Articles | Volume 19, issue 7
https://doi.org/10.5194/hess-19-3181-2015
© 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-19-3181-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Jul 2015
Research article |
| 23 Jul 2015
Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments
UNESCO-IHE Institute for Water Education, Delft, the Netherlands
currently at: Department of Civil Engineering, Middle East Technical University, Ankara, Turkey
P. López López
UNESCO-IHE Institute for Water Education, Delft, the Netherlands
Deltares, Delft, the Netherlands
now at: Utrecht University (Utrecht) and Deltares (Delft), the Netherlands
D. P. Solomatine
UNESCO-IHE Institute for Water Education, Delft, the Netherlands
Delft University of Technology, Delft, the Netherlands
A. H. Weerts
Deltares, Delft, the Netherlands
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands
D. L. Shrestha
CSIRO Land and Water, Highett, Victoria, Australia
Related authors
Caitlyn A. Hall, Sheila M. Saia, Andrea L. Popp, Nilay Dogulu, Stanislaus J. Schymanski, Niels Drost, Tim van Emmerik, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 647–664, https://doi.org/10.5194/hess-26-647-2022, https://doi.org/10.5194/hess-26-647-2022, 2022
Short summary
Short summary
Impactful open, accessible, reusable, and reproducible hydrologic research practices are being embraced by individuals and the community, but taking the plunge can seem overwhelming. We present the Open Hydrology Principles and Practical Guide to help hydrologists move toward open science, research, and education. We discuss the benefits and how hydrologists can overcome common challenges. We encourage all hydrologists to join the open science community (https://open-hydrology.github.io).
Jitao Zhang, Dimitri Solomatine, and Zengchuan Dong
Hydrol. Earth Syst. Sci., 28, 3739–3753, https://doi.org/10.5194/hess-28-3739-2024, https://doi.org/10.5194/hess-28-3739-2024, 2024
Short summary
Short summary
Faced with the problem of uncertainty in the field of water resources management, this paper proposes the Copula Multi-objective Robust Optimization and Probabilistic Analysis of Robustness (CM-ROPAR) approach to obtain robust water allocation schemes based on the uncertainty of drought and wet encounters and the uncertainty of inflow. We believe that this research article not only highlights the significance of the CM-ROPAR approach but also provides a new concept for uncertainty analysis.
Devi Purnamasari, Adriaan J. Teuling, and Albrecht H. Weerts
EGUsphere, https://doi.org/10.5194/egusphere-2024-1929, https://doi.org/10.5194/egusphere-2024-1929, 2024
Short summary
Short summary
This paper introduces a method to identify irrigated areas by combining hydrology models with satellite temperature data. Our method was tested in the Rhine basin which aligns well with official statistics. It performs best in regions with large farms and less well in areas with small farms. Observed differences with existing data are influenced by data resolution and methods.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-211, https://doi.org/10.5194/hess-2024-211, 2024
Revised manuscript under review for HESS
Short summary
Short summary
Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping better prepare for and respond to floods.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
Short summary
Short summary
We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Steven Reinaldo Rusli, Victor F. Bense, Syed M. T. Mustafa, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-26, https://doi.org/10.5194/hess-2024-26, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
In this paper, we investigate the impact of climatic and anthropogenic factors on future groundwater availability. The changes are simulated using hydrological and groundwater flow models. We found out that the future groundwater status is influenced more so by anthropogenic factors compared to climatic factors. The results are beneficial to inform the responsible parties in operational water management to achieve future (ground)water governance.
Ana Paez-Trujilo, Jeffer Cañon, Beatriz Hernandez, Gerald Corzo, and Dimitri Solomatine
Nat. Hazards Earth Syst. Sci., 23, 3863–3883, https://doi.org/10.5194/nhess-23-3863-2023, https://doi.org/10.5194/nhess-23-3863-2023, 2023
Short summary
Short summary
This study uses a machine learning technique, the multivariate regression tree approach, to assess the hydroclimatic characteristics that govern agricultural and hydrological drought severity. The results show that the employed technique successfully identified the primary drivers of droughts and their critical thresholds. In addition, it provides relevant information to identify the areas most vulnerable to droughts and design strategies and interventions for drought management.
Marjanne J. Zander, Pety J. Viguurs, Frederiek C. Sperna Weiland, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-274, https://doi.org/10.5194/hess-2023-274, 2023
Manuscript not accepted for further review
Short summary
Short summary
Flash floods are damaging natural hazard which often occur in the European Alps. High resolution climate model output is combined with high resolution distributed hydrological models to model changes in flash flood frequency and intensity. Results show a similar flash flood frequency for autumn in the future, but a decrease in summer. However, the future discharge simulations indicate an increase in the flash flood severity in both summer and autumn leading to more severe flash flood impacts.
Bas J. M. Wullems, Claudia C. Brauer, Fedor Baart, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci., 27, 3823–3850, https://doi.org/10.5194/hess-27-3823-2023, https://doi.org/10.5194/hess-27-3823-2023, 2023
Short summary
Short summary
In deltas, saltwater sometimes intrudes far inland and causes problems with freshwater availability. We created a model to forecast salt concentrations at a critical location in the Rhine–Meuse delta in the Netherlands. It requires a rather small number of data to make a prediction and runs fast. It predicts the occurrence of salt concentration peaks well but underestimates the highest peaks. Its speed gives water managers more time to reduce the problems caused by salt intrusion.
Mohamed Elneel Elshaikh Eltayeb Elbasheer, Gerald Augusto Corzo, Dimitri Solomatine, and Emmanouil Varouchakis
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-98, https://doi.org/10.5194/hess-2023-98, 2023
Revised manuscript not accepted
Short summary
Short summary
In this research, we explored the use of machine learning (ML) to improve the S2S ensemble precipitation forecast, different approaches were used as exploratory experiments to see which approach is better addressing the improvement of the ensemble probabilistic forecast, as a conclusion of our research, we found that the concept of committee model (CM) is a promising approach that can be further studied and evaluated using a different combination of the state of the art ML techniques.
Mohamed Elneel Elshaikh Eltayeb Elbasheer, Gerald Augusto Corzo, Dimitri Solomatine, and Emmanouil Varouchakis
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-348, https://doi.org/10.5194/hess-2022-348, 2022
Manuscript not accepted for further review
Short summary
Short summary
In this research, we explored the use of machine learning (ML) to improve the ECMWF S2S ensemble precipitation forecast, different approaches were used as exploratory experiments to see which approach is better addressing the improvement of the ensemble probabilistic forecast, as a conclusion of our research, we found that the concept of committee model (CM) is a promising approach that can be further studied and evaluated using a different combination of the state of the art ML techniques.
Jerom P. M. Aerts, Rolf W. Hut, Nick C. van de Giesen, Niels Drost, Willem J. van Verseveld, Albrecht H. Weerts, and Pieter Hazenberg
Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022, https://doi.org/10.5194/hess-26-4407-2022, 2022
Short summary
Short summary
In recent years gridded hydrological modelling moved into the realm of hyper-resolution modelling (<10 km). In this study, we investigate the effect of varying grid-cell sizes for the wflow_sbm hydrological model. We used a large sample of basins from the CAMELS data set to test the effect that varying grid-cell sizes has on the simulation of streamflow at the basin outlet. Results show that there is no single best grid-cell size for modelling streamflow throughout the domain.
Vitali Diaz, Ahmed A. A. Osman, Gerald A. Corzo Perez, Henny A. J. Van Lanen, Shreedhar Maskey, and Dimitri Solomatine
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-252, https://doi.org/10.5194/hess-2022-252, 2022
Preprint withdrawn
Short summary
Short summary
Drought impacts on crops can be assessed in terms of crop yield (CY) variation. The hypothesis is that the spatiotemporal change of drought area is a good input to predict CY. A step-by-step approach for predicting CY is built based on two types of machine learning models. Drought area was found suitable for predicting CY. Since it is currently possible to calculate drought areas within drought monitoring systems, the prediction of drought impacts can be integrated directly into them.
Mar J. Zander, Pety J. Viguurs, Frederiek C. Sperna Weiland, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-207, https://doi.org/10.5194/hess-2022-207, 2022
Manuscript not accepted for further review
Short summary
Short summary
We perform a modelling study to research potential future changes in flash flood occurrence in the European Alps. We use new high-resolution numerical climate simulations, which can simulate the type of local, intense rainstorms which trigger flash floods, combined with high-resolution hydrological modelling. We find that flash floods would become less frequent in summers in our future climate scenario, with little change in autumns. However, the maximal severity would increase in both seasons.
Laurène J. E. Bouaziz, Emma E. Aalbers, Albrecht H. Weerts, Mark Hegnauer, Hendrik Buiteveld, Rita Lammersen, Jasper Stam, Eric Sprokkereef, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 26, 1295–1318, https://doi.org/10.5194/hess-26-1295-2022, https://doi.org/10.5194/hess-26-1295-2022, 2022
Short summary
Short summary
Assuming stationarity of hydrological systems is no longer appropriate when considering land use and climate change. We tested the sensitivity of hydrological predictions to changes in model parameters that reflect ecosystem adaptation to climate and potential land use change. We estimated a 34 % increase in the root zone storage parameter under +2 K global warming, resulting in up to 15 % less streamflow in autumn, due to 14 % higher summer evaporation, compared to a stationary system.
