Articles | Volume 26, issue 4
https://doi.org/10.5194/hess-26-1111-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-1111-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data
Alby Duarte Rocha
CORRESPONDING AUTHOR
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, 10623 Berlin, Germany
Stenka Vulova
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, 10623 Berlin, Germany
Christiaan van der Tol
University of Twente, Faculty of Geo-Information Science and Earth
Observation (ITC), P.O. Box 217, AE 7500 Enschede, the Netherlands
Michael Förster
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, 10623 Berlin, Germany
Birgit Kleinschmit
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, 10623 Berlin, Germany
Related authors
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-106, https://doi.org/10.5194/gmd-2024-106, 2024
Revised manuscript under review for GMD
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation generally hindered by lack of knowing the truth. We propose a virtual framework, in which this truth is fully known and the sensor observations for Cosmic Ray Neutron Sensing, Remote Sensing, and Hydrogravimetry are simulated. This allows the rigourous testing of these virtual sensors to understand their effectiveness and limitations.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-106, https://doi.org/10.5194/gmd-2024-106, 2024
Revised manuscript under review for GMD
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation generally hindered by lack of knowing the truth. We propose a virtual framework, in which this truth is fully known and the sensor observations for Cosmic Ray Neutron Sensing, Remote Sensing, and Hydrogravimetry are simulated. This allows the rigourous testing of these virtual sensors to understand their effectiveness and limitations.
Katharina Heike Horn, Stenka Vulova, Hanyu Li, and Birgit Kleinschmit
EGUsphere, https://doi.org/10.5194/egusphere-2024-1380, https://doi.org/10.5194/egusphere-2024-1380, 2024
Short summary
Short summary
In this study we applied Random Forest machine learning algorithm to model current and future forest fire susceptibility (FFS) in north-east Germany using anthropogenic, climatic, topographic, soil, and vegetation variables. Model accuracy ranged between 69 % to 71 % showing a moderately high model reliability for predicting FFS. The model results underline the importance of anthropogenic and vegetation parameters for FFS. This study will support regional forest fire prevention and management.
Enting Tang, Yijian Zeng, Yunfei Wang, Zengjing Song, Danyang Yu, Hongyue Wu, Chenglong Qiao, Christiaan van der Tol, Lingtong Du, and Zhongbo Su
Biogeosciences, 21, 893–909, https://doi.org/10.5194/bg-21-893-2024, https://doi.org/10.5194/bg-21-893-2024, 2024
Short summary
Short summary
Our study shows that planting shrubs in a semiarid grassland reduced the soil moisture and increased plant water uptake and transpiration. Notably, the water used by the ecosystem exceeded the rainfall received during the growing seasons, indicating an imbalance in the water cycle. The findings demonstrate the effectiveness of the STEMMUS–SCOPE model as a tool to represent ecohydrological processes and highlight the need to consider energy and water budgets for future revegetation projects.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
Short summary
Short summary
Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
P. E. K. Campbell, K. F. Huemmrich, E. M. Middleton, J. Alfieri, C. van der Tol, and C. S. R. Neigh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 1–8, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-1-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-1-2022, 2022
Peiqi Yang, Egor Prikaziuk, Wout Verhoef, and Christiaan van der Tol
Geosci. Model Dev., 14, 4697–4712, https://doi.org/10.5194/gmd-14-4697-2021, https://doi.org/10.5194/gmd-14-4697-2021, 2021
Short summary
Short summary
Since the first publication 12 years ago, the SCOPE model has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary productivity (GPP), and directional thermal signals. Here, we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements.
