Articles | Volume 19, issue 1
https://doi.org/10.5194/hess-19-275-2015
© Author(s) 2015. 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-19-275-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Jan 2015
Research article |
| 16 Jan 2015
The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands
current address: Portland State University, Department of Civil & Environmental Engineering, 1930 S. W. 4th Avenue, Suite 200, Portland, OR 97201, USA
M. J. Booij
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands
A. Y. Hoekstra
Water Engineering and Management, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands
Related authors
E. Bontempo, M. C. Demirel, C. Corsini, F. Martins, and D. Valeriano
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 201–206, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, 2020
Kamal Ahmed, Dhanapala A. Sachindra, Shamsuddin Shahid, Mehmet C. Demirel, and Eun-Sung Chung
Hydrol. Earth Syst. Sci., 23, 4803–4824, https://doi.org/10.5194/hess-23-4803-2019, https://doi.org/10.5194/hess-23-4803-2019, 2019
Short summary
Short summary
This study evaluated the performance of 36 CMIP5 GCMs in simulating seasonal precipitation and maximum and minimum temperature over Pakistan using spatial metrics (SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency) for the period 1961–2005. NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 were identified as the most suitable GCMs for simulating all three climate variables over Pakistan.
Julian Koch, Mehmet Cüneyd Demirel, and Simon Stisen
Geosci. Model Dev., 11, 1873–1886, https://doi.org/10.5194/gmd-11-1873-2018, https://doi.org/10.5194/gmd-11-1873-2018, 2018
Short summary
Short summary
Our work addresses a key challenge in earth system modelling: how to optimally exploit the information contained in satellite remote sensing observations in the calibration of such models. For this we thoroughly test a number of measures that quantify the fit between an observed and a simulated spatial pattern. We acknowledge the difficulties associated with such a comparison and suggest using measures that regard multiple aspects of spatial information, i.e. magnitude and variability.
Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, and Simon Stisen
Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, https://doi.org/10.5194/hess-22-1299-2018, 2018
Short summary
Short summary
Satellite data offer great opportunities to improve spatial model predictions by means of spatially oriented model evaluations. In this study, satellite images are used to observe spatial patterns of evapotranspiration at the land surface. These spatial patterns are utilized in combination with streamflow observations in a model calibration framework including a novel spatial performance metric tailored to target the spatial pattern performance of a catchment-scale hydrological model.
Oleksandr Mialyk, Joep F. Schyns, Martijn J. Booij, and Rick J. Hogeboom
Hydrol. Earth Syst. Sci., 26, 923–940, https://doi.org/10.5194/hess-26-923-2022, https://doi.org/10.5194/hess-26-923-2022, 2022
Short summary
Short summary
As the global demand for crops is increasing, it is vital to understand spatial and temporal patterns of crop water footprints (WFs). Previous studies looked into spatial patterns but not into temporal ones. Here, we present a new process-based gridded crop model to simulate WFs and apply it for maize in 1986–2016. We show that despite the average unit WF reduction (−35 %), the global WF of maize production has increased (+50 %), which might harm ecosystems and human livelihoods in some regions.
E. Bontempo, M. C. Demirel, C. Corsini, F. Martins, and D. Valeriano
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 201–206, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, 2020
Chao Gao, Martijn J. Booij, and Yue-Ping Xu
Hydrol. Earth Syst. Sci., 24, 3251–3269, https://doi.org/10.5194/hess-24-3251-2020, https://doi.org/10.5194/hess-24-3251-2020, 2020
Short summary
Short summary
This paper studies the impact of climate change on high and low flows and quantifies the contribution of uncertainty sources from representative concentration pathways (RCPs), global climate models (GCMs) and internal climate variability in extreme flows. Internal climate variability was reflected in a stochastic rainfall model. The results show the importance of internal climate variability and GCM uncertainty in high flows and GCM and RCP uncertainty in low flows especially for the far future.
