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Kira Kalinski The Author Kira Kalinski was born in Hanover. She studied Ecosystem services of urban geosciences with a focus on soil science at Universität Hamburg and graduated with a floodplain soils under changing Master of Science. In her master's thesis, she analyzed soil respiration of savanna sites in climate and water management northern Namibia embedded in the research project “The Future of Okavango”. Afterwards she did her PhD as part of the research project "Sicherstellung der Entwässerung küstennaher, urbaner Räume unter Berücksichtigung des Klimawandels". Within this project, she analyzed ecosystem services of urban floodplain soils under changing climate and water management. Kira's scientific focus is on developing practical solutions to current and future environmental problems. K. Kalinski
Band 100
Verein zur Förderung der Bodenkunde Hamburg 100 2021 c/o Institut für Bodenkunde - Universität Hamburg
https://www.geo.uni-hamburg.de/de/bodenkunde.html Band ISSN: 0724-6382
Hamburger Bodenkundliche Arbeiten Hamburger Bodenkundliche Arbeiten HBA
Ecosystem services of urban floodplain soils under changing climate and water management
Dissertation
with the aim of achieving the doctoral degree of natural sciences at the Faculty of Mathematics, Informatics and Natural Sciences
submitted by Kira Kalinski
Department of Earth Sciences UNIVERSITÄT HAMBURG
Hamburg 2021
Tag der Disputation: 08.02.2021
Dissertation angenommen aufgrund der Gutachten von
Prof. Dr. Annette Eschenbach Prof. Dr. Kai Jensen
Erschienen als:
Hamburger Bodenkundliche Arbeiten, Band 100
Herausgeber: Verein zur Förderung der Bodenkunde Hamburg Allende‐Platz 2, D‐20146 Hamburg https://www.geo.uni‐hamburg.de/bodenkunde/ueber‐das‐institut/hba.html Schriftleitung: Dr. Klaus Berger
Dedicated to my grandparents!
Acknowledgments 5
Acknowledgments
This research was funded by the BMBF (Bundesministerium für Bildung und Forschung) Germany, as part of the project STUCK (FKZ: 033W031) within the FONA³ (Forschung für Nachhaltigkeit) funding measure ReWaM (Regionales Wasserressourcen‐Management). This study contributed to the Cluster of Excellence CLICSS (Climate, Climatic Change, and Society) and to CEN (Center for Earth System Research and Sustainability) of Universität Hamburg. Special thanks also to the LSBG (Landesbetrieb für Straßen, Brücken und Gewässer) for their project coordination and to the Bezirksamt Eimsbüttel and Bezirksamt Bergedorf for their permission to work within the Kollau and Dove‐Elbe area.
I would like to thank my supervisor Annette Eschenbach for her support during my PhD. Working on a doctoral thesis, as a mother of a little child, is only possible if a flexible working time is created ‐ thank you very much for that! The continuous panel meetings were very helpful in completing my doctoral thesis. Many thanks to SICSS (School of Integrated Climate System Sciense) for setting up these meetings and many thanks to Annette Eschenbach, Alexander Gröngröft, Kai Jensen and Felix Ament for their regular participation and helpful comments. Furthermore, I would like to thank Alexander Gröngröft for his help with designing experimental concepts and the data assessment. I would also like to thank Volker Kleinschmidt for his help during the soil surveys and setting up field experiments. Without the support in the lab, no doctoral thesis in soil science is possible, many thanks to Monika Voss, Angelika Meier, Deborah Harms and Sumita Rui for that! Special thanks go to Jana Bräuner for her long time of support. Without her help, some of the field and lab experiments would not have been possible, due to the great amount of time involved. Furthermore, I would like to thank Alexander Grasmik, Jonas Reinhardt, Stefan Assall, Markus Kiedrizyn, Florian Zander, Stephan Baumann and Alexander Schütt for their help with the field‐ and lab work. Thanks Sara, for the helpful language corrections!
Miri, Liz, Mathias, Adrian, and Jona have always paid attention to the compliance of lunch breaks, coffee breaks, and after work beer ‐ thank you!
Thank you, Olly, for being the way you are! Nothing better could happen to me!
My family, especially my parents, supported me a lot during the time of my PhD. Help with childcare, creating time offs, coping with highs and downs, and all the love ‐ thank you very much!
I also thank all my roommates from Schomi, Moorweg and Sardinenbüchse and all my friends for all the different kinds of help! Without my second home with Janne and Timo the time in Hamburg would have been only half as nice!
Contents 7
Contents
Acknowledgments ...... 5
Contents ...... 7
Figures ...... 9
Tables ...... 11
Equations ...... 13
Symbols and abbreviations...... 14
Abstract ...... 15 Zusammenfassung ...... 17
1 Introduction ...... 19 1.1 Urban floodplain soils under transition ...... 19 1.2 Chapter overview ...... 26
2 Soil related ecosystem services ...... 27 2.1 Classifying and valuing ecosystem services – an overview ...... 27 2.2 Ecosystem services of floodplain soils ...... 31
3 Study areas ...... 33 3.1 Climate in the City of Hamburg ...... 34 3.2 Study area of Kollau River ...... 35 3.3 Study area of Dove‐Elbe River ...... 37
4 Material and Methods ...... 39 4.1 Soil survey and sampling ...... 39 4.2 Field Experiments ...... 43 4.3 Laboratory analyses...... 46 4.4 Data correction and calculation ...... 49 4.5 Statistical analyses ...... 52
5 Anthropogenic influences on floodplain soils ...... 53 5.1 Soil types of both study areas ...... 53 5.2 Overview of reference soil profiles of both study areas ...... 58 5.3 Consequences of anthropogenic influences on floodplain soils ...... 61
6 Relevance of water retention ponds for the retention of pollutants ...... 63 6.1 Pollutant retention characteristics in urban floodplain soils ...... 63 6.1.1 Pollutant levels in topsoils of floodplains and ponds of the Kollau area ...... 63 8 Contents
6.1.2 Origin of floodplain soil substrate and pollution level...... 64 6.1.3 Controlling factors of water retention pond pollution ...... 65 6.1.4 Total accumulation of pollutants in topsoils of water retention ponds ...... 67 6.2 Discussion ...... 70 6.3 Conclusion ...... 74
7 Potentials of water retention in urban floodplain soils ...... 75 7.1 Water retention characteristics of urban floodplain soils ...... 75 7.1.1 Water balances in floodplain soils of the Kollau area ...... 75 7.1.2 Influencing factors on the soil water balance in the Kollau area ...... 81 7.1.3 Sources of water rise during flood events in floodplain soils ...... 82 7.1.4 Modelling of water storage capacities in bank soils of floodplains ...... 87 7.2 Discussion ...... 90 7.3 Conclusion ...... 95
8 Carbon storage and processes in urban floodplain soils ...... 97 8.1 Carbon storage characteristics in urban floodplain soils...... 97 8.1.1 Soil carbon pools ...... 97 8.1.2 Influencing factors on soil carbon pools ...... 100 8.1.3 Mineralization of organic material typical for urban floodplains ...... 103 8.1.4 Influencing factors on organic carbon loss and mineralization rate ...... 107 8.2 Discussion ...... 108 8.3 Conclusion ...... 113
9 Synthesis ...... 115 9.1 Valuation of soil related ecosystem services ...... 115 9.2 Urban floodplain design under transition ...... 120 9.3 Synergies and conflicts of optimization strategies ...... 125
10 Outlook ...... 127
References...... 129
Appendix ...... 143 A1 Soil parameters ...... 143 A2 Soil pollutants ...... 151 A3 Soil water ...... 156
Figures 9
Figures
Figure 1: Profiles of floodplain soils before and after rates of sediment deposition ...... 21 Figure 2: Categories of ecosystem services ...... 28 Figure 3: Framework for the provision of ecosystem services from soil natural capital ...... 30 Figure 4: Location of the study areas investigated in this research project ...... 33 Figure 5: Annual mean temperatures for Hamburg‐Fuhlsbüttel station ...... 34 Figure 6: Time series of the annual rain sums of Hamburg‐Fuhlsbüttel ...... 34 Figure 7: Catchment area of the Kollau River ...... 36 Figure 8: Catchment area of the Dove‐Elbe River ...... 38 Figure 9: Soil survey of Kollau area ...... 40 Figure 10: Designs of water retention ponds ...... 42 Figure 11: Soil survey of Dove‐Elbe ...... 4 3 Figure 12: Construction of a soil water station in the soil profile ...... 44 Figure 13: Double ring infiltrometer ...... 45 Figure 14: Soil types within both study areas ...... 53 Figure 15: Soil types of the Kollau area ...... 54 Figure 16: Soil types of the Dove‐Elbe area ...... 56 Figure 17: Location and soil types of the 23 reference soil profiles of the Kollau area ...... 58 Figure 18: Location and soil types of the nine reference soil profiles of the Dove‐Elbe area . 60 Figure 19: Relation between organic carbon contents and Pb levels in the topsoils of water retention ponds ...... 66 Figure 20: Calculated annual accumulation masses of ponds of sub catchment 1 (G1 – G4) . 68 Figure 21: Calculated annual accumulation masses of ponds of sub catchment 2 (M1 – M4) ...... 69 Figure 22: Calculated annual accumulation masses of ponds of sub catchment 3 (S1 – S3) .. 70 Figure 23: Optimization of pollutant retention in urban water retention ponds ...... 74 Figure 24: Precipitation (top), river water level (middle) and water storage (bottom) between October 2016 and December 2017 for all plots, equipped with a soil water station...... 77 Figure 25: Precipitation (top), river water level (middle) and water storage in mm per 1 m soil depth (bottom) from all plots equipped with a soil water station during one isolated flood event...... 77 Figure 26: Precipitation (top), river water level (middle) and water storage capacity (bottom) between October 2016 and December 2017 for all plots, equipped with a soil water stations...... 78 10 Figures
Figure 27: Precipitation (top), river water level (middle) and water storage capacity (bottom) for all plots equipped with a soil water station during one isolated flood event...... 79 Figure 28: Precipitation (top), river water level (middle) and groundwater level (bottom) between October 2016 and December 2017 for all plots, equipped with a soil water station...... 80 Figure 29: Precipitation (top), river water level (middle) and groundwater level (bottom) for all plots equipped with a soil water station during one isolated flood event. .. 80 Figure 30: Comparison of precipitation, river water level, groundwater level and water storage capacity during a flood event in December 2016...... 84 Figure 31: Comparison of precipitation, river water level, groundwater level and amount of water storage capacity during a flood event in June 2017...... 86 Figure 32: Typical riverbank designs of the Kollau River ...... 87 Figure 33: Optimization of water retention ...... 94 Figure 34: Carbon pools of reference profiles in Kollau area ...... 98 Figure 35: Carbon pools of reference profiles in bank soils of water retention ponds in the Kollau area ...... 98 Figure 36: Carbon pools of reference profiles in Dove‐Elbe area ...... 99 Figure 37: Carbon pools of topsoils (0.0 – 0.1 m depth) in the Dove‐Elbe area ...... 99 Figure 38: Losses of organic carbon, hemicellulose, cellulose, and lignin for the two litter materials ...... 104 Figure 39: Losses of organic carbon of topsoil materials ...... 105 Figure 40: First‐order kinetic fitting curves of soil organic carbon mineralization ...... 106 Figure 41: Optimization of carbon storage ...... 113 Figure 42: Schematic approach of a sponge city ...... 122
Tables 11
Tables
Table 1: Characteristics of the investigated water retention ponds in the Kollau area ...... 41 Table 2: Water sensors installed at six soil profiles ...... 44 Table 3: Methods for analyzing soil physical and soil chemical parameters...... 46 Table 4: Methods for analyzing inorganic and organic soil pollutants...... 47 Table 5: Statistical methods used for data assessment...... 52 Table 6: Distribution of all mapped soils (n = 130) of the Kollau area ...... 55 Table 7: Distribution of all mapped soils (n = 83) in the Dove‐Elbe area ...... 57 Table 8: Characteristics of reference soil profiles in the Kollau area ...... 59 Table 9: Characteristics of reference soil profiles in the Dove‐Elbe area ...... 60 Table 10: Pollutant levels of trace metals, metalloids, and organic pollutants in topsoil samples ...... 64 Table 11: Pollutant levels of trace metals, metalloid, and organic pollutants in floodplain soils ...... 65 Table 12: Results of regression calculations between pollutant levels and organic carbon contents...... 66 Table 13: Accretion of sludge layers in all ponds of the Kollau area...... 67 Table 14: Characterization of six plots installed with a soil water station ...... 75 Table 15: Infiltration rates of plots F1, F2, F3 and F7 ...... 81 Table 16: Spearman correlation matrix of water‐ soil‐ and terrain properties ...... 82 Table 17: Mean amount of soil water rise (mm) during eleven flood events (October 2016 – December 2017) recorded for all six plots ...... 82 Table 18: Scenario’s climate, urbanization, and soil condition. The calculation of soil water retention in bank soils is based on these scenarios...... 87 Table 19: Water storage capacities in riverbank soils for 1 m flow section along the river .... 88 Table 20: Water storage capacities in riverbank soils of m³ per 1 m flow section along the river for the runoffs predicted for the year 2035 ...... 89 Table 21: Spearman correlation coefficient matrix of Kollau carbon pools correlated with soil‐, terrain‐ and soil water properties ...... 100 Table 22: ANOVA of Kollau soil carbon pools with land uses and degrees of urbanity ...... 101 Table 23: Spearman correlation coefficient matrix of Dove‐Elbe carbon pools and soil‐ and terrain properties ...... 102 Table 24: ANOVA of the soil carbon pools of the Dove Elbe area with land uses and degrees of urbanity ...... 102 Table 25: Characterization of organic material, used in the incubation experiment ...... 103 Table 26: Fitting parameters of organic carbon loss for all organic materials ...... 107 12 Tables
Table 27: Spearman correlation coefficient matrix of organic carbon losses and mineralization rates, soil‐ and litter properties ...... 108 Table 28: ANOVA of organic carbon losses and water content ...... 108 Table 29: Soil ecosystem service and valuation summary ...... 116 Table 30: Services provided by pollutant retention in urban floodplain soils...... 117 Table 31: Valuing soil pollutant retention in urban floodplain soils...... 118 Table 32: Services provided by soil water retention in urban floodplain soils...... 118 Table 33: Valuing water retention in urban floodplain soils...... 119 Table 34: Services provided by carbon storage in urban floodplain soils...... 119 Table 35: Valuing carbon storage in urban floodplain soils...... 120
Equations 13
Equations
Equation 1: Infiltration rate, according to Durner (2012)...... 45 Equation 2: Mass calculation of sludge, organic carbon, and pollutants...... 49 Equation 3: Calculation of water storage and water storage capacity ...... 50 Equation 4: Water storage capacity after flood event...... 50 Equation 5: Transformation of Gauckler‐Manning‐Strickler‐Formular...... 51 Equation 6: Carbon pools in floodplain soils per horizon...... 51 Equation 7: Calculation of the parameter’s hemicellulose, cellulose, and lignin ...... 51 Equation 8: Exponential function of first order mineralization kinetics ...... 52
14 Symbols and abbreviations
Symbols and abbreviations
LR litter origin from rural location LU litter origin from urban location
MOH C10‐C40 sum of mineral oil hydrocarbons C10 – C40 n.e. not existent n.m. not measured
PAHEPA sum of 16 EPA polycyclic aromatic hydrocarbons
PCBEPA sum of 6 polychlorinated biphenyls congeners according to EPA rs Spearman correlation coefficient T1 Topsoil with 1 % organic carbon T6 Topsoil with 6 % organic carbon T8 Topsoil with 8 % organic carbon
Abstract 15
Abstract
With this study, a high potential for the optimization of the ecosystem services of urban floodplain soils was identified. Scholz et al. (2012) named water retention, pollutant retention and carbon storage as the most important ecosystem services of active floodplain soils. Flood events can be mitigated, ecosystems and people protected from high levels of pollution and carbon storage enlarged. Especially in cities, these ecosystem services are increasingly exposed to the stressors of urbanization and climate change. Floodplains are being decimated in favor of settlement construction, with a simultaneous increase in heavy rain and flood events. So far, ecosystem services in urban floodplain soils and their processes have not been sufficiently researched. Previous studies have examined individual ecosystem services in urban floodplains focusing on strategies to improve urban planning concepts. The aim of this study is to analyze the most important ecosystem services of soils in urban floodplains combined. Based on the gained results, optimization strategies of each ecosystem service considering increasing stressors of urbanization and climate change are developed. The urban floodplain soils of the Kollau River and the Dove‐Elbe River in the City of Hamburg were investigated for this purpose. The current state of water retention, pollutant retention, and carbon storage were analyzed and controlling factors on the respective ecosystem services identified. Field and laboratory experiments were performed to improve the process understanding of (i) accumulation processes of pollutants, (ii) water balances and sources during flood events, and (iii) mineralization of organic materials in urban floodplain soils. In the Kollau area, significantly higher levels of pollutants were analyzed in the sediments of water retention ponds compared to the topsoils of the floodplains. As an example, zinc levels of 74.35 mg kg‐1 in the topsoils and 266.71 mg kg‐1 in the sediments were measured. Within the ponds, highest accumulation masses were calculated in the shallow water zones overgrown with a plant cover. By increasing and extending these zones in water retention ponds, the pollutant retention can be optimized. Depending on location and season, groundwater levels varied from 0 to 110 cm below surface and water storage capacities ranged from 16 to 265 cm within 1 m soil depth in the Kollau area. Optimal water storage capacities were determined in soils with low water contents, low groundwater levels, and a sandy soil texture. These soils were identified especially at the edges of the designated floodplains. Water retention of bank soils was calculated for different bank morphologies and scenarios consisting of climate, urbanization, and soil condition. Flat bank morphologies, sandy soil substrate and low water content favor water retention of bank soils. Overall, only a portion of runoff can be retained during flood events in bank soils. The flood wave can be flattened, but not completely retained. For an optimal water retention in floodplain soils, the designated floodplains should be extended considering small‐scale differences of soil properties and bank areas flatten at suitable sites.
16 Abstract
In the soils of the Kollau and Dove‐Elbe areas, low to very high carbon pools between 0.44 kg m‐2 and 260.99 kg m‐2 were analyzed. Fossil peat bands, burial of former topsoils, and technogenic organic rich substrates, are the main reasons for the high carbon storages. Water contents and groundwater levels mainly influences these carbon pools. In addition, carbon mineralization is controlled by the composition of the organic matter components, which seems to be influenced by urban factors. Higher mineralization rates were determined for litter from an urban site compared to litter of a rural surrounding. Existing high carbon pools can be maintained and increased by creating near‐natural floodplain areas with high water contents in urban floodplain soils. Following on from previous studies, this study presents a combination of the important ecosystem services of pollutant retention, water retention and carbon storage of urban floodplain soils. The optimization of these ecosystem services could be developed based on the gained results. Through the specific redesign of floodplains and water retention ponds, planning concepts such as water management can be improved and ecological flood protection in cities further advanced. The creation of near‐natural floodplains and the associated increase in biodiversity and provision of recreational areas represents synergies. Conflicts arise in the simultaneous implementation of mutually exclusive optimization strategies. For example, low soil water contents were derived to optimize water retention and high soil water contents were derived to optimize carbon storage. In the future, urban planning processes should focus on providing sufficient floodplain areas in cities for the optimization of its ecosystem services. This process can positively influence the important mitigation of climate change and urbanization in cities.
Zusammenfassung 17
Zusammenfassung
Diese Studie verdeutlicht das hohe Potential zur Optimierung von Ökosystemleistungen urbaner Überschwemmungsböden. Scholz et al. (2012) nannten neben der Wasserretention die Schadstoffretention und die Kohlenstoffspeicherung als die wichtigsten Ökosystemleistungen intakter Überschwem‐ mungsböden. Hochwasserereignisse können abgemildert, Ökosysteme und Menschen vor hohen Schadstoffleveln geschützt und Kohlenstoffspeicher erhöht werden. Insbesondere in Städten sind diese Ökosystemleistungen den Stressoren der Urbanisierung und des Klimawandels zunehmend ausgesetzt. Überschwemmungsflächen werden zu Gunsten von Siedlungsbau stark dezimiert, bei gleichzeitiger Zunahme von Starkregen‐ und Hochwasserereignissen. Bisher sind die Ökosystemleistungen urbaner Überschwemmungs‐ böden und ihre Prozesse nicht ausreichend erforscht. Vorherige Studien untersuchten einzelne Ökosystemleistungen urbaner Überschwemmungsböden und konzentrierten sich dabei auf Strategien zur Verbesserung städtebaulicher Konzepte. Das Ziel dieser Studie ist es, die wichtigsten Ökosystemleistungen von Böden in urbanen Überschwemmungsgebieten zu analysieren und kombiniert darzustellen. Basierend auf den gewonnenen Ergebnissen wurden Optimierungsstrategien der einzelnen Ökosystemdienstleistungen unter Berücksichtigung der zunehmenden Stressoren der Urbanisierung und des Klimawandels entwickelt. Die urbanen Überschwemmungsböden des Kollau Flusses und des Dove‐Elbe Flusses in der Hansestadt Hamburg wurden zu diesem Zweck untersucht. Der Ist‐Zustand der Wasserretention, der Schadstoffretention, und der Kohlenstoffspeicherung wurde analysiert und steuernde Faktoren auf die jeweilige Ökosystemleistung identifiziert. Feld‐ und Laborexperimente wurden durchgeführt, um das Prozessverständnis von (i) Akkumu‐ lationsprozessen von Schadstoffen, (ii) Wasserbilanzen und Quellen während Hochwasser‐ ereignissen und (iii) der Mineralisierung organischer Materialien in urbanen Überschwem‐ mungsböden zu verbessern. Im Kollau Einzugsgebiet wurden in den Sedimenten der Hochwasserrückhaltebecken signifikant höhere Schadstofflevels verglichen zu den Oberböden der Überschwemmungs‐ gebiete ermittelt. Als Beispiel für Schwermetalle wurden Zinklevel in Höhe von 74.35 mg kg‐1 in den Oberböden und 266.71 mg kg‐1 in den Sedimenten gemessen. Innerhalb der Hochwasserrückhaltebecken wurden die deutlich höchsten Akkumulationsmassen in den Flachwasserbereichen bewachsen mit einer dichten Pflanzendecke berechnet. Durch eine flächenhafte Ausdehnung dieser Zonen in Hochwasserrückhaltebecken kann die Schad‐ stoffretention optimiert werden. Abhängig von Standort und Jahreszeit variierten die Grundwasserstände von 0 bis 110 cm unter Geländeoberfläche und die Wasserspeicherkapazitäten lagen zwischen 16 und 265 cm innerhalb 1 m Bodentiefe im Kollau Einzugsgebiet. Optimale Wasserspeicherkapazitäten während Hochwasserereignissen wiesen Böden mit anfänglich geringen Wassergehalten, tiefen Grundwasserständen und einer sandigen Bodenart auf. Böden mit diesen Eigenschaften wurden am Rande der bisher ausgewiesenen Überschwemmungsgebiete identifiziert. Die
18 Zusammenfassung
Wasserretention von Uferböden wurde für verschiedene Ufermorphologien und Szenarien bestehend aus Klima, Urbanisierung und Bodenzustand berechnet. Flache Ufermorphologien, sandiges Bodensubstrat und geringe Wassergehalte begünstigen die Wasserretention von Uferböden. Insgesamt kann jedoch nur ein Teil des Abflusses während Hochwasserereignissen in den Uferböden gespeichert werden. Die Hochwasserwelle kann abgeflacht, jedoch nicht komplett zurückgehalten werden. Für eine optimale Ausnutzung der Wasserretention in Überschwemmungsböden sollten die bisher ausgewiesenen Überschwemmungsgebiete unter Berücksichtigung kleinräumiger Unterschiede der Bodeneigenschaften deutlich vergrößert und Uferpassagen an geeigneten Stellen abgeflacht werden. In den Überschwemmungsböden der Kollau und Dove‐Elbe wurden geringe bis sehr hohe Kohlenstoffpools zwischen 0.44 kg m‐2 und 260.99 kg m‐2 analysiert. Fossile Torfbänder, vergrabene ehemalige Oberbodenhorizonte und technogenes organikreiches Substrat sind die Hauptgründe der hohen Kohlenstoffpools. Wassergehalte und Grundwasserstände beeinflussen diese Kohlenstoffpools maßgeblich. Darüber hinaus wird die Kohlenstoff‐ mineralisierung durch die Zusammensetzung der Bestandteile der organischen Substanz gesteuert, welche durch urbane Faktoren beeinflusst wird. Es wurden höhere Mineralisierungsraten für Streu aus einem städtischen Standort im Vergleich zu Streu aus einer ländlichen Umgebung ermittelt. Vorhandene hohe Kohlenstoffpools können in urbanen Überschwemmungsböden erhalten und erhöht werden, indem naturnahe Überschwem‐ mungsgebiete mit hohen Bodenwassergehalten geschaffen werden. Diese Studie stellt anknüpfend an vorherige Studien eine kombinierte Darstellung der wichtigen Ökosystemleistungen der Schadstoffretention, der Wasserretention und der Kohlenstoffspeicherung urbaner Überschwemmungsböden dar. Die Optimierung der jeweiligen Ökosystemleistung konnte basierend auf den gewonnenen Ergebnissen fachlich weiterentwickelt werden. Durch die spezifische Umgestaltung von Überschwemmungs‐ gebieten und Hochwasserrückhaltebecken können stadtplanerische Konzepte wie das städtische Wassermanagement verbessert und der ökologische Hochwasserschutz in Städten weiter vorangetrieben werden. Bei gleichzeitiger Umsetzung der erarbeiteten Optimierungsstrategien kommt es zu Synergien und Konflikten. Die Schaffung von naturnahen Überschwemmungsgebieten und die damit verbundene Erhöhung der Biodiversität und die Bereitstellung von Erholungsflächen stellen Synergieeffekte dar. Konflikte entstehen bei der gleichzeitigen Umsetzung sich gegenseitig ausschließender Optimierungsstrategien. So wurden beispielsweise niedrige Bodenwassergehalte zur Optimierung des Wasserrückhalts und hohe Bodenwassergehalte zur Optimierung der Kohlenstoffspeicherung abgeleitet. In Zukunft sollte in stadtplanerischen Prozessen ein Fokus auf die Bereitstellung von Flächen gelegt werde, um Raum für nötige Optimierungen von Ökosystemleistungen urbaner Überschwemmungsböden bereitzustellen, welches zu einer Abmilderung der Folgen des Klimawandels und der Urbanisierung in Städten beitragen kann.
1 Introduction 19
1 Introduction 1.1 Urban floodplain soils under transition
Urban floodplain soils are exposed to increased stressors due to ongoing urbanization (i.a. Scalenghe et al. 2009; Schober et al. 2020) and climate change (IPCC 2018; Schlünzen et al. 2018). Water retention, pollutant retention, and carbon storage are the most important ecosystem services of active floodplain soils (Scholz et al. 2012). Flood events can be mitigated, ecosystems and people protected from high levels of pollution and carbon storages enlarged. Especially in cities, floodplains are decimated in favor of settlement construction (Schober et al. 2020). Simultaneously, extreme rain and flood events occur, which causes severe damage in densely populated areas (Raadgever et al. 2018b). Thus, natural floodplains have been greatly reduced and at the same time the exposed to intense climatic events. In order to preserve the ecosystem services of urban floodplain soils in future and to generate the greatest possible benefit for the human well‐being, floodplain planning concepts must be adapted in order to deal with the ongoing urbanization and climate change (Han et al. 2011; Hobbie et al. 2020). Previous planning concepts in urban floodplains were based primarily on resistance to natural disasters as floods (Richards et al. 2017). In recent years, however, it has become clear that planning concepts, mainly based on grey infrastructure and hard engineering management, are no longer sufficient to deal with the stressors of urbanization and climate change (Chan et al. 2018). In addition, the ecosystems are fragmented and isolated in urban floodplains (Depietri et al. 2012). In consequence, the capacity of providing ecosystem services is reduced. As a result, cities becoming more vulnerable to natural hazards (Depietri et al. 2012). Many studies on planning concepts for urban floodplains call for less destructive approaches with the aim of creating a resilient city based on a blue‐green infrastructure. This infrastructure aims to restore and use the natural ecosystem services of floodplains. Common approaches are the sponge city, low‐impact development, and sustainable urban management design (i.a. Jiang et al. 2018; Raadgever et al. 2018b; Tillie 2017). However, these concepts are not yet sufficiently implemented in urban floodplains. Within the extension of active floodplains in urban areas, as a measure of the blue‐green infrastructure, soil‐related ecosystem services are subsequently considered. The area size rather than the small‐scale soil properties serves as a basis for the designation of active floodplains. In addition, only few studies address the ecosystem services of urban floodplain soils within urban planning concepts. Previous studies focused on modeling of water storage in the whole floodplain area (Collentine et al. 2018; Gunnell et al. 2019), the valuation of ecosystem services (Hopkins et al. 2018; Peters 2016; Tomscha et al. 2016), on riparian forests (Haase 2017), and on sediment retention (Hopkins et al. 2018; McMillan et al. 2017). In order to integrate the ecosystem services of urban floodplain soils into planning processes, a holistic knowledge is needed about their status, processes and limitations due to future changes and the resulting effects on the community. With further research, recommendations for their
20 1 Introduction optimization can be derived, which facilitates the integration in the landscape planning of urban floodplains. The aim of this study is to characterize the most important ecosystem services of urban floodplain soils and to point out potentials for their respective optimization under changing climate and water management. The main ecosystem services of floodplain soils, water retention, pollutant retention and carbon storage are recorded and valuated in two urban floodplain areas in the City of Hamburg.
Land use change in floodplains Timber exploitation and fishing alongside livestock grazing on the floodplain meadows were the first uses of European floodplains. Due to short transport distances and fertile alluvial soils, floodplains became preferred settlement areas. As cities emerged and urbanization increases, roads were built through the floodplains and hydraulic engineering schemes such as river regulation measures were built for flood protection (Roccati et al. 2018). These first interventions in the natural ecosystems of floodplains were exacerbated by industrialization in the 20th century, which was accompanied with an increase in the emissions of pollutants (Haase et al. 1999; Kilianova et al. 2017). Due to the ongoing settlement process along with the construction of flood protection measures, only relicts of active floodplains remain in cities (Müller 1992). As a result, large parts of urban floodplain soils were disconnected from the flood regime, becoming less flooded and drier, so their natural functions of pollutant retention, water retention and carbon storage were reduced. The relevance of floodplains in connection with flood protection is discussed again after major flood events occurring during the 21st century. While increasing heavy rain and flood events the disadvantage of the destruction of natural functions of the floodplain becomes apparent. The restoration of urban floodplains to cope with future extreme climatic events is becoming more and more important (Haase 2009; Raadgever et al. 2018a; Scharf 2017).
Soils of urban floodplains Soils with a changing water table are typical for floodplains. Input of sediment and organic materials is enhanced by recurrent flooding, resulting in the typical layering of floodplain soils and organic‐rich horizons. Furthermore, floodplain soils are characterized by redoximorph features that are a product of chemical reactions developed over a long time, primarily the oxidation, reduction, and solubilization of iron. The typical grey colors in floodplain soils are generated under anaerobic conditions when iron is reduced, solubilized, and leached (Reddy et al. 1993). Reduction and oxidation spots are formed due to the fluctuating water table that are used to classify floodplain soils. Location‐dependent former peat bands lead to organic rich soils horizons. Changes in hydrology, associated with urbanization, create disturbed horizon sequences (De Kimpe et al. 2000; Groffman et al. 2003). Lowering of the water table, dynamic cycles of erosion and deposition due to agriculture and residential construction leading in buried horizons. Additionally, anthropogenic activities such as mixing, sealing, refilling, and polluting with technogenic materials (Lehmann et al. 2007; Nakamura et al. 2000) result in an alteration of the former natural floodplain soil sequence (Amosse et al. 2015). This
1 Introduction 21 massively affects the natural function of carbon storage (Figure 1) as well as other important natural functions, especially the ability to buffer and purify pollutants. Thus, the formation and characteristics of urban soils are strongly affected by human activities, and so are their functions (Yang et al. 2015).
Figure 1: Profiles of floodplain soils before and after rates of sediment deposition associated with agriculture and residential construction, and a lowered water table accompanying urbanization. Horizon symbols are defined as follow: O – horizon dominated by organic material, A ‐ topsoil, Ab – buried topsoil, AB – transition from topsoil to subsoil, BC – transition from subsoil to sediment, Bt – subsoil with alluvial accumulation of silicate clay, Bu – subsoil with urban/technogenic material, Cg – sediment in transition to parent material with static conditions. Figure adapted from Groffman et al. (2003), extended by K. Kalinski.
Relevance of water retention ponds for pollutant retention Previous studies on trace metals and polycyclic aromatic hydrocarbons have shown high levels of pollutants in urban floodplain soils (Bain et al. 2011; Lintern et al. 2015; Simon et al. 2012) and pond sediment (Clozel et al. 2006; Duff 2017; Istenič et al. 2012; Weiss et al. 2006). The important retention function in urban floodplain areas is thereby illustrated (Podschun et al. 2018). Water retention ponds become more important for the pollutant retention in urban floodplains. With ongoing soil sealing due to land requirements and infrastructural development (Scalenghe et al. 2009; Schober et al. 2020), the urban floodplain areas, important for the retention of pollutants, are moderately to severely reduced. As a result, the input of pollutants via surface runoff into the river systems increases. Thus, the retention of pollutants takes place mainly in water retention ponds, which are primarily designed for water management and water retention. Hence, the dispersion of pollutants downstream along the
22 1 Introduction rivers can be avoided. Only a few studies in urban floodplains report this trend and give recommendations for an optimization of pollutant retention in urban ponds. It is well known that various pollutants, which are transported by urban surface runoff, are adsorbed to fine grained particles like clay or organic carbon. These absorption and binding processes are responsible for the immobilization of pollutants in sediment (Polprasert et al. 1989; Scheunert et al. 1992). Entering zones of reduced flow velocity, the pollutant‐ carrying sediment is incorporated into the upper soil layers (Berndtsson 1990). Several studies investigate the accumulation processes of sediment in urban water retention ponds. The main foci were on the conservation of the water retention volume for effective flood protection (Gu et al. 2017; Koskiaho et al. 2003), and the future improvement of the management and recycling of polluted sludge (Keffala et al. 2013). Concerning this topic, accumulation processes within the urban ponds were investigated to optimize the costly desludging measures. Previous studies determine the highest accumulations of sludge associated with pollutants in pond zones of inflow (Franci 1999; Middlebrooks et al. 1965; Schneiter et al. 1983), zones of outflow (Gratziou et al. 2015), zones with aquatic vegetation (Istenič et al. 2012), and the corners (Picot et al. 2005). Papadopoulos et al. (2003) also reported that the accumulation of sludge depends on the geometric shape of ponds, which creates sedimentation conditions of different quality for the settling process. In addition to the accumulation rates of sludge, the total mass of solids can be determined if the thickness of the sludge layer is known (Nelson et al. 2004). Some studies have calculated the masses of sludge in the different pond zones (Gratziou et al. 2015; Istenič et al. 2012; Nelson et al. 2004). However, no study to date separately calculates the masses of the individual components of sludge, organic carbon, and pollutants. This partial study aims to highlight the importance of pollutant retention in urban water retention ponds and deepen the knowledge of the accumulation process. In addition to previous studies, the annual masses of each individual pollutant are calculated for the respective pond zone. Firstly, the actual state and origin of pollutant levels in floodplain and pond soils are determined. Secondly, optimization strategies of the pollutant retention within ponds based on the mass calculations of the sludge components are derived. By the knowledge of the dominant accumulation zones of polluted sludge, pond design and desludging measures can be optimized. This should aim at a sustainable removal of pollutants from ecosystem cycles.