Jan G. Hofste, Rogier van der Velde, Jun Wen, Xin Wang, Zuoliang Wang, Donghai Zheng, Christiaan van der Tol, and Zhongbo Su
Earth Syst. Sci. Data, 13, 2819–2856, https://doi.org/10.5194/essd-13-2819-2021, https://doi.org/10.5194/essd-13-2819-2021, 2021
Short summary
Short summary
The dataset reported in this paper concerns the measurement of microwave reflections from an alpine meadow over the Tibetan Plateau. These microwave reflections were measured continuously over 1 year. With it, variations in soil water content due to evaporation, precipitation, drainage, and soil freezing/thawing can be seen. A better understanding of the effects aforementioned processes have on microwave reflections may improve methods for estimating soil water content used by satellites.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Yunfei Wang, Yijian Zeng, Lianyu Yu, Peiqi Yang, Christiaan Van der Tol, Qiang Yu, Xiaoliang Lü, Huanjie Cai, and Zhongbo Su
Geosci. Model Dev., 14, 1379–1407, https://doi.org/10.5194/gmd-14-1379-2021, https://doi.org/10.5194/gmd-14-1379-2021, 2021
Short summary
Short summary
This study integrates photosynthesis and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model. The results indicated that the simulation of land surface fluxes was significantly improved by considering the root water uptake, especially when vegetation was experiencing severe water stress. This finding highlights the importance of enhanced soil heat and moisture transfer in simulating ecosystem functioning.
Lena-Marie Kuhlemann, Doerthe Tetzlaff, Aaron Smith, Birgit Kleinschmit, and Chris Soulsby
Hydrol. Earth Syst. Sci., 25, 927–943, https://doi.org/10.5194/hess-25-927-2021, https://doi.org/10.5194/hess-25-927-2021, 2021
Short summary
Short summary
We studied water partitioning under urban grassland, shrub and trees during a warm and dry growing season in Berlin, Germany. Soil evaporation was highest under grass, but total green water fluxes and turnover time of soil water were greater under trees. Lowest evapotranspiration losses under shrub indicate potential higher drought resilience. Knowledge of water partitioning and requirements of urban green will be essential for better adaptive management of urban water and irrigation strategies.
Peiqi Yang, Christiaan van der Tol, Petya K. E. Campbell, and Elizabeth M. Middleton
Biogeosciences, 18, 441–465, https://doi.org/10.5194/bg-18-441-2021, https://doi.org/10.5194/bg-18-441-2021, 2021
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) has the potential to facilitate the monitoring of photosynthesis from space. This study presents a systematic analysis of the physical and physiological meaning of the relationship between fluorescence and photosynthesis at both leaf and canopy levels. We unravel the individual effects of incoming light, vegetation structure and leaf physiology and highlight their joint effects on the relationship between canopy fluorescence and photosynthesis.
Bart Schilperoort, Miriam Coenders-Gerrits, César Jiménez Rodríguez, Christiaan van der Tol, Bas van de Wiel, and Hubert Savenije
Biogeosciences, 17, 6423–6439, https://doi.org/10.5194/bg-17-6423-2020, https://doi.org/10.5194/bg-17-6423-2020, 2020
Short summary
Short summary
With distributed temperature sensing (DTS) we measured a vertical temperature profile in a forest, from the forest floor to above the treetops. Using this temperature profile we can see which parts of the forest canopy are colder (thus more dense) or warmer (and less dense) and study the effect this has on the suppression of turbulent mixing. This can be used to improve our knowledge of the interaction between the atmosphere and forests and improve carbon dioxide flux measurements over forests.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Javier Pacheco-Labrador, Tarek S. El-Madany, M. Pilar Martin, Rosario Gonzalez-Cascon, Arnaud Carrara, Gerardo Moreno, Oscar Perez-Priego, Tiana Hammer, Heiko Moossen, Kathrin Henkel, Olaf Kolle, David Martini, Vicente Burchard, Christiaan van der Tol, Karl Segl, Markus Reichstein, and Mirco Migliavacca
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-501, https://doi.org/10.5194/bg-2019-501, 2020
Revised manuscript not accepted
Short summary
Short summary
The new generation of sensors on-board satellites have the potential to provide richer information about the function of vegetation than before. This information, nowadays missing, is fundamental to improve our understanding and prediction of carbon and water cycles, and therefore to anticipate effects and responses to Climate Change. In this manuscript we propose a method to exploit the data provided by these satellites to successfully obtain this information key to face Climate Change.