Caitlyn A. Hall, Sheila M. Saia, Andrea L. Popp, Nilay Dogulu, Stanislaus J. Schymanski, Niels Drost, Tim van Emmerik, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 647–664, https://doi.org/10.5194/hess-26-647-2022, https://doi.org/10.5194/hess-26-647-2022, 2022
Short summary
Short summary
Impactful open, accessible, reusable, and reproducible hydrologic research practices are being embraced by individuals and the community, but taking the plunge can seem overwhelming. We present the Open Hydrology Principles and Practical Guide to help hydrologists move toward open science, research, and education. We discuss the benefits and how hydrologists can overcome common challenges. We encourage all hydrologists to join the open science community (https://open-hydrology.github.io).
Vitali Diaz, Ahmed A. A. Osman, Gerald A. Corzo Perez, Henny A. J. Van Lanen, Shreedhar Maskey, and Dimitri Solomatine
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-600, https://doi.org/10.5194/hess-2021-600, 2021
Preprint withdrawn
Short summary
Short summary
Drought effects on crops are usually evaluated through crop yield (CY). The hypothesis is that the drought spatial extent is a good input to predict CY. A machine learning approach to predict crop yield is introduced. The use of drought area was found suitable. Since it is currently possible to calculate drought areas within drought monitoring systems, the direct application to predict drought effects can be integrated into them by following approaches such as the one presented or similar.
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
Short summary
Short summary
Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
Ruben Imhoff, Claudia Brauer, Klaas-Jan van Heeringen, Hidde Leijnse, Aart Overeem, Albrecht Weerts, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 25, 4061–4080, https://doi.org/10.5194/hess-25-4061-2021, https://doi.org/10.5194/hess-25-4061-2021, 2021
Short summary
Short summary
Significant biases in real-time radar rainfall products limit the use for hydrometeorological forecasting. We introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors to correct radar rainfall products and to benchmark other correction algorithms. When tested for 12 Dutch basins, estimated rainfall and simulated discharges with CARROTS generally outperform those using the operational mean field bias adjustments.
Laurène J. E. Bouaziz, Fabrizio Fenicia, Guillaume Thirel, Tanja de Boer-Euser, Joost Buitink, Claudia C. Brauer, Jan De Niel, Benjamin J. Dewals, Gilles Drogue, Benjamin Grelier, Lieke A. Melsen, Sotirios Moustakas, Jiri Nossent, Fernando Pereira, Eric Sprokkereef, Jasper Stam, Albrecht H. Weerts, Patrick Willems, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, https://doi.org/10.5194/hess-25-1069-2021, 2021
Short summary
Short summary
We quantify the differences in internal states and fluxes of 12 process-based models with similar streamflow performance and assess their plausibility using remotely sensed estimates of evaporation, snow cover, soil moisture and total storage anomalies. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Therefore, we invite modelers to evaluate their models using multiple variables and to rely on multi-model studies.
Shaokun He, Shenglian Guo, Chong-Yu Xu, Kebing Chen, Zhen Liao, Lele Deng, Huanhuan Ba, and Dimitri Solomatine
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-586, https://doi.org/10.5194/hess-2019-586, 2020
Manuscript not accepted for further review
Short summary
Short summary
Aiming at cascade impoundment operation, we develop a classification-aggregation-decomposition method to overcome the
curse of dimensionalityand inflow stochasticity problem. It is tested with a mixed 30-reservoir system in China. The results show that our method can provide lots of schemes to refer to different flood event scenarios. The best scheme outperforms the conventional operating rule, as it increases impoundment efficiency and hydropower generation while flood control risk is less.
Imme Benedict, Chiel C. van Heerwaarden, Albrecht H. Weerts, and Wilco Hazeleger
Hydrol. Earth Syst. Sci., 23, 1779–1800, https://doi.org/10.5194/hess-23-1779-2019, https://doi.org/10.5194/hess-23-1779-2019, 2019
Short summary
Short summary
The spatial resolution of global climate models (GCMs) and global hydrological models (GHMs) is increasing. This model study examines the benefits of a very high-resolution GCM and GHM in representing the hydrological cycle in the Rhine and Mississippi basins. We find that a higher-resolution GCM results in an improved precipitation budget, and therefore an improved hydrological cycle for the Rhine. For the Mississippi, no substantial improvements are found with increased resolution.
Bart van Osnabrugge, Remko Uijlenhoet, and Albrecht Weerts
Hydrol. Earth Syst. Sci., 23, 1453–1467, https://doi.org/10.5194/hess-23-1453-2019, https://doi.org/10.5194/hess-23-1453-2019, 2019
Short summary
Short summary
A correct estimate of the amount of future precipitation is the most important factor in making a good streamflow forecast, but evaporation is also an important component that determines the discharge of a river. However, in this study for the Rhine River we found that evaporation forecasts only give an almost negligible improvement compared to methods that use statistical information on climatology for a 10-day streamflow forecast. This is important to guide research on low flow forecasts.
Laurène Bouaziz, Albrecht Weerts, Jaap Schellekens, Eric Sprokkereef, Jasper Stam, Hubert Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 22, 6415–6434, https://doi.org/10.5194/hess-22-6415-2018, https://doi.org/10.5194/hess-22-6415-2018, 2018
Short summary
Short summary
We quantify net intercatchment groundwater flows in the Meuse basin in a complementary three-step approach through (1) water budget accounting, (2) testing a set of conceptual hydrological models and (3) evaluating against remote sensing actual evaporation data. We show that net intercatchment groundwater flows can make up as much as 25 % of mean annual precipitation in the headwaters and should therefore be accounted for in conceptual models to prevent overestimating actual evaporation rates.
Albert I. J. M. van Dijk, Jaap Schellekens, Marta Yebra, Hylke E. Beck, Luigi J. Renzullo, Albrecht Weerts, and Gennadii Donchyts
Hydrol. Earth Syst. Sci., 22, 4959–4980, https://doi.org/10.5194/hess-22-4959-2018, https://doi.org/10.5194/hess-22-4959-2018, 2018
Short summary
Short summary
Evaporation from wetlands, lakes and irrigation areas needs to be measured to understand water scarcity. So far, this has only been possible for small regions. Here, we develop a solution that can be applied at a very high resolution globally by making use of satellite observations. Our results show that 16% of global water resources evaporate before reaching the ocean, mostly from surface water. Irrigation water use is less than 1% globally but is a very large water user in several dry basins.
David R. Casson, Micha Werner, Albrecht Weerts, and Dimitri Solomatine
Hydrol. Earth Syst. Sci., 22, 4685–4697, https://doi.org/10.5194/hess-22-4685-2018, https://doi.org/10.5194/hess-22-4685-2018, 2018
Short summary
Short summary
In high-latitude (> 60° N) watersheds, measuring the snowpack and predicting of snowmelt runoff are uncertain due to the lack of data and complex physical processes. This provides challenges for hydrological assessment and operational water management. Global re-analysis datasets have great potential to aid in snowpack representation and snowmelt prediction when combined with a distributed hydrological model, though they still have clear limitations in remote boreal forest and tundra environments.
Anouk I. Gevaert, Luigi J. Renzullo, Albert I. J. M. van Dijk, Hans J. van der Woerd, Albrecht H. Weerts, and Richard A. M. de Jeu
Hydrol. Earth Syst. Sci., 22, 4605–4619, https://doi.org/10.5194/hess-22-4605-2018, https://doi.org/10.5194/hess-22-4605-2018, 2018
Short summary
Short summary
We assimilated three satellite soil moisture retrievals based on different microwave frequencies into a hydrological model. Two sets of experiments were performed, first assimilating the retrievals individually and then assimilating each set of two retrievals jointly. Overall, assimilation improved agreement between model and field-measured soil moisture. Joint assimilation resulted in model performance similar to or better than assimilating either retrieval individually.
Alexander Gelfan, Vsevolod Moreydo, Yury Motovilov, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 22, 2073–2089, https://doi.org/10.5194/hess-22-2073-2018, https://doi.org/10.5194/hess-22-2073-2018, 2018
Short summary
Short summary
We describe a forecasting procedure that is based on a semi-distributed hydrological model using two types of weather ensembles for the lead time period: observed weather data, constructed on the basis of the ESP methodology, and synthetic weather data, simulated by a weather generator. We compare the described methodology with the regression-based operational forecasts that are currently in practice and show the increased informational content of the ensemble-based forecasts.
Sanjeev K. Jha, Durga L. Shrestha, Tricia A. Stadnyk, and Paulin Coulibaly
Hydrol. Earth Syst. Sci., 22, 1957–1969, https://doi.org/10.5194/hess-22-1957-2018, https://doi.org/10.5194/hess-22-1957-2018, 2018
Short summary
Short summary
The output from numerical weather prediction (NWP) models is known to have errors. River forecast centers in Canada mostly use precipitation forecasts directly obtained from American and Canadian NWP models. In this study, we evaluate the forecast performance of ensembles generated by a Bayesian post-processing approach in cold climates. We demonstrate that the post-processing approach generates bias-free forecasts and provides a better picture of uncertainty in the case of an extreme event.