Hatem Chouchane, Maarten S. Krol, and Arjen Y. Hoekstra
Hydrol. Earth Syst. Sci., 24, 3015–3031, https://doi.org/10.5194/hess-24-3015-2020, https://doi.org/10.5194/hess-24-3015-2020, 2020
Short summary
Short summary
Previous studies on water saving through food trade focussed either on comparing water productivities among countries or on analysing food trade in relation to national water endowments. Here, we consider, for the first time, both differences in water productivities and water endowments to analyse national comparative advantages. Our study reveals that blue water scarcity can be reduced to sustainable levels by changing cropping patterns while maintaining current levels of global production.
Kamal Ahmed, Dhanapala A. Sachindra, Shamsuddin Shahid, Mehmet C. Demirel, and Eun-Sung Chung
Hydrol. Earth Syst. Sci., 23, 4803–4824, https://doi.org/10.5194/hess-23-4803-2019, https://doi.org/10.5194/hess-23-4803-2019, 2019
Short summary
Short summary
This study evaluated the performance of 36 CMIP5 GCMs in simulating seasonal precipitation and maximum and minimum temperature over Pakistan using spatial metrics (SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency) for the period 1961–2005. NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 were identified as the most suitable GCMs for simulating all three climate variables over Pakistan.
Pute Wu, La Zhuo, Guoping Zhang, Mesfin M. Mekonnen, Arjen Y. Hoekstra, Yoshihide Wada, Xuerui Gao, Xining Zhao, Yubao Wang, and Shikun Sun
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-436, https://doi.org/10.5194/hess-2018-436, 2018
Manuscript not accepted for further review
Short summary
Short summary
This study estimates the concomitant economic benefits and values to the crop-related (physical and virtual) water flows at a basin level. The net benefit of blue water was 13–42 % lower than that of green water in the case for the Yellow River Basin. The basin got a net income through the virtual water exports. It is necessary to manage the internal trade-offs between the water consumption and economic returns, for maximizing both the water use efficiency and water economic productivities.
Abebe D. Chukalla, Maarten S. Krol, and Arjen Y. Hoekstra
Hydrol. Earth Syst. Sci., 22, 3245–3259, https://doi.org/10.5194/hess-22-3245-2018, https://doi.org/10.5194/hess-22-3245-2018, 2018
Short summary
Short summary
This paper provides the first detailed and comprehensive study regarding the potential for reducing the grey WF of crop production by changing management practice such as the nitrogen application rate, nitrogen form (inorganic N or manure N), tillage practice and irrigation strategy. The paper shows that although water pollution (grey WF) can be reduced dramatically, this comes together with a great yield reduction.
Julian Koch, Mehmet Cüneyd Demirel, and Simon Stisen
Geosci. Model Dev., 11, 1873–1886, https://doi.org/10.5194/gmd-11-1873-2018, https://doi.org/10.5194/gmd-11-1873-2018, 2018
Short summary
Short summary
Our work addresses a key challenge in earth system modelling: how to optimally exploit the information contained in satellite remote sensing observations in the calibration of such models. For this we thoroughly test a number of measures that quantify the fit between an observed and a simulated spatial pattern. We acknowledge the difficulties associated with such a comparison and suggest using measures that regard multiple aspects of spatial information, i.e. magnitude and variability.
Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, and Simon Stisen
Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, https://doi.org/10.5194/hess-22-1299-2018, 2018
Short summary
Short summary
Satellite data offer great opportunities to improve spatial model predictions by means of spatially oriented model evaluations. In this study, satellite images are used to observe spatial patterns of evapotranspiration at the land surface. These spatial patterns are utilized in combination with streamflow observations in a model calibration framework including a novel spatial performance metric tailored to target the spatial pattern performance of a catchment-scale hydrological model.
Harm-Jan F. Benninga, Martijn J. Booij, Renata J. Romanowicz, and Tom H. M. Rientjes
Hydrol. Earth Syst. Sci., 21, 5273–5291, https://doi.org/10.5194/hess-21-5273-2017, https://doi.org/10.5194/hess-21-5273-2017, 2017
Short summary
Short summary
Accurate flood and low-streamflow forecasting are important. The paper presents a methodology to evaluate ensemble streamflow-forecasting systems for different lead times; low, medium and high streamflow; and related runoff-generating processes. We applied the methodology to a study forecasting system of the Biała Tarnowska River in Poland. The results provide valuable information about the forecasting system: in which conditions it can be used and how the system can be improved effectively.
