Articles | Volume 17, issue 11
08 Nov 2013
Research article | 08 Nov 2013
Considering rating curve uncertainty in water level predictions
A. E. Sikorska et al.
No articles found.
Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci. Discuss.,
Preprint under review for HESSShort summary
For stream flow predictions in hydrology, commonly two types of models are used: deep learning models (high predictive performance) and ODE-based conceptual hydrologic models (fully interpretable, encoding scientific assumptions). We introduce hydrologic Neural ODE models that fuse both approaches and have their benefits: We obtain state-of the-art predictive performance and gain insights into dynamics of model processes and states. We demonstrate the approach on a large real-world data set.
Anna Špačková, Vojtěch Bareš, Martin Fencl, Marc Schleiss, Joël Jaffrain, Alexis Berne, and Jörg Rieckermann
Earth Syst. Sci. Data, 13, 4219–4240,Short summary
An original dataset of microwave signal attenuation and rainfall variables was collected during 1-year-long field campaign. The monitored 38 GHz dual-polarized commercial microwave link with a short sampling resolution (4 s) was accompanied by five disdrometers and three rain gauges along its path. Antenna radomes were temporarily shielded for approximately half of the campaign period to investigate antenna wetting impacts.
Mark Honti, Nele Schuwirth, Jörg Rieckermann, and Christian Stamm
Hydrol. Earth Syst. Sci., 21, 1593–1609,Short summary
We present a new catchment model that covers most major pollutants and is suitable for uncertainty analysis. The effects of climate change, population dynamics, socio-economic development, and management strategies on water quality are demonstrated in a small catchment in the Swiss Plateau. Models and data are still the largest sources of uncertainty for some water quality parameters. Uncertainty assessment helps to select robust management and focus research and monitoring efforts.
Martin Fencl, Michal Dohnal, Jörg Rieckermann, and Vojtěch Bareš
Hydrol. Earth Syst. Sci., 21, 617–634,Short summary
Commercial microwave links (CMLs) can provide rainfall observations with high space–time resolution. Unfortunately, CML rainfall estimates are often biased because we lack detailed information on the processes that attenuate the transmitted microwaves. We suggest removing the bias by continuously adjusting CMLs to cumulative data from rain gauges (RGs), which can be remote from the CMLs. Our approach practically eliminates the bias, which we demonstrate on unique data from several CMLs and RGs.
João P. Leitão, Matthew Moy de Vitry, Andreas Scheidegger, and Jörg Rieckermann
Hydrol. Earth Syst. Sci., 20, 1637–1653,Short summary
Precise and detailed DEMs are essential to accurately predict overland flow in urban areas. In this this study we evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs. In addition, we compared the best quality UAV DEM to a conventional lidar-based DEM; the two DEMs are of comparable quality.
P. Tokarczyk, J. P. Leitao, J. Rieckermann, K. Schindler, and F. Blumensaat
Hydrol. Earth Syst. Sci., 19, 4215–4228,Short summary
We investigate for the first time the possibility of deriving high-resolution imperviousness maps for urban areas from UAV imagery and using this information as input for urban drainage models. We show that imperviousness maps generated using UAV imagery processed with modern classification methods achieve accuracy comparable with standard, off-the-shelf aerial imagery. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications.
L. Hejduk, A. Hejduk, and K. Banasik
Proc. IAHS, 370, 167–170,Short summary
This paper presents an attempt for application of the CN method for snowmelt runoff events based on measured snow water content, rainfall and runoff in a small lowland catchment. The CN calculated from measured snowmelt-runoff events show a large variation from 64 to 94.8 with the average value calculated as 79.3. The comparison of CN and MP suggests decreasing the relation in a similar way to decreasing the CN and rainfall relation already investigated in this catchment.
M. Honti, A. Scheidegger, and C. Stamm
Hydrol. Earth Syst. Sci., 18, 3301–3317,
D. Del Giudice, M. Honti, A. Scheidegger, C. Albert, P. Reichert, and J. Rieckermann
Hydrol. Earth Syst. Sci., 17, 4209–4225,
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysisGuidance on evaluating parametric model uncertainty at decision-relevant scalesQuantifying input uncertainty in the calibration of water quality models: reordering errors via the secant methodSequential data assimilation for real-time probabilistic flood inundation mappingBenchmarking global hydrological and land surface models against GRACE in a medium-size tropical basinKey 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)The critical role of uncertainty in projections of hydrological extremesResidual 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 computationTechnical 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 modelling
Jared D. Smith, Laurence Lin, Julianne D. Quinn, and Lawrence E. Band
Hydrol. Earth Syst. Sci., 26, 2519–2539,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,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,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.
