A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 2: Scenario-Based Flood Characteristics for the Planned State of Land Use
Abstract
:1. Introduction
- The DSS has to address the actual goals of the decision-makers and stakeholder wishes
- The DSS should provide a user-friendly interface and good visualization capabilities for a real participatory use by the stakeholders and decision-makers
- The stakeholder and decision-makers should have a clear understanding of the model concept and should ideally be able to edit it by themselves e.g., for scenario analysis purposes
- a physical process model of the catchment hydrology and river hydraulics, set up for the current state of land use [22] and
- a GIS-routine calculating the additional runoff for land use change scenarios and its routing through the stream system.
2. Materials and Methods
2.1. Study Area
2.2. Basic Data
2.2.1. Land Use Map
2.2.2. Watercourse Cadastre
2.2.3. Flood Characteristics
2.2.4. Maximum Rainfall Intensities
2.3. Detection of Flood Characteristics for Planned Land Use Changes
2.3.1. Pre-Processing of Functions to Calculate Peak Runoff Coefficients
2.3.2. The Storm Water Routine
2.4. Validation of the Storm Water Routine
3. Results
3.1. Derived Functions for the Determination of Peak Runoff Coefficients
3.2. Validation of the GIS-DSS Storm Water Routine
3.2.1. Comparison of Model Results for Actual and Plan State in SWMM
3.2.2. Comparison of Storm Water Routine Results with Model Results for the Plan State
4. Discussion
- Setup and parametrization of a detailed hydrologic/hydrodynamic model
- Forecasting runoff change induced by land use changes and downstream flood risks applying newly developed GIS routines
- The modeling concept has been developed jointly
- The data for the model are accessible for or provided by the decision-makers and stakeholders
- The tool is designed interactively and embedded in a familiar GIS environment
- Results are processed and visualized for direct use (interactive planning, decision on storm water discharge applications, etc.)
5. Conclusions and Outlook
- -
- Uncertainty of the input data (object data, time series of rainfall and flow) for set-up and calibration/parametrization of the process model for the current state (in the investigated river basins the resulting peak runoff error was 8–26 %)
- -
- Inaccuracies in the simplified calculation of the peak runoff of a newly planned site: 0–32 % (depending on rain scenario)
- -
- Inaccuracies in the propagation of additional peak runoff in the watercourse: 0–1.07 m3s−1 downstream of discharge point and 0.06–1.05 m3s−1 upstream (depending on rain scenario)
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Poelmans, L.; van Rompaey, A.; Batelaan, O. Coupling urban expansion models and hydrological models: How important are spatial patterns? Land Use Policy 2010, 27, 965–975. [Google Scholar] [CrossRef]
- Park, G.; Park, H. Influence analysis of land use by population growth on urban flood risk using system dynamics using system dynamics. In Environmental Impact IV, Proceedings of the Environmental Impact 2018, Naples, Italy, 20–22 June 2018; Casares, J., Passerini, G., Perillo, G., Eds.; WIT Press: Southampton, UK, 2018; pp. 195–205. [Google Scholar]
- Shi, P.-J.; Yuan, Y.; Zheng, J.; Wang, J.-A.; Ge, Y.; Qiu, G.-Y. The effect of land use/cover change on surface runoff in Shenzhen region, China. CATENA 2007, 69, 31–35. [Google Scholar] [CrossRef]
- Weng, Q. Modeling urban growth effects on surface runoff with the integration of remote sensing and GIS. Environ. Manag. 2001, 28, 737–748. [Google Scholar] [CrossRef]
- Kind, J.; Wouter Botzen, W.J.; Aerts, J.C. Accounting for risk aversion, income distribution and social welfare in cost-benefit analysis for flood risk management. WIREs Clim. Chang. 2017, 8, e446. [Google Scholar] [CrossRef]
- Alexakis, D.D.; Grillakis, M.G.; Koutroulis, A.G.; Agapiou, A.; Themistocleous, K.; Tsanis, I.K.; Michaelides, S.; Pashiardis, S.; Demetriou, C.; Aristeidou, K.; et al. GIS and remote sensing techniques for the assessment of land use change impact on flood hydrology: The case study of Yialias basin in Cyprus. Nat. Hazards Earth Syst. Sci. 2014, 14, 413–426. [Google Scholar] [CrossRef] [Green Version]
- Deguid, K. Here’s What Flood Damage Is Predicted to Cost America by 2051. Available online: https://www.weforum.org/agenda/2021/02/cost-of-flood-damage-to-u-s-homes-will-increase-by-61-in-30-years/ (accessed on 19 July 2021).