Thaine H. Assumpção, Ioana Popescu, Andreja Jonoski, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 22, 1473–1489, https://doi.org/10.5194/hess-22-1473-2018, https://doi.org/10.5194/hess-22-1473-2018, 2018
Short summary
Short summary
Citizens can contribute to science by providing data, analysing them and as such contributing to decision-making processes. For example, citizens have collected water levels from gauges, which are important when simulating/forecasting floods, where data are usually scarce. This study reviewed such contributions and concluded that integration of citizen data may not be easy due to their spatio-temporal characteristics but that citizen data still proved valuable and can be used in flood modelling.
Anqi Wang and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-78, https://doi.org/10.5194/hess-2018-78, 2018
Manuscript not accepted for further review
Short summary
Short summary
This paper presents a brief review and classification of sensitivity analysis (SA) methods. Six different global SA methods: Sobol, FAST, Morris, LH-OAT, RSA and PAWN are tested on the three conceptual rainfall-runoff models with varying complexity: (GR4J, Hymod and HBV), with respect to effectiveness, efficiency and convergence. Practical framework of selecting and using the SA methods is presented, which may be of assistance for practitioners assessing reliability of their models.
Fabio Sai, Lydia Cumiskey, Albrecht Weerts, Biswa Bhattacharya, and Raihanul Haque Khan
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2018-26, https://doi.org/10.5194/nhess-2018-26, 2018
Revised manuscript not accepted
Short summary
Short summary
The research tackled the challenge of flood impact-based forecasting and service for Bangladesh by proposing an approach based on colour coded as mean for linking forecasted water levels to possible impacts. This was tested at the local level and, although limited to the case study, the results encouraged us to share our outcomes for triggering interest in such approach and to foster further research aimed to move it forward.
Maurizio Mazzoleni, Vivian Juliette Cortes Arevalo, Uta Wehn, Leonardo Alfonso, Daniele Norbiato, Martina Monego, Michele Ferri, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 22, 391–416, https://doi.org/10.5194/hess-22-391-2018, https://doi.org/10.5194/hess-22-391-2018, 2018
Short summary
Short summary
We investigate the usefulness of assimilating crowdsourced observations from a heterogeneous network of sensors for different scenarios of citizen involvement levels during the flood event occurred in the Bacchiglione catchment in May 2013. We achieve high model performance by integrating crowdsourced data, in particular from citizens motivated by their feeling of belonging to a community. Satisfactory model performance can still be obtained even for decreasing citizen involvement over time.
Imme Benedict, Chiel C. van Heerwaarden, Albrecht H. Weerts, and Wilco Hazeleger
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-473, https://doi.org/10.5194/hess-2017-473, 2017
Revised manuscript not accepted
Short summary
Short summary
The spatial resolution of global climate models (GCMs) and global hydrological models (GHMs) is increasing. This study examines the benefits of a very high resolution GCM and GHM on representing the hydrological cycle in the Rhine and Mississippi basin. We conclude that increasing the resolution of a GCM is the most straightforward route to better precipitation and thereby discharge results, although this is depending on the climatic drivers of the basin.
Naze Candogan Yossef, Rens van Beek, Albrecht Weerts, Hessel Winsemius, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 21, 4103–4114, https://doi.org/10.5194/hess-21-4103-2017, https://doi.org/10.5194/hess-21-4103-2017, 2017
Short summary
Short summary
This paper presents a skill assessment of the global seasonal streamflow forecasting system FEWS-World. For 20 large basins of the world, forecasts using the ESP procedure are compared to forecasts using actual S3 seasonal meteorological forecast ensembles by ECMWF. The results are discussed in the context of prevailing hydroclimatic conditions per basin. The study concludes that in general, the skill of ECMWF S3 forecasts is close to that of the ESP forecasts.
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.
Patricia López López, Edwin H. Sutanudjaja, Jaap Schellekens, Geert Sterk, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 21, 3125–3144, https://doi.org/10.5194/hess-21-3125-2017, https://doi.org/10.5194/hess-21-3125-2017, 2017
Short summary
Short summary
We perform various calibration experiments of a large-scale hydrological model using satellite-based products of evapotranspiration and soil moisture in the Oum Er Rbia River basin in Morocco. In addition, we study the impact on discharge estimates of three global precipitation products in comparison with model parameter calibration. Results show that evapotranspiration and soil moisture observations can be used for model calibration, resulting in discharge estimates of acceptable accuracy.
Juan C. Chacon-Hurtado, Leonardo Alfonso, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 21, 3071–3091, https://doi.org/10.5194/hess-21-3071-2017, https://doi.org/10.5194/hess-21-3071-2017, 2017
Short summary
Short summary
This paper compiles most of the studies (as far as the authors are aware) on the design of sensor networks for measurement of precipitation and streamflow. The literature shows that there is no overall consensus on the methods for the evaluation of sensor networks, as different design criteria often lead to different solutions. This paper proposes a methodology for the classification of methods, and a general framework for the design of sensor networks.
Maurizio Mazzoleni, Martin Verlaan, Leonardo Alfonso, Martina Monego, Daniele Norbiato, Miche Ferri, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 21, 839–861, https://doi.org/10.5194/hess-21-839-2017, https://doi.org/10.5194/hess-21-839-2017, 2017
Short summary
Short summary
This study assesses the potential use of crowdsourced data in hydrological modeling, which are characterized by irregular availability and variable accuracy. We show that even data with these characteristics can improve flood prediction if properly integrated into hydrological models. This study provides technological support to citizen observatories of water, in which citizens can play an active role in capturing information, leading to improved model forecasts and better flood management.
Joost V. L. Beckers, Albrecht H. Weerts, Erik Tijdeman, and Edwin Welles
Hydrol. Earth Syst. Sci., 20, 3277–3287, https://doi.org/10.5194/hess-20-3277-2016, https://doi.org/10.5194/hess-20-3277-2016, 2016
Short summary
Short summary
Oceanic–atmospheric climate modes, such as El Niño–Southern Oscillation (ENSO), are known to affect the streamflow regime in many rivers around the world. A new method is presented for ENSO conditioning of the ensemble streamflow prediction (ESP) method, which is often used for seasonal streamflow forecasting. The method was tested on three tributaries of the Columbia River, OR. Results show an improvement in forecast skill compared to the standard ESP.
Patricia López López, Niko Wanders, Jaap Schellekens, Luigi J. Renzullo, Edwin H. Sutanudjaja, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 20, 3059–3076, https://doi.org/10.5194/hess-20-3059-2016, https://doi.org/10.5194/hess-20-3059-2016, 2016
Short summary
Short summary
We perform a joint assimilation experiment of high-resolution satellite soil moisture and discharge observations in the Murrumbidgee River basin with a large-scale hydrological model. Additionally, we study the impact of high- and low-resolution meteorological forcing on the model performance. We show that the assimilation of high-resolution satellite soil moisture and discharge observations has a significant impact on discharge simulations and can bring them closer to locally calibrated models.
O. Rakovec, A. H. Weerts, J. Sumihar, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 19, 2911–2924, https://doi.org/10.5194/hess-19-2911-2015, https://doi.org/10.5194/hess-19-2911-2015, 2015
Short summary
Short summary
This is the first analysis of the asynchronous ensemble Kalman filter in hydrological forecasting. The results of discharge assimilation into a hydrological model for the catchment show that including past predictions and observations in the filter improves model forecasts. Additionally, we show that elimination of the strongly non-linear relation between soil moisture and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting.
N. Tangdamrongsub, S. C. Steele-Dunne, B. C. Gunter, P. G. Ditmar, and A. H. Weerts
Hydrol. Earth Syst. Sci., 19, 2079–2100, https://doi.org/10.5194/hess-19-2079-2015, https://doi.org/10.5194/hess-19-2079-2015, 2015
A. Hally, O. Caumont, L. Garrote, E. Richard, A. Weerts, F. Delogu, E. Fiori, N. Rebora, A. Parodi, A. Mihalović, M. Ivković, L. Dekić, W. van Verseveld, O. Nuissier, V. Ducrocq, D. D'Agostino, A. Galizia, E. Danovaro, and A. Clematis
Nat. Hazards Earth Syst. Sci., 15, 537–555, https://doi.org/10.5194/nhess-15-537-2015, https://doi.org/10.5194/nhess-15-537-2015, 2015
A. Md Ali, D. P. Solomatine, and G. Di Baldassarre
Hydrol. Earth Syst. Sci., 19, 631–643, https://doi.org/10.5194/hess-19-631-2015, https://doi.org/10.5194/hess-19-631-2015, 2015
P. López López, J. S. Verkade, A. H. Weerts, and D. P. Solomatine
Hydrol. Earth Syst. Sci., 18, 3411–3428, https://doi.org/10.5194/hess-18-3411-2014, https://doi.org/10.5194/hess-18-3411-2014, 2014
N. Kayastha, J. Ye, F. Fenicia, V. Kuzmin, and D. P. Solomatine
Hydrol. Earth Syst. Sci., 17, 4441–4451, https://doi.org/10.5194/hess-17-4441-2013, https://doi.org/10.5194/hess-17-4441-2013, 2013
D. E. Robertson, D. L. Shrestha, and Q. J. Wang
Hydrol. Earth Syst. Sci., 17, 3587–3603, https://doi.org/10.5194/hess-17-3587-2013, https://doi.org/10.5194/hess-17-3587-2013, 2013
M. B. Mabrouk, A. Jonoski, D. Solomatine, and S. Uhlenbrook
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-10873-2013, https://doi.org/10.5194/hessd-10-10873-2013, 2013
Revised manuscript not accepted
D. L. Shrestha, D. E. Robertson, Q. J. Wang, T. C. Pagano, and H. A. P. Hapuarachchi
Hydrol. Earth Syst. Sci., 17, 1913–1931, https://doi.org/10.5194/hess-17-1913-2013, https://doi.org/10.5194/hess-17-1913-2013, 2013
D. Leedal, A. H. Weerts, P. J. Smith, and K. J. Beven
Hydrol. Earth Syst. Sci., 17, 177–185, https://doi.org/10.5194/hess-17-177-2013, https://doi.org/10.5194/hess-17-177-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysis
A data-centric perspective on the information needed for hydrological uncertainty predictions
A decomposition approach to evaluating the local performance of global streamflow reanalysis
How much water vapour does the Tibetan Plateau release into the atmosphere?
Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems
Technical note: The CREDIBLE Uncertainty Estimation (CURE) toolbox: facilitating the communication of epistemic uncertainty
On the importance of observation uncertainty when evaluating and comparing models: a hydrological example
Why do our rainfall–runoff models keep underestimating the peak flows?
Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies
Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example
Benchmarking global hydrological and land surface models against GRACE in a medium-sized tropical basin
Guidance on evaluating parametric model uncertainty at decision-relevant scales
Quantifying input uncertainty in the calibration of water quality models: reordering errors via the secant method
Sequential data assimilation for real-time probabilistic flood inundation mapping
Key challenges facing the application of the conductivity mass balance method: a case study of the Mississippi River basin
Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model
A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda
Technical note: Uncertainty in multi-source partitioning using large tracer data sets
Assessment of climate change impact and difference on the river runoff in four basins in China under 1.5 and 2.0 °C global warming
A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation
Technical note: Analytical sensitivity analysis and uncertainty estimation of baseflow index calculated by a two-component hydrograph separation method with conductivity as a tracer
Understanding the water cycle over the upper Tarim Basin: retrospecting the estimated discharge bias to atmospheric variables and model structure
The effect of input data resolution and complexity on the uncertainty of hydrological predictions in a humid vegetated watershed
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches
Technical note: Pitfalls in using log-transformed flows within the KGE criterion
Improvement of model evaluation by incorporating prediction and measurement uncertainty
Transferability of climate simulation uncertainty to hydrological impacts
Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)
Mapping (dis)agreement in hydrologic projections
Consistency assessment of rating curve data in various locations using Bidirectional Reach (BReach)
The critical role of uncertainty in projections of hydrological extremes
Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting
Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding
Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales
Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model
Quantifying uncertainty on sediment loads using bootstrap confidence intervals
Event-scale power law recession analysis: quantifying methodological uncertainty
Disentangling timing and amplitude errors in streamflow simulations
Reliability of lumped hydrological modeling in a semi-arid mountainous catchment facing water-use changes
Using dry and wet year hydroclimatic extremes to guide future hydrologic projections
Uncertainty contributions to low-flow projections in Austria
Accounting for dependencies in regionalized signatures for predictions in ungauged catchments
Climate change and its impacts on river discharge in two climate regions in China
Uncertainty in hydrological signatures
Climate model uncertainty versus conceptual geological uncertainty in hydrological modeling
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
Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz
Hydrol. Earth Syst. Sci., 28, 4099–4126, https://doi.org/10.5194/hess-28-4099-2024, https://doi.org/10.5194/hess-28-4099-2024, 2024
Short summary
Short summary
This work examines the impact of temporal and spatial information on the uncertainty estimation of streamflow forecasts. The study emphasizes the importance of data updates and global information for precise uncertainty estimates. We use conformal prediction to show that recent data enhance the estimates, even if only available infrequently. Local data yield reasonable average estimations but fall short for peak-flow events. The use of global data significantly improves these predictions.
Tongtiegang Zhao, Zexin Chen, Yu Tian, Bingyao Zhang, Yu Li, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 28, 3597–3611, https://doi.org/10.5194/hess-28-3597-2024, https://doi.org/10.5194/hess-28-3597-2024, 2024
Short summary
Short summary
The local performance plays a critical part in practical applications of global streamflow reanalysis. This paper develops a decomposition approach to evaluating streamflow analysis at different timescales. The reanalysis is observed to be more effective in characterizing seasonal, annual and multi-annual features than daily, weekly and monthly features. Also, the local performance is shown to be primarily influenced by precipitation seasonality, longitude, mean precipitation and mean slope.
Chaolei Zheng, Li Jia, Guangcheng Hu, Massimo Menenti, and Joris Timmermans
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-55, https://doi.org/10.5194/hess-2024-55, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Significant changes are occurring in the Tibetan Plateau, but the amount and variations of evapotranspiration (ET) are with large uncertainty. This study compares 22 ET products and finds that the mean annual ET is 350.34 mm/yr over the Tibetan Plateau, with soil water contribute most to total ET. It also find most products showing an increasing trend. It provides a comprehensive study that supports further ET estimation and potential use of ET data for relevant water and climate studies.
Uwe Ehret and Pankaj Dey
Hydrol. Earth Syst. Sci., 27, 2591–2605, https://doi.org/10.5194/hess-27-2591-2023, https://doi.org/10.5194/hess-27-2591-2023, 2023
Short summary
Short summary
We propose the
c-u-curvemethod to characterize dynamical (time-variable) systems of all kinds.
Uis for uncertainty and expresses how well a system can be predicted in a given period of time.
Cis for complexity and expresses how predictability differs between different periods, i.e. how well predictability itself can be predicted. The method helps to better classify and compare dynamical systems across a wide range of disciplines, thus facilitating scientific collaboration.
Trevor Page, Paul Smith, Keith Beven, Francesca Pianosi, Fanny Sarrazin, Susana Almeida, Liz Holcombe, Jim Freer, Nick Chappell, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 27, 2523–2534, https://doi.org/10.5194/hess-27-2523-2023, https://doi.org/10.5194/hess-27-2523-2023, 2023
Short summary
Short summary
This publication provides an introduction to the CREDIBLE Uncertainty Estimation (CURE) toolbox. CURE offers workflows for a variety of uncertainty estimation methods. One of its most important features is the requirement that all of the assumptions on which a workflow analysis depends be defined. This facilitates communication with potential users of an analysis. An audit trail log is produced automatically from a workflow for future reference.
Jerom P.M. Aerts, Jannis M. Hoch, Gemma Coxon, Nick C. van de Giesen, and Rolf W. Hut
EGUsphere, https://doi.org/10.5194/egusphere-2023-1156, https://doi.org/10.5194/egusphere-2023-1156, 2023
Short summary
Short summary
Hydrological model performance involves comparing simulated states and fluxes with observed counterparts. Often, it is overlooked that there is inherent uncertainty surrounding the observations. This can significantly impact the results. In this publication, we emphasize the significance of accounting for observation uncertainty in model comparison. We propose a practical method that is applicable for any observational time series with available uncertainty estimations.
András Bárdossy and Faizan Anwar
Hydrol. Earth Syst. Sci., 27, 1987–2000, https://doi.org/10.5194/hess-27-1987-2023, https://doi.org/10.5194/hess-27-1987-2023, 2023
Short summary
Short summary
This study demonstrates the fact that the large river flows forecasted by the models show an underestimation that is inversely related to the number of locations where precipitation is recorded, which is independent of the model. The higher the number of points where the amount of precipitation is recorded, the better the estimate of the river flows.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Short summary
Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Yan Liu, Jaime Fernández-Ortega, Matías Mudarra, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5341–5355, https://doi.org/10.5194/hess-26-5341-2022, https://doi.org/10.5194/hess-26-5341-2022, 2022
Short summary
Short summary
We adapt the informal Kling–Gupta efficiency (KGE) with a gamma distribution to apply it as an informal likelihood function in the DiffeRential Evolution Adaptive Metropolis DREAM(ZS) method. Our adapted approach performs as well as the formal likelihood function for exploring posterior distributions of model parameters. The adapted KGE is superior to the formal likelihood function for calibrations combining multiple observations with different lengths, frequencies and units.
Silvana Bolaños Chavarría, Micha Werner, Juan Fernando Salazar, and Teresita Betancur Vargas
Hydrol. Earth Syst. Sci., 26, 4323–4344, https://doi.org/10.5194/hess-26-4323-2022, https://doi.org/10.5194/hess-26-4323-2022, 2022
Short summary
Short summary
Using total water storage (TWS) from GRACE satellites, we assess the reliability of global hydrological and land surface models over a medium-sized tropical basin with a well-developed gauging network. We find the models poorly represent TWS for the monthly series, but they improve in representing seasonality and long-term trends. We conclude that GRACE provides a valuable dataset to benchmark global simulations of TWS change, offering a useful tool to improve global models in tropical basins.