Silvana Bolaños Chavarría, Micha Werner, and Juan Fernando Salazar
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript accepted for HESSShort 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.
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.
Hadush K. Meresa and Renata J. Romanowicz
Hydrol. Earth Syst. Sci., 21, 4245–4258,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,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,
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,
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.
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.
Barszcz, M.: Forecast of probably flows caused by heavy rainfall on urbanized drainage basin of Słu\.zew Stream, Scientific Review Engineering and Environmental Sciences, 4, 3–21, 2009.
Beck, M. B.: Principles of Modelling, Water Sci. Technol., 24, 1–8, IWA Publishing, 1991.
Blöschl, G. and Montanari, A.: Climate change impacts – throwing the dice?, Hydrol. Process., 24, 374–381, https://doi.org/10.1002/hyp.7574, 2010.
Chivers, C.: MHadaptive: General Markov Chain Monte Carlo for Bayesian Inference using adaptive Metropolis-Hastings sampling. R package, available at: http://www.R-project.org (last access: 4 March 2013), 2012.
Coccia, G. and Todini, E.: Recent developments in predictive uncertainty assessment based on the model conditional processor approach, Hydrol. Earth Syst. Sci., 15, 3253–3274, https://doi.org/10.5194/hess-15-3253-2011, 2011.
Coutu, S., Del Giudice, D., Rossi, L., and Barry, D.: Parsimonious hydrological modeling of urban sewer and river catchments, J. Hydrol., 464–465, 477-484, https://doi.org/10.1016/j.jhydrol.2012.07.039, 2012.
Del Giudice, D., Honti, M., Scheidegger, A., Albert, C., Reichert, P., and Rieckermann, J.: Improving uncertainty estimation in urban hydrological modeling by statistically describing bias, Hydrol. Earth Syst. Sci., 17, 4209–4225, https://doi.org/10.5194/hess-17-4209-2013, 2013.
Deletic, A., Dotto, C. B. S., McCarthy, D. T., Kleidorfer, M., Freni, G., Mannina, G., Uhl, M., Henrichs, M., Fletcher, T. D., Rauch, W., Bertrand-Krajewski, J. L., and Tait, S.: Assessing uncertainties in urban drainage models, Phys. Chem. Earth 42–44, 3–10, https://doi.org/10.1016/j.pce.2011.04.007, 2012.
Di Baldassarre, G. and Claps, P.: A hydraulic study on the applicability of flood rating curves, Hydrol. Res., 42, 10–19, https://doi.org/10.2166/nh.2010.098, 2011.
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.
Di Baldassarre, G. and Uhlenbrook, S.: Is the current flood of data enough? A treatise on research needs for the improvement of flood modelling, Hydrol. Process., 26, 153–158, https://doi.org/10.1002/hyp.8226, 2012.
Di Baldassarre, G., Laio, F., and Montanari, A.: Effect of observation errors on the uncertainty of design floods, Phys. Chem. Earth, 42–44, 85–90, https://doi.org/10.1016/j.pce.2011.05.001, 2012.
Domeneghetti, A., Castellarin, A., and Brath, A.: Assessing rating-curve uncertainty and its effects on hydraulic model calibration, Hydrol. Earth Syst. Sci., 16, 1191–1202, https://doi.org/10.5194/hess-16-1191-2012, 2012.
Dotto, C. B. S, Mannina, G., Kleidorfer, M., Vezzaro, L., Henrichs, M., McCarthy, D. T., Freni, G., Rauch, W., and Deletic, A.: Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling, Water Res., 46, 2545–2558, 2012.
Dottori, F., Martina, M. L. V., and Todini, E.: A dynamic rating curve approach to indirect discharge measurement, Hydrol. Earth Syst. Sci., 13, 847–863, https://doi.org/10.5194/hess-13-847-2009, 2009.
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B.: Bayesian Data Analysis, 2nd Edn., Chapman & Hall, Boca Raton, Florida, 2003.
Haario, H., Saksman, E., and Tamminen, J.: An adaptive Metropolis algorithm, Bernoulli, 7, 223–242, 2001.
Harrell, F. E.: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer Series in Statistics), Springer, New York, 2010.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res., 42, W03407, https://doi.org/10.1029/2005WR004368, 2006.
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.
Le Coz, J.: A literature review of methods for estimating the uncertainty associated with stage-discharge relations, WMO, 2012.
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., Clark, M., Kavetski, D., and Woods, R.: Rainfall Uncertainty in Hydrological Modelling: An Evaluation of Multiplicative Error Models/Mendeley, J. Hydrol., 400, 83–94, https://doi.org/10.1016/j.jhydrol.2011.01.026, 2011.