Abebe D. Chukalla, Maarten S. Krol, and Arjen Y. Hoekstra
Hydrol. Earth Syst. Sci., 21, 3507–3524, https://doi.org/10.5194/hess-21-3507-2017, https://doi.org/10.5194/hess-21-3507-2017, 2017
Short summary
Short summary
In the current study, we have developed a method to obtain marginal cost curves (MCCs) for WF reduction in crop production. The method is innovative by employing a model that combines soil water balance accounting and a crop growth model and assessing costs and WF reduction for all combinations of irrigation techniques, irrigation strategies and mulching practices. While this approach has been used in the field of constructing MCCs for carbon footprint reduction, this has never been done before.
Tom Brouwer, Dirk Eilander, Arnejan van Loenen, Martijn J. Booij, Kathelijne M. Wijnberg, Jan S. Verkade, and Jurjen Wagemaker
Nat. Hazards Earth Syst. Sci., 17, 735–747, https://doi.org/10.5194/nhess-17-735-2017, https://doi.org/10.5194/nhess-17-735-2017, 2017
Short summary
Short summary
The increasing number and severity of floods, driven by e.g. urbanization, subsidence and climate change, create a growing need for accurate and timely flood maps. At the same time social media is a source of much real-time data that is still largely untapped in flood disaster management. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.
La Zhuo, Mesfin M. Mekonnen, and Arjen Y. Hoekstra
Hydrol. Earth Syst. Sci., 20, 4547–4559, https://doi.org/10.5194/hess-20-4547-2016, https://doi.org/10.5194/hess-20-4547-2016, 2016
Short summary
Short summary
Benchmarks for the water footprint (WF) of crop production can serve as a reference and be helpful in setting WF reduction targets. The study explores which environmental factors should be distinguished when determining benchmarks for the consumptive (green and blue) WF of crops. Through a case study for winter wheat in China over 1961–2008, we find that when determining benchmark levels for the consumptive WF of a crop, it is most useful to distinguish between different climate zones.
A. D. Chukalla, M. S. Krol, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 19, 4877–4891, https://doi.org/10.5194/hess-19-4877-2015, https://doi.org/10.5194/hess-19-4877-2015, 2015
Short summary
Short summary
This paper provides the first detailed and comprehensive study regarding the potential for reducing the consumptive WF of a crop by changing management practice such as irrigation technique, irrigation strategy and mulching practice. If we consider all the cases of drip or subsurface drip irrigation with synthetic mulching, including all crops and environments, we find an average consumptive WF reduction of 28-29%. The corresponding blue WF reduction is 44% and the green WF reduction 14%.
J. F. Schyns, A. Y. Hoekstra, and M. J. Booij
Hydrol. Earth Syst. Sci., 19, 4581–4608, https://doi.org/10.5194/hess-19-4581-2015, https://doi.org/10.5194/hess-19-4581-2015, 2015
Short summary
Short summary
The paper draws attention to the fact that green water (soil moisture returning to the atmosphere through evaporation) is a scarce resource, because its availability is limited and there are competing demands for green water. Around 80 indicators of green water availability and scarcity are reviewed and classified based on their scope and purpose of measurement. This is useful in order to properly include limitations in green water availability in water scarcity assessments.
L. Zhuo, M. M. Mekonnen, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 18, 2219–2234, https://doi.org/10.5194/hess-18-2219-2014, https://doi.org/10.5194/hess-18-2219-2014, 2014
H. H. G. Savenije, A. Y. Hoekstra, and P. van der Zaag
Hydrol. Earth Syst. Sci., 18, 319–332, https://doi.org/10.5194/hess-18-319-2014, https://doi.org/10.5194/hess-18-319-2014, 2014
W. R. van Esse, C. Perrin, M. J. Booij, D. C. M. Augustijn, F. Fenicia, D. Kavetski, and F. Lobligeois
Hydrol. Earth Syst. Sci., 17, 4227–4239, https://doi.org/10.5194/hess-17-4227-2013, https://doi.org/10.5194/hess-17-4227-2013, 2013
M. C. Demirel, M. J. Booij, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 17, 4241–4257, https://doi.org/10.5194/hess-17-4241-2013, https://doi.org/10.5194/hess-17-4241-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysis
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
Why our rainfall-runoff models keep underestimating the peak flows?
