., 49, 1523–1536, https://doi.org/10.1029/2011WR011533, 2013.
Coles, S.: An introduction to statistical modelling of extreme values, Springer-Verlag London Limited, 208 pp., 2001.
Deckers, D. L., Booij, M. J., Rientjes, Th. M., and Krol, M. S.: Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall-runoff model, Water Resour. Manag., 24, 3961–3985, 2010.
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: Impacts of climate change on the seasonality of low flows in 134 catchments in the River Rhine basin using an ensemble of bias-corrected regional climate simulations, Hydrol. Earth Syst. Sci., 17, 4241–4257, https://doi.org/10.5194/hess-17-4241-2013, 2013a.
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models, Water Resour. Res., 49, 4035–4053, https://doi.org/10.1002/wrcr.20294, 2013b.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions “Should we apply bias correction to global and regional climate model data”, Hydrol. Earth Syst. Sci., 16, 3391–3404, 2012.
Falloon, P., Challinor, A., Dessai, S., Hoang, L., Johnson, J., and Koehler, A. K.: Ensembles and uncertainty in climate change impacts, Front. Environ. Sci., 2, 7 pp., https://doi.org/10.3389/fenvs.2014.00033, 2014.
Ghosh, S. and Katkar, S.: Modeling uncertainty resulting from multiple downscaling methods in assessing hydrological impacts of climate change, Water Resour. Manag., 26, 3559–3579, 2012.
Giuntoli, J., Vidal, J.-P., Prudhomme, C., and Hannah, D. M.: Future hydrological extremes: the uncertainty from multiple global climate and global hydrological models, Earth Syst. Dynam., 6, 267–285, https://doi.org/10.5194/esd-6-267-2015, 2015.
Gosling, S. N. and Arnell, N. W.: Simulating current global river runoff with a global hydrological model: Model revisions, validation, and sensitivity analysis, Hydrol. Process., 25, 1129–1145, https://doi.org/10.1002/hyp.7727, 2011.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations-a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
Gupta, H. V., H. Kling, K. K. Yilmaz, 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/10.016/j.jhydrol.2009.08.003, 2009.
Gutjahr, O. and Heinemann, G. : Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM – Effects on extreme values and climate change signal, Theor. Appl. Climatol., 114, 511–529, https://doi.org/10.1007/s00704-013-0834-z, 2013.
Hamon, W. R.: Estimating potential evapotranspiration, J. Hydraul. Div., Proc. Am. Soc. Civil Eng., 87, 107–120, 1961.
Honti, M., Scheidegger, A., and Stamm, C.: The importance of hydrological uncertainty assessment methods in climate change impact studies, Hydrol. Earth Syst. Sci., 18, 3301–3317, https://doi.org/10.5194/hess-18-3301-2014, 2014.
Houska, T., Multsch, S., Kraft, P., Frede, H.-G., and Breuer, L.: Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model, Biogeosciences, 11, 2069–2082, https://doi.org/10.5194/bg-11-2069-2014, 2014.
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., Kroner, 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.
Jin, X., Xu, C.-Y., Zhang, Q., and Singh, V.: Parameter and modelling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model, J. Hydrol., 383, 147–155, 2010.
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014.
Knutti, R. and Sedlacek, J.: Robustness and uncertainties in the new CMIP5 climate model projections, Nat. Clim. Change, 3, 369–373, https://doi.org/10.1038/nclimate1716, 2012.
Kundzewicz, Z. K, Krysanova, V., Dankers, R., Hirabayashi,Y., Kanae,S., Hattermann, F. F., Huang, S., Milly, P. C. D., Stoffel, M., Driessen, P. P. J., Matczak, P., Quevauviller, P., and Schellnhuber, H.-J.: Differences in flood hazard projections in Europe – their causes and consequences for decision making, Hydrol. Sci. J., 62, 1–14, https://doi.org/10.1080/02626667.2016.1241398, 2017.
Lagerwalla, G., Kiker, G., Muñoz-Carpena, R., and Wang, N.: Global uncertainty and sensitivity analysis of a spatially distributed ecological model, Ecol. Model., 275, 22–30, 2014.
Lawrence, D. and Hisdal, H.: Hydrological projections for floods in Norway under a future climate, NVE Report 5-2011, Oslo, 2011.