Mishra, S. K. and Singh, V. P.: Soil Conservation Service Curve Number (SCS-CN) Methodology, 1st Edn., Springer, Netherlands, 2010.
Montanari, A.: What do we mean by "uncertainty"? The need for a consistent wording about uncertainty assessment in hydrology, Hydrol. Process., 21, 841–845, https://doi.org/10.1002/hyp.6623, 2007.
Montanari, A. and Koutsoyiannis D.: A blueprint for processbased modeling of uncertain hydrological systems, Water Resour. Res., 48, W09555, https://doi.org/10.1029/2011WR011412, 2012.
Nash, J. E.: The form of instantaneous unit hydrograph, Int. Assoc. Sci. Hydrol., 45, 114–121, 1957.
Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models part I : A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Pappenberger, F., Matgen, P., Beven, K. J., Henry, J. B., Pfister, L., and De Fraipont, P.: Influence of uncertain boundary conditions and model structure on flood inundation predictions, Adv. Water Resour., 29, 1430–1449, 2006.
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.
R Development Core Team: R: A language and environment for statistical computing, available at: http://www.R-project.org (last access: 4 March 2013), 2011.
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 bias description to multiobjective model calibration, Water Resour. Res., 48, W09543, https://doi.org/10.1029/2011WR011391, 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.
Sadegh, M. and Vrugt, J. A.: Approximate Bayesian Computation in hydrologic modeling: equifinality of formal and informal approaches, Hydrol. Earth Syst. Sci. Discuss., 10, 4739–4797, https://doi.org/10.5194/hessd-10-4739-2013, 2013.
Sauer, V. B. and Meyer, R. W.: Determination of error in individual discharge measurements, USGS Open File Report, USGS, Washington, DC, 1992.
Seibert, J.: Conceptual runoff models – fiction or representation of reality? Acta Univ. Ups., Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 436, 52 pp., Uppsala, ISBN 91-554-4402-4, 1999.
Seibert, J. and McDonnell, J.: Gauging the Ungauged Basin: The Relative Value of Soft and Hard Data, J. Hydrol. Eng., https://doi.org/10.1061/(ASCE)HE.1943-5584.0000861, 2013.
Sikorska, A. E.: Interactive comment on "Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models" by A. E. Sikorska et al., 2012, Hydrol. Earth Syst. Sci. Discuss., 8, C6284–C6310, 2012.
Sikorska, A. E.: Uncertainty analysis of rainfall-runoff predictions for a small urbanized basin, doctoral thesis, Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences – SGGW, 2013.
Sikorska, A. and Banasik, K.: Parameter identification of a conceptual rainfall-runoff model for a small urban catchment, Annals of Warsaw University of Life Sciences-SGGW, Land Reclamation, 42, 279–293, 2010.
Sikorska, A. E., Scheidegger, A., Banasik, K., and Rieckermann, J.: Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models, Hydrol. Earth Syst. Sci., 16, 1221–1236, https://doi.org/10.5194/hess-16-1221-2012, 2012.
Uhlenbrook, S., Seibert, J., Leibundgut, C., and Allan Rodhe, A.: Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure, Hydrolog. Sci. J., 44, 779–797, https://doi.org/10.1080/02626669909492273, 1999.
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, 2008.
Wagener, T. and Gupta, H. V.: Model identification for hydrological forecasting under uncertainty, Stoch. Env. Res. Risk A., 19, 378–387, https://doi.org/10.1007/s00477-005-0006-5, 2005.
Wagener, T. and Montanari, A.: Convergence of approaches toward reducing uncertainty in predictions in ungauged basins, Water Resour. Res., 47, W06301, https://doi.org/10.1029/2010WR009469, 2011.
Wagener, T., Gupta, H. V., and Sorooshian, S.: Stochastic formulation of a conceptual hydrological model, in: Hydrology: science and practice for the 21st century, Proceedings of the British Hydrological Society International Conference, Imperial College, London, 12–16 July 2004, 398–405, 2004.
Wang, Q. J. and Robertson, D. E.: Multisite probabilistic forecasting of seasonal flows for streams with zero values occurences, Water Resour. Res., 47, W02546, https://doi.org/10.1029/2010WR009333, 2011.
Westerberg, I., Guerrero, J.-L., Seibert, J., Beven, K. J., and Halldin, S.: Stage-discharge uncertainty derived with a non-stationary rating curve in the Choluteca River, Honduras, Hydrol. Process., 25, 603–613, https://doi.org/0.1002/hyp.7848, 2011.
WMO: Guide to Hydrological Practice, Volume I, Hydrology – From Measurement to Hydrological Information, 6th Edn., World Meteorological Organisation, Geneva, Switzerland, 2008.
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, 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.