Use of Soil Infiltration Capacity and Stream Flow Velocity to Estimate Physical Flood Vulnerability under Land-Use Change Scenarios
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Hydrologic and Hydraulic Modeling
2.3. Physical Vulnerability Assessment
2.3.1. Vulnerability Based on Flow Velocity and Curve Number (VFVCN)
2.3.2. Vulnerability Based on Soil Water Storage Variation (VVHu)
2.3.3. Vulnerability and Mapping of Floods Based on Land-Use Patterns (MFLUP)
2.3.4. Vulnerability in Land-Use Change Scenarios Based on Morpho-Edaphological Attributes (VLUCS)
2.3.5. Mapping Physical Vulnerability Levels
2.4. Similarity between Vulnerability Assessment Methods
2.5. Statistical Scaling of Physical Vulnerability
3. Results and Discussion
3.1. Calibration and Validation of Models
3.2. Effect of Land-Use Change on the Flood Regime
3.3. Effect of Land-Use Change on the Physical Vulnerability
3.4. Analysis of Similarity between Methods
3.5. Scaling Physical Vulnerability in a Land-Use Change Scenario
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- WMO. 2018 Annual Report: WMO for the Twenty-First Century, No. 1229. 2018. Available online: https://library.wmo.int/doc_num.php?explnum_id=6264 (accessed on 11 February 2023).
- Erlick, J.C. Natural Disasters in Latin America and the Caribbean; Routledge: London, UK, 2021. [Google Scholar] [CrossRef]
- Bhatt, C.; Rao, G.; Diwakar, P.; Dadhwal, V. Development of flood inundation extent libraries over a range of potential flood levels: A practical framework for quick flood response. Geomat. Nat. Hazards Risk 2016, 8, 384–401. [Google Scholar] [CrossRef] [Green Version]
- Baeck, S.H.; Choi, S.J.; Choi, G.W.; Lee, N.R. A study of evaluating and forecasting watersheds using the flood vulnerability assessment index in Korea. Geomat. Nat. Hazards Risk 2014, 5, 208–231. [Google Scholar] [CrossRef] [Green Version]
- Ye, B.; Jiang, J.; Liu, J.; Zheng, Y.; Zhou, N. Research on quantitative assessment of climate change risk at an urban scale: Review of recent progress and outlook of future direction. Renew. Sustain. Energy Rev. 2021, 135, 110415. [Google Scholar] [CrossRef]
- Yang, Y.-C.; Ge, Y.-E. Adaptation strategies for port infrastructure and facilities under climate change at the Kaohsiung port. Transp. Policy 2020, 97, 232–244. [Google Scholar] [CrossRef]
- Dandapat, K.; Panda, G.K. Flood vulnerability analysis and risk assessment using analytical hierarchy process. Model. Earth Syst. Environ. 2017, 3, 1627–1646. [Google Scholar] [CrossRef]
- Gain, A.K.; Mojtahed, V.; Biscaro, C.; Balbi, S.; Giupponi, C. An integrated approach of flood risk assessment in the eastern part of Dhaka City. Nat. Hazards 2015, 79, 1499–1530. [Google Scholar] [CrossRef] [Green Version]
- Marques, G.F.; de Souza, V.B.; Moraes, N.V. The economic value of the flow regulation environmental service in a Brazilian urban watershed. J. Hydrol. 2017, 554, 406–419. [Google Scholar] [CrossRef]
- Chowdhuri, I.; Pal, S.C.; Chakrabortty, R. Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv. Space Res. 2020, 65, 1466–1489. [Google Scholar] [CrossRef]
- Haque, M.; Islam, S.; Sikder, B.; Islam, S. Community flood resilience assessment in Jamuna floodplain: A case study in Jamalpur District Bangladesh. Int. J. Disaster Risk Reduct. 2022, 72, 102861. [Google Scholar] [CrossRef]
