Thorsten Wagener

A decade of Predictions in Ungauged Basins (PUB)—a review

Abstract The Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium 2012 held in Delft (23–25 October 2012), set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with increasing realism and predictive power. This paper reviews the work that has been done under the six science themes of the PUB Decade and outlines the challenges ahead for the hydrological sciences community. Editor D. Koutsoyiannis Citation Hrachowitz, M., Savenije, H.H.G., Blöschl, G., McDonnell, J.J., Sivapalan, M., Pomeroy, J.W., Arheimer, B., Blume, T., Clark, M.P., Ehret, U., Fenicia, F., Freer, J.E., Gelfan, A., Gupta, H.V., Hughes, D.A., Hut, R.W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P.A., Uhlenbrook, S., Wagener, T., Winsemius, H.C., Woods, R.A., Zehe, E., and Cudennec, C., 2013. A decade of Predictions in Ungauged Basins (PUB)—a review. Hydrological Sciences Journal, 58 (6), 1198–1255.

The future of hydrology: An evolving science for a changing world

Human activities exert global-scale impacts on our environment with significant implications for freshwater-driven services and hazards for humans and nature. Our approach to the science of hydrology needs to significantly change so that we can understand and predict these implications. Such an adjustment is a necessary prerequisite for the development of sustainable water resource management strategies and to achieve long-term water security for people and the environment. Hydrology requires a paradigm shift in which predictions of system behavior that are beyond the range of previously observed variability or that result from significant alterations of physical (structural) system characteristics become the new norm. To achieve this shift, hydrologists must become both synthesists, observing and analyzing the system as a holistic entity, and analysts, understanding the functioning of individual system components, while operating firmly within a well-designed hypothesis testing framework. Cross-disciplinary integration must become a primary characteristic of hydrologic research, catalyzing new research and nurturing new educational models. The test of our quantitative understanding across atmosphere, hydrosphere, lithosphere, biosphere, and anthroposphere will necessarily lie in new approaches to benchmark our ability to predict the regional hydrologic and connected implications of environmental change. To address these challenges and to serve as a catalyst to bring about the necessary changes to hydrologic science, we call for a long-term initiative to address the regional implications of environmental change.

Panta Rhei-Everything Flows: Change in hydrology and society-The IAHS Scientific Decade 2013-2022

Abstract The new Scientific Decade 2013–2022 of IAHS, entitled “Panta Rhei—Everything Flows”, is dedicated to research activities on change in hydrology and society. The purpose of Panta Rhei is to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems. The practical aim is to improve our capability to make predictions of water resources dynamics to support sustainable societal development in a changing environment. The concept implies a focus on hydrological systems as a changing interface between environment and society, whose dynamics are essential to determine water security, human safety and development, and to set priorities for environmental management. The Scientific Decade 2013–2022 will devise innovative theoretical blueprints for the representation of processes including change and will focus on advanced monitoring and data analysis techniques. Interdisciplinarity will be sought by increased efforts to connect with the socio-economic sciences and geosciences in general. This paper presents a summary of the Science Plan of Panta Rhei, its targets, research questions and expected outcomes. Editor Z.W. Kundzewicz Citation Montanari, A., Young, G., Savenije, H.H.G., Hughes, D., Wagener, T., Ren, L.L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S.J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and Belyaev, V., 2013. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrological Sciences Journal. 58 (6) 1256–1275.

A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

Received 29 November 2007; revised 12 May 2008; accepted 23 May 2008; published 11 September 2008. [1] Distributed hydrological models have the potential to provide improved streamflow forecasts along the entire channel network, while also simulating the spatial dynamics of evapotranspiration, soil moisture content, water quality, soil erosion, and land use change impacts. However, they are perceived as being difficult to parameterize and evaluate, thus translating into significant predictive uncertainty in the model results. Although a priori parameter estimates derived from observable watershed characteristics can help to minimize obstacles to model implementation, there exists a need for powerful automated parameter estimation strategies that incorporate diagnostic information regarding the causes of poor model performance. This paper investigates a diagnostic approach to model evaluation that exploits hydrological context and theory to aid in the detection and resolution of watershed model inadequacies, through consideration of three of the four major behavioral functions of any watershed system; overall water balance, vertical redistribution, and temporal redistribution (spatial redistribution was not addressed). Instead of using classical statistical measures (such as mean squared error), we use multiple hydrologically relevant ‘‘signature measures’’ to quantify the performance of the model at the watershed outlet in ways that correspond to the functions mentioned above and therefore help to guide model improvements in a meaningful way. We apply the approach to the Hydrology Laboratory Distributed Hydrologic Model (HL-DHM) of the National Weather Service and show that diagnostic evaluation has the potential to provide a powerful and intuitive basis for deriving consistent estimates of the parameters of watershed models. Citation: Yilmaz, K. K., H. V. Gupta, and T. Wagener (2008), A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, doi:10.1029/2007WR006716.

