Articles | Volume 16, issue 4
https://doi.org/10.5194/hess-16-1221-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-16-1221-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
12 Apr 2012
Research article |
| 12 Apr 2012
Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models
A. E. Sikorska
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dept. of Urban Water Management, Überlandstrasse 133, Dübendorf, 8600, Switzerland
Warsaw University of Life Sciences – SGGW, Dept. of Hydraulic Engineering, 166 Nowoursynowska Street Warsaw, 02-787, Poland
A. Scheidegger
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dept. of Urban Water Management, Überlandstrasse 133, Dübendorf, 8600, Switzerland
K. Banasik
Warsaw University of Life Sciences – SGGW, Dept. of Hydraulic Engineering, 166 Nowoursynowska Street Warsaw, 02-787, Poland
J. Rieckermann
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dept. of Urban Water Management, Überlandstrasse 133, Dübendorf, 8600, Switzerland
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysis
A data-centric perspective on the information needed for hydrological uncertainty predictions
A decomposition approach to evaluating the local performance of global streamflow reanalysis
Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems
Technical note: The CREDIBLE Uncertainty Estimation (CURE) toolbox: facilitating the communication of epistemic uncertainty
Why do our rainfall–runoff models keep underestimating the peak flows?
Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies
Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example
Benchmarking global hydrological and land surface models against GRACE in a medium-sized tropical basin
Guidance on evaluating parametric model uncertainty at decision-relevant scales
Quantifying input uncertainty in the calibration of water quality models: reordering errors via the secant method
Sequential data assimilation for real-time probabilistic flood inundation mapping
Key challenges facing the application of the conductivity mass balance method: a case study of the Mississippi River basin
Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model
A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda
Technical note: Uncertainty in multi-source partitioning using large tracer data sets
Assessment of climate change impact and difference on the river runoff in four basins in China under 1.5 and 2.0 °C global warming
A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation
Technical note: Analytical sensitivity analysis and uncertainty estimation of baseflow index calculated by a two-component hydrograph separation method with conductivity as a tracer
Understanding the water cycle over the upper Tarim Basin: retrospecting the estimated discharge bias to atmospheric variables and model structure
The effect of input data resolution and complexity on the uncertainty of hydrological predictions in a humid vegetated watershed
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches
Technical note: Pitfalls in using log-transformed flows within the KGE criterion
Improvement of model evaluation by incorporating prediction and measurement uncertainty
Transferability of climate simulation uncertainty to hydrological impacts
Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)
Mapping (dis)agreement in hydrologic projections
Consistency assessment of rating curve data in various locations using Bidirectional Reach (BReach)
The critical role of uncertainty in projections of hydrological extremes
Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting
Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding
Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales
Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model
Quantifying uncertainty on sediment loads using bootstrap confidence intervals
Event-scale power law recession analysis: quantifying methodological uncertainty
Disentangling timing and amplitude errors in streamflow simulations
Reliability of lumped hydrological modeling in a semi-arid mountainous catchment facing water-use changes
Using dry and wet year hydroclimatic extremes to guide future hydrologic projections
Uncertainty contributions to low-flow projections in Austria
Accounting for dependencies in regionalized signatures for predictions in ungauged catchments
Climate change and its impacts on river discharge in two climate regions in China
Uncertainty in hydrological signatures
Climate model uncertainty versus conceptual geological uncertainty in hydrological modeling
Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments
Transferring global uncertainty estimates from gauged to ungauged catchments
Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model
Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data
The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models
Flow pathways and nutrient transport mechanisms drive hydrochemical sensitivity to climate change across catchments with different geology and topography
The importance of hydrological uncertainty assessment methods in climate change impact studies
Regional water balance modelling using flow-duration curves with observational uncertainties
Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz
Hydrol. Earth Syst. Sci., 28, 4099–4126, https://doi.org/10.5194/hess-28-4099-2024, https://doi.org/10.5194/hess-28-4099-2024, 2024
Short summary
Short summary
This work examines the impact of temporal and spatial information on the uncertainty estimation of streamflow forecasts. The study emphasizes the importance of data updates and global information for precise uncertainty estimates. We use conformal prediction to show that recent data enhance the estimates, even if only available infrequently. Local data yield reasonable average estimations but fall short for peak-flow events. The use of global data significantly improves these predictions.
Tongtiegang Zhao, Zexin Chen, Yu Tian, Bingyao Zhang, Yu Li, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 28, 3597–3611, https://doi.org/10.5194/hess-28-3597-2024, https://doi.org/10.5194/hess-28-3597-2024, 2024
Short summary
Short summary
The local performance plays a critical part in practical applications of global streamflow reanalysis. This paper develops a decomposition approach to evaluating streamflow analysis at different timescales. The reanalysis is observed to be more effective in characterizing seasonal, annual and multi-annual features than daily, weekly and monthly features. Also, the local performance is shown to be primarily influenced by precipitation seasonality, longitude, mean precipitation and mean slope.
