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1. Hydrological model calibration with uncertain discharge data

Author

Westerberg, Ida K, Sikorska-Senoner, Anna E, Viviroli, Daniel, Vis, Marc, Seibert, Jan, University of Zurich, and Westerberg, Ida K

Subjects
10122 Institute of Geography, 2312 Water Science and Technology, 910 Geography & travel, Water Science and Technology
Abstract

Discharge data used to calibrate and evaluate hydrological models can be highly uncertain and this uncertainty affects the conclusions that we can draw from modelling results. We investigated the role of discharge data uncertainty and its representation in hydrological model calibration to give recommendations on methods to account for data uncertainty. We tested five different representations of discharge data uncertainty in calibrating the HBV-model for three Swiss catchments, ranging from using no information to using full empirical probability distributions for each time step. We developed a new objective function to include discharge data uncertainty, as quantified by these distributions directly in calibration to hydrological time series. This new objective function provided more reliable results than using no data uncertainty or multiple realizations of discharge time series. We recommend using the new objective function in combination with empirical or triangular distributions of the discharge data uncertainty.

Published
2022

2. Estimating uncertainties in hydraulicallymodelled rating curves for discharge time series assessment

Author

Mansanarez Valentin, Westerberg Ida K., Lyon Steve W., and Lam Norris

Subjects
Environmental sciences, GE1-350
Abstract

Establishing a reliable stage-discharge (SD) rating curve for calculating discharge at a hydrological gauging station normally takes years of data collection. Estimation of high flows is particularly difficult as they occur rarely and are often difficult to gauge in practice. At a minimum, hydraulicallymodelled rating curves could be derived with as few as two concurrent SD and water-surface slope measurements at different flow conditions. This means that a reliable rating curve can, potentially, be developed much faster via hydraulic modelling than using a traditional rating curve approach based on numerous stage-discharge gaugings. In this study, we use an uncertainty framework based on Bayesian inference and hydraulic modelling for developing SD rating curves and estimating their uncertainties. The framework incorporates information from both the hydraulic configuration (bed slope, roughness, vegetation) using hydraulic modelling and the information available in the SD observation data (gaugings). Discharge time series are estimated by propagating stage records through the posterior rating curve results. Here we apply this novel framework to a Swedish hydrometric station, accounting for uncertainties in the gaugings and the parameters of the hydraulic model. The aim of this study was to assess the impact of using only three gaugings for calibrating the hydraulic model on resultant uncertainty estimations within our framework. The results were compared to prior knowledge, discharge measurements and official discharge estimations and showed the potential of hydraulically-modelled rating curves for assessing uncertainty at high and medium flows, while uncertainty at low flows remained high. Uncertainty results estimated using only three gaugings for the studied site were smaller than ±15% for medium and high flows and reduced the prior uncertainty by a factor of ten on average and were estimated with only 3 gaugings.

Published
2018
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3. Hydrological model calibration with uncertain discharge data.

Author

Westerberg, Ida K., Sikorska-Senoner, Anna E., Viviroli, Daniel, Vis, Marc, and Seibert, Jan

Subjects
*HYDROLOGIC models, *CALIBRATION, *TIME series analysis, *DISTRIBUTION (Probability theory), *GLOW discharges, *DATA distribution
Abstract

Discharge data used to calibrate and evaluate hydrological models can be highly uncertain and this uncertainty affects the conclusions that we can draw from modelling results. We investigated the role of discharge data uncertainty and its representation in hydrological model calibration to give recommendations on methods to account for data uncertainty. We tested five different representations of discharge data uncertainty in calibrating the HBV-model for three Swiss catchments, ranging from using no information to using full empirical probability distributions for each time step. We developed a new objective function to include discharge data uncertainty, as quantified by these distributions directly in calibration to hydrological time series. This new objective function provided more reliable results than using no data uncertainty or multiple realizations of discharge time series. We recommend using the new objective function in combination with empirical or triangular distributions of the discharge data uncertainty. [ABSTRACT FROM AUTHOR]

Published
2022
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5. Twenty-three unsolved problems in hydrology (UPH)–a community perspective

Author

Blöschl, Günter, Bierkens, Marc F.P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H.G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Josè, Széles, Borbála, Viglione, Alberto, Aksoy, Hafzullah, Allen, Scott T., Amin, Anam, Andréassian, Vazken, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, Borges de Amorim, Pablo, Böttcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Chen, Xiaohong, Chen, Yangbo, Chen, Yuanfang, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., de Barros, Felipe P.J., de Rooij, Gerrit, Di Baldassarre, Giuliano, Driscoll, Jessica M., Duethmann, Doris, Dwivedi, Ravindra, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Fowler, Keirnan, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Gharari, Shervan, Gleeson, Tom, Glendell, Miriam, Gonzalez Bevacqua, Alena, González-Dugo, María P., Grimaldi, Salvatore, Gupta, A. B., Guse, Björn, Han, Dawei, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlaváčiková, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Illangasekare, Tissa H., Inam, Azhar, Innocente, Camyla, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Khanal, Sonu, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnová, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L.R., Lindquist, Eric, Link, Timothy, Liu, Junguo, Loucks, Daniel Peter, Luce, Charles, Mahé, Gil, Makarieva, Olga, Malard, Julien, Mashtayeva, Shamshagul, Maskey, Shreedhar, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Müller-Thomy, Hannes, Nabizadeh, Alireza, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Nurtaev, Bakhram, Odongo, Vincent O., Panda, Subhabrata, Pande, Saket, Pang, Zhonghe, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, María J., Post, David, Prieto Sierra, Cristina, Ramos, Maria Helena, Renner, Maik, Reynolds, José Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Roy, Tirthankar, Sá, João H.M., Salvadori, Gianfausto, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Steinsland, Ingelin, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Tian, Fuqiang, Tong, Rui, Tussupova, Kamshat, Tyralis, Hristos, Uijlenhoet, Remko, van Beek, Rens, van der Ent, Ruud J., van der Ploeg, Martine, Van Loon, Anne F., van Meerveld, Ilja, van Nooijen, Ronald, van Oel, Pieter R., Vidal, Jean Philippe, von Freyberg, Jana, Vorogushyn, Sergiy, Wachniew, Przemyslaw, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Xu, Zongxue, Yilmaz, Koray K., Zhang, Yongqiang, Blöschl, Günter, Bierkens, Marc F.P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H.G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Josè, Széles, Borbála, Viglione, Alberto, Aksoy, Hafzullah, Allen, Scott T., Amin, Anam, Andréassian, Vazken, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, Borges de Amorim, Pablo, Böttcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Chen, Xiaohong, Chen, Yangbo, Chen, Yuanfang, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., de Barros, Felipe P.J., de Rooij, Gerrit, Di Baldassarre, Giuliano, Driscoll, Jessica M., Duethmann, Doris, Dwivedi, Ravindra, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Fowler, Keirnan, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Gharari, Shervan, Gleeson, Tom, Glendell, Miriam, Gonzalez Bevacqua, Alena, González-Dugo, María P., Grimaldi, Salvatore, Gupta, A. B., Guse, Björn, Han, Dawei, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlaváčiková, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Illangasekare, Tissa H., Inam, Azhar, Innocente, Camyla, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Khanal, Sonu, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnová, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L.R., Lindquist, Eric, Link, Timothy, Liu, Junguo, Loucks, Daniel Peter, Luce, Charles, Mahé, Gil, Makarieva, Olga, Malard, Julien, Mashtayeva, Shamshagul, Maskey, Shreedhar, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Müller-Thomy, Hannes, Nabizadeh, Alireza, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Nurtaev, Bakhram, Odongo, Vincent O., Panda, Subhabrata, Pande, Saket, Pang, Zhonghe, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, María J., Post, David, Prieto Sierra, Cristina, Ramos, Maria Helena, Renner, Maik, Reynolds, José Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Roy, Tirthankar, Sá, João H.M., Salvadori, Gianfausto, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Steinsland, Ingelin, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Tian, Fuqiang, Tong, Rui, Tussupova, Kamshat, Tyralis, Hristos, Uijlenhoet, Remko, van Beek, Rens, van der Ent, Ruud J., van der Ploeg, Martine, Van Loon, Anne F., van Meerveld, Ilja, van Nooijen, Ronald, van Oel, Pieter R., Vidal, Jean Philippe, von Freyberg, Jana, Vorogushyn, Sergiy, Wachniew, Przemyslaw, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Xu, Zongxue, Yilmaz, Koray K., and Zhang, Yongqiang

Abstract

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

Published
2019

6. Characterising droughts in Central America with uncertain hydro-meteorological data

Author

Quesada Montano, Beatriz, Wetterhall, Fredrik, Westerberg, Ida K., Hidalgo, Hugo G., Halldin, Sven, Quesada Montano, Beatriz, Wetterhall, Fredrik, Westerberg, Ida K., Hidalgo, Hugo G., and Halldin, Sven

Abstract

Central America is frequently affected by droughts that cause significant socio-economic and environmental problems. Drought characterisation, monitoring and forecasting are potentially useful to support water resource management. Drought indices are designed for these purposes, but their ability to characterise droughts depends on the characteristics of the regional climate and the quality of the available data. Local comprehensive and high-quality observational networks of meteorological and hydrological data are not available, which limits the choice of drought indices and makes it important to assess available datasets. This study evaluated which combinations of drought index and meteorological dataset were most suitable for characterising droughts in the region. We evaluated the standardised precipitation index (SPI), a modified version of the deciles index (DI), the standardised precipitation evapotranspiration index (SPEI) and the effective drought index (EDI). These were calculated using precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), the CRN073 dataset, the Climate Research Unit (CRU), ECMWF Reanalysis (ERA-Interim) and a regional station dataset, and temperature from the CRU and ERA-Interim datasets. The gridded meteorological precipitation datasets were compared to assess how well they captured key features of the regional climate. The performance of all the drought indices calculated with all the meteorological datasets was then evaluated against a drought index calculated using river discharge data. Results showed that the selection of database was more important than the selection of drought index and that the best combinations were the EDI and DI calculated with CHIRPS and CRN073. Results also highlighted the importance of including indices like SPEI for drought assessment in Central America.