Jared D. Smith, Laurence Lin, Julianne D. Quinn, and Lawrence E. Band
Hydrol. Earth Syst. Sci., 26, 2519–2539, https://doi.org/10.5194/hess-26-2519-2022, https://doi.org/10.5194/hess-26-2519-2022, 2022
Short summary
Short summary
Watershed models are used to simulate streamflow and water quality, and to inform siting and sizing decisions for runoff and nutrient control projects. Data are limited for many watershed processes that are represented in such models, which requires selecting the most important processes to be calibrated. We show that this selection should be based on decision-relevant metrics at the spatial scales of interest for the control projects. This should enable more robust project designs.
Xia Wu, Lucy Marshall, and Ashish Sharma
Hydrol. Earth Syst. Sci., 26, 1203–1221, https://doi.org/10.5194/hess-26-1203-2022, https://doi.org/10.5194/hess-26-1203-2022, 2022
Short summary
Short summary
Decomposing parameter and input errors in model calibration is a considerable challenge. This study transfers the direct estimation of an input error series to their rank estimation and develops a new algorithm, i.e., Bayesian error analysis with reordering (BEAR). In the context of a total suspended solids simulation, two synthetic studies and a real study demonstrate that the BEAR method is effective for improving the input error estimation and water quality model calibration.
Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021, https://doi.org/10.5194/hess-25-4995-2021, 2021
Short summary
Short summary
In this study, daily observations are assimilated into a hydrodynamic model to update the performance of modeling and improve the flood inundation mapping skill. Results demonstrate that integrating data assimilation with a hydrodynamic model improves the performance of flood simulation and provides more reliable inundation maps. A flowchart provides the overall steps for applying this framework in practice and forecasting probabilistic flood maps before the onset of upcoming floods.
Hang Lyu, Chenxi Xia, Jinghan Zhang, and Bo Li
Hydrol. Earth Syst. Sci., 24, 6075–6090, https://doi.org/10.5194/hess-24-6075-2020, https://doi.org/10.5194/hess-24-6075-2020, 2020
Short summary
Short summary
Baseflow separation plays a critical role in science-based management of water resources. This study addressed key challenges hindering the application of the generally accepted conductivity mass balance (CMB). Monitoring data for over 200 stream sites of the Mississippi River basin were collected to answer the following questions. What are the characteristics of a watershed that determine the method suitability? What length of monitoring data is needed? How can the parameters be more accurate?
Aynom T. Teweldebrhan, Thomas V. Schuler, John F. Burkhart, and Morten Hjorth-Jensen
Hydrol. Earth Syst. Sci., 24, 4641–4658, https://doi.org/10.5194/hess-24-4641-2020, https://doi.org/10.5194/hess-24-4641-2020, 2020
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020, https://doi.org/10.5194/hess-24-4463-2020, 2020
Short summary
Short summary
The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
Alicia Correa, Diego Ochoa-Tocachi, and Christian Birkel
Hydrol. Earth Syst. Sci., 23, 5059–5068, https://doi.org/10.5194/hess-23-5059-2019, https://doi.org/10.5194/hess-23-5059-2019, 2019
Short summary
Short summary
The applications and availability of large tracer data sets have vastly increased in recent years leading to research into the contributions of multiple sources to a mixture. We introduce a method based on Taylor series approximation to estimate the uncertainties of such sources' contributions. The method is illustrated with examples of hydrology (14 tracers) and a MATLAB code is provided for reproducibility. This method can be generalized to any number of tracers across a range of disciplines.
Hongmei Xu, Lüliu Liu, Yong Wang, Sheng Wang, Ying Hao, Jingjin Ma, and Tong Jiang
Hydrol. Earth Syst. Sci., 23, 4219–4231, https://doi.org/10.5194/hess-23-4219-2019, https://doi.org/10.5194/hess-23-4219-2019, 2019
Short summary
Short summary
1.5 and 2 °C have become targets in the discussion of climate change impacts. However, climate research is also challenged to provide more robust information on the impact of climate change at local and regional scales to assist the development of sound scientific adaptation and mitigation measures. This study assessed the impacts and differences of 1.5 and 2.0 °C global warming on basin-scale river runoff by examining four river basins covering a wide hydroclimatic setting in China.
Lorenz Ammann, Fabrizio Fenicia, and Peter Reichert
Hydrol. Earth Syst. Sci., 23, 2147–2172, https://doi.org/10.5194/hess-23-2147-2019, https://doi.org/10.5194/hess-23-2147-2019, 2019
Short summary
Short summary
The uncertainty of hydrological models can be substantial, and its quantification and realistic description are often difficult. We propose a new flexible probabilistic framework to describe and quantify this uncertainty. It is show that the correlation of the errors can be non-stationary, and that accounting for temporal changes in correlation can lead to strongly improved probabilistic predictions. This is a promising avenue for improving uncertainty estimation in hydrological modelling.
Weifei Yang, Changlai Xiao, and Xiujuan Liang
Hydrol. Earth Syst. Sci., 23, 1103–1112, https://doi.org/10.5194/hess-23-1103-2019, https://doi.org/10.5194/hess-23-1103-2019, 2019
Short summary
Short summary
This paper analyzed the sensitivity of the baseflow index to the parameters of the conductivity two-component hydrograph separation method. The results indicated that the baseflow index is more sensitive to the conductivity of baseflow and the separation method may be more suitable for the long time series in a small watershed. After considering the mutual offset of the measurement errors of conductivity and streamflow, the uncertainty in baseflow index was reduced by half.
Xudong Zhou, Jan Polcher, Tao Yang, Yukiko Hirabayashi, and Trung Nguyen-Quang
Hydrol. Earth Syst. Sci., 22, 6087–6108, https://doi.org/10.5194/hess-22-6087-2018, https://doi.org/10.5194/hess-22-6087-2018, 2018
Short summary
Short summary
Model bias is commonly seen in discharge simulation by hydrological or land surface models. This study tested an approach with the Budyko hypothesis to retrospect the estimated discharge bias to different bias sources including the atmospheric variables and model structure. Results indicate that the bias is most likely caused by the forcing variables, and the forcing bias should firstly be assessed and reduced in order to perform pertinent analysis of the regional water cycle.
Linh Hoang, Rajith Mukundan, Karen E. B. Moore, Emmet M. Owens, and Tammo S. Steenhuis
Hydrol. Earth Syst. Sci., 22, 5947–5965, https://doi.org/10.5194/hess-22-5947-2018, https://doi.org/10.5194/hess-22-5947-2018, 2018
Short summary
Short summary
The paper analyzes the effect of two input data (DEMs and the combination of soil and land use data) with different resolution and complexity on the uncertainty of model outputs (the predictions of streamflow and saturated areas) and parameter uncertainty using SWAT-HS. Results showed that DEM resolution has significant effect on the spatial pattern of saturated areas and using complex soil and land use data may not necessarily improve model performance or reduce model uncertainty.
Aynom T. Teweldebrhan, John F. Burkhart, and Thomas V. Schuler
Hydrol. Earth Syst. Sci., 22, 5021–5039, https://doi.org/10.5194/hess-22-5021-2018, https://doi.org/10.5194/hess-22-5021-2018, 2018
Léonard Santos, Guillaume Thirel, and Charles Perrin
Hydrol. Earth Syst. Sci., 22, 4583–4591, https://doi.org/10.5194/hess-22-4583-2018, https://doi.org/10.5194/hess-22-4583-2018, 2018
Short summary
Short summary
The Kling and Gupta efficiency (KGE) is a score used in hydrology to evaluate flow simulation compared to observations. In order to force the evaluation on the low flows, some authors used the log-transformed flow to calculate the KGE. In this technical note, we show that this transformation should be avoided because it produced numerical flaws that lead to difficulties in the score value interpretation.
Lei Chen, Shuang Li, Yucen Zhong, and Zhenyao Shen
Hydrol. Earth Syst. Sci., 22, 4145–4154, https://doi.org/10.5194/hess-22-4145-2018, https://doi.org/10.5194/hess-22-4145-2018, 2018
Short summary
Short summary
In this study, the cumulative distribution function approach (CDFA) and the Monte Carlo approach (MCA) were used to develop two new approaches for model evaluation within an uncertainty framework. These proposed methods could be extended to watershed models to provide a substitution for traditional model evaluations within an uncertainty framework.
Hui-Min Wang, Jie Chen, Alex J. Cannon, Chong-Yu Xu, and Hua Chen
Hydrol. Earth Syst. Sci., 22, 3739–3759, https://doi.org/10.5194/hess-22-3739-2018, https://doi.org/10.5194/hess-22-3739-2018, 2018
Short summary
Short summary
Facing a growing number of climate models, many selection methods were proposed to select subsets in the field of climate simulation, but the transferability of their performances to hydrological impacts remains doubtful. We investigate the transferability of climate simulation uncertainty to hydrological impacts using two selection methods, and conclude that envelope-based selection of about 10 climate simulations based on properly chosen climate variables is suggested for impact studies.
