- Articles & preprints
- Submission
- Policies
- Peer review
- Editorial board
- About
- EGU publications
- Manuscript tracking
Journal cover
Journal topic
Hydrology and Earth System Sciences
An interactive open-access journal of the European Geosciences Union
Journal topic
- Articles & preprints
- Submission
- Policies
- Peer review
- Editorial board
- About
- EGU publications
- Manuscript tracking
Journal metrics
IF5.153
IF 5-year
5.460
5.460
CiteScore
7.8
7.8
SNIP1.623
IPP4.91
SJR2.092
Scimago H
index123
index123
h5-index65
Abstracted/indexed
Abstracted/indexed
Volume 21, issue 1
Hydrol. Earth Syst. Sci., 21, 571–588, 2017
https://doi.org/10.5194/hess-21-571-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-571-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 571–588, 2017
https://doi.org/10.5194/hess-21-571-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-571-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article 27 Jan 2017
Research article | 27 Jan 2017
Quantifying uncertainty on sediment loads using bootstrap confidence intervals
Johanna I. F. Slaets et al.
Related authors
Johanna I. F. Slaets, Petra Schmitter, Thomas Hilger, Tran Duc Vien, and Georg Cadisch
Biogeosciences, 13, 3267–3281, https://doi.org/10.5194/bg-13-3267-2016, https://doi.org/10.5194/bg-13-3267-2016, 2016
Short summary
Short summary
Maize production on steep slopes causes erosion. Where the eroded material ends up is not well understood. This study assessed transport of sediment in mountainous Vietnam, where maize is cultivated on slopes and rice is cultivated in valleys. Per year, 64 tons per hectare of sediments are brought into the rice fields and 28 tons of those are deposited there. The sediment fraction captured by the paddies is mostly sandy, while fertile silt and clay are exported. Upland erosion thus impacts rice production.
Moritz Laub, Michael Scott Demyan, Yvonne Funkuin Nkwain, Sergey Blagodatsky, Thomas Kätterer, Hans-Peter Piepho, and Georg Cadisch
Biogeosciences, 17, 1393–1413, https://doi.org/10.5194/bg-17-1393-2020, https://doi.org/10.5194/bg-17-1393-2020, 2020
Short summary
Short summary
Loss of soil carbon to the atmosphere represents a global challenge. We tested an innovative way to reduce the high uncertainty related to turnover of carbon stored in soils. With the use of infrared spectra of soils from model bare fallow systems, we were able to better assess the current state of soil carbon and predict its behavior in overdecadal time spans. In agreement with recent studies, carbon turnover seems faster than earlier assumed, with potential for high loss under mismanagement.
Johanna I. F. Slaets, Petra Schmitter, Thomas Hilger, Tran Duc Vien, and Georg Cadisch
Biogeosciences, 13, 3267–3281, https://doi.org/10.5194/bg-13-3267-2016, https://doi.org/10.5194/bg-13-3267-2016, 2016
Short summary
Short summary
Maize production on steep slopes causes erosion. Where the eroded material ends up is not well understood. This study assessed transport of sediment in mountainous Vietnam, where maize is cultivated on slopes and rice is cultivated in valleys. Per year, 64 tons per hectare of sediments are brought into the rice fields and 28 tons of those are deposited there. The sediment fraction captured by the paddies is mostly sandy, while fertile silt and clay are exported. Upland erosion thus impacts rice production.
M. S. Demyan, F. Rasche, M. Schütt, N. Smirnova, E. Schulz, and G. Cadisch
Biogeosciences, 10, 2897–2913, https://doi.org/10.5194/bg-10-2897-2013, https://doi.org/10.5194/bg-10-2897-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysis
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
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
Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures
From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation
Considering rating curve uncertainty in water level predictions
Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models
The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments
An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources
Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling
Adaptive correction of deterministic models to produce probabilistic forecasts
Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models
Hydrological education and training needs in sub-Saharan Africa: requirements, constraints and progress
Technical Note: Analytical sensitivity analysis of a two parameter recursive digital baseflow separation filter
Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother
Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: a case study of SWAT model applied to Three Gorges Reservoir Region, China
Impact of the European Russia drought in 2010 on the Caspian Sea level
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.