Relevance of urban floodplain soils for water retention Of all the natural hazards in Europe, flooding is the most common, and accounts for the largest number of casualties and highest economic damage (Raadgever et al. 2018b). Flood events have become more severe in recent years due to extreme rain events caused by climate change and strengthened by ongoing urbanization (i.a. IPCC 2018). The natural function of water retention in urban floodplain soils is becoming more important (Haase 2019). Flood protection measures in cities are often characterized by the traditional grey infrastructure. In combination with changing climate and urbanization, such as blocking and drainage of channels, soil sealing and deforestation, particularly strong and
1 Introduction 23 destructive flood events are generated (e.g. Guerreiro et al. 2018; Hughes et al. 2014; Kaspersen et al. 2017). Based on the apprehension, that traditional grey infrastructure may no longer be sufficient for flood protection in urban areas; rising attention is paid on the preventive flood risk management and blue‐green infrastructure by politics and urban planners. Former studies, dealing with the optimization of flood protection in urban areas, recommend a land use change from grey infrastructure to a mixture with blue‐green infrastructure, which is less destructive for the ecosystems (Raadgever et al. 2018a; Scharf 2017; Tingsanchali 2012). Creating more spaces for rivers to increase the ecosystem service of water retention is one of the major goal of approaches based on blue‐green infrastructure, such as Sponge Cities (China) and Bluegreensolutions (Europe), as well as projects like Room for River (Belgium), and LAND4FLOOD (international) (Fokkens 2006; Hartmann et al. 2019; Jiang et al. 2018; Raadgever et al. 2018b). Within the extension of urban floodplains, as one measure of the blue‐green infrastructure, water retention processes including small‐scale soil properties and processes and its controlling factors are often considered subsequently. Studies of the controlling factors on the water retention in floodplain soils are located mostly in less populated areas. Schwartz et al. (2000) and Mc Millan et al. (2015) identified substrate properties and distance to the river as the strongest factors influencing the water balance of floodplains. Locations at higher elevations are also influenced by vegetation (McMillan et al. 2015; Schwartz et al. 2000), while plots at lower elevations by groundwater levels (Hardison et al. 2009; Schwartz et al. 2000). Seasonal fluctuations also have a significant influence on the water content in floodplain soils over the course of the year (McMillan et al. 2015). Infiltration rates into floodplain soils are significantly increased by forests and natural vegetation structures (Hubbart et al. 2011), while anthropogenic activities such as soil sealing and soil compaction have a significant negative impact on soil infiltration rates (Yang et al. 2011). However, studies concerning controlling factors on water retention in urban floodplain soils are still rare. The processes and sources of water rise in soil profiles during flood events are insufficiently investigated so far. Chormanski et al. (2011) analyzed the spatial distribution of water types during floods in an active floodplain, based on a GPS remote sensing method. They concluded that the distribution of floodwater is correlated with different water sources (river water, atmospheric water, and groundwater) and to the spatial distribution of vegetation types in floodplains. In addition, Haga et al. (2005) and Chifflard et al. (2018) highlighted that soil moisture is an important indicator for explaining lag times and subsurface water movements. Initial wet conditions of areas may yield valuable information for flood prediction and warning systems. With the detailed knowledge of the water retention processes in urban floodplain soils, the floodplain areas can be designated more effectively, and the water retention optimized. This partial study aims to highlight the importance of water retention in urban floodplain soils. Further to previous studies, the focus is set on the processes and influencing factors of the soil water balance during flood events in urban floodplains. First, the actual state of water retention will be investigated, followed by the determination of controlling factors and the distribution of floodwater within the urban soils during floods. Finally, optimization strategies
24 1 Introduction of the ecosystem service of water retention in urban floodplain soils are derived, based on the investigated results.
Carbon storage and processes in urban floodplain soils Natural rivers and adjacent floodplains are hotspots of carbon storage (Samaritani et al. 2011; Wohl & Pfeiffer 2017). Due to their dynamic water levels and spatial complexity, floodplains provide ideal conditions for the input and storage of carbon. Besides the high carbon content in the above‐ground biomass, a large part of the stored carbon is located in the floodplain soils (Sutfin et al. 2016). In addition to autochthonous organic material, recurrent flooding introduces exogenous organic material into the soils creating the floodplain‐typical organic‐rich layers. Due to the consequences of ongoing urbanization, e.g. soil sealing, urban floodplain soils are minimized and their carbon storage function severely (Lorenz et al. 2017a). Furthermore, dry periods caused by climate change can massively alter the carbon storage (von Lützow et al. 2009; Wohl et al. 2017; Xiong et al. 2014). However, the effects of ongoing urbanization and climate change on the high carbon storages in active floodplain soils, especially in densely populated areas, are insufficiently determined (Sutfin et al. 2016). A precise process understanding is important to preserve the remaining carbon storages in urban floodplain soils under future conditions and to adapt urban planning accordingly. Many studies investigated high carbon storage in active floodplain soils in less populated areas (Cierjacks et al. 2010; Hoffmann et al. 2007; Hoffmann et al. 2009; Rieger et al. 2014). In contrast, studies that investigate the carbon storage of floodplain soils in urban areas are rare. These studies focus mainly on the characterization of controlling factors on carbon storage. Land use change and ongoing urbanization were identified as factors which could massively decrease carbon storage in urban floodplain soils (Brown et al. 2018). Especially the loss of spaces with former high carbon storage and the reduction of lateral connectivity between the rivers and floodplains, caused by dikes, constitutes the most substantial change of carbon dynamics in floodplain areas (Tockner et al. 2000). The ability to store, transform and transport organic matter is thus massively decreased and could transform carbon sinks into carbon sources (Wohl et al. 2017). In contrast, studies by D’Elia et al. (2017b) and Rees et al. (2019) state that the refilling of organic rich technogenic substrates and the burial of former topsoil horizons, as a consequence of construction processes, can significantly increase the carbon storage in urban floodplain soils. In addition, soil properties and floodplain morphology (Bullinger‐Weber et al. 2014) followed by seasonality’s and flooding alongside with temperature and water content were identified as controlling factors (Samaritani et al. 2011). Above all, the water table in soil profiles, especially altered within the course of climate change (IPCC 2018), seems to be the main driving force for soil carbon pools in urban floodplain soils (Davidson et al. 2006). Changes in soil organic carbon storage could greatly affect the organic carbon emissions in floodplains, even if the change is very small (Tian et al. 2015). Therefore, the mineralization of organic carbon in soils of floodplain ecosystems have received increasing attention in recent years (Chen et al. 2018; Sihi et al. 2016; Yin et al. 2019). In some studies, models have been
1 Introduction 25 developed to better describe the mineralization processes of organic carbon. These models are based on single and double exponential fitting curves (Alvarez et al. 2000; Cooper et al. 2011). In addition to the parameters temperature, moisture, soil texture, pH, microbes, and vegetation, soil water content could be identified as the largest influencing factor on the mineralization of organic carbon in soils (Feng et al. 2016). The highest mineralization rates were found at water holding capacities between 60 % and 75 %. In addition, high rates have also been analyzed at lower and higher water contents (Wang et al. 2003; Yin et al. 2019; Zhang et al. 2015). Yin et al. (2019) stated that different flooding frequencies could greatly affect the soil carbon pool of active floodplains. All these studies focused on soil organic carbon mineralization in farmlands, forests, peatlands, and floodplains in less populated areas (Feng et al. 2016; Jiang et al. 2012; Wang et al. 2003; Yin et al. 2019). However, verification of these results in soils of urban floodplains is still missing. This partial study aims to highlight the importance of carbon storage in urban floodplain soils. Following to previous studies on carbon storage in natural floodplain soils, a special focus is set on the carbon pools of anthropogenic influenced soils and their mineralization process. First, the current state of carbon storage in urban floodplain soils are analyzed followed by the determination of controlling. Secondly, the carbon mineralization of organic material origin from rural and urban surroundings is characterized. Finally, optimization strategies for the ecosystem service of carbon storage in urban floodplains soils are developed with the aim to protect and increase urban carbon storages in future.
Objectives The aim of this study is to generate a holistic overview of the soil‐related ecosystem services in urban floodplain soils. In the course of a changing climate and increasing urbanization, it is important to adapt the ecosystem services of water retention, pollutant retention, and carbon storage to these changes in order to support its conservation and to generate the highest possible benefit for the human well‐being. In addition to studies on ecosystem services of floodplains in less populated areas, this study combines detailed research on all important ecosystem services of urban floodplain soils. Two urban floodplain areas in Hamburg City are investigated for this purpose. The current state of pollutant retention, water retention and carbon storage are analyzed and compared with former studies in urban areas. Controlling factors on the respective ecosystem service are identified. Field and laboratory experiments are conducted to increase the process understanding of (i) accumulation processes of pollutants, (ii) water balances and sources during flood events within soil profiles and (iii) mineralization of organic materials originating from a rural and an urban surrounding. Based on these results, the respective ecosystem services are valuated, and strategies for their optimal use are developed, considering future changes. These strategies should serve as a basis for political decision makers and urban planners to create sustainable and future‐adapted urban floodplain designs. The research questions are:
26 1 Introduction
1. How is the current state of ecosystem services of pollutant retention, water retention and carbon storage in soils of two urban floodplain areas in the City of Hamburg?
2. Which factors influence the processes of the respective ecosystem service?
2.1. Pollutant retention: Which parameters are the main drivers of pollutant accumulation in urban floodplain soils and sediments of water retention ponds? 2.2. Water retention: How much water can be stored in floodplain soils and which sources determine the water rise during flood events (precipitation, flood, groundwater rise)? What is the contribution of bank morphology to water retention in floodplain soils? 2.3. Carbon storage: Which processes and factors influence the pools and mineralization of organic material originated from urban floodplains?
3. How can each of these ecosystem services be optimized within this specific floodplain area?
4. What overall optimization strategies can be implemented in urban floodplains under changing climate and water management and ongoing urbanization?
Research questions 1 to 3 are answered in the main chapters 4 to 8. In chapter 9, research question 4 is discussed.
1.2 Chapter overview This thesis is based on three main chapters that resulted from research conducted in the frame of the BMBF financed project “STUCK (Sicherstellung der Entwässerung küstennaher und urbaner Räume unter Berücksichtigung des Klimawandels)” (FKZ: 033W031) which deals with adaptation strategies for urban flood risk management while changing climate and flood risk management in the City of Hamburg, Germany. Results of Chapter 6 and Chapter 7 will be published in scientific journals.
(Chap. 6) K. Kalinski, A. Gröngröft and A. Eschenbach (submitted): Relevance of water retention ponds for the retention of pollutants. (Chap. 7) K. Kalinski, A. Gröngröft and A. Eschenbach (in preparation): Urban floodplains for improved stormwater retention. (Chap. 8) Soil carbon pools and processes in urban floodplains.
I was largely responsible for laboratory‐ and fieldwork and for the writing process. I carried out the entire field and laboratory work, the evaluation procedures, the production of graphics and the elaboration of the manuscript. Student assistants collected some of the data. The manuscript was revised by the co‐authors.
2 Soil related ecosystem services 27
2 Soil related ecosystem services
Soils are characterized by a variety of processes and interactions such as biomass production, nutrient cycles, chemical recycling and water storage (Blum 2005). Like other compartments of ecosystems, this resource is under pressure from anthropogenic activities and climate change. In order to counteract this trend in future, one approach is to demonstrate the value of soils and its related ecosystem services. The generated concepts have been increasingly used over the last two decades by academics, NGOs and governments (Gómez‐Baggethun et al. 2010) whereby in existing classifications of ecosystem services (MEA 2005; TEEB 2016) a holistic validation of economic values of soils is not uniformly possible (Dominati et al. 2010). In the following chapter, previous research on classification systems of ecosystem services followed by classification systems of soil‐based ecosystem services is presented. Finally, the soil‐based ecosystem services of urban floodplains are introduced.
2.1 Classifying and valuing ecosystem services – an overview
Ecosystem services Since the 1990s, there has been an immense increase in studies on ecosystem functions and nature capital (Costanza et al. 2017; De Groot 1992; De Groot et al. 2002; Douguet et al. 2003; Noël et al. 1998; Robinson et al. 2012; Robinson et al. 2010). In summary, the services that nature capital provides are grouped into ecosystem services and defined as benefits people obtain from the ecosphere and its ecosystems (MEA 2005). In order to determine the value of an ecosystem service, it must first be defined, classified and finally economically assessed (Kumar 2012; MEA 2005). For this purpose, classification systems have been compiled. The classification systems of De Groot (2002), the Millennium Ecosystem Assessment (MEA) (2005), and the Economics of Ecosystems and Biodiversity (TEEB) (2010) present concepts for a uniform and holistic economic assessment of ecosystem services. Four main categories can be derived from the above‐mentioned classification systems: supporting services, provisioning services, regulating services and cultural services. The differences of these categories are shown in Figure 2, derived by the Millennium Ecosystem Assessment Report (MEA 2005). This concept of the ecosystem services on a global agenda provides an important link between demonstrating the importance of ecosystem services and the usage of the concept in political structures as a basis for the development of cities (De Groot et al. 2012).
28 2 Soil related ecosystem services
Figure 2: Categories of ecosystem services. Strength of linkages between commonly encountered categories of ecosystem services and components of human well‐being. Source: (MEA 2005).
TEEB as the latest concept concentrate mostly on urban areas and the economic value on ecosystem services and not on the ecosystems itself. While the concepts of ecosystem services and natural capital have been broadly accepted and their potential contribution to better environmental management widely acknowledged, there are conceptual criticisms and various calls for improvement. Wallace et al. (2007) criticizes that the classification of MEAs is based on mixed processes in which achieving ecosystems and ecosystem services themselves are classified simultaneously. This limits their contribution to decisions concerning biodiversity. Ambiguity in the definitions of key terms, such as ecosystem processes, functions and services, exacerbates this situation (Wallace 2007). Schröter et al. (2014) criticize that the MEA classification system is too anthropocentric and that economic values seems to be more important than ecosystem services themselves. In a study by Bürgi et al. in 2015, it became clear that the possibility of applying the classifications of ecosystem services strongly depends on the history of a landscape structure. For example not all ecosystem services are available everywhere and the specific historical, political, socio‐economic, cultural, and technological context influence which ecosystem services are realized in a specific place and at a specific time (Bürgi et al. 2015). Costanza et al. (2017) stated that practical applications of the classification concepts of ecosystem services are still limited. Limited factors include (1) inconsistent approaches to ecosystem service modelling, assessment, and valuation; (2) the
2 Soil related ecosystem services 29 expense of applying sophisticated methods to answer research questions; (3) the lack of appropriate institutional frameworks; and (4) mistrust or misunderstanding of the science. In general, ecosystem functions are defined as a subset of ecological processes and ecosystem structures, whereas the ecosystem services are the benefits society obtain produced by the ecosystem functions. Fisher et al. (2009) recommend a frequently check on the validity of early valuation concepts to avoid inconsistent definitions of ecosystem processes, ecosystem functions and ecosystem services. This check should include how ecosystems are defined, and how a wide range of stakeholders including scientists, economists, practitioners, policy makers, land managers and environmental educators can use these concepts. The scientific community needs to continue to develop improved methods to measure, monitor, map, model, value, and manage ecosystem services at a multiple scale. Scientists also needs to communicate the concepts and results more effectively to the public, ideally using transdisciplinary teams and strategies in close collaboration with stakeholders. Moreover, the concepts must be provided to decision makers in an appropriate and transparent way to clearly identify differences in outcomes among policy choices (Costanza et al. 2017).
Soil related ecosystem services The first study of soil‐based ecosystem services appeared in Daily et al. (1997) where six services are classified: buffering of the hydrological cycle, physical support of plants, retention and delivery of nutrients to plants, disposal of waste and dead organic matter, renewal of soil fertility and regulation of major element cycles. This classification summarized the ecosystem functions that soil generates but did not establish a direct link to ecosystem services that benefit humans. After the publication of the MEA in which this link was generated, further classifications of ecosystem services with a specific focus on soil functions increased. While Andrews et al. (2004) presented a framework for assessing the impact of soil management practices on soil function, Barrios et al. (2007) focused on a linkage between soil biota and soil ecosystem functions. Furthermore Robinson et al. (2009) developed a definition of soil natural capital based on the parameters of mass, energy and organization/entropy. Other studies focused on soil‐based ecosystem services in the context of agro‐ecosystems (Porter et al. 2009; Sandhu et al. 2010; Swinton et al. 2007; Zhang et al. 2007). Dominati et al. (2010) developed a more holistic classification system where the concept of natural capital of soils and the related ecosystem services are connected. In this concept, soil‐based ecosystem services are derived from the nature capital of soils and categorized into the groups of cultural services, regulating services, and supporting services. These groups are then directly linked to human needs (Figure 3). Within all described soil‐based classification systems of ecosystem services, some of the following points were missing (1) connection between soil natural capital and soil function; (2) categorization of the different services; and (3) potential beneficiaries of the soil and an explanation how to value the economic benefits. All studies until today were created with a goal in mind like determining management scenarios (Andrews et al. 2004), the importance
30 2 Soil related ecosystem services of soil fauna to ecosystem services (Barrios 2007; Lavelle et al. 2006) and the role of soil‐based ecosystem services in the context of agro‐ecosystems (Sandhu et al. 2010; Swinton et al. 2007; Zhang et al. 2007). Also in the framework of Dominati et al. (2010), the final economic evaluation of soil ecosystem functions is still missing. Jónnson et al. (2016) used the framework published by Dominati et al. (2010) to prove whether soil ecosystem services could be evaluated by basic economic methods. They provide examples on how soil ecosystem services can be classified and valued using standard economic methods and established economic frameworks. This study is the latest holistic approach of valuing soil ecosystem services based on different economic methods (Jónsson et al. 2016).
Figure 3: Framework for the provision of ecosystem services from soil natural capital (Dominati et al. 2010).
The latest studies concerning the valuation of ecosystem services in floodplain soils followed different approaches. In a study by Peters et al. (2016) the preliminary stage for an economic valuation of the ecosystem services of floodplains is given. A monetary valuation is missing here. Other studies that calculate a monetary value for the ecosystem services of floodplain soils are based on costs for damage or waste/recycling (Hopkins et al. 2018; Watson et al. 2016). A holistic concept for the valuation of ecosystem services, also for floodplain soils, is still missing.
2 Soil related ecosystem services 31
2.2 Ecosystem services of floodplain soils Urban watercourses and their floodplains provide a variety of ecosystem services all categorized in the group of regulating services of existing ecosystem service classifications (Dominati et al. 2010; MEA 2005; Robinson et al. 2012; Robinson et al. 2010). In addition to the function of water retention, Scholz et al. (2012) identified pollutant retention and carbon storage as the most important ecosystem services of active floodplain soils. Pollutants are discharged into rivers and adjacent floodplain areas due to surface runoff and flood events. Active floodplain soils can filter pollutants out of the surface runoffs and floods in a sustainable manner and thus contribute to an optimal ecosystem service of pollutant retention. This process takes place primarily when soils are flooded. The pollutants bind with the organic matter, sediment and incorporate into the upper soil layers. The pollutant load of floodplain ecosystems varies widely, depending on use and history and on inputs from upper reaches and parameters such as flooding frequency and duration (Du Laing et al. 2009; Gröngröft et al. 2005). In urban spaces, the proximity to traffic routes and the presence of polluted areas are of further significance for pollution of floodplain ecosystems. Floodplain soils retain and store water temporarily. This ecosystem service of water retention leads to a significant reduction of floods caused by extreme rain events, thus preventing major damage in densely populated areas. The soil water balance of floodplain soils is a complex system characterized by substrate and elevation above the river water table, flooding frequency and duration and groundwater level (Schwartz et al. 2000). High amounts of carbon are stored in floodplain soils. Besides the storage of autochthonous plant material, organic material is introduced by flooding. The temporary anaerobic conditions contribute to the fixation of organic matter in the soil. Because of the high carbon storage, active floodplains act as CO2 sinks and are therefore of great importance for climate regulation. In river systems, changes in the soil water balance lead on the one hand to an influence on the mineralization processes of the produced plant biomass and on the other hand activate the conversion processes of the organic carbon stored in the soil (Blagodatskaya et al. 2008; Guenet et al. 2010; Kuzyakov 2002).
3 Study areas 33
3 Study areas
Two areas in the City of Hamburg were selected to study ecosystem services of urban floodplain soils. The selection is based on the following criteria (i) frequent occurrence of flood events, (ii) occurrence of urban and rural areas and (iii) demonstration of different land use conflicts. In the urban Kollau area, located in the north‐west of Hamburg, settlements and traffic is the dominant land use partly interrupted by agricultural areas. In turn, the southeastern rural Dove‐Elbe area is characterized by extensive agricultural areas, which are crossed by smaller streets flanked with settlements. In the Kollau area, settlement areas are mainly affected by flooding and in the Dove‐Elbe area, agricultural uses (Figure 4). In both areas, flood events have increased in intensity in recent years, resulting in an increased damage potential (LSBG 2016). In the following, Hamburg's climate and land uses are described, including degrees of soil sealing, flood management strategies and vegetation structures for both areas.
Figure 4: Location of the study areas investigated in this research project. Grey: Kollau area; Green: Dove‐Elbe area.
34 3 Study areas
3.1 Climate in the City of Hamburg Hamburg is located in the warm and humid temperate climate zone, maritime influenced due to prevailing winds from the west. This leads to mild winters and cool summers by an annual mean temperature of 9.0 °C with a maximum of 12.7 °C and a minimum of 5.2 °C between the years 1971 ‐2000 (Riecke et al. 2010). Schlünzen et al. (2009 a) noted an increase in the mean annual temperature between 1891 and 2007 with a higher slope in recent years (Figure 5). Annual rain averages 772 mm in the years 1971‐2000. Most rain falls in August and the lowest in March (Rosenhagen et al. 2011). Also the rain increased between the years 1891 and 2007 (Figure 6), measured by the weather station in Hamburg‐Fuhlsbüttel located in the Kollau area (Schlünzen et al. 2009 a).
Figure 5: Annual mean temperatures for Hamburg‐Fuhlsbüttel station from 1891 to 2007 (homogenized data series) and linear trends for 1948‐2007 and 1978‐2007 (Schlünzen et al. 2009 a).
Figure 6: Time series of the annual rain sums of Hamburg‐Fuhlsbüttel between 1891 and 2007 (Schlünzen et al. 2009 a).
3 Study areas 35
In urban areas, the so‐called urban climate is created, which differs from the regional climate. The best‐known feature of the urban climate is the heat island, which describes the increased temperatures of urban areas compared to temperatures in rural areas (Parlow et al. 2014). It results, among other things, from an increase in heat storage due to the urban structure, lower evaporation, anthropogenic heat emissions and altered wind fields. Urban heat islands are particularly noticeable on calm summer nights with clear skies (Richter et al. 2013; Schlünzen et al. 2009 a; Wienert et al. 2013). Along with the urban heat island convergences and updrafts can occur, leading to a higher amount of rain in the lee of a city (Shepherd et al. 2002). Due to climate change, an increase in heavy rain events and summer droughts (KLIMZUG‐NORD 2014; Trusilova et al. 2015; Von Storch et al. 2018) is predicted for the area of Hamburg, provided that the urban structure does not change. However, these scenarios can be mitigated by the existence and expansion of green areas, unsealing and green roofs (Schlünzen et al. 2018).
3.2 Study area of Kollau River The study area of the Kollau River in the northwest of Hamburg is a small area on the former moraine with a size of 32 km². The River originates in the northwest of Hamburg City and flows into the Tarpenbek after a flow distance of about 7.3 km, which in turn flows into the Alster. Due to hydraulic engineering and deepening of the riverbed, the morphology of the Kollau changed to a trapezoid cross‐section. The drainage of this urban area largely takes place via a canal network. Important tributaries of the Kollau are the Grothwischgraben (area: 3.8 km²), the Mühlenau (area: 13.3 km²) and the Schillingsbek (area: 3.1 km²) each of which represents its own sub catchment (Figure 7). In the following, the sub catchments are defined as: Grothwischgraben – sub catchment 1; Mühlenau – sub catchment 2 and Schillingsbek – sub catchment 3. The average gradient of the Kollau is 0.1 % with a terrain height between +0.67 m and +58 m above sea level. The southern area has larger elevations than the northern area (Hesser et al. 2017). Different land uses are developed in this urban area. In the rural northern part green areas, agricultural uses and small forests dominate alongside single‐family house settlements. Dense residential buildings, apartment settlements and industrial areas dominate the urban southern part of the Kollau. Traffic roads cross the entire area (Figure 7) (LSBG 2017). Due to the predominant density of buildings, high soil sealing occurs. As shown in Figure 7, areas with a low soil sealing of 0‐10 % up to a bottom sealing of 90‐100 % are present, depending on the respective land use. Some of these areas are directly adjacent to each other. The highest degree of sealing is alongside roads and industrial areas with values of 80 – 100 %. Green areas, which are centrally located, indicate the lowest soil sealing with values between 0 – 20 % respectively. The surrounding residential development shows a soil sealing of 20 ‐ 80 % (LSBG 2016).
36 3 Study areas
Figure 7: Catchment area of the Kollau River. Rural areas indicate forests, green spaces and agricultural uses and sealed areas indicate settlements, traffic, and industry. Ratio of soil sealing (%) is given according to “Behörde für Umwelt, Klima, Energie und Agrarwirtschaft” (BUKEA) authority of Hamburg.
The small size of the Kollau area and the high degree of settlement areas with high sealing ratios lead, especially during heavy rain events, to a high surface runoff, which can cause water levels rising sharply in a very short time, sometimes within 30‐60 minutes. This problem occurs at the Mühlenau, the main tributary of the Kollau. Downstream of the confluence of the rivers Mühlenau and Kollau, flooding problems often occur in the southern settlement areas. Active floodplains are only present in small areas along the water bodies directly bordered with commercial areas, residential buildings with gardens, and allotments. In addition to these small floodplains, 23 water retention ponds are installed to mitigate the flood wave (LSBG 2016). The water retention ponds differ in construction and design further described in chapter 4.
3 Study areas 37
The Hamburg biotope mapping describes the Kollau as a "near‐natural water body with impairments" due to steep bank passages in longer sections. These restrictions lead to a decrease of flood events, which in turn reduce the development of typical floodplain vegetation. Only in small areas, reed beds and perennial meadows are present along the watercourse. The upper edges of the slopes of Kollau and Mühlenau are often planted with trees and shrubs (EGL 2012). In the floodplains along the Kollau, small‐scale moist woody structures with Salix species and nitrophytes developed. Furthermore, Alnus glutinosa frequently appears in these areas as a dominant species of woody plant, which is not a typical softwood species in the lowlands, but rather indicates forest plots (Pott 1997). Smaller pioneer forests, mainly consisting of Fraxinus excelsior and Acer species, can be found in the floodplains. Even though the Kollau area has currently no typical soft and hardwood floodplains or wood‐free floodplain structures, it is noticeable that there is a small‐scale potential to induce a typical floodplain vegetation development following water engineering measures and the possibility of flooding (LSBG 2016).
3.3 Study area of Dove‐Elbe River The study area of the Dove‐Elbe River, located in the southeastern part of Hamburg City, is a large area of 159.9 km² and a river length of 19.6 km. The river originates at Gammer Ort in the southeastern district of Hamburg, runs in northwestern direction and flows through the Tatenberger sluice into the Tideelbe. Altitudes between ‐1 m a.s.l. and +5 m a.s.l. are reached in the area, with lowest elevations in the southeastern and highest elevations in the northwestern part. The Dove‐Elbe area is characterized by different land uses. Residential and commercial uses are common for the northern part with a high soil sealing of at least 60 %, in some cases, where industrial areas are densely built, also 90 %. This reflects urbanization also in the outskirts of Hamburg City. In the southern districts, small‐scale agricultural use with a lower soil sealing of less than 10 % is dominant. The development of settlements takes place mainly along old dike lines, which also serve as transport axes. In these areas, average soil sealing of around 40‐60 % are achieved (LSBG 2016).
In the lower parts of the Dove‐Elbe area, there is a complex drainage system consisting of ditches, weirs, and pumping stations, which are controlled to mitigate flood waves. Prior to the closure of the Tatenberger sluice in 1952, the Dove‐Elbe was connected to the tidal Elbe and its area was affected by tidal flooding. Currently, the origin of floods is caused by heavy rain events and discharges from adjacent water bodies. In 1966, a designated floodplain was established along the lower Dove‐Elbe, which serves as an intermediate water storage in the event of restricted drainage into the Elbe River. The floodplain with a size of about 5 km² is located between water and foreland area, which is bounded by the old dike lines (LSBG 2017). Due to the intensive agricultural use and accompanying drainage measures, the formerly typical floodplain vegetation has changed considerably. At present, reed beds, high perennial meadows and restored sections, such as the eastern part of the nature reserve "Die Reit", can be found in the floodplain of the Dove Elbe. Willow bushes and forests are relics of the former
38 3 Study areas softwood floodplains at the Dove Elbe. Since the damming of the Elbe River Salix plant communities dominate the actual vegetation structures. Occasionally, small‐scale relics of hardwood floodplain forests can still be found in the floodplains. Due to the dominant use of floodplains as grassland, there are currently only a few active floodplain areas, on which a natural succession of vegetation occurs. On these areas, however, a development of vegetation towards characteristic soft and hardwood floodplains as well as wood‐free floodplains can be induced when flooding is permitted (Asdonk et al. 2019; LSBG 2016).
Figure 8: Catchment area of the Dove‐Elbe River. Rural areas indicate forests, green spaces and agricultural uses and sealed areas indicate settlements, traffic, and industry. Ratio of soil sealing (%) is given according to “Behörde für Umwelt, Klima, Energie und Agrarwirtschaft” (BUKEA) authority of Hamburg.
4 Material and Methods 39
4 Material and Methods
Investigation concept for both study areas In both study areas, the investigation concept was applied in the following order. First, the study sites where selected based on the following criteria: (i) the study sites are located within a floodplain area, (ii) all occurring land uses are covered, (iii) all occurring soil properties are covered and (iv) the study sites are evenly distributed over the entire area. Secondly, a soil mapping was performed using rod drillings. The distance between the rod drillings depends on the respective site characteristics. The soils were described based on the Soil Science Mapping Instruction (KA5) (Arbeitsgruppe‐Boden 2005) and later translated into soil types of the international soil classification called world reference base for soil resources (WRB) (WRB 2015). As a third step, reference profiles were selected out of all mapping points. They should represent all occurring site characteristics within each study area. Fourthly, reference profiles were created, and soil samples were taken. Depending on the object of investigation, topsoil samples, sludge samples, horizon wise mixed samples and undisturbed soil samples were taken. The common soil parameters were analyzed in the laboratory as a fifth step. Finally, field experiments concerning water retention and carbon storage were carried out on selected study sites. In the following, the investigation steps for both study sites, Kollau and Dove‐Elbe, are described in detail and the various laboratory analyses and field experiments are listed.
4.1 Soil survey and sampling
Kollau area Soil mapping was carried out along the Kollau River in designated floodplains and water retention ponds. In total 130 plots were mapped within the Kollau area. On ten selected floodplain areas, a total of 35 rod drillings were carried out. Every 50 m a 2 m rod drill followed by a soil description based on the KA5 was generated. In the bank and underwater areas of 11 water retention ponds, a total of 95 rod drillings were carried out. Because of the higher soil heterogeneity of the constructed ponds, the distance between the mapping points was 10 m in the bank areas. The rod drilling was also followed by a soil description based on the KA5 and later translated into soil types of the WRB. The mapping and sampling of the underwater soils in the ponds will be described in the next paragraph. Out of all mapped soils, 23 plots were chosen for the creation of a reference profile. Eleven reference profiles were generated on the floodplains and 12 references profiles in the bank areas of the ponds (Figure 8). Horizon wise mixed samples were taken from each reference profile to perform detailed soil laboratory analyses. In addition, undisturbed soil samples in five 100 cm³ and two 250 cm³ rings were extracted from each horizon of the eleven reference profiles on the floodplains for the analysis of soil‐physical parameters.
40 4 Material and Methods
Figure 9: Soil survey of Kollau area. Brown dots represent soil mapping points on floodplain areas and black dots soil mapping points in pond areas. Black circles indicate plots of reference profiles. Numbers in brackets define the quantity of the respective soil profile. Rural areas indicate forests, green spaces and agricultural uses and sealed areas indicate settlements, traffic, and industry.
In the eleven water retention ponds, a special focus was set on the pollutant analyses of the sludge. Hence, sludge thickness mapping and sludge sampling was done in the eleven selected ponds. In addition to the criteria listed in the paragraph ‘Investigation concept for both study areas`, the selected ponds should represent all pond designs existing in the Kollau area. The examined ponds were distributed in the three sub catchments of the Kollau area (cf. Figure 4) as follows: four ponds in sub catchment 1 (G1, G2, G3 and G4), four ponds in sub catchment 2 (M1, M2, M3 and M4) and three ponds in sub catchment 3 (S1, S2 and S3). The numbering was based on the direction of flow. In total four types of ponds were classified. Table 1 gives general information of the eleven selected ponds. Type A ponds consist of steep shallow water areas and extensive deep‐water areas and a straight flow direction, whereas type B ponds are characterized by larger shallow water areas and a non‐linear flow direction. Type B ponds are
4 Material and Methods 41 further divided as follows: (1) ponds with large shallow water areas, (2) ponds with a central island and (3) ponds with a reed area in the shallow water (Figure 10).
Table 1: Characteristics of the investigated water retention ponds in the Kollau area divided into the three sub catchments. Dominant land uses of sub catchment areas were estimated based on the data from the BUKEA authority of Hamburg. The order follows the frequency of land use within the respective sub catchment. The total area of each pond, based on the mean water level, were taken from hydraulic engineering reports of the Technical University of Hamburg. Desludging dates are provided by the district office Eimsbüttel, Hamburg.