Debsunder Dutta, David S. Schimel, Ying Sun, Christiaan van der Tol, and Christian Frankenberg
Biogeosciences, 16, 77–103, https://doi.org/10.5194/bg-16-77-2019, https://doi.org/10.5194/bg-16-77-2019, 2019
Short summary
Short summary
Canopy structural and leaf photosynthesis parameterizations are often fixed over time in Earth system models and represent large sources of uncertainty in predictions of carbon and water fluxes. We develop a moving window nonlinear optimal parameter inversion framework using constraining flux and satellite reflectance observations. The results demonstrate the applicability of the approach for error reduction and capturing the seasonal variability of key ecosystem parameters.
César Cisneros Vaca, Christiaan van der Tol, and Chandra Prasad Ghimire
Hydrol. Earth Syst. Sci., 22, 3701–3719, https://doi.org/10.5194/hess-22-3701-2018, https://doi.org/10.5194/hess-22-3701-2018, 2018
Short summary
Short summary
The influence of long-term changes in canopy structure on rainfall interception loss was studied in a 55-year old forest. Interception loss was similar at the same site (38 %), when the forest was 29 years old. In the past, the forest was denser and had a higher storage capacity, but the evaporation rates were lower. We emphasize the importance of quantifying downward sensible heat flux and heat release from canopy biomass in tall forest in order to improve the quantification of evaporation.
Rahul Raj, Christiaan van der Tol, Nicholas Alexander Samuel Hamm, and Alfred Stein
Geosci. Model Dev., 11, 83–101, https://doi.org/10.5194/gmd-11-83-2018, https://doi.org/10.5194/gmd-11-83-2018, 2018
C. Rumbaur, N. Thevs, M. Disse, M. Ahlheim, A. Brieden, B. Cyffka, D. Duethmann, T. Feike, O. Frör, P. Gärtner, Ü. Halik, J. Hill, M. Hinnenthal, P. Keilholz, B. Kleinschmit, V. Krysanova, M. Kuba, S. Mader, C. Menz, H. Othmanli, S. Pelz, M. Schroeder, T. F. Siew, V. Stender, K. Stahr, F. M. Thomas, M. Welp, M. Wortmann, X. Zhao, X. Chen, T. Jiang, J. Luo, H. Yimit, R. Yu, X. Zhang, and C. Zhao
Earth Syst. Dynam., 6, 83–107, https://doi.org/10.5194/esd-6-83-2015, https://doi.org/10.5194/esd-6-83-2015, 2015
Related subject area
Subject: Urban Hydrology | Techniques and Approaches: Remote Sensing and GIS
Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modelling in urban areas
High-quality observation of surface imperviousness for urban runoff modelling using UAV imagery
High-resolution land surface modeling utilizing remote sensing parameters and the Noah UCM: a case study in the Los Angeles Basin
João P. Leitão, Matthew Moy de Vitry, Andreas Scheidegger, and Jörg Rieckermann
Hydrol. Earth Syst. Sci., 20, 1637–1653, https://doi.org/10.5194/hess-20-1637-2016, https://doi.org/10.5194/hess-20-1637-2016, 2016
Short summary
Short summary
Precise and detailed DEMs are essential to accurately predict overland flow in urban areas. In this this study we evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs. In addition, we compared the best quality UAV DEM to a conventional lidar-based DEM; the two DEMs are of comparable quality.
P. Tokarczyk, J. P. Leitao, J. Rieckermann, K. Schindler, and F. Blumensaat
Hydrol. Earth Syst. Sci., 19, 4215–4228, https://doi.org/10.5194/hess-19-4215-2015, https://doi.org/10.5194/hess-19-4215-2015, 2015
Short summary
Short summary
We investigate for the first time the possibility of deriving high-resolution imperviousness maps for urban areas from UAV imagery and using this information as input for urban drainage models. We show that imperviousness maps generated using UAV imagery processed with modern classification methods achieve accuracy comparable with standard, off-the-shelf aerial imagery. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications.
P. Vahmani and T. S. Hogue
Hydrol. Earth Syst. Sci., 18, 4791–4806, https://doi.org/10.5194/hess-18-4791-2014, https://doi.org/10.5194/hess-18-4791-2014, 2014
Cited articles
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration: Guidelines for computing crop requirements, FAO
Irrigation and drainage paper 56, ISBN 92-5-104219-5, https://www.fao.org/3/x0490e/x0490e00.htm (last access: 14 November 2021), 1998.