Published
2019
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7. Twenty-three unsolved problems in hydrology (UPH) - a community perspective

Author

Bloeschl, Gunter, Bierkens, Marc F. P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H. G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Jose, Szeles, Borbala, Viglione, Alberto, Aksoy, Hafzullah, Allen, Scott T., Amin, Anam, Andreassian, Vazken, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, de Amorim, Pablo Borges, Boettcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Chen, Xiaohong, Chen, Yangbo, Chen, Yuanfang, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., de Barros, Felipe P. J., de Rooij, Gerrit, Di Baldassarre, Giuliano, Driscoll, Jessica M., Duethmann, Doris, Dwivedi, Ravindra, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Fowler, Keirnan, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Gharari, Shervan, Gleeson, Tom, Glendell, Miriam, Bevacqua, Alena Gonzalez, Gonzalez-Dugo, Maria P., Grimaldi, Salvatore, Gupta, A. B., Guse, Bjoern, Han, Dawei, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlavacikova, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Illangasekare, Tissa H., Inam, Azhar, Innocente, Camyla, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Khanal, Sonu, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnova, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L. R., Lindquist, Eric, Link, Timothy, Liu, Junguo, Loucks, Daniel Peter, Luce, Charles, Mahe, Gil, Makarieva, Olga, Malard, Julien, Mashtayeva, Shamshagul, Maskey, Shreedhar, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Mueller-Thomy, Hannes, Nabizadeh, Alireza, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Nurtaev, Bakhram, Odongo, Vincent, Panda, Subhabrata, Pande, Saket, Pang, Zhonghe, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, Maria J., Post, David, Sierra, Cristina Prieto, Ramos, Maria-Helena, Renner, Maik, Reynolds, Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Roy, Tirthankar, Sa, Joao H. M., Salvadori, Gianfausto, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Steinsland, Ingelin, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Tian, Fuqiang, Tong, Rui, Tussupova, Kamshat, Tyralis, Hristos, Uijlenhoet, Remko, van Beek, Rens, van der Ent, Ruud J., van der Ploeg, Martine, Van Loon, Anne F., van Meerveld, Ilja, van Nooijen, Ronald, van Oel, Pieter R., Vidal, Jean-Philippe, von Freyberg, Jana, Vorogushyn, Sergiy, Wachniew, Przemyslaw, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Xu, Zongxue, Yilmaz, Koray K., Zhang, Yongqiang, Bloeschl, Gunter, Bierkens, Marc F. P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H. G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Jose, Szeles, Borbala, Viglione, Alberto, Aksoy, Hafzullah, Allen, Scott T., Amin, Anam, Andreassian, Vazken, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, de Amorim, Pablo Borges, Boettcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Chen, Xiaohong, Chen, Yangbo, Chen, Yuanfang, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., de Barros, Felipe P. J., de Rooij, Gerrit, Di Baldassarre, Giuliano, Driscoll, Jessica M., Duethmann, Doris, Dwivedi, Ravindra, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Fowler, Keirnan, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Gharari, Shervan, Gleeson, Tom, Glendell, Miriam, Bevacqua, Alena Gonzalez, Gonzalez-Dugo, Maria P., Grimaldi, Salvatore, Gupta, A. B., Guse, Bjoern, Han, Dawei, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlavacikova, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Illangasekare, Tissa H., Inam, Azhar, Innocente, Camyla, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Khanal, Sonu, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnova, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L. R., Lindquist, Eric, Link, Timothy, Liu, Junguo, Loucks, Daniel Peter, Luce, Charles, Mahe, Gil, Makarieva, Olga, Malard, Julien, Mashtayeva, Shamshagul, Maskey, Shreedhar, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Mueller-Thomy, Hannes, Nabizadeh, Alireza, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Nurtaev, Bakhram, Odongo, Vincent, Panda, Subhabrata, Pande, Saket, Pang, Zhonghe, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, Maria J., Post, David, Sierra, Cristina Prieto, Ramos, Maria-Helena, Renner, Maik, Reynolds, Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Roy, Tirthankar, Sa, Joao H. M., Salvadori, Gianfausto, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Steinsland, Ingelin, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Tian, Fuqiang, Tong, Rui, Tussupova, Kamshat, Tyralis, Hristos, Uijlenhoet, Remko, van Beek, Rens, van der Ent, Ruud J., van der Ploeg, Martine, Van Loon, Anne F., van Meerveld, Ilja, van Nooijen, Ronald, van Oel, Pieter R., Vidal, Jean-Philippe, von Freyberg, Jana, Vorogushyn, Sergiy, Wachniew, Przemyslaw, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Xu, Zongxue, Yilmaz, Koray K., and Zhang, Yongqiang

Abstract

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

Published
2019
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8. Rapid Stage-Discharge Rating Curve Assessment Using Hydraulic Modeling in an Uncertainty Framework

Author

Mansanarez, Valentin, Westerberg, Ida K., Lam, Norris, Lyon, Steve W., Mansanarez, Valentin, Westerberg, Ida K., Lam, Norris, and Lyon, Steve W.

Abstract

Establishing reliable streamflow time series is essential for hydrological studies and water-related decisions, but it can be both time-consuming and costly since streamflow is typically calculated from water level using rating curves based on numerous calibration measurements (gaugings). It can take many years of gauging data collection to estimate reliable rating curves, and even then extreme-flow estimates often still depend on rating curve extrapolation. Hydraulically modeled rating curves are a promising alternative to traditional methods as they can be rapidly derived with few concurrent stage-discharge gaugings. We introduce a novel framework for Rating curve Uncertainty estimation using Hydraulic Modelling (RUHM), based on Bayesian inference and physically based hydraulic modeling for estimating stage-discharge rating curves and their associated uncertainty. The framework incorporates information from the river shape, hydraulic configuration, and the control gaugings as well as uncertainties in the gaugings and model parameters. We explored the interaction of uncertainty sources within RUHM by (1) assessing its performance at two Swedish stations, (2) investigating the sensitivity of the results to the number and magnitude of the calibration gaugings, and (3) evaluating the importance of prior information on the model parameters. We found that rating curves with constrained uncertainty could be estimated using only three gaugings for either low or low and medium flows that have a high probability of occurrence, thereby enabling rapid rating curve estimation. Prior information about the water-surface slope-stage relation, obtainable from site surveys, was needed to adequately constrain uncertainty estimates. Plain Language Summary Reliable streamflow time series are essential for water-related decisions. However, it can take several years and numerous measurements to establish a reliable streamflow time series, and these may still be associated with large unc

Published
2019
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9. Twenty-three unsolved problems in hydrology (UPH) - a community perspective