Characteristics Sub catchment 1 Sub catchment 2 Sub catchment 3 G1 G2 G3 G4 M1 M2 M3 M4 S1 S2 S3 Pond type acc. A A B1 A B3 B2 B3 B3 B2 B2 B2 to Figure 2 Total area of the 1094 231 2139 3688 1094 5545 14430 12853 281 3023 1190 pond [m²] Period since last 6 23 23 33 13 ‐ 10 7 3 17 17 measure [a] Main land use Settlement, Industry, Traffic, Settlement, Traffic, Settlement, Traffic, Green area Agriculture, Green area Green area
Topsoil (0‐10 cm below surface) samples in each water retention pond were collected in areas classified as either inflow (i), outflow (o), slope (sl), shallow (sh), deep (d) or reed (r) (Figure 10). Slope and shallow water were defined as areas with a water depth less than 1 m, and deep‐water as areas with a water depth greater than 1 m, based on the mean water level. Drilling of the sludge layers was done with a Beeker sampler pipe. At least five samples were taken in each area, depending on the size and condition of each pond. All samples from one area were then mixed to form an average sample. Sludge sampling was done in the year 2016. Additionally, in 2018 a mapping of the sludge thickness from the sludge surface up to the pond bottom in all eleven ponds were carried out. At each mapping point, sludge samples were taken for a further analysis of water content and organic carbon and trace metals in samples of reed areas.
42 4 Material and Methods
Figure 10: Designs of water retention ponds. Ponds of design A are characterized by a straight flow direction, whereas the flow in ponds designed by type B is not linear. Further distinctions are as follows: A: deep pond; B1: shallow pond; B2: shallow pond with centralized island; B3: shallow pond with reed area. Sampling zones of sludge samples inside pond – inflow, outflow, slope and shallow (up to 1 m depth), reed and deep (over 1 m depth).
Dove‐Elbe area In the Dove‐Elbe area, sites between dike line and water body were mapped. Based on a study by Meyer et al. (1954), 83 plots were selected on the basis of a biotope mapping in 1954 following the criteria listed in paragraph ‘investigation concept for both study areas. Additionally, the 83 plots were divided into different height classes to capture all past and present influences of flooding. The height classes based on the mean water level are divided as follows: 0 to 50 cm, 50 to 100 cm, 100 to 150 cm, and 150 to 200 cm. Rod drilling to a depth of 2 m was performed followed by a soil description based on the KA5 and later translated into the soil classification WRB. Out of the 83 mapped soils, nine plots were selected for the construction of a reference profile. Soil samples were taken from all nine reference profiles for a comprehensive soil chemical and soil physical analysis. A mixed sample and undisturbed samples were distributed from each soil horizon. Undisturbed soil sampling includes the extraction of five 100 cm³ and two 250 cm³ rings. In the Dove‐Elbe area, a special focus was set on the analysis of carbon pool distribution in the topsoils. Hence, mixed topsoil samples from a depth of 0 to 10 cm were taken from 40
4 Material and Methods 43 mapping points. These were selected out of the 83 mapping plots by randomly determining of ten points within each of the four height classes. Sampling was carried out directly at the borehole and four further times at approximately three meters around the borehole. This procedure was chosen to compensate any small‐scale inhomogeneity. The five individual samples per borehole were mixed and homogenized to an average sample.
Figure 11: Soil survey of Dove‐Elbe. Brown dots represent soil surveys on floodplain and black circles indicate plots of reference profiles. Numbers in brackets define the quantity of the respective soil profile. Rural areas indicate forests, green spaces and agricultural uses and sealed areas indicate settlements, traffic, and industry.
4.2 Field Experiments Field experiments were conducted in the Kollau area to investigate the detailed processes of the ecosystem service of water retention in urban floodplain soils. Because of its urban character the Kollau area was selected for the investigation of the consequences of extreme flood events on the surrounding land uses.
Soil water stations Six soil water stations were equipped with sensors for soil water content, soil water tension, soil temperature, and groundwater level monitoring (Figure 12). Additionally, the water level of the nearest water body was measured at four soil water stations (No F1, F2, F3, and F7). For the other two stations (No F5 and F6) data from permanently installed gauges were used. As described in Table 4 the volumetric soil water content and soil temperature were measured in a combined sensor (VWC). Campbell VWC sensors were used on three soil profiles (No F1, F2 and F7) and Decagon sensors on four soil profiles (No F3, F5 and F6). All soil
44 4 Material and Methods water stations were equipped with the same tensiometer probes (Delta‐T Devices) to measure soil water tension. For the groundwater level measurements, the lowest tensiometer was reversed so that the pressure of the water column above the ceramic candle could be recorded. The sensors were installed in undisturbed soil profiles from 10 cm to 100 cm in the middle of each horizon. Using data loggers, data was recorded at 30‐minute intervals. The water level measurements were carried out with a TD diver water level logger directly in the sensor. Due to the rapidly changing water levels, a measuring interval of 10 minutes was set. In addition, rain data were provided by a climate station from the research project ‘Hamburg urban soil climate observatory (HUSCO)’. Data were recorded between June 2016 and December 2017. Few soil water stations suffered data failures due to vandalism and weather conditions.
Table 2: Water sensors installed at six soil profiles for the recording of soil water balance in the Kollau study area.
Measurement Sensor type Data logger Accuracy Company Soil profiles Campbell CS 650 soil Volumetric CR 300 Scientific water content ± 3 % F1, F2 and F7 water content Campbell Ltd., Bremen reflectometer combined with Germany soil Decagon ‐ temperature 5 TM Decagon ECH2O Em50 ± 0.03 m³ m ³ Devices Inc. F3, F5, F6 (2010) T4e UMS GmbH Water tension Delta‐T DL6‐te ± 0.5 kPa all profiles Tensiometer (2009) Eijkelkamp Water level TD Diver water level logger ± 0.05 % F1, F2, F3, F7 Soil & Water
Figure 12: Construction of a soil water station in the soil profile. (A) Box for storing the loggers; (B) Sensors in a soil profile with deep groundwater level; (C) Sensors in a soil profile with low groundwater level.
4 Material and Methods 45
Infiltration rate Infiltration rates were determined at plots F1, F2, F3 and F7. The infiltration rates at the individual plots were carried out on undisturbed soil profiles using a double ring infiltrator according to DIN 19682‐7. The infiltrator consists of two rings made of stainless steel, which are driven into the topsoil at a depth of approx. 10 cm. If necessary, the vegetation on the surface was shortened to better insert the rings into the soil (Figure 13).
Figure 13: Double ring infiltrometer. Source: Assall (2017), unpublished.
For the measurements, the larger ring was first filled with water to a level of 10 cm, followed immediately by the smaller one. The use of the outer ring, which is also filled with water, is important because it guarantees that the water from the inner ring infiltrates vertically into the soil and that no water moves laterally towards the water‐unsaturated soil (cf. Equipment 2015). For an accurate measurement, it is important to keep lateral water movement to a minimum. This can be achieved by identical water levels of both rings over the entire measuring time.
The infiltration rate i thus results from the cumulative infiltration I (mm) per time t (min). According to Durner (2012) the corresponding formula for calculating the infiltration rates is as follows: Equation 1: Infiltration rate, according to Durner (2012).
𝑑𝐼 ∆𝐼 𝑖 (1) 𝑑𝑡 ∆𝑡
46 4 Material and Methods
4.3 Laboratory analyses To characterize the soil properties, basic soil analyses were conducted. Methods and procedure are summarized in Table 3 and Table 4.
Table 3: Methods for analyzing soil physical and soil chemical parameters.
Parameter Method Instrument Instruction sieving /sedimentation Sedimat 4‐12, UGT Particle size method in accordance DIN ISO 11277 GmbH, distribution with Köhn analysis (2002) Müncheberg, Germany method undisturbed depth Klute and Bulk density (pb) method Dirksen (1986) Hartge and gravimetric technique Drainage branch of Horn (2006), of a porous plate retention curve Richards apparatus (1948) Water holding water that soils can DIN ISO 11274
capacity hold against gravity (2018) AccuPyc II 1340, helium pycnometer Micromeritics DIN 66137‐2 Particle density method Instrument Corporation, (2019) Norcross, GA pH meter, Xylem determination of pH DIN ISO 10390 pH analytics, Weilheim, with CaCl2 (2005) Germany Conductivity meter Electrical determination of (WTW LF90), Xylem DIN ISO 11265 conductivity electrical conductivity analytics, Weilheim, (1997) Germany Total carbon/organic laboratory analyzer for vario MAX CNS, carbon the determination of Elementar DIN ISO 10694 Total nitrogen carbon, nitrogen, and Analysensysteme (1996) Total sulfur sulfur GmbH, Hanau, Germany Plant available Flame AAS Varian, LabX, Determination of potassium Midland, ON, Canada phosphorus and DIN 38405, UV/VIS Photometer, DR Plant available potassium in double Part 1 (1983) 5000, Hach Lange phosphorus lactate (DL) extract GmbH, Germany NDF (neutral detergent fiber); ADF Detergent analysis of ANKOM 2000, ANKOM ANKOM (acid detergent the cell wall Technology, Macedon, (2014) fiber); ADL (acid components NY, USA detergent lignin)
4 Material and Methods 47
Table 4: Methods for analyzing inorganic and organic soil pollutants.
Parameter Method Instrument Instruction Microwave, company Soil condition trace metals (Cd, Cr, CEM, model MarsXPress extraction in aqua DIN 11466 Cu, Fe, Pb and Zn) and Flame AAS Varian, regia of soluble trace (1997) and metalloid (As) LabX, Midland, ON, elements Canada ∑16 EPA polycyclic DIN 18287 aromatic Determination of PAH GC‐MS triple squad, (2006) hydrocarbons (PAH) and PCB by Soxhlet 7000C, Agilent ∑6 EPA polycyclic extraction Technologies, USA DIN 51527 biphenyls (PCB) (1987) GC‐FID, Shimadzu 2010, Mineral organic Determination of MKW type FID and AOC20i, DIN EN ISO hydrocarbons (MOH by extraction with n‐ Shimadzu Deutschland 9377‐2 (2001) C10‐C40) hexane and acetone GmbH, Duisburg, Germany
In the following the analyzation of soil pollutants is described in detail due to necessary adaption of the methods.
Metalloid and trace metals Aqua‐regia extraction of trace metals cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) and the metalloid arsenic (As) was performed using the microwave method (Mars Xpress, CEM GmbH, Germany) according to DIN11466 (1997). Samples of oven‐dried fine‐grained soils were put into Teflon vessels and treated with aqua regia solution (13.4 mL of HCl 30%, and 3.5 mL of HNO3 60%) in the microwave. Extracted solutions were decanted to glass flasks of volume 50 mL and made up to the volume by bidistilled water. The element content was analyzed using the atomic absorption spectrometer (AAS Varian AA 280 Series, Germany).
Polycyclic aromatic hydrocarbons The US Environmental Protection Agency (EPA) has defined the sum parameters of 16
PAH (PAHEPA) and six PCB congeners ‐ 28, 52, 101, 138, 153, and 180 ‐ (PCBEPA) as priority environmental pollutants that functions as representatives of the entire substance group. The extraction of PAHEPA (DIN18287 2006) and PCBEPA (DIN51527 1987) was conducted with a Soxhlet apparatus (Behr Labor‐Technik, Germany) using n‐Hexane. 6.0 g of air‐dried and homogenized soil sample was filled into a cellulose extraction thimble, covered with quartz wadding, and put into the Soxhlet apparatus. Together with the solvent, a deuterated PAHEPA internal standard solution was added to each sample. The extraction procedure ran for two hours. Afterwards extracts were analyzed with a mass spectrometer (GC‐MS triple squad,
7000C, Agilent Technologies, USA) to quantify the PAHEPA and PCBEPA.
48 4 Material and Methods
Mineral organic hydrocarbons According to DIN EN ISO 9377‐2 (2001) the determination of total mineral oil hydrocarbon
(MOH C10 – C40) content was analyzed. An amount of 10.0 g of air‐dried and homogenized soil was weighed into a 50 ml flask. For extraction, 20 ml of n‐hexane marked with decane (C10), eicosane (C20), and tetracontane (C40), and 20 ml of acetone were added. The mixture was agitated for 30 minutes in a horizontal shaker at 150 r min‐1. The hexane phase with the solved mineral hydrocarbons was separated from the rest by adding distilled water, placing it in a centrifuge for 15 minutes and taking the buoyant phase off with a glass pipette. This procedure was repeated twice. Afterwards the extract was cleaned up by column filtration. Columns were filled with quartz wadding, 2 g Florisil® (magnesium silicate gel) and 2 g sodium sulfate. Extract was placed on column until 10 ml extract for analysis were obtained. From this sample, three aliquots were taken in 1.5 ml crimp‐neck vials for analysis with gas chromatograph and flame ionization detector (GC‐FID, Shimadzu 2010, type FID and AOC20i, Shimadzu Deutschland GmbH, Duisburg, Germany). After correction of the baseline, based on blind and standard values, a sum parameter between C10 and C40 was determined.
Procedure of an Incubation Experiment The incubation experiment was performed to characterize the carbon mineralization of different organic materials, typical for the Kollau area, under controlled conditions. Two litter materials and three organic‐rich topsoil materials were chosen for the long‐term incubation over six months under constant climatic conditions (temperature of 20°C) and different water contents, adjusted based on the water holding capacity of each organic material. Populus litter material was collected at an urban park in the Hamburg City center directly among large roads (LU) and at a rural forest at the boundaries of Hamburg City (LR). Topsoils were selected from reference profiles within the Kollau area. T8 material represents a topsoil with 8 % organic carbon collected from a rural plot, T6 material indicates a topsoil with 6 % organic carbon and anthropogenic influences and T1 material a topsoil with 1 % organic carbon also anthropogenic influenced. For each organic material three water contents of 55 %, 75 % and 95 %, calculated based on water holding capacity, were adjusted, and filled into incubation bottles. The different water contents should simulate the fluctuating water levels of floodplain areas. All bottles were sealed with butyl rubber stoppers to prevent gas exchange with the ambient air and to keep water content constant. The headspace of all samples was exchanged with synthetic air (20% O2, 80% N2). Three parallel measurements each result in 45 incubation bottles. Additionally, an empty incubation bottle for the blank value was measured. The concentration of CO2 and CH4 inside the headspace of each bottle were measured repeatedly via gas chromatography (7890A and 6890N, Agilent Technologies, Santa Clara, CA, USA). The gas chromatograph was equipped with a nickel catalyst to reduce CO2 to CH4 and a flame‐ ionizing detector (FID). Gases were separated on a PorapakQ column with helium as carrier gas. If the concentration of CO2 in the headspace of aerobic incubations approached 3%, the headspace was exchanged again with synthetic air. On the starting day of incubation, the CO2 contents of all bottles were determined. During six months of incubation, the measuring intervals for the CO2 content varied from daily measurements during the first weeks to two
4 Material and Methods 49 weekly measurements in the last month. The amount of gas inside each bottle was calculated from the gas concentration, headspace volume, incubation temperature, and pressure inside the bottle using the ideal gas law. The amount of dissolved gas was calculated from the gas concentration in the headspace, pressure inside the bottle, water content, and gas solubility in water. The conversion of CO2 (µmol/g) into Corg (%) was calculated with the atomic mass of
CO2 (44). To characterize the selected five materials, the following laboratory analyses were performed before incubation experiments: amount of carbon and nitrogen and cell wall components of neutral detergent fiber (NDF), acid detergent fiber (ADF) and acid detergent lignin (ADL) for the characterization of the litter cell components. After the incubation, again the carbon and nitrogen values of all organic materials were determined.
4.4 Data correction and calculation
Pollutant retention Masses of sludge, organic carbon and pollutants were calculated for each zone within each water retention pond. The calculation is based on the parameters: sludge thickness, dry bulk density and organic carbon content of sludge samples taken in 2018 and the pollutant level and organic carbon content of sludge samples from 2016. The bulk density was derived from water and organic carbon content. The pollutant level for the year 2018 were calculated using regression equations based on the correlation of pollutant level and organic carbon content from 2016 (Table 12) The equations are as follows: Equation 2: Mass calculation of sludge, organic carbon, and pollutants.
𝑆 𝑠𝑡 ∗ 𝜌 / 1000 𝐶𝑜𝑟𝑔 (2) 𝐶 𝑆𝑚 ∗ 100 𝑃 𝑔 ∗𝐶𝑜𝑟𝑔 ∗ 𝑘 / 10000 .
Sm = sludge mass (kg), Cm = organic carbon mass (kg), Pm = pollutant mass (kg), stn = sludge thickness
(m), ρn = particle density (g/cm³), Corgn = organic carbon content (%), gn = gradient from a regression equation, kn = constant from a regression equation
Water sensors At the soil water stations, different types of sensors were used due to practical reasons. The used water content probes CS616 (Campbell Scientific Inc.) are sensitive to changes in soil temperature (Seyfried and Murdock, 2001). Therefore, a temperature‐correction was carried out in accordance with the user manual’s calibration equation (Campbell Scientific Inc., 2006a). According to the manufacturer, the water content sensors 5 TM by Decagon are only weakly sensitive to temperature fluctuations and do not need a temperature correction (Campbell, 2001). The Campell CS616 sensors as well as the 5 TM sensors measure water contents between 0 and 50 Vol‐% (according to the manufacturer).
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Water storage and water storage capacity From the water content data, recorded with the Campell and Decagon sensors, the water storage and the water storage capacity were calculated. Both parameters are indicated in mm per 1 m soil depth. The water storage was determined based on the water content and the thickness of the soil horizon. The water storage subtracted from the total pore volume (pore volume) within 1 m soil depth results in the water storage capacity. Equation 3: Calculation of water storage and water storage capacity (mm per 1 m soil depth).
WS WC ∗st ∗10 (3) WSC PV PV ∗st WS
WS = water storage (mm per 1 m soil depth), WSC = water storage capacity (mm per 1m soil depth),
WCn = water content (Vol‐%), st = thickness of respective soil horizon (m), PVs = pore volume of 1 m soil (mm per 1 m soil depth), PVn = pore volume of respective soil horizon (Vol‐%)
Sources of water rise during flood events The available water storage capacity after a flood event in 1m soil depth was calculated with the parameters of water rise in the soil profile, sum of rain and total pore volume within 1 m soil depth. Equation 4: Water storage capacity after flood event.
𝑊𝑆𝐶 𝑃𝑉 𝑃 𝑅 𝑊𝑅 𝑃 (4)
WSCn = water storage capacity (mm), PVn = total pore volume within 1 m soil depth (mm), Pn = sum of rain during flood event (mm), Rn = water rise during flood event except rain (mm), WRn = total water rises in soil profile during flood event (mm)
Calculation of water storage capacity in bank soils Floodwater is infiltrated in bank soils during flood events. When water saturation is reached, surface runoff occurs. The extent, to which water storage capacity of soils is effective, depends on several factors. The aim of the following calculation is to determine, the influence of changed morphology of water bodies and floodplains on the height of flooding of the river cross‐section during defined floods and to estimate the water storage capacity in bank soils. The boundary conditions for the calculation are as followed (1) the height of the riverbed is flat, (2) the water shoulder lies at the same height on both sides and (3) the two bank sides lead to a uniform height, which must be greater than the water shoulder. This results in a river cross‐section that can be calculated from the water body trapezoid up to the height of the water shoulder and from the slope of the flooded area up to the height of the bank side. To determine the height of the water level, the following data were used: (1) typical outlets of the Kollau recorded from the main water level, (2) the existing gradient is starting at the lower reaches of the Kollau, (3) the Gauckler‐Manning‐Strickler equation was used to calculate the flow velocity and (4) the roughness coefficient kst has been varied between 15 and 25. The equation of Gauckler‐Manning‐Strickler was changed as follows:
4 Material and Methods 51
Equation 5: Transformation of Gauckler‐Manning‐Strickler‐Formular.
/ (i) 𝑣 𝑘 ∗ 𝑅 ∗𝐼 (ii) 𝑄 𝑣 ∗ 𝐴 / (iii) 𝑄 𝑘 ∗ 𝑅 ∗𝐴∗𝐼 (5) (iv) 𝑅 𝐴/𝑈 / (v) / 𝐴 ∗ ∗
R = hydraulic radius (m), A = flowed through cross‐section (m²), U = wetted perimeter (m), I = flow gradient (m/m), vm = average flow velocity (m/s), Q = discharge (m³/s), kst = coefficient according to Strickler (m1/3/s).
The calculation is based on (1) the determination of A and U as a function of the water level in the given river cross‐section (right side of equation v), (2) the calculation of the quotient of runoff, gradient, and roughness (left side of equation v) and (3) the determination of the resulting water level height, bank, and floodplain area. For a given water level, a cross‐ sectional area is calculated which indicates the soil area where water can be stored, consisting of the zone of the slope and the floodplain area. Considering an air volume of the unsaturated zone, a potential water storage capacity in m3 per m flood area is determined.
Soil Carbon Pools Soil carbon pools were calculated in kg m‐2 of both study areas. The carbon pools of the surface vegetation were not calculated. For each horizon of the reference soil profiles, the carbon pool was extrapolated using dry bulk density and carbon content. In the presence of coarse soil, their volume was substracted as percentages from the thickness of the soil horizons. The same calculation was carried out for the topsoils in the Dove‐Elbe area. Equation 6: Carbon pools in floodplain soils per horizon.
𝐶𝑃 𝐶𝑜𝑟𝑔 ∗𝜌 ∗𝑠𝑡 ∗ 10 ∗ 100 𝑠𝑐/100 (6)
CPn = soil carbon pools (kg/m²), Corgn = organic carbon content (%), ρn = particle density (g/cm³), stn = soil horizon thickness (m), sc = amount of coarse soil (%).
After the horizontal wise calculation of the soil carbon pools, these were grouped into carbon pools of topsoil (0.0 ‐ 0.3 m soil depth) and subsoil (0.3 ‐ 1.0 m soil depth).
Incubation experiment For the characterization of the litter material, the parameters cellulose, hemicellulose, and lignin are calculated from the determined components of the cell walls NDF (neutral detergent fiber), ADF (acid detergent fiber) and ADL (acid detergent lignin): Equation 7: Calculation of the parameter’s hemicellulose, cellulose, and lignin using the components of the cell walls NDF (neutral detergent fiber), ADF (acid detergent fiber) and ADL (acid detergent lignin).
52 4 Material and Methods
Hemicellulose = NDF – ADF Cellulose = ADF – ADL (7) Lignin = ADL
The mineralization parameter k for each incubation was calculated using the first order kinetic function ‘ExpDec1’ provided by OriginPro 9.1G Software (OriginLab Corporation, Northampton, USA) based on the following exponential function (Ottow 2011b): Equation 8: Exponential function of first order mineralization kinetics to calculate the mineralization rate k.
𝐶 𝐶 ∗ 𝑒 (8)
Ct = concentration of the organic material after time t, C0 = initial concentration at time t = 0, k = mineralization parameter, t = time
4.5 Statistical analyses For all data, basic statistics of mean, standard deviation, minimum and maximum were performed. Principal analysis of variance (ANOVA) followed by a Tuckey‐HSD honest Posthoc Test (significance level of p < 0.05) was used to identify significant differences within pollutant levels, water content and groundwater levels, subsoil‐ and topsoil carbon pools and organic carbon losses. These data are categorized into the different land uses, degrees of urbanity location within study areas, sub catchments of Kollau area, position within ponds and soil texture. Spearman correlations were implemented to determine controlling factors on pollutant levels, water content and groundwater levels, topsoil and subsoil carbon pools, organic carbon losses and mineralization rates (Table 5).
Table 5: Statistical methods used for data assessment.
Statistics Application Variables Output pollutant levels; land uses; ANOVA followed by a water content and groundwater degrees of urbanity; Tuckey‐HSD Posthoc Test levels; location; sub catchment; p 0;1 (significance level of p < 0.05) topsoil and subsoil carbon pools position within pond; soil and organic carbon losses texture pollutant levels; water content and groundwater basic soil parameters; levels; Spearman correlations soil water parameters; rs ‐1;1 topsoil and subsoil carbon pools, terrain morphologies organic carbon losses and mineralization rates
For statistical approach, the programs Excel 2013 (Microsoft), OriginPro 9.1G (OriginLab Corporation, Northampton, USA) and SPSS 16.1 (IBM Corp., Armonk, USA) were used.
5 Anthropogenic influences on floodplain soils 53
5 Anthropogenic influences on floodplain soils
An extensive soil survey served as a basis for the characterization of soil properties and further investigations on soil ecosystem services in both study areas. Within the Kollau area, 130 soils were mapped on floodplain and pond areas. Out of these 130 soils, 23 plots were selected as reference profiles. Of these, eleven reference profiles were generated on floodplains and 12 reference profiles in the bank areas of ponds. In the Dove‐Elbe area, 83 soils were mapped in the area between river and dike line. All these plots were divided into four height classes starting from the mean water level: a) 1.0 – 1.5 m; b) 1.5 – 2.0 m; c) 2.0 – 2.5 m and d) 2.5 – 3.0 m. Out of the 83 soils, nine plots were chosen for a reference profile.
5.1 Soil types of both study areas The groundwater influenced Gleysol was the most frequently mapped soil in both study areas. Out of 130 mapped soils in the Kollau area, 99 Gleysols were determined. Also, in the Dove‐ Elbe area, 77 Gleysols out of 83 mapped soils were identified. Furthermore, the soil types Fluvisol as soil with flooding horizons, Histosol as organic rich soil and Technosol as anthropogenic influenced soil occurred in smaller quantities in both areas. In the Dove‐Elbe area, the soil types Anthrosol, as soil influenced by horticulture and plaque management, and Regosol, as soil with little to no soil genesis, were identified in smaller parts (Figure 14). Specific site characteristics were indicated by the principal qualifiers (prefixes) of Fluvic, Gleyic, Haplic, Histic and Subaquatic and the supplementary qualifier (suffixes) of Fluvic, Gleyic, Humic and Technic, further explained in the next paragraphs.
Figure 14: Soil types within both study areas. Numbers represent the quantity of mapped soils in.
54 5 Anthropogenic influences on floodplain soils
Soil types of the Kollau area The substrate genesis in the Kollau area comprises sediments of the Weichselian period in the northern areas, while Holocene peat bands are present in the southern areas. Subsequent to the floodplains, Saalian elutriation masses from silt occur (Miehlich et al. 2010).
Figure 15: Soil types of the Kollau area. Colored dots indicate the different soil types of the 130 mapped soil profiles in the floodplains of the Kollau River. Numbers represent the quantity of each soil type.
Holocene peat bands, fluctuating groundwater levels, flooding events and anthropogenic interventions, due to the refilling of technogenic substrates and the burial of former topsoil horizons, were identified as the main factors forming the soils of the Kollau area. Gleysols with a high amount of soil organic carbon were dominant in this study area. In the rural northern part, soils with a natural pedogenesis were mapped. In depressions and near the river system Haplic Gleysols, indicating typical Gleysols, and Histic Gleysols, reflecting the high amount of carbon turnover, were mapped. A combination with the suffix of Humic, indicating high amounts of soil organic carbon, was identified. In the southern area, Histosols and Technosols occurred in addition to Gleysols. Near areas with dense settlement and traffic, anthropogenic influenced soils occurred caused by former and recent building activities. The naturally grown soils changed in their horizon sequence due to the refilling of technogenic substrates and the burial of former topsoil horizons. In addition, the Holocene peat bands led to organic‐rich subsoil horizons (Figure 1). In depressions with peat bands, Haplic Histosols and Histic Gleysols
5 Anthropogenic influences on floodplain soils 55 were mapped. Gleysols and Haplic Technosols were identified on floodplains, in areas close to settlements and in the banks of water retention ponds. Due to the artificial construction of the ponds, former construction activities in bank areas led to strongly disturbed soil horizons. Depending on the site characteristics, Gleysols were combined with the prefixes Haplic and Histic and with the suffixes Humic and Technic. For the Haplic Technols a combination with the suffix Humic occurred. Fluvisols with the suffix Subaquatic, which defines the underwater location, were identified in the water areas of ponds (Figure 15).
Table 6: Distribution of all mapped soils (n = 130) of the Kollau area . Total number and percentage of soils within one land use is indicated.
Land use Fluvisol Gleysol Histosol Technosol Total % 0 100 0 0 100 Agriculture n 0 1 0 0 1 % 0 100 0 0 100 Fallow n 0 13 0 0 13 % 17 73 2 8 100 Floodplain n 16 70 2 8 96 % 0 100 0 0 100 Forest n 0 5 0 0 5 % 0 92 8 0 100 Grassland n 0 11 1 0 12 % 0 100 0 0 100 Settlement n 0 3 0 0 3
Only 47% of all investigated soils showed a natural substrate genesis, while 53% were characterized by an anthropogenic‐influenced substrate genesis (Table 11). This result reflects the consequences of urban activities on the soils of a city. The distribution of all soil types within one land use of the Kollau area are illustrated in Table 6. Gleysols were dominant in every land use. Histosols occurred sporadically within grasslands and floodplains. Technosols and Fluvisols were mapped only on floodplains including water retention ponds.
Soil types of the Dove‐Elbe area Holocene sediments of rivers and river deposits, partly peri marine influenced, dominated the substrate genesis in the Dove‐Elbe area. Main sediments were river sand and floodplain sediments. Clay and silt horizons occurred in small‐scale areas (Miehlich et al. 2010).
56 5 Anthropogenic influences on floodplain soils
Figure 16: Soil types of the Dove‐Elbe area. Colored dots indicate the different soil types of the 130 mapped soil profiles in the floodplains of the Dove‐Elbe River. Numbers represent the quantity of each soil type.
Former influences by brackish water, the partly shallow groundwater levels, and anthropogenic interventions, due to the expansion of the Dove‐Elbe River, were identified as the main factors forming the soils of the Dove‐Elbe area. The soil types Gleysol, Fluvisol, Technosol, Anthrosol, and Regosol were mapped in this area. In the entire area, agricultural land uses, grasslands and forest strips formed the landscape between the dike line and the water body. Near the Dove‐Elbe River and in depressions Gleysols were dominant. Depending on the site characteristics a combination with the prefixes Fluvic, resulting in former influence with brackish water, and Haplic and the suffixes Humic and Technic were determined. At several plots along the waterbody, massive anthropogenic interventions in the soils were visible through the expansion of the Dove‐Elbe River and the resultant refilling of technogenic substrates. The refilling of technogenic substrates interrupted the natural sequence of the soil horizons, which led to a massive disturbance of the former natural grown soils. Along the watercourse, anthropogenic influenced soils were described as Haplic Technosol, Haplic
5 Anthropogenic influences on floodplain soils 57
Anthrosol and Haplic Regosol. Suffixes of Fluvic, Gleyic, indicating fluctuating soil water levels, Humic and Technic represented different site characteristics (Figure 16).
Table 7: Distribution of all mapped soils (n = 83) in the Dove‐Elbe area. Total number and percentage of soils within one land use is indicated.
Land use Anthrosol Fluvisol Gleysol Technosol Regosol Total % 0 0 96 0 4 100 Agriculture n 0 0 21 0 1 22 % 0 0 96 0 4 100 Fallow n 0 0 26 0 1 27 % 0 0 75 25 0 100 Floodplain n 0 0 3 1 0 4 % 0 0 100 0 0 100 Forest n 0 0 4 0 0 4 % 0 5 90 0 5 100 Grassland n 0 1 19 0 1 21 % 20 0 80 0 0 100 Settlement n 1 0 4 0 0 5
The distribution of soils within land uses of the Dove‐Elbe area is indicated in Table 7. As already stated in the Kollau area, Gleysol were the dominant soil in all land uses. Within the land use forest only Gleysols were mapped. In addition to Gleysols, Anthrosol, Fluvisol, Technosol and Regosols were mapped on agriculture, fallow, floodplain, grassland, and settlement sites.
58 5 Anthropogenic influences on floodplain soils
5.2 Overview of reference soil profiles of both study areas In this chapter, the reference soil profiles of both study areas are presented. The reference soil profiles covered all site characteristics that occurred in the respective study area (cf. chapter 4). In the Kollau area 23 reference soil profiles and in the Dove‐Elbe area nine reference soil profiles were selected. Extensive soil samples were taken, typical soil parameters analyzed, field experiments on soil water and carbon carried out. A complete overview of all laboratory results is attached in the appendix (A1‐A8).
Reference soil profiles of the Kollau area
Figure 17: Location and soil types of the 23 reference soil profiles of the Kollau area. Reference soil profiles labelled with F are located within a floodplain area and reference soil profiles labelled with P within a water retention pond.
5 Anthropogenic influences on floodplain soils 59
Table 8: Characteristics of reference soil profiles in the Kollau area. Reference soil profiles are divided into soil profiles near water retention ponds and on floodplains. n.e. not existent; n.m not measured. Plot Sub Label Soil type (WRB) Dominant Technogenic Distance to catchment Prefix Reference Suffix soil texture substrate water body soil group (cm depth) (m) P01 Haplic Gleysol Technic sand yes (25) 1.45 P02 Haplic Gleysol Humic, sand yes (10) 0.00 1 Technic P03 Haplic Technosol sand yes (00) 2.4 P04 Haplic Gleysol sand yes (00) 3.98 P05 Haplic Gleysol sand yes (00) 1.87 P06 Haplic Gleysol Humic, sand yes (32) 4.21 Technic 2 P07 Haplic Gleysol Technic loamy sand yes (00) 0.00 P08 Haplic Histosol clay loam yes (20) 0.00
Water retention pond P09 Haplic Gleysol Humic loamy sand yes (00) 4.51 P10 Haplic Technosol loamy sand yes (00) 1.52 P11 Haplic Gleysol Humic Sand yes (15) 0.52 3 P12 Haplic Gleysol Humic, Sand yes (00) 0.98 Technic F01 Haplic Gleysol Humic loamy sand n.e 57.13 1 F02 Histic Gleysol loamy sand n.e 35.18 F03 Haplic Gleysol Humic, sandy loam yes (18) 123.83 Technic 3 F04 Haplic Technosol Humic loamy sand yes (05) 20.11 F05 Haplic Gleysol Technic loamy sand yes (08) 69.21 F06 Histic Gleysol Technic loamy sand yes (10) 33.27 2 F07 Haplic Gleysol Technic Sand yes (00) 18.78
Floodplain F08 Haplic Gleysol loamy sand n.e 114.59 1 F09 Haplic Gleysol Humic Sand n.e 13.91 F10 Haplic Gleysol Humic, loamy sand yes (25) 79.58 Technic 3 F11 Haplic Gleysol Humi, Sand yes (12) 41.23 Technic
60 5 Anthropogenic influences on floodplain soils
Reference soil profiles of Dove‐Elbe area
Figure 18: Location and soil types of the nine reference soil profiles of the Dove‐Elbe area. Reference soil profiles labelled with F are located within a floodplain area.
Table 9: Characteristics of reference soil profiles in the Dove‐Elbe area. n.e. not existent; n.m not measured. Plot Label Soil type (WRB) Dominant Technogenic Prefix Reference Suffix soil texture substrate soil group (cm depth) F12 Haplic Gleysol Humic, clay loam yes (13) Technic F13 Haplic Technosol Fluvic sand n.e. F14 Haplic Gleysol Humic clay loam n.e. F15 Fluvic Gleysol Humic clay loam n.e. F16 Haplic Gleysol Technic loamy sand yes (12) F17 Haplic Regosol Humic, sand yes (10) Floodplain Technic F18 Fluvic Gleysol clay loam n.e. F19 Haplic Gleysol clay loam n.e. F20 Haplic Gleysol Humic clay loam n.e.