Allen, R. G., Walter, I. A., Elliott, R. L., Howell, T. A., Itenfisu, D.,
Jensen, M. E., and Snyder, R. L.: The ASCE Standardized Reference
Evapotranspiration Equation, American Society of Civil Engineers, https://epic.awi.de/id/eprint/42362/1/ascestzdetmain2005.pdf (last access: 10 February 2021), 2005.
Bauer-Marschallinger, B. and Paulik, C.: Copernicus Global Land Operations
”Vegetation and Energy”, Validation Report (QAR), SWI1km-V1_I1.11, https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS1_SQE2020_SWI1km_I1.00.pdf (last access: 25 May 2021), 2019.
Bayat, B., van der Tol, C., and Verhoef, W.: Integrating satellite optical
and thermal infrared observations for improving daily ecosystem functioning
estimations during a drought episode, Remote Sens. Environ., 209, 375–394, https://doi.org/10.1016/j.rse.2018.02.027, 2018.
Devia, G. K., Ganasri, B. P., and Dwarakish, G. S.: A Review on Hydrological
Models, Aquat. Procedia, 4, 1001–1007, https://doi.org/10.1016/j.aqpro.2015.02.126, 2015.
Duarte Rocha, A.: AlbyDR/rSCOPE: rSCOPE v1.0 (Evapotranspiration), Zenodo [code], https://doi.org/10.5281/zenodo.6204580, 2022.
Dwarakish, G. S., Ganasri, B. P., and De Stefano, L.: Impact of land use
change on hydrological systems: A review of current modeling approaches,
Cogent Geosci., 1, 1115691, https://doi.org/10.1080/23312041.2015.1115691, 2015.
DWD: DWD Climate Data Center (CDC), http://ftp-cdc.dwd.de/climate_environment/CDC/ (last access: 15 October 2021), 2020.
Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G., Ceulemans, R., Clement, R., Dolman, H., Granier, A., Gross, P., Grünwald, T., Hollinger, D., Jensen, N. O., Katul, G., Keronen, P., Kowalski, A., Lai, C. T., Law, B. E., Meyers, T., Moncrieff, J., Moors, E.,
Munger, J. W., Pilegaard, K., Rannik, Ü., Rebmann, C., Suyker, A., Tenhunen, J., Tu, K., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: Gap
filling strategies for defensible annual sums of net ecosystem exchange, Agr. Forest Meteorol., 107, 43–69, https://doi.org/10.1016/S0168-1923(00)00225-2, 2001.
Feigenwinter, C., Vogt, R., and Christen, A.: Eddy Covariance, Springer, 377–397, https://doi.org/10.1007/978-94-007-2351-1, 2012.
Feigenwinter, C., Vogt, R., Parlow, E., Lindberg, F., Marconcini, M., Del Frate, F., and Chrysoulakis, N.: Spatial Distribution of Sensible and Latent Heat Flux in the City of Basel (Switzerland), IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 11, 2717–2723, https://doi.org/10.1109/JSTARS.2018.2807815, 2018.
Foken, T.: The energy balance closure problem: An overview, Ecol. Appl., 18, 1351–1367, https://doi.org/10.1890/06-0922.1, 2008.
Foltýnová, L., Fischer, M., and McGloin, R. P.: Recommendations for
gap-filling eddy covariance latent heat flux measurements using marginal
distribution sampling, Theor. Appl. Climatol., 139, 677–688,
https://doi.org/10.1007/s00704-019-02975-w, 2020.
Gillefalk, M., Tetzlaff, D., Hinkelmann, R., Kuhlemann, L., Meier, F., Maneta, M. P., and Soulsby, C.: Quantifying the effects of urban green space
on water partitioning and ages using an isotope-based ecohydrological model,
Hydrol. Earth Syst. Sci., 25, 3635–3652, https://doi.org/10.5194/hess-25-3635-2021, 2021.
Hörnschemeyer, B., Henrichs, M., and Uhl, M.: Swmm-urbaneva: A model for
the evapotranspiration of urban vegetation, Water, 13, 243, https://doi.org/10.3390/w13020243, 2021.