Ensemble predictions of runoff in ungauged catchments

A new approach to regionalization of conceptual rainfall-runoff models is presented on the basis of ensemble modeling and model averaging. It is argued that in principle, this approach represents an improvement on the established procedure of regressing parameter values against numeric catchment descriptors. Using daily data from 127 catchments in the United Kingdom, alternative schemes for defining prior and posterior likelihoods of candidate models are tested in terms of accuracy of ungauged catchment predictions. A probability distributed model structure is used, and alternative parameter sets are identified using data from each of a number of gauged catchments. Using the models of the 10 gauged catchments most similar to the ungauged catchment provides generally the best results and performs significantly better than the regression method, especially for predicting low flows. The ensemble of candidate models provides an indication of uncertainty in ungauged catchment predictions, although this is not a robust estimate of possible flow ranges, and frequently fails to encompass flow peaks. Options for developing the new method to resolve these problems are discussed.

Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting

Abstract This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but ov...

Runoff prediction in ungauged basins : synthesis across processes, places and scales

List of contributors Foreword Thomas Dunne Preface Gunter Bloschl, Murugesu Sivapalan, Thorsten Wagener, Alberto Viglione and Hubert Savenije 1. Introduction Gunter Bloschl, Murugesu Sivapalan, Thorsten Wagener, Alberto Viglione and Hubert Savenije 2. A synthesis framework for runoff predictions in ungauged basins Thorsten Wagener, Gunter Bloschl, David Goodrich, Hoshin V. Gupta, Murugesu Sivapalan, Yasuto Tachikawa, Peter Troch and Markus Weiler 3. A data acquisition framework for predictions of runoff in ungauged basins Brian McGlynn, Gunter Bloschl, Marco Borga, Helge Bormann, Ruud Hurkmans, Jurgen Komma, Lakshman Nandagiri, Remko Uijlenhoet and Thorsten Wagener 4. Process realism: flow paths and storage Dorthe Tetzlaff, Ghazi Al-Rawas, Gunter Bloschl, Sean K. Carey, Ying Fan, Markus Hrachowitz, Robert Kirnbauer, Graham Jewitt, Hjalmar Laudon, Kevin J. McGuire, Takahiro Sayama, Chris Soulsby, Erwin Zehe and Thorsten Wagener 5. Prediction of annual runoff in ungauged basins Thomas McMahon, Gregor Laaha, Juraj Parajka, Murray C. Peel, Hubert Savenije, Murugesu Sivapalan, Jan Szolgay, Sally Thompson, Alberto Viglione, Ross Woods and Dawen Yang 6. Prediction of seasonal runoff in ungauged basins R. Weingartner, Gunter Bloschl, David Hannah, Danny Marks, Juraj Parajka, Charles Pearson, Magdalena Rogger, Jose Luis. Salinas, Eric Sauquet, Sri Srikanthan, Sally Thompson and Alberto Viglione 7. Prediction of flow duration curves in ungauged basins Attilio Castellarin, Gianluca Botter, Denis A. Hughes, Suxia Liu, Taha B. M. J. Ouarda, Juraj Parajka, David Post, Murugesu Sivapalan, Christopher Spence, Alberto Viglione and Richard Vogel 8. Prediction of low flows in ungauged basins Gregor Laaha, Siegfried Demuth, Hege Hisdal, Charles N. Kroll, Henny A. J. van Lanen, Thomas Nester, Magdalena Rogger, Eric Sauquet, Lena M. Tallaksen, Ross Woods and Andy Young 9. Prediction of floods in ungauged basins Dan Rosbjerg, Gunter Bloschl, Donald H. Burn, Attilio Castellarin, Barry Croke, Guliano Di Baldassarre, Vito Iacobellis, Thomas Kjeldsen, George Kuczera, Ralf Merz, Alberto Montanari, David Morris, Taha B. M. J. Ouarda, Liliang Ren, Magdalena Rogger, Jose Luis Salinas, Elena Toth and Alberto Viglione 10. Predictions of runoff hydrographs in ungauged basins Juraj Parajka, Vazken Andreassian, Stacey Archfield, Andras Bardossy, Francis Chiew, Qingyun Duan, Alexander Gelfan, Kamila Hlavcova, Ralf Merz, Neil McIntyre, Ludovic Oudin, Charles Perrin, Magdalena Rogger, Jose Luis Salinas, Hubert Savenije, Jon Olav Skoien, Thorsten Wagener, Erwin Zehe and Yongqiang Zhang 11. Case studies Hubert Savenije, Murugesu Sivapalan, Trent Biggs, Shaofeng Jia, Leonid M. Korytny, E.A.Ilyichyova, Boris Gartsman, John W. Pomeroy, Kevin Shook, Xing Fang, Tom Brown, Denis A. Hughes, Stacey Archfield, Jos Samuel, Paulin Coulibaly, Robert A. Metcalfe, Attilio Castellarin, Ralf Merz, Gunter Humer, Ataur Rahman, Khaled Haddad, Erwin Weinmann, George Kuczera, Theresa Blume, Armand Crabit, Francois Colin, Roger Moussa, Hessel Winsemius, Hubert Savenije, Jens Liebe, Nick van de Giesen, M. Todd Walter, Tammo S. Steenhuis, Jeffrey R. Kennedy, David Goodrich, Carl L. Unkrich, Dominic Mazvimavi, Neil R. Viney, Kuniyoshi Takeuchi, H. A. P. Hapuarachchi, Anthony S. Kiem, Hiroshi Ishidaira, Tianqi Ao, Jun Magome, Maichun C. Zhou, Mikhail Georgievski, Guoqiang Wang, Chihiro Yoshimura, Berit Arheimer, Goran Lindstrom and Shijun Lin 12. Synthesis across processes, places and scales Hoshin V. Gupta, Gunter Bloschl, Jeffrey McDonnell, Hubert Savenije, Murugesu Sivapalan, Alberto Viglione and Thorsten Wagener 13. Recommendations Kuniyoshi Takeuchi, Gunter. Bloschl, Hubert Savenije, John Schaake, Murugesu Sivapalan, Alberto Viglione, Thorsten Wagener and Gordon Young Appendix: summary of studies used in the comparative assessments References Index.This book is devoted to predicting runoff in ungauged basins (PUB), i.e., predicting runoff at those locations where no runoff data are available. It aims at a synthesis of research on predictions of runoff in ungauged basins across processes, places and scales as a response to the dilemma of fragmentation in hydrology. It takes a comparative approach to learning from the differences and similarities between catchments around the world. The book also provides a comparative performance assessment (in the form of blind testing) of methods that are being used for predictions in ungauged basins, interpreted in a hydrologically meaningful way. It therefore throws light on the status of PUB at the present moment and can serve as a benchmark against which future progress on PUB can be judged. In so doing, the book has also come out with a new scientific framework that can guide the advances that are needed to underpin PUB and to advance the science of hydrology as a whole. The synthesis presented in the book is built on the collective experience of a large number of researchers around the world inspired by the PUB initiative of the International Association of Hydrological Sciences, which makes it truly a community effort. It has provided insights into the scientific, technical and societal factors that contribute to PUB. On the basis of the synthesis presented in this book, recommendations are made on the predictive, scientific and community aspects of PUB and of hydrology as a whole. www.cambridge.org