Uwe Ehret and Pankaj Dey
Hydrol. Earth Syst. Sci., 27, 2591–2605, https://doi.org/10.5194/hess-27-2591-2023, https://doi.org/10.5194/hess-27-2591-2023, 2023
Short summary
Short summary
We propose the
c-u-curvemethod to characterize dynamical (time-variable) systems of all kinds.
Uis for uncertainty and expresses how well a system can be predicted in a given period of time.
Cis for complexity and expresses how predictability differs between different periods, i.e. how well predictability itself can be predicted. The method helps to better classify and compare dynamical systems across a wide range of disciplines, thus facilitating scientific collaboration.
Trevor Page, Paul Smith, Keith Beven, Francesca Pianosi, Fanny Sarrazin, Susana Almeida, Liz Holcombe, Jim Freer, Nick Chappell, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 27, 2523–2534, https://doi.org/10.5194/hess-27-2523-2023, https://doi.org/10.5194/hess-27-2523-2023, 2023
Short summary
Short summary
This publication provides an introduction to the CREDIBLE Uncertainty Estimation (CURE) toolbox. CURE offers workflows for a variety of uncertainty estimation methods. One of its most important features is the requirement that all of the assumptions on which a workflow analysis depends be defined. This facilitates communication with potential users of an analysis. An audit trail log is produced automatically from a workflow for future reference.
András Bárdossy and Faizan Anwar
Hydrol. Earth Syst. Sci., 27, 1987–2000, https://doi.org/10.5194/hess-27-1987-2023, https://doi.org/10.5194/hess-27-1987-2023, 2023
Short summary
Short summary
This study demonstrates the fact that the large river flows forecasted by the models show an underestimation that is inversely related to the number of locations where precipitation is recorded, which is independent of the model. The higher the number of points where the amount of precipitation is recorded, the better the estimate of the river flows.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Short summary
Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Yan Liu, Jaime Fernández-Ortega, Matías Mudarra, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5341–5355, https://doi.org/10.5194/hess-26-5341-2022, https://doi.org/10.5194/hess-26-5341-2022, 2022
Short summary
Short summary
We adapt the informal Kling–Gupta efficiency (KGE) with a gamma distribution to apply it as an informal likelihood function in the DiffeRential Evolution Adaptive Metropolis DREAM(ZS) method. Our adapted approach performs as well as the formal likelihood function for exploring posterior distributions of model parameters. The adapted KGE is superior to the formal likelihood function for calibrations combining multiple observations with different lengths, frequencies and units.
Silvana Bolaños Chavarría, Micha Werner, Juan Fernando Salazar, and Teresita Betancur Vargas
Hydrol. Earth Syst. Sci., 26, 4323–4344, https://doi.org/10.5194/hess-26-4323-2022, https://doi.org/10.5194/hess-26-4323-2022, 2022
Short summary
Short summary
Using total water storage (TWS) from GRACE satellites, we assess the reliability of global hydrological and land surface models over a medium-sized tropical basin with a well-developed gauging network. We find the models poorly represent TWS for the monthly series, but they improve in representing seasonality and long-term trends. We conclude that GRACE provides a valuable dataset to benchmark global simulations of TWS change, offering a useful tool to improve global models in tropical basins.
Jared D. Smith, Laurence Lin, Julianne D. Quinn, and Lawrence E. Band
Hydrol. Earth Syst. Sci., 26, 2519–2539, https://doi.org/10.5194/hess-26-2519-2022, https://doi.org/10.5194/hess-26-2519-2022, 2022
Short summary
Short summary
Watershed models are used to simulate streamflow and water quality, and to inform siting and sizing decisions for runoff and nutrient control projects. Data are limited for many watershed processes that are represented in such models, which requires selecting the most important processes to be calibrated. We show that this selection should be based on decision-relevant metrics at the spatial scales of interest for the control projects. This should enable more robust project designs.
Xia Wu, Lucy Marshall, and Ashish Sharma
Hydrol. Earth Syst. Sci., 26, 1203–1221, https://doi.org/10.5194/hess-26-1203-2022, https://doi.org/10.5194/hess-26-1203-2022, 2022
Short summary
Short summary
Decomposing parameter and input errors in model calibration is a considerable challenge. This study transfers the direct estimation of an input error series to their rank estimation and develops a new algorithm, i.e., Bayesian error analysis with reordering (BEAR). In the context of a total suspended solids simulation, two synthetic studies and a real study demonstrate that the BEAR method is effective for improving the input error estimation and water quality model calibration.
Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021, https://doi.org/10.5194/hess-25-4995-2021, 2021
Short summary
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, https://doi.org/10.5194/hess-24-6075-2020, https://doi.org/10.5194/hess-24-6075-2020, 2020
Short summary
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, https://doi.org/10.5194/hess-24-4641-2020, https://doi.org/10.5194/hess-24-4641-2020, 2020
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.
Alicia Correa, Diego Ochoa-Tocachi, and Christian Birkel
Hydrol. Earth Syst. Sci., 23, 5059–5068, https://doi.org/10.5194/hess-23-5059-2019, https://doi.org/10.5194/hess-23-5059-2019, 2019
Short summary
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, https://doi.org/10.5194/hess-23-4219-2019, https://doi.org/10.5194/hess-23-4219-2019, 2019
Short summary
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, https://doi.org/10.5194/hess-23-2147-2019, https://doi.org/10.5194/hess-23-2147-2019, 2019
Short summary
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, https://doi.org/10.5194/hess-23-1103-2019, https://doi.org/10.5194/hess-23-1103-2019, 2019
Short summary
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, https://doi.org/10.5194/hess-22-6087-2018, https://doi.org/10.5194/hess-22-6087-2018, 2018
Short summary
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, https://doi.org/10.5194/hess-22-5947-2018, https://doi.org/10.5194/hess-22-5947-2018, 2018
Short summary
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, https://doi.org/10.5194/hess-22-5021-2018, https://doi.org/10.5194/hess-22-5021-2018, 2018
Léonard Santos, Guillaume Thirel, and Charles Perrin
Hydrol. Earth Syst. Sci., 22, 4583–4591, https://doi.org/10.5194/hess-22-4583-2018, https://doi.org/10.5194/hess-22-4583-2018, 2018
Short summary
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, https://doi.org/10.5194/hess-22-4145-2018, https://doi.org/10.5194/hess-22-4145-2018, 2018
Short summary
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, https://doi.org/10.5194/hess-22-3739-2018, https://doi.org/10.5194/hess-22-3739-2018, 2018
Short summary
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, https://doi.org/10.5194/hess-22-3125-2018, https://doi.org/10.5194/hess-22-3125-2018, 2018
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, https://doi.org/10.5194/hess-22-1775-2018, https://doi.org/10.5194/hess-22-1775-2018, 2018
Short summary
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, https://doi.org/10.5194/hess-21-5315-2017, https://doi.org/10.5194/hess-21-5315-2017, 2017
Short summary
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.
Hadush K. Meresa and Renata J. Romanowicz
Hydrol. Earth Syst. Sci., 21, 4245–4258, https://doi.org/10.5194/hess-21-4245-2017, https://doi.org/10.5194/hess-21-4245-2017, 2017
Short summary
Short summary
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.
Omar Wani, Joost V. L. Beckers, Albrecht H. Weerts, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 21, 4021–4036, https://doi.org/10.5194/hess-21-4021-2017, https://doi.org/10.5194/hess-21-4021-2017, 2017
Short summary
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, https://doi.org/10.5194/hess-21-3325-2017, https://doi.org/10.5194/hess-21-3325-2017, 2017
Short summary
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, https://doi.org/10.5194/hess-21-2301-2017, https://doi.org/10.5194/hess-21-2301-2017, 2017
Short summary
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, https://doi.org/10.5194/hess-21-1279-2017, https://doi.org/10.5194/hess-21-1279-2017, 2017
Short summary
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, https://doi.org/10.5194/hess-21-571-2017, https://doi.org/10.5194/hess-21-571-2017, 2017
Short summary
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, https://doi.org/10.5194/hess-21-65-2017, https://doi.org/10.5194/hess-21-65-2017, 2017
Short summary
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, https://doi.org/10.5194/hess-20-3745-2016, https://doi.org/10.5194/hess-20-3745-2016, 2016
Short summary
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, https://doi.org/10.5194/hess-20-3691-2016, https://doi.org/10.5194/hess-20-3691-2016, 2016
Short summary
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, https://doi.org/10.5194/hess-20-2811-2016, https://doi.org/10.5194/hess-20-2811-2016, 2016
Short summary
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, https://doi.org/10.5194/hess-20-2085-2016, https://doi.org/10.5194/hess-20-2085-2016, 2016