- Fernández-Montblanc, T.; Duo, E.; Ciavola, P. Dune reconstruction and revegetation as a potential measure to decrease coastal erosion and flooding under extreme storm conditions. Ocean Coast. Manag. 2019, 188, 105075. [Google Scholar] [CrossRef]
- Ettinger, S.; Mounaud, L.; Magill, C.; Yao-Lafourcade, A.-F.; Thouret, J.-C.; Manville, V.; Negulescu, C.; Zuccaro, G.; De Gregorio, D.; Nardone, S.; et al. Building vulnerability to hydro-geomorphic hazards: Estimating damage probability from qualitative vulnerability assessment using logistic regression. J. Hydrol. 2016, 541, 563–581. [Google Scholar] [CrossRef]
- Laudan, J.; Rözer, V.; Sieg, T.; Vogel, K.; Thieken, A.H. Damage assessment in Braunsbach 2016: Data collection and analysis for an improved understanding of damaging processes during flash floods. Nat. Hazards Earth Syst. Sci. 2017, 17, 2163–2179. [Google Scholar] [CrossRef] [Green Version]
- Guidolin, M.; Chen, A.S.; Ghimire, B.; Keedwell, E.C.; Djordjević, S.; Savić, D.A. A weighted cellular automata 2D inundation model for rapid flood analysis. Environ. Model. Softw. 2016, 84, 378–394. [Google Scholar] [CrossRef] [Green Version]
- Van Westen, C.J. Remote Sensing and GIS for Natural Hazards Assessment and Disaster Risk Management. In Treatise on Geomorphology; Academic Press: Cambridge, MA, USA, 2013; Volume 3, pp. 259–298. [Google Scholar] [CrossRef]
- Hendrawan, V.S.A.; Komori, D. Developing flood vulnerability curve for rice crop using remote sensing and hydrodynamic modeling. Int. J. Disaster Risk Reduct. 2021, 54, 102058. [Google Scholar] [CrossRef]
- Karagiorgos, K.; Thaler, T.; Hübl, J.; Maris, F.; Fuchs, S. Multi-vulnerability analysis for flash flood risk management. Nat. Hazards 2016, 82, 63–87. [Google Scholar] [CrossRef] [Green Version]
- Bankoff. Mapping Vulnerability: Disasters, Development and People, Earthscan, 1st ed.; Taylor & Francis: London, UK, 2004. [Google Scholar]
- Gabel, F. Chancen dynamischer Konzeptionen von Vulnerabilität für den Katastrophenschutz. In Resilienz im Katastrophenfall Konzepte zur Stärkung von Pflege- und Hilfsbedürftigen im Bevölkerungsschutz; Marco Krüger, Matthias Max—Bielefeld Transcr: Gnoien, Germany, 2019; pp. 77–96. [Google Scholar]
- Malik, S.; Pal, S.C.; Sattar, A.; Singh, S.K.; Das, B.; Chakrabortty, R.; Mohammad, P. Trend of extreme rainfall events using suitable Global Circulation Model to combat the water logging condition in Kolkata Metropolitan Area. Urban Clim. 2020, 32, 100599. [Google Scholar] [CrossRef]
- Blöschl, G. Three hypotheses on changing river flood hazards. Hydrol. Earth Syst. Sci. 2022, 26, 5015–5033. [Google Scholar] [CrossRef]
- Messner, V.; Meyer, F. Flood Damage, Vulnerability and Risk Perception—Challenges for Flood Damage Research; Springer: Berlin/Heidelberg, Germany, 2005. [Google Scholar]
- Liu, J.; Shi, Z.; Wang, D. Measuring and mapping the flood vulnerability based on land-use patterns: A case study of Beijing, China. Nat. Hazards 2016, 83, 1545–1565. [Google Scholar] [CrossRef]
- Wu, F.; Sun, Y.; Sun, Z.; Wu, S.; Zhang, Q. Assessing agricultural system vulnerability to floods: A hybrid approach using emergy and a landscape fragmentation index. Ecol. Indic. 2019, 105, 337–346. [Google Scholar] [CrossRef]