Järvi, L., Grimmond, C. S. B. B., and Christen, A.: The Surface Urban
Energy and Water Balance Scheme (SUEWS): Evaluation in Los Angeles and
Vancouver, J. Hydrol., 411, 219–237, https://doi.org/10.1016/j.jhydrol.2011.10.001, 2011.
Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., Oleson, K. W., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki, Q. J. Roy. Meteorol. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016.
Kent, C. W., Grimmond, S., and Gatey, D.: Aerodynamic roughness parameters in
cities: Inclusion of vegetation, J. Wind Eng. Ind. Aerodynam., 169, 168–176,
https://doi.org/10.1016/j.jweia.2017.07.016, 2017.
Kljun, N., Rotach, M. W., and Schmid, H. P.: A three-dimensional backward
lagrangian footprint, Bound.-Lay. Meteorol., 103, 205–226, 2002.
Kljun, N., Calanca, P., Rotach, M. W., and Schmid, H. P.: A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP), Geosci. Model Dev., 8, 3695–3713, https://doi.org/10.5194/gmd-8-3695-2015, 2015.
Knauer, J., El-Madany, T. S., Zaehle, S., and Migliavacca, M.: Bigleaf – An R package for the calculation of physical and physiological ecosystem
properties from eddy covariance data, PLoS One, 13, e0201114,
https://doi.org/10.1371/journal.pone.0201114, 2018.
Kormann, R. and Meixner, F. X.: An analytical footprint model for non-neutral stratification, Bound.-Lay. Meteorol., 99, 207–224, https://doi.org/10.1023/A:1018991015119, 2001.
Kotthaus, S. and Grimmond, C. S. B.: Identification of Micro-scale Anthropogenic CO2, heat and moisture sources – Processing eddy covariance fluxes for a dense urban environment, Atmos. Environ., 57, 301–316, https://doi.org/10.1016/j.atmosenv.2012.04.024, 2012.
Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban
environment – Part II: Impact of spatial heterogeneity of the surface, Urban
Clim., 10, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014.
Kovats, R. S. and Hajat, S.: Heat stress and public health: A critical
review, Annu. Rev. Publ. Health, 29, 41–55, https://doi.org/10.1146/annurev.publhealth.29.020907.090843, 2008.
Kracher, D., Mengelkamp, H. T., and Foken, T.: The residual of the energy
balance closure and its influence on the results of three SVAT models, Meteorol. Z., 18, 647–661, https://doi.org/10.1127/0941-2948/2009/0412, 2009.
Kuhlemann, L.-M., Tetzlaff, D., Smith, A., Kleinschmit, B., and Soulsby, C.:
Using soil water isotopes to infer the influence of contrasting urban green
space on ecohydrological partitioning, Hydrol. Earth Syst. Sci., 25, 927–943, https://doi.org/10.5194/hess-25-927-2021, 2021.
Liang, S. and Wang, J. (Eds.): Chapter 17 – Terrestrial evapotranspiration,
in: Advanced Remote Sensing, 2nd Edn., Academic Press, 649–684, ISBN 9780128158265, 2020.
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F.,
Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The
Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric
and oceanic flows: Model formulation, recent developments, and future
perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015, 2015.
Martens, B., Miralles, D. G., Lievens, H., Van Der Schalie, R., De Jeu, R.
A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N.
E. C.: GLEAM v3: Satellite-based land evaporation and root-zone soil
moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Meili, N., Manoli, G., Burlando, P., Bou-Zeid, E., Chow, W. T. L., Coutts,
A. M., Daly, E., Nice, K. A., Roth, M., Tapper, N. J., Velasco, E., Vivoni,
E. R., and Fatichi, S.: An urban ecohydrological model to quantify the effect
of vegetation on urban climate and hydrology (UT&C v1.0), Geosci. Model Dev., 13, 335–362, https://doi.org/10.5194/gmd-13-335-2020, 2020.
Miralles, D. G., Brutsaert, W., Dolman, A. J., and Gash, J. H.: On the Use of
the Term “Evapotranspiration”, Water Resour. Res., 56, e2020WR028055, https://doi.org/10.1029/2020WR028055, 2020.