Short summary
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, https://doi.org/10.5194/hess-20-887-2016, https://doi.org/10.5194/hess-20-887-2016, 2016
Short summary
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, https://doi.org/10.5194/hess-19-4609-2015, https://doi.org/10.5194/hess-19-4609-2015, 2015
Short summary
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, https://doi.org/10.5194/hess-19-3951-2015, https://doi.org/10.5194/hess-19-3951-2015, 2015
Short summary
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, https://doi.org/10.5194/hess-19-3891-2015, https://doi.org/10.5194/hess-19-3891-2015, 2015
Short summary
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, https://doi.org/10.5194/hess-19-3181-2015, https://doi.org/10.5194/hess-19-3181-2015, 2015
F. Bourgin, V. Andréassian, C. Perrin, and L. Oudin
Hydrol. Earth Syst. Sci., 19, 2535–2546, https://doi.org/10.5194/hess-19-2535-2015, https://doi.org/10.5194/hess-19-2535-2015, 2015
T. Berezowski, J. Nossent, J. Chormański, and O. Batelaan
Hydrol. Earth Syst. Sci., 19, 1887–1904, https://doi.org/10.5194/hess-19-1887-2015, https://doi.org/10.5194/hess-19-1887-2015, 2015
F. Silvestro, S. Gabellani, R. Rudari, F. Delogu, P. Laiolo, and G. Boni
Hydrol. Earth Syst. Sci., 19, 1727–1751, https://doi.org/10.5194/hess-19-1727-2015, https://doi.org/10.5194/hess-19-1727-2015, 2015
M. C. Demirel, M. J. Booij, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 19, 275–291, https://doi.org/10.5194/hess-19-275-2015, https://doi.org/10.5194/hess-19-275-2015, 2015
Short summary
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, https://doi.org/10.5194/hess-18-5125-2014, https://doi.org/10.5194/hess-18-5125-2014, 2014
Short summary
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, https://doi.org/10.5194/hess-18-3301-2014, https://doi.org/10.5194/hess-18-3301-2014, 2014
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, https://doi.org/10.5194/hess-18-2993-2014, https://doi.org/10.5194/hess-18-2993-2014, 2014
Cited articles
Ahmad, M. M., Ghumman, A. R., and Ahmad, S.: Estimation of Clark’s Instantaneous Unit Hydrograph Parameters and Development of Direct Surface Runoff Hydrograph, Water Resour. Manage., 23, 2417–2435, https://doi.org/10.1007/s11269-008-9388-8, 2009.
Ahmad, M. M., Ghumman, A. R., Ahmad, S., and Hashmi, H. N.: Estimation of a Unique Pair of Nash Model Parameters: An Optimization Approach, Water Resour. Manage., 24, 2971–2989, https://doi.org/10.1007/s11269-010-9590-3, 2010.
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, https://doi.org/10.1029/2005WR004745, 2007.
Balin, D., Lee, H., and Rode, M.: Is point uncertain rainfall likely to have a great impact on distributed complex hydrological modeling?, Water Resour. Res., 46, W11520, https://doi.org/10.1029/2009WR007848, 2010.
Banasik, K., Hejduk, L., and Barszcz, M.: Flood flow consequences of land use changes in a small urban catchment of Warsaw, in: 11th International Conference on Urban Drainage, 31, 10 pp., 2008.
Bárdossy, A. and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89, https://doi.org/10.5194/hess-12-77-2008, 2008.
Barron, O., Pollock, D., and Dawes, W.: Evaluation of catchment contributing areas and storm runoff in flat terrain subject to urbanisation, Hydrol. Earth Syst. Sci., 15, 547–559, https://doi.org/10.5194/hess-15-547-2011, 2011.
Beck, J. L. and Katafygiotis, L. S.: Updating models and their uncertainties. I: Bayesian Statistical Framework, J. Eng. Mech., 124, 455–461, https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(455), 1998.
Berne, A. and Uijlenhoet, R.: Path-averaged rainfall estimation using microwave links: Uncertainty due to spatial rainfall variability, Geophys. Res. Lett., 34, L07403, https://doi.org/10.1029/2007GL029409, 2007.
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.
Bhunya, P. K., Mishra, S. K., and Berndtsson, R.: Simplified Two-Parameter Gamma Distribution for Derivation of Synthetic Unit Hydrograph, J. Hydrol. Eng., 8, 226, https://doi.org/10.1061/(ASCE)1084-0699(2003)8:4(226), 2003.
Bocchiola, D., Diolaiuti, G., Soncini, A., Mihalcea, C., D'Agata, C., Mayer, C., Lambrecht, A., Rosso, R., and Smiraglia, C.: Prediction of future hydrological regimes in poorly gauged high altitude basins: the case study of the upper Indus, Pakistan, Hydrol. Earth Syst. Sci, 15, 2059–2075, https://doi.org/10.5194/hess-15-2059-2011, 2011.
Box, G. E. P. and Cox, D. R.: An Analysis of Transformations, J. Royal Stat. Soc., 26, 211–252, 1964.