Lespinas, F., Ludwig, W., and Heussner, S.: Hydrological and climatic uncertainties associated with modeling the impact of climate change on water resources of small Mediterranean coastal rivers, J. Hydrol., 511, 403–422, https://doi.org/10.1016/j.jhydrol.2014.01.033, 2014.
Lindstrom, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, 1997.
Maraun, D.: Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue, J. Climate, 26, 2137–2143, https://doi.org/10.1175/JCLI-D-12-00821.1,2013.
Meresa, H., Osuch, M., and Romanowicz, R.: Hydro-meteorological drought projection, Water, 8, 206, 22 pp., https://doi.org/10.3390/w8050206, 2016.
Meresa, H., Romanowicz, R. J., and Napiórkowski, J. J.: Trends of projections of hydrological extreme indices in the 21st century, Acta Geophys., accepted, 20 pp., https://doi.org/10.1007/s11600-017-0062-5, 2017.
Montgomery, D. C.: Design and Analysis of Experiments, Wiley and Sons Ltd., New York, 704 pp., 1997.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models, Part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970.
Osuch, M.: Sensitivity and uncertainty analysis of precipitation-runoff models for the Middle Vistula Basin, Chapter in GeoPlanet: Earth and Planetary Sciences, 61–81, https://doi.org/10.1007/978-3-319-18854-6_5, 2015.
Osuch, M., Romanowicz, R. J., and Booij, M. J.: The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics, Hydrol. Sci. J., 60, 1299–1316, https://doi.org/10.1080/02626667.2014.967694, 2015.
Osuch, M., Lawrence, D., Meresa, K. H, Napiorkowski, J. J., and Romanowicz, J.: Projected changes in flood indices in selected catchments in Poland in the 21st century, Stoch. Environ. Res. Risk Assess., 23 pp., https://doi.org/10.1007/s00477-016-1296-5, 2016.
Osuch, M., Romanowicz, R. J., and Wong, W.: Analysis of low flow indices under varying climatic conditions in Poland, Hydrol. Res., 48, 18 pp., doi:10.2166/nh.2017.021, 2017.
Ouyang, F., Lu, H., Zhu, Y., Zhang, J., Yu, Z., Chen, X., and Li M.: Uncertainty analysis of downscaling methods in assessing the influence of climate change on hydrology, Stoch. Environ. Res. Risk Assess., 28, 991–1010, 2014.
Poulin, A., Brissette, F., Leconte, R., Arsenault, R., and Malo, J.: Uncertainty of hydrological modelling in climate change impact studies in a Canadian, snow-dominated river basin, J. Hydrol., 409, 626–636, 2011.
Romanowicz, R. J. and Macdonald, R.: Modelling Uncertainty and Variability in Environmental Systems, Acta Geophys. Polonica, 53, 401–417, 2005.
Romanowicz, J., Osuch, M., and Grabowiecka, M.: On the Choice of Calibration Periods and Objective Functions: A Practical Guide to Model Parameter Identification, Acta Geophys., 61, 1477–1503, https://doi.org/10.2478/s11600-013-0157-6, 2013.
Romanowicz J., Bogdanowicz, E., Debele, E., Doroszkiewcz, J., Hisdal, H., Lawrence, D., Meresa, K. Hadush, Jaroslaw, J. Napiorkowski, J. N., Marzena Osuch, M., Witold, G. Strupczewski, W. G., Donna Wilson, D., and Wong, W. K.: Climate Change Impact on Hydrological Extremes: Preliminary Results from the Polish-Norwegian Project, Acta Geophys., 64, 477–509, https://doi.org/10.1515/acgeo-2016-0009, 2016a.
Romanowicz, R. J., Kundzewicz, Z. W., Meresa, H. K., Stoffel, M., Krysanova, V., and Doroszkiewicz, J.: Projections of changes in flood hazard in two headwater catchments of the Vistula in the context of European-scale studies, in: Flood Risk in the Upper Vistula Basin, 341–359, Springer International Publishing, 2016b.
Saltelli, A., Ratto, M., Tarantola, S., Campolongo, F., and European Commission, Joint Research Centre of Ispra (I): Review: Sensitivity analysis practices: Strategies for model-based inference, Reliab. Eng. Syst. Safe., 91, 1109–1125, https://doi.org/10.1016/j.ress.2005.11.014, 2006.