Moncrieff, J. B., Massheder, J. M., De Bruin, H., Elbers, J., Friborg, T.,
Heusinkveld, B., Kabat, P., Scott, S., Soegaard, H., and Verhoef, A.: A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide, J. Hydrol., 188–189, 589–611, https://doi.org/10.1016/S0022-1694(96)03194-0, 1997.
Nordbo, A., Järvi, L., and Vesala, T.: Revised eddy covariance flux
calculation methodologies – effect on urban energy balance, Tellus B, 64, 18184, https://doi.org/10.3402/tellusb.v64i0.18184, 2012.
Nouri, H., Beecham, S., Kazemi, F., and Hassanli, A. M.: A review of ET measurement techniques for estimating the water requirements of urban
landscape vegetation, Urban Water J., 10, 247–259,
https://doi.org/10.1080/1573062X.2012.726360, 2013.
Nouri, H., Beecham, S., Anderson, S., Hassanli, A. M., and Kazemi, F.: Remote
sensing techniques for predicting evapotranspiration from mixed vegetated
surfaces, Urban Water J., 12, 380–393, https://doi.org/10.1080/1573062X.2014.900092,
2015.
Nouri, H., Borujeni, S. C., and Hoekstra, A. Y.: The blue water footprint of
urban green spaces: An example for Adelaide, Australia, Landsc. Urban Plan.,
190, 103613, https://doi.org/10.1016/j.landurbplan.2019.103613, 2019.
Olmedo, G. F., Ortega-Farías, S., de la Fuente-Sáiz, D., Fonseca-Luengo, D., and Fuentes-Peñailillo, F.: water: Tools and Functions to Estimate Actual Evapotranspiration Using Land Surface Energy Balance Models in R, R J., 8, 352–370, https://doi.org/10.32614/rj-2016-051, 2016.
Ortega-Farias, S., Olioso, A., Antonioletti, R., and Brisson, N.: Evaluation
of the Penman-Monteith model for estimating soybean evapotranspiration,
Irrig. Sci., 23, 1–9, https://doi.org/10.1007/s00271-003-0087-1, 2004.
Petropoulos, G., Carlson, T. N., and Wooster, M. J.: An overview of the use
of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) model for the
study of land-atmosphere interactions, Sensors, 9, 4286–4308,
https://doi.org/10.3390/s90604286, 2009.
Quanz, J. A.: Impact of spatial heterogeneity on energy exchange in an urban environment in Berlin, MS thesis, Institute of Ecology Chair of Climatology, Technische Universität Berlin, Berlin, Germany, 93 pp., 2018.
Rafael, S., Rodrigues, V., Fernandes, A. P., Augusto, B., Borrego, C., and
Lopes, M.: Evaluation of urban surface parameterizations in WRF model using
energy fluxes measurements in Portugal, Urban Clim., 28, 100465,
https://doi.org/10.1016/j.uclim.2019.100465, 2019.
Rafael, S., Martins, H., Matos, M. J., Cerqueira, M., Pio, C., Lopes, M., and
Borrego, C.: Application of SUEWS model forced with WRF: Energy fluxes
validation in urban and suburban Portuguese areas, Urban Clim., 33, 100662, https://doi.org/10.1016/j.uclim.2020.100662, 2020.
Raj, R., Bayat, B., Lukeš, P., Šigut, L., and Homolová, L.: Analyzing Daily Estimation of Forest Gross Primary Production Based on Harmonized Landsat-8 and Sentinel-2 Product Using SCOPE Process-Based Model,
Remote Sens., 12, 3773, https://doi.org/10.3390/rs12223773, 2020.
Ramamurthy, P. and Bou-Zeid, E.: Contribution of impervious surfaces to urban evaporation, Water Resour. Res., 50, 2889–2902, https://doi.org/10.1002/2013WR013909, 2014.
R Core Team: R: A Language and Environment for Statistical Computing,
https://www.r-project.org/ (last access: 5 January 2022), 2020.
Rocha, A. D., Groen, T. A., Skidmore, A. K., Darvishzadeh, R., and Willemen,
L.: Machine learning using hyperspectral data inaccurately predicts plant
traits under spatial dependency, Remote Sens., 10, 1263, https://doi.org/10.3390/rs10081263, 2018.