5 Anthropogenic influences on floodplain soils 61
5.3 Consequences of anthropogenic influences on floodplain soils Groundwater influences, organic‐rich peat bands of former Holocene peatlands, former influences of brackish water and anthropogenic interventions such as refilling of technogenic substrates and the burial of former topsoil horizons are the dominant soil formation processes of both study areas. Soils with an undisturbed sequence of soil horizons were present in areas where no anthropogenic activities took place. These soils could be mapped on green areas, floodplains and in forest strips. The influences of anthropogenic activities were clearly visible in the soil profiles of urban areas, as already described in chapter 1. The introduction of technogenic substrates, including building rubble deposits, interrupted the typical sequence of soil horizons, whereupon anthropogenic influenced soils were mapped. Urban floodplains show large differences in soil genesis in small areas. Punctual anthropogenic changes, peat bands and depressions can result in very different soils in small areas. The description of anthropogenic influenced soils is still limited in soil classifications. Within the international soil classification WRB, soils with a respective amount of building rubble deposits can be defined as Technosol. In addition, the soil type Antrhrosol offers the possibility of depicting anthropogenic activities such as horticulture and plague management. Other anthropogenic influences on soils in urban areas, such as soil compaction, pollution, and acidification, have not been adequately reflected in soil classifications until now. To generate a better description of urban soils and their properties, it is important to adapt soil classifications to the urban influences in populated areas.
Soils in urban floodplains can only be used to a limited extend by humans. Due to the high groundwater levels and frequent flood events, the urban floodplain soils are unsuitable for a wide range of land uses. However, these soils are used for building and agriculture due to the high land use pressure. By means of drainage systems, groundwater levels are lowered, and the soils are made available for different land uses. This in turn leads to a change in natural soil functions, which are characteristic for floodplain soils. The extent to which natural ecosystem functions are altered in anthropogenic influenced floodplain soils will be investigated in detail in the next chapters.
For this study, the floodplain soils are divided into natural and anthropogenic influenced soils. Natural soils represent soils with an undisturbed sequence of soil horizons. Indirect influences, as changes in soil hydrology caused by caused by drainage measures, are not considered for this definition. Anthropogenic influenced soils show a disturbed sequence of soil horizons, which include the refilling of technogenic substrates and the burial of former topsoil horizons and all resulting soil properties.
6 Relevance of water retention ponds for the retention of pollutants 63
6 Relevance of water retention ponds for the retention of pollutants
Ongoing urbanization leads to significant sealing of areas in cities (Scalenghe et al. 2009). In urban floodplains, this results in a loss of areas that are of great importance for pollutant retention (McMillan et al. 2017). Based on this development, the retention of pollutants will take place mainly in urban water retention ponds. This chapter aims to develop strategies for an optimization of the ecosystem service of pollutant retention in urban floodplain soils. First, the in‐situ pollutant levels of floodplain and pond topsoils were presented, and the pollution hotspots identified. Next, the factors that control the accumulation of pollutants in urban ponds were investigated. Finally, the accumulation masses of pollutants within different zones of water retention ponds were determined and optimization strategies for the retention process developed.
6.1 Pollutant retention characteristics in urban floodplain soils
In this chapter, abbreviations were used for all pollutants. The heavy metals and the metalloid were abbreviated with their element symbols: Arsenic ‐ As, Cadmium ‐ Cd, Copper ‐ Cu, Lead ‐ Pb and Zinc ‐ Zn. The organic pollutants were abbreviated as follows: sum of 16 polycyclic aromatic hydrocarbons ‐ PAHEPA, sum of six polycyclic byphenil ‐ PCBEPA, and mineral organic hydrocarbons ‐ MOH C10‐C40.
6.1.1 Pollutant levels in topsoils of floodplains and ponds of the Kollau area In the topsoils, levels of pollutants were significantly higher in the water retention ponds than in the floodplains (Table 10). As an example for trace metals, mean Zn level was 262 mg kg‐1 ‐1 in ponds, compared to 67 mg kg in floodplains. For the MOH C10–C40 representing organic pollutants, a mean of 402 mg kg‐1 in ponds and 20 mg kg‐1 in floodplains was analyzed. Based on an ANOVA followed by a Tuckey‐HSD Posthoc Test (significance level of p < 0.05) significant differences of pollutant levels between ponds and floodplains were distinguished for all analyzed pollutants, except for As, Pb and PAHEPA. High punctual pollution loads, due to the refilling of technogenic substrates, can cause the high pollutant levels in the floodplain topsoils, which will be described in more detail in the following subchapter. However, as indicated by the range, a high variability of pollutant levels could be shown for both units of ponds and floodplains.
64 6 Relevance of water retention ponds for the retention of pollutants
Table 10: Pollutant levels of trace metals, metalloids, and organic pollutants in topsoil samples of ponds (n=69) and floodplains (n=45). Comparison of means with ANOVA followed by a Tuckey‐HSD Posthoc Test with significance level of p < 0.05: aa – no significance; ab ‐ significance.
Water retention ponds Floodplains Mean Min Max Mean Min Max ‐1 ‐1 ‐1 ‐1 ‐1 ‐1 mg kg mg kg mg kg mg kg mg kg mg kg Trace metals and metalloid As 5.92a 1.04 29.41 4.63a < 1 15.80 Cd 0.51a < 0.1 2.67 0.24b 0.02 0.82 Cu 66.04a < 1 451.29 22.84b 1.62 64.00 Pb 59.09a < 1 259.41 56.38a 1.86 144.08 Zn 266.71a < 1 1600.89 74.35b 7.25 288.87
Organic pollutants a b MOH C10–C40 402.09 11.09 2116.63 10.05 < 1 33.39 a a PAHEPA 3.18< 1 11.92 1.24 < 1 5.13 a b PCBEPA 0.03< 0.01 0.22 < 0.01 < 0.01 0.01
6.1.2 Origin of floodplain soil substrate and pollution level Topsoils within the floodplains were distinguished into natural and anthropogenic topsoils according to the presence of technogenic substrates or the burial of topsoil horizons. Only 47 % of the investigated floodplain topsoil samples consisted of natural substrates while 53 % exhibited an anthropogenic influence. Higher mean pollutant levels were determined for the anthropogenic‐influenced floodplain topsoils compared to topsoils with a natural substrate genesis. As an example for trace metals, mean Zn level was 90 mg kg‐1 in anthropogenic‐ ‐1 influenced topsoils and 31 mg kg in natural topsoils. For MOH C10‐C40, as a representative of organic pollutants, anthropogenic substrates showed a mean value of 26 mg kg‐1 and in natural substrates only 11 mg kg‐1. Similar trends were generated for all other investigated pollutants of topsoil samples. An ANOVA followed by Tuckey‐HSD Posthoc Test (p < 0.05) were used to analyze the differences between pollutant levels of natural and anthropogenic topsoils. Significant differences were calculated for all pollutants (Table 11). The interpretation of these differences is that in the urban floodplains the refilling of technogenic substrates and the burial of former topsoil material mainly cause the high pollution levels. Therefore, the focus was set on pond sediments to characterize different pollutant levels due to surface runoff and flooding.
6 Relevance of water retention ponds for the retention of pollutants 65
Table 11: Pollutant levels of trace metals, metalloid, and organic pollutants in floodplain soils separated into anthropogenic influenced topsoils (n=24) and natural topsoils (n=21) using ANOVA followed by a Tuckey‐HSD Posthoc Test with a significance level of p < 0.05, * p < 0.06 and ** p < 0.07: aa – no significance; ab – significance.
Anthropogenic soils Natural soils Mean Min Max Mean Min Max ‐1 ‐1 ‐1 ‐1 ‐1 ‐1 mg kg mg kg mg kg mg kg mg kg mg kg Trace metals and metalloid As 5.63a < 1 15.80 3.05b** < 1 6.33 Cd 0.29a 0.04 0.82 0.17b** 0.02 0.32 Cu 27.37a 4.10 64.00 15.66b** 1.62 54.37 Pb 68.22a 3.79 144.08 37.65b** 1.86 81.59 Zn 96.91a 22.45 288.87 38.62b** 7.25 116.14 Organic pollutants a b** MOH C10–C40 25.04 < 1 119.37 10.05 < 1 33.39 a b** PAHEPA 4.76< 1 30.68 1.24 < 1 5.13 a b** PCBEPA 0.01< 0.01 0.02 < 0.01 < 0.01 0.01
6.1.3 Controlling factors of water retention pond pollution As described in the previous subchapter, the analyses of the pollutant levels and processes was carried out further in the topsoils of the water retention ponds, as their pollution were mainly caused by surface runoff and flooding. First, it was tested if the pollutant levels of pond topsoils varied between the sub catchments. No significant differences were found (ANOVA and Tukey‐HSD Posthoc Test with p < 0.05). By integrating the data of all sub catchments, significant differences between the pollutant levels in the various zones of the ponds were analyzed (see Appendix A9 and A10). Secondly, spearman correlations between pollutant levels and soil properties of pond topsoils were tested. Correlations to the organic carbon content were identified for all pollutants. Thus, increasing concentration of organic matter in the upper pond topsoils was reflected by higher levels of pollutants. These correlations differed slightly between the sub catchments. In Figure 19, the relations of Pb levels and organic carbon content are illustrated for all three sub catchments. In four samples of sub catchment 1, high organic carbon contents in relation to Pb levels were found, indicating the in‐situ‐accumulation of organic matter within the pond topsoils. Thus, the correlation coefficient was the smallest (rs = 0.62). In turn in sub catchment 3, partly low organic carbon contents in relation to Pb levels were achieved. Here a correlation coefficient of rs 0.70 was calculated. In sub catchment 2, Pb levels were clearly correlated with the organic carbon contents, rs 0.89.
66 6 Relevance of water retention ponds for the retention of pollutants
Figure 19: Relation between organic carbon contents and Pb levels in the topsoils of water retention ponds of three sub catchments abbreviated with sub. rs = Spearman correlation coefficient.
Table 12: Results of regression calculations between pollutant levels and organic carbon contents.
Sub Pb As Cd Cu Zn PAHEPA PCBEPA MOH
catchment C10‐C40 constant 33.28 ‐0.98 0.12 ‐27.47 ‐48.13 ‐0.55 ‐0.010 ‐137.03
1 slope 5.92 0.65 0.12 26.16 99.02 0.68 0.012 127.67
rs 0.62 0.26 0.65 0.83 0.73 0.73 0.61 0.42
constant 9.95 1.51 0.09 ‐39.43 ‐99.92 ‐0.28 0.004 ‐84.73
2 slope 10.34 0.54 0.08 24.91 89.58 0.65 0.005 117.14
rs 0.89 0.69 0.57 0.52 0.23 0.30 0.34 0.66
constant 13.93 ‐1.75 ‐0.23 ‐28.13 ‐22.44 0.79 0.014 156.17
3 slope 13.95 1.58 0.32 32.15 121.92 0.89 0.006 53.59
rs 0.70 0.27 0.44 0.53 0.93 0.57 0.77 0.44
For each pollutant (trace metals, metalloid, and organic pollutants), regression equations were calculated for all three sub catchments with the respective content of organic carbon (Table 12). The equations served as a basis for a calculation of the pollutant levels for a given content of organic carbon.
6 Relevance of water retention ponds for the retention of pollutants 67
6.1.4 Total accumulation of pollutants in topsoils of water retention ponds Table 13 lists the calculated annual sludge accretion within each pond zone based on the sludge thickness survey 2018 and the time until the construction or last desludging measure. The annual accretion ranged between 0.07 and 6.39 cm a‐1. On average, in sub catchment 1 the highest accretion was calculated for the shallow zones with 0.39 ‐ 6.39 cm a‐1 and lowest accretion for inflow zones with 0.69 ‐ 0.92 cm a‐1. In sub catchment 2, highest accretion was calculated for the reed zones with 1.93 ‐ 3.73 cm a‐1 and the lowest accretion for the inflow zones with 0.07 – 0.67 cm a‐1. In sub catchment 3, highest accretion was found in the shallow zone with values between 2.91 ‐ 4.99 cm a‐1 and lowest in inflow zones with values between 1.45 – 4.74 cm a‐1. The mean accretion was low (1.0 – 1.8 cm a‐1) for sub catchment 2, higher for sub catchment 3 (2.2 – 3.8 cm a‐1) and variable in sub catchment 1 (1.2 – 5.3 cm a‐1).
Table 13: Accretion of sludge layers in all ponds of the Kollau area. Slope, reed, and deep areas are not existing (n.e.) in several ponds. In pond G3, inflow and outflow were not measured (n.m.) because of inaccessibility. Mean = areal weighed mean.
Sub Pond Accretion rates of pond area [cm a‐1] catchment Inflow Outflow Slope Shallow Deep Reed Mean G1 0.75 1.31 n.e. 6.39 4.25 n.e. 5.30 G2 0.92 0.80 0.98 n.e. 2.15 n.e. 1.21 1 G3 n.m. n.m. n.e. 3.04 4.22 n.e. 3.35 G4 0.69 0.94 0.39 n.e. 2.30 n.e. 1.62 M1 0.07 1.13 n.e. 2.93 1.27 3.73 1.83 M2 0.61 1.67 n.e. 0.98 0.48 n.e. 1.01 2 M3 0.67 1.25 n.e. 1.32 n.e. 2.79 1.51 M4 0.44 0.78 n.e. 1.11 n.e. 1.93 1.06 S1 1.90 3.00 n.e. 3.62 3.06 n.e. 3.79 3 S2 1.45 3.08 n.e. 2.91 1.25 n.e. 2.17 S3 4.74 1.26 n.e. 4.99 3.58 n.e. 3.64
The calculated annual accumulation of sludge, organic carbon, and pollutants per square meter within each pond zone is presented in Figure 21 to Figure 23. The sludge thicknesses and water and carbon contents measured in 2018, and the regression equations developed based on the data from 2016 (Table 4) were used for the calculations. With the inclusion of the time of the last measure (last desludging and commissioning) the annual accumulation was derived (Table 1). Lowest calculated annual accumulation masses were determined in ponds of sub catchment 2, while ponds of sub catchment 1 and 3 indicated medium to high calculated annual accumulation masses.
68 6 Relevance of water retention ponds for the retention of pollutants
Figure 20: Calculated annual accumulation masses of ponds of sub catchment 1 (G1 – G4) divided into the different zones i (inflow), o (outflow), sl (slope), sh (shallow) and d (deep). For Pb and MOH C10‐C40, masses are calculated on the basis of regression equations.
In sub catchment 1, the highest annual accumulation masses were found in the shallow water zone of ponds G1 and G3. Here, accumulation masses for sludge ranged between 17.0 and 34.5 kgm‐2a‐1, for organic carbon between 2.11 and 2.83 kgm‐2a‐1, for Pb between 1.25 and ‐2 ‐1 ‐2 ‐1 1.68 gm a and for MOH C10–C40 between 27.0 and 36.2 gm a . In contrary, the lowest annual accumulation masses were estimated in the inflow water zones of all ponds. Values for sludge ranged between 2.98 and 4.48 kgm‐2a‐1, for organic carbon between 0.16 and 0.18 kgm‐ 2 ‐1 ‐2 ‐1 ‐2 ‐ a , for Pb between 0.10 and 0.11 gm a and for MOH C10–C40 between 2.06 and 2.39 gm a 1. In ponds G1 and G3, significantly higher accumulation masses were determined in shallow water zones, while in ponds G2 and G4 higher accumulation took place in the deep‐water zone (Figure 20).
Especially for the pollutants in sub catchment 2, the highest accumulation masses were determined in the reed zones. In this zone, annual sludge accumulation masses ranged between 3.9 and 10.2 kgm‐2a‐1, for organic carbon between 0.58 and 1.98 kg m‐2a‐1, for Pb ‐2 ‐1 ‐2 ‐1 between 0.60 and 2.05 gm a , and for MOH C10–C40 between 6.8 and 23.2 gm a . The inflow water zones could be identified as low annual accumulation zones with values for sludge between 0.16 and 2.45 kgm‐2a‐1, for organic carbon between 0.01 and 0.28 kg m‐2a‐1, for Pb ‐2 ‐1 ‐2 ‐1 between 0.01 and 0.29 gm a , and for MOH C10–C40 between 0.37 and 3.30 gm a . The shallow zones showed medium to high annual accumulation masses. In general, reed zones
6 Relevance of water retention ponds for the retention of pollutants 69 indicated highest annual accumulation masses followed by shallow and outflow zones and deep and inflow zones with lowest annual accumulation masses (Figure 21).
Figure 21: Calculated annual accumulation masses of ponds of sub catchment 2 (M1 – M4) divided into the different zones i (inflow), o (outflow), sh (shallow), r (reed), and d (deep). For Pb and MOH C10‐C40 annual accumulation masses are calculated on the basis of regression equations.
In sub catchment 3, the lowest annual accumulation masses were achieved in the inflow zone of each pond, except for pond S2. Here values for sludge ranged between 6.54 and 12.47 kg m‐²a‐1, for organic carbon between 0.41 and 0.92 gm‐²a‐1, for Pb between 0.59 and 1.29 gm‐ ‐1 ‐ ‐1 ²a and for MOH C10–C40 between 2.40 and 4.96 gm ²a . Shallow water zones indicated higher values between 10.48 and 16.64 kgm‐²a‐1 for sludge, 1.18 and 1.83 gm‐²a‐1 for organic carbon, ‐1 ‐2 ‐1 1.64 and 2.55 gm‐²a for Pb and 6.31 and 9.80 gm a for MOH C10–C40 (Figure 22).
70 6 Relevance of water retention ponds for the retention of pollutants
Figure 22: Calculated annual accumulation masses of ponds of sub catchment 3 (S1 – S3) divided into the different zones i (inflow), o (outflow), sh (shallow) and d (deep). For Pb and MOH C10‐C40 annual accumulation masses are calculated on the basis of regression equations.
6.2 Discussion Pollutant levels of trace metals, metalloid and organic pollutants were investigated in topsoils of floodplains and water retention ponds in the Kollau area. Significantly higher pollutant levels were measured in the pond topsoils compared to the floodplain topsoils. Anthropogenic influences, rather than introduction of polluted surface runoff, were the main reasons for the pollutant levels in the floodplain topsoils. The soil organic carbon content correlated best with all pollutants within the water retention ponds. Accumulation masses of sludge, soil organic carbon and respective pollutant in pond zones could be calculated based on the sludge layer thickness, the bulk density, the soil organic carbon content and the pollutant content. Within the pond zones, highest accumulation masses were determined in the reed zones of the ponds and lowest in the inflow and outflow zones.
The pollutant levels in topsoils of urban floodplains and water retention ponds of the Kollau area in Hamburg City (Table 2) are comparable with other studies of urban environments. In floodplain soils of Baltimore City, United States, Bain et al. (2011) found trace metal levels of 35 mg kg‐1 for Cu, 89 mg kg‐1 for Pb and 81 mg kg‐1 for Zn. Furthermore, Pies et al. (2007) ‐1 investigated PAHEPA levels of up to 81 mg kg in floodplains of Saar and Mosel River in Germany. In former studies, conducted in urban floodplain soils, hot spots of trace metal pollution could be associated with technogenic substrates and the proximity to former
6 Relevance of water retention ponds for the retention of pollutants 71 pollutant emitters, especially industries (Konstantinova et al. 2019; Osipova et al. 2014; Zhornyak et al. 2016). In topsoils of urban water retention ponds of Maryland, United States, Casey et al. (2005) determined levels of Cu between 5 and 18 mg kg‐1, of Pb between 6 and 18 mg kg‐1 and of Zn between 14 and 344 mg kg‐1. In urban ponds of Danish cities Istenic et al. (2012) measured levels of Cu between 4 and 3293 mg kg‐1, Pb between < 2 and 220 mg kg‐1 and Zn between 26 and 1361 mg kg‐1. In French urban ponds, El‐Mufleh et al. (2013) analyzed ‐1 levels of PAHEPA between 0.1 and 14 mg kg and levels of MOH C10‐C40 between < 1 and 57 mg kg‐1.
Urban soil pollution may be caused by various sources, and pollutants found in urban soils varies from almost none to extremely high levels (Levin et al. 2017). According to Meuser (2010), soil pollution can be differentiated between site‐related and non‐site related sources. Site‐related pollutions are caused by industrial or horticultural activities, the application of sludge, or the impact of technogenic substrates. Non‐site‐related sources include pollution from dusts, atmospheric deposits, and linear pollutions by roads or flood events (Meuser 2010). In densely populated areas, emissions of gases and aerosols from various emitters such as traffic, industry, and domestic fuel consumption play an important role. Pollution levels in urban floodplain soils are expected to be caused by non‐site‐related sources such as diffuse atmospheric deposition, flooding and the subsequent sedimentation of transported pollutions originating from artificial surfaces such as roads, roofs, industrial plants or re‐suspended polluted soil particles (Levin et al. 2017). Roads and dense settlements, partly interrupted by green areas such as forests, parks and agricultural uses, characterize the Kollau area. Within the investigated floodplain topsoils, significantly higher pollution levels were recorded in the anthropogenic‐influenced soil substrates, such as kolluvial organic layers with technogenic artefacts and construction waste. This indicates site‐related pollutions mainly caused by historic land uses such as horticultural activity or the refilling of technogenic substrates. Therefore, these point pollutions cannot be linked to an input of pollutants caused by flooding and were not included in the further assessment of pollutant retention processes. However, the topsoils in the water retention ponds showed higher levels of trace metals and organic pollutants than the adjacent floodplain topsoils, which has already been confirmed in a study carried out by Lee et al. (1997). According to German Waste Regulation, individual values for
Cu, Zn, PAHEPA, and MOH C10‐C40 exceed the classification Z2. This means that soils, if removed due to construction or desludging processes, have to be deposited at landfill sites (Bertram et al. 2004).
An ANOVA analysis did not reveal any significant differences in the pond pollutant levels between the land use mixtures within the respective sub catchment. The headwaters of the three tributaries of the Kollau are located in settlement or industrial areas with a dense net of traffic routes. Additionally, the lower reaches of sub catchment 1 flows through green areas and of sub catchment 2 through green areas and agricultural sites. Accordingly, similar pollutant distribution can be assumed for all ponds. However, small differences of pollutant levels were identified between the different sub catchments, illustrated in Figure 3. The
72 6 Relevance of water retention ponds for the retention of pollutants pollutant:organic‐carbon ratio was highest in sub catchment 3 compared to sub catchment 1 and 2. Sub catchment 3 is located in a strongly urbanized area with settlement areas and traffic routes, which can cause this specific pollution of Pb. Conversely, green areas and agricultural sites characterize the lower reaches of sub catchment 1 and sub catchment 2, which could cause an increased introduction of organic matter in the ponds.
It is still unclear to what extent the accumulated pollutants in the topsoils of urban ponds are transported further with the flowing water. The mobility of pollutants from river sediments has been investigated in a few studies (Barbosa et al. 2012; Durand et al. 2004) where different solubility’s of heavy metals and their further transport have been described. Investigations about the dissolved concentration of pollutants in the water phase have not been conducted within this study. However, especially in the water retention ponds, trace metals, metalloid and organic pollutants show a high correlation with the organic carbon content (Figure 3); thus, particle‐driven pollutant transport and sedimentation is assumed to be the key process that causes higher concentration at positions with reduced water flow velocity and optimal sedimentation conditions. This strong correlation of organic carbon and pollutants in topsoil layers of urban ponds was also observed in studies of Du Laing et al. (2009) and Hayat et al. (2009).
The design of the water retention ponds (see Figure 10) control the annual accumulation of sediments. Ponds G2 and G4 of sub catchment 1 are assigned to pond type A, while pond G1 and G3 represents design B1. Lower accumulation masses were calculated for ponds of design A. The low accumulation results firstly in large deep‐water zone with steep slope areas, where accumulation is limited. Secondly, the flow passes unhindered through these straight ponds, which reduce accumulation. In contrast to pond type A, ponds of type B are characterized by nonlinear water flow. In design B1, the highest masses of sediments occurred in the shallow water zones and slightly less in the deep‐water zones. This results in the higher areal proportion of shallow water. In sub catchment 2, ponds M1, M3 and M4 are designed according to B3. In type B3 ponds, the highest accumulation was observed in the reed zones. The reed plants in shallow water zones reduce flow velocity and act as a natural filter system for the flowing water, thus increasing the amount of sediment accumulation significantly. Pond M2 with a B2 design showed low accumulation masses in comparison to the other ponds of this sub catchment. In this situation, the inflow and outflow zones are located on the same side, which might lead to shortcutting water flow and an increased accumulation in the outflow zone. In sub catchment 3, all ponds are designed as pond type B2. Here, significantly higher accumulation masses were calculated for the shallow water zones. Due to the central location of the island, extensive shallow water zones with partly flow stabilization exist, which optimize the accumulation of sediments. The amount of the accumulation at the inflow and outflow zones are individually determined by the width and depth of the river morphology. In most ponds, the inflows are wide and the outflows rather narrow, resulting in higher accumulation in the outflow zones. In general, ponds designed by type B generate significantly higher accumulation masses. For the ponds at sub catchment 2 the higher terrain gradient
6 Relevance of water retention ponds for the retention of pollutants 73
(LSBG 2016) associated with a greater flow velocity in the river tributary contribute to general lower masses of sediments due to a faster removal of dispersed particles in flow direction. Including this terrain gradient in the comparison of accumulation masses within all pond designs, the pond design B3 can be derived as the optimum for pollutant accumulation. Because of their filtering function, reeds and sedges can contribute to an increased accumulation of sludge and therefore higher rates of pollutant retention.
The total amount of sludge deposits also depends on the absolute size of ponds. In a large pond, higher amount of sludge can accumulate. At the same time, it can be assumed that the specific pollutant rate in a larger pond is smaller, since the pollutants are distributed over a larger area. This means that a larger amount of sludge must be removed in the desludging measures, but the pollutant levels would be lower. Investigations of the accumulation process of pollutants in ponds, which differs with significantly in size are recommended in the future.
At certain intervals, ponds must be desludged to preserve the retention volume for water before and during flood events. A further function of the desludging measures is the removal of pollutants bound to the sludge layers, which prevents the transport of pollutants downstream. In future, ponds can be designed with reed areas in places that are easily accessible and facilitate desludging. Through this process, the pollutants are excavated and landfilled at given pollution levels. Thus, the pollutants can be sustainably removed from the whole ecosystem cycle. Furthermore, in areas with low sludge accumulation there is no need for sludge removal, as sludge is consolidated and mineralized with depth over a longer period of time which will not negatively affect the pond volume (Bouza Deaño et al. 2012).
Effective pollutant retention for water retention ponds in urban areas can be concluded. They prevent the widespread transportation of pollutants downstream and help to concentrate pollutants closer to the source. Extended shallow water zones with plant cover of reeds and sedges create the optimal situation for pollutant retention in ponds (Figure 23). With the knowledge of the sludge thickness, water content and the correlation of organic carbon and specific pollutants, the accumulated masses of each substance can be easily calculated. On this basis, the design and management of urban water retention ponds can be adapted to increase the accumulation of pollutants in areas, which are easily accessible for desludging measures.
Finally, it must be noted that the most sustainable way to avoid pollution in urban floodplains is to prevent polluted surface runoff. If surface runoff from roofs, pavements and other anthropogenic surfaces is clean before it enters urban drainage systems, this is by far the best way to protect downstream ecosystems from pollution.
74 6 Relevance of water retention ponds for the retention of pollutants
Figure 23: Optimization of pollutant retention in urban water retention ponds. A transition from ponds with deep water zones and no vegetation cover (picture top) into ponds with large areas of shallow water, covered with dense vegetation of reeds and sedges (picture bottom), is recommended. Rural areas indicate forests, green spaces and agricultural uses and sealed areas indicate settlements, traffic, and industry.
6.3 Conclusion Urban water retention ponds are important for the retention of pollutants in urban catchments. Refilling of technogenic substrates and construction waste could be identified as major sources of pollutants in the floodplain soils. Pollutants from surface runoff and flooding events are retained in the urban water retention ponds, if apparent. Correlations between organic carbon and relevant pollutants indicate that the pollutants settle in association with the organic matter in the sediments of the ponds and accumulate there over a longer time. The masses and accumulation masses of sludge and pollutants can be calculated based on the sludge thickness, water contents and the correlation between organic carbon and pollutants for each pond zone. Thereby, the accumulation of sludge and pollutants depends on the respective pond design. The highest annual accumulation rates were calculated particularly in shallow water zones covered with dense vegetation of reeds and sedges. The ponds prevent the downstream transport of pollutants and help to concentrated them closer to the source. With the knowledge of the total masses of pollutants in the respective pond areas, desludging measures can be improved in future.
7 Potentials of water retention in urban floodplain soils 75
7 Potentials of water retention in urban floodplain soils
With increasing intensity of flood events (IPCC 2007; Schlünzen et al. 2018) and the loss of floodplains due to ongoing urbanization (Adobati et al. 2020; Scalenghe et al. 2009), the natural ecosystem service of water retention of urban floodplain soils is exposed to massive stressors. To maintain and improve this function in cities, a detailed process understanding of the soil water balance in urban floodplains necessary. This chapter aims to develop strategies for an optimization of the ecosystem service of water retention in urban floodplain soils. First, the in‐situ water storages, water storage capacities and groundwater levels were characterized at six different plots. Secondly, the sources that are responsible for the water rise in soils during flood events were elaborated. In addition, the time lags between rain event and rise in river water levels and groundwater levels were investigated. Finally, water storage capacities of bank soils were determined and evaluated for varying bank and terrain morphologies, soil properties and runoffs by a model based on the Manning‐Gauckler‐ Strickler‐Formular. The aim is to develop strategies that optimize the soil related ecosystem service of water retention within planning processes of urban floodplain design.
7.1 Water retention characteristics of urban floodplain soils
7.1.1 Water balances in floodplain soils of the Kollau area
Soil properties of plots, equipped with a soil water station Soil water stations were set up at six plots in the Kollau area. The plots reflect different land uses, distances to the nearest water body, soil textures, carbon contents and pore volumes.
Table 14: Characterization of six plots installed with a soil water station. Site characteristics, soil texture, carbon contents and pore volumes are listed for each plot.
Plot Land use Distance Dominant Soil carbon Soil carbon Pore volume to water soil texture pool pool body 0‐ 30 cm 30 ‐ 100 cm m % kg m‐2 kg m‐2 mm per 1m soil depth F01 Fallow 57.13 loamy sand 14.24 2.73 477.34 F02 Grassland 35.18 loamy sand 24.19 1.40 493.55 F03 Forest 123.83 sandy loam 13.02 77.19 481.80 F05 Settlement 69.21 loamy sand 4.66 23.82 436.11 F06 Settlement 33.27 loamy sand 15.35 36.60 626.78 F07 Floodplain 18.78 sand 2.58 0.44 439.27
The plots equipped with a soil water station differed in their distance to the nearest water body. Plot F07 with 18.78 m was located in close distance to the nearest water body, plot F01 with 57.13 m in medium distance and plot F03 with 123.83 m was farthest away from the
76 7 Potentials of water retention in urban floodplain soils nearest water body. The land uses ranged from floodplains, grasslands, forests, fallows to settlements. The soil textures showed different compositions from sand to loamy sand and sandy loam. Furthermore, differences within the carbon pools were investigated (see chapter 8). Plots F07 and F05 showed comparatively low carbon pools with amounts between 0.44 and 23.82 mg kg‐2 while plots F01, F02, F03 and F06 showed higher soil carbon pools with amounts between 2.73 and 77.19 mg kg‐2. Pore volumes ranged from 436.11 mm per 1 m soil depth at plot F05 to 626.78 mm per 1 m soil depth at plot F06.
Water storage, water storage capacity and groundwater level Over a period of 1.5 years characteristic components of the water balance was investigated in the Kollau area. Rain, further defined as precipitation, was recorded by a climate station, river water level by a bario diver in the water body next to each soil water station and the water content and groundwater level in the respective soil profiles (see chapter 4.2). The water storage and water storage capacity were calculated as described in chapter 4.4. Overall, high amounts of precipitation were recorded in 2017. Compared to Hamburg’s annual average precipitation of 772 l m‐2, a higher precipitation of 990 l m‐2 for 2017 was recorded in this study. After a precipitation event of 8 mm, the Kollau River reacted with a flood wave. Between October 2016 and December 2017, wide ranges of precipitation events were recorded leading to flood events of different intensities. In particular, the events in mid‐ December 2016 and early June 2017 led to strong flooding that are discussed in more detail in chapter 7.1.4. Figure 24 shows the water storages of all six plots during the whole investigation time. In summer months (June – August) water storage was lowest at all plots and highest in the winter months (December – February). A decrease of the water storage within 1m soil depth was accompanied by an increase of the lateral distance to the water bodies. The lowest water storages were recorded for plot F3 with values between 217 and 382 mm per 1m soil depth. This plot showed the highest lateral distance to the adjacent water body. Plots with middle lateral distance to the water bodies indicated water storages between 317 and 470 mm per 1m soil depth at plot F1 and 287 and 391 per 1 m soil depth at plot F5. Highest water storages were measured at plot F2 with values between 462 and 478 mm per 1 m soil depth, 479 ‐ 602 mm per 1 m soil depth at plot F6 and 347 ‐ 410 mm per 1 m soil depth at plot F7. These plots indicated low lateral distances to the nearest water bodies. The course of water storages during one isolated flood event in June 2017 are illustrated in Figure 25. After the precipitation and flood event, the water storages at plots F3, F5 and F6 increased immediately. Plot F1 showed a slower increase of the water storage while at plots F2 and F7 no alteration of the water storages after the precipitation and flood event was recorded. Especially at plots F3 and F5, the water storages decreased again a few hours after the flood event.
7 Potentials of water retention in urban floodplain soils 77
Figure 24: Precipitation (top), river water level (middle) and water storage (bottom) between October 2016 and December 2017 for all plots, equipped with a soil water station.
Figure 25: Precipitation (top), river water level (middle) and water storage in mm per 1 m soil depth (bottom) from all plots equipped with a soil water station during one isolated flood event.
78 7 Potentials of water retention in urban floodplain soils
Over the year, highest water storage capacities were measured in summer months (June – August) and lowest in winter months (December – February). Figure 26 indicates the water storage capacities during the whole investigation time, and Figure 27 water storage capacities for one isolated flood event of all six plots. Plots near the respective water bodies, F2, F6 and F7, indicated lowest water storage capacities. Water storage capacities ranged between 16 ‐ 34 mm per 1m soil depth at plot F2, 25 ‐ 148 mm per 1 m soil depth at plot F6 and 29 ‐ 93 mm per 1m soil depth at plot F7. Slightly higher water storage capacities were calculated for the plots more distant to the water bodies. Water storage capacities ranged between 7 and 160 mm per 1 m soil depth at plot F1, and at plot F5 between 45 and 149 mm per 1 m soil depth. Plot F3 with the highest lateral distance to the water body indicated the highest water storage capacities between 100 and 265 mm per 1 m soil depth. The course of water storage capacities during a flood event in June 2017 are illustrated in Figure 27. After the precipitation and flood event, plots F1, F3 and F5 indicated an immediate decrease in the water storage capacities. Slight decreases of water storage capacities were recorded for plots F2, F6 and F7.