Sensitivity analysis of environmental models

Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research. We present an overview of SA and its link to uncertainty analysis, model calibration and evaluation, robust decision-making.We provide a systematic review of existing approaches, which can support users in the choice of an SA method.We provide practical guidelines by developing a workflow for the application of SA and discuss critical choices.We give best practice examples from the literature and highlight trends and gaps for future research.

Karst water resources in a changing world: Review of hydrological modeling approaches

Karst regions represent 7–12% of the Earths continental area, and about one quarter of the global population is completely or partially dependent on drinking water from karst aquifers. Climate simulations project a strong increase in temperature and a decrease of precipitation in many karst regions in the world over the next decades. Despite this potentially bleak future, few studies specifically quantify the impact of climate change on karst water resources. This review provides an introduction to karst, its evolution, and its particular hydrological processes. We explore different conceptual models of karst systems and how they can be translated into numerical models of varying complexity and therefore varying data requirements and depths of process representation. We discuss limitations of current karst models and show that at the present state, we face a challenge in terms of data availability and information content of the available data. We conclude by providing new research directions to develop and evaluate better prediction models to address the most challenging problems of karst water resources management, including opportunities for data collection and for karst model applications at so far unprecedented scales.

Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox

The detailed evaluation of mathematical models and the consideration of uncertainty in the modeling of hydrological and environmental systems are of increasing importance, and are sometimes even demanded by decision makers. At the same time, the growing complexity of models to represent real-world systems makes it more and more difficult to understand model behavior, sensitivities and uncertainties. The Monte Carlo Analysis Toolbox (MCAT) is a Matlab library of visual and numerical analysis tools for the evaluation of hydrological and environmental models. Input to the MCAT is the result of a Monte Carlo or population evolution based sampling of the parameter space of the model structure under investigation. The MCAT can be used off-line, i.e. it does not have to be connected to the evaluated model, and can thus be used for any model for which an appropriate sampling can be performed. The MCAT contains tools for the evaluation of performance, identifiability, sensitivity, predictive uncertainty and also allows for the testing of hypotheses with respect to the model structure used. In addition to research applications, the MCAT can be used as a teaching tool in courses that include the use of mathematical models.