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
G. Seiller and F. Anctil
Hydrol. Earth Syst. Sci., 18, 2033–2047, https://doi.org/10.5194/hess-18-2033-2014, https://doi.org/10.5194/hess-18-2033-2014, 2014
J. D. Herman, J. B. Kollat, P. M. Reed, and T. Wagener
Hydrol. Earth Syst. Sci., 17, 5109–5125, https://doi.org/10.5194/hess-17-5109-2013, https://doi.org/10.5194/hess-17-5109-2013, 2013
M. Sadegh and J. A. Vrugt
Hydrol. Earth Syst. Sci., 17, 4831–4850, https://doi.org/10.5194/hess-17-4831-2013, https://doi.org/10.5194/hess-17-4831-2013, 2013
A. E. Sikorska, A. Scheidegger, K. Banasik, and J. Rieckermann
Hydrol. Earth Syst. Sci., 17, 4415–4427, https://doi.org/10.5194/hess-17-4415-2013, https://doi.org/10.5194/hess-17-4415-2013, 2013
J. D. Herman, J. B. Kollat, P. M. Reed, and T. Wagener
Hydrol. Earth Syst. Sci., 17, 2893–2903, https://doi.org/10.5194/hess-17-2893-2013, https://doi.org/10.5194/hess-17-2893-2013, 2013
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, https://doi.org/10.5194/hess-17-2613-2013, https://doi.org/10.5194/hess-17-2613-2013, 2013
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, https://doi.org/10.5194/hess-17-565-2013, https://doi.org/10.5194/hess-17-565-2013, 2013
L. Loosvelt, H. Vernieuwe, V. R. N. Pauwels, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 461–478, https://doi.org/10.5194/hess-17-461-2013, https://doi.org/10.5194/hess-17-461-2013, 2013
P. J. Smith, K. J. Beven, A. H. Weerts, and D. Leedal
Hydrol. Earth Syst. Sci., 16, 2783–2799, https://doi.org/10.5194/hess-16-2783-2012, https://doi.org/10.5194/hess-16-2783-2012, 2012
A. E. Sikorska, A. Scheidegger, K. Banasik, and J. Rieckermann
Hydrol. Earth Syst. Sci., 16, 1221–1236, https://doi.org/10.5194/hess-16-1221-2012, https://doi.org/10.5194/hess-16-1221-2012, 2012
D. A. Hughes
Hydrol. Earth Syst. Sci., 16, 861–871, https://doi.org/10.5194/hess-16-861-2012, https://doi.org/10.5194/hess-16-861-2012, 2012
K. Eckhardt
Hydrol. Earth Syst. Sci., 16, 451–455, https://doi.org/10.5194/hess-16-451-2012, https://doi.org/10.5194/hess-16-451-2012, 2012
R. T. Bailey and D. Baù
Hydrol. Earth Syst. Sci., 16, 287–304, https://doi.org/10.5194/hess-16-287-2012, https://doi.org/10.5194/hess-16-287-2012, 2012
Z. Y. Shen, L. Chen, and T. Chen
Hydrol. Earth Syst. Sci., 16, 121–132, https://doi.org/10.5194/hess-16-121-2012, https://doi.org/10.5194/hess-16-121-2012, 2012
K. Arpe, S. A. G. Leroy, H. Lahijani, and V. Khan
Hydrol. Earth Syst. Sci., 16, 19–27, https://doi.org/10.5194/hess-16-19-2012, https://doi.org/10.5194/hess-16-19-2012, 2012
Cited articles
ASTM: Standard D3977-97, Standard test methods for determining sediment concentration in water samples, ASTM International, West Conshohocken, PA, 7 pp., 2013.
Berk, R. A.: Statistical learning from a regression perspective, Springer, New York, ISBN-10: 0387775013, 347 pp., 2008.
Burnham, K. P. and Anderson, D. R.: Model selection and multimodel inference: a practical information-theoretic approach, Springer, New York, ISBN-10: 0387953647, 454 pp., 2002.
Clemens, G., Fiedler, S., Cong, N. D., Van Dung, N., Schuler, U., and Stahr, K.: Soil fertility affected by land use history, relief position, and parent material under a tropical climate in NW-Vietnam, Catena, 81, 87–96, 2010.