Figure 26: Precipitation (top), river water level (middle) and water storage capacity (bottom) between October 2016 and December 2017 for all plots, equipped with a soil water stations.
7 Potentials of water retention in urban floodplain soils 79
Figure 27: Precipitation (top), river water level (middle) and water storage capacity (bottom) for all plots equipped with a soil water station during one isolated flood event.
Figure 28 illustrates the groundwater levels over the whole investigation time of all six plots. Groundwater levels were recorded between 0 and 110 cm under soil surface. Deepest groundwater levels were measured in summer months while the shallowest groundwater levels occurred in winter months. Shallow groundwater levels were measured at plots close to the waterbodies and deep groundwater levels at plots more distant to the waterbodies. At plot F2, the groundwater levels ranged between 0 and 85 cm below surface, at plot F6 between 29 and 83 cm below surface and at plot F7 between 18 and 82 cm below surface. Clearly deeper groundwater levels were recorded at the following plots: F1 18 ‐ >110 cm below surface, F3 42 – >110 cm below surface and F5 49 – >110 cm below surface. In Figure 29, the courses of groundwater levels during one isolated flood event in June 2017 is presented. While at plots F6 and F7 a steep increase of the groundwater levels after precipitation and flood event was recorded, plots F1, F2 and F3 were characterized by a flat increase in groundwater levels. At plot F7, a delayed increase of the groundwater level after the events was recorded. At plot F5, groundwater level rose above 110 cm depth after 12 hours delayed to the precipitation and flood event.
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Figure 28: Precipitation (top), river water level (middle) and groundwater level (bottom) between October 2016 and December 2017 for all plots, equipped with a soil water station.
Figure 29: Precipitation (top), river water level (middle) and groundwater level (bottom) for all plots equipped with a soil water station during one isolated flood event.
7 Potentials of water retention in urban floodplain soils 81
Infiltration capacity Infiltration capacity was determined at plots F1, F2, F3 and F7 (Table 15). Highest infiltration rates were investigated at plot F1 with 23.79 mm min‐1 for the start infiltration and 10.22 mm min‐1 for the end infiltration, followed by F3 with start infiltration of 17.80 mm min‐ 1 and end infiltration of 9.44 mm min‐1. Medium infiltration rates were recorded at plot F7 with 9.13 mm min‐1 for start infiltration and 2.28 mm min‐1 for end infiltration. Lowest infiltration rates were measured for plot F2 with 1.80 mm min‐1 for start infiltration and 0.39 mm min‐1 for end infiltration.
Table 15: Infiltration rates of plots F1, F2, F3 and F7. Start and End infiltration rates were calculated out of whole dataset.
Plot Infiltration rate (mm min‐1) Start End F1 23.79 10.22 F2 1.80 0.39 F3 17.08 9.44 F7 9.13 2.28
7.1.2 Influencing factors on the soil water balance in the Kollau area The main factors influencing the water balance in urban floodplain soils are presented in this chapter. The influences of soil related parameters (pore volume and infiltration capacity) and terrain properties (river water level, lateral distance to adjacent water body and elevation, based on the river water level) on mean values of water contents and groundwater levels were determined based on a spearman correlation analysis (Table 16). The parameter water content represented indirectly the parameters water storage and water storage capacity, as water content functioned as the basis for their calculation. Water content correlated with the groundwater level (rs ‐0.82), with the pore volume (rs 0.75), with the lateral distance to adjacent water body (rs ‐0.64) and with elevation, based on the mean water level (rs 0.71). Furthermore, high spearman correlation coefficients were determined for the correlation of groundwater level with infiltration capacity (rs 0.64) and lateral distance to adjacent water body (rs 0.82). The correlation analyses of time series data showed also high correlations between water contents and groundwater levels (see Appendix A 21 ‐ A 26). Based on the results of an ANOVA followed by a Tuckey‐HSD Posthoc Test (significance level of p < 0.05) no significant differences of water contents and groundwater levels could be determined within the categories of sub catchments, land uses and soil properties (see Appendix A 27).
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Table 16: Spearman correlation matrix of water‐ soil‐ and terrain properties: water content (WC), groundwater level (GWL), river water level (RWL), pore volume (PV), infiltration capacity (IC), lateral distance to adjacent water body (LD) and elevation, based on the mean river water level, (EL).
WC GWL RWL PV IC LD EL WC 1.00 ‐0.82 ‐0.15 0.75 ‐0.50 ‐0.64 0.71 GWL 1.00 ‐0.04 ‐0.46 0.64 0.82 ‐0.32 RWL 1.00 ‐0.41 ‐0.19 0.04 0.22 PV 1.00 ‐0.32 ‐0.14 0.46 IC 1.00 0.57 ‐0.29 D 1.00 ‐0.21 EL 1.00
7.1.3 Sources of water rise during flood events in floodplain soils Eleven flood events were recorded between October 2016 and December 2017. Thereby, flood events that followed a precipitation event of more than 8 mm h‐1 were defined as significant in this study. The mean water rises in the soil profiles during all eleven flood events were highest at plots F1, F3 and F5 and lowest at plots F2, F6 and F7. The highest mean water rises were recorded for plot F3 with 26.6 mm followed by plot F5 with 15.6 mm, and plot F1 with 13.6 mm. Plot F6 generated a mean water rise of 7.8 mm and the lowest mean water rises were calculated at plot F2 with 0.6 mm and plot F7 with 1.7 mm. The calculation of the different sources of water rise (precipitation and flood/groundwater) showed that at plots F2, F6 and F7 only precipitation caused the water rise in the soil profiles. At plots F1 and F5, the water rise was caused by precipitation and a smaller amount of flood or groundwater, whereas both components were equally responsible for the soil water rise at plot F3.
Table 17: Mean amount of soil water rise (mm) during eleven flood events (October 2016 – December 2017) recorded for all six plots . The quantities of water amount from precipitation and flood/groundwater, which are responsible for the water rise in the floodplain soils during flood events, were calculated. Plot Annual mean Sources of water rise water rise Precipitation Flood/Groundwater mm mm mm F1 13.6 10.7 2.9 F2 0.6 0.6 0.0 F3 26.6 14.8 11.8 F5 15.9 14.2 1.7 F6 7.8 7.8 0.0 F7 1.7 1.7 0.0
With the following analyses, the small‐scale processes of the soil water balance in urban floodplain soils during flood events was determined. Therefore, two characteristic flood events in December 2016 and in June 2017 where precipitation led to great discharge peaks were chosen. In Figure 30 and Figure 31, the components precipitation, river water level,
7 Potentials of water retention in urban floodplain soils 83 groundwater level and water storage capacities per soil horizon are compared with each other. In addition, theoretical calculations of the amounts of soil water prior and after the flood event and the remaining amount of soil air are illustrated for each flood event and plot. To capture all site characteristics of the Kollau area, plots with different distances to the nearest water body were compared: plot F7 with 18.78 m distance, plot F1 with 50.13 m distance and plot F3 with 123.83 m distance to the nearest water body. Similar trends of precipitation, river water level and groundwater level were observed at all plots. A few hours after the precipitation event, the flood wave followed in the waterbodies. Twenty to thirty hours after the precipitation event, the groundwater level rose. In Figure 30, the components of the water balance during the flood event in December 2016 and the theoretical calculation of water masses and remaining soil air are illustrated for each plot. Different water storage capacities were calculated. In general, plot F7 showed small water storage capacities, plot F1 indicated medium and plot F3 high water storage capacities. With increasing distance to the water bodies, also the water storage capacity increased. At plot F7, the flood wave reached its maximum with three hours delay to the precipitation event. Prior to the flood event, the horizon in 10 cm showed only 2 % of water storage capacity, in 20 cm 6 % water storage capacity and in 40 cm 3 % water storage capacity. Water saturation was achieved in the horizon of 60 cm. With nearly complete water saturation, the 10 cm horizon reacted as a water impermeable layer, which inhibited the percolation of water in deeper horizons. The groundwater level rose after eleven hours up to a height of 30 cm below surface and then decreased again. Based on the theoretical mass calculation it could be derived, that only small parts of the precipitation were absorbed in the uppermost horizon and the remaining precipitation is discharged as surface runoff. No water storage capacity was available for the potential water uptake if the plot was flooded (Figure 30, top). The water body near plot F1 was reached by the flood wave with two hours delay to the precipitation event. With an initial water storage capacity of 36 % at a depth of 10 cm, 24 % at a depth of 20 cm, 23 % at a depth of 40 cm and 3 % at a depth of 60 cm, the precipitation infiltrated at the soil surface and percolated into the deeper horizons. The groundwater level reached its maximum of 45 cm below surface by a 37‐hour delay to the precipitation event and then decreased again with a simultaneous increase in the amount of water storage capacities. The infiltration of precipitation generated water‐saturated soil horizons from 60 cm depth. Only small amounts of water storage capacities were available for a potential water uptake after this flood event (Figure 30, middle). Two hours delayed to the precipitation event, the flood wave reached the water body near plot F3. However, this plot was not flooded during this event. In the soil profile, no water‐ saturated horizon occurred during the flood event. The water storage capacities ranged between 41 % in 10 cm and 12 % in 100 cm depth. After the precipitation event, water was infiltrated into the upper horizons and percolated into the lower horizons. Groundwater level did not reach the 100 cm horizon. At plot F3 226 mm per 1m soil depth of pore space was still filled with air and was available for further intermediate storage of water after the flood event (Figure 30, bottom).
84 7 Potentials of water retention in urban floodplain soils
Figure 30: Comparison of precipitation, river water level, groundwater level and water storage capacity during a flood event in December 2016. The time‐delays of river water level maxima and groundwater level maxima, starting from the precipitation maxima, are indicated as red numbers in the graphs. Plot F7 represent a plot near the water body (18.78 m), plot F3 more distant to the water body (123.83 m) and plot F1 a medium distance to the water body (57.13 m). The left bars illustrate calculations of amounts of soil water before flood event in December 2016 (grey), water rise due to precipitation (blue), water rise due to groundwater level increase or flooding (turquoise) and the remaining soil air after the flood event (white).
7 Potentials of water retention in urban floodplain soils 85
In addition to the flood event in December 2016, the same assessment for a flood event in June 2017 is shown in Figure 31. In contrast to the water balances during the flood event in December 2016, the plot close to water bodies showed slightly higher water storage capacities and shallower groundwater levels. Due the high amount of precipitation in the year 2017, the groundwater levels rose in the whole Kollau area. The flood wave passed the water body near plot F7 two hours delayed to the precipitation event. Water saturation was reached in horizons of 10 cm and 60 cm. Prior to the flood event, the 20 and 40 cm horizons indicated 3 % of water storage capacities. During the precipitation event, no water was percolated into the lower horizons. In addition to the results presented in Figure 30, the upper horizon at plot F7 reacted as a water impermeable horizon, which hindered the percolation of water in deeper horizons (cf. Figure 30, above). The groundwater level rose after a time delay of 15 hours until a height of 55 cm below soil surface. Runoff caused by limited infiltration of precipitation was theoretically calculated for the plot F7 during the flood event in June 2017 (Figure 31, top). With a time‐delay of six hours, the flood wave reached the water body close to plot F1. Water storage capacities prior to the flood event ranged from 32 % in the 10 cm horizon to 4 % in 60 cm depth. The 80 and 100 cm horizons reached water saturation. Due to the infiltration and the percolation processes, the water storage capacities ranged from 28 % in the 10 cm horizon and 3 % in the 60 cm horizon. After the percolation of precipitation, the groundwater level rose after a time delay of 26 hours to a minimum depth of 60 cm below surface. Plot F1 showed low amount of water storage capacity with 21 mm per 1m soil depth after the infiltration of precipitation. Not enough pore space was available at this plot for the storage of possible floodwater followed the precipitation event (Figure 31, middle). With a two‐hour time‐delay, the flood wave reached the water body at plot F3. Simultaneously with the precipitation event, the water storage capacity decreased in each horizon. In the 10 cm horizon water storage capacity decreased from 40 % to 34 % and in the 100 cm horizon from 3 % to 0 %. Water saturation in the 100 cm horizon arose parallel to the groundwater level, which increased 14 hours delayed to the precipitation event up to a height of 85 cm below surface. Plot F3 indicated the highest amounts of water storage capacity with 198 mm per 1 m soil depth after the flood event, resulting in an optimal provision of water retention in the soil (Figure 31, bottom).
86 7 Potentials of water retention in urban floodplain soils
Figure 31: Comparison of precipitation, river water level, groundwater level and amount of water storage capacity during a flood event in June 2017. The time‐delays of river water level maxima and groundwater level maxima starting from the precipitation maxima are indicated as red numbers in the graphs. Plot F7 represent a plot near the water body (18.78 m), plot F3 more distant to the water body (123.83 m) and plot F1 a medium distance to the water body (57.13 m). The left bars illustrate calculations of amounts of soil water before flood event in December 2016 (grey), water rise due to precipitation (blue), water rise due to groundwater level increase or flooding (turquoise) and the remaining soil air after the flood event (white).
7 Potentials of water retention in urban floodplain soils 87
7.1.4 Modelling of water storage capacities in bank soils of floodplains The riverbank and floodplain terrain morphology controls the flooding of floodplain areas. Additionally, the amount of water that can infiltrate into the floodplain soils depends on the site and soil properties, as described in the previous chapters. In this chapter, water storage capacities in riverbank soils within different scenarios were calculated using a model based on the Gauckler‐Manning‐Strickler‐Formula explained in chapter 4.4. The generated model calculated the water storage capacities in riverbank soils within 1 m flow section along the river. In addition, the duration until the riverbank soils is water saturated were estimated. Different bank morphologies and the variables runoff, initial water storage capacity, representing the pore volume and water content, and the coefficient of roughness for the characterization of the condition of the riverbed and the vegetation on the riverbank soils are included in the calculations. The morphology of the riverbank was defined in advance. Two different riverbank morph‐ ologies were chosen for comparison. Type A was defined as steep riverbank areas and deep riverbed, while type B represented a shallow river depth and flat riverbank areas (Figure 32).
Figure 32: Typical riverbank designs of the Kollau River. Morphologies were used within different scenarios for the calculation of water storage capacities in riverbank soils.
Runoffs and initial water storage capacities were calculated based on scenarios presented in Table 18. Runoffs for a 30‐yearly and a 100 yearly flood event were calculated for the actual flood events of the Kollau. In addition, calculations on predicted runoffs for the year 2035 included 15 % increase in precipitation and a 2.5 % increase in soil sealing. The in‐situ runoff from a 30‐ yearly flood event (4.5 m³ s‐1) and from a 100‐yearly flood event (8.1 m³ s‐1) were used in Table 19. In Table 20 predicted runoffs for the year 2035, were used for a 30‐yearly flood event (5.4 m³ s‐1) and for a 100‐yearly flood event (10.3 m³ s‐1), provided by LSBG (2017). For the variation the initial water storage capacity, in‐situ determined values from the data investigated in this study were derived. Sandy soils generated a water storage capacity of 23 %, loamy dominated soils a water storage capacity of 16 % and clay dominated soils a water storage capacity of 5 %.
Table 18: Scenario’s climate, urbanization, and soil condition. The calculation of soil water retention in bank soils is based on these scenarios.
Scenario 2015 2035 Data source Climate Actual precipitation + 15.0 % StucK Urbanization Actual soil sealing + 2.5 % Soil condition Actual pore volume + water content own data
88 7 Potentials of water retention in urban floodplain soils
A roughness coefficient for straight riverbed (rst 25) was chosen to represent the urban water bodies. Further boundary conditions were: (i) infiltration rate was not considered and (ii) only the maximum water storage was calculated.
During an actual 30‐yearly flood event, the calculated runoffs indicated that water bodies with a riverbank design of type A will not flood the connecting riverbank soils. A storage in the riverbank soils of 0 % was calculated. With a riverbank design of type B, flooding of riverbank soils was possible which resulted in a storage volume of 42 % in sandy soils, 33 % in loamy soils and 13 % in clayey soils from total flood volume per m flow distance. Sandy soils were filled within 9.62 min, loamy soils within 6.69 min and clay dominated soils within 2.09 min of the whole flood event (Table 19, top). Even within an actual 100‐yearly flood event, flooding of the riverbank soils was not possible based on design A. Within design B, riverbank soils stored water of 38 % in sandy soils, 30 % in loamy soils and 12 % in clayey soils from total flood volume per m³. Slightly shorter time was needed to fill up the riverbank soils: sandy soils in 9.31 min, loamy soils in 6.47 min and clay dominated soils in 2.02 min (Table 19, bottom).
Table 19: Water storage capacities in riverbank soils for 1 m flow section along the river for actual runoffs from a 30‐yearly flood event (top) and a 100‐yearly flood event (bottom).
Bank Initial Remaining Duration of Flood volume Water storage in morphology Water storage water storage storage riverbank soil capacity capacity % m³ m‐1 min m³ m‐1 % 23 0.92 3.39 0.00 0 A 16 0.64 2.36 0.00 0 5 0.20 0.74 0.00 0 23 2.60 9.62 3.63 42 B 16 1.81 6.69 3.63 33 5 0.56 2.09 3.63 13
Bank Initial Remaining Duration of Flood volume Water storage in morphology water storage water storage storage riverbank soil capacity capacity % m³ m‐1 min m³ m‐1 % 23 1.09 3.42 0.00 0 A 16 0.76 2.38 0.00 0 5 0.24 0.74 0.00 0 23 2.96 9.31 4.74 38 B 16 2.06 6.47 4.74 30 5 0.64 2.02 4.74 12
In Table 20, results from a calculation of water storage capacities in riverbank soils based on predicted runoffs for the year 2035 are illustrated. Within a 30‐yearly runoff event, the riverbank soils could not be flooded because of the steep riverbanks caused by design A.
7 Potentials of water retention in urban floodplain soils 89
Within design B, flooding occurred and could be stored in riverbank soils with 32 % in sandy soils, 25 % in loamy soils and 9 % in clayey soils of total flood volume per m flow distance. Storage time was calculated for sandy soils with 8.03 min, for loamy soils with 5.59 min and for clay‐dominated soils with 1.75 min (Table 20, top). In Table 20 bottom, the calculated water storage capacities based on the runoff predicted for a 100‐yearly runoff event in 2035 are indicated. In design A, the flood reached the riverbank soils and could be stored there with 30 % in sandy soils, 23 % in loamy soils and 8 % in clayey soils of total flood volume per m³. Storage times of 7.31 min for sandy soils, 5.08 min for loamy soils and 1.59 min for clay‐ dominated soils were calculated. Within design B, floodwater could be stored by 68 % in sandy soils, 59 % in loamy soils and 31 % in clayey soils. For our scenarios, these were the highest calculated water storage capacities. Significantly higher storage times were calculated for this event with 41 min for sandy soils, 28.52 min for loamy soils and 8.91 min for clay dominated soils. Very small events could be stored in the riverbank soils almost completely (Q 2.6m³; water storage capacity of 96%) if flooding of riverbank soils were possible.
Table 20: Water storage capacities in riverbank soils of m³ per 1 m flow section along the river for the runoffs predicted for the year 2035 including land use change and climate change from a 30‐yearly event (top) and a 100‐yearly event (bottom).
Bank Initial Remaining Duration of Flood volume Water storage in morphology water storage water storage storage riverbank soil capacity capacity % m³ m‐1 min m³ m‐1 % 23 1.64 3.37 0 0 A 16 1.14 2.35 0 0 5 0.36 0.73 0 0 23 3.90 8.03 8.25 32 B 16 2.72 5.59 8.25 25 5 0.85 1.75 8.25 9
Bank Initial Remaining Duration of Flood volume Water storage in morphology water storage water storage storage riverbank soil capacity capacity % m³ m‐1 min m³ m‐1 % 23 4.34 7.31 10.16 30 A 16 3.02 5.08 10.16 23 5 0.94 1.59 10.16 8 23 24.36 41.00 11.64 68 B 16 16.94 28.52 11.64 59 5 5.29 8.91 11.64 31
90 7 Potentials of water retention in urban floodplain soils
7.2 Discussion Over 1.5 years, the soil water balances at six soil water stations in the Kollau area were recorded. At plots near the water bodies, lower water storage capacities were measured compared to plots more distant to the water bodies. Seasonal groundwater levels and lateral distances to the nearest water body were identified as the main influencing factors on the water contents, reflecting water storage capacity. During precipitation and flood events, only soils with a deep groundwater level and an initial low water content (present at the edges of the Kollau floodplains) indicated water storage capacities for a possible intermediate water storage. A model based on the Gauckler‐Manning‐Strickler‐Formula was created to calculate water storage capacities in riverbank soils under different scenarios. Riverbank and floodplain morphology and soil properties are the main factors controlling the water storage capacities in riverbank soils. Floodplains generate a high potential of water storage capacity due to the varying groundwater levels and the continuous restoration of the water storage capacity in soils between floods (Samaritani et al. 2011). However, formerly active floodplains have undergone massive modifications due to land use changes in the last century (Haase 2017, 2019). Land use change from forest to agricultural sites results in a limitation in dynamic groundwater flow (Gori et al. 2019) and reduction of plant water uptake, groundwater recharge, and subsurface hydraulic conductivity (Kellner et al. 2016; Zell et al. 2015). In addition urban sprawl limit connection between river channel and floodplain areas, which interfered with water storage capacity and the connectivity between sub‐surface flows (Baptista et al. 2017). Settlements within and adjacent to riverine floodplains exacerbates flood losses by increasing peak discharge and runoff volume, shortening the time to flood peak, and altering the extent of the floodplain (Gori et al. 2019). Gainsborough et al. (2002) stated that long term urban growth and sprawling land consumption associated with surface sealing is likely to impair the urban water storage more than short‐term environmental changes. Not only the amount of urban land uptake, but the spatial pattern of land conversion is also mainly responsible for the limitations of water storage in urban floodplain soils (Burchell et al. 2003; Newton 2000; Nuissl et al. 2008). Furthermore, urban sprawl potentially leads to an increased flood risk produced by increasing direct runoff and a resulting higher release of water out of the urban system (Haase 2009). The floodplains along the Kollau have already undergone the described land use change. Only a few remnants of wooded floodplain vegetation (LSBG 2017) and small active floodplains are present. In the northern part of the Kollau area, agriculture and green areas dominate the landscape, while in the southern part extensive settlement areas are dominant. Still existing green spaces are often separated by settlements.
In a study of Hughes et al. (2014) they summarized the results of urbanization of floodplain areas in a so called “urban water body syndrome,” where hydrographs become flashier, water quality is degraded, channels are homogenized, biological richness declines, and disturbance‐ tolerant increased in prevalence. Some of these trends can also be observed in the urban Kollau area. In the populated southern part of the Kollau area, deep riverbeds and steep banks are intended to protect settlements areas from floods. In addition, the interruption of former
7 Potentials of water retention in urban floodplain soils 91 green areas due to settlement construction massively interferes with the availability of possible active areas for flooding and intermediate storage of water during floods. In addition, soil compaction in built‐up areas leads to limited infiltration capacities and reduced pore volume in floodplain soils. In the southern part, high soil sealing (cf. Figure 7) and soil compaction due to construction processes is present. Here the infiltration capacity is severely restricted (Assall 2017 unpublished). In summary, only a few floodplains in the Kollau area are still active and separated from each other, which leads to an insufficient buffering of flood events. The in‐situ water storage capacities and its influencing factors of the remaining active floodplain soils in the Kollau area are discussed in the next paragraphs.
The floodplain soils investigated in this study are characterized by a high variability of water storage capacities. Factors leading to different water storage capacities have been investigated in previous studies and are mostly consistent with findings of this study. Mc Millan et al. (2015) found that the most important control on the variability in water storage is distance from the adjacent water body. In addition, the seasonality affects the water tables significantly with shallower groundwater table in winter and deeper groundwater table in summer. Also, in this study, plots near the water bodies (FP2, FP6 and FP7) generated low water storage capacities in connection with shallow groundwater levels. Significantly higher water storage capacities were measured at plots more distant to the water bodies (FP1, FP3 and FP5), where also deep groundwater levels were estimated. Differences in the seasonality of the water storage capacity and groundwater levels were particularly apparent at plots far away from the waterbodies. Here, shallower groundwater levels in winter and deeper groundwater levels in summer were observed. Seasonal trends were less pronounced at plots close to the riverbank. The high water contents and the rainy year in 2017 (annual average precipitation of 990 l/m²), could be reasons for this. Schwartz et al. (2000) identified the substrate sequence as the most important influencing factor on water balances in active floodplain soils. The soil texture and the coarse pore volume, which in turn results in the infiltration capacity, play a secondary role in the soil water balance of the Kollau floodplain soils (see Appendix A 27). High infiltration rates are accompanied with plots of vegetation (Hubbart et al. 2011; Kellner et al. 2016) and low infiltration rates with anthropogenic soil compaction (Yang et al. 2011) which agreed with findings of this study. Highest infiltration rates at fallow and forest plots (F1 and F3) and the lowest infiltration rates at plots with increased soil compaction (F2 and F7) were determined (Assall 2017). In the floodplain soils of the Kollau area, water storage capacities depend on groundwater levels, the lateral distance to the nearest water body and the seasonality. Soil properties show only subordinate influences. How soil water storage reacts during flood events is discussed in the next paragraph.
For an optimization of the ecosystem service of water retention in urban floodplain soils, it is important to characterize the limiting factors of soil water storage capacities during flood events. With this knowledge, the designation of floodplain areas could be optimized by selecting soils with high potential of water storage capacities. Three plots with different
92 7 Potentials of water retention in urban floodplain soils distances to the Kollau River were analyzed for this purpose. As the initial water content become lower and the initial groundwater level deeper, the potential to absorb and temporarily store water during flood events in soils increase. Prior to a flood event, higher air‐ filled pore spaces were determined in soils at the edges of the floodplains compared to the soils near the riverbanks. In few studies, initial soil water content was identified as influencing factor on the water storage capacities during flood events. Within a study on runoff prediction in an urban area, it was concluded, that point data of soil water content describe the areas initial conditions well and may yield valuable information for flood prediction (Chifflard et al. 2018). Additional in a study by Haga et al. (2005), the effects of precipitation, initial soil water conditions, and flow paths on runoff response in a forested unchanneled area, were tested to characterize lag times during flood events. They concluded that consideration of initial soil water conditions as well as precipitation amount and intensity is essential for understanding the regional characteristics of subsurface water movement. Additionally, to a high initial water content, water saturated topsoil horizons at plots F07 and F02 inhibited the infiltration of water during flood events, although in the lower horizons water storage capacities were available. The reasons for the inhibited percolation of water in these situations and to what extent lateral flows contribute to a utilization of the available soil pore space for water retention, can be investigated in further studies. Within this study, it could be proved that the possible intermediate storage of floodwater in floodplain soils can be well estimated by the initial water contents and groundwater levels.
Different water storage capacities during flood events in riverbank soils, varying in soil texture, and initial water content, for different bank and terrain morphologies were calculated with a model based on the Gauckler‐Manning‐Strickler‐Formula.
Bank and terrain morphology is of great importance for floodplains to be reached by floodwater. Within the floodplain soils, sandy soil substrate and low initial water content generate the highest possible water storage capacity per 1 m flow section along the Kollau River. In particular, sandy soils generate the highest pore volume for water storage. In addition, a medium amount of humus could increase the water holding capacity. Due to their hydrophilic properties, humus particles can retain water over a longer time (Beyer et al. 1995). Also in a study by Sofia et al. (2019) spatio‐temporal patterns of precipitation, and soil properties were identified as the main drivers of urban runoff events. It should be noted that times of 3 to 41 min were calculated, until the riverbank soils were filled up with water. Analyses on the restauration times of the water storage capacity in soils indicated times of 28 to 59 hours after the flood events. It follows, that only a small part of the floodwater can be stored in the riverbank soils during flood events. Extreme small flood events with runoffs of Q = 2.4 m³ s‐1 and less, could completely be stored within the riverbank soils. Concluding flat riverbank areas with a sandy and humus rich soil substrate and initial low water content, contribute to an increase in water retention in riverbank soils during flood events. However, the riverbank soils do not store the complete floodwater during flood events but can minimize discharge peaks, which could contribute to a more effective flood risk management. Improved
7 Potentials of water retention in urban floodplain soils 93 integration of urban floodplains into urban flood management is becoming increasingly important as urbanization continues and extreme precipitation events become more frequent and intense (Marelle et al. 2020). Only a mixture of technical and natural flood protection measures can mitigate the consequences of climate change and urbanization in cities.
An optimization of the water retention function in urban floodplains can lead to a more sustainable buffering of the predicted increasing flood events (IPCC 2018). With the realization that grey infrastructure is no longer sufficient for flood protection in cities, has prompted the scientific world to combine urban planning with the natural resources of water retention (Haase 2019). Approaches, which are based on the implementation of a mixture of grey‐ and blue‐green infrastructure in cities, have been developed within this century. Most common approaches are the Sponge City (China), Best management practice (USA and Canada), Low impact development (Great Britain), Sustainable urban drainage system (Asia), Water sensitive urban design (Australia and New Zealand) and Bluegreensolution (Europe). All of them aim to develop a sustainable rainwater management, which contribute to a minimization of flood waves and cooling the urban heat island. (e.g. Jiang et al. 2018; Löschner et al. 2018; Poleto et al. 2012; Raadgever et al. 2018b; Tillie 2017). They all recommend measures as green roofs, porous pavements, water tanks, precipitation gardens, bioswales and optimized use of floodplains, retention ponds and river channels. With these measures, the increased urban surface runoff during heavy precipitation events can be reduced in two ways. Firstly, measures in densely populated areas as green roofs, porous pavements, water tanks and bioswales (LSBG 2017; Qin et al. 2013; Raadgever et al. 2018b) can store rainwater where it falls prior to the input into the river systems. Also forested areas in less populated areas outside the floodplains contribute to a storing of rainwater before entering the water system (Kellner et al. 2016; Zell et al. 2015). In addition to measures in densely populated areas, an expansion of floodplains, retention ponds and flattening of bank areas is recommended in less populated areas. An explicit recommendation of the expansion of urban floodplains is given by the projects of Room for the River and LAND4FLOOD (Groot et al. 2009; Jiang et al. 2018; Löschner et al. 2018; Rijke et al. 2012; Tillie 2017) and others. In order to allow flooding of floodplains the morphology of riverbanks need to be adapted. Results of Cliverd et al. (2016) suggest that steep riverbank removal can increase floodplain hydrological connectivity to form a more natural floodplain ecotone, driven by frequent localized flood disturbance. This has important implications for the planning and management of river restoration projects that aim to enhance floodwater storage, floodplain species composition and biogeochemical cycling of nutrients. A sustainable management approach of urban floodplain should include the goals of restoration and protection of natural hydraulic processes, particularly those that support ecological and geomorphic functioning of water bodies (Anim et al. 2018). Furthermore, studies of Carmon et al. (2010), Qin et al. (2013) and Nilsson et al. (2018) made clear that an improvement of water retention in urban floodplains can only be achieved by a combination of several sensitive measures, as recommended by the above described approaches for an adapted floodplain design.
94 7 Potentials of water retention in urban floodplain soils
In the Kollau area itself, floodplain areas can be extended to increase water retention in soils. Already designated floodplains along the Kollau River (Figure 33) are located in relatively small areas in the north, middle and south of the watercourse. In these areas, soils with low water storage capacities, shallow groundwater levels and steep riverbanks were estimated which led to floodplains where the retention of water is not optimally exploited. Substantial greater floodplains must be designated in order to increase the water storage capacities during flood events. Sandy and humus rich soils with a deep groundwater level represents effective plot characteristics for optimal water retention. In addition, flat riverbank designs enable possible flooding of floodplain areas. There is a high potential of an expansion of floodplain areas within the green areas and forests in the north and middle of the Kollau River (Figure 33, pictures). These areas could be partially transformed into floodplains. Land use conflicts and possible synergies resulting from these recommendations are discussed in chapter 9.
Figure 33: Optimization of water retention. Designated floodplains along the Kollau River (orange). Grey areas indicate areas with high soil sealing (areas with settlements, industries and traffic) and turquoise areas with low soil sealing (areas with agriculture, grassland and forests). Two sites for possible flooding are illustrated (pictures). Blue arrows indicate the direction of possible flooding.
7 Potentials of water retention in urban floodplain soils 95
7.3 Conclusion Water storage capacities are highly variable in the Kollau area. With increasing distance to the water body, water storage capacities increase, and groundwater levels deepen. Soil properties and infiltration capacities were identified as secondary factors influencing the water storage capacities in urban floodplain soils. During flood events, the water storage capacities can be predicted with the initial soil water contents and groundwater levels. With the developed model, based on the Gauckler‐Manning‐Strickler formula, water storage capacities in riverbank soils for different scenarios, variable in riverbank and terrain morphology, soil texture and initial water content, could be calculated. These calculations can be used, to optimize the small‐scale assessment of possible intermediate water retention in riverbank soils. So far, lateral flows in soils during flood events have been insufficiently investigated. A better understanding of these processes can contribute to an increased understanding and prediction of water retention in soils during flood events. In the actual designated floodplains of the Kollau River, soils with low water storage capacities, shallow groundwater levels and partly steep riverbank passages were identified. For an optimization of water retention in floodplain soils, an extension of the designated floodplains in suitable areas and a flattening of the riverbank sections are recommended.
8 Carbon storage and processes in urban floodplain soils 97
8 Carbon storage and processes in urban floodplain soils
In urban floodplain soils, carbon pools and their processes are still insufficiently investigated. There is no consensus in the scientific world, on how future changes due to climate change and ongoing urbanization will affect the carbon pools in urban floodplain soils (Sutfin et al. 2016). This chapter aims to develop strategies for an optimization of the ecosystem service of carbon storage in urban floodplain soils. First, the in‐situ soil carbon pools were calculated followed by an analysis of their spatial distribution and genesis. Secondly, influencing factors on the soil carbon pools were characterized and assessed. Finally, the mineralization of different organic materials, typical for urban floodplains, were investigated in an incubation experiment. The overall aim is to develop optimization strategies to preserve and increase carbon storage in urban floodplain soils.