Author

Blösch, Günter, Bierkens, Marc F. P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H. G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Jose, Szeles, Borbala, Viglione, Alberto, Aksoy, Hafzullah, Allen, Scott T., Amin, Anam, Andreassian, Vazken, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, de Amorim, Pablo Borges, Boettcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Chen, Xiaohong, Chen, Yangbo, Chen, Yuanfang, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., de Barros, Felipe P. J., de Rooij, Gerrit, Di Baldassarre, Giuliano, Driscoll, Jessica M., Duethmann, Doris, Dwivedi, Ravindra, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Fowler, Keirnan, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Gharari, Shervan, Gleeson, Tom, Glendell, Miriam, Bevacqua, Alena Gonzalez, Gonzalez-Dugo, Maria P., Grimaldi, Salvatore, Gupta, A. B., Guse, Bjoern, Han, Dawei, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlavacikova, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Illangasekare, Tissa H., Inam, Azhar, Innocente, Camyla, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Khanal, Sonu, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnova, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L. R., Lindquist, Eric, Link, Timothy, Liu, Junguo, Loucks, Daniel Peter, Luce, Charles, Mahe, Gil, Makarieva, Olga, Malard, Julien, Mashtayeva, Shamshagul, Maskey, Shreedhar, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Mueller-Thomy, Hannes, Nabizadeh, Alireza, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Nurtaev, Bakhram, Odongo, Vincent O., Panda, Subhabrata, Pande, Saket, Pang, Zhonghe, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, Maria J., Post, David, Sierra, Cristina Prieto, Ramos, Maria-Helena, Renner, Maik, Reynolds, Jose Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Roy, Tirthankar, Sa, Joao H. M., Salvadori, Gianfausto, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Steinsland, Ingelin, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Tian, Fuqiang, Tong, Rui, Tussupova, Kamshat, Tyralis, Hristos, Uijlenhoet, Remko, van Beek, Rens, van der Ent, Ruud J., van der Ploeg, Martine, Van Loon, Anne F., van Meerveld, Ilja, van Nooijen, Ronald, van Oel, Pieter R., Vidal, Jean-Philippe, von Freyberg, Jana, Vorogushyn, Sergiy, Wachniew, Przemyslaw, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Xu, Zongxue, Yilmaz, Koray K., Zhang, Yongqiang, Blösch, Günter, Bierkens, Marc F. P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H. G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Jose, Szeles, Borbala, Viglione, Alberto, Aksoy, Hafzullah, Allen, Scott T., Amin, Anam, Andreassian, Vazken, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, de Amorim, Pablo Borges, Boettcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Chen, Xiaohong, Chen, Yangbo, Chen, Yuanfang, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., de Barros, Felipe P. J., de Rooij, Gerrit, Di Baldassarre, Giuliano, Driscoll, Jessica M., Duethmann, Doris, Dwivedi, Ravindra, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Fowler, Keirnan, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Gharari, Shervan, Gleeson, Tom, Glendell, Miriam, Bevacqua, Alena Gonzalez, Gonzalez-Dugo, Maria P., Grimaldi, Salvatore, Gupta, A. B., Guse, Bjoern, Han, Dawei, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlavacikova, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Illangasekare, Tissa H., Inam, Azhar, Innocente, Camyla, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Khanal, Sonu, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnova, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L. R., Lindquist, Eric, Link, Timothy, Liu, Junguo, Loucks, Daniel Peter, Luce, Charles, Mahe, Gil, Makarieva, Olga, Malard, Julien, Mashtayeva, Shamshagul, Maskey, Shreedhar, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Mueller-Thomy, Hannes, Nabizadeh, Alireza, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Nurtaev, Bakhram, Odongo, Vincent O., Panda, Subhabrata, Pande, Saket, Pang, Zhonghe, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, Maria J., Post, David, Sierra, Cristina Prieto, Ramos, Maria-Helena, Renner, Maik, Reynolds, Jose Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Roy, Tirthankar, Sa, Joao H. M., Salvadori, Gianfausto, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Steinsland, Ingelin, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Tian, Fuqiang, Tong, Rui, Tussupova, Kamshat, Tyralis, Hristos, Uijlenhoet, Remko, van Beek, Rens, van der Ent, Ruud J., van der Ploeg, Martine, Van Loon, Anne F., van Meerveld, Ilja, van Nooijen, Ronald, van Oel, Pieter R., Vidal, Jean-Philippe, von Freyberg, Jana, Vorogushyn, Sergiy, Wachniew, Przemyslaw, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Xu, Zongxue, Yilmaz, Koray K., and Zhang, Yongqiang

Abstract

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

Published
2019
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10. Twenty-three unsolved problems in hydrology (UPH)–a community perspective

Author

Hydrologie, Landscape functioning, Geocomputation and Hydrology, Blöschl, Günter, Bierkens, Marc F.P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H.G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Josè, Széles, Borbála, Viglione, Alberto, Aksoy, Hafzullah, Allen, Scott T., Amin, Anam, Andréassian, Vazken, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, Borges de Amorim, Pablo, Böttcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Chen, Xiaohong, Chen, Yangbo, Chen, Yuanfang, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., de Barros, Felipe P.J., de Rooij, Gerrit, Di Baldassarre, Giuliano, Driscoll, Jessica M., Duethmann, Doris, Dwivedi, Ravindra, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Fowler, Keirnan, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Gharari, Shervan, Gleeson, Tom, Glendell, Miriam, Gonzalez Bevacqua, Alena, González-Dugo, María P., Grimaldi, Salvatore, Gupta, A. B., Guse, Björn, Han, Dawei, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlaváčiková, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Illangasekare, Tissa H., Inam, Azhar, Innocente, Camyla, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Khanal, Sonu, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnová, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L.R., Lindquist, Eric, Link, Timothy, Liu, Junguo, Loucks, Daniel Peter, Luce, Charles, Mahé, Gil, Makarieva, Olga, Malard, Julien, Mashtayeva, Shamshagul, Maskey, Shreedhar, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Müller-Thomy, Hannes, Nabizadeh, Alireza, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Nurtaev, Bakhram, Odongo, Vincent O., Panda, Subhabrata, Pande, Saket, Pang, Zhonghe, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, María J., Post, David, Prieto Sierra, Cristina, Ramos, Maria Helena, Renner, Maik, Reynolds, José Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Roy, Tirthankar, Sá, João H.M., Salvadori, Gianfausto, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Steinsland, Ingelin, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Tian, Fuqiang, Tong, Rui, Tussupova, Kamshat, Tyralis, Hristos, Uijlenhoet, Remko, van Beek, Rens, van der Ent, Ruud J., van der Ploeg, Martine, Van Loon, Anne F., van Meerveld, Ilja, van Nooijen, Ronald, van Oel, Pieter R., Vidal, Jean Philippe, von Freyberg, Jana, Vorogushyn, Sergiy, Wachniew, Przemyslaw, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Xu, Zongxue, Yilmaz, Koray K., Zhang, Yongqiang, Hydrologie, Landscape functioning, Geocomputation and Hydrology, Blöschl, Günter, Bierkens, Marc F.P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H.G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Josè, Széles, Borbála, Viglione, Alberto, Aksoy, Hafzullah, Allen, Scott T., Amin, Anam, Andréassian, Vazken, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, Borges de Amorim, Pablo, Böttcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Chen, Xiaohong, Chen, Yangbo, Chen, Yuanfang, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., de Barros, Felipe P.J., de Rooij, Gerrit, Di Baldassarre, Giuliano, Driscoll, Jessica M., Duethmann, Doris, Dwivedi, Ravindra, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Fowler, Keirnan, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Gharari, Shervan, Gleeson, Tom, Glendell, Miriam, Gonzalez Bevacqua, Alena, González-Dugo, María P., Grimaldi, Salvatore, Gupta, A. B., Guse, Björn, Han, Dawei, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlaváčiková, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Illangasekare, Tissa H., Inam, Azhar, Innocente, Camyla, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Khanal, Sonu, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnová, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L.R., Lindquist, Eric, Link, Timothy, Liu, Junguo, Loucks, Daniel Peter, Luce, Charles, Mahé, Gil, Makarieva, Olga, Malard, Julien, Mashtayeva, Shamshagul, Maskey, Shreedhar, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Müller-Thomy, Hannes, Nabizadeh, Alireza, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Nurtaev, Bakhram, Odongo, Vincent O., Panda, Subhabrata, Pande, Saket, Pang, Zhonghe, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, María J., Post, David, Prieto Sierra, Cristina, Ramos, Maria Helena, Renner, Maik, Reynolds, José Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Roy, Tirthankar, Sá, João H.M., Salvadori, Gianfausto, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Steinsland, Ingelin, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Tian, Fuqiang, Tong, Rui, Tussupova, Kamshat, Tyralis, Hristos, Uijlenhoet, Remko, van Beek, Rens, van der Ent, Ruud J., van der Ploeg, Martine, Van Loon, Anne F., van Meerveld, Ilja, van Nooijen, Ronald, van Oel, Pieter R., Vidal, Jean Philippe, von Freyberg, Jana, Vorogushyn, Sergiy, Wachniew, Przemyslaw, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Xu, Zongxue, Yilmaz, Koray K., and Zhang, Yongqiang

Published
2019

11. Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment?