Box, G. E. P. and Cox, D. R.: An Analysis of Transformations Revisited, Rebutted, J. Am. Stat. Ass., 77, 209–210, 1982.
Brath, A., Montanari, A., and Moretti, G.: Assessing the effect on flood frequency of land use change via hydrological simulation (with uncertainty), J. Hydrol., 324, 141–153, https://doi.org/10.1016/j.jhydrol.2005.10.001, 2006.
Brooks, S., Gelman, A., Jones, G., and Meng, X. L.: Handbook of Markov Chain Monte Carlo, Chapman & Hall., 2011.
Cawley, G. C., Janacek, G. J., Haylock, M. R., and Dorling, S. R.: Predictive uncertainty in environmental modelling, Neural networks, 20, 537–549, https://doi.org/10.1016/j.neunet.2007.04.024, 2007.
Chatfield, C.: Model uncertainty and forecast accuracy, J. Forecasting, 15, 495–508, https://doi.org/10.1002/(SICI)1099-131X(199612)15:7<495::AID-FOR640>3.0.CO;2-O, 1996.
Chatfield, C.: The Analysis of Time Series, 6th Edn, Chapman & Hall, London., 2003.
Clarke, R. T.: Hydrological prediction in a non-stationary world, Hydrol. Earth Syst. Sci., 11, 408–414, https://doi.org/10.5194/hess-11-408-2007, 2007.
Di Baldassarre, G. and Montanari, A.: Uncertainty in river discharge observations: a quantitative analysis, Hydrol. Earth Syst. Sci., 13, 913–921, https://doi.org/10.5194/hess-13-913-2009, 2009.
Dietz, M. E. and Clausen, J. C.: Stormwater runoff and export changes with development in a traditional and low impact subdivision, J. Environ. Manage., 87, 560–566, https://doi.org/10.1016/j.jenvman.2007.03.026, 2008.
Dietzel, A. and Reichert, P.: Using Statistical Bias Description for Multi-objective Calibration of a Lake Water Quality Model, International Environmental Modelling and Software Society (iEMSs), Ottawa, Canada, 2010.
EEA: Urban sprawl in Europe – The ignored challenge, European Environment Agency – EEA, Copenhagen, 2006.
Franks, S. W.: Integrating models, methods and measurements for prediction in ungauged basins, in Predictions in: Ungauged Basins: PUB Kick-off, 13–21, 2002.
Garthwaite, P. H., Kadane, J. B., and O'Hagan, A.: Statistical Methods for Eliciting Probability Distributions, J. Am. Stat. Ass., 100, 680–701, https://doi.org/10.1198/016214505000000105, 2005.
Gelman, A., Meng, X. L., and Stern, H.: Posterior predictive assessment of model fitness via realized discrepancies, Statist. Sinica, 6, 733–759, 1996.
Gilks, W. R., Richardson, S., and Spiegelhalter, D.: Markov Chain Monte Carlo in Practice, Chapman & Hall., 1995.
Gironás, J., Niemann, J. D., Roesner, L. A., Rodriguez, F., and Andrieu, H.: A morphoclimatic instantaneous unit hydrograph model for urban catchments based on the kinematic wave approximation, J. Hydrol., 377, 317–334, https://doi.org/10.1016/j.jhydrol.2009.08.030, 2009.
Gourley, J. J. and Vieux, B. E.: A method for identifying sources of model uncertainty in rainfall-runoff simulations, J. Hydrol., 327, 68–80, https://doi.org/10.1016/j.jhydrol.2005.11.036, 2006.
Haan, C. T., Barfield, B. J., and Hayes, J. C.: Rainfall-Runoff Estimation in Stormwater Computations, in Design hydrology and sedimentology for small catchments, 37–103, San Diego, Calif., Academic Press, 1994.
Hall, M. J., Zaki, A. F., and Shahin, M. M. A.: Regional analysis using the geomorphoclimatic instantaneous unit hydrograph, Hydrol. Earth Syst. Sci., 5, 93–102, https://doi.org/10.5194/hess-5-93-2001, 2001.
Hawkins, R. H., Ward, T. J., Woodward, D. E., and Van Mullem, J. A.: Curve number hydrology: state of the practice, Report of ASCE/EWRI Task Committee, American Society of Civil Engineers, Reston, Virginia, US, 2009.
Honti, M., Stamm, C., and Reichert, P.: Integrated uncertainty assessment of discharge predictions with a statistical error model, Water Resour. Res., submitted, 2012.
Jain, M. K., Mishra, S. K., and Singh, V. P.: Evaluation of AMC-Dependent SCS-CN-Based Models Using Watershed Characteristics, Water Resour. Manage., 20, 531–552, https://doi.org/10.1007/s11269-006-3086-1, 2006.