8.1 Carbon storage characteristics in urban floodplain soils
8.1.1 Soil carbon pools The soil carbon pools of both study areas are presented and compared in this subchapter. The calculations of the carbon pools referred to the soil organic carbon content. Organic carbon from aboveground vegetation and inorganic carbon in soils were not included. Carbon pools of all reference profiles within 0.0 – 0.3 m depth, defined as topsoils, and carbon pools within 0.3 – 1.0 m depth, defined as subsoils, are illustrated in Figure 34 ‐Figure 36. In addition, carbon pools down to 10 cm depth were calculated for the 40 topsoil samples of the Dove‐ Elbe area (Figure 37). For the floodplain soils of the Kollau area, carbon pools in the topsoils varied between 2.58 and 67.74 kg m‐2 and in subsoils between 0.29 and 260.99 kg m‐2. Plot F4 must be highlighted because of the extreme high carbon pools in the topsoil with 67.74 kg m‐2 and subsoil with 260.99 kg m‐2. Furthermore, high subsoil carbon pools could be determined at plots F3, F5, F6, F10 and F11 with carbon pools between 4.66 and 77.19 kg m‐2. All these plots were influenced by anthropogenic activities such as refilling of technogenic substrates and burial of former topsoil horizons. Low carbon pools were estimated for plots F1, F2, F7, F8 and F9 with carbon pools between 0.44 and 24.19 kg m‐2. At these plots, except F8, low carbon pools were detected in the subsoils. Plot F7 showed lowest carbon pools of 2.58 kg m‐2 in the topsoil and 0.44 kg m‐2 in the subsoil (Figure 34). Carbon pools of the soils in bank areas of water retention ponds ranged between 6.19 – 81.75 kg m‐2 in topsoils and 2.92 – 81.75 kg m‐2 in subsoils. All these soils were characterized by anthropogenic activities such as refilling of technogenic substrates and burial of former topsoil horizons. Higher carbon pools between 6.19 and 81.74 kg m‐2 were recorded for the plots P6 – P12 located in densely populated areas, compared to carbon pools between 2.91 and 32.93 kg m‐2 of plots P1 – P5, which were situated in an agricultural dominated landscape (Figure 35).
98 8 Carbon storage and processes in urban floodplain soils
Figure 34: Carbon pools of reference profiles in Kollau area. White bars represent carbon pools in 0.0 – 0.3 cm depth and grey bars carbon pools in 0.3 – 1.0 cm depth. Soils with layers of technogenic substrates are labelled with hatched bars.
Figure 35: Carbon pools of reference profiles in bank soils of water retention ponds in the Kollau area. White bars represent carbon pools in 0.0 – 0.3 cm depth and grey bars carbon pools in 0.3 – 1.0 cm depth. Soils with layers of technogenic substrates are labelled with hatched bars.
8 Carbon storage and processes in urban floodplain soils 99
Figure 36: Carbon pools of reference profiles in Dove‐Elbe area. White bars represent carbon pools in 0.0 – 0.3 cm depth and grey bars carbon pools in 0.3 – 1.0 cm depth. Soils with layers of technogenic substrates are labelled with hatched bars.
Figure 37: Carbon pools of topsoils (0.0 – 0.1 m depth) in the Dove‐Elbe area. The topsoils are grouped into different elevation levels above the mean water level of the Dove‐Elbe. Elevation level 1: 1.0 – 1.5 m; elevation level 2: 1.5 – 2.0 m; elevation level 3: 2.0 – 2.5 m and elevation level 4: 2.5 – 3.0 m. Topsoils with layers of technogenic substrates are labelled with hatched bars.
100 8 Carbon storage and processes in urban floodplain soils
In the reference profiles of the Dove‐Elbe, the carbon pools in floodplain soils varied between 6.64 and 34.10 kg m‐2 in topsoils and 1.66 ‐ 60.04 kg m‐2 in subsoils (Figure 36). Lower carbon pools were determined at plots F16, F18 and F19 with carbon pools between 6.64 and 13.45 kg m‐2 in topsoils and 1.66 kg m‐2 ‐ 9.20 kg m‐2 in subsoils. Soil horizons of plots F12, F16 and F17 were characterized by anthropogenic activities such as refilling of technogenic substrates and burial of former topsoil horizons. The carbon pools of the 40 topsoil samples in the Dove‐Elbe area, categorized into four elevation levels, are illustrated in Figure 37. In elevation level 1 carbon pools ranged between 17.57 and 105.27 kg m‐2. In comparison, elevation levels 1‐3 showed lower carbon pools. In elevation level 2 carbon pools ranged between 11.37 and 49.00 kg m‐2, in elevation level 3 between 12.55 and 41.64 kg m‐2 and in elevation level 4 between 15.04 and 38.27 kg m‐2.
8.1.2 Influencing factors on soil carbon pools The following paragraphs describe the influencing factors on the carbon pools categorized in topsoil carbon pools and subsoil carbon pools of both study areas.
In Table 21, the results from a spearman correlation between topsoil and subsoil carbon pools and soil‐, terrain‐ and soil water properties are illustrated for the Kollau area.
Table 21: Spearman correlation coefficient matrix of Kollau carbon pools correlated with soil‐, terrain‐ and soil water properties. Correlation coefficients of topsoil carbon pools are indicated in the top table and correlation coefficients of subsoil carbon pools are shown in the bottom table. CP carbon pool; C/N C/N ratio; LD lateral distance to water body, EL elevation based on the mean river water level; GWL annual average of groundwater level and WC annual average of water content.
Topsoil CP C/N clay pH LD EL GWL WC CP 1.00 0.22 0.42 ‐0.30 ‐0.46 ‐0.10 ‐0.61 0.73 C/N 1.00 0.03 0.48 ‐0.29 ‐0.15 0.03 ‐0.05 Clay 1.00 0.09 ‐0.07 0.20 ‐0.38 0.54 pH 1.00 ‐0.19 0.14 0.28 ‐0.39 LD 1.00 ‐0.10 0.58 ‐0.77 EL 1.00 0.29 0.12 GWL 1.00 ‐0.87 WL 1.00
Subsoil CP C/N clay pH LD EL GWL WC CP 1.00 0.04 0.59 0.02 ‐0.09 ‐0.09 0.06 ‐0.06 C/N 1.00 ‐0.06 ‐0.03 0.07 ‐0.50 ‐0.11 ‐0.17 Clay 1.00 0.08 0.01 ‐0.11 0.22 ‐0.17 pH 1.00 ‐0.14 0.11 0.20 ‐0.44 LD 1.00 ‐0.10 0.58 ‐0.77 EL 1.00 0.29 0.12 GWL 1.00 ‐0.87 WL 1.00
8 Carbon storage and processes in urban floodplain soils 101
Highest correlations were identified between topsoil carbon pools and groundwater levels, rs
‐0.61, and between topsoil carbon pools and water contents, rs 0.73. Weak correlations were calculated for the parameters of clay, pH, C/N ratio, lateral distance to the adjacent water body, and elevation, based on the mean river water level. Here, Spearman coefficients varied between rs 0.22 and rs ‐0.46. Within the subsoil carbon pools, only weak correlations were identified between carbon pools, soil‐, and terrain‐ and soil water properties. Correlations of carbon pools with clay showed a spearman correlation coefficient of rs 0.59 while all other parameter indicated coefficients between rs 0.04 and rs ‐0.09.
Table 22: ANOVA of Kollau soil carbon pools with land uses and degrees of urbanity. Land uses: fallow, floodplain, forest, grassland, and settlement. Degrees of urbanity: natural and urban. A Tuckey‐HSD Posthoc Test (significance level of p > 0.05) followed the ANOVA. Subsoil was abbreviated with Sub and topsoil with Top.
Carbon pools Kollau Df F p Land use 4 1.43 0.22
Top Urbanity 1 1.02 0.98 Land use 4 2.85 0.07
Sub Urbanity 1 7.34 0.01
Based on an ANOVA followed by a Tukey‐HSD Posthoc test (significance level of p < 0.05), significant differences were identified in the subsoil carbon pools categorized into different degrees of urbanity (Table 22). Soils with horizons of technogenic substrates and burial of former topsoil horizons were assigned to the category ‘urban’ and soils with a natural soil genesis into the category ‘natural’. Nearly significant differences with p of 0.07 was analyzed for the subsoil carbon pools categorized into different land uses. No significant differences were calculated for the topsoil carbon pools categorized into different land uses and different degrees of urbanity. It should be noted that an F‐value above one was estimated for each analysis. Thereby differences can be assumed but could not be confirmed by the p‐value (significance level of p<0.05) (Table 21).
Spearman coefficients of carbon pools in the Dove‐Elbe area correlated with soil‐ and terrain properties indicated weak correlations (Table 23). Highest correlation was identified between topsoil carbon pools and clay, rs 0.55, and topsoil carbon pools and C/N ratio, rs 0.32.
Spearman coefficients between rs 0.14 and rs ‐0.11 were determined for the topsoil carbon pools correlated with pH, lateral distance to adjacent water body and elevation, based on the mean river water level.
In the subsoil carbon pools, weak correlation coefficients were found for C/N (rs 0.48) and clay (rs 0.25). For the correlation of subsoil carbon pools with pH, lateral distance to adjacent water body and elevation, based on the mean water level, coefficients between rs 0.14 and rs ‐0.11 were estimated.
102 8 Carbon storage and processes in urban floodplain soils
Table 23: Spearman correlation coefficient matrix of Dove‐Elbe carbon pools and soil‐ and terrain properties. Correlation coefficients of topsoil carbon pools are indicated in the top table and correlation coefficients of subsoil carbon pools are shown in the table below. CP carbon pool; C/N C/N ratio; LD lateral distance to water body and EL elevation based on the mean water level.
Topsoil CP C/N clay pH LD EL CP 1.00 0.32 0.55 ‐0.11 ‐0.07 0.14 C/N 1.00 ‐0.30 0.47 ‐0.64 0.07 Clay 1.00 ‐0.39 0.32 ‐0.11 pH 1.00 ‐0.14 ‐0.07 LD 1.00 0.36 EL 1.00
Subsoil CP C/N clay pH LD EL CP 1.00 0.48 0.25 0.05 ‐0.07 0.14 C/N 1.00 ‐0.13 0.58 ‐0.64 ‐0.43 clay 1.00 ‐0.33 0.00 0.00 pH 1.00 ‐0.29 0.18 LD 1.00 0.36 EL 1.00
An ANOVA followed by a Tukey‐HSD Posthoc Test (significance level of p < 0.05) identified no significant differences between the topsoil and subsoil carbon pools grouped into different land uses and different degrees of urbanity in the Dove‐Elbe area. It should be noted that an F‐value above one was estimated for each analysis. Thereby differences can be assumed but could not be confirmed by the p‐value (significance level of p<0.05) (Table 24).
Table 24: ANOVA of the soil carbon pools of the Dove Elbe area with land uses and degrees of urbanity. Land uses: fallow, floodplain, forest, grassland, and settlement. Degrees of urbanity: natural and urban. A Tuckey‐HSD Posthoc Test (significance level of p > 0.05) followed the ANOVA. Subsoil was abbreviated with Sub and topsoil with Top.
Carbon pools Dove‐Elbe Df F p Land use 4 1.12 0.09
Top Urbanity 1 1.75 0.68 Land use 4 1.34 0.42
Sub Urbanity 1 2.00 0.25
8 Carbon storage and processes in urban floodplain soils 103
8.1.3 Mineralization of organic material typical for urban floodplains In the next paragraphs, the results of the incubation experiment, described in chapter 4.4, are presented.
Characterization of organic materials Within an incubation experiment, carbon losses and mineralization rates of litter and topsoil materials, typical for urban floodplains, were investigated. The litter material of Populus tremula was extracted at a rural site on a grassland and at an urban site in a central city park. Accordingly, the litter origin from a rural site is abbreviated with LR and the litter origin from an urban site with LU. LR showed slightly higher amounts of organic carbon with 50.01 % compared to 47.62 % for LU, while the C/N ratio with 48.3 for LR is slightly lower compared to 51.4 for LU. In addition, hemicellulose showed only small differences between the two litter materials. For LR 12.80 % and for LU 12.53 % were analyzed. The amount of cellulose and lignin differed significantly between the two litter materials. While LR showed a cellulose amount of 8.95 %, LU had an amount of 12.53 %. Inversely, LR indicate 24.92 % and LU 17.74 % of lignin (Table 25, top).
Table 25: Characterization of organic material, used in the incubation experiment. Organic materials are abbreviated as LR: Populus litter with origin form a rural site; LU: Populus litter with origin from an urban site; T8: topsoil material with organic carbon content of 8%, T6: topsoil material with organic carbon content of 6% and T1: topsoil material with organic carbon content of 1%. Land uses refer to the respective sampling area of the litter and topsoil materials. Occurrence of soil layers with technogenic substrates and burial of former topsoil horizons were defined as indicator of anthropogenic influences.
Organic Land use Anthropogenic Organic C/N Hemi‐ Cellulose Lignin material influences carbon ratio cellulose % % % % LR Grassland No 50.01 48.3 12.80 8.95 24.92 LU Settlement Yes 47.26 51.4 11.98 12.53 17.74
Organic Land use Anthropogenic Organic Clay Silt Sand pH material influences carbon % % % % T8 Grassland No 8.13 4.28 15.1 80.64 4.05 T6 Grassland Yes 6.70 23.34 35.9 40.71 6.57 T1 Settlement Yes 0.81 1.33 1.84 96.84 6.90
The topsoil materials differed for organic carbon. Thus, the topsoil with an organic carbon amount of 8.13 % was abbreviated with T8, the topsoil with an organic carbon amount of 6.70 % was abbreviated with T6 and the topsoil with an organic carbon amount of 0.81 % was abbreviated with T1. Topsoil material T8 was extracted from a natural grassland site, material T6 from an urban grassland site and topsoil material T1 from an urban pond site. Furthermore,
104 8 Carbon storage and processes in urban floodplain soils the topsoils differed in soil texture and pH. Clay content amounted 4.28 % for T8, 23.34 % for T6 and 1.33 % for T1. Silt contents were investigated as follow: 15.10 % for T8, 35.94 % for T6 and 1.84 % for T1. High amounts of sand were analyzed for T8 with 80.64 % and for T1 with 96.84 %. For T6 medium amount of sand with 40.71 % were analyzed. Values of pH were 4.05 for T8, 6.57 for T6 and 6.90 for T1 (Table 25, bottom). All organic materials were incubated for 200 days. For each material three different water contents in percentage of the maximum water holding capacity of 55 %, 75 % and 100 % were adjusted to simulate the fluctuating water, groundwater levels and flood events.
Carbon losses and mineralization rates In Figure 38, the losses of carbon, cellulose, hemicellulose, and lignin of the litter material during the incubation experiment are illustrated. It is clearly visible that the respective losses were highest at 75 % water content and lowest at 100 % water content, also confirmed by an ANOVA followed by a Tukey‐HSD Posthoc Test (significance level of p < 0.05). LU indicated higher losses of organic carbon and cellulose compared to LR, while the losses of hemicellulose and lignin were higher within LR compared to LU.
Figure 38: Losses of organic carbon, hemicellulose, cellulose, and lignin for the two litter materials. Losses of organic carbon are mean values measured during an incubation experiment over 200 days. Losses of hemicellulose, cellulose and lignin were calculated based analyses before and after the incubation experiment. All parameters are categorized into three different water contents in percentage of the maximum water holding capacity. White bars present the litter originating from a rural site (LR) and diagonally hatched bars present the litter originating from an urban site (LU). ANOVA followed by a Tukey‐HSD Posthoc Test (significance level of p < 0.05) indicated significant differences, of organic carbon loss categorized into the different water contents (abc).
8 Carbon storage and processes in urban floodplain soils 105
Organic carbon losses of 2.33 % at 55 % water content, 2.74 % at 75 % water content and 1.64 % at 100 % water content were measured for LR. In contrast, organic losses of 2.83 % at 55 % water content, 3.25 % at 75 % water content and 2.29 % at 100 % water content were analyzed for LU. Additionally, cellulose losses of 1.07 % at 55% water content, 1.17 % at 75 % water content and 0.48 % at 100 % water content were measured for LR and cellulose losses of LU were investigated as follow: 4.92 % at 55 % water content, 6.13 % at 75 % water content and 3.81 % at 100 % water content. The parameters hemicellulose and lignin showed opposite trends. For the parameter of hemicellulose, LR indicated losses of 5.98 % at 55 % water content, 7.08 % at 75 % water content and 5.39 % at 100 % water content. In contrast, lower hemicellulose losses were analyzed for LU with 4.35 % at 55 % water content, 6.66 % at 75 % water content and 3.98 % at 100 % water content. Same tendencies were investigated for the parameter lignin. LR indicated higher losses of 14.52 % at 55 % water content, 18.31 % at 75 % water content and 12.78 % at 100 % water content compared to LU, which indicated lower losses of 9.20 % at 55 % water content, 12.42 % at 75 % water content and 3.16 % at 100 % water content.
Figure 39: Losses of organic carbon of topsoil materials, set in an incubation experiment over 200 days. A mean of three parallel measurements were calculated. They are categorized into three different water contents in percentage of the maximum water holding capacity. Grey bars indicate the topsoil material with 8 % organic carbon, grey wide hatched bars topsoil material with 6 % organic carbon and grey thinly hatched bars represents topsoil material with 1 % organic carbon.
In Figure 39, the carbon losses of all topsoil materials during the incubation experiment are illustrated. It is clearly visible that the respective losses were highest at 75 % water content
106 8 Carbon storage and processes in urban floodplain soils and lowest at 100 % water content, confirmed by an ANOVA followed by a Tukey‐HSD Posthoc Test (significance level of p < 0.05). Within the topsoils, organic carbon losses were highest for topsoil material T8, followed by topsoil material T6 and lowest for topsoil material T1. T8 showed organic carbon losses of 0.28 % at 55 % water content, 0.37 % at 75 % water content and 0.16 % at 100 % water content. Organic carbon losses of 0.21 % at 55 % water content, 0.28 % at 75 % water content and 0.11 % at 100 % water content were reached within T6 topsoil material. Topsoil material T1 showed lowest organic carbon losses of 0.09 % at 55 % water content, 0.08 % at 75 % water content and 0.01 % at 100 % water content. An ANOVA followed by a Tukey‐HSD Posthoc Test (significance level of 0.05) indicated significant differences, of organic carbon loss categorized into the different water contents (ab).
Figure 40: First‐order kinetic fitting curves of soil organic carbon mineralization, for three different water contents in percentage of the maximum water holding capacity (55%, 75% and 100%), categorized into the incubated organic materials. LR litter origin from a rural site; LU litter origin from an urban site; T8 topsoil with organic carbon content of 8%: T6 topsoil with organic carbon content of 6% and T1 topsoil with organic carbon content of 1%.
Figure 40 shows the fitted curves of cumulative organic carbon loss of each incubated organic material with three different water contents in percentage of the maximum water holding capacity each. The cumulative organic carbon loss will be further named as mineralization of organic carbon. All mineralization curves showed an exponential course and could be fitted using the exponential function ‘ExpDec1’ (provided by OriginPro 9.1G). R‐values of over 0.95 confirmed the exponential nature of the incubation data. Based on Equation 8 (chapter 4.5.3), the mineralization rate k was estimated using the first order kinetic function. For all
8 Carbon storage and processes in urban floodplain soils 107 investigated organic materials, the k‐values showed a clear sequence: LR < LU < T8 < T6 < T1. The k values for LR ranged between 51.23 and 53.20, for LU between 57.71 and 61.43, for T8 between 100.79 and 109.89, for T6 between 101.02 and 112.20 and for T1 between 303.41 and 349.50. Highest k values within each organic material were found at water contents of 75% (Table 25 and Figure 40).
Table 26: Fitting parameters of organic carbon loss for all organic materials, categorized into three water contents in percentage of the maximum water holding capacity. Parameter k defines the mineralization rate of organic carbon during the whole incubation experiment. LR litter origin from a rural site; LU litter origin from an urban site; T8 topsoil with organic carbon content of 8%: T6 topsoil with organic carbon content of 6% and T1 topsoil with organic carbon content of 1%.
Organic material Water content y A k Statstics % d‐1 r² LR 55 24800.59 ‐18591.98 51.23 0.96 LR 75 30799.83 ‐22858.90 55.85 0.95 LR 100 16806.52 ‐12627.27 53.20 0.95 LU 55 27907.35 ‐22948.17 57.71 0.98 LU 75 31659.93 ‐25316.47 61.43 0.96 LU 100 12654.25 ‐9314.51 59.27 0.95 T8 55 939.99 ‐965.20 100.79 0.99 T8 75 912.01 ‐952.06 109.89 0.99 T8 100 922.14 ‐950.30 104.68 0.99 T6 55 930.73 ‐957.92 101.02 0.99 T6 75 1031.03 ‐1056.93 112.20 0.99 T6 100 888.33 ‐915.70 104.67 0.99 T1 55 1094.60 ‐1100.87 304.85 1.00 T1 75 1286.56 ‐1294.63 349.50 1.00 T1 100 939.16 ‐944.56 303.41 1.00
8.1.4 Influencing factors on organic carbon loss and mineralization rate Spearman correlation coefficients are illustrated in Table 26 for the organic carbon losses correlated with the mineralization rate k, C/N, hemicellulose, cellulose, and lignin. Organic carbon loss and mineralization rate k indicated highest negative correlation with a coefficient of rs ‐0.91. With a correlation coefficient of rs 0.75 organic carbon loss and C/N indicated dependencies, as well as organic carbon loss and hemicellulose with a coefficient of rs 0.77. Only weak correlations were calculated for organic carbon losses with cellulose and lignin with spearman coefficients between rs 0.54 and rs 0.37.
An ANOVA followed by a Tukey‐HSD Posthoc Test (significance level of p < 0.05) confirmed significant differences between the organic carbon losses categorized into the different water contents in percentage of the maximum water holding capacity for each topsoil material and litter material (Table 27).
108 8 Carbon storage and processes in urban floodplain soils
Table 27: Spearman correlation coefficient matrix of organic carbon losses and mineralization rates, soil‐ and litter properties. Corg_loss organic carbon loss; HC hemicellulose; C cellulose; L lignin and k organic carbon mineralization rate.
Corg_loss k C/N HC C L Corg_loss 1.00 ‐0.91 0.75 0.77 0.54 0.37 k 1.00 ‐0.72 0.31 ‐0.83 0.71 C/N 1.00 ‐0.83 0.54 ‐1.00 HC 1.00 ‐0.03 0.83 C 1.00 ‐0.54 L 1.00
Table 28: ANOVA of organic carbon losses and water content for all organic material. ANOVA followed by a Tukey‐HSD Posthoc Test (significance level of p < 0.05). LR: Litter origin from a rural site; LU: Litter origin from an urban site; T8: topsoil with organic carbon content of 8%, T6: topsoil with organic carbon content of 6% and T1: topsoil with organic carbon content of 1% Organic material Water content in percentage of the maximum water holding capacity DF F p
LR 2 131.68549 1.11E‐05 LU 2 59.99549 1.08E‐04 T8 2 5.36246 0.04617 T6 2 8.99646 0.01564 T1 2 11.35219 0.00913
8.2 Discussion In this study, low to high soil organic carbon pools were determined in the Kollau and Dove‐ Elbe area. Former Peat bands and anthropogenic activities such as refilling of technogenic substrates and burial of former topsoil horizons result in high carbon pools. Groundwater levels and water contents are the main influencing factors on carbon pools in the Kollau area. Subordinated influences on carbon pools were analyzed for C/N ratio, clay content, pH, lateral distance to adjacent water body, elevation, based on the mean river water level, and land uses in both areas. With an incubation experiment, organic carbon losses and mineralization rates of organic materials, typical for urban floodplains, were determined. Litter materials indicate the highest organic carbon losses and topsoil material medium to low losses. Within the litter materials, higher organic carbon losses were achieved for litter origin from an urban surrounding compared to litter origin from a rural surrounding. All materials were incubated within three different water content treatments in percentage of the maximum water holding capacity. With a water content of 75 %, each organic material generates the highest carbon losses and mineralization rates and with a water content of 100 % the lowest carbon losses and mineralization rates.
8 Carbon storage and processes in urban floodplain soils 109
Floodplain soils act as important carbon sinks (Cierjacks et al. 2011; Zehetner et al. 2009). The continuous alternation of flooding and dry periods contributes to the transportation by organic carbon on the floodplain soils and the incorporation into the upper soil layers of autochthonous and allochthonous organic carbon. However, these carbon pools are strongly altered by the disturbances of anthropogenic activities in urban areas, which will be discussed in the next paragraphs. Studies in European floodplain soils measured carbon pools in the Rhine floodplains between 53.8 and 67.1 kg m‐² (Hoffmann et al. 2007; Hoffmann et al. 2009), on the Danube floodplains between 14.4 and 18.6 kg m‐² (Cierjacks et al. 2010), and between 18.8 and 31.3 kg m‐² in riparian forests (Rieger et al. 2013). Floodplain soils with organic‐rich horizons due former peatlands reached carbon pools of 25.0 to 140.0 kg m‐² (Wohl 2013; Wohl et al. 2012). In a study by Steger (2018), carbon pools of 26.6 kg m‐² were determined for buried former topsoil horizons, which, in contrast to other horizons in greater depths, had 130 % higher values. In addition, further studies identified buried horizons or organic‐rich materials as reasons for higher carbon pools in the deeper soil horizons (Chaopricha et al. 2014; D’Elia et al. 2017a; Lorenz et al. 2017b; Rees et al. 2019). The carbon pools determined in this study ranged from 0.29 to 260.92 kg m‐2, reflecting low to very high carbon pools in comparison to former studies. In soils with a natural substrate genesis, carbon pools between 0.29 and 105.27 kg m‐2 and in anthropogenic influenced soils, carbon pools between 0.44 and 260.92 kg m‐2 were analyzed. Especially the carbon pools of anthropogenic influenced soils clearly exceed carbon pools determined in former studies so far. Relic horizons of peat bands and the refilling of technogenic and organic rich substrates, reflecting anthropogenic activities, are the main processes that are responsible for the high carbon pools in both study areas. Due to the formerly natural floodplain activities, organic‐ rich horizons are relict in the subsoils of many urban floodplains (Wohl 2013; Wohl et al. 2012). Furthermore, technogenic substrates with an initial high carbon content are often refilled in subsoils of urban floodplains which significantly increase the carbon storage of those areas (D’Elia et al. 2017b). Confirmed by a study of Rees et al. (2019), the results in this study reveal high potential of urban soils to store and keep large amounts of organic carbon over time, when they are constructed from an adequate set of parent materials. To exploit this potential, the quality of organic carbon and the associated mineralization rate, needs to be further investigated to find suitable organic rich substrates, which can be stored in soils for a long time.
Soil carbon pools in the Kollau area are directly influenced by soil water properties such as water contents and groundwater levels. Soil and terrain properties such as clay and lateral distance to adjacent water body are subordinate factors influencing the carbon pools of topsoils as well as subsoils in the Kollau area (Table 21 and Table 23). Especially in active floodplain areas, former studies achieve similar results. Sutfin et al. (2016) reviewed a strong dependency of temperature and moisture on carbon pools in floodplain ecosystems.
Groundwater levels were identified as controlling factor on carbon pools and the resulting CO2 emission (Atkins et al. 2013). In addition, Samaritani et al. (2011) identified all parameters
110 8 Carbon storage and processes in urban floodplain soils linked to seasonality’s and floods rather than soil properties as main influencing factors. Soil texture indirectly control the carbon pools, due to the absorption facies and the resulting provision of organic carbon for microbial mineralization (Sutfin et al. 2016). However, frequent disturbance by flood events led to high heterogeneity with temporarily and locally increased carbon pools and soil respiration (Samaritani et al. 2011). Through this study, the influencing factors of carbon pools identified in former studies could also be investigated for the carbon pools of soils in two urban floodplains in the City of Hamburg.
It is still unclear, what influences climate change will have on the carbon pools in urban floodplain soils. Longer dry periods and extreme rain events (IPCC 2018) will influence the soil water balance of urban floodplains, which could have a significant impact on carbon pools. Some studies stated, that high temperatures tended to accelerate soil organic carbon accumulation while extreme rain events reduce the soil organic carbon storage rate (Xiong et al. 2014). Again other studies concluded that under global warming the temperature dependence of soil carbon pools will be decisive for soil carbon storages (von Lützow et al. 2009). In a study by Merriman et al. (2017) an outweighed carbon turnover was investigated by extreme rain events and growing season length. Further research is needed to understand the effects of climate change on carbon pools in urban floodplain soils.
The organic carbon loss during the incubation experiment increased gradually and tended to stabilize in the end of the incubation time. From the data obtained, it can be concluded that the amount of organic carbon losses increases with higher initial amount of organic carbon of the respective material. Following sequence of organic carbon losses were investigated for the topsoil materials: T1 < T6 < T8. Less organic carbon, available for microbial degradation, seems to result in lower organic carbon losses. With a higher initial organic carbon content, both litter materials indicate higher losses of organic carbon compared to the topsoil materials. However, LU with a lower initial organic carbon content of 47.26 % produces higher losses of organic carbon compared to LR with a higher initial carbon content of 50.01 %. This tendency can be explained by the different composition of the organic matter (Dorendorf et al. 2015a; Xie et al. 2017), further explained in the next paragraphs. From an exponential fit, based on a first order kinetic function, the mineralization rate k could be determined (Ottow 2011b; Yin et al. 2019). Due to the different persistence of the individual components within organic carbon, the mineralization corresponds to a sequence according to increasing resistance to microbial degradation, which is expressed in decreasing mineralization rates. Thus, the mineralization rate decreases significantly from the labile organic components (e.g. proteins, sugar and starch), the easily degradable components (e.g. lipids, hemicellulose and cellulose), the difficult degradable components (e.g. lignin, aromatic substances and Polyphenole) to the recalcitrant and chemico‐physically stabilized components (permanent humus). (Ottow 2011b). The litter material LU, which originates from an urban surrounding, and the litter material LR, which originates from a rural surrounding, indicate differences in the composition of the organic matter. LU consist of more easily degradable substances such as cellulose and
8 Carbon storage and processes in urban floodplain soils 111 hemicellulose, while LR has a significant higher amount of lignin, which is more difficult to degrade by microorganisms. This results in lower mineralization rates for LR and in slightly higher mineralization rates for LU. Despite the fact that the litter origins from the same tree species, the differences in litter quality between rural and urban locations are clearly visible, also investigated in a study by Dorendorf et al. (2015a). To what extent urban factors (e.g. air pollution) influence the composition of litter and what effects this has on future carbon storage is still unclear. The topsoils T8 and T6 showed medium and the topsoil T1 the highest k values. This reflects the different composition of organic carbon within these materials. Material T1 consists mainly of technogenic substrate with a low carbon content. In turn, material T6 and T8 consist of decomposed former peat bands with organic carbon components more difficult to degrade. From the mineralization rate k, the composition of the organic carbon and their degradability of the materials examined in this study can be derived. In addition, hemicellulose and cellulose showed strong correlations with the organic carbon loss, which indicates the predominant mineralization of these organic components. As these components are easily degradable, they are also the first to be mineralized within the incubation process (Ottow 2011a). Significant differences between the carbon losses and the mineralization rate k divided into the different water contents could be determined. The clearly highest values were investigated within the water content of 75 %, followed by water contents of 55 % and the lowest losses at water contents of 100 %. This is confirmed by the studies of Yin et al. (2019) and Wang et al. (2010). In the incubated materials, typical for urban floodplains, the microbial activity is highest within 75 % water content. In order to protect the urban carbon storages from microbial mineralization, a water content of 75 % should be avoided. Soils with near water‐saturated conditions provide ideal situations for carbon storage, as evidenced by the low mineralization rates at 100% water content, determined in this study.
Partly high soil carbon pools can be concluded for both study areas. These pools will be exposed to future stressors, especially through climate change (IPCC 2018) and urbanization (Brown et al. 2018; Lorenz et al. 2017a). Since soil carbon pools react strongly to changes in temperature and water content, they will be particularly vulnerable in future. To protect these high carbon pools and to exploit the carbon storage potential of urban floodplain soils, changes in the environmental design of floodplain areas are necessary. Former studies suggest that areas with already existing high carbon pools in floodplain soils need to be protected. For example, a study by Bruland and Richardson (2006) compared the floodplain carbon pools of restored and active floodplains. They stated that carbon pools, which have been enriched for thousands of years, simply could not be replaced with created floodplains. This is also confirmed by Peach et al. (2019) who compared soil carbon pools of yard and forests sites. The land use change from forests to yards reduced the soil carbon pools significantly. In In the future, the protection of high carbon pools origin from tecnogenic and organic rich substrates needs to be considered, as these account for a significant amount of carbon storage in urban floodplain soils (D’Elia et al. 2017b; Rees et al. 2019). Sustainable protection of carbon pools includes the restoration of areas along the river system near‐
112 8 Carbon storage and processes in urban floodplain soils natural floodplain areas. These plots will contribute to an increase of carbon fixation in the soil and simultaneously could have a cooling effect, positive for the city climate (Wiesner et al. 2016). Restoration of floodplains should include broad unconfined valleys with complex channel geometry and wet conditions (Sutfin et al. 2016). Additionally, forest plots along the watercourse can increase the carbon pools in urban floodplains to a high extent (Cariñanos et al. 2018; Kellner et al. 2016; Pouyat et al. 2002). In order to find suitable areas for floodplain restoration, Wiesmeier et al. (2012) recommended that carbon pools need to be determined by horizon for the entire soil profile in order to evaluate carbon storage potentials more accurately. In turn, considering the cost and labor in measuring soil properties, results by Li et al. (2018) suggests that soil organic carbon at the lowest depth can provide good estimates of the vertical distribution in soil organic carbon in a floodplain.
In the Kollau area, areas along the watercourse already represent suitable sites for storing carbon over a longer time (Figure 41). A higher amount of these sites would increase the carbon storage within urban floodplain soils. Restored areas suitable for carbon storage should provide following criteria: (1) high carbon pools, especially in subsoils, (2) soil properties where adsorption of organic carbon is possible, (3) high water contents up to water saturation, and (4) typical floodplain vegetation, which provide organic material for carbon storage. Possible areas that fulfill these criteria are located in the upper reaches of the Kollau. In Figure 41, areas are illustrated, where restoration of floodplain areas along the Kollau River is possible (indicated as blue areas). In future, a goal for science, politics and urban planners is the generation of the best possible compromise for uses of urban floodplain areas by the population, which is compatible with nature and their ecosystem services, further discussed in chapter 9.
8 Carbon storage and processes in urban floodplain soils 113
Figure 41: Optimization of carbon storage. Designated floodplains (orange) are situated along the Kollau River. Grey areas indicate areas with high soil sealing (areas with settlements, industries and traffic) and turquoise areas with low soil sealing (areas with agriculture, grassland and forests). Two sites along the watercourse with medium to high organic carbon pools are illustrated (pictures).