- Pitt, M. The Pitt ReviewLearning Lessons from the 2007 Floods. Available online: https://webarchive.nationalarchives.gov.uk/20100812084907/http://archive.cabinetoffice.gov.uk/pittreview/_/media/assets/www.cabinetoffice.gov.uk/flooding_review/pitt_review_full%20pdf.pdf (accessed on 2 February 2021).
- Tsatsaris, A.; Kalogeropoulos, K.; Stathopoulos, N.; Louka, P.; Tsanakas, K.; Tsesmelis, D.E.; Krassanakis, V.; Petropoulos, G.P.; Pappas, V.; Chalkias, C. Geoinformation Technologies in Support of Environmental Hazards Monitoring under Climate Change: An Extensive Review. IJGI 2021, 10, 94. [Google Scholar] [CrossRef]
- Mysiak, J.; Giupponi, C.; Rosato, P. Towards the development of a decision support system for water resource management. Environ. Model. Softw. 2005, 20, 203–214. [Google Scholar] [CrossRef]
- Levy, J.K. Multiple criteria decision making and decision support systems for flood risk management. Stoch. Environ. Res. Risk Assess. 2005, 19, 438–447. [Google Scholar] [CrossRef]
- Wang, L.; Cheng, Q. Design and implementation of a web-based spatial decision support system for flood forecasting and flood risk mapping. In Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007; IEEE: Piscataway, NJ, USA, 2007; pp. 4588–4591, ISBN 978-1-4244-1211-2. [Google Scholar]
- Todini, E. An operational decision support system for flood risk mapping, forecasting and management. Urban Water 1999, 1, 131–143. [Google Scholar] [CrossRef]
- Mahmoud, S.H.; Gan, T.Y. Urbanization and climate change implications in flood risk management: Developing an efficient decision support system for flood susceptibility mapping. Sci. Total Environ. 2018, 636, 152–167. [Google Scholar] [CrossRef] [PubMed]
- Basco-Carrera, L.; Warren, A.; van Beek, E.; Jonoski, A.; Giardino, A. Collaborative modelling or participatory modelling? A framework for water resources management. Environ. Model. Softw. 2017, 91, 95–110. [Google Scholar] [CrossRef]
- Maskrey, S.A.; Mount, N.J.; Thorne, C.R.; Dryden, I. Participatory modelling for stakeholder involvement in the development of flood risk management intervention options. Environ. Model. Softw. 2016, 82, 275–294. [Google Scholar] [CrossRef]
- Luke, A.; Sanders, B.F.; Goodrich, K.A.; Feldman, D.L.; Boudreau, D.; Eguiarte, A.; Serrano, K.; Reyes, A.; Schubert, J.E.; AghaKouchak, A.; et al. Going beyond the flood insurance rate map: Insights from flood hazard map co-production. Nat. Hazards Earth Syst. Sci. 2018, 18, 1097–1120. [Google Scholar] [CrossRef] [Green Version]
- Costabile, P.; Costanzo, C.; de Lorenzo, G.; de Santis, R.; Penna, N.; Macchione, F. Terrestrial and airborne laser scanning and 2-D modelling for 3-D flood hazard maps in urban areas: New opportunities and perspectives. Environ. Model. Softw. 2021, 135, 104889. [Google Scholar] [CrossRef]
- Macchione, F.; Costabile, P.; Costanzo, C.; de Santis, R. Moving to 3-D flood hazard maps for enhancing risk communication. Environ. Model. Softw. 2019, 111, 510–522. [Google Scholar] [CrossRef]
- Sanders, B.F.; Schubert, J.E.; Goodrich, K.A.; Houston, D.; Feldman, D.L.; Basolo, V.; Luke, A.; Boudreau, D.; Karlin, B.; Cheung, W.; et al. Collaborative Modeling With Fine-Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps. Earth’s Future 2020, 7, e2019EF001391. [Google Scholar] [CrossRef] [Green Version]
- Hoffmann, T.; Mehl, D.; Schilling, J.; Chen, S.; Tränckner, J.; Hinz, M.; Bill, R. GIS-basiertes Entscheidungsunterstützungssystem für die prospektive synergistische Planung von Entwicklungsoptionen in Regiopolen am Beispiel des Stadt-Umland-Raums Rostock. gis.Science 2021. Manuscript submitted for publication. [Google Scholar]
- Kachholz, F.; Tränckner, J. A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use. Hydrology 2021, 8, 102. [Google Scholar] [CrossRef]
- Kunze, U. Die Geschichte der Hansestadt. Available online: https://www.rostock.de/kultur/historisches/geschichte-der-hansestadt.html (accessed on 11 May 2021).