Seibert, J. and Vis, M. J.: How informative are stream level observations in different geographic regions?, Hydrol. Process., 30, 2498–2508, 2016.
Sellami, H., La Jeunesse, I., Benabdallah, S., Baghdadi, N., and Vanclooster, M.: Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France), Hydrol. Earth Syst. Sci., 18, 2393–2413, https://doi.org/10.5194/hess-18-2393-2014, 2014.
Steinschneider, S., Polebitski, A., Brown, C., and Letcher, B. H.: Toward a statistical framework to quantify the uncertainties of hydrologic response under climate change, Water Resour. Res., 48, W11525, https://doi.org/10.1029/2011WR011318, 2012.
Steinschneider, S., Sungwook, W., and Casey Brown, C.: The integrated effects of climate and hydrologic uncertainty on future flood risk assessments, Hydrol. Process., 29, 2823–2839, https://doi.org/10.1002/hyp.10409, 2015.
Strupczewski, W., Kochanek, K., Markiewicz, I., Bogdanowicz, E., Weglarczyk, S., and Singh, P. V.: On the tails of distributions of annual peak flow, IWA Publishing, Hydrol. Res., 9, 71–192, https://doi.org/10.2166/nh.2011.062, 2011.
Sunyer, M. A., Hundecha, Y., Lawrence, D., Madsen, H., Willems, P., Martinkova, M., Vormoor, K., Bürger, G., Hanel, M., Kriaučiūnienė, J., Loukas, A., Osuch, M., and Yücel, I.: Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe, Hydrol. Earth Syst. Sci., 19, 1827–1847, https://doi.org/10.5194/hess-19-1827-2015, 2015.
Teng, J., Potter, N. J., Chiew, F. H. S., Zhang, L., Wang, B., Vaze, J., and Evans, J. P.: How does bias correction of regional climate model precipitation affect modelled runoff?, Hydrol. Earth Syst. Sci., 19, 711–728, https://doi.org/10.5194/hess-19-711-2015, 2015.
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.
Thompson, J. R., Green, A. J., Kingston, D. G., and Gosling, S. N.: Assessment of uncertainty in river flow projections for the Mekong River using multiple GCMs and hydrological models, J. Hydrol. 486, 1–30, 2013.
Tian, Y., Xu, Y.-P., Booij, M. J., and Cao, L.: Impact assessment of multiple uncertainty sources on high flows under climate change, Hydrol. Res., 47.1, 61–74, https://doi.org/10.2166/nh.2015.008, 2016.
Todd, M. C., Taylor, R. G. Osborn, T. J., Kingston, D. G. Arnell, N. W., and Gosling, S. N.: Uncertainty in climate change impacts on basin-scale freshwater resources-preface to the special issue: the QUEST-GSI methodology and synthesis of results, Hydrol. Earth Syst. Sci., 15, 1035–1046, https://doi.org/10.5194/hess-15-1035-2011, 2011.
Vormoor, K., Lawrence, D., Heistermann, M., and Bronstert, A.: Climate change impacts on the seasonality and generation processes of floods-projections and uncertainties for catchments with mixed snowmelt/rainfall regimes, Hydrol. Earth Syst. Sci., 19, 913–931, https://doi.org/10.5194/hess-19-913-2015, 2015.
Wilby, R. L. and Harris, I.: A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the River Thames, UK, Water Resour. Res., 42, W02419, https://doi.org/10.1029/2005WR004065, 2006.
Zhan, Y. and Zhang, M.: Application of a combined sensitivity analysis approach on a pesticide environmental risk indicator, Environ. Model. Softw., 49, 129–140, 2013.
Evaluation of the uncertainty in projections of future hydrological extremes in the mountainous catchment was performed. The uncertainty of the estimate of 1-in-100-year return maximum flow based on the 1971–2100 time series exceeds 200 % of its median value with the largest influence of the climate model uncertainty, while the uncertainty of the 1-in-100-year return minimum flow is of the same order (i.e. exceeds 200 %) but it is mainly influenced by the hydrological model parameter uncertainty.
Evaluation of the uncertainty in projections of future hydrological extremes in the mountainous...