8.3 Conclusion With this study, partly high carbon pools were analyzed for urban floodplain soils. Especially the subsoil carbon pools in the Kollau and Dove‐Elbe area clearly exceed carbon pools investigated in former studies in floodplain soils. Relic peat bands and organic rich technogenic substrates, reflecting anthropogenic influences, are the main reasons for the high carbon pools of urban floodplain soils. To what extent the technogenic substrate can be fixed in the soils of urban floodplains has to be analyzed in further studies. Soil water properties directly control the carbon pools while soil properties and terrain morphology react as subordinate influencing factors. Carbon mineralization of litter‐ and topsoil material, typical for an urban floodplain, is directly controlled by water content and the composition of the organic matter. Highest mineralization rates were measured at 75 % water content in percentage of the maximum water holding capacity. Faster degradability’s were estimated for the litter originated from an urban surrounding, compared to the litter origin from a rural surrounding, due to the higher proportion of easily degradable organic substances in the cell wall components. Urban influences such as soil pollution and city climate on litter quality is assumed from this result but needs further investigation and verification. Existing carbon pools should be protected in urban floodplain soils on suitable areas to preserve and increase the ecosystem service of carbon storage. These areas should provide the following criteria:
114 8 Carbon storage and processes in urban floodplain soils high carbon pools, soil properties that allow the adsorption of organic carbon, high soil water contents up to water saturation and typical floodplain vegetation.
9 Synthesis 115
9 Synthesis
In the previous chapters, the relevant ecosystem services of urban floodplain soils, pollutant retention, water retention and carbon storage (Scholz et al. 2012), were investigated and their influencing factors analyzed. Based on the in‐situ investigation and process analysis of each ecosystem service, optimization potentials and strategies were derived. To facilitate ecosystem services in urban planning processes, urban planners and politicians are calling for an economic valuation of the respective ecosystem services. Section 9.1 gives a valuation of soil related ecosystem services in urban floodplains, based on the approaches that exist until today. The individual optimization strategies are summarized in chapter 9.2 to a common recommendation for urban floodplains embedded in current international design concepts of urban areas. Finally, synergies and conflicts of land uses in urban floodplain areas are discussed in chapter 9.3.
9.1 Valuation of soil related ecosystem services
Previous approaches and difficulties The various concepts of ecosystem services and their classification were presented in chapter 2. Through the concepts of MEA (2005) and TEEB (2016), services can be assigned to ecosystem functions and classified into different categories of supporting, provisioning, regulating, and cultural services. These concepts served as a basis for the development of classifications of soil‐related ecosystem services (Adhikari et al. 2016; Dominati et al. 2010; Jónsson et al. 2016; Schwilch et al. 2016). In recent years, it has become increasingly clear that the implementation of soil‐related ecosystem services in urban planning processes requires valuation. Therefore, politicians and urban planners are calling for a monetary assessment of soil ecosystem services (Baveye et al. 2016; da Silva et al. 2018; Jónsson et al. 2016). Possible monetary values have been calculated in several studies based on different economic methods for individual soil ecosystem services. The study by Jónsson et al. (2016) is the latest study that brought together all existing approaches. They summarize the monetary benefit of soil related ecosystem services, provided by former studies, for all categories of supporting, provisioning, regulating and cultural services (Table 29). In order to implement the economic approaches summarized by Jónnson et al. (2016) to the soil related ecosystem services, area‐ related data that covers a period of several years is required. For example, in the case of carbon storage, the carbon content in kg m‐³ and its annual increase or decrease must be known for it to be economically evaluated.
116 9 Synthesis
Table 29: Soil ecosystem service and valuation summary ‐ provided by Jónsson et al. (2016).
All the economic valuations summarized in Table 29 are based on an anthropocentric view of the respective ecosystem services. Many studies criticize that monetary valuations are not reflecting the natural value of the ecosystem itself (i.a. Costanza et al. 2017). Other studies focus on decision‐making‐methods instead of monetary valuation, because it is not clear what economic and financial markets might do with prices of soil ecosystem services (Baveye et al. 2016). Research on soil health assessment (Rinot et al. 2019) and life cycle assessment (Pavan et al. 2018) focus among other studies on decision‐making methods. Future research will have to elucidate to what extent economic valuation can suitably represent the soil ecosystem services and whether non‐monetary valuation methods are preferable. To date, the required level of quality is not given in soil ecosystem valuation and the informational value of existing valuation results is low (Bartkowski et al. 2020).
Up until now, only few studies exist concerning the valuation of ecosystem services in urban floodplain soils, all following different approaches. A non‐monetary approach is given in a study by Peters et al. (2016), which provides the preliminary stage for an economic valuation of the ecosystem services of floodplains. They state that the capacity of a particular floodplain function to provide services depends on its processes and structures. To value the services provided by floodplain functions, the key ecological processes and ecosystems structures that enable the functions to occur must be identified and appropriate measures developed as a first step. Secondly, these measures are then valued as high or low (Peters 2016). Studies that calculate a monetary value for the ecosystem services of floodplain soils are based on economic approaches of damage or waste and recycling costs. In a study by Hopkins et al. (2018) the valuation of nitrogen retention in floodplain areas was derived from wastewater treatment costs. The method presented is scalable and transferable to other areas if appropriate datasets are available for validation. Damages caused by floods are economically valued in a study by Watson et al. (2016). Economic impacts stress the importance of floodplain and wetland conservation, warrant the consideration of ecosystem services in land use decisions, and make a compelling case for the role of green infrastructure in building resilience to climate change. A uniform assessment of soil‐related ecosystem services of urban floodplain soils has not been provided until now.
9 Synthesis 117
With the data generated in this study, economic valuation is not possible. For the soil‐related ecosystem services of pollutant retention, water retention and carbon sequestration, mainly in‐situ data are available. Annual increases or decreases of these services were only partially measured. However, a general assessment can be made based on the study by Peters et al. (2016). For each ecosystem service in the Kollau area, the benefits for humans (anthropocentric) and the ecosystem itself (biocentric) are described and followed by a valuation of the measures, which influences the respective ecosystem services. The measures recommended in this study to optimize ecosystem services in urban floodplains are then ranged with high and low values.
General Valuation of pollutant retention in urban floodplain soils In urban floodplain soils, pollutant retention takes place mainly in the topsoils of water retention ponds. The urban polluted surface runoff is channeled into urban water systems. Due to reduced flow and areas with ideal accumulation conditions, the pollutants associated with the organic matter, sediment and are incorporated into the upper soil layers of ponds (see chapter 6). Table 30 lists the services pollutant retention provide for the population and the ecosystem itself. Urban floodplain soils can protect the population and the vegetation from the widespread of pollutants. With the sedimentation of the particles associated with pollutants in water retention ponds, the pollutants are kept near to the source and could be easily removed within desludging processes of ponds. When sludge is deposited in landfills, due to high levels of pollutants, these pollutants are removed from the ecosystem cycle. A redesign of ponds can also provide areas for leisure activities that can be used by the population.
Table 30: Services provided by pollutant retention in urban floodplain soils.
Anthropocentric Biocentric Protection of population from pollution Protection from pollution levels that have Generating of areas for leisure activities negative effects on certain plant species
Pollution of surface runoff and pond design are the most important processes influencing the ecosystem service of pollutant retention in urban floodplain soils. The level of pollution of surface runoff determines the pollution load introduced in the water systems. Pond design in turn determines the accumulation and sedimentation of pollutants in the water retention ponds. Table 31 lists the valuation of the respective processes of pollutant retention.
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Table 31: Valuing soil pollutant retention in urban floodplain soils.
Low value High value Pollution of surface runoff Polluted Unpolluted Pond design Linear flow Nonlinear flow No vegetation cover Vegetation cover of reeds and sedges in shallow water zones
In summary, the pollutant retention in the Kollau area can be valuated with low values, though partly with high values in areas where ponds are already optimal designed. Polluted surface runoff and a range of different pond designs control the actual function of pollutant retention in the Kollau floodplains. Ponds with linear flow and without flow‐calming situations indicate very low accumulation of pollutants. In turn, in ponds with non‐linear flow direction and areas with vegetation cover in shallow water zones, significantly higher accumulation of pollutants was estimated. A high potential for an optimization of pollutant retention, by redesigning urban ponds, is derived by this study. With a larger area of vegetation cover in shallow water areas and flow‐calmed situations within the ponds, the accumulation of pollutants could be significantly increased.
General valuation of water retention in urban floodplain soils Depending on the site characteristics, urban floodplain soils can temporarily store a significant amount of rain and floodwater and thus mitigate flood events (see chapter 7). In addition to the protection of the population from damage caused by severe floods, the regular flooding of urban floodplains provides the following services: recharge of groundwater and using restored river sections as areas for leisure activities by the population. By optimizing the ecosystem services of water retention, a floodplain‐like ecosystem can be generated, which provides space for typical floodplain vegetation (Table 31).
Table 32: Services provided by soil water retention in urban floodplain soils.
Anthropocentric Biocentric Protection of population from flood Generation of a near‐natural floodplain damages ecosystem Water supply – groundwater recharge Generating of areas for leisure activities
Bank and floodplain design are the factors that control the service of water retention in floodplain soils. Only if flooding is possible, can water be temporarily stored in the floodplain soils. Thereby, dams, dikes, and steep bank passages prevent the spread of water in floodplain areas. The amount of water that can infiltrate into the soil depends on the initial water content and groundwater levels, the soil properties, and the size of the floodplain area. Floodplain
9 Synthesis 119 soils with low water contents, deep groundwater levels, and a sandy and humus‐rich soil texture are optimal for the intermediate storage of water. To catch these site characteristics, the designated floodplains should cover a large area. Table 33 lists the valuation of bank design and floodplain design within urban floodplain soils.
Table 33: Valuing water retention in urban floodplain soils.
Low value High value Bank design Steep Flat Floodplain design High water content Low water content Shallow groundwater level Deep groundwater level Water logging soil horizons (e.g. peatbands) Sandy and organic rich soil substrate Small floodplain area Large floodplain area
In summary, the ecosystem service of water retention in the Kollau area can be valuated with low values. Steep bank passages dominate the middle and southern parts of the Kollau River. In the northern Kollau area, the banks are also characterized by flat and restored sections. Here the river can easily overflow its banks. Due to the site characteristics of partly high water contents, shallow groundwater levels, and only small designated areas for flooding, the ecosystem service of water retention is valued low in the Kollau area. Sites, where a higher potential for water retention has been identified, are located at the edges of the actual designated floodplains. To increase the ecosystem service of water retention, the floodplain areas should be expanded to cover sites where water storage in the soil is possible.
General valuation of carbon storage in urban floodplain soils Carbon storage takes place in urban floodplains due to the input of allochthones and autochthonous organic material and the partially water‐saturated soil conditions. If soils are mostly water‐saturated, accumulation and fixation of carbon increases. In addition to the input of organic material by floods and autochthonous organic material, the refilling of organic‐rich and technogenic substrates results in high carbon storage in urban floodplains (Chapter 8). In a near‐natural floodplain area, typical floodplain vegetation can establish itself. The population could also use these areas as recreational sites (Table 34).
Table 34: Services provided by carbon storage in urban floodplain soils.
Anthropocentric Biocentric
Reduction of CO2 emissions Generation of a near‐natural floodplain Generation of areas for leisure activities ecosystem
Vegetation cover, soil water content, and soil properties are the main processes controlling the ecosystem service of carbon storage in urban floodplain soils. Valuation of these processes
120 9 Synthesis are given in Table 35. The highest carbon storages can be generated in areas with initial high soil carbon contents and a soil type that promotes the binding of organic substances.
Table 35: Valuing carbon storage in urban floodplain soils.
Low value High value Vegetation cover No vegetation Typical floodplain vegetation Soil water content 75 % 100 % Soil properties Soils without binding forces for organic Soils with high binding forces for organic carbon carbon Low carbon content High carbon content (natural and anthropogenic origin)
In summary the ecosystem service of carbon storage in the Kollau can be valuated with high values. In some areas, very high carbon pools have been measured in the Kollau area. There are already areas that provide suitable site characteristics for carbon storage. An increase in restored sections along the Kollau, which are characterized by typical floodplain vegetation, a high soil water content up to water saturation and an already existing high carbon content, would have a positive effect on the ecosystem service of carbon storage.
9.2 Urban floodplain design under transition
Adaption strategies of urban floodplain design – an overview With the clear prognoses that climate change will become even more severe in the future (i.a. IPCC 2018), many countries are applying adaptation strategies concerning the design and management of urban areas. The overall goal is to create sustainable cities that are resilient to natural disasters caused by extreme rain events and dry periods, and flood events are becoming a particular focus of attention. In recent years, a massive increase in the intensity of flood events has been observed (Haase 2019). Until now, the common measures to protect cities and the population from flood events were based on the construction of grey infrastructure (Brears 2018; Perini 2017). Among a wide range of measures, dikes, elevated bank passages of urban rivers, and a targeted drainage of rainwater were used in cities to increase resistance against flood events. However, with the increase in the intensity of flood events, it became clear that these measures of grey infrastructure were no longer sufficient to mitigate flood events and protect the population from flood damages (Haase 2019). The transformation into sustainable cities based on a mixture of grey and blue‐green infrastructure is increasingly recommended by several studies and research projects to create a city which is resilient to natural hazards as floods (Alves et al. 2019; Alves et al. 2020; Liao et
9 Synthesis 121 al. 2017). Blue‐green infrastructure describes all elements of a network of connected green spaces and creates the spatial basis for a sustainable use of ecosystems and their services. Protected areas are integrated into the existing landscape. Some of these elements can be reforestation, green bridges, roofs, or walls. Through strategic spatial planning, room for nature will be extended to promote the preservation of biodiversity and ecosystem services (Lucius et al. 2011). Based on the blue‐green infrastructure, several approaches concerning a holistic and sustainable management of rainwater were derived with the objective to mitigate extreme flood events (Sörensen et al. 2019; Suter 2018; Thorne et al. 2018). Through improved percolation, intermediate storage, and evaporation of rainwater, flood events are thought to be mitigated. In addition, pollutants that enter the ecosystems via surface runoff are filtered. Many studies in different countries have developed approaches based on the measures mentioned above. Important approaches include:
Sponge Cities (China) (Figure 42) Best management practices (USA and Canada) Low impact development (Great Britain) Sustainable urban drainage system (Asia) Water sensitive urban design (Australia and New Zealand) Bluegreen solutions (Europe)
All approaches aim to green cities, storing rainwater, increasing evaporation, mitigating flood events and give urban rivers and adjacent floodplains larger room (e.g. Jiang et al. 2018; Löschner et al. 2018; Poleto et al. 2012; Raadgever et al. 2018b; Tillie 2017). They all recommend measures as green roofs, porous pavements, water tanks, rain gardens, bio swales and optimized use of floodplains, retention ponds and river channels (see Figure 42). A distinction can be made between measures that can be implemented in densely populated areas and measures that require a larger area with a lower population density. In densely populated areas, porous pavements, green roofs, water tanks, green facades, and rain gardens could be easily implemented, while an optimization of floodplains, retention ponds and river‐ channels often require larger spaces and less density of population. Optimization, restoration, and expansion of these areas compete directly with the demand for space in the course of ongoing urbanization and former uses. As a result, the transformation of urban floodplains where blue‐green infrastructure is established is only implemented slowly (Dawson 2017).
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Figure 42: Schematic approach of a sponge city Source: https://www.dsd.gov.hk/Documents/.../sponge_city.html
The blue‐green infrastructure in urban floodplains is not yet sufficiently implemented. In particular, the generation of larger areas for urban rivers and their floodplains is often in direct competition with ongoing urbanization. To counteract this tendency, more projects are emerging that demand a larger area for rivers in natural and urban areas. LAND4FLOOD is an international project that reclaims and redesign areas in different countries for the flooding of rivers (Löschner et al. 2018). Furthermore, Room for Rivers was a project in the Netherlands based on a government design plan that intended to address flood protection, master landscaping, and the improvement of environmental conditions in the areas surrounding the Netherlands' rivers (Fokkens 2006). Within these studies however, soil‐related ecosystem services have been insufficiently addressed so far. The holistic overview of the current state, the processes, and recommendations for the optimization of soil‐related ecosystem services in urban floodplains, developed in this study, provides a basis to better integrate these ecosystem services into future urban planning processes. This generated holistic process understanding demonstrate the benefits people obtain from soil ecosystem services in urban floodplains. Making politicians, urban planners, and the broader population aware of a greater number of ecosystem services may encourage them to design ecosystems that avoid the occurrence of land use conflicts. Simply describing more ecosystem services to people had no effect on their acceptance for management strategies, suggesting that detailed, empirical data on ecosystem services and their processes are required to affect decision‐making (Richards et al. 2017). With this study, it could be shown that there is a high potential concerning the optimization of soil‐related ecosystem services in urban floodplain areas. Through an increased understanding of the respective processes of pollutant retention, water retention, and carbon storage, recommendations for the redesign of urban floodplains can be given. The derived recommendations for a floodplain design could
9 Synthesis 123 be used in the future by politicians and urban planners to create sustainable floodplains, which contribute to a resilient city.
Optimizing soil ecosystem services in floodplains of the Kollau area In Germany, the sponge city approach will be implemented in future city plans (Figure 42). In recent years, infiltration has become a further component of the rainwater management that is relevant to practice. Based on the political guidance, "Principles for the management and treatment of rainwater runoff for discharge into surface waters," the preservation of the local water balance is formulated as a target value and must be implemented in future development processes in cities (DWA 2013). One example in which a rainwater‐sensitive management for cities was developed is the RISA study conducted in Hamburg City (RISA 2015). This study aimed to present new approaches for Hamburg’s water management to cope with increasing soil sealing and extreme rain and flood events caused by climate change. In RISA, different Ministries of Hamburg are working together to implement concepts and solutions for the sustainable use of rainwater. The concept relates to the following areas of activities: (i) urban water management, (ii) urban and landscape planning, (iii) traffic planning and (iv) watercourse planning. The RISA project is a good example of the sustainable use of rainwater and the integration of rainwater management in cities. In urban floodplains in specific, rainwater management is strongly related to flood risk management. In the City of Hamburg, the urban floodplains are designed based on the existing flood risk management which consists of three pillars (LSBG 2016):
Preventive flood protection (retention, floodplain designation, risk communication) Technical flood protection (management of discharge with structural measures) Operational flood protection (warning service, flood defense, maintenance)
Within preventive flood protection, the floodplain areas are designated according to the affected area of a 100‐yearly flood event. In addition, water retention ponds are evenly spread over the whole floodplain area to store water temporally, resulting in a minimized peak flow. The preventive flood protection is to be strengthened in the future in order to enable more ecological flood protection. For an optimal use of the soil related ecosystem services of pollutant retention, water retention, and carbon storage, the following recommendations are given for floodplains and their design.
The retention of pollutants in the floodplain soils of the Kollau was classified in chapter 9.1 with a high value and further optimization potential. By adapting the design of water retention ponds, the ecosystem service of pollutant retention can be significantly increased in more than half of the ponds in the Kollau area. Following criteria should be fulfilled for an optimization of pollutant retention in water retention ponds:
(1) Establishing of a vegetation cover of reeds and sedges in shallow water zones and (2) Creation of a nonlinear flow direction to increase flow stabilized areas.
124 9 Synthesis
The polluted sludge masses must be removed at continuous intervals and, depending on the pollution level, either reused or landfilled. Accordingly, the pollutants are sustainably removed from the ecosystem cycle. This contributes to the obligation of the EU Water Framework Directive where all EU Member States need to achieve a "good ecological" and "good chemical status" for all waters by 2015 and in expendable cases by 2027. Finally, it should also be noted that the most effective prevention of pollution in urban floodplains is the introduction of unpolluted surface runoff.
Optimization of water retention requires a significant extension of floodplains in the Kollau area. As described in chapter 9.1, the current state of water retention in Kollau’s floodplain soils were assessed with low values. Future floodplain areas with an optimized water retention should provide following criteria:
(1) Soils with a low water content and deep groundwater levels, (2) Sandy soil substrates with a high amount of humus and (3) Riverbank and terrain morphology that allows fluctuating water table and flooding.
As mentioned in chapter 7.2, open areas in the upper reaches of the Kollau are available to implement these recommendations. An extension of floodplain areas in the upper reaches of the Kollau River contributes to a storing of floodwater close to the source and thus being able to mitigate the flood wave.
The carbon storage in the Kollau area was evaluated in chapter 9.1. Because of the actual high carbon pools, the ecosystem service of carbon storage was valuated with a high value. Additionally, the fixation of the high carbon storages in floodplain soils could be optimized. Areas along the Kollau River already provide optimal conditions for carbon accumulation and fixation (cf. chapter 8.2). A higher number of areas for carbon storage and fixation should be generated, based on the following criteria:
(1) High carbon pools, especially in subsoils, (2) Soil properties where absorption of organic carbon is possible, (3) High water contents up to water saturation, (4) Typical floodplain vegetation including forest vegetation
9 Synthesis 125
9.3 Synergies and conflicts of optimization strategies In the next paragraphs, the conflicts, and synergies, which occur by integrating the recommended optimization strategies in urban floodplains, are discussed for each ecosystem service of pollutant retention, water retention, and carbon storage.
Optimizing the retention of pollutants in water retention ponds may lead to conflicts regarding the storage volume for rainwater in ponds. Synergies are possible with the features of biodiversity and recreation. By redesigning water retention ponds with extensive shallow water including vegetation zones and flow‐stabilized areas, the original function of water retention can be preserved. As part of flood risk management in urban areas, the main function of urban ponds is to store water temporarily and to contribute to the protection of the population from damage caused by floods. Furthermore, an increase of vegetation zones within the ponds provide space for a variety of plant species and animals. A study by Holtmann et al. (2019) points out that water retention ponds provide habitats for rare species, which could increase biodiversity. Also a study by Lenzewski (2020) emphasizes the importance of urban wetlands, although they are highly variable in terms of their geomorphology and chemical status, for supporting a wide range of species, including highly endangered species. Near‐natural pond ecosystems lead to the features of local leisure and recreation activities for the population. With hiking trails, lookout points, and information boards, the population can use the redesigned ponds. In summary, the ecosystem itself and the population both benefit from the redesign of the water retention ponds.
A significant extension of floodplains to increase water retention leads to numerous conflicts of land use especially in densely populated areas. However, there are also possible synergies with aspects of agricultural uses and local recreation. The current Kollau area is characterized by settlements of single and multi‐family houses with surrounding gardens, allotment gardens, livestock agriculture, and grasslands. Except of grasslands, no land use is compatible with fluctuating water tables and flooding. Additionally, continuing urbanization is exerting enormous land use pressure on the remaining open spaces in cities, which are often located along urban rivers. In many cases, the land use of urban floodplains is decided in favor of construction projects. The remaining open spaces where flooding would be possible are often under land uses which are not compatible with the activities of a typical floodplain. In the future, concepts must be developed which provide floodplain designs where mixed uses are possible. Mixed uses where flooding is possible could be grasslands and fallows as areas for natural compensation and leisure and recreation activities. Through hiking and bike trails, viewing platforms, and information boards, the created near‐natural floodplain ecosystem could be easily accessible and perceptible for the population. Effective designated floodplains can also be used as connecting axes into the rural environment outside the city. This contributes to an increase in the quality of life (Tillie 2017). Overall, there are wide ranges of synergies that can be created by expanding urban floodplain areas. On the other hand, there are land use conflicts occurring when water retention in
126 9 Synthesis floodplain soils will be optimized, especially with ongoing urbanization and the demand for settlement areas.
The creation of near‐natural floodplain ecosystems along the river systems for an optimization of carbon storage leads to minor land use conflicts and holds various synergies with the aspects of biodiversity and local leisure and recreation activities for the population. Within the Kollau area, grasslands, fallows, and agricultural uses dominate the land uses in the upper reaches of the Kollau, while single‐ and multi‐family houses with surrounding gardens and settlement complexes dominate the lower reaches. In the already less populated areas, a transformation of areas into near‐natural floodplain ecosystems is possible. In the more densely populated southern areas of the Kollau, land use conflicts will occur. In some cases, high carbon pools have also been recorded for settlement areas where conversion to a near‐natural floodplain is not possible. In the future, strategies must be generated to protect high carbon pools in densely populated areas with a simultaneous maintenance of the settlement areas. A transformation into a near‐natural floodplain ecosystem along urban river creates synergies especially with the aspects of biodiversity and leisure and recreation by the population. Biodiversity can be increased in restored floodplain areas. Additionally, hiking and bike trails, viewing platforms, and information boards create recreational areas easily accessible for the population. In summary, the ecosystem itself and the population both benefit from the generation of near‐natural floodplain areas along the River.
Based on the results of water retention and carbon storage, it is evident that the derived optimization strategies can be mutually exclusive. Low water contents are necessary for the optimization of water retention and high water contents are necessary for the optimization of carbon storage in the urban floodplain soils. Both optimization strategies cannot be implemented on the same floodplain area. In the future, urban planning processes should consider whether a mosaic structure of areas can be established for the implementation of mutually exclusive optimization strategies, or whether a prioritization of ecosystem services to be optimized can be made for the respective areas.
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10 Outlook
Within this study, the processes of each ecosystem service of pollutant retention, water retention and carbon storage were investigated. In addition to the process understanding generated, there are other open issues that should be investigated in subsequent studies. Within pollutant retention, it is necessary to clarify to what extent the accumulated pollutants are dissolved in the water phase and further transported downstream. Furthermore, the desludging processes, which represent a massive intervention in the ecosystems, needs to be optimized. The development of possibilities for the undisturbed removal of polluted sludge should be welcomed in further studies. The knowledge of the process of water retention can be enhanced by research on lateral flows. So far, there are findings on how rainwater and floodwater infiltrates into the soil and is percolated to the lower horizons. Measuring lateral fluxes in situ remains difficult but would increase the understanding of the water balance processes in floodplain soils. This study demonstrated that large carbon stocks exist in urban floodplain soils. The factors that control the fixation of urban‐derived carbon stocks in urban floodplain soils should be further investigated in addition to this study. Furthermore, it is unclear what influence urban aspects such as air pollution on soils have on the composition of organic material. This composition appears to affect the degradability of these materials, which can have a negative effect on the carbon storage in the soil.
More studies highlighting the importance of soil‐related ecosystem services in urban floodplains are needed to draw attention of politicians and urban planners concerning the redesigning of floodplain areas in cities. In this context, approaches, or concepts to assess soil‐ related ecosystem services in a monetary or comparable way should be derived. Only with a suitable valuation, soil‐related ecosystem services can be optimally integrated into urban planning processes.
With this study, the high potential for an optimization of soil‐related ecosystem services in urban floodplains is illustrated. Future urban planning concepts should focus on the provision of and unsealing of land through for the implementation of the necessary optimization strategies of the ecosystem services of floodplain soils in cities. This process will contribute to the mitigation of the consequences of climate change and urbanization in cities.
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Appendix
A1 Soil parameters A 1: Results of laboratory analyses of typical soil parameters for pots P01 to P06. The analyses were performed for each soil horizon.
Plot Horizon Clay Silt Sand Coarse Soil pH Electrical Ctotal Corg C/N Ntotal K P As Pb Cd Cu Zn PAHEPA PCBEPA MOH bottom soil texture conductivity ratio C10‐C40 m % % % % (KA5) % % % % mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg µg/kg µg/kg mg/kg P01 0.25 6.99 9.02 83.99 4.99 St2 5.60 40 3.06 3.06 15.36 0.20 19.68 10.22 4.00 60.69 0.31 54.37 116.14 1507.13 5.42 0.00 P01 0.53 6.00 8.75 85.24 13.90 St2 7.10 120 1.03 0.84 16.89 0.05 9.13 8.49 1.89 16.35 0.08 8.68 32.67 656.71 0.00 0.00 P01 0.70 4.84 6.40 88.75 22.06 Ss 6.20 47 0.66 0.66 14.52 0.05 6.58 3.78 P01 1.38 4.18 5.58 90.24 10.33 Ss 6.00 44 0.18 0.18 14.00 0.01 7.28 14.94 P02 0.10 11.72 12.75 75.53 2.94 Sl3 5.40 63 4.75 4.75 15.34 0.31 130.18 43.59 5.18 62.44 0.27 25.72 62.56 1899.87 0.00 13.04 P02 0.40 13.38 17.19 69.42 2.92 Sl4 5.00 42 4.23 4.23 15.41 0.27 15.68 20.80 7.30 105.45 0.31 48.73 57.58 2791.94 0.00 0.00 P02 0.80 2.12 4.75 93.13 0.02 Ss 6.40 38 0.11 0.11 10.33 0.01 P02 1.38 2.49 2.97 94.54 0.33 Ss 6.40 160 0.29 0.29 12.52 0.02 0.98 4.12 0.02 2.76 15.75 88.59 0.00 0.00 P03 0.32 5.30 12.88 81.82 23.64 Sl2 6.30 53 3.37 3.37 15.51 0.22 31.18 30.03 4.46 57.08 0.20 17.26 49.20 1856.66 0.00 11.70 P03 0.64 7.88 12.16 79.96 2.78 Sl2 7.40 117 0.98 0.17 8.61 0.02 35.18 0.13 3.53 3.79 0.04 4.10 22.45 99.24 0.00 0.00 P04 0.30 5.78 12.88 81.35 6.99 Sl2 4.60 52 1.96 1.96 13.08 0.15 20.00 3.92 3.72 33.87 0.12 8.31 21.57 879.89 0.00 8.44 P04 0.50 5.50 8.99 85.51 12.11 St2 5.10 46 0.66 0.66 12.64 0.05 11.70 1.36 1.73 12.76 0.07 5.43 15.42 324.93 0.00 3.56 P04 0.65 3.78 4.25 91.96 1.56 Ss 5.50 25 0.14 0.14 9.77 0.01 P04 0.78 6.49 6.73 86.78 1.23 St2 6.00 25 0.08 0.08 6.87 0.01
P04 1.00 10.73 13.79 75.48 4.18 Sl3 6.40 51 0.21 0.21 11.04 0.02 P05 0.25 4.12 6.56 89.32 3.54 Ss 5.20 29.30 3.03 3.03 15.94 0.19 38.65 18.57 2.21 72.41 0.18 25.25 37.38 2231.42 3.98 29.39 P05 0.57 3.60 6.51 89.89 0.00 Ss 5.50 7.10 0.29 0.29 14.91 0.02 3.65 18.19 0.04 1.86 0.02 1.62 7.25 25.41 0.00 2.31
P05 1.00 14.89 21.44 63.66 0.00 Sl4 5.30 7.90 0.14 0.14 14.12 0.01 5.05 8.37 P06 0.15 7.18 13.41 79.41 10.38 Sl2 6.10 54 3.87 3.87 15.38 0.25 89.00 74.29 3.72 63.10 0.27 26.91 109.44 3854.20 9.54 8.29 P06 0.90 3.21 12.62 84.17 8.83 Su2 6.70 94 3.62 3.56 16.82 0.21 60.00 79.68 3.65 101.64 0.38 41.76 164.45 6315.82 18.84 0.00 P06 1.00 3.15 11.72 85.12 0.05 Su2 5.80 24 0.25 0.25 13.95 0.02 0.00 0.00
144 Appendix
A 2: Results of laboratory analyses of typical soil parameters for pots P07 to P12. The analyses were performed for each soil horizon.
Plot Horizon Clay Silt Sand Coarse Soil pH Electrical Ctotal Corg C/N Ntotal K P As Pb Cd Cu Zn PAHEPA PCBEPA MOH bottom soil texture conductivity ratio C10‐C40 m % % % % (KA5) % % % % mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg µg/kg µg/kg mg/kg P07 0.14 12.76 16.94 70.30 7.50 Sl4 7.60 78.40 2.19 2.16 14.39 0.15 43.00 26.28 3.58 54.79 0.21 16.04 65.41 1094.11 0.00 33.39 P07 0.37 6.52 7.48 86.00 21.07 St2 8.40 87.40 0.71 0.34 16.07 0.02 24.75 17.37 2.37 4.00 0.07 3.80 16.57 65.82 0.00 0.79
P07 0.46 8.01 20.64 71.35 2.98 Sl3 8.30 99.50 1.30 0.06 2.64 0.02 33.50 0.00
P07 0.70 2.06 2.23 95.71 4.43 Ss 7.20 105.90 0.36 0.36 13.95 0.03 21.55 16.81
P07 1.00 12.97 17.74 69.29 1.30 Sl4 5.00 775.00 3.85 3.85 14.54 0.26 40.00 25.06 P08 0.50 14.47 28.73 56.80 1.55 Sl4 5.25 308.00 10.80 10.80 16.86 0.64 39.00 141.51 P09 0.25 11.18 14.56 74.27 5.62 Sl3 6.31 82.80 3.43 3.43 7.50 0.46 31.60 15.33 12.52 85.75 0.41 25.93 56.45 694.69 0.00 19.22 P09 0.58 6.95 12.77 80.29 15.20 Sl2 7.14 76.30 1.83 1.75 0.87 2.02 10.35 30.51 5.19 33.09 0.25 12.04 42.51 789.98 0.00 7.55 P09 0.85 7.95 15.08 76.97 9.88 Sl2 6.76 136.50 2.30 2.24 2.31 0.97 19.30 68.71 P09 1.00 6.57 17.15 76.28 10.27 Sl2 7.86 169.00 1.31 0.88 0.64 1.36 21.80 27.75 P10 0.15 4.77 11.86 83.37 4.14 Su2 7.20 89.00 2.65 2.56 15.03 0.17 18.65 80.11 2.91 38.26 0.23 27.30 107.96 1.39 16.74 119.37 P10 0.32 6.28 21.69 72.02 3.07 Sl2 6.40 59.00 2.26 2.26 15.05 0.15 8.10 55.98 P10 0.56 3.89 10.99 85.11 0.12 Su2 6.40 44.00 0.42 0.42 12.69 0.03 9.00 15.74 P10 1.00 2.88 5.03 92.09 0.00 Ss 6.40 28.00 0.23 0.23 10.10 0.02 12.30 11.88 P11 0.15 4.42 6.08 89.50 3.33 Ss 5.27 40.40 2.30 2.30 12.17 0.19 17.65 52.38 5.19 102.64 0.56 36.21 143.12 9008.12 20.92 40.41 P11 0.49 6.09 8.22 85.70 7.85 St2 6.23 46.20 2.73 2.73 14.26 0.19 19.50 93.35 9.46 144.08 0.82 51.03 259.84 5416.29 20.55 56.82 P11 0.85 5.06 8.97 85.97 6.75 St2 6.42 58.30 1.53 1.53 4.46 0.34 33.60 83.03
P11 1.00 11.04 18.55 70.40 1.63 Sl3 6.10 280.00 4.25 4.25 12.86 0.33 28.10 225.62 P12 0.14 5.04 11.51 83.45 5.37 Sl2 5.19 70.60 4.07 4.06 11.13 0.37 73.50 31.90 3.53 93.79 0.20 31.60 67.63 2224.83 0.00 10.16 P12 0.37 4.44 11.22 84.34 0.83 Su2 4.86 31.40 2.64 2.63 9.67 0.27 7.15 7.43 1.30 38.82 0.12 11.29 37.92 1026.29 0.00 0.00 P12 0.46 3.28 7.86 88.86 0.44 Ss 5.00 44.80 1.56 1.56 14.52 0.11 2.40 7.12 P12 0.70 2.49 2.19 95.32 0.11 Ss 4.94 171.00 0.19 0.18 3.46 0.05 5.00 6.93
Appendix 145
A 3: Results of laboratory analyses of typical soil parameters for pots F01 to F06. The analyses were performed for each soil horizon.