- Caldas, A.M.; Pissarra, T.C.T.; Costa, R.C.A.; Neto, F.C.R.; Zanata, M.; da Parahyba, R.B.V.; Fernandes, L.F.S.; Pacheco, F.A.L. Flood Vulnerability, Environmental Land Use Conflicts, and Conservation of Soil and Water: A Study in the Batatais SP Municipality, Brazil. Water 2018, 10, 1357. [Google Scholar] [CrossRef] [Green Version]
- USDA-SCS. Section 4: Hidrology. In National Engineering Handbook; Soil Conservation Service; United States Department of Agriculture: Washington, DC, USA, 1972; p. 127. [Google Scholar]
- Peña, L.E.; Barrios, M.; Francés, F. Flood quantiles scaling with upper soil hydraulic properties for different land uses at catchment scale. J. Hydrol. 2016, 541, 1258–1272. [Google Scholar] [CrossRef] [Green Version]
- Saxton, K.E.; Rawls, W.J. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions. Soil Sci. Soc. Am. J. 2006, 70, 1569–1578. [Google Scholar] [CrossRef] [Green Version]
- Soil Survey Staff. Soil Taxonomy, 2nd ed.; U.S. Government Printing Office: Washington, DC, USA, 1999. [Google Scholar]
- United States Departament of Agriculture. Keys to Soil Taxonomy; SMSS Technical monograph No. 19; Pocahontas Press, Inc.: Blacksburg, VA, USA, 1992. [Google Scholar]
- Francés, F.; Vélez, J.I.; Vélez, J.J. Split-parameter structure for the automatic calibration of distributed hydrological models. J. Hydrol. 2007, 332, 226–240. [Google Scholar] [CrossRef]
- Medici, C.; Butturini, A.; Bernal, S.; Vázquez, E.; Sabater, F.; Vélez, J.I.; Francés, F. Modelling the non-linear hydrological behaviour of a small Mediterranean forested catchment. Hydrol. Process. 2008, 22, 3814–3828. [Google Scholar] [CrossRef]
- Salazar, S.; Francés, F.; Komma, J.; Blume, T.; Francke, T.; Bronstert, A.; Bloschl, G. A comparative analysis of the effectiveness of flood management measures based on the concept of “retaining water in the landscape” in different European hydro-climatic regions. Nat. Hazards Earth Syst. Sci. 2012, 12, 3287–3306. [Google Scholar] [CrossRef] [Green Version]
- Francésa, F.; Bussib, G. Análisis del impacto del cambio climático en el ciclo de sedimentos de la cuenca del río Ésera (España) mediante un modelo hidrológico distribuido. Rev. Iberoam. Ribagua 2014, 1, 14–25. [Google Scholar] [CrossRef] [Green Version]
- Siswanto, S.Y.; Francés, F. How land use/land cover changes can affect water, flooding and sedimentation in a tropical watershed: A case study using distributed modeling in the Upper Citarum watershed, Indonesia. Environ. Earth Sci. 2019, 78, 550. [Google Scholar] [CrossRef]
- Teng, J.; Jakeman, A.J.; Vaze, J.; Croke, B.F.W.; Dutta, D.; Kim, S. Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environ. Model. Softw. 2017, 90, 201–216. [Google Scholar] [CrossRef]
- Bozzi, S.; Passoni, G.; Bernardara, P.; Goutal, N.; Arnaud, A. Roughness and Discharge Uncertainty in 1D Water Level Calculations. Environ. Model. Assess. 2014, 20, 343–353. [Google Scholar] [CrossRef]
- Liu, J.; Shi, Z.; Tan, X. Measuring the dynamic evolution of road network vulnerability to floods: A case study of Wuhan, China. Travel Behav. Soc. 2020, 23, 13–24. [Google Scholar] [CrossRef]