Author

Quesada Montano, Beatriz, Westerberg, Ida K., Fuentes–Andino, Diana, Hidalgo, Hugo G., and Halldin, Sven

Subjects
climate variability, Oceanography, Hydrology and Water Resources, hydrological model, Central America, Oceanografi, hydrologi och vattenresurser, process constraints, uncertainty, ungauged basins
Abstract

Long‐term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, such as Central America, hydrological models are an alternative for reproducing historical streamflow series. Additional types of information—to locally observed discharge—can be used to constrain model parameter uncertainty for ungauged catchments. Given the strong influence that climatic large‐scale processes exert on streamflow variability in the Central American region, we explored the use of climate variability knowledge as process constraints to constrain the simulated discharge uncertainty for a Costa Rican catchment, assumed to be ungauged. To reduce model uncertainty, we first rejected parameter relationships that disagreed with our understanding of the system. Then, based on this reduced parameter space, we applied the climate‐based process constraints at long‐term, inter‐annual, and intra‐annual timescales. In the first step, we reduced the initial number of parameters by 52%, and then, we further reduced the number of parameters by 3% with the climate constraints. Finally, we compared the climate‐based constraints with a constraint based on global maps of low‐flow statistics. This latter constraint proved to be more restrictive than those based on climate variability (further reducing the number of parameters by 66% compared with 3%). Even so, the climate‐based constraints rejected inconsistent model simulations that were not rejected by the low‐flow statistics constraint. When taken all together, the constraints produced constrained simulation uncertainty bands, and the median simulated discharge followed the observed time series to a similar level as an optimized model. All the constraints were found useful in constraining model uncertainty for an—assumed to be—ungauged basin. This shows that our method is promising for modelling long‐term flow data for ungauged catchments on the Pacific side of Central America and that similar methods can be developed for ungauged basins in other regions where climate variability exerts a strong control on streamflow variability. UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI) UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Física

Published
2018

12. Twenty-three unsolved problems in hydrology (UPH) – a community perspective

Author

Blöschl, Günter, primary, Bierkens, Marc F.P., additional, Chambel, Antonio, additional, Cudennec, Christophe, additional, Destouni, Georgia, additional, Fiori, Aldo, additional, Kirchner, James W., additional, McDonnell, Jeffrey J., additional, Savenije, Hubert H.G., additional, Sivapalan, Murugesu, additional, Stumpp, Christine, additional, Toth, Elena, additional, Volpi, Elena, additional, Carr, Gemma, additional, Lupton, Claire, additional, Salinas, Josè, additional, Széles, Borbála, additional, Viglione, Alberto, additional, Aksoy, Hafzullah, additional, Allen, Scott T., additional, Amin, Anam, additional, Andréassian, Vazken, additional, Arheimer, Berit, additional, Aryal, Santosh K., additional, Baker, Victor, additional, Bardsley, Earl, additional, Barendrecht, Marlies H., additional, Bartosova, Alena, additional, Batelaan, Okke, additional, Berghuijs, Wouter R., additional, Beven, Keith, additional, Blume, Theresa, additional, Bogaard, Thom, additional, Borges de Amorim, Pablo, additional, Böttcher, Michael E., additional, Boulet, Gilles, additional, Breinl, Korbinian, additional, Brilly, Mitja, additional, Brocca, Luca, additional, Buytaert, Wouter, additional, Castellarin, Attilio, additional, Castelletti, Andrea, additional, Chen, Xiaohong, additional, Chen, Yangbo, additional, Chen, Yuanfang, additional, Chifflard, Peter, additional, Claps, Pierluigi, additional, Clark, Martyn P., additional, Collins, Adrian L., additional, Croke, Barry, additional, Dathe, Annette, additional, David, Paula C., additional, de Barros, Felipe P. J., additional, de Rooij, Gerrit, additional, Di Baldassarre, Giuliano, additional, Driscoll, Jessica M., additional, Duethmann, Doris, additional, Dwivedi, Ravindra, additional, Eris, Ebru, additional, Farmer, William H., additional, Feiccabrino, James, additional, Ferguson, Grant, additional, Ferrari, Ennio, additional, Ferraris, Stefano, additional, Fersch, Benjamin, additional, Finger, David, additional, Foglia, Laura, additional, Fowler, Keirnan, additional, Gartsman, Boris, additional, Gascoin, Simon, additional, Gaume, Eric, additional, Gelfan, Alexander, additional, Geris, Josie, additional, Gharari, Shervan, additional, Gleeson, Tom, additional, Glendell, Miriam, additional, Gonzalez Bevacqua, Alena, additional, González-Dugo, María P., additional, Grimaldi, Salvatore, additional, Gupta, A. B., additional, Guse, Björn, additional, Han, Dawei, additional, Hannah, David, additional, Harpold, Adrian, additional, Haun, Stefan, additional, Heal, Kate, additional, Helfricht, Kay, additional, Herrnegger, Mathew, additional, Hipsey, Matthew, additional, Hlaváčiková, Hana, additional, Hohmann, Clara, additional, Holko, Ladislav, additional, Hopkinson, Christopher, additional, Hrachowitz, Markus, additional, Illangasekare, Tissa H., additional, Inam, Azhar, additional, Innocente, Camyla, additional, Istanbulluoglu, Erkan, additional, Jarihani, Ben, additional, Kalantari, Zahra, additional, Kalvans, Andis, additional, Khanal, Sonu, additional, Khatami, Sina, additional, Kiesel, Jens, additional, Kirkby, Mike, additional, Knoben, Wouter, additional, Kochanek, Krzysztof, additional, Kohnová, Silvia, additional, Kolechkina, Alla, additional, Krause, Stefan, additional, Kreamer, David, additional, Kreibich, Heidi, additional, Kunstmann, Harald, additional, Lange, Holger, additional, Liberato, Margarida L. R., additional, Lindquist, Eric, additional, Link, Timothy, additional, Liu, Junguo, additional, Loucks, Daniel Peter, additional, Luce, Charles, additional, Mahé, Gil, additional, Makarieva, Olga, additional, Malard, Julien, additional, Mashtayeva, Shamshagul, additional, Maskey, Shreedhar, additional, Mas-Pla, Josep, additional, Mavrova-Guirguinova, Maria, additional, Mazzoleni, Maurizio, additional, Mernild, Sebastian, additional, Misstear, Bruce Dudley, additional, Montanari, Alberto, additional, Müller-Thomy, Hannes, additional, Nabizadeh, Alireza, additional, Nardi, Fernando, additional, Neale, Christopher, additional, Nesterova, Nataliia, additional, Nurtaev, Bakhram, additional, Odongo, Vincent O., additional, Panda, Subhabrata, additional, Pande, Saket, additional, Pang, Zhonghe, additional, Papacharalampous, Georgia, additional, Perrin, Charles, additional, Pfister, Laurent, additional, Pimentel, Rafael, additional, Polo, María J., additional, Post, David, additional, Prieto Sierra, Cristina, additional, Ramos, Maria-Helena, additional, Renner, Maik, additional, Reynolds, José Eduardo, additional, Ridolfi, Elena, additional, Rigon, Riccardo, additional, Riva, Monica, additional, Robertson, David E., additional, Rosso, Renzo, additional, Roy, Tirthankar, additional, Sá, João H.M., additional, Salvadori, Gianfausto, additional, Sandells, Mel, additional, Schaefli, Bettina, additional, Schumann, Andreas, additional, Scolobig, Anna, additional, Seibert, Jan, additional, Servat, Eric, additional, Shafiei, Mojtaba, additional, Sharma, Ashish, additional, Sidibe, Moussa, additional, Sidle, Roy C., additional, Skaugen, Thomas, additional, Smith, Hugh, additional, Spiessl, Sabine M., additional, Stein, Lina, additional, Steinsland, Ingelin, additional, Strasser, Ulrich, additional, Su, Bob, additional, Szolgay, Jan, additional, Tarboton, David, additional, Tauro, Flavia, additional, Thirel, Guillaume, additional, Tian, Fuqiang, additional, Tong, Rui, additional, Tussupova, Kamshat, additional, Tyralis, Hristos, additional, Uijlenhoet, Remko, additional, van Beek, Rens, additional, van der Ent, Ruud J., additional, van der Ploeg, Martine, additional, Van Loon, Anne F., additional, van Meerveld, Ilja, additional, van Nooijen, Ronald, additional, van Oel, Pieter R., additional, Vidal, Jean-Philippe, additional, von Freyberg, Jana, additional, Vorogushyn, Sergiy, additional, Wachniew, Przemyslaw, additional, Wade, Andrew J., additional, Ward, Philip, additional, Westerberg, Ida K., additional, White, Christopher, additional, Wood, Eric F., additional, Woods, Ross, additional, Xu, Zongxue, additional, Yilmaz, Koray K., additional, and Zhang, Yongqiang, additional

Published
2019
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13. A Comparison of Methods for Streamflow Uncertainty Estimation

Author

Kiang, Julie E., Gazoorian, Chris, McMillan, Hilary, Coxon, Gemma, Le Coz, Jérôme, Westerberg, Ida K., Belleville, Arnaud, Sevrez, Damien, Sikorska, Anna E., Petersen-Øverleir, Asgeir, Reitan, Trond, Freer, Jim, Renard, Benjamin, Mansanarez, Valentin, Mason, Robert, Kiang, Julie E., Gazoorian, Chris, McMillan, Hilary, Coxon, Gemma, Le Coz, Jérôme, Westerberg, Ida K., Belleville, Arnaud, Sevrez, Damien, Sikorska, Anna E., Petersen-Øverleir, Asgeir, Reitan, Trond, Freer, Jim, Renard, Benjamin, Mansanarez, Valentin, and Mason, Robert

Abstract

Streamflow time series are commonly derived from stage-discharge rating curves, but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage-discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage-discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Is&e River (France), full width 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast, uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time-varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates. Plain Language Summary Knowledge of the uncertainty in streamflow discharge measured at gauging stations is important for water management applications and scientific analysis. This paper shows that uncertainty estimates vary widely

Published
2018
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14. Reply to Discussion of 'Perceptual models of uncertainty for socio-hydrological systems:a flood risk change example'(*)

Author

Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith J., Coxon, Gemma, Krueger, Tobias, Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith J., Coxon, Gemma, and Krueger, Tobias

Abstract

Ertsen discusses the representation of reality and uncertainty in our paper, raising three critical points. In response to the first, we agree that discussion of different interpretations of the concept of uncertainty is important when developing perceptual models - making different uncertainty interpretations explicit was a key motivation behind our method. Secondly, we do not, as Ertsen suggests, deny anyone who is not a "certified" scientist to have relevant knowledge. The elicitation of diverse views by discussing perceptual models is a basis for open discussion and decision making. Thirdly, Ertsen suggests that it is not useful to treat socio-hydrological systems as if they exist. We argue that we act as "pragmatic realists" in most practical applications by treating socio-hydrological systems as an external reality that can be known. But the uncertainty that arises from our knowledge limitations needs to be recognized, as it may impact on practical decision making and associated costs.

