Holger Lange


2019

DOI bib
Twenty-three unsolved problems in hydrology (UPH) – a community perspective
Günter Blöschl, M. F. Bierkens, António Chambel, Christophe Cudennec, Georgia Destouni, Aldo Fiori, J. W. Kirchner, Jeffrey J. McDonnell, H. H. G. Savenije, Murugesu Sivapalan, Christine Stumpp, Elena Toth, Elena Volpi, Gemma Carr, Claire Lupton, José Luis Salinas, Borbála Széles, Alberto Viglione, Hafzullah Aksoy, Scott T. Allen, Anam Amin, Vazken Andréassian, Berit Arheimer, Santosh Aryal, Victor R. Baker, Earl Bardsley, Marlies Barendrecht, Alena Bartošová, Okke Batelaan, Wouter Berghuijs, Keith Beven, Theresa Blume, Thom Bogaard, Pablo Borges de Amorim, Michael E. Böttcher, Gilles Boulet, Korbinian Breinl, Mitja Brilly, Luca Brocca, Wouter Buytaert, Attilio Castellarin, Andrea Castelletti, Xiaohong Chen, Yangbo Chen, Yuanfang Chen, Peter Chifflard, Pierluigi Claps, Martyn P. Clark, Adrian L. Collins, Barry Croke, Annette Dathe, Paula Cunha David, Felipe P. J. de Barros, Gerrit de Rooij, Giuliano Di Baldassarre, Jessica M. Driscoll, Doris Duethmann, Ravindra Dwivedi, Ebru Eriş, William Farmer, James Feiccabrino, Grant Ferguson, Ennio Ferrari, Stefano Ferraris, Benjamin Fersch, David C. Finger, Laura Foglia, Keirnan Fowler, Б. И. Гарцман, Simon Gascoin, Éric Gaumé, Alexander Gelfan, Josie Geris, Shervan Gharari, Tom Gleeson, Miriam Glendell, Alena Gonzalez Bevacqua, M. P. González‐Dugo, Salvatore Grimaldi, A.B. Gupta, Björn Guse, Dawei Han, David M. Hannah, A. A. Harpold, Stefan Haun, Kate Heal, Kay Helfricht, Mathew Herrnegger, Matthew R. Hipsey, Hana Hlaváčiková, Clara Hohmann, Ladislav Holko, C. Hopkinson, Markus Hrachowitz, Tissa H. Illangasekare, Azhar Inam, Camyla Innocente, Erkan Istanbulluoglu, Ben Jarihani, Zahra Kalantari, Andis Kalvāns, Sonu Khanal, Sina Khatami, Jens Kiesel, M. J. Kirkby, Wouter Knoben, Krzysztof Kochanek, Silvia Kohnová, Alla Kolechkina, Stefan Krause, David K. Kreamer, Heidi Kreibich, Harald Kunstmann, Holger Lange, Margarida L. R. Liberato, Eric Lindquist, Timothy E. Link, Junguo Liu, Daniel P. Loucks, Charles H. Luce, Gil Mahé, Olga Makarieva, Julien Malard, Shamshagul Mashtayeva, Shreedhar Maskey, Josep Mas-Plá, Maria Mavrova-Guirguinova, Maurizio Mazzoleni, Sebastian H. Mernild, Bruce Misstear, Alberto Montanari, Hannes Müller-Thomy, Alireza Nabizadeh, Fernando Nardi, Christopher M. U. Neale, Nataliia Nesterova, Bakhram Nurtaev, V.O. Odongo, Subhabrata Panda, Saket Pande, Zhonghe Pang, Georgia Papacharalampous, Charles Perrin, Laurent Pfister, Rafael Pimentel, María José Polo, David Post, Cristina Prieto, Maria‐Helena Ramos, Maik Renner, José Eduardo Reynolds, Elena Ridolfi, Riccardo Rigon, Mònica Riva, David E. Robertson, Renzo Rosso, Tirthankar Roy, João Henrique Macedo Sá, Gianfausto Salvadori, Melody Sandells, Bettina Schaefli, Andreas Schumann, Anna Scolobig, Jan Seibert, Éric Servat, Mojtaba Shafiei, Ashish Sharma, Moussa Sidibé, Roy C. Sidle, Thomas Skaugen, Hugh G. Smith, Sabine M. Spiessl, Lina Stein, Ingelin Steinsland, Ulrich Strasser, Bob Su, Ján Szolgay, David G. Tarboton, Flavia Tauro, Guillaume Thirel, Fuqiang Tian, Rui Tong, Kamshat Tussupova, Hristos Tyralis, R. Uijlenhoet, Rens van Beek, Ruud J. van der Ent, Martine van der Ploeg, Anne F. Van Loon, Ilja van Meerveld, Ronald van Nooijen, Pieter van Oel, Jean‐Philippe Vidal, Jana von Freyberg, Sergiy Vorogushyn, Przemysław Wachniew, Andrew J. Wade, Philip J. Ward, Ida Westerberg, Christopher White, Eric F. Wood, Ross Woods, Zongxue Xu, Koray K. Yılmaz, Yongqiang Zhang
Hydrological Sciences Journal, Volume 64, Issue 10

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.

2018

DOI bib
Application of Photon Recollision Probability Theory for Compatibility Check Between Foliage Clumping and Leaf Area Index Products Obtained from Earth Observation Data
Jan Písek, Henning Buddenbaum, Fernando Camacho, Joachim Hill, Jennifer Jensen, Holger Lange, Zhili Liu, Arndt Piayda, Yonghua Qu, Olivier Roupsard, Shawn P. Serbin, Svein Solberg, Oliver Sonnentag, Anne Thimonier, Francesco Vuolo
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given value of leaf area index (LAI). Both the CI and LAI can be obtained from global Earth Observing (EO) systems such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the compatibility between CI and LAI products derived from EO data is examined independently using the theory of spectral invariants, also referred to as photon recollision probability theory (i.e. ‘ $p$ -theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types (PFTs). The $p$ -theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. Our results indicate that the integration of empirically-based CI maps with the MODIS LAI product is feasible, providing a potential means to improve the accuracy of LAI EO data products. Given the strong results for the large range of PFTs explored here, we demonstrate the capacity to obtain p-values for any location solely from EO data. This is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using EO data.

DOI bib
Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory
Jan Písek, Henning Buddenbaum, Fernando Camacho, Joachim Hill, Jennifer Jensen, Holger Lange, Zhili Liu, Arndt Piayda, Yonghua Qu, Olivier Roupsard, Shawn P. Serbin, Svein Solberg, Oliver Sonnentag, Anne Thimonier, Francesco Vuolo
Remote Sensing of Environment, Volume 215

Abstract Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘p-theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p-theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.
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