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
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
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.
András Bárdossy and Faizan Anwar
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-281, https://doi.org/10.5194/hess-2022-281, 2022
Preprint under review for HESS
Short summary
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
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
Cited articles
Adamowski, J., Chan, H. F., Prasher, S. O., Ozga-Zielinski, B., and Sliusarieva, A.: Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada, Water Resour. Res., 48, W01528, https://doi.org/10.1029/2010wr009945, 2012.
Archer, D. R. and Fowler, H. J.: Using meteorological data to forecast seasonal runoff on the River Jhelum, Pakistan, J. Hydrol., 361, 10–23, https://doi.org/10.1016/j.jhydrol.2008.07.017, 2008.
ATV-DVWK: Verdunstung in Bezug zu Landnutzung, Bewuchs und Boden, Merkblatt ATV-DVWK-M 504, Hennef, 2002.
Bell, V. A., Davies, H. N., Kay, A. L., Marsh, T. J., Brookshaw, A., and Jenkins, A.: Developing a large-scale water-balance approach to seasonal forecasting: application to the 2012 drought in Britain, Hydrol. Process., 27, 3003–3012, https://doi.org/10.1002/hyp.9863, 2013.
Bierkens, M. F. P. and van Beek, L. P. H.: Seasonal Predictability of European Discharge: NAO and Hydrological Response Time, J. Hydrometeorol., 10, 953–968, https://doi.org/10.1175/2009jhm1034.1, 2009.
Booij, M. J.: Impact of climate change on river flooding assessed with different spatial model resolutions, J. Hydrol., 303, 176–198, https://doi.org/10.1016/j.jhydrol.2004.07.013, 2005.
Bormann, H.: Runoff regime changes in German rivers due to climate change, Erdkunde, 64, 257–279, https://doi.org/10.3112/erdkunde.2010.03.04, 2010.
Bormann, H., Pinter, N., and Elfert, S.: Hydrological signatures of flood trends on German rivers: Flood frequencies, flood heights and specific stages, J. Hydrol., 404, 50–66, https://doi.org/10.1016/j.jhydrol.2011.04.019, 2011.
Chiew, F. H. S., Zhou, S. L., and McMahon, T. A.: Use of seasonal streamflow forecasts in water resources management, J. Hydrol., 270, 135–144, 2003.
Chowdhury, S. and Sharma, A.: Multisite seasonal forecast of arid river flows using a dynamic model combination approach, Water Resour. Res., 45, W10428, https://doi.org/10.1029/2008wr007510, 2009.
Coley, D. M. and Waylen, P. R.: Forecasting dry season streamflow on the Peace River at Arcadia, Florida, USA, J. Am. Water Resour. Assoc., 42, 851–862, 2006.
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times, Hydrol. Process., 27, 2742–2758, https://doi.org/10.1002/hyp.9402, 2013a.
Demirel, M. C., Booij, M. J., and Hoekstra, A. Y.: Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models, Water Resour. Res., 49, 4035–4053, https://doi.org/10.1002/wrcr.20294, 2013b.
Devineni, N., Sankarasubramanian, A., and Ghosh, S.: Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations, Water Resour. Res., 44, W09404, https://doi.org/10.1029/2006wr005855, 2008.
De Vos, N. J. and Rientjes, T. H. M.: Multiobjective training of artificial neural networks for rainfall-runoff modeling, Water Resour. Res., 44, W08434, https://doi.org/10.1029/2007wr006734, 2008.
Doblas-Reyes, F. J., Weisheimer, A., Déqué, M., Keenlyside, N., McVean, M., Murphy, J. M., Rogel, P., Smith, D., and Palmer, T. N.: Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts, Q. J. Roy. Meteorol. Soc., 135, 1538–1559, https://doi.org/10.1002/qj.464, 2009.