Published
2018

15. Reply to Discussion of 'Perceptual models of uncertainty for socio-hydrological systems : a flood risk change example'(*)

Author

Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith J., Coxon, Gemma, Krueger, Tobias, Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith J., Coxon, Gemma, and Krueger, Tobias

Abstract

Ertsen discusses the representation of reality and uncertainty in our paper, raising three critical points. In response to the first, we agree that discussion of different interpretations of the concept of uncertainty is important when developing perceptual models - making different uncertainty interpretations explicit was a key motivation behind our method. Secondly, we do not, as Ertsen suggests, deny anyone who is not a "certified" scientist to have relevant knowledge. The elicitation of diverse views by discussing perceptual models is a basis for open discussion and decision making. Thirdly, Ertsen suggests that it is not useful to treat socio-hydrological systems as if they exist. We argue that we act as "pragmatic realists" in most practical applications by treating socio-hydrological systems as an external reality that can be known. But the uncertainty that arises from our knowledge limitations needs to be recognized, as it may impact on practical decision making and associated costs.

Published
2018

16. A comparison of methods for streamflow uncertainty estimation

Author

Kiang, Julie E; https://orcid.org/0000-0003-0653-4225, Gazoorian, Chris; https://orcid.org/0000-0002-5408-6212, McMillan, Hilary; https://orcid.org/0000-0002-9330-9730, Coxon, Gemma, Le Coz, Jérôme; https://orcid.org/0000-0003-1243-6955, Westerberg, Ida K, Belleville, Arnaud, Sevrez, Damien, Sikorska, Anna E; https://orcid.org/0000-0002-5273-1038, Petersen-Øverleir, Asgeir, Reitan, Trond, Freer, Jim, Renard, Benjamin; https://orcid.org/0000-0001-8447-5430, Mansanarez, Valentin; https://orcid.org/0000-0002-0130-815X, Mason, Robert; https://orcid.org/0000-0002-3998-3468, Kiang, Julie E; https://orcid.org/0000-0003-0653-4225, Gazoorian, Chris; https://orcid.org/0000-0002-5408-6212, McMillan, Hilary; https://orcid.org/0000-0002-9330-9730, Coxon, Gemma, Le Coz, Jérôme; https://orcid.org/0000-0003-1243-6955, Westerberg, Ida K, Belleville, Arnaud, Sevrez, Damien, Sikorska, Anna E; https://orcid.org/0000-0002-5273-1038, Petersen-Øverleir, Asgeir, Reitan, Trond, Freer, Jim, Renard, Benjamin; https://orcid.org/0000-0001-8447-5430, Mansanarez, Valentin; https://orcid.org/0000-0002-0130-815X, and Mason, Robert; https://orcid.org/0000-0002-3998-3468

Abstract

Streamflow time series are commonly derived from stage-discharge rating curves, but theuncertainty of the rating curve and resulting streamflow series are poorly understood. While differentmethods to quantify uncertainty in the stage-discharge relationship exist, there is limited understanding ofhow uncertainty estimates differ between methods due to different assumptions and methodologicalchoices. We compared uncertainty estimates and stage-discharge rating curves from seven methods at threeriver locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a widerange of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), fullwidth 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast,uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of therating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (UnitedKingdom), where the hydraulic control is unstable at low flows. Differences between methods result fromdifferences in the sources of uncertainty considered, differences in the handling of the time-varying nature ofrating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptionswhen extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of anuncertainty method requires a match between user requirements and the assumptions made by theuncertainty method. Given the signi ficant differences in uncertainty estimates between methods, we suggestthat a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.

Published
2018

18. Perceptual models of uncertainty for socio-hydrological systems : a flood risk change example

Author

Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith, Coxon, Gemma, Krueger, Tobias, Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith, Coxon, Gemma, and Krueger, Tobias

Abstract

Characterizing, understanding and better estimating uncertainties are key concerns for drawing robust conclusions when analyzing changing socio-hydrological systems. Here we suggest developing a perceptual model of uncertainty that is complementary to the perceptual model of the socio-hydrological system and we provide an example application to flood risk change analysis.Such a perceptual model aims to make all relevant uncertainty sources - and different perceptions thereof - explicit in a structured way. It is a first step to assessing uncertainty in system outcomes that can help to prioritize research efforts and to structure dialogue and communication about uncertainty in interdisciplinary work.

Published
2017
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19. Perceptual models of uncertainty for socio-hydrological systems:a flood risk change example

Author

Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith J., Coxon, Gemma, Krueger, Tobias, Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith J., Coxon, Gemma, and Krueger, Tobias

Abstract

Characterizing, understanding and better estimating uncertainties are key concerns for drawing robust conclusions when analyzing changing socio-hydrological systems. Here we suggest developing a perceptual model of uncertainty that is complementary to the perceptual model of the socio-hydrological system and we provide an example application to flood risk change analysis. Such a perceptual model aims to make all relevant uncertainty sources–and different perceptions thereof–explicit in a structured way. It is a first step to assessing uncertainty in system outcomes that can help to prioritize research efforts and to structure dialogue and communication about uncertainty in interdisciplinary work.

Published
2017

22. Observational uncertainties in hypothesis testing : investigating the hydrological functioning of a tropical catchment

Author

Westerberg, Ida K., Birkel, Christian, Westerberg, Ida K., and Birkel, Christian

Abstract

Hypothesis testing about catchment functioning with conceptual hydrological models is affected by uncertainties in the model representation of reality as well as in the observed data used to drive and evaluate the model. We formulated a learning framework to investigate the role of observational uncertainties in hypothesis testing using conceptual models and applied it to the relatively data-scarce tropical Sarapiqui catchment in Costa Rica. Observational uncertainties were accounted for throughout the framework that incorporated different choices of model structures to test process hypotheses, analyses of parametric uncertainties and effects of likelihood choice, a posterior performance analysis and (iteratively) formulation of new hypotheses. Estimated uncertainties in precipitation and discharge were linked to likely non-linear near-surface runoff generation and the potentially important role of soils in mediating the hydrological response. Some model-structural inadequacies could be identified in the posterior analyses (supporting the need for an explicit soil-moisture routine to match streamflow dynamics), but the available information about the observational uncertainties prevented conclusions about other process representations. The importance of epistemic data errors, the difficulty in quantifying them and their effect on model simulations was illustrated by an inconsistent event with long-term effects. Finally we discuss the need for new data, new process hypotheses related to deep groundwater losses, and conclude that observational uncertainties need to be accounted for in hypothesis testing to reduce the risk of drawing incorrect conclusions.

Published
2015
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23. Smiling in the rain: Seven reasons to be positive about uncertainty in hydrological modelling

Author

Juston, John M, Kauffeldt, Anna, Quesada Montano, Beatriz, Seibert, Jan, Beven, Keith J, Westerberg, Ida K, University of Zurich, and Juston, John M

Subjects
10122 Institute of Geography, 2312 Water Science and Technology, uncertainty estimation, positive uncertainty, 910 Geography & travel, hydrological modelling
Abstract

Artículo científico -- Universidad de Costa Rica. Centro de Investigaciones Geofísicas, 2012 Over 20 years ago, the notion of ‘positive uncertainty’ was introduced in the field of psychology (Gelatt, 1989). This was coincident to some developments in the hydrological sciences (e.g. Beven and Binley, 1992; Grayson et al., 1992) which laid the foundation for today’s continued efforts to explicitly acknowledge and quantify uncertainties inherent in our modelling efforts. ‘Positive uncertainty’ urges moving beyond deterministic frameworks of the past, but doing so not just by regrettably accepting that uncertainties are inevitable, but by positively thriving in the new perspectives that accompany this recognition (Gelatt, 1989). During a recent small workshop in Sweden – while the rain was pouring down – we discussed some of the positive developments in recent years that have resulted directly from explicit recognitions of uncertainty in hydrological modelling.With the earlier paper of Pappenberger and Beven (2006) in mind, we here summarise that workshop and elaborate seven reasons to be positive about uncertainty in hydrological modelling. How have the hydrological sciences benefitted in the last 20 years from the increased recognition of uncertainties, particularly in the modelling of time series data? In our view, uncertainty estimation is a means to address hydrologic research questions in an honest and robust way. There have also been (and will continue to be) new research questions and opportunities initiated by the recognition of what needs to be done to quantify and then constrain uncertainties. This notion fits intrinsically with the view of modelling as a learning process (Box, 1976), learning about the nature of the uncertainties involved and the possibility of reducing them by improving our models or our data. Universidad de Costa Rica. Centro de Investigaciones Geofísicas. UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)