Plot Horizon Clay Silt Sand Coarse Soil Bulk Particle Pore Air Field Usable filed pH Electrical bottom soil texture density density volume capacity capacity capacity conductivity m % % % % (KA5) g/cm3 g/cm3 Vol % Vol % Vol % Vol % F01 0.20 8.14 12.94 78.92 0.53 Sl3 0.84 2.49 66.11 27.50 38.61 32.41 4.40 166.00 F01 0.40 9.16 13.46 77.37 1.10 Sl3 1.21 2.53 52.15 12.51 39.63 37.54 4.20 67.00 F01 0.72 3.22 10.38 86.40 0.01 Su2 1.54 2.63 41.55 14.44 27.11 24.86 4.90 18.00 F01 1.50 4.91 19.65 75.43 0.01 Su2 1.62 2.63 38.53 8.13 30.06 27.67 4.90 20.00 F02 0.30 4.28 15.08 80.64 4.53 Su2 0.82 2.28 64.15 7.23 56.92 44.90 4.10 261.00 F02 1.40 1.87 0.80 97.33 0.35 Ss 1.49 2.62 43.01 23.29 19.72 18.52 4.90 52.00 F03 0.10 5.12 6.89 87.99 St2 5.91 176.00 F03 0.25 4.50 6.70 88.81 Ss 6.12 58.00 F03 0.45 3.81 4.11 92.07 Ss 6.83 48.50 F03 0.67 9.25 14.01 76.74 Sl3 7.18 81.80 F03 0.73 7.59 11.07 81.34 Sl2 7.06 73.40 F03 0.90 16.78 28.21 55.01 Sl4 6.97 189.30 F03 1.00 27.70 63.87 8.43 Lu 6.31 285.00 F04 0.25 23.34 35.94 40.71 Ls3 0.45 2.26 79.89 12.09 67.79 56.26 6.53 103.00 F04 0.45 14.26 26.94 58.80 Sl4 0.89 2.46 63.77 8.90 54.88 44.55 66.00 66.00 F04 0.75 36.93 29.74 33.33 Lts 0.42 1.99 79.02 5.61 73.41 34.49 202.00 202.00 F04 1.36 33.73 31.67 34.60 Lt2 0.19 1.69 88.95 9.05 79.90 67.90 420.00 420.00 F05 0.12 1.91 4.76 93.33 0.94 Ss 1.41 2.58 45.36 21.67 23.69 19.81 6.50 53.00 F05 0.19 1.81 4.39 93.79 1.66 Ss 1.43 2.60 45.08 22.60 22.48 19.47 6.60 47.00 F05 0.57 5.81 14.98 79.20 6.13 Sl2 1.39 2.58 46.04 15.49 30.56 24.55 6.50 48.00 F05 1.00 7.57 16.37 76.07 5.17 Sl2 1.30 2.54 48.87 12.80 36.07 28.71 6.20 37.00 F06 0.18 10.10 18.29 71.62 3.95 Sl3 0.76 3.39 77.47 17.83 59.64 49.44 5.80 109.00 F06 0.43 10.36 21.61 68.03 6.23 Sl3 1.21 2.53 52.00 6.86 45.13 34.94 7.00 91.00 F06 0.50 12.17 20.87 66.96 7.58 Sl4 1.21 2.55 52.70 5.46 47.24 37.19 7.20 152.00 F06 0.78 14.08 24.35 61.56 7.21 Sl4 1.14 2.52 54.63 7.66 46.97 38.45 5.50 149.00 F06 1.00 45.80 41.94 12.26 0.76 Tu2 0.24 1.69 65.24 4.59 81.50 59.38 3.60 2600.00
146 Appendix
A 4: Results of laboratory analyses of typical soil parameters for pots F07 to F11. The analyses were performed for each soil horizon.
Plot Horizon Clay Silt Sand Coarse Soil Bulk Particle Pore Air Field Usable filed pH Electrical Bottom soil texture density density volume capacity capacity capacity conductivity m % % % % (KA5) g/cm3 g/cm3 Vol % Vol % Vol % Vol % F07 0.12 1.33 1.84 96.84 5.64 Ss 1.64 2.64 37.93 16.53 21.40 19.55 6.90 99.00 F07 0.25 2.46 5.80 91.75 12.93 Ss 1.56 2.62 37.93 15.59 24.87 22.17 7.10 146.00 F07 0.35 2.61 6.04 91.35 9.75 Ss 6.40 54.00 F08 0.20 5.26 11.59 83.15 0.00 Sl2 1.34 2.54 47.05 1.76 45.29 32.42 64.40 F08 0.33 6.16 11.32 82.52 0.00 Sl2 1.33 2.53 47.64 9.47 38.17 27.69 38.70 F08 0.56 2.48 5.14 92.38 0.00 Ss 1.55 2.63 41.23 15.16 26.06 21.04 27.50 F08 0.86 12.76 37.72 49.51 0.00 Sl4 1.36 2.61 47.87 3.00 44.87 31.08 37.30 F09 0.10 10.93 13.12 75.95 0.39 Sl3 0.80 2.37 66.13 5.37 60.75 44.17 0.01 F09 0.15 11.60 11.77 76.64 2.09 Sl4 1.01 2.40 57.81 1.44 56.37 39.40 0.51 F09 0.33 11.33 11.19 77.47 0.29 Sl3 1.10 2.47 55.62 3.79 51.83 37.96 0.50 F09 0.60 0.64 1.24 98.12 0.10 Ss 1.64 2.64 37.94 9.55 28.40 27.25 0.53 F10 0.08 6.83 9.26 83.91 n.d. St2 0.84 2.41 65.18 17.24 47.94 32.29 88.40 F10 0.24 5.40 9.41 85.19 n.d. St2 1.05 2.52 58.10 23.27 34.83 22.97 85.50 F10 0.39 5.77 10.63 83.60 n.d. Sl2 1.31 2.56 48.58 7.74 40.85 32.51 67.10 F10 1.05 5.99 12.22 81.79 n.d. Sl2 1.45 2.55 43.20 7.59 35.61 24.92 69.40 F11 0.15 8.21 10.07 81.72 n.d. Sl3 1.02 2.40 57.64 1.37 56.27 43.49 44.20 F11 0.25 9.41 8.93 81.66 n.d. St2 1.29 2.50 48.28 1.10 47.18 34.17 31.00 F11 0.45 2.97 5.98 91.05 n.d. Ss 1.41 2.46 42.73 0.29 42.44 40.79 26.90 F11 0.57 2.83 9.26 87.91 n.d. Ss 1.46 2.52 42.26 1.93 40.32 33.05 20.70 F11 0.75 4.47 9.22 86.32 n.d. Ss 1.62 2.60 37.64 2.44 35.20 29.39 24.70 F11 0.90 1.90 9.69 88.40 n.d. Ss 2.63 30.60
Appendix 147
A 5: Results of laboratory analyses of typical soil parameters for pots F01 to F06. The analyses were performed for each soil horizon.
Plot Horizon Ctotal Corg C/N ratio Ntotal K P As Pb Cd Cu Zn PAHEPA PCBEPA MOH bottom C10‐C40 m % % % % mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg µg/kg µg/kg mg/kg F01 0.20 4.04 4.04 10.69 0.38 83.50 2.64 4.47 56.13 0.32 15.61 41.46 2482.90 0.00 3.32 F01 0.40 2.90 2.90 11.18 0.26 34.00 4.37 6.33 81.59 0.32 20.99 40.41 2.86
F01 0.72 0.15 0.15 7.47 0.02
F01 1.50 0.24 0.24 9.04 0.03 F02 0.30 10.06 10.06 12.16 0.83 46.00 23.84 15.80 113.21 0.30 35.07 32.73 1046.77 0.00 40.28
F02 1.40 0.31 0.31 11.40 0.03
F03 0.10 2.49 2.49 12.40 0.20 115.43 117.82 5130.42 4.86 F03 0.25 1.81 1.81 11.94 0.15 75.95 102.80 6886.94 7.74 F03 0.45 0.79 0.79 10.19 0.08 F03 0.67 3.85 3.85 13.98 0.28 F03 0.73 2.22 2.22 14.01 0.16 F03 0.90 5.04 5.04 12.32 0.41 F03 1.00 10.05 11.20 13.68 0.82 F04 0.25 9.38 9.38 14.04 0.67 42.53 47.87 F04 0.45 5.62 5.62 15.74 0.36 F04 0.75 15.01 15.01 11.56 1.30 F04 1.36 22.09 22.09 10.07 2.19 F05 0.12 1.20 1.10 13.65 0.08 1.60 49.43 2.03 7.15 0.08 7.98 23.57 260.43 0.00 3.47 F05 0.19 1.07 1.02 12.12 0.08 2.50 32.16 2.78 39.54 0.13 12.97 48.74 1382.98 0.00 13.10
F05 0.57 2.26 2.22 13.73 0.16 13.34
F05 1.00 3.21 3.21 15.04 0.21 F06 0.18 6.35 6.35 15.18 0.42 19.50 56.01 9.92 112.98 0.53 52.33 244.77 26816.58 13.55 5.82 F06 0.43 4.84 4.70 17.86 0.26 7.90 8.39 11.71 123.31 0.57 64.00 288.87 30675.57 12.98 33.35
F06 0.50 3.25 3.03 14.17 0.21 F06 0.78 3.76 3.76 12.95 0.29 F06 1.00 33.23 33.23 13.85 2.40
148 Appendix
A 6: Results of laboratory analyses of typical soil parameters for pots F07 to F11. The analyses were performed for each soil horizon.
Plot Horizon Ctotal Corg C/N ratio Ntotal K P As Pb Cd Cu Zn PAHEPA PCBEPA MOH bottom C10‐C40 m % % % % mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg µg/kg µg/kg mg/kg F07 0.12 1.12 1.12 44.74 0.03 17.35 1.81 0.65 5.56 0.08 4.15 30.51 510.95 0.00 14.25 F07 0.25 0.33 0.25 4.62 0.05 65.00 2.18 1.53 17.66 0.09 8.67 44.50 1664.62 0.00 22.98 F07 0.35 0.19 0.19 11.11 0.02 9.02
F08 0.20 2.79 2.77 10.19 0.27 38.00 24.45 326.67
F08 0.33 2.60 2.59 10.07 0.26 13.80 20.58 337.34
F08 0.56 0.52 0.51 9.40 0.06 11.10 9.73 27.24
F08 0.86 1.06 1.05 11.94 0.09 25.80 8.79 F09 0.10 5.43 5.37 10.32 0.53 54.50 8.61 1295.86 F09 0.15 4.41 4.36 9.76 0.45 21.15 7.58 1811.67 F09 0.33 3.79 3.74 9.43 0.40 17.60 2.93 710.20 F09 0.60 0.06 0.06 1.45 0.04 0.40 1.21 F10 0.08 5.79 5.74 12.52 0.46 5.91 324.78 5136.87 F10 0.24 3.68 3.61 11.16 0.32 4.48 243.42 4485.73 F10 0.39 2.23 2.21 11.20 0.20 1.85 107.34 1472.67 F10 1.05 2.27 2.25 11.68 0.19 1.47 29.07 F11 0.15 4.47 4.45 12.30 0.36 0.75 55.22 2823.96 F11 0.25 3.35 3.33 12.72 0.26 0.24 25.64 715.66 F11 0.45 5.59 5.58 25.70 0.22 0.05 6.34 113.98 F11 0.57 3.19 3.19 25.62 0.12 0.01 2.53
F11 0.75 1.05 1.05 14.65 0.07 0.04 0.82
F11 0.90 0.16 0.15 2.25 0.05 0.00 0.00
Appendix 149
A 7: Results of laboratory analyses of typical soil parameters for pots F12 to F16. The analyses were performed for each soil horizon.
Plot Horizon Clay Silt Sand Coarse soil Soil texture pH Electric conductivity Ctotal Corg C/N ratio Ntotal As Cd Cu Pb Zn PAHEPA bottom m % % % % (KA5) µS/cm % % % mg/kg mg/kg mg/kg mg/kg mg/kg µg/kg F12 0.20 31.14 36.47 32.39 0.05 Lt2 97.10 4.64 4.59 11.31 0.41 74.14 1.50 99.52 182.34 224.75 5411.86
F12 0.40 34.52 42.67 22.81 0.08 Lt2 93.90 2.56 2.53 10.69 0.24
F12 0.55 33.65 57.42 8.94 0.07 Tu3 140.70 2.50 2.48 10.51 0.24
F12 0.80 38.94 57.95 3.11 0.02 Tu3 252.00 2.82 2.77 10.41 0.27 F13 0.32 33.59 45.71 20.70 0.06 Lt2 63.40 2.24 2.21 9.94 0.22 50.70 1.83 73.39 126.43 293.92 2053.42
F13 0.59 31.74 44.10 24.16 0.00 Lt2 45.70 0.94 0.92 8.54 0.11
F13 1.00 42.18 52.82 5.01 0.00 Tu3 50.60 1.15 1.14 8.02 0.14 F13 1.15 2.30 1.10 96.60 0.01 Ss 21.80 0.09 0.09 3.73 0.02 F14 0.13 8.82 13.82 77.36 14.47 Sl3 247.00 6.66 6.54 14.29 0.46 14.91 2.57 89.77 367.11 415.83 F14 0.37 9.63 14.02 76.35 23.81 Sl3 119.00 4.13 4.02 15.79 0.25 F14 1.00 10.74 17.97 71.28 3.39 Sl3 120.00 4.82 4.34 20.23 0.21 F15 0.28 43.89 47.19 8.09 0.00 Lt3 100.60 3.65 3.60 9.26 0.39 71.65 2.37 84.98 163.63 310.87 1484.41 F15 0.50 44.98 46.22 4.40 0.00 Tu2 73.80 1.24 1.22 8.18 0.15 F15 0.85 37.36 49.61 11.95 0.05 Tu3 94.30 2.33 2.30 9.72 0.24 F15 0.95 38.43 56.24 4.91 0.02 Tu3 188.50 1.99 1.79 10.37 0.19 F16 0.12 6.60 8.79 84.61 8.76 St2 5.70 64.00 2.72 2.72 10.75 0.25 2.50 0.15 7.34 14.34 28.94 243.51 F16 0.32 5.97 11.08 82.95 8.97 Sl2 6.50 44.00 1.22 1.22 10.31 0.12 F16 0.50 0.54 0.47 98.99 0.74 Ss 7.80 80.00 0.51 0.51 25.21 0.02
150 Appendix
A 8: Results of laboratory analyses of typical soil parameters for pots F17 to F20. The analyses were performed for each soil horizon.
Plot Horizon Clay Silt Sand Coarse soil Soil texture pH Electric conductivity Ctotal Corg C/N ratio Ntotal As Cd Cu Pb Zn PAHEPA bottom m % % % % (KA5) µS/cm % % % mg/kg mg/kg mg/kg mg/kg mg/kg µg/kg F17 0.30 26.47 20.89 52.64 4.06 Lts 6.80 54.00 4.42 4.42 15.32 0.29 84.43 5.94 216.26 409.49 1061.27 12515.71
F17 0.35 23.63 24.55 51.82 5.27 Lts 7.20 103.00 5.17 5.17 18.49 0.28
F17 0.45
F17 0.50
F17 0.68
F17 1.00 28.05 24.79 47.16 0.03 Lts 6.70 156.00 5.53 5.53 14.51 0.38 F18 0.12 25.35 40.34 34.31 0.61 Lt2 5.20 60.00 2.96 2.96 9.84 0.30 24.37 0.80 35.66 230.97 135.02 661.47 F18 0.50 22.96 41.12 35.91 0.05 Ls2 6.00 24.00 1.08 1.08 8.50 0.13 F18 0.63 17.04 24.78 58.18 0.02 Ls4 6.20 16.00 0.42 0.42 7.01 0.06 F18 0.80 26.50 49.47 24.02 0.00 Lt2 6.30 20.00 0.59 0.59 7.48 0.08 F19 0.10 7.75 11.65 80.61 1.29 Sl2 5.30 80.00 1.43 1.43 10.22 0.14 5.00 0.28 9.66 21.15 44.24 497.85 F19 0.40 6.41 12.46 81.14 0.22 Sl2 5.50 27.00 0.54 0.54 8.09 0.07 F19 0.65 4.73 4.83 90.44 0.02 Ss 6.10 15.00 0.12 0.12 4.47 0.03 F19 0.95 1.25 0.47 98.28 0.00 Ss 6.20 12.00 0.04 0.04 1.97 0.02 F19 1.00 11.72 23.57 64.71 0.00 Sl3 6.20 16.00 0.19 0.19 4.76 0.04 F19 1.05 F20 0.10 39.22 48.67 12.11 0.22 Lt3 5.10 78.00 4.84 4.84 9.80 0.49 50.59 1.95 71.35 136.69 299.31 1614.62 F20 0.30 39.01 51.32 9.67 0.00 Tu3 5.40 34.00 2.24 2.24 8.86 0.25 F20 0.70 39.66 54.93 5.41 0.00 Tu3 5.90 39.00 1.27 1.27 8.20 0.16
F20 0.95 40.39 53.05 6.56 0.10 Tu3 6.20 43.00
A2 Soil pollutants
A 9: Results of ANOVA followed by a Tuckey‐HSD Posthoc Test (significance level of p < 0.05) of all trace metal data categorized in different land uses (agriculture, green areas, industry, settlement and traffic), location of plot (pond and floodplain), different degrees of urbanity (natural and urban), different sub catchments (1‐3) and positions within ponds (inflow (i), outflow (o), deep (d), shallow (sh), slope (sl) and reed (r)).
As Cd Cu Pb Zn DF F p DF F p DF F p DF F p DF F p Land use 4 2.54 0.87 4 1.16 0.34 4 1.11 0.33 4 0.18 0.83 4 1.09 0.33 Location 1 0.29 0.52 1 0.95 0.06 1 1.42 0.19 1 0.91 0.52 1 1.68 0.10 Urbanity 1 0.39 0.67 1 1.42 0.29 1 1.28 0.28 1 0.62 0.53 1 1.95 0.14 Sub catchment 2 0.02 0.98 2 1.40 0.25 2 6.54 0.01 2 3.02 0.06 2 6.21 0.01 Position 5 0.34 0.94 5 1.22 0.31 5 2.22 0.04 5 0.93 0.04 5 2.41 0.02
A 10: Results of ANOVA followed by a Tuckey‐HSD Posthoc Test (significance level of p < 0.05) of all organic pollutant data categorized in different land uses (agriculture, green areas, industry, settlement and traffic), location of plot (pond and floodplain), different degrees of urbanity (natural and urban), different sub catchments (1‐3) and positions within ponds (inflow (i), outflow (o), deep (d), shallow (sh), slope (sl) and reed (r)).
PAHEPA PCBEPA MOH C10‐C40 DF F p DF F p DF F p Land use 4 0.35 0.70 4 0.68 0.51 4 1.14 0.32 Location 1 0.66 0.73 1 0.45 0.89 1 3.03 0.01 Urbanity 1 3.15 0.04 1 1.62 0.20 1 3.55 0.03 Sub catchment 2 3.29 0.04 2 3.89 0.02 2 5.18 0.01 Position 5 1.97 0.05 5 4.11 0.01 5 1.27 0.02
A 11: Spearman correlation matrix of soil properties and pollutants of floodplain soil data.
Floodplain soils Clay Corg N As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 Clay 1.00 0.56 0.55 0.60 0.34 0.42 0.33 0.26 0.13 ‐0.46 ‐0.15
Corg 1.00 0.88 0.65 0.76 0.66 0.69 0.49 0.42 ‐0.02 0.17 N 1.00 0.74 0.73 0.73 0.62 0.48 0.33 ‐0.08 0.27 As 1.00 0.80 0.85 0.73 0.54 0.56 0.01 0.22 Pb 1.00 0.91 0.93 0.76 0.85 0.36 0.38 Cd 1.00 0.90 0.80 0.81 0.35 0.33 Cu 1.00 0.85 0.85 0.46 0.26 Zn 1.00 0.82 0.53 0.38
PAHEPA 1.00 0.61 0.31
PCBEPA 1.00 0.30 MOH C10‐C40 1.00
152 Appendix
A 12: Spearman correlation matrix of soil properties and pollutants of pond sediment data.
Pond sediments Clay Corg N S As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 Clay 1.00 0.43 0.36 0.04 ‐0.04 0.15 0.15 0.15 0.14 0.00 ‐0.03 0.16
Corg 1.00 0.84 0.32 0.48 0.76 0.56 0.67 0.52 0.58 0.46 0.36 N 1.00 0.29 0.48 0.65 0.50 0.56 0.45 0.43 0.37 0.28 S 1.00 0.29 0.39 0.65 0.74 0.84 0.57 0.74 0.86 As 1.00 0.58 0.36 0.34 0.23 0.41 0.24 0.12 Pb 1.00 0.78 0.75 0.60 0.73 0.68 0.44 Cd 1.00 0.87 0.83 0.74 0.86 0.74 Cu 1.00 0.94 0.81 0.89 0.83 Zn 1.00 0.76 0.89 0.93
PAHEPA 1.00 0.83 0.66
PCBEPA 1.00 0.83 MOH C10‐C40 1.00
A 13: Spearman correlation matrix of soil properties and pollutants of deep zone of pond sediment data.
Deep Clay Corg N S As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 Clay 1.00 0.62 0.53 0.21 0.28 0.41 0.15 0.43 0.43 0.43 0.42 0.53
Corg 1.00 0.77 0.84 0.56 0.96 0.77 0.85 0.85 0.77 0.88 0.86 N 1.00 0.61 0.27 0.78 0.70 0.79 0.79 0.75 0.68 0.73 S 1.00 0.47 0.89 0.95 0.88 0.88 0.82 0.88 0.83 As 1.00 0.43 0.41 0.42 0.42 0.41 0.47 0.43 Pb 1.00 0.95 0.99 0.99 0.98 0.97 0.92 Cd 1.00 0.94 0.94 0.90 0.93 0.83 Cu 1.00 1.00 0.95 0.95 0.93 Zn 1.00 0.95 0.95 0.93
PAHEPA 1.00 0.97 0.93
PCBEPA 1.00 0.83 MOH C10‐C40 1.00
Appendix 153
A 14: Spearman correlation matrix of soil properties and pollutants of shallow and slope zone of pond sediment data.
Shallow and slope Clay Corg N S As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 Clay 1.00 0.14 ‐0.05 ‐0.44 ‐0.50 ‐0.18 ‐0.11 ‐0.15 ‐0.21 ‐0.22 ‐0.13 ‐0.14
Corg 1.00 0.84 0.17 0.27 0.71 0.52 0.81 0.75 0.54 0.33 0.65 N 1.00 0.40 0.38 0.69 0.56 0.77 0.77 0.56 0.44 0.73 S 1.00 0.76 0.58 0.62 0.51 0.51 0.70 0.55 0.57 As 1.00 0.49 0.32 0.53 0.55 0.65 0.41 0.54 Pb 1.00 0.91 0.91 0.87 0.90 0.83 0.86 Cd 1.00 0.79 0.77 0.84 0.85 0.83 Cu 1.00 0.98 0.81 0.80 0.92 Zn 1.00 0.77 0.80 0.93
PAHEPA 1.00 0.89 0.90
PCBEPA 1.00 0.92 MOH C10‐C40 1.00
A 15: Spearman correlation matrix of soil properties and pollutants of inflow zone of pond sediment data.
Inflow Clay Corg N S As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 Clay 1.00 0.73 0.73 0.04 0.35 0.53 0.58 0.75 0.58 0.56 0.40 0.65
Corg 1.00 0.56 0.48 0.28 0.83 0.82 0.93 0.88 0.60 0.67 0.85 N 1.00 0.03 ‐0.22 0.38 0.56 0.60 0.55 0.15 0.18 0.53 S 1.00 0.07 0.39 0.27 0.28 0.37 0.27 0.60 0.47 As 1.00 0.14 0.14 0.15 0.03 0.45 0.22 0.01 Pb 1.00 0.85 0.92 0.96 0.73 0.92 0.93 Cd 1.00 0.83 0.87 0.55 0.72 0.82 Cu 1.00 0.95 0.60 0.73 0.93 Zn 1.00 0.62 0.87 0.92
PAHEPA 1.00 0.88 0.62
PCBEPA 1.00 0.85 MOH C10‐C40 1.00
154 Appendix
A 16: Spearman correlation matrix of soil properties and pollutants of outflow zone of pond sediment data.
Outflow Clay Corg N S As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 clay 1.00 ‐0.02 ‐0.22 ‐0.09 ‐0.20 ‐0.27 ‐0.39 ‐0.31 ‐0.33 ‐0.20 ‐0.12 ‐0.07
Corg 1.00 0.92 0.95 0.90 0.77 0.71 0.81 0.76 0.77 0.64 0.87 N 1.00 0.87 0.92 0.89 0.85 0.87 0.87 0.83 0.76 0.88 S 1.00 0.78 0.73 0.75 0.89 0.81 0.79 0.71 0.90 As 1.00 0.83 0.71 0.75 0.76 0.77 0.60 0.75 Pb 1.00 0.92 0.82 0.82 0.93 0.93 0.72 Cd 1.00 0.84 0.87 0.82 0.79 0.76 Cu 1.00 0.96 0.90 0.86 0.93 Zn 1.00 0.87 0.74 0.93
PAHEPA 1.00 0.98 0.79
PCBEPA 1.00 0.64 MOH C10‐C40 1.00
A 17: Spearman correlation matrix of soil properties and pollutants of reed zone of pond sediment data.
Reed Clay Corg N S As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 clay 1.00 0.40 0.40 0.40 ‐0.20 0.80 0.80 1.00 0.80 1.00 1.00 0.80
Corg 1.00 1.00 1.00 0.80 0.80 0.80 0.50 0.80 0.50 1.00 0.80 N 1.00 1.00 0.80 0.80 0.80 0.50 0.80 0.50 1.00 0.80 S 1.00 0.80 0.80 0.80 0.50 0.80 0.50 1.00 0.80 As 1.00 0.40 0.40 ‐0.50 0.40 ‐0.50 1.00 0.40 Pb 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Cd 1.00 1.00 1.00 1.00 1.00 1.00 Cu 1.00 1.00 1.00 1.00 1.00 Zn 1.00 1.00 1.00 1.00
PAHEPA 1.00 1.00 1.00
PCBEPA 1.00 1.00 MOH C10‐C40 1.00
Appendix 155
A 18: Spearman correlation matrix of soil properties and pollutants of pond sediment data within sub catchment 1.
Sub catchment 1 Clay Corg N S As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 Clay 1.00 0.81 0.82 0.33 0.27 0.67 0.45 0.62 0.49 0.51 0.34 0.37
Corg 1.00 0.95 0.56 0.26 0.80 0.65 0.83 0.73 0.74 0.61 0.60 N 1.00 0.42 0.22 0.79 0.54 0.70 0.59 0.64 0.49 0.42 S 1.00 0.26 0.54 0.83 0.82 0.87 0.66 0.85 0.91 As 1.00 0.46 0.32 0.30 0.27 0.37 0.18 0.28 Pb 1.00 0.79 0.82 0.76 0.87 0.72 0.65 Cd 1.00 0.91 0.92 0.83 0.92 0.89 Cu 1.00 0.96 0.85 0.90 0.89 Zn 1.00 0.87 0.96 0.94
PAHEPA 1.00 0.84 0.78
PCBEPA 1.00 0.93 MOH C10‐C40 1.00
A 19: Spearman correlation matrix of soil properties and pollutants of pond sediment data within sub catchment 2.
Sub catchment 2 Clay Corg N S As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 Clay 1.00 0.10 ‐0.10 ‐0.01 ‐0.24 ‐0.14 ‐0.03 ‐0.19 ‐0.06 ‐0.07 ‐0.22 ‐0.07
Corg 1.00 0.68 0.22 0.70 0.82 0.58 0.53 0.23 0.30 0.34 0.09 N 1.00 0.36 0.70 0.60 0.49 0.46 0.33 0.07 0.23 0.15 S 1.00 0.18 0.29 0.67 0.73 0.86 0.45 0.84 0.78 As 1.00 0.74 0.42 0.42 0.17 0.14 0.17 ‐0.14 Pb 1.00 0.75 0.73 0.40 0.53 0.47 0.15 Cd 1.00 0.92 0.79 0.69 0.77 0.63 Cu 1.00 0.84 0.76 0.93 0.72 Zn 1.00 0.67 0.89 0.94
PAHEPA 1.00 0.82 0.69
PCBEPA 1.00 0.86 MOH C10‐C40 1.00
156 Appendix
A 20: Spearman correlation matrix of soil properties and pollutants of pond sediment data within sub catchment 3.
Sub catchment 3 Clay Corg N S As Pb Cd Cu Zn PAHEPA PCBEPA MOH C10‐C40 Clay 1.00 0.52 0.55 ‐0.04 0.05 0.21 0.30 0.29 0.21 0.09 0.04 0.18
Corg 1.00 0.95 ‐0.03 0.27 0.70 0.44 0.54 0.41 0.57 ‐0.09 0.19 N 1.00 0.06 0.21 0.69 0.50 0.63 0.49 0.58 ‐0.02 0.28 S 1.00 0.19 0.17 0.41 0.54 0.68 0.25 0.51 0.88 As 1.00 0.47 0.20 0.29 0.31 0.43 0.22 0.20 Pb 1.00 0.77 0.78 0.70 0.73 0.49 0.42 Cd 1.00 0.86 0.82 0.59 0.61 0.69 Cu 1.00 0.96 0.73 0.75 0.79 Zn 1.00 0.69 0.82 0.88
PAHEPA 1.00 0.62 0.48
PCBEPA 1.00 0.77 MOH C10‐C40 1.00
A3 Soil water
A 21: Spearman correlation matrix of groundwater level, water content, water storage capacity and river water level for the time series data of plot F1.
Plot F1 GWL WC WSC RWL GWL 1.00 ‐0.87 0.87 ‐0.74 WC 1.00 ‐1.00 0.59 WSC 1.00 ‐0.59 RWL 1.00
A 22: Spearman correlation mnatrix of groundwater level, water content, water storage capacity and river water level for the time series data of plot F2.
Plot F2 GWL WC WSC RWL GWL 1.00 ‐0.81 0.80 ‐0.59 WC 1.00 ‐1.00 0.14 WSC 1.00 ‐0.17 RWL 1.00
A 23: Spearman correlation matrix of groundwater level, water content, water storage capacity and river water level for the time series data of plot F3.
Plot F3 GWL WC WSC RWL GWL 1.00 ‐0.61 0.61 ‐0.06 WC 1.00 ‐1.00 0.09 WSC 1.00 ‐0.09 RWL 1.00
Appendix 157
A 24: Spearman correlation matrix of groundwater level, water content, water storage capacity and river water level for the time series data of plot F5.
Plot F5 GWL WC WSC RWL GWL 1.00 ‐0.08 0.08 ‐0.29 WC 1.00 ‐1.00 0.12 WSC 1.00 ‐0.12 RWL 1.00
A 25: Spearman correlation matrix of groundwater level, water content, water storage capacity and river water level for the time series data of plot F6.
Plot F6 GWL WC WSC RWL GWL 1.00 ‐0.08 0.08 ‐0.40 WC 1.00 ‐1.00 0.10 WSC 1.00 ‐0.10 RWL 1.00
A 26: Spearman correlation matrix of groundwater level, water content, water storage capacity and river water level for the time series data of plot F7.
Plot F7 GWL WC WSC RWL GWL 1.00 ‐0.23 0.23 ‐0.59 WC 1.00 ‐1.00 0.54 WSC 1.00 ‐0.59 RWL 1.00
A 27: Results of ANOVA followed by a Tuckey‐HSD Posthoc Test (significance level of p < 0.05) of all water contents and groundwater levels categorized in different land uses (fallow, floodplain, forest, grassland and settlement), soil types (sand, loam and organic horizon) and degree of urbanity (natural and urban).
Water content Groundwater level DF F p DF F p Land use 4 0.60 0.70 4 1.83 0.38 Soil type 2 2.877 0.20 2 2.52 0.23 Urbanity 1 0.66 0.47 1 0.08 0.79
158 Appendix
A 28: Comparison of rain, river water level, groundwater level and amount of water storage capacity for a flood event in December 2016 for plot F2.The time‐delays of river water level maxima and groundwater level maxima starting from the rain maxima are indicates as red numbers in the graphs. The left bar illustrates calculations of the amounts of soil water before flood event in December 2016 (grey), water rise due to rain (blue), water rise due to groundwater level increase or flooding (turquoise) and the remaining soil air after the flood event (white).
A 29: Comparison of rain, river water level, groundwater level and amount of water storage capacity for a flood event in December 2016 for plot F5.The time‐delays of river water level maxima and groundwater level maxima starting from the rain maxima are indicates as red numbers in the graphs. The left bar illustrates calculations of the amounts of soil water before flood event in December 2016 (grey), water rise due to rain (blue), water rise due to groundwater level increase or flooding (turquoise) and the remaining soil air after the flood event (white).
Appendix 159
A 30: Comparison of rain, river water level, groundwater level and amount of water storage capacity for a flood event in December 2016 for plot F6.The time‐delays of river water level maxima and groundwater level maxima starting from the rain maxima are indicates as red numbers in the graphs. The left bar illustrates calculations of the amounts of soil water before flood event in December 2016 (grey), water rise due to rain (blue), water rise due to groundwater level increase or flooding (turquoise) and the remaining soil air after the flood event (white).
A 31: Comparison of rain, river water level, groundwater level and amount of water storage capacity for a flood event in June 2017 for plot F2.The time‐delays of river water level maxima and groundwater level maxima starting from the rain maxima are indicates as red numbers in the graphs. The left bar illustrates calculations of the amounts of soil water before flood event in June 2017 (grey), water rise due to rain (blue), water rise due to groundwater level increase or flooding (turquoise) and the remaining soil air after the flood event (white).
160 Appendix
A 32: Comparison of rain, river water level, groundwater level and amount of water storage capacity for a flood event in June 2017 for plot F5.The time‐delays of river water level maxima and groundwater level maxima starting from the rain maxima are indicates as red numbers in the graphs. The left bar illustrates calculations of the amounts of soil water before flood event in June 2017 (grey), water rise due to rain (blue), water rise due to groundwater level increase or flooding (turquoise) and the remaining soil air after the flood event (white).
A 33: Comparison of rain, river water level, groundwater level and amount of water storage capacity for a flood event in June 2017 for plot F6.The time‐delays of river water level maxima and groundwater level maxima starting from the rain maxima are indicates as red numbers in the graphs. The left bar illustrates calculations of the amounts of soil water before flood event in June 2017 (grey), water rise due to rain (blue), water rise due to groundwater level increase or flooding (turquoise) and the remaining soil air after the flood event (white).