- Olaf. Rostock Wächst Langsamer als Erwartet: Die Tatsächliche Bevölkerungsentwicklung Liegt in Rostock Deutlich Unter der Prognose von 2016—Die Grünen Wollen Mehr Grünflächen Erhalten. Available online: https://www.rostock-heute.de/rostock-einwohnerzahl-bevoelkerungsprognose/105402 (accessed on 12 May 2021).
- Wimes—Stadt- und Regionalentwicklung. Bevölkerungsprognose 2030 für den LK Rostock: Endfassung. 2017. Available online: http://213.254.33.168/landkreis/daten_fakten/Bevxlkerungsprognose_2030_LK_Rostock.pdf (accessed on 12 May 2021).
- Chen, S.; Hoffmann, T.G.; Mehl, D. Digitale Gewässerkataster: Grundlage von system- und prozessorientierter Raumanalyse und -planung. RaumPlanung 2021, 211/2-2021, 44–51. [Google Scholar]
- Überflutungsvorsorge—Kommunale Gemeinschaftsaufgabe und verteilte Zuständigkeiten. Wasser Abfall 2017, 34–38.
- Junghänel, T.; Ertel, H.; Deutschländer, T. KOSTRA-DWD-2010R Deutscher Wetterdienst: Bericht zur Revision der Koordinierten Starkregenregionalisierung und -Auswertung des Deutschen Wetterdienstes in der Version 2010; Offenbach am Main. 2017. Available online: https://www.dwd.de/DE/leistungen/kostra_dwd_rasterwerte/download/bericht_revision_kostra_dwd_2010.pdf?__blob=publicationFile&v=6 (accessed on 7 July 2021).
- DWA. Arbeitsblatt DWA-A 118. Hydraulische Bemessung und Nachweis von Entwässerungssystemen; DWA: Hennef, Germany, 2006; ISBN 3939057150. [Google Scholar]
- Rollason, E.; Bracken, L.J.; Hardy, R.J.; Large, A.R.G. Rethinking flood risk communication. Nat. Hazards 2018, 92, 1665–1686. [Google Scholar] [CrossRef] [Green Version]
- Pilla, F.; Gharbia, S.S.; Lyons, R. How do households perceive flood-risk? The impact of flooding on the cost of accommodation in Dublin, Ireland. Sci. Total Environ. 2019, 650, 144–154. [Google Scholar] [CrossRef]
- Sy, B.; Frischknecht, C.; Dao, H.; Consuegra, D.; Giuliani, G. Flood hazard assessment and the role of citizen science. J. Flood Risk Manag. 2019, 12, e12519. [Google Scholar] [CrossRef]
- Voinov, A.; Kolagani, N.; McCall, M.K.; Glynn, P.D.; Kragt, M.E.; Ostermann, F.O.; Pierce, S.A.; Ramu, P. Modelling with stakeholders—Next generation. Environ. Model. Softw. 2016, 77, 196–220. [Google Scholar] [CrossRef]
- Vettermann, F.; Weinzierl, T.; Bill, R. Monitoring Twitter Messages for Spatio-Temporal and Thematic Analysis—Twittermonitor Rostock. gis.Science 2018, 1/2018, 1–9. [Google Scholar]
- Eckart, K.; McPhee, Z.; Bolisetti, T. Multiobjective optimization of low impact development stormwater controls. J. Hydrol. 2018, 562, 564–576. [Google Scholar] [CrossRef]
Duration | Return Period |
---|---|
1 h | 2 a, 100 a |
3 h | 10 a, 25 a, 50 a, 100 a |
6 h | 10 a, 25 a, 50 a, 100 a |
9 h | 10 a, 25 a, 50 a, 100 a |
12 h | 10 a, 25 a, 50 a, 100 a |
Designation | Unit | Declaration | |
---|---|---|---|
Watercourses | Qfull | m3 s−1 | Maximum possible flow at normal flow (water level gradient = bottom gradient) |
Qmax,act | m3 s−1 | Maximum flow | |