- Ologunorisa, T.E. An assessment of flood vulnerability zones in the Niger delta, Nigeria. Int. J. Environ. Stud. 2004, 61, 31–38. [Google Scholar] [CrossRef]
- Sokal, R.R.; Michener, C.D. A statistical method for evaluating systematic relationships. Univ. Kansas Sci. Bull. 1958, 38, 1409–1438. [Google Scholar]
- Huang, L.; Wang, G.; Wang, Y.; Blanzieri, E.; Su, C. Link Clustering with Extended Link Similarity and EQ Evaluation Division. PLoS ONE 2013, 8, e66005. [Google Scholar] [CrossRef] [PubMed]
- Xu, H.; Ma, C.; Lian, J.; Xu, K.; Chaima, E. Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China. J. Hydrol. 2018, 563, 975–986. [Google Scholar] [CrossRef]
- Burlando, P.; Rosso, R. Scaling and muitiscaling models of depth-duration-frequency curves for storm precipitation. J. Hydrol. 1996, 187, 45–64. [Google Scholar] [CrossRef]
- Moriasi, D.N.; Arnold, J.G.; van Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 2007, 50, 885–900. [Google Scholar] [CrossRef]
- Kundu, S.; Khare, D.; Mondal, A. Individual and combined impacts of future climate and land use changes on the water balance. Ecol. Eng. 2017, 105, 42–57. [Google Scholar] [CrossRef]
- Marshall, M.R.; Ballard, C.E.; Frogbrook, Z.L.; Solloway, I.; McIntyre, N.; Reynolds, B.; Wheater, H.S. The impact of rural land management changes on soil hydraulic properties and runoff processes: Results from experimental plots in upland UK. Hydrol. Process. 2014, 28, 2617–2629. [Google Scholar] [CrossRef]
- GEOTEC. Estudio de Amenazas Naturales, Vulnerabilidad y Escenarios de Riesgo en los Centros Poblados de Villarestrepo, Llanitos, Juntas, Pastales, Pico de Oro, Bocatoma Combeima y Cay, por Flujos Torrenciales en las Microcuencas del Río Combeima; Geotec Group—Alcaldía de Ibagué—Cortolima: Ibagué, Colombia, 2007. [Google Scholar]
- Alaoui, A.; Rogger, M.; Peth, S.; Blöschl, G. Does soil compaction increase floods? A review. J. Hydrol. 2018, 557, 631–642. [Google Scholar] [CrossRef]
- Odunuga, S.; Adegun, O.; Raji, S.A.; Udofia, S. Changes in flood risk in Lower Niger–Benue catchments. Proc. Int. Assoc. Hydrol. Sci. 2015, 370, 97–102. [Google Scholar] [CrossRef] [Green Version]
- Jobe, A.; Kalra, A.; Ibendahl, E. Conservation Reserve Program effects on floodplain land cover management. J. Environ. Manag. 2018, 214, 305–314. [Google Scholar] [CrossRef] [PubMed]
- Horton, A.J.; Nygren, A.; Diaz-Perera, M.A.; Kummu, M. Flood severity along the Usumacinta River, Mexico: Identifying the anthropogenic signature of tropical forest conversion. J. Hydrol. X 2020, 10, 100072. [Google Scholar] [CrossRef]
- Andréassian, V. Waters and forests: From historical controversy to scientific debate. J. Hydrol. 2004, 291, 1–27. [Google Scholar] [CrossRef]
- Tanir, T.; Sumi, S.J.; Lima, A.D.S.D.; Coelho, G.D.A.; Uzun, S.; Cassalho, F.; Ferreira, C.M. Multi-scale comparison of urban socio-economic vulnerability in the Washington, DC metropolitan region resulting from compound flooding. Int. J. Disaster Risk Reduct. 2021, 61, 102362. [Google Scholar] [CrossRef]
- Czech, W.; Radecki-Pawlik, A.; Wyżga, B.; Hajdukiewicz, H. Modelling the flooding capacity of a Polish Carpathian river: A comparison of constrained and free channel conditions. Geomorphology 2016, 272, 32–42. [Google Scholar] [CrossRef]