Jung, I.-W., Chang, H., and Moradkhani, H.: Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects, Hydrol. Earth Syst. Sci., 15, 617–633, https://doi.org/10.5194/hess-15-617-2011, 2011.
Kapangaziwiri, E. and Hughes, D. A.: Towards revised physically based parameter estimation methods for the Pitman monthly rainfall-runoff model, Water SA, 34, 183–192, 2008.
Kavetski, D., Franks, S. W., and Kuczera, G.: Confronting Input Uncertainty in Environmental Modelling, Am. Geophys. Union, 6, 49–68, 2002.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrologicalmodeling: 1. Theory, Water Resour. Res., 42, W03407, https://doi.org/10.1029/2005WR004368, 2006a.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrologicalmodeling: 2. Application, Water Resour. Res., 42, W03408, https://doi.org/10.1029/2005WR004376, 2006b.
Khaleghi, M. R., Gholami, V., Ghodusi, J., and Hosseini, H.: Efficiency of the geomorphologic instantaneous unit hydrograph method in flood hydrograph simulation, Catena, 87, 163–171, https://doi.org/10.1016/j.catena.2011.04.005, 2011.
Kuczera, G., Kavetski, D., Franks, S. W., and Thyer, M.: Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters, J. Hydrol., 331, 161–177, https://doi.org/10.1016/j.jhydrol.2006.05.010, 2006.
Kumar, R., Chatterjee, C., Singh, R. D., Lohani, A. K., and Kumar, S.: Runoff estimation for an ungauged catchment using geomorphological instantaneous unit hydrograph (GIUH) models, Hydrol. Process., 21, 1829–1840, https://doi.org/10.1002/hyp.6318, 2007.
Leonard, J., Mietton, M., Najib, H., and Gourbesville, P.: Rating curve modelling with Manning's equation to manage instability and improve extrapolation, Hydrolog. Sci. J., 45, 739–750, 2000.
Lutz, W.: Calculation of flood discharges using area characteristics, Universität Karlsruhe, IHW, 24, 235, 1984.
Madsen, H., Wilson, G., and Ammentorp, H. C.: Comparison of different automated strategies for calibration of rainfall-runoff models, J. Hydrol., 261, 48–59, https://doi.org/10.1016/S0022-1694(01)00619-9, 2002.
Majewski, G., Przewo\'{z}niczuk, W., and Kleniewska, M.: Precipitation at the meteorological station in Ursynów WULS-SGGW in 1960–2009, Sci. Rev. Eng. Env. Sci., 2, 3–22, 2010 (in Polnish).
Mantovan, P. and Todini, E.: Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology, J. Hydrol., 330, 368–381, https://doi.org/10.1016/j.jhydrol.2006.04.046, 2006.
McIntyre, N., Lee, H., Wheater, H., Young, A., and Wagener, T.: Ensemble predictions of runoff in ungauged catchments, Water Resour. Res., 41, W12434, https://doi.org/10.1029/2005WR004289, 2005.
McMillan, H., Freer, J., Pappenberger, F., Krueger, T., and Clark, M.: Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions, Hydrol. Process., 24, 1270–1284, https://doi.org/10.1002/hyp.7587, 2010.
McMillan, H., Jackson, B., Kavetski, D., and Woods, R.: Rainfall Uncertainty in Hydrological Modelling: An Evaluation of Multiplicative Error Models, J. Hydrol., 400, 83–94, https://doi.org/10.1016/j.jhydrol.2011.01.026, 2011.
Merz, R. and Blöschl, G.: Regionalisation of catchment model parameters, J. Hydrol., 287, 95–123, https://doi.org/10.1016/j.jhydrol.2003.09.028, 2004.
Mishra, S. K. and Singh, V. P.: Soil Conservation Service Curve Number (SCS-CN) Methodology, 1st Ed., Springer, 2003.
Montanari, A. and Di Baldassarre, G.: Data errors and hydrological modelling: reducing uncertainty in flood risk assessment, Adv. Water Resour., under review, 2012.
Moulin, L., Gaume, E., and Obled, C.: Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations, Hydrol. Earth Syst. Sci., 13, 99–114, https://doi.org/10.5194/hess-13-99-2009, 2009.
Nash, J. E.: The form of instantaneous unit hydrograph, Int. Assoc. Sci. Hydrol., 45, 114–121, 1957.
Nowicka, B. and Soczynska, U.: Application of GIUH and dimensionless hydrograph models in ungauged basins, in: Friends in Hydrology, edited by: Roald, L., Nordseth, K., and Hassel, K. A., Proc. Bolkesjø Symp., 197–203, Int. Ass. Hydrol. Sci. Pubn., 187., 1989.