Dutra, E., Di Giuseppe, F., Wetterhall, F., and Pappenberger, F.: Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index, Hydrol. Earth Syst. Sci., 17, 2359–2373, https://doi.org/10.5194/hess-17-2359-2013, 2013.
Dutra, E., Pozzi, W., Wetterhall, F., Di Giuseppe, F., Magnusson, L., Naumann, G., Barbosa, P., Vogt, J., and Pappenberger, F.: Global meteorological drought – Part 2: Seasonal forecasts, Hydrol. Earth Syst. Sci., 18, 2669–2678, https://doi.org/10.5194/hess-18-2669-2014, 2014.
Eberle, M.: Hydrological Modelling in the River Rhine Basin Part III – Daily HBV Model for the Rhine Basin BfG-1451, Institute for Inland Water Management and Waste Water Treatment (RIZA) and Federal Institute of Hydrology (BfG) Koblenz, Germany, 2005.
ECMWF: Describing ECMWF's forecasts and forecasting system, ECMWF newsletter 133, available from: http://old.ecmwf.int/publications/manuals/mars/ (last access: 26 July 2014), 2012.
Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D. P.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 1: Concepts and methodology, Hydrol. Earth Syst. Sci., 14, 1931–1941, https://doi.org/10.5194/hess-14-1931-2010, 2010.
Engeland, K., Renard, B., Steinsland, I., and Kolberg, S.: Evaluation of statistical models for forecast errors from the HBV model, J. Hydrol., 384, 142–155, 2010.
EU: Horizon 2020 – Work Programme 2014–2015: Water 7_2015: Increasing confidence in seasonal-to-decadal predictions of the water cycle, http://www.aber.ac.uk/en/media/departmental/researchoffice/funding/UKRO-Horizon-2020_climate change draft wp.pdf, last access: 4 September 2013.
Felipe, P.-S. and Nelson, O.-N.: Forecasting of Monthly Streamflows Based on Artificial Neural Networks, J. Hydrol. Eng., 14, 1390–1395, 2009.
Förster, K., Meon, G., Marke, T., and Strasser, U.: Effect of meteorological forcing and snow model complexity on hydrological simulations in the Sieber catchment (Harz Mountains, Germany), Hydrol. Earth Syst. Sci., 18, 4703–4720, https://doi.org/10.5194/hess-18-4703-2014, 2014.
Fundel, F., Jörg-Hess, S., and Zappa, M.: Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices, Hydrol. Earth Syst. Sci., 17, 395–407, https://doi.org/10.5194/hess-17-395-2013, 2013.
Ganguli, P. and Reddy, M. J.: Ensemble prediction of regional droughts using climate inputs and SVM-copula approach, Hydrol. Process., 28, 4989–5009, https://doi.org/10.1002/hyp.9966, 2014.
Gaume, E. and Gosset, R.: Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology?, Hydrol. Earth Syst. Sci., 7, 693–706, https://doi.org/10.5194/hess-7-693-2003, 2003.
Giuntoli, I., Renard, B., Vidal, J. P., and Bard, A.: Low flows in France and their relationship to large-scale climate indices, J. Hydrol., 482, 105–118, https://doi.org/10.1016/j.jhydrol.2012.12.038, 2013.
Gobena, A. K. and Gan, T. Y.: Incorporation of seasonal climate forecasts in the ensemble streamflow prediction system, J. Hydrol., 385, 336–352, https://doi.org/10.1016/j.jhydrol.2010.03.002, 2010.
Görgen, K., Beersma, J., Brahmer, G., Buiteveld, H., Carambia, M., de Keizer, O., Krahe, P., Nilson, E., Lammersen, R., Perrin, C., and Volken, D.: Assessment of Climate Change Impacts on Discharge in the Rhine River Basin: Results of the RheinBlick 2050 Project, Lelystad, CHR, p. 211, available from: http://www.news.admin.ch/NSBSubscriber/message/attachments/20770.pdf (last access: 30 October 2014), 2010.
Govindaraju, R. S. and Rao, A. R.: Artificial Neural Networks in Hydrology, Kluwer Academic Publishers Norwell, MA, USA, 329 pp., 2000.