Duan, N.: Smearing estimate: a nonparametric retransformation method, J. Am. Stat. Assoc., 78, 605–610, 1983.
Ebtehaj, M., Moradkhani, H., and Gupta, H. V.: Improving robustness of hydrologic parameter estimation by the use of moving block bootstrap resampling, Water Resour. Res., 46, W07515, https://doi.org/10.1029/2009WR007981, 2010.
Efron, B. and Tibshirani, R. J.: An introduction to the bootstrap, Chapman & Hall/CRC, Boca Raton, ISBN-13: 9780412042317, 436 pp., 1993.
Ferguson, R. I.: River loads underestimated by rating curves, Water Resour. Res., 22, 74–76, 1986.
Freeman, J. and Modarres, R.: Inverse Box-Cox: the power-normal distribution, Stat. Probabil. Lett., 76, 764–772, 2006.
Gao, P.: Understanding watershed suspended sediment transport, Prog. Phys. Geog., 32, 243–263, 2008.
Hamilton, A. S. and Moore, R. D.: Quantifying uncertainty in streamflow records, Can. Water Resour. J., 37, 3–21, 2012.
Harmel, R. D., Smith, D. R., King, K. W., and Slade, R. M.: Estimating storm discharge and water quality data uncertainty: A software tool for monitoring and modeling applications, Environ. Modell. Softw., 24, 832–842, 2009.
Herschy, R. W.: Streamflow measurement, CRC Press, Boca Raton, ISBN-13: 978-0-415-41342-8, 507 pp., 1995.
Hirsch, R. M., Archfield, S. A., and De Cicco, L. A.: A bootstrap method for estimating uncertainty of water quality trends, Environ. Modell. Softw., 73, 148–166, 2015.
Horowitz, A. J.: Determining annual suspended sediment and sediment-associated trace element and nutrient fluxes, Sci. Total Environ., 400, 315–343, 2008.
Krueger, T., Quinton, J. N., Freer, J., Macleod, C. J., Bilotta, G. S., Brazier, R. E., Butler, P., and Haygarth, P. M.: Uncertainties in data and models to describe event dynamics of agricultural sediment and phosphorus transfer, J. Environ. Qual., 38, 1137–1148, 2009.
Kuhnert, P. M., Henderson, B. L., Lewis, S. E., Bainbridge, Z. T., Wilkinson, S. N., and Brodie, J. E.: Quantifying total suspended sediment export from the Burdekin River catchment using the loads regression estimator tool, Water Resour. Res., 48, W04533, https://doi.org/10.1029/2011WR011080, 2012.
Kulasova, A., Smith, P. J., Beven, K. J., Blazkova, S. D., and Hlavacek, J.: A method of computing uncertain nitrogen and phosphorus loads in a small stream from an agricultural catchment using continuous monitoring data, J. Hydrol., 458–459, 1–8, 2012.
Lahiri, S. N.: Resampling methods for dependent data, Springer, New York, ISBN-13: 978-1-4419-1848-2, 2003.
Lessels, J. S. and Bishop, T. F. A.: Estimating water quality using linear mixed models with stream discharge and turbidity, J. Hydrol., 498, 13–22, 2013.
Mailhot, A., Rousseau, A. N., Talbot, G., Gagnon, P., and Quilbé, R.: A framework to estimate sediment loads using distributions with covariates: Beaurivage River watershed (Québec, Canada), Hydrol. Process., 22, 4971–4985, 2008.
McMillan, H., Krueger, T., and Freer, J.: Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality, Hydrol. Process., 26, 4078–4111, 2012.
Moatar, F. and Meybeck, M.: Compared performances of different algorithms for estimating annual nutrient loads discharged by the eutrophic River Loire, Hydrol. Process., 19, 429–444, 2005.
Nearing, M. A.: Why soil erosion models over-predict small soil losses and under-predict large soil losses, Catena, 32, 15–22, 1998.
Némery, J., Mano, V., Coynel, A., Etcheber, H., Moatar, F., Meybeck, M., Belleudy, P., and Poirel, A.: Carbon and suspended sediment transport in an impounded alpine river (Isère, France), Hydrol. Process., 27, 2498–2508, 2013.