O'Hagan, A.: Eliciting Expert Beliefs in Substantial Practical Applications, Statistician, 47, 21–35, 1998.
Ott, B. and Uhlenbrook, S.: Quantifying the impact of land-use changes at the event and seasonal time scale using a process-oriented catchment model, Hydrol. Earth Syst. Sci., 2004, 62–78, https://doi.org/10.5194/hess-8-62-2004, 2004.
Oudin, L., Kay, A., Andréassian, V., and Perrin, C.: Are seemingly physically similar catchments truly hydrologically similar?, Water Resour. Res., 46, W11558, https://doi.org/10.1029/2009WR008887, 2010.
Patil, S. and Stieglitz, M.: Hydrologic similarity among catchments under variable flow conditions, Hydrol. Earth Syst. Sci., 15, 989–997, https://doi.org/10.5194/hess-15-989-2011, 2011.
Quilbé, R., Rousseau, A. N., Moquet, J.-S., Savary, S., Ricard, S., and Garbouj, M. S.: Hydrological responses of a watershed to historical land use evolution and future land use scenarios under climate change conditions, Hydrol. Earth Syst. Sci., 12, 101–110, https://doi.org/10.5194/hess-12-101-2008, 2008.
R Development Core Team: R: A language and environment for statistical computing, available at: http://www.R-project.org, last access: 1 March 2012, 2011.
Ramos, M.-H., Mathevet, T., Thielen, J., and Pappenberger, F.: Communicating uncertainty in hydro-meteorological forecasts: mission impossible?, Meteorol. Appl., 17, 223–235, https://doi.org/10.1002/met.202, 2010.
Rao, R. A., Delleur, J. W., and Sarma, S. P.: Conceptual Hydrologic Models for Urbanizing Basins, J. Hydraulic Div., HY7, 1972.
Reichert, P. and Mieleitner, J.: Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters, Water Resour. Res., 45, W10402, https://doi.org/10.1029/2009WR007814, 2009.
Reichert, P. and Schuwirth, N.: Linking statistical description of bias to multi-objective model calibration, Water Resour. Res., in review, 2012.
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.: Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009WR008328, 2010.
Renard, B., Kavetski, D., Leblois, M., Thyer, M., Kuczera, G., and Franks, S. W.: Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation, Water Resour. Res., 47, W11516, https://doi.org/10.1029/2011WR010643, 2011.
Rodríguez-Iturbe, I. and Valdés, J. B.: The geomorphologic structure of hydrologic response, Water Resour. Res., 15, 1409–1420, https://doi.org/10.1029/WR015i006p01409, 1979.
Rodr\'iguez-Iturbe, I., Gonzalez-Sanabria, M., and Bras, R. L.: A Geomorphoclimatic Theory of the Instantaneous Unit Hydrograph, Water Resour. Res., 18, 877–886, https://doi.org/10.1029/WR018i004p00877, 1982.
Romanowicz, R. J., Beven, K. J., and Tawn, J. A.: Evaluation of predictive uncertainty in nonlinear hydrological models using a Bayesian Approach, in Statistics for the Environment (2), Water Related Issues, 297–318, Wiley, Chichester., 1994.
Rosso, R. and Rulli, M. C.: An integrated simulation method for flash-flood risk assessment: 2. Effects of changes in land-use under a historical perspective, Hydrol. Earth Syst. Sci., 6, 285–294, https://doi.org/10.5194/hess-6-285-2002, 2002.
Schaefli, B., Harman, C., Sivapalan, M., and Schymanski, S.: HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling, Hydrol. Earth Syst. Sci., 15, 635–646, https://doi.org/10.5194/hess-15-635-2011, 2011.
Scholten, L., Scheidegger, A., Maurer, M., and Reichert, P.: Combining quantitative expert knowledge and local data for enhanced service life modeling of water pipes under lacking data, in preparation, 2012.
Seibert, J. and Beven, K. J.: Gauging the ungauged basin: how many discharge measurements are needed?, Hydrol. Earth Syst. Sci., 13, 883–892, https://doi.org/10.5194/hess-13-883-2009, 2009.
Shepherd, J. M.: A review of current investigations of urban-induced rainfall and recommendations for the future, Earth Interact., 9, 1–27, https://doi.org/10.1175/EI156.1, 2005.
Sikorska, A. and Banasik, K.: Parameter identification of a conceptual rainfall-runoff model for a small urban catchment, Ann. Warsaw Univ. of Life Sci. – SGGW, Land Reclam, 42, 279–293, 2010.
Singh, S. K.: Identifying Representative Parameters of IUH, J. Irrig. Drain Eng., 133, 602, https://doi.org/10.1061/(ASCE)0733-9437(2007)133:6(602), 2007.