Hamlet, A. F., Elsner, M. M., Mauger, G. S., Lee, S.-Y., Tohver, I., and Norheim, R. A.: An Overview of the Columbia Basin Climate Change Scenarios Project: Approach, Methods, and Summary of Key Results, Atmos.-Ocean, 51, 392–415, https://doi.org/10.1080/07055900.2013.819555, 2013.
Hartmann, H. C., Pagano, T. C., Sorooshian, S., and Bales, R.: Confidence builders: Evaluating seasonal climate forecasts from user perspectives, B. Am. Meteorol. Soc., 83, 683–698, 2002.
Jaun, S. and Ahrens, B.: Evaluation of a probabilistic hydrometeorological forecast system, Hydrol. Earth Syst. Sci., 13, 1031–1043, https://doi.org/10.5194/hess-13-1031-2009, 2009.
Kahya, E. and Dracup, J. A.: U.S. streamflow patterns in relation to the El Niño/Southern Oscillation, Water Resour. Res., 29, 2491–2503, https://doi.org/10.1029/93wr00744, 1993.
Kalra, A., Ahmad, S., and Nayak, A.: Increasing streamflow forecast lead time for snowmelt-driven catchment based on large-scale climate patterns, Adv. Water Resour., 53, 150–162, https://doi.org/10.1016/j.advwatres.2012.11.003, 2013.
Kasiviswanathan, K. S., Raj, C., Sudheer, K. P., and Chaubey, I.: Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations, J. Hydrol., 499, 275–288, https://doi.org/10.1016/j.jhydrol.2013.06.043, 2013.
Kuo, C.-C., Gan, T. Y., and Yu, P.-S.: Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan, J. Hydrol., 387, 292–303, 2010.
Li, H., Luo, L., and Wood, E. F.: Seasonal hydrologic predictions of low-flow conditions over eastern USA during the 2007 drought, Atmos. Sci. Lett., 9, 61–66, 2008.
Li, H., Luo, L., Wood, E. F., and Schaake, J.: The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting, J. Geophys. Res., 114, D04114, https://doi.org/10.1029/2008jd010969, 2009.
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergstrom, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, 1997.
Luo, L., Wood, E. F., and Pan, M.: Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions, J. Geophys. Res., 112, D10102, https://doi.org/10.1029/2006jd007655, 2007.
Madadgar, S. and Moradkhani, H.: A Bayesian Framework for Probabilistic Seasonal Drought Forecasting, J. Hydrometeorol., 14, 1685–1705, https://doi.org/10.1175/JHM-D-13-010.1, 2013.
Martina, M. L. V., Todini, E., and Libralon, A.: A Bayesian decision approach to rainfall thresholds based flood warning, Hydrol. Earth Syst. Sci., 10, 413–426, https://doi.org/10.5194/hess-10-413-2006, 2006.
Nicolle, P., Pushpalatha, R., Perrin, C., François, D., Thiéry, D., Mathevet, T., Le Lay, M., Besson, F., Soubeyroux, J.-M., Viel, C., Regimbeau, F., Andréassian, V., Maugis, P., Augeard, B., and Morice, E.: Benchmarking hydrological models for low-flow simulation and forecasting on French catchments, Hydrol. Earth Syst. Sci., 18, 2829–2857, https://doi.org/10.5194/hess-18-2829-2014, 2014.
Olsson, J. and Lindström, G.: Evaluation and calibration of operational hydrological ensemble forecasts in Sweden, J. Hydrol., 350, 14–24, 2008.
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, 2003.
Pokhrel, P., Wang, Q. J., and Robertson, D. E.: The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia, Water Resour. Res., 49, 6671–6687, https://doi.org/10.1002/wrcr.20449, 2013.
Pushpalatha, R., Perrin, C., Moine, N. L., Mathevet, T., and Andréassian, V.: A downward structural sensitivity analysis of hydrological models to improve low-flow simulation, J. Hydrol., 411, 66–76, 2011.
Pushpalatha, R., Perrin, C., Moine, N. L., and Andréassian, V.: A review of efficiency criteria suitable for evaluating low-flow simulations, J. Hydrol., 420–421, 171–182, https://doi.org/10.1016/j.jhydrol.2011.11.055, 2012.