Rocha, A. D., Groen, T. A., Skidmore, A. K., and Willemen, L.: Role of
Sampling Design When Predicting Spatially Dependent Ecological Data With
Remote Sensing, IEEE T. Geosci. Remote, 59, 663–674, https://doi.org/10.1109/TGRS.2020.2989216, 2020.
Scherer, D., Fehrenbach, U., Lakes, T., Lauf, S., Meier, F., and Schuster, C.: Quantification of heat-Stress related mortality hazard, vulnerability and risk in Berlin, Germany, Erde, 144, 238–259, https://doi.org/10.12854/erde-144-17, 2013.
Scherer, D., Ament, F., Emeis, S., Fehrenbach, U., Leitl, B., Scherber, K.,
Schneider, C., and Vogt, U.: Three-dimensional observation of atmospheric
processes in cities, Meteorol. Z., 28, 121–138, https://doi.org/10.1127/metz/2019/0911, 2019.
Schmid, H. P. and Oke, T. R.: A model to estimate the source area contributing to turbulent exchange in the surface layer over patchy terrain,
Q. J. Roy. Meteorol. Soc., 116, 965–988, https://doi.org/10.1002/qj.49711649409, 1990.
Senate Department for Urban Development and Housing: Berlin Environmental Atlas, Long-term Development of Selected Climate Parameters, Edition 2015,
https://www.berlin.de/umweltatlas/_assets/klima/klimaparameter/langjaehrig/en-texte/ek413.pdf (last access: 21 December 2020), 2015.
Senate Department for Urban Development and Housing: Berlin Environmental
Atlas, Green Volume, Edition 2017, https://www.berlin.de/umweltatlas/_assets/biotope/gruenvolumen/en-texte/ek509.pdf (last access: 8 December 2020), 2017.
Senate Department for Urban Planning and the Environment: Berlin
Environmental Atlas, Building and Vegetation Heights, 2014 Edition,
https://www.berlin.de/umweltatlas/_assets/nutzung/gebaeude-und-vegetationshoehen/en-texte/ek610.pdf (last access: 6 January 2021), 2014.
Senate Department for Urban Planning and the Environment: Berlin
Environmental Atlas, Surface Runoff, Percolation, Total Runoff and
Evaporation from Precipitation, 2019 Edition, https://www.berlin.de/umweltatlas/_assets/wasser/wasserhaushalt/en-texte/ekd213.docx
(last access: 4 December 2020), 2019.
Timmermans, J., Su, Z., van der Tol, C., Verhoef, A., and Verhoef, W.:
Quantifying the uncertainty in estimates of surface-atmosphere fluxes through joint evaluation of the SEBS and SCOPE models, Hydrol. Earth Syst. Sci., 17, 1561–1573, https://doi.org/10.5194/hess-17-1561-2013, 2013.
van der Tol, C. and Norberto, G.: Guidelines for Remote Sensing of
Evapotranspiration, Evapotranspiration – Remote Sens. Model., https://doi.org/10.5772/18582, 2012.
van der Tol, C., Verhoef, W., Timmermans, J., Verhoef, A., and Su, Z.: An
integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance, Biogeosciences, 6, 3109–3129, https://doi.org/10.5194/bg-6-3109-2009, 2009.
van der Tol, C., Prikaziuk, and akornfeld: Christiaanvandertol/SCOPE: SCOPEv2.0 (v2.0), Zenodo [code], https://doi.org/10.5281/zenodo.4309327, 2020.
Vickers, D. and Mahrt, L.: Quality control and flux sampling problems for
tower and aircraft data, J. Atmos. Ocean Tech., 14, 512–526,
https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2, 1997.
Vitale, D., Fratini, G., Bilancia, M., Nicolini, G., Sabbatini, S., and Papale, D.: A robust data cleaning procedure for eddy covariance flux
measurements, Biogeosciences, 17, 1367–1391, https://doi.org/10.5194/bg-17-1367-2020, 2020.