Published
2013

24. Regional water balance modelling using flow-duration curves with observational uncertainties

Author

Westerberg, Ida K., Gong, Lebing, Beven, Keith J., Seibert, J., Semedo, A., Xu, Chong-Yu, Halldin, Sven, Westerberg, Ida K., Gong, Lebing, Beven, Keith J., Seibert, J., Semedo, A., Xu, Chong-Yu, and Halldin, Sven

Abstract

Robust and reliable water-resource mapping in ungauged basins requires estimation of the uncertainties in the hydrologic model, the regionalisation method, and the observational data. In this study we investigated the use of regionalised flow-duration curves (FDCs) for constraining model predictive uncertainty, while accounting for all these uncertainty sources. A water balance model was applied to 36 basins in Central America using regionally and globally available precipitation, climate and discharge data that were screened for inconsistencies. A rating-curve analysis for 35 Honduran discharge stations was used to estimate discharge uncertainty for the region, and the consistency of the model forcing and evaluation data was analysed using two different screening methods. FDCs with uncertainty bounds were calculated for each basin, accounting for both discharge uncertainty and, in many cases, uncertainty stemming from the use of short time series, potentially not representative for the modelling period. These uncertain FDCs were then used to regionalise a FDC for each basin, treating it as ungauged in a cross-evaluation, and this regionalised FDC was used to constrain the uncertainty in the model predictions for the basin. There was a clear relationship between the performance of the local model calibration and the degree of data set consistency - with many basins with inconsistent data lacking behavioural simulations (i.e. simulations within predefined limits around the observed FDC) and the basins with the highest data set consistency also having the highest simulation reliability. For the basins where the regionalisation of the FDCs worked best, the uncertainty bounds for the regionalised simulations were only slightly wider than those for a local model calibration. The predicted uncertainty was greater for basins where the result of the FDC regionalisation was more uncertain, but the regionalised simulations still had a high reliability compared to the locally

Published
2014
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25. Regional water balance modelling using flow-duration curves with observational uncertainties

Author

Westerberg, Ida K, Gong, Lebing, Beven, Keith J, Seibert, Jan; https://orcid.org/0000-0002-6314-2124, Semedo, Alvaro, Xu, Chong-Yu, Halldin, Sven, Westerberg, Ida K, Gong, Lebing, Beven, Keith J, Seibert, Jan; https://orcid.org/0000-0002-6314-2124, Semedo, Alvaro, Xu, Chong-Yu, and Halldin, Sven

Abstract

Robust and reliable water-resource mapping in ungauged basins requires estimation of the uncertainties in the hydrologic model, the regionalisation method, and the observational data. In this study we investigated the use of regionalised flow-duration curves (FDCs) for constraining model predictive uncertainty, while accounting for all these uncertainty sources. A water balance model was applied to 36 basins in Central America using regionally and globally available precipitation, climate and discharge data that were screened for inconsistencies. A rating-curve analysis for 35 Honduran discharge stations was used to estimate discharge uncertainty for the region, and the consistency of the model forcing and evaluation data was analysed using two different screening methods. FDCs with uncertainty bounds were calculated for each basin, accounting for both discharge uncertainty and, in many cases, uncertainty stemming from the use of short time series, potentially not representative for the modelling period. These uncertain FDCs were then used to regionalise a FDC for each basin, treating it as ungauged in a cross-evaluation, and this regionalised FDC was used to constrain the uncertainty in the model predictions for the basin. There was a clear relationship between the performance of the local model calibration and the degree of data set consistency – with many basins with inconsistent data lacking behavioural simulations (i.e. simulations within predefined limits around the observed FDC) and the basins with the highest data set consistency also having the highest simulation reliability. For the basins where the regionalisation of the FDCs worked best, the uncertainty bounds for the regionalised simulations were only slightly wider than those for a local model calibration. The predicted uncertainty was greater for basins where the result of the FDC regionalisation was more uncertain, but the regionalised simulations still had a high reliability compared to the locally

Published
2014

26. Disinformative data in large-scale hydrological modelling

Author

Kauffeldt, Anna, Halldin, Sven, Rodhe, Allan, Xu, Chong-Yu, Westerberg, Ida K., Kauffeldt, Anna, Halldin, Sven, Rodhe, Allan, Xu, Chong-Yu, and Westerberg, Ida K.

Abstract

Large-scale hydrological modelling has become an important tool for the study of global and regional water resources, climate impacts, and water-resources management. However, modelling efforts over large spatial domains are fraught with problems of data scarcity, uncertainties and inconsistencies between model forcing and evaluation data. Model-independent methods to screen and analyse data for such problems are needed. This study aimed at identifying data inconsistencies in global datasets using a pre-modelling analysis, inconsistencies that can be disinformative for subsequent modelling. The consistency between (i) basin areas for different hydrographic datasets, and (ii) between climate data (precipitation and potential evaporation) and discharge data, was examined in terms of how well basin areas were represented in the flow networks and the possibility of water-balance closure. It was found that (i) most basins could be well represented in both gridded basin delineations and polygon-based ones, but some basins exhibited large area discrepancies between flow-network datasets and archived basin areas, (ii) basins exhibiting too-high runoff coefficients were abundant in areas where precipitation data were likely affected by snow undercatch, and (iii) the occurrence of basins exhibiting losses exceeding the potential-evaporation limit was strongly dependent on the potential-evaporation data, both in terms of numbers and geographical distribution. Some inconsistencies may be resolved by considering subgrid variability in climate data, surface-dependent potential-evaporation estimates, etc., but further studies are needed to determine the reasons for the inconsistencies found. Our results emphasise the need for pre-modelling data analysis to identify dataset inconsistencies as an important first step in any large-scale study. Applying data-screening methods before modelling should also increase our chances to draw robust conclusions from subsequent model simulations

Published
2013
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29. Temporal variability in stage-discharge relationships

Author

Guerrero, Jose-Luis, Westerberg, Ida K., Halldin, Sven, Xu, Chong-Yu, Lundin, Lars-Christer, Guerrero, Jose-Luis, Westerberg, Ida K., Halldin, Sven, Xu, Chong-Yu, and Lundin, Lars-Christer

Abstract

Although discharge estimations are central for water management and hydropower, there are few studies on the variability and uncertainty of their basis; deriving discharge from stage heights through the use of a rating curve that depends on riverbed geometry. A large fraction of the world's river-discharge stations are presumably located in alluvial channels where riverbed characteristics may change over time because of erosion and sedimentation. This study was conducted to analyse and quantify the dynamic relationship between stage and discharge and to determine to what degree currently used methods are able to account for such variability. The study was carried out for six hydrometric stations in the upper Choluteca River basin, Honduras, where a set of unusually frequent stage-discharge data are available. The temporal variability and the uncertainty of the rating curve and its parameters were analysed through a Monte Carlo (MC) analysis on a moving window of data using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. Acceptable ranges for the values of the rating-curve parameters were determined from riverbed surveys at the six stations, and the sampling space was constrained according to those ranges, using three-dimensional alpha shapes. Temporal variability was analysed in three ways: (i) with annually updated rating curves (simulating Honduran practices), (ii) a rating curve for each time window, and (iii) a smoothed, continuous dynamic rating curve derived from the MC analysis. The temporal variability of the rating parameters translated into a high rating-curve variability. The variability could turn out as increasing or decreasing trends and/or cyclic behaviour. There was a tendency at all stations to a seasonal variability. The discharge at a given stage could vary by a factor of two or more. The quotient in discharge volumes estimated from dynamic and static rating curves varied between 0.5 and 1.5. The difference between discharge

Published
2012
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30. Reply to Discussion of "Perceptual models of uncertainty for socio-hydrological systems: a flood risk change example"*.

Author

Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith J., Coxon, Gemma, and Krueger, Tobias

Subjects
*HYDROLOGY, *FLOOD risk, *DECISION making, *CIVIL society, *BOUNDARY value problems
Abstract

Ertsen discusses the representation of reality and uncertainty in our paper, raising three critical points. In response to the first, we agree that discussion of different interpretations of the concept of uncertainty is important when developing perceptual models - making different uncertainty interpretations explicit was a key motivation behind our method. Secondly, we do not, as Ertsen suggests, deny anyone who is not a "certified" scientist to have relevant knowledge. The elicitation of diverse views by discussing perceptual models is a basis for open discussion and decision making. Thirdly, Ertsen suggests that it is not useful to treat socio-hydrological systems as if they exist. We argue that we act as "pragmatic realists" in most practical applications by treating socio-hydrological systems as an external reality that can be known. But the uncertainty that arises from our knowledge limitations needs to be recognized, as it may impact on practical decision making and associated costs. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
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31. Reply to Discussion of "Perceptual models of uncertainty for socio-hydrological systems: a flood risk change example"*.