Sivapalan, M.: Prediction in ungauged basins: a grand challenge for theoretical hydrology, Hydrol. Process., 17, 3163–3170, https://doi.org/10.1002/hyp.5155, 2003.
Sivapalan, M., Takeuchi, K., Franks, S. W., Gupta, V. K., Karambiri, H., Lakshmi, V., Liang, X., McDonnell, J. J., Mendiondo, E. M., O'connell, P. E., Oki, T., Pomeroy, J. W., Schertzer, D., Uhlenbrook, S., and Zehe, E.: IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences, Hydrol. Sci. J., 48, 857–880, 2003.
Sivia, D. S. and Skilling, J.: Data analysis: a Bayesian tutorial, Oxford University Press, 2006.
Sorooshian, S. and Dracup, J. A.: Stochastic parameter estimation procedures for hydrologic rainfall-runoff models: Correlated and heteroscedastic error cases, Water Resour. Res., 16, 430–442, https://doi.org/10.1029/WR016i002p00430, 1980.
Soulis, K. X., Valiantzas, J. D., Dercas, N., and Londra, P. A.: Analysis of the runoff generation mechanism for the investigation of the SCS-CN method applicability to a partialarea experimental watershed, Hydrol. Earth Syst. Sci., 13, 605–615, https://doi.org/10.5194/hess-13-605-2009, 2009.
Tarboton, D. G.: Fractal river networks, Horton's laws and Tokunaga cyclicity, J. Hydrol., 187, 105–117, https://doi.org/10.1016/S0022-1694(96)03089-2, 1996.
USDA-SCS: Urban hydrology for small watersheds, Technical Release 55, Soil Conservation Service, USDA, Engineering Division, Washington DC, 1986.
USDA-SCS: Engineering field manual, Chapter 2, Estimating runoff and peak discharges, Soil Conservation Service, USDA, Washington DC, 1989.
Valdés, J. B., Fiallo, Y., and Rodríguez-Iturbe, I.: A rainfall-runoff analysis of the geomorphologic IUH, Water Resour. Res., 15, 1421–1434, https://doi.org/10.1029/WR015i006p01421, 1979.
Vihola, M.: Robust adaptive Metropolis algorithm with coerced acceptance rate, Stat. Comput., 1–12, https://doi.org/10.1007/s11222-011-9269-5, 2011.
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., and Verstraten, J. M.: Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, https://doi.org/10.1029/2004WR003059, 2005.
Vrugt, J. A., Braak, C. J. F., Gupta, H. V., and Robinson, B. A.: Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?, Stoch. Env. Res. Risk A., 23, 1011–1026, https://doi.org/10.1007/s00477-008-0274-y, 2008a.
Vrugt, J. A., ter Braak, C. J. F., Clark, M. P., Hyman, J. M., and Robinson, B. A.: Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resour. Res., 44, W00B09, https://doi.org/10.1029/2007WR006720, 2008b.
Wagener, T. and Gupta, H. V.: Model identification for hydrological forecasting under uncertainty, Stoch. Environ. Res. Ris. Assess., 19, 378–387, https://doi.org/10.1007/s00477-005-0006-5, 2005.
Walker, S. E., Banasik, K., Mitchell, J. K., Nothcott, W. J., Yuan, Y., and Jiang, N.: Applicability of the SCS curve number method to tile-drained watersheds., Ann. Warsaw Univ. of Life Sci. – SGGW, Land Reclam., 30, 3–14, 2000.
Wang, Z., Schleiss, M., Jaffrain, J., Berne, A., and Rieckermann, J.: Identification of dry and rainy periods using telecommunication microwave links, Atmos. Meas. Tech., submitted, 2012.
WAU: Hydrologic documentation of the Sluzew Creek at Przyczolkowa gauge station (in Polish), Internal Report (led by K. Banasik), Warsaw Agricultural University – SGGW, Dept. of Water Engineering and Environmental Restoration, Warsaw., 2002.
Winkler, R. L.: The assessment of prior distributions in Bayesian analysis, J. Am. Stat. Ass., 62, 776–800, 1967.
Yang, J., Reichert, P., Abbaspour, K. C., and Yang, H.: Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference, J. Hydrol., 340, 167–182, https://doi.org/10.1016/j.jhydrol.2007.04.006, 2007.
Yang, J., Reichert, P., Abbaspour, K., Xia, J., and Yang, H.: Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China, J. Hydrol., 358, 1–23, https://doi.org/10.1016/j.jhydrol.2008.05.012, 2008.
Zhang, E., Feissel, P., and Antoni, J.: A comprehensive Bayesian approach for model updating and quantification of modeling errors, Probabilist. Eng. Mech., 26, 550–560, https://doi.org/10.1016/j.probengmech.2011.07.001, 2011.