Andreas M. Jobst, Daniel G. Kingston, Nicolas J. Cullen, and Josef Schmid
Hydrol. Earth Syst. Sci., 22, 3125–3142, https://doi.org/10.5194/hess-22-3125-2018, https://doi.org/10.5194/hess-22-3125-2018, 2018
Lieke A. Melsen, Nans Addor, Naoki Mizukami, Andrew J. Newman, Paul J. J. F. Torfs, Martyn P. Clark, Remko Uijlenhoet, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 22, 1775–1791, https://doi.org/10.5194/hess-22-1775-2018, https://doi.org/10.5194/hess-22-1775-2018, 2018
Short summary
Short summary
Long-term hydrological predictions are important for water management planning, but are also prone to uncertainty. This study investigates three sources of uncertainty for long-term hydrological predictions in the US: climate models, hydrological models, and hydrological model parameters. Mapping the results revealed spatial patterns in the three sources of uncertainty: different sources of uncertainty dominate in different regions.
Katrien Van Eerdenbrugh, Stijn Van Hoey, Gemma Coxon, Jim Freer, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 21, 5315–5337, https://doi.org/10.5194/hess-21-5315-2017, https://doi.org/10.5194/hess-21-5315-2017, 2017
Short summary
Short summary
Consistency in stage–discharge data is investigated using a methodology called Bidirectional Reach (BReach). Various measurement stations in the UK, New Zealand and Belgium are selected based on their historical ratings information and their characteristics related to data consistency. When applying a BReach analysis on them, the methodology provides results that appear consistent with the available knowledge and thus facilitates a reliable assessment of (in)consistency in stage–discharge data.
Hadush K. Meresa and Renata J. Romanowicz
Hydrol. Earth Syst. Sci., 21, 4245–4258, https://doi.org/10.5194/hess-21-4245-2017, https://doi.org/10.5194/hess-21-4245-2017, 2017
Short summary
Short summary
Evaluation of the uncertainty in projections of future hydrological extremes in the mountainous catchment was performed. The uncertainty of the estimate of 1-in-100-year return maximum flow based on the 1971–2100 time series exceeds 200 % of its median value with the largest influence of the climate model uncertainty, while the uncertainty of the 1-in-100-year return minimum flow is of the same order (i.e. exceeds 200 %) but it is mainly influenced by the hydrological model parameter uncertainty.
Omar Wani, Joost V. L. Beckers, Albrecht H. Weerts, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 21, 4021–4036, https://doi.org/10.5194/hess-21-4021-2017, https://doi.org/10.5194/hess-21-4021-2017, 2017
Short summary
Short summary
We generate uncertainty intervals for hydrologic model predictions using a simple instance-based learning scheme. Errors made by the model in some specific hydrometeorological conditions in the past are used to predict the probability distribution of its errors during forecasting. We test it for two different case studies in England. We find that this technique, even though conceptually simple and easy to implement, performs as well as some other sophisticated uncertainty estimation methods.
Christa Kelleher, Brian McGlynn, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 21, 3325–3352, https://doi.org/10.5194/hess-21-3325-2017, https://doi.org/10.5194/hess-21-3325-2017, 2017
Short summary
Short summary
Models are tools for understanding how watersheds function and may respond to land cover and climate change. Before we can use models towards these purposes, we need to ensure that a model adequately represents watershed-wide observations. In this paper, we propose a new way to evaluate whether model simulations match observations, using a variety of information sources. We show how this information can reduce uncertainty in inputs to models, reducing uncertainty in hydrologic predictions.
Gabriele Baroni, Matthias Zink, Rohini Kumar, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 2301–2320, https://doi.org/10.5194/hess-21-2301-2017, https://doi.org/10.5194/hess-21-2301-2017, 2017
Short summary
Short summary
Three methods are used to characterize the uncertainty in soil properties. The effect on simulated states and fluxes is quantified using a distributed hydrological model. Different impacts are identified as function of the perturbation method, of the model outputs and of the spatio-temporal resolution. The study underlines the importance of a proper characterization of the uncertainty in soil properties for a correct assessment of their role and further improvements in the model application.
Ji Li, Yangbo Chen, Huanyu Wang, Jianming Qin, Jie Li, and Sen Chiao
Hydrol. Earth Syst. Sci., 21, 1279–1294, https://doi.org/10.5194/hess-21-1279-2017, https://doi.org/10.5194/hess-21-1279-2017, 2017
Short summary
Short summary
Quantitative precipitation forecast produced by the WRF model has a similar pattern to that estimated by rain gauges in a southern China large watershed, hydrological model parameters should be optimized with QPF produced by WRF, and simulating floods by coupling the WRF QPF with a distributed hydrological model provides a good reference for large watershed flood warning and could benefit the flood management communities due to its longer lead time.
Johanna I. F. Slaets, Hans-Peter Piepho, Petra Schmitter, Thomas Hilger, and Georg Cadisch
Hydrol. Earth Syst. Sci., 21, 571–588, https://doi.org/10.5194/hess-21-571-2017, https://doi.org/10.5194/hess-21-571-2017, 2017
Short summary
Short summary
Determining measures of uncertainty on loads is not trivial, as a load is a product of concentration and discharge per time point, summed up over time. A bootstrap approach enables the calculation of confidence intervals on constituent loads. Ignoring the uncertainty on the discharge will typically underestimate the width of 95 % confidence intervals by around 10 %. Furthermore, confidence intervals are asymmetric, with the largest uncertainty on the upper limit.
David N. Dralle, Nathaniel J. Karst, Kyriakos Charalampous, Andrew Veenstra, and Sally E. Thompson
Hydrol. Earth Syst. Sci., 21, 65–81, https://doi.org/10.5194/hess-21-65-2017, https://doi.org/10.5194/hess-21-65-2017, 2017
Short summary
Short summary
The streamflow recession is the period following rainfall during which flow declines. This paper examines a common method of recession analysis and identifies sensitivity of the technique's results to necessary, yet subjective, methodological choices. The results have implications for hydrology, sediment and solute transport, and geomorphology, as well as for testing numerous hydrologic theories which predict the mathematical form of the recession.
Simon Paul Seibert, Uwe Ehret, and Erwin Zehe
Hydrol. Earth Syst. Sci., 20, 3745–3763, https://doi.org/10.5194/hess-20-3745-2016, https://doi.org/10.5194/hess-20-3745-2016, 2016
Short summary
Short summary
While the assessment of "vertical" (magnitude) errors of streamflow simulations is standard practice, "horizontal" (timing) errors are rarely considered. To assess their role, we propose a method to quantify both errors simultaneously which closely resembles visual hydrograph comparison. Our results reveal differences in time–magnitude error statistics for different flow conditions. The proposed method thus offers novel perspectives for model diagnostics and evaluation.
Paul Hublart, Denis Ruelland, Inaki García de Cortázar-Atauri, Simon Gascoin, Stef Lhermitte, and Antonio Ibacache
Hydrol. Earth Syst. Sci., 20, 3691–3717, https://doi.org/10.5194/hess-20-3691-2016, https://doi.org/10.5194/hess-20-3691-2016, 2016
Short summary
Short summary
Our paper explores the reliability of conceptual catchment models in the dry Andes. First, we show that explicitly accounting for irrigation water use improves streamflow predictions during dry years. Second, we show that sublimation losses can be easily incorporated into temperature-based melt models without increasing model complexity too much. Our work also highlights areas requiring additional research, including the need for a better conceptualization of runoff generation processes.
Stephen Oni, Martyn Futter, Jose Ledesma, Claudia Teutschbein, Jim Buttle, and Hjalmar Laudon
Hydrol. Earth Syst. Sci., 20, 2811–2825, https://doi.org/10.5194/hess-20-2811-2016, https://doi.org/10.5194/hess-20-2811-2016, 2016
Short summary
Short summary
This paper presents an important framework to improve hydrologic projections in cold regions. Hydrologic modelling/projections are often based on model calibration to long-term data. Here we used dry and wet years as a proxy to quantify uncertainty in projecting hydrologic extremes. We showed that projections based on long-term data could underestimate runoff by up to 35% in boreal regions. We believe the hydrologic modelling community will benefit from new insights derived from this study.
Juraj Parajka, Alfred Paul Blaschke, Günter Blöschl, Klaus Haslinger, Gerold Hepp, Gregor Laaha, Wolfgang Schöner, Helene Trautvetter, Alberto Viglione, and Matthias Zessner
Hydrol. Earth Syst. Sci., 20, 2085–2101, https://doi.org/10.5194/hess-20-2085-2016, https://doi.org/10.5194/hess-20-2085-2016, 2016
Short summary
Short summary
Streamflow estimation during low-flow conditions is important for estimation of environmental flows, effluent water quality, hydropower operations, etc. However, it is not clear how the uncertainties in assumptions used in the projections translate into uncertainty of estimated future low flows. The objective of the study is to explore the relative role of hydrologic model calibration and climate scenarios in the uncertainty of low-flow projections in Austria.