- McEachran, Z.P.; Karwan, D.L.; Sebestyen, S.D.; Slesak, R.A.; Ng, G.-H.C. Nonstationary flood-frequency analysis to assess effects of harvest and cover type conversion on peak flows at the Marcell Experimental Forest, Minnesota, USA. J. Hydrol. 2021, 596, 126054. [Google Scholar] [CrossRef]
- Zhao, L.; Liu, F. Land-use planning adaptation in response to SLR based on a vulnerability analysis. Ocean Coast. Manag. 2020, 196, 105297. [Google Scholar] [CrossRef]
- Rahman, M.; Ningsheng, C.; Mahmud, G.I.; Islam, M.; Pourghasemi, H.R.; Ahmad, H.; Habumugisha, J.M.; Washakh, R.M.A.; Alam, M.; Liu, E.; et al. Flooding and its relationship with land cover change, population growth, and road density. Geosci. Front. 2021, 12, 101224. [Google Scholar] [CrossRef]
Soil Unit * | Forest | Grassland | Crop | ||||||
---|---|---|---|---|---|---|---|---|---|
(mm) | (mm h−1) | (mm h−1) | (mm) | (mm h−1) | (mm h−1) | (mm) | (mm h−1) | (mm h−1) | |
MKB | 102.6 | 75.7 | 68.2 | 10.3 | 7.6 | 6.8 | 24.6 | 18.2 | 16.4 |
MKG | 153.2 | 58.2 | 57.9 | 22.8 | 8.8 | 8.7 | 54.6 | 21.0 | 20.8 |
MQC | 149.3 | 28.0 | 28.6 | 11.6 | 2.0 | 2.0 | 27.9 | 4.8 | 4.8 |
MQD | 140.1 | 177.7 | 114.7 | 11.9 | 14.5 | 9.4 | 28.6 | 34.9 | 22.5 |
MQE | 194.9 | 22.8 | 22.2 | 18.0 | 2.1 | 2.1 | 43.3 | 5.1 | 4.9 |
MDA | 100.8 | 105.8 | 80.9 | 8.5 | 8.9 | 6.8 | 20.4 | 21.4 | 16.3 |
MGA | 54.9 | 236.7 | 114.5 | 54.9 | 236.7 | 114.5 | 54.9 | 236.7 | 114.5 |
MGB | 133.3 | 576.5 | 104.6 | 4.5 | 21.5 | 10.5 | 10.9 | 51.5 | 25.2 |
MGC | 93.2 | 119.4 | 78.0 | 23.0 | 99.4 | 18.0 | 55.2 | 238.6 | 43.3 |
MWD | 0.0 | 3011.7 | 3008.6 | 0.0 | 3011.7 | 3008.6 | 0.0 | 3011.7 | 3008.6 |
PWD | 117.7 | 14.9 | 14.1 | 9.0 | 1.1 | 1.0 | 21.6 | 2.7 | 2.5 |
PWL | 112.6 | 203.0 | 53.6 | 5.6 | 10.6 | 2.7 | 13.5 | 24.4 | 6.4 |
MQO | 126.5 | 29.7 | 20.9 | 19.8 | 4.6 | 3.3 | 47.4 | 11.1 | 7.8 |
MKI | 40.4 | 454.4 | 127.4 | 3.2 | 36.1 | 10.1 | 7.7 | 86.5 | 24.3 |
MQH | 130.8 | 12.4 | 11.6 | 21.4 | 2.0 | 1.9 | 51.3 | 4.9 | 4.6 |
MQJ | 129.6 | 9.9 | 7.9 | 13.9 | 1.1 | 851.6 | 33.4 | 2.5 | 2.0 |
MWA | 0.0 | 10.6 | 10.0 | 0.0 | 1.9 | 1.8 | 0.0 | 4.5 | 4.3 |
MWC | 0.0 | 14.4 | 13.9 | 0.0 | 2.9 | 2.8 | 0.0 | 6.9 | 6.6 |
MWJ | 123.1 | 20.1 | 12.1 | 14.3 | 2.3 | 1.4 | 34.3 | 5.6 | 3.4 |
PWH | 50.5 | 186.9 | 155.6 | 3.4 | 12.6 | 10.5 | 8.2 | 30.3 | 25.2 |
Year | Cross-Section Number | Velocity (m s−1) | Water Depth (m) | Qmax (m3 s−1) | (mm) | (mm h−1) | |
---|---|---|---|---|---|---|---|
1976 | 53 | 3.19 | 1.35 | 41.12 | 60.73 | 15.52 | 58 |
52 | 1.48 | 2.94 | 41.12 | 82.99 | 14.34 | 58 | |
51 | 3.49 | 2.20 | 41.12 | 126.50 | 20.86 | 58 | |
49 | 3.59 | 1.58 | 41.12 | 67.27 | 24.74 | 58 | |
48 | 4.45 | 1.33 | 41.12 | 37.65 | 19.10 | 58 | |
42 | 4.76 | 1.09 | 41.12 | 54.05 | 15.52 | 58 | |
1987 | 53 | 3.57 | 1.61 | 57.56 | 86.68 | 15.14 | 59 |
52 | 1.18 | 3.34 | 57.56 | 81.05 | 14.34 | 59 | |
51 | 3.64 | 2.62 | 57.56 | 126.50 | 14.34 | 59 | |
49 | 3.99 | 1.91 | 57.56 | 107.73 | 15.14 | 59 | |
48 | 4.84 | 1.66 | 57.56 | 137.90 | 11.22 | 59 | |
42 | 5.50 | 1.24 | 57.56 | 80.72 | 15.52 | 58 | |
1991 | 53 | 3.44 | 1.52 | 51.61 | 74.74 | 3.57 | 58 |
52 | 1.69 | 3.21 | 51.61 | 43.29 | 3.91 | 58 | |
51 | 3.64 | 2.45 | 51.61 | 47.40 | 3.91 | 58 | |