Author

Westerberg, Ida K., Di Baldassarre, Giuliano, Beven, Keith J., Coxon, Gemma, and Krueger, Tobias

Subjects
HYDROLOGY, FLOOD risk, DECISION making, CIVIL society, BOUNDARY value problems
Abstract

Ertsen discusses the representation of reality and uncertainty in our paper, raising three critical points. In response to the first, we agree that discussion of different interpretations of the concept of uncertainty is important when developing perceptual models - making different uncertainty interpretations explicit was a key motivation behind our method. Secondly, we do not, as Ertsen suggests, deny anyone who is not a "certified" scientist to have relevant knowledge. The elicitation of diverse views by discussing perceptual models is a basis for open discussion and decision making. Thirdly, Ertsen suggests that it is not useful to treat socio-hydrological systems as if they exist. We argue that we act as "pragmatic realists" in most practical applications by treating socio-hydrological systems as an external reality that can be known. But the uncertainty that arises from our knowledge limitations needs to be recognized, as it may impact on practical decision making and associated costs. [ABSTRACT FROM AUTHOR]

Published
2018
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32. Rating curve uncertainty assessment using hydraulic modelling.

Author

Mansanarez, Valentin, Westerberg, Ida K., Norris, Lam, and Lyon, Steve W.

Subjects
*HYDRAULIC models, *EXTRAPOLATION, *UNCERTAINTY, *WATERSHEDS, *RATING curve (Hydrology), *ACQUISITION of data
Abstract

Traditional methods for estimating stage–discharge rating curves and their uncertainties need numerous calibration gaugings. Years of data collection efforts are often needed to gauge the stage–discharge relation across the flow range to establish a reliable rating curve. In particular, high-flow discharge estimation is often highly uncertain since these flows rarely occur and are practically difficult to gauge. Therefore, the portion of the rating curve representing most extreme flows typically needs to be extrapolated. Hydraulic modelling can be used to derive rating curves based on only a few calibration gaugings and can therefore potentially be a good alternative for quickly estimating rating curves. In particular, they have potential to improve high flow discharge estimation as they are based on hydraulic theory rather than extrapolation techniques. However, rating curve estimation with hydraulic models is also associated with multiple sources of uncertainty that have not yet been comprehensively assessed. These uncertainties need to be accounted for and estimated to evaluate the full potential of hydraulic rating-curve modelling.We developed the Rating curve Uncertainty estimation using Hydraulic Modelling (RUHM) framework to investigate and estimate these uncertainties. The framework combines a one dimensional hydraulic model and Bayesian inference to incorporate information from both hydraulic knowledge (bed slope, roughness, topography and vegetation) and the (uncertain) calibration gauging data. The framework was applied at the Röån River catchment in Sweden. We investigated the number of gaugings needed to reliably calibrate the model, the sensitivity of the results to the prior hydraulic information quality (water-surface slope measurements and roughness), and the effect of the vegetation survey data on the high flow discharge estimation.We found that the rating curve uncertainty could be estimated reliably with only a few gauging and water-slope measurements, and that the uncertainty was insensitive to the number of gaugings as long as they covered low and medium flows. We found that at least one (uncertain) water-surface slope measurement was needed, and that precise information about the roughness parameter was not needed. The impact of the vegetation survey data on the high flow discharge estimation was investigated to assess its importance for extrapolation at extreme flows. Our results at this site show that hydraulic rating curve uncertainty estimation is a promising tool for quickly estimating rating curves and their uncertainties. It can be particularly useful at previously ungauged sites or at established sites that have experienced major temporal changes to the stage–discharge relation. [ABSTRACT FROM AUTHOR]

Published
2019

33. Twenty-three unsolved problems in hydrology (UPH) – a community perspective

Author

Blöschl, Günter, Bierkens, Marc F.P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H.G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Josè, Széles, Borbála, Viglione, Alberto, Hafzullah Aksoy, Allen, Scott T., Anam Amin, Vazken Andréassian, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, Amorim, Pablo Borges De, Böttcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Xiaohong Chen, Yangbo Chen, Yuanfang Chen, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., Barros, Felipe P. J. De, Rooij, Gerrit De, Baldassarre, Giuliano Di, Driscoll, Jessica M., Duethmann, Doris, Ravindra Dwivedi, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Keirnan Fowler, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Shervan Gharari, Gleeson, Tom, Glendell, Miriam, Bevacqua, Alena Gonzalez, González-Dugo, María P., Grimaldi, Salvatore, A. B. Gupta, Guse, Björn, Dawei Han, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlaváčiková, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Tissa H. Illangasekare, Inam, Azhar, Camyla Innocente, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Sonu Khanal, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnová, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L. R., Lindquist, Eric, Link, Timothy, Junguo Liu, Loucks, Daniel Peter, Luce, Charles, Mahé, Gil, Makarieva, Olga, Malard, Julien, Shamshagul Mashtayeva, Shreedhar Maskey, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Müller-Thomy, Hannes, Alireza Nabizadeh, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Bakhram Nurtaev, Odongo, Vincent O., Subhabrata Panda, Saket Pande, Zhonghe Pang, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, María J., Post, David, Sierra, Cristina Prieto, Maria-Helena Ramos, Renner, Maik, Reynolds, José Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Tirthankar Roy, Sá, João H.M., Gianfausto Salvadori, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Ingelin Steinsland, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Fuqiang Tian, Tong, Rui, Kamshat Tussupova, Tyralis, Hristos, Uijlenhoet, Remko, Beek, Rens Van, Ent, Ruud J. Van Der, Ploeg, Martine Van Der, Loon, Anne F. Van, Meerveld, Ilja Van, Nooijen, Ronald Van, Oel, Pieter R. Van, Jean-Philippe Vidal, Freyberg, Jana Von, Vorogushyn, Sergiy, Przemyslaw Wachniew, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Zongxue Xu, Yilmaz, Koray K., and Yongqiang Zhang

Subjects
13. Climate action, 6. Clean water
Abstract

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

34. Twenty-three unsolved problems in hydrology (UPH) – a community perspective

Author

Blöschl, Günter, Bierkens, Marc F.P., Chambel, Antonio, Cudennec, Christophe, Destouni, Georgia, Fiori, Aldo, Kirchner, James W., McDonnell, Jeffrey J., Savenije, Hubert H.G., Sivapalan, Murugesu, Stumpp, Christine, Toth, Elena, Volpi, Elena, Carr, Gemma, Lupton, Claire, Salinas, Josè, Széles, Borbála, Viglione, Alberto, Hafzullah Aksoy, Allen, Scott T., Anam Amin, Vazken Andréassian, Arheimer, Berit, Aryal, Santosh K., Baker, Victor, Bardsley, Earl, Barendrecht, Marlies H., Bartosova, Alena, Batelaan, Okke, Berghuijs, Wouter R., Beven, Keith, Blume, Theresa, Bogaard, Thom, Amorim, Pablo Borges De, Böttcher, Michael E., Boulet, Gilles, Breinl, Korbinian, Brilly, Mitja, Brocca, Luca, Buytaert, Wouter, Castellarin, Attilio, Castelletti, Andrea, Xiaohong Chen, Yangbo Chen, Yuanfang Chen, Chifflard, Peter, Claps, Pierluigi, Clark, Martyn P., Collins, Adrian L., Croke, Barry, Dathe, Annette, David, Paula C., Barros, Felipe P. J. De, Rooij, Gerrit De, Baldassarre, Giuliano Di, Driscoll, Jessica M., Duethmann, Doris, Ravindra Dwivedi, Eris, Ebru, Farmer, William H., Feiccabrino, James, Ferguson, Grant, Ferrari, Ennio, Ferraris, Stefano, Fersch, Benjamin, Finger, David, Foglia, Laura, Keirnan Fowler, Gartsman, Boris, Gascoin, Simon, Gaume, Eric, Gelfan, Alexander, Geris, Josie, Shervan Gharari, Gleeson, Tom, Glendell, Miriam, Bevacqua, Alena Gonzalez, González-Dugo, María P., Grimaldi, Salvatore, A. B. Gupta, Guse, Björn, Dawei Han, Hannah, David, Harpold, Adrian, Haun, Stefan, Heal, Kate, Helfricht, Kay, Herrnegger, Mathew, Hipsey, Matthew, Hlaváčiková, Hana, Hohmann, Clara, Holko, Ladislav, Hopkinson, Christopher, Hrachowitz, Markus, Tissa H. Illangasekare, Inam, Azhar, Camyla Innocente, Istanbulluoglu, Erkan, Jarihani, Ben, Kalantari, Zahra, Kalvans, Andis, Sonu Khanal, Khatami, Sina, Kiesel, Jens, Kirkby, Mike, Knoben, Wouter, Kochanek, Krzysztof, Kohnová, Silvia, Kolechkina, Alla, Krause, Stefan, Kreamer, David, Kreibich, Heidi, Kunstmann, Harald, Lange, Holger, Liberato, Margarida L. R., Lindquist, Eric, Link, Timothy, Junguo Liu, Loucks, Daniel Peter, Luce, Charles, Mahé, Gil, Makarieva, Olga, Malard, Julien, Shamshagul Mashtayeva, Shreedhar Maskey, Mas-Pla, Josep, Mavrova-Guirguinova, Maria, Mazzoleni, Maurizio, Mernild, Sebastian, Misstear, Bruce Dudley, Montanari, Alberto, Müller-Thomy, Hannes, Alireza Nabizadeh, Nardi, Fernando, Neale, Christopher, Nesterova, Nataliia, Bakhram Nurtaev, Odongo, Vincent O., Subhabrata Panda, Saket Pande, Zhonghe Pang, Papacharalampous, Georgia, Perrin, Charles, Pfister, Laurent, Pimentel, Rafael, Polo, María J., Post, David, Sierra, Cristina Prieto, Maria-Helena Ramos, Renner, Maik, Reynolds, José Eduardo, Ridolfi, Elena, Rigon, Riccardo, Riva, Monica, Robertson, David E., Rosso, Renzo, Tirthankar Roy, Sá, João H.M., Gianfausto Salvadori, Sandells, Mel, Schaefli, Bettina, Schumann, Andreas, Scolobig, Anna, Seibert, Jan, Servat, Eric, Shafiei, Mojtaba, Sharma, Ashish, Sidibe, Moussa, Sidle, Roy C., Skaugen, Thomas, Smith, Hugh, Spiessl, Sabine M., Stein, Lina, Ingelin Steinsland, Strasser, Ulrich, Su, Bob, Szolgay, Jan, Tarboton, David, Tauro, Flavia, Thirel, Guillaume, Fuqiang Tian, Tong, Rui, Kamshat Tussupova, Tyralis, Hristos, Uijlenhoet, Remko, Beek, Rens Van, Ent, Ruud J. Van Der, Ploeg, Martine Van Der, Loon, Anne F. Van, Meerveld, Ilja Van, Nooijen, Ronald Van, Oel, Pieter R. Van, Jean-Philippe Vidal, Freyberg, Jana Von, Vorogushyn, Sergiy, Przemyslaw Wachniew, Wade, Andrew J., Ward, Philip, Westerberg, Ida K., White, Christopher, Wood, Eric F., Woods, Ross, Zongxue Xu, Yilmaz, Koray K., and Yongqiang Zhang