Susana Almeida, Nataliya Le Vine, Neil McIntyre, Thorsten Wagener, and Wouter Buytaert
Hydrol. Earth Syst. Sci., 20, 887–901, https://doi.org/10.5194/hess-20-887-2016, https://doi.org/10.5194/hess-20-887-2016, 2016
Short summary
Short summary
The absence of flow data to calibrate hydrologic models may reduce the ability of such models to reliably inform water resources management. To address this limitation, it is common to condition hydrological model parameters on regionalized signatures. In this study, we justify the inclusion of larger sets of signatures in the regionalization procedure if their error correlations are formally accounted for and thus enable a more complete use of all available information.
H. Xu and Y. Luo
Hydrol. Earth Syst. Sci., 19, 4609–4618, https://doi.org/10.5194/hess-19-4609-2015, https://doi.org/10.5194/hess-19-4609-2015, 2015
Short summary
Short summary
This study quantified the climate impact on river discharge in the River Huangfuchuan in semi-arid northern China and the River Xiangxi in humid southern China. Climate projections showed trends toward warmer and wetter conditions, particularly for the River Huangfuchuan. The main projected hydrologic impact was a more pronounced increase in annual discharge in both catchments. Peak flows are projected to appear earlier than usual in the River Huangfuchuan and later than usual in River Xiangxi.
I. K. Westerberg and H. K. McMillan
Hydrol. Earth Syst. Sci., 19, 3951–3968, https://doi.org/10.5194/hess-19-3951-2015, https://doi.org/10.5194/hess-19-3951-2015, 2015
Short summary
Short summary
This study investigated the effect of uncertainties in data and calculation methods on hydrological signatures. We present a widely applicable method to evaluate signature uncertainty and show results for two example catchments. The uncertainties were often large (i.e. typical intervals of ±10–40% relative uncertainty) and highly variable between signatures. It is therefore important to consider uncertainty when signatures are used for hydrological and ecohydrological analyses and modelling.
T. O. Sonnenborg, D. Seifert, and J. C. Refsgaard
Hydrol. Earth Syst. Sci., 19, 3891–3901, https://doi.org/10.5194/hess-19-3891-2015, https://doi.org/10.5194/hess-19-3891-2015, 2015
Short summary
Short summary
The impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Geology is the dominating uncertainty source for travel time and capture zones, while climate dominates for hydraulic heads and steam flow.
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
Cited articles
Aubert, D., Loumagne, C., and Oudin, L.: Sequential assimilation of soil moisture and streamflow data in a conceptual rainfallrunoff model, J. Hydrol., 280, 145–161, https://doi.org/10.1016/S0022-1694(03)00229-4a, 2003.
Bailey, R. and Dobson, C.: Forecasting for floods in the Severn catchment, J. Inst. Water Eng. Sci., 35, 168–178, 1981.
Barnwal, P. and Kotani, K.: Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India, Ecol. Econ., 87, 95–109, 2013.
Battiti, R.: Using mutual information for selecting features in supervised neural net learning, IEEE T. Neural Networ., 5, 537–550, 1994.
Baur, D., Saisana, M., and Schulze, N.: Modelling the effects of meteorological variables on ozone concentration – a quantile regression approach, Atmos. Environ., 38, 4689–4699, 2004.
Bell, V. A. and Moore, R. J.: The sensitivity of catchment runoff models to rainfall data at different spatial scales, Hydrol. Earth Syst. Sci., 4, 653–667, https://doi.org/10.5194/hess-4-653-2000, 2000.
Bensaid, A. M., Hall, L. O., Bezdek, J. C., Clarke, L. P., Silbiger, M. L., Arrington, J. A., and Murtagh, R. F.: Validity-guided (re) clustering with applications to image segmentation, IEEE T. Fuzzy Syst., 4, 112–123, 1996.
Bergström, S.: Development and application of a conceptual runoff model for Scandinavian catchments, report RHO7, 118, Swed. Meteorol. Hydrol. Inst., Norrköping, Sweden, 1976.
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992.
Bogner, K. and Pappenberger, F.: Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system, Water Resour. Res., 47, W07524, https://doi.org/10.1029/2010WR009137, 2011.
Bondell, H. D., Reich, B. J., and Wang, H.: Noncrossing Quantile Regression curve estimation, Biometrika, 97, 825–838, https://doi.org/10.1093/biomet/asq048, 2010.
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C.: Time series analysis: forecasting and control (4th ed.), p. 16, John Wiley & Sons, Inc., Hoboken, New Jersey, 2008.
Bremnes, J. B.: Probabilistic wind power forecasts using local quantile regression, Wind Energy, 7, 47–54, https://doi.org/10.1002/we.107, 2004.
Brown, J. D. and Heuvelink, G. B. M.: Assessing uncertainty propagation through physically based models of soil water flow and solute transport, in: Encyclopedia of Hydrological Sciences, 6:79, edited by: Anderson, M. G., John Wiley, New York, 2005.
Cannon, A. J.: Quantile regression neural networks: Implementation in R and application to precipitation downscaling, Comput. Geosci., 37, 1277–1284, 2011.
Coccia, G. and Todini, E.: Recent developments in predictive uncertainty assessment based on the model conditional processor approach, Hydrol. Earth Syst. Sci., 15, 3253–3274, https://doi.org/10.5194/hess-15-3253-2011, 2011.
Coxon, G., Freer, J., Westerberg, I., Woods, R., Smith, P., and Wagener, T.: A generalised framework for large-scale evaluation of discharge uncertainties across England and Wales, EGU General Assembly, Vienna, Austria, 27 April–2 May 2014, EGU2014-10157, 2014.
Di Baldassarre, G. and Montanari, A.: Uncertainty in river discharge observations: a quantitative analysis, Hydrol. Earth Syst. Sci., 13, 913–921, https://doi.org/10.5194/hess-13-913-2009, 2009.
Dogulu, N., Solomatine, D. P., and Shrestha, D. L.: Applying clustering approach in predictive uncertainty estimation: a case study with the UNEEC method, EGU General Assembly, Vienna, Austria, 27 April–2 May 2014, EGU2014-5992, 2014.
EA: Environment Agency: River levels: Midlands, available at: http://www.environment-agency.gov.uk/homeandleisure/floods/riverlevels/, (last access: 1 October 2013), 2009.
Friederichs, P. and Hense, A.: Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression, Mon. Weather Rev., 135, 2365–2378, https://doi.org/10.1175/MWR3403.1, 2007.
Gupta, H. V., Beven, K. J., and Wagener, T.: Model calibration and uncertainty estimation, in: Encyclopedia of Hydrological Sciences, 11:131, edited by: Anderson, M. G., John Wiley, New York, 2005.
Halkidi, M., Batistakis, Y., and Vazirgiannis, M.: On clustering validation techniques, J. Intell. Inf. Syst., 17, 107–145, https://doi.org/10.1023/A:1012801612483, 2001.
Hill, T. and Neal, C.: Spatial and temporal variation in pH, alkalinity and conductivity in surface runoff and groundwater for the Upper River Severn catchment. Hydrol. Earth Syst. Sci., 1, 697–715, https://doi.org/10.5194/hess-1-697-1997, 1997.
Jin, X., Xu, C. Y., Zhang, Q., and Singh, V. P.: Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model, J. Hydrol., 383, 147–155, https://doi.org/10.1016/j.jhydrol.2009.12.028, 2010.
Kobold, M. and Sušelj, K.: Precipitation forecasts and their uncertainty as input into hydrological models, Hydrol. Earth Syst. Sci., 9, 322–332, https://doi.org/10.5194/hess-9-322-2005, 2005.
Koenker, R.: Quantile Regression, Cambridge University Press, New York, 2005.
Koenker, R. and Bassett Jr., G.: Regression Quantiles, Econometrica, 1, 33–50, 1978.
Koenker, R. and Hallock, K.: Quantile Regression, J. Econ. Perspect., 15, 143–156, 2001.
Krzysztofowicz, R.: Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35, 2739–2750, https://doi.org/10.1029/1999WR900099, 1999.
Krzysztofowicz, R.: The case for probabilistic forecasting in hydrology, J. Hydrol., 249, 2–9, 2001.
Krzysztofowicz, R. and Kelly, K. S.: Hydrologic uncertainty processor for probabilistic river stage forecasting, Water Resour. Res., 36, 3265–3277, https://doi.org/10.1029/2000WR900108, 2000.
Kudryavtsev, A. A.: Using quantile regression for rate-making, Insur. Math. Econ., 45, 296–304, https://doi.org/10.1016/j.insmatheco.2009.07.010, 2009.
Laio, F. and Tamea, S.: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrol. Earth Syst. Sci., 11, 1267–1277, https://doi.org/10.5194/hess-11-1267-2007, 2007.
Lee, H., Seo, D.-J., and Koren, V.: Assimilation of streamflow and in-situ soil moisture data into operational distributed hydrologic models: Effects of uncertainties in the data and initial model soil moisture states, Adv. Water Resour., 34, 1597–1615, https://doi.org/10.1016/j.advwatres.2011.08.012, 2011.
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, https://doi.org/10.1016/S0022-1694(97)00041-3, 1997.
Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework, Water Resour. Res., 43, W07401, https://doi.org/10.1029/2006WR005756, 2007.
López López, P., Verkade, J. S., Weerts, A. H., and Solomatine, D. P.: Alternative configurations of Quantile Regression for estimating predictive uncertainty in water level forecasts for the Upper Severn River: a comparison, Hydrol. Earth Syst. Sci., 18, 3411–3428, https://doi.org/10.5194/hess-18-3411-2014, 2014.