Vulova, S., Meier, F., Duarte, A., Quanz, J., Nouri, H., and Kleinschmit, B.:
Science of the Total Environment Modeling urban evapotranspiration using
remote sensing, flux footprints, and artificial intelligence, Sci. Total Environ., 786, 147293, https://doi.org/10.1016/j.scitotenv.2021.147293, 2021.
Wang, Y., Zhang, Y., Ding, N., Qin, K., and Yang, X.: Simulating the impact
of urban surface evapotranspiration on the urban heat island effect using
the modified RS-PM model: A case study of Xuzhou, China, Remote Sens., 12, 578, https://doi.org/10.3390/rs12030578, 2020.
Ward, H. C. and Grimmond, C. S. B.: Assessing the impact of changes in
surface cover, human behaviour and climate on energy partitioning across
Greater London, Landsc. Urban Plan., 165, 142–161,
https://doi.org/10.1016/j.landurbplan.2017.04.001, 2017.
Ward, H. C., Evans, J. G., and Grimmond, C. S. B. B.: Multi-season eddy
covariance observations of energy, water and carbon fluxes over a suburban
area in Swindon, UK, Atmos. Chem. Phys., 13, 4645–4666,
https://doi.org/10.5194/acp-13-4645-2013, 2013.
Ward, H. C., Kotthaus, S., Järvi, L., and Grimmond, C. S. B. B.: Surface
Urban Energy and Water Balance Scheme (SUEWS): Development and evaluation at
two UK sites, Urban Clim., 18, 1–32, https://doi.org/10.1016/j.uclim.2016.05.001, 2016.
Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of flux measurements
for density effects due to heat and water vapour transfer, Q. J. Roy. Meteorol. Soc., 106, 85–100, https://doi.org/10.1002/qj.49710644707, 1980.
Westerhoff, R. S.: Using uncertainty of Penman and Penman-Monteith methods
in combined satellite and ground-based evapotranspiration estimates, Remote
Sens. Environ., 169, 102–112, https://doi.org/10.1016/j.rse.2015.07.021, 2015.
Wickham, H.: ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag, New York, ISBN 978-3-319-24277-4, 2016.
Wouters, H., Demuzere, M., De Ridder, K., and Van Lipzig, N. P. M.: The
impact of impervious water-storage parametrization on urban climate modelling, Urban Clim., 11, 24–50, https://doi.org/10.1016/j.uclim.2014.11.005, 2015.
Wutzler, T., Lucas-Moffat, A., Migliavacca, M., Knauer, J., Sickel, K., Šigut, L., Menzer, O., and Reichstein, M.: Basic and extensible post-processing of eddy covariance flux data with REddyProc, Biogeosciences, 15, 5015–5030, https://doi.org/10.5194/bg-15-5015-2018, 2018.
Xenakis, G.: FREddyPro: Post-Processing EddyPro Full Output File (R package
version 1.0), https://cran.r-project.org/package=FREddyPro (last access: 29 July 2020), 2016.
Yang, P., Prikaziuk, E., Verhoef, W., and van der Tol, C.: SCOPE 2.0: a model to simulate vegetated land surface fluxes and satellite signals, Geosci. Model Dev., 14, 4697–4712, https://doi.org/10.5194/gmd-14-4697-2021, 2021.
Zhao, L., Xia, J., Xu, C. yu, Wang, Z., Sobkowiak, L., and Long, C.:
Evapotranspiration estimation methods in hydrological models, J. Geogr. Sci., 23, 359–369, https://doi.org/10.1007/s11442-013-1015-9, 2013.
Zheng, Q., Hao, L., Huang, X., Sun, L., and Sun, G.: Effects of urbanization
on watershed evapotranspiration and its components in southern China, Water, 12, 645, https://doi.org/10.3390/w12030645, 2020.
Short summary
Evapotranspiration (ET) is a sum of soil evaporation and plant transpiration. ET produces a cooling effect to mitigate heat waves in urban areas. Our method uses a physical model with remote sensing and meteorological data to predict hourly ET. Designed for uniform vegetation, it overestimated urban ET. To correct it, we create a factor using vegetation fraction that proved efficient for reducing bias and improving accuracy. This approach was tested on two Berlin sites and can be used to map ET.
Evapotranspiration (ET) is a sum of soil evaporation and plant transpiration. ET produces a...