Subjects
13. Climate action, 6. Clean water
Abstract

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

38. Automation of hydrological drought typology to study drought propagation in a tropical catchment

Author

Quesada-Montano, Beatriz, Van Loon, A. F., Hidalgo, Hugo G., Birkel, Christian, Westerberg, Ida K., Wetterhall, Fredrik, Quesada-Montano, Beatriz, Van Loon, A. F., Hidalgo, Hugo G., Birkel, Christian, Westerberg, Ida K., and Wetterhall, Fredrik

Abstract

Understanding different types of drought and how they propagate through the hydrological cycle from precipitation to streamflow and groundwater deficits is important for improving water and risk management policies. Drought in the tropics is a recurrent phenomenon, but limited knowledge exists about drought severity and duration as well as the processes that cause different types of drought. At the catchment scale, analysing drought propagation is usually done manually. This can be time consuming (e.g. when dealing with long time series or many catchments) and may introduce subjective elements into the analysis that affect the comparability between catchments and studies. In this study, we developed a methodology to provide an automated objective procedure for drought typology to study hydrological drought propagation in the tropics. We selected the Savegre catchment in Costa Rica as a proof-of-concept pilot study. The first step was to analyse if the types of drought affecting this catchment could be explained in terms of the process-based typology available in the literature: classical rainfall deficit drought, wet-to-dry season drought, and composite drought. Then, based on the manual typology, we defined different criteria for the hydrological drought types to make the typology automated and objective. Finally, we analysed drought propagation using a set of duration, timing and deficit indicators. We found that the process-based hydrological typology available in the literature is suitable to describe the different drought processes occurring in Savegre. The classification obtained with the automated typology was highly similar to the manual typology, with the exception of one event. We found that most of the detected droughts (71% and 73% from all river discharge and groundwater droughts, respectively) were classical rainfall deficits droughts, which suggests that droughts in this catchment are highly climate dominated. However, the importance of storage contr

39. Uncertainty in hydrological signatures for gauged and ungauged catchments

Author

Ida Westerberg, Alberto Montanari, Attilio Castellarin, Gemma Coxon, Hilary McMillan, Thorsten Wagener, Jim Freer, Westerberg, Ida K., Wagener, Thorsten, Coxon, Gemma, Mcmillan, Hilary K., Castellarin, Attilio, Montanari, Alberto, and Freer, Jim

Subjects
Hydrology, Regionalization, 0208 environmental biotechnology, Monte Carlo method, Autocorrelation, Flow (psychology), Magnitude (mathematics), Regionalisation, 02 engineering and technology, Rating curve, 020801 environmental engineering, Hydrological signatures, Hydrology (agriculture), Climatology, Computer Science::Computer Vision and Pattern Recognition, Eco-hydrological flow indices, Environmental science, Ungauged basins, Mean flow, Discharge uncertainty, Hydrology, ungauged catchment, Water Science and Technology
Abstract

Reliable information about hydrological behavior is needed for water-resource management and scientific investigations. Hydrological signatures quantify catchment behavior as index values, and can be predicted for ungauged catchments using a regionalization procedure. The prediction reliability is affected by data uncertainties for the gauged catchments used in prediction and by uncertainties in the regionalization procedure. We quantified signature uncertainty stemming from discharge data uncertainty for 43 UK catchments and propagated these uncertainties in signature regionalization, while accounting for regionalization uncertainty with a weighted-pooling-group approach. Discharge uncertainty was estimated using Monte Carlo sampling of multiple feasible rating curves. For each sampled rating curve, a discharge time series was calculated and used in deriving the gauged signature uncertainty distribution. We found that the gauged uncertainty varied with signature type, local measurement conditions and catchment behavior, with the highest uncertainties (median relative uncertainty ±30–40% across all catchments) for signatures measuring high- and low-flow magnitude and dynamics. Our regionalization method allowed assessing the role and relative magnitudes of the gauged and regionalized uncertainty sources in shaping the signature uncertainty distributions predicted for catchments treated as ungauged. We found that 1) if the gauged uncertainties were neglected there was a clear risk of over-conditioning the regionalization inference, e.g. by attributing catchment differences resulting from gauged uncertainty to differences in catchment behavior, and 2) uncertainty in the regionalization results was lower for signatures measuring flow distribution (e.g. mean flow) than flow dynamics (e.g. autocorrelation), and for average flows (and then high flows) compared to low flows. This article is protected by copyright. All rights reserved.

Published
2016

40. Regional water balance modelling using flow-duration curves with observational uncertainties

Author

Chong-Yu Xu, Lebing Gong, Ida Westerberg, Jan Seibert, Keith Beven, Alvaro Semedo, Sven Halldin, University of Zurich, and Westerberg, Ida K

Subjects
010504 meteorology & atmospheric sciences, Calibration (statistics), 0207 environmental engineering, 02 engineering and technology, Forcing (mathematics), Structural basin, lcsh:Technology, 01 natural sciences, lcsh:TD1-1066, Water balance, 2312 Water Science and Technology, Consistency (statistics), Econometrics, lcsh:Environmental technology. Sanitary engineering, 910 Geography & travel, 020701 environmental engineering, lcsh:Environmental sciences, Reliability (statistics), 0105 earth and related environmental sciences, lcsh:GE1-350, lcsh:T, 1901 Earth and Planetary Sciences (miscellaneous), lcsh:Geography. Anthropology. Recreation, Regionalisation, Data set, 10122 Institute of Geography, lcsh:G, 13. Climate action, Environmental science
Abstract

Robust and reliable water-resource mapping in ungauged basins requires estimation of the uncertainties in the hydrologic model, the regionalisation method, and the observational data. In this study we investigated the use of regionalised flow-duration curves (FDCs) for constraining model predictive uncertainty, while accounting for all these uncertainty sources. A water balance model was applied to 36 basins in Central America using regionally and globally available precipitation, climate and discharge data that were screened for inconsistencies. A rating-curve analysis for 35 Honduran discharge stations was used to estimate discharge uncertainty for the region, and the consistency of the model forcing and evaluation data was analysed using two different screening methods. FDCs with uncertainty bounds were calculated for each basin, accounting for both discharge uncertainty and, in many cases, uncertainty stemming from the use of short time series, potentially not representative for the modelling period. These uncertain FDCs were then used to regionalise a FDC for each basin, treating it as ungauged in a cross-evaluation, and this regionalised FDC was used to constrain the uncertainty in the model predictions for the basin. There was a clear relationship between the performance of the local model calibration and the degree of data set consistency – with many basins with inconsistent data lacking behavioural simulations (i.e. simulations within predefined limits around the observed FDC) and the basins with the highest data set consistency also having the highest simulation reliability. For the basins where the regionalisation of the FDCs worked best, the uncertainty bounds for the regionalised simulations were only slightly wider than those for a local model calibration. The predicted uncertainty was greater for basins where the result of the FDC regionalisation was more uncertain, but the regionalised simulations still had a high reliability compared to the locally calibrated simulations and often encompassed them. The regionalised FDCs were found to be useful on their own as a basic signature constraint; however, additional regionalised signatures could further constrain the uncertainty in the predictions and may increase the robustness to severe data inconsistencies, which are difficult to detect for ungauged basins.

Published
2014

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