Renner, M., Werner, M. G. F., Rademacher, S., and Sprokkereef, E.: Verification of ensemble flow forecasts for the River Rhine, J. Hydrol., 376, 463–475, 2009.
Robertson, D. E., Pokhrel, P., and Wang, Q. J.: Improving statistical forecasts of seasonal streamflows using hydrological model output, Hydrol. Earth Syst. Sci., 17, 579–593, https://doi.org/10.5194/hess-17-579-2013, 2013.
Roulin, E.: Skill and relative economic value of medium-range hydrological ensemble predictions, Hydrol. Earth Syst. Sci., 11, 725–737, https://doi.org/10.5194/hess-11-725-2007, 2007.
Rutten, M., van de Giesen, N., Baptist, M., Icke, J., and Uijttewaal, W.: Seasonal forecast of cooling water problems in the River Rhine, Hydrol. Process., 22, 1037–1045, 2008.
Saadat, S., Khalili, D., Kamgar-Haghighi, A., and Zand-Parsa, S.: Investigation of spatio-temporal patterns of seasonal streamflow droughts in a semi-arid region, Nat. Hazards, 69, 1697–1720, https://doi.org/10.1007/s11069-013-0783-y, 2013.
Sauquet, E., Lerat, J., and Prudhomme, C.: La prévision hydro-météorologique à 3-6 mois, Etat des connaissances et applications, La Houille Blanche, 6, 77–84, https://doi.org/10.1051/lhb:2008075, 2008.
Schubert, S., Koster, R., Hoerling, M., Seager, R., Lettenmaier, D., Kumar, A., and Gutzler, D.: Predicting Drought on Seasonal-to-Decadal Time Scales, B. Am. Meteorol. Soc., 88, 1625–1630, https://doi.org/10.1175/bams-88-10-1625, 2007.
Shamseldin, A. Y.: Application of a neural network technique to rainfall-runoff modelling, J. Hydrol., 199, 272–294, https://doi.org/10.1016/s0022-1694(96)03330-6, 1997.
Shukla, S. and Lettenmaier, D. P.: Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill, Hydrol. Earth Syst. Sci., 15, 3529–3538, https://doi.org/10.5194/hess-15-3529-2011, 2011.
Shukla, S., Voisin, N., and Lettenmaier, D. P.: Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill, Hydrol. Earth Syst. Sci., 16, 2825–2838, https://doi.org/10.5194/hess-16-2825-2012, 2012.
Shukla, S., Sheffield, J., Wood, E. F., and Lettenmaier, D. P.: On the sources of global land surface hydrologic predictability, Hydrol. Earth Syst. Sci., 17, 2781–2796, https://doi.org/10.5194/hess-17-2781-2013, 2013.
Soukup, T. L., Aziz, O. A., Tootle, G. A., Piechota, T. C., and Wulff, S. S.: Long lead-time streamflow forecasting of the North Platte River incorporating oceanic-atmospheric climate variability, J. Hydrol., 368, 131–142, 2009.
Thirel, G., Rousset-Regimbeau, F., Martin, E., and Habets, F.: On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions, J. Hydrometeorol., 9, 1301–1317, https://doi.org/10.1175/2008jhm959.1, 2008.
Tian, Y., Booij, M. J., and Xu, Y.-P.: Uncertainty in high and low flows due to model structure and parameter errors, Stoch. Environ. Res. Risk A., 28, 319–332, https://doi.org/10.1007/s00477-013-0751-9, 2014.
Tootle, G. A. and Piechota, T. C.: Suwannee River Long Range Streamflow Forecasts Based On Seasonal Climate Predictors, J. Am. Water Resour. Assoc., 40, 523–532, 2004.
Towler, E., Roberts, M., Rajagopalan, B., and Sojda, R. S.: Incorporating probabilistic seasonal climate forecasts into river management using a risk-based framework, Water Resour. Res., 49, 4997–5008, https://doi.org/10.1002/wrcr.20378, 2013.
Van den Tillaart, S. P. M., Booij, M. J., and Krol, M. S.: Impact of uncertainties in discharge determination on the parameter estimation and performance of a hydrological model, Hydrol. Res., 44, 454–466 2013.