Qfree,act | m3 s−1 | Flow rate that would additionally fit into the cross profile at maximum flow rate; value calculated from model results (Figure 2d): Qfree,act = Qfull − Qmax,act | |
Subcatchments | Rmax,act | m3 s−1 | Maximum direct runoff (surface runoff) |
Return Period | ||||||
---|---|---|---|---|---|---|
2a | 10a | 25a | 50a | 100a | ||
Duration | 1 h | y = 1E − 07 × 3 + 1E − 05x2 + 2.1E − 03x | y = 1E − 06x3 − 9E − 05x2 + 6.2E − 03x | |||
3 h | y = 4E − 07x3 + 4E − 05x2 + 2.1E − 03x | y = 7E − 07x3 + 6E − 07x2 +4E − 03x | y = 8E − 07x3 − 1E − 05x2 + 4.8E − 03x | y = 7E − 07x3 − 1E − 05x2 + 5.4E − 03x | ||
6 h | y = −2E − 10x3 + 1.0E − 04x2 + 5E − 08x | y = 3E − 08x3 + 1E − 04x2 + 2.0E − 04x | y = 2E − 07x3 + 7E − 05x2 + 1.2E − 03x | y = 4E − 07x3 + 4E − 05x2 + 2.4E − 03x | ||
9 h | y = 1E − 11x3 + 1.0E − 04x2 + 5E − 08x | y = 1E − 11x3 + 1E − 04x2 + 3E − 08x | y = 8E − 12x3 + 1E − 04x2 + 3E − 08x | y = 2E − 08x3 + 1E − 04x2 + 9E − 05x | ||
12 h | y = −2E − 08x3 + 1.0E − 04x2 − 1.0E − 04x | y = −5E − 12x3 + 1.0E − 04x2 − 3E − 08x | y = −7E − 10x3 + 1.0E − 04x2 − 3E − 06x | y = 7E − 09x3 + 1E − 04x2 + 3E − 05x |
Return Period | ||||||
---|---|---|---|---|---|---|
2 a | 10 a | 25 a | 50 a | 100 a | ||
Duration | 1 h | 0.601 | 0.658 | |||
3 h | 0.972 | 0.945 | 0.929 | 0.918 | ||
6 h | 1.000 | 0.997 | 0.982 | 0.960 | ||
9 h | 1.000 | 1.000 | 1.000 | 0.997 | ||
12 h | 0.996 | 1.000 | 1.000 | 1.000 |
Peak Runoff Simulated with SWMM (m3s−1) | Peak Runoff Calculated with SWR (m3s−1) | Runoff Difference (m3s−1) | Relative Deviation (%) | |
---|---|---|---|---|
1 h–2 a | 0.209 | 0.309 | 0.100 | 32 |
1 h–100 a | 0.820 | 1.169 | 0.349 | 30 |
3 h–10 a | 0.131 | 0.123 | −0.008 | −7 |
3 h–25 a | 0.163 | 0.158 | −0.005 | −3 |
3 h–50 a | 0.191 | 0.192 | 0.001 | 0 |
3 h–100 a | 0.229 | 0.224 | −0.005 | −2 |
6 h–10 a | 0.071 | 0.071 | 0.000 | 0 |
6 h–25 a | 0.083 | 0.087 | 0.004 | 5 |
6 h–50 a | 0.097 | 0.094 | −0.004 | −4 |
6 h–100 a | 0.119 | 0.109 | −0.009 | −9 |
9 h–10 a | 0.051 | 0.051 | 0.000 | 0 |
9 h–25 a | 0.059 | 0.059 | 0.000 | 0 |
9 h–50 a | 0.066 | 0.066 | 0.000 | 0 |
9 h–100 a | 0.076 | 0.078 | 0.002 | 2 |
12 h–10 a | 0.040 | 0.038 | −0.001 | −3 |
12 h–25 a | 0.046 | 0.046 | 0.000 | 0 |
12 h–50 a | 0.052 | 0.052 | 0.000 | 0 |
12 h–100 a | 0.059 | 0.059 | 0.001 | 1 |
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Kachholz, F.; Schilling, J.; Tränckner, J. A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 2: Scenario-Based Flood Characteristics for the Planned State of Land Use. Hydrology 2021, 8, 130. https://doi.org/10.3390/hydrology8030130
Kachholz F, Schilling J, Tränckner J. A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 2: Scenario-Based Flood Characteristics for the Planned State of Land Use. Hydrology. 2021; 8(3):130. https://doi.org/10.3390/hydrology8030130
Chicago/Turabian StyleKachholz, Frauke, Jannik Schilling, and Jens Tränckner. 2021. "A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 2: Scenario-Based Flood Characteristics for the Planned State of Land Use" Hydrology 8, no. 3: 130. https://doi.org/10.3390/hydrology8030130