49 | 3.86 | 1.79 | 51.61 | 40.90 | 4.20 | 58 | |
48 | 4.75 | 1.54 | 51.61 | 37.65 | 4.20 | 58 | |
42 | 5.18 | 1.20 | 51.61 | 106.02 | 3.15 | 59 | |
2000 | 53 | 3.48 | 1.54 | 53.18 | 74.74 | 12.41 | 59 |
52 | 1.72 | 3.24 | 53.18 | 43.29 | 14.34 | 59 | |
51 | 3.64 | 2.50 | 53.18 | 47.40 | 14.34 | 59 | |
49 | 3.90 | 1.83 | 53.18 | 40.90 | 11.15 | 59 | |
48 | 4.76 | 1.56 | 53.18 | 37.65 | 11.15 | 59 | |
42 | 5.27 | 1.21 | 53.18 | 106.02 | 15.52 | 60 | |
2007 | 53 | 3.30 | 1.43 | 45.84 | 74.74 | 15.52 | 48 |
52 | 1.57 | 3.07 | 45.84 | 43.29 | 7.82 | 48 | |
51 | 3.62 | 2.30 | 45.84 | 47.40 | 7.82 | 48 | |
49 | 3.73 | 1.68 | 45.84 | 40.90 | 6.30 | 48 | |
48 | 4.61 | 1.432 | 45.84 | 37.65 | 6.30 | 48 | |
42 | 4.92 | 1.146 | 45.84 | 106.02 | 6.30 | 47 | |
2017 | 53 | 3.25 | 1.39 | 43.56 | 74.74 | 8.36 | 41 |
52 | 1.53 | 3.01 | 43.56 | 43.29 | 14.34 | 41 | |
51 | 3.56 | 2.25 | 43.56 | 47.40 | 14.34 | 41 | |
49 | 3.67 | 1.63 | 43.56 | 40.90 | 19.10 | 41 | |
48 | 4.56 | 1.34 | 43.56 | 37.65 | 19.10 | 41 | |
42 | 4.87 | 1.34 | 43.56 | 106.02 | 15.52 | 43 |
Cross | Scenario 1976 | Scenario 1987 | Scenario 1991 | Scenario 2000 | Scenario 2007 | Scenario 2017 | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Section | MFLUP | VLUCS | VFVCN | VVHu | MFLUP | VLUCS | VFVCN | VVHu | MFLUP | VLUCS | VFVCN | VVHu | MFLUP | VLUCS | VFVCN | VVHu | MFLUP | VLUCS | VFVCN | VVHu | MFLUP | VLUCS | VFVCN | VVHu |
147 | ||||||||||||||||||||||||
146 | ||||||||||||||||||||||||
145 | ||||||||||||||||||||||||
144 | ||||||||||||||||||||||||
138 | ||||||||||||||||||||||||
121 | ||||||||||||||||||||||||
120 | ||||||||||||||||||||||||
119 | ||||||||||||||||||||||||
117 | ||||||||||||||||||||||||
103 | ||||||||||||||||||||||||
102 | ||||||||||||||||||||||||
95 | ||||||||||||||||||||||||
87 | ||||||||||||||||||||||||
86 | ||||||||||||||||||||||||
53 | ||||||||||||||||||||||||
52 | ||||||||||||||||||||||||
51 | ||||||||||||||||||||||||
49 | ||||||||||||||||||||||||
48 | ||||||||||||||||||||||||
42 | ||||||||||||||||||||||||
16 | ||||||||||||||||||||||||
15 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hernández-Atencia, Y.; Peña, L.E.; Muñoz-Ramos, J.; Rojas, I.; Álvarez, A. Use of Soil Infiltration Capacity and Stream Flow Velocity to Estimate Physical Flood Vulnerability under Land-Use Change Scenarios. Water 2023, 15, 1214. https://doi.org/10.3390/w15061214
Hernández-Atencia Y, Peña LE, Muñoz-Ramos J, Rojas I, Álvarez A. Use of Soil Infiltration Capacity and Stream Flow Velocity to Estimate Physical Flood Vulnerability under Land-Use Change Scenarios. Water. 2023; 15(6):1214. https://doi.org/10.3390/w15061214
Chicago/Turabian StyleHernández-Atencia, Yelena, Luis E. Peña, Jader Muñoz-Ramos, Isabel Rojas, and Alexander Álvarez. 2023. "Use of Soil Infiltration Capacity and Stream Flow Velocity to Estimate Physical Flood Vulnerability under Land-Use Change Scenarios" Water 15, no. 6: 1214. https://doi.org/10.3390/w15061214
APA StyleHernández-Atencia, Y., Peña, L. E., Muñoz-Ramos, J., Rojas, I., & Álvarez, A. (2023). Use of Soil Infiltration Capacity and Stream Flow Velocity to Estimate Physical Flood Vulnerability under Land-Use Change Scenarios. Water, 15(6), 1214. https://doi.org/10.3390/w15061214