Pagendam, D. E., Kuhnert, P. M., Leeds, W. B., Wikle, C. K., Bartley, R., and Peterson, E. E.: Assimilating catchment processes with monitoring data to estimate sediment loads to the great barrier reef, Environmetrics, 25, 214–229, 2014.
Patterson, H. D. and Thompson, R.: Recovery of inter-block information when block sizes are unequal, Biometrika, 58, 545–554, 1971.
Piepho, H. P.: Data transformation in statistical analysis of field trials with changing treatment variance, Agron. J., 101, 865–869, https://doi.org/10.2134/agronj2008.0226x, 2009.
Piepho, H. P., Möhring, J., Pflugfelder, M., Hermann, W., and Williams, E. R.: Problems in parameter estimation for power and AR(1) models of spatial correlation in designed field experiments, Communications in Biometry and Crop Science, 10, 3–16, 2015.
Rustomji, P. and Wilkinson, S. N.: Applying bootstrap resampling to quantify uncertainty in fluvial suspended sediment loads estimated using rating curves, Water Resour. Res., 44, W09435, https://doi.org/10.1029/2007WR006088, 2008.
Sauer, V. B. and Meyer, R. W.: Determination of error in individual discharge measurements, US Department of the Interior, US Geological Survey, Washington, DC, 21 pp., 1992.
Schmitter, P., Fröhlich, H. L., Dercon, G., Hilger, T., Huu Thanh, N., Lam, N. T., Vien, T. D., and Cadisch, G.: Redistribution of carbon and nitrogen through irrigation in intensively cultivated tropical mountainous watersheds, Biogeochemistry, 109, 133–150, 2012.
Selle, B. and Hannah, M.: A bootstrap approach to assess parameter uncertainty in simple catchment models, Environ. Modell. Softw., 25, 919–926, 2010.
Slaets, J. I. F., Schmitter, P., Hilger, T., Lamers, M., Piepho, H. P., Vien, T. D., and Cadisch, G.: A turbidity-based method to continuously monitor sediment, carbon and nitrogen flows in mountainous watersheds, J. Hydrol., 513, 45–57, 2014.
Slaets, J. I. F., Piepho, H. P., Schmitter, P., Hilger, T., and Cadisch, G.: Quantifying uncertainty on sediment loads using bootstrap confidence intervals, available at: https://www.uni-hohenheim.de/bioinformatik/beratung/index.htm (last access: 21 January 2017), 2016.
Smith, C. and Croke, B.: Sources of uncertainty in estimating suspended sediment load, IAHS-AISH Publication, 292, 136–143, 2005.
Tomkins, K. M.: Uncertainty in streamflow rating curves: Methods, controls, and consequences, Hydrol. Processes, 28, 464–481, 2014.
Verbeke, G. and Molenberghs, G.: Linear mixed models for longitudinal data, Springer, New York, ISBN-10: 1441903003, 484 pp., 2009.
Vigiak, O. and Bende-Michl, U.: Estimating bootstrap and Bayesian prediction intervals for constituent load rating curves, Water Resour. Res., 49, 8565–8578, 2013.
Walling, D. E.: Limitations of the rating curve technique for estimating suspended sediment loads, with particular reference to British rivers, IAHS-AISH Publication, 122, 34–118, 1977.
Walling, D. E. and Webb, B. W.: The reliability of rating curve estimates of suspended sediment yield: Some further comments, in: Variability in stream erosion and sediment transport, edited by: Olive, L. J., Loughran, R. J., and Kesby, J. A., IAHS Publ., 269–279, 1988.
Walling, D. E. and Webb, B. W.: Erosion and sediment yield: A global overview, IAHS-AISH Publication, 236, 3–19, 1996.
Wang, Y. G., Kuhnert, P., and Henderson, B.: Load estimation with uncertainties from opportunistic sampling data – A semiparametric approach, J. Hydrol., 396, 148–157, 2011.
Search articles
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.
Determining measures of uncertainty on loads is not trivial, as a load is a product of...
Hydrology and Earth System Sciences
An interactive open-access journal of the European Geosciences Union
All site content, except where otherwise noted, is licensed under the Creative Commons Attribution 4.0 License.