Van Dijk, A. I. J. M., Peña-Arancibia, J. L., Wood, E. F., Sheffield, J., and Beck, H. E.: Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide, Water Resour. Res., 49, 2729–2746, https://doi.org/10.1002/wrcr.20251, 2013.
van Ogtrop, F. F., Vervoort, R. W., Heller, G. Z., Stasinopoulos, D. M., and Rigby, R. A.: Long-range forecasting of intermittent streamflow, Hydrol. Earth Syst. Sci., 15, 3343–3354, https://doi.org/10.5194/hess-15-3343-2011, 2011.
Velázquez, J. A., Anctil, F., and Perrin, C.: Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments, Hydrol. Earth Syst. Sci., 14, 2303–2317, https://doi.org/10.5194/hess-14-2303-2010, 2010.
Vidal, J.-P., Martin, E., Franchistéguy, L., Habets, F., Soubeyroux, J.-M., Blanchard, M., and Baillon, M.: Multilevel and multiscale drought reanalysis over France with the Safran-Isba-Modcou hydrometeorological suite, Hydrol. Earth Syst. Sci., 14, 459–478, https://doi.org/10.5194/hess-14-459-2010, 2010.
Wang, E., Zhang, Y., Luo, J., Chiew, F. H. S., and Wang, Q. J.: Monthly and seasonal streamflow forecasts using rainfall-runoff modeling and historical weather data, Water Resour. Res., 47, W05516, https://doi.org/10.1029/2010wr009922, 2011.
Wang, W., Gelder, P. H. A. J. M. V., Vrijling, J. K., and Ma, J.: Forecasting daily streamflow using hybrid ANN models, J. Hydrol., 324, 383–399, https://doi.org/10.1016/j.jhydrol.2005.09.032, 2006.
Wedgbrow, C. S., Wilby, R. L., Fox, H. R., and O'Hare, G.: Prospects for seasonal forecasting of summer drought and low river flow anomalies in England and Wales, Int. J. Climatol., 22, 219–236, https://doi.org/10.1002/joc.735, 2002.
Wedgbrow, C. S., Wilby, R. L., and Fox, H. R.: Experimental seasonal forecasts of low summer flows in the River Thames, UK, using Expert Systems, Clim. Res., 28, 133–141, 2005.
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Elsevier, New York, 1995.
Winsemius, H. C., Dutra, E., Engelbrecht, F. A., Archer Van Garderen, E., Wetterhall, F., Pappenberger, F., and Werner, M. G. F.: The potential value of seasonal forecasts in a changing climate in southern Africa, Hydrol. Earth Syst. Sci., 18, 1525–1538, https://doi.org/10.5194/hess-18-1525-2014, 2014.
WMO: Forecastverification – issues, methods and faq. WWRP/WGNE, Joint Working Group on Verification, available at: www.cawcr.gov.au/projects/verification (last access: 24 September 2013), 2012.
Wood, A. W. and Lettenmaier, D. P.: A Test Bed for New Seasonal Hydrologic Forecasting Approaches in the Western United States, B. Am. Meteorol. Soc., 87, 1699-1712, https://doi.org/10.1175/bams-87-12-1699, 2006.
Wood, A. W., Maurer, E. P., Kumar, A., and Lettenmaier, D. P.: Long-range experimental hydrologic forecasting for the eastern United States, J. Geophys. Res, 107, 4429, https://doi.org/10.1029/2001JD000659, 2002.
Yossef, N. C., van Beek, L. P. H., Kwadijk, J. C. J., and Bierkens, M. F. P.: Assessment of the potential forecasting skill of a global hydrological model in reproducing the occurrence of monthly flow extremes, Hydrol. Earth Syst. Sci., 16, 4233–4246, https://doi.org/10.5194/hess-16-4233-2012, 2012.
Yossef, N. C., Winsemius, H., Weerts, A., van Beek, R., and Bierkens, M. F. P.: Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing, Water Resour. Res., 49, 4687–4699, https://doi.org/10.1002/wrcr.20350, 2013.
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.
This paper investigates the skill of 90-day low-flow forecasts using three models. From the...