Marsh, T. and Hannaford, J.: UK Hydrometric Register, Hydrological Data UK Series, Centre for Ecology and Hydrology, Wallingford, UK, 1–210, 2008.
Møller, J. K., Nielsen, H. A., and Madsen, H.: Time-adaptive quantile regression, Comput. Stat. Data An., 52, 1292–1303, 2008.
Montanari, A.: What do we mean by 'uncertainty'? The need for a consistent wording about uncertainty assessment in hydrology, Hydrol. Process., 21, 841–845, 2007.
Montanari, A. and Brath, A.: A stochastic approach for assessing the uncertainty of rainfall–runoff simulations, Water Resour. Res., 40, W01106, https://doi.org/10.1029/2003WR002540, 2004.
Moore, R. J., Jones, D. A., Cox, D. R., and Isham, V. S.: Design of the HYREX raingauge network, Hydrol. Earth Syst. Sci., 4, 523–530, https://doi.org/10.5194/hess-4-523-2000, 2000.
Mukolwe, M. M., Di Baldassarre, G., Werner, M., and Solomatine, D. P.: Flood modelling: parameterisation and inflow uncertainty, P. I. Civil Eng.-Wat. M., 167, 51–60, 2014.
Munir, S., Chen, H., and Ropkins, K.: Modelling the impact of road traffic on ground level ozone concentration using a quantile regression approach, Atmos. Environ., 60, 283–291, 2012.
Nasseri, M. and Zahraie, B.: Application of simple clustering on space-time mapping of mean monthly rainfall pattern, Int. J. Climatol., 31, 732–741, https://doi.org/10.1002/joc.2109, 2011.
Nasseri, M., Zahraie, B., Ansari, A., and Solomatine, D. P.: Uncertainty assessment of monthly water balance models based on Incremental Modified Fuzzy Extension Principle method, J. Hydroinform., 15, 1340–1360, https://doi.org/10.2166/hydro.2013.159, 2013.
Nielsen, H. A., Madsen, H., and Nielsen, T. S.: Using Quantile Regression to extend an existing wind power forecasting system with probabilistic forecasts, Wind Energy, 9, 95–108, 2006.
Pappenberger, F. and Beven, K. J.: Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, https://doi.org/10.1029/2005WR004820, 2006.
Patil, S. and Stieglitz, M.: Hydrologic similarity among catchments under variable flow conditions, Hydrol. Earth Syst. Sci., 15, 989–997, https://doi.org/10.5194/hess-15-989-2011, 2011.
Pianosi, F. and Raso, L.: Dynamic modeling of predictive uncertainty by regression on absolute errors, Water Resour. Res., 48, W03516, https://doi.org/10.1029/2011WR010603, 2012.
Pianosi, F., Shrestha, D. L., and Solomatine, D. P.: ANN-based representation of parametric and residual uncertainty of models, IEEE IJCNN, 1–6, https://doi.org/10.1109/IJCNN.2010.5596852, 2010.
Quinlan, J. R.: Learning with continuous classes, in: Proceedings of the 5th Australian joint Conference on Artificial Intelligence, Hobart, Tasmania, 16–18 November 1992, 343–348, 1992.
Reggiani, P. and Weerts, A.: A Bayesian approach to decision-making under uncertainty: An application to real-time forecasting in the river Rhine, J. Hydrol., 356, 56–59, https://doi.org/10.1016/j.jhydrol.2008.03.027, 2008.
Reggiani, P., Renner, M., Weerts, A., and Van Gelder, P.: Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system, Water Resour. Res., 45, W02428, https://doi.org/10.1029/2007WR006758, 2009.
Roscoe, K. L., Weerts, A. H., and Schroevers, M.: Estimation of the uncertainty in water level forecasts at ungauged river locations using Quantile Regression, Int. J. River Basin Manage., 10, 383–394, 2012.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Shrestha, D. L. and Solomatine, D. P.: Machine learning approaches for estimation of prediction interval for the model output, Neural Networks, 19, 225–235, https://doi.org/10.1016/j.neunet.2006.01.012, 2006.
Shrestha, D. L. and Solomatine, D. P.: Data-driven approaches for estimating uncertainty in rainfall-runoff modelling, Int. J. River Basin Manage., 6, 109–122, 2008.
Shrestha, D. L., Rodriguez, J., Price, R. K., and Solomatine, D. P.: Assessing model prediction limits using fuzzy clustering and machine learning, Proc. 7th Int. Conf. On Hydroinformatics, 4–8 September, Nice, France, 2006.
Shrestha, D. L., Robertson, D. E., Wang, Q. J., Pagano, T. C., and Hapuarachchi, H. A. P.: Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose, Hydrol. Earth Syst. Sci., 17, 1913–1931, https://doi.org/10.5194/hess-17-1913-2013, 2013.
Sikorska, A. E., Scheidegger, A., Banasik, K., and Rieckermann, J.: Considering rating curve uncertainty in water level predictions, Hydrol. Earth Syst. Sci., 17, 4415–4427, https://doi.org/10.5194/hess-17-4415-2013, 2013.
Singh, S. K., McMillan, H., and Bárdossy, A.: Use of the data depth function to differentiate between case of interpolation and extrapolation in hydrological model prediction, J. Hydrol., 477, 213–228, https://doi.org/10.1016/j.jhydrol.2012.11.034, 2013.
Sivakumar, B., Singh, V. P., Berndtsson, R., and Khan, S. K.: Catchment Classification Framework in Hydrology: Challenges and Directions, J. Hydrol. Eng., https://doi.org/10.1061/(ASCE)HE.1943- 5584.0000837, 2015.
Solomatine, D. P.: Modelling Theory and Uncertainty (An Introduction) – Part 2: Analysis of model uncertainty, Lecture Notes. UNESCO-IHE Institute for Water Education, Delft, the Netherlands, 2013.
Solomatine, D. P. and Dulal, K. N.: Model trees as an alternative to neural networks in rainfall–runoff modelling, Hydrol. Sci. J., 48, 399–411, 2003.
Solomatine, D. P. and Shrestha, D. L.: A novel method to estimate model uncertainty using machine learning techniques, Water Resour. Res., 45, W00B11, https://doi.org/10.1029/2008WR006839, 2009.
Solomatine, D. P. and Wagener, T.: Hydrological Modeling, in: Treatise on Water Science, vol. 2, edited by: Wilderer, P., Academic Press, Oxford, 435–457, 2011.
Solomatine, D. P., Dibike, Y. B., and Kukuric, N.: Automatic calibration of groundwater models using global optimization techniques, Hydrol. Sci. J., 44, 879–894, 1999.
Tayfur, G., Zucco, G., Brocca, L., and Moramarco, T.: Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale, J. Hydrol., 510, 363–371, https://doi.org/10.1016/j.jhydrol.2013.12.045, 2014.
Taylor, J. W.: Forecasting daily supermarket sales using exponentially weighted quantile regression, Eur. J. Oper. Res., 178, 154–167, 2007.
Todini, E.: A model conditional processor to assess predictive uncertainty in flood forecasting, Int. J. River Basin Manage., 6, 123–137, 2008.
Toth, E.: Catchment classification based on characterisation of streamflow and precipitation time series, Hydrol. Earth Syst. Sci., 17, 1149–1159, https://doi.org/10.5194/hess-17-1149-2013, 2013.
van Andel, S. J., Weerts, A., Schaake, J., and Bogner, K.: Post-processing hydrological ensemble predictions intercomparison experiment, Hydrol. Process., 27, 158–161, https://doi.org/10.1002/hyp.9595, 2013.
Verkade, J. S. and Werner, M. G. F.: Estimating the benefits of single value and probability forecasting for flood warning, Hydrol. Earth Syst. Sci., 15, 3751–3765, https://doi.org/10.5194/hess-15-3751-2011, 2011.
Wagener, T. and Gupta, H. V.: Model identification for hydrological forecasting under uncertainty, Stoch. Environ. Res. Risk A., 19, 378–387, https://doi.org/10.1007/s00477-005-0006-5, 2005.
Wallingford 1994: Wallingford Water, a food forecasting and warning system for the river Soar, Wallingford Water, Wallingford, UK, 1994.
Wallingford 1997: HR Wallingford, ISIS software, available at: http://www.isisuser.com/isis/ (last access: 1 October 2013), HR Wallingford, Hydraluic Unit, Wallingford, UK, 1997.
Weerts, A. H., Winsemius, H. C., and Verkade, J. S.: Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales), Hydrol. Earth Syst. Sci., 15, 255–265, https://doi.org/10.5194/hess-15-255-2011, 2011.
Werner, M., Schellekens, J., Gijsbers, P., van Dijk, M., van den Akker, O., and Heynert, K.: The Delft-FEWS flow forecasting system, Environ. Modell. Softw., 40, 65–77, 30 https://doi.org/10.1016/j.envsoft.2012.07.010, 2013.
Xie, X. L. and Beni, G.: A validity measure for fuzzy clustering, IEEE T. Pattern. Anal., 13, 841–847, 1991.
