Abstract
We analysed in the work how change in land use/land cover influences on flood characteristics (frequency and magnitude) using a model inter-comparison approach, statistical methods and two land use scenarios (land use scenario A and land use scenario B) for three time horizons. The derived land use maps from these scenarios were considered as forcing inputs to two physically based hydrological models (SWAT and WaSiM). The generalized Pareto distribution combined with the Poisson distribution was used to compute flood frequency and magnitude. Under land use scenario A, croplands increase at the annual rate of 0.7% while under land use scenario B, it increases by 1.13% between 2003 and 2029. The expansion of croplands indubitably enhances flood risks. Although there was a general agreement about the sense of the variation, the magnitude of change in flood characteristics was highly influenced by the model type. The rate of increase in flood quantiles simulated from SWAT (0.36–1.3% for 10-year flood) was smaller than the corresponding magnitude of changes simulated from WaSiM (2.6–7.0% for 10-year flood) whatever the scenarios. The expansion of agricultural and pasture lands at the yearly rate of 0.7% under land use scenario A (respectively, 1.13% under land use scenario B) leads to an increase of 3.6% (respectively, 5.4%) in 10-year flood by considering WaSiM. This study is among the first of its kind to establish a strong statistical relation between flood severity/frequency and agricultural land expansion and natural vegetation reduction. The results of this study are relevant and useful to the scientific research community as well as the decision makers for framing appropriate policy decisions towards the management of extreme events and the land use planning/management in future in the region.
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References
Alila Y, Kura PK, Schnorbus M, Hudson R (2009) Forests and floods: a new paradigm sheds light on age-old controversies. Water Resour Res 45:1–24. https://doi.org/10.1029/2008WR007207
Arnell NW (1999) The effect of climate change on hydrological regimes in Europe: a continental perspective. Glob Environ Change 9:5–23
Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development. J Am Water Resour As 34:73–89
Beguería S (2005) Uncertainties in partial duration series modelling of extremes related to the choice of the threshold value. J Hydrol 303:215–230. https://doi.org/10.1016/j.jhydrol.2004.07.015
Beven K (2012) Rainfall-runoff modelling, the primer, 2nd edn. Wiley, Oxford, p 472
Beven K, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J Hydrol 249:11–29. https://doi.org/10.1016/S0022-1694(01)00421-8
Bormann H, Breuer L, Gra T (2009) Assessing the impact of land use change on hydrology by ensemble modelling: IV. Model sensitivity to data aggregation and spatial (re-) distribution. Adv Water Resour 32:171–192. https://doi.org/10.1016/j.advwatres.2008.01.002
Bossa YA (2012) Multi-scale modeling of sediment and nutrient flow dynamics in the Ouémé catchment (Benin)–towards an assessment of global change effects on soil degradation and water quality. PhD thesis. University of Bonn
Bossa A, Diekkrüger B, Agbossou E (2014) Scenario-based impacts of land use and climate change on land and water degradation from the meso to regional scale. Water 6:3152–3181. https://doi.org/10.3390/w6103152
Bronstert A, Niehoff D, Gerd B (2002) Effects of climate and land-use change on storm runoff generation: present knowledge and modelling capabilities. Hydrol Process 529:509–529. https://doi.org/10.1002/hyp.326
Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, New York, p 570
Cornelissen T, Diekkrüger B, Giertz S (2013) A comparison of hydrological models for assessing the impact of land use and climate change on discharge in a tropical catchment. J Hydrol. https://doi.org/10.1016/j.jhydrol.2013.06.016
Costa MH, Botta A, Cardille JA (2003) Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. J Hydrol 283:206–217. https://doi.org/10.1016/S0022-1694(03)00267-1
Crochet P (2012) Flood-duration-frequency modeling application to ten catchments in Northern Iceland. Report. http://www.vedur.is/media/2012_006.pdf. Accessed 10 Nov 2016
Crooks S, Davies H (2001) Assessment of land use change in the Thames catchment and its effect on the flood regime of the river. Phys Chem Earth Part B Hydrol Ocean Atmos 26:583–591. https://doi.org/10.1016/S1464-1909(01)00053-3
Cullmann J, Mishra V, Peters R (2006) Flow analysis with WaSiM-ETH—model parameter sensitivity at different scales. Adv Geosci 9:73–77
D’Orgeval T (2006) Impact du changement climatique sur le cycle de l’eau en Afrique de l’Ouest: Modélisation et incertitudes, Ph.D. thesis. Universite Paris 6
De Roo A, Odijk M, Schmuck G et al (2001) Assessing the effects of land use changes on floods in the Meuse and Oder catchment. Phys Chem Earth 26:593–599
De Roo A, Schmuck G, Perdigao V, Thielen J (2003) The influence of historic land use changes and future planned land use scenarios on floods in the Oder catchment. Phys Chem Earth 28:1291–1300. https://doi.org/10.1016/j.pce.2003.09.005
Diekkrüger B, Söndgerath D, Kersebaum KC, McVoy CW (1995) Validity of agroecosystem models. A comparison of results of different models applied to the same data set. Ecol Modell 81:3–29. https://doi.org/10.1016/0304-3800(94)00157-D
Farley KA, Jobbagy EG, Jackson RB (2005) Effects of afforestation on water yield: a global synthesis with implications for policy. Glob Change Biol 11:1565–1576. https://doi.org/10.1111/j.1365-2486.2005.01011.x
Götzinger J (2007) Distributed conceptual hydrological modelling—simulation of climate, land use change impact and uncertainty analysis, Ph.D. thesis. University of Stuttgart
Guillemette F, Plamondon AP, Prévost M, Lévesque D (2005) Rainfall generated stormflow response to clearcutting a boreal forest: peak flow comparison with 50 world-wide basin studies. J Hydrol 302:137–153. https://doi.org/10.1016/j.jhydrol.2004.06.043
Guo H, Hu Q, Jiang T (2008) Annual and seasonal streamflow responses to climate and land-cover changes in the Poyang Lake basin. J Hydrol 355:106–122. https://doi.org/10.1016/j.jhydrol.2008.03.020
Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J Hydrol 377:80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
Herbst M, Casper MC, Grundmann J, Buchholz O (2009) Comparative analysis of model behaviour for flood prediction purposes using Self-Organizing Maps. Nat Hazards Earth Syst Sci 9:373–392. https://doi.org/10.5194/nhess-9-373-2009
Hounkpè J (2016) Assessing the climate and land use changes impact on flood hazard in Ouémé River Basin, Benin (West Africa), Ph.D. thesis. University of Abomey Calavi
Huisman JA, Breuer L, Bormann H et al (2009) Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) III: scenario analysis. Adv Water Resour 32:159–170. https://doi.org/10.1016/j.advwatres.2008.06.009
Igué AM, Houndagba CJ, Gaiser T, Stahr K (2006) Land use/cover map and its accuracy in the Oueme Basin of Benin (West Africa). In: Conference on international agricultural research for development land. University of Bonn, Bonn, Germany, p 1:4
IPCC (2001) Climate change, 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 881
Jarvis A, Reuter HI, Nelson A, Guevara E (2008) Hole-filled seamless SRTM data V4. International Centre for Tropical Agriculture (CIAT). www.cgiar-csi.org/data/srtm-90m-digital-elevation-database-v4-1. Accessed 4 May 2013
Jasper K, Gurtz J, Lang H (2002) Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model. J Hydrol 267:40–52. https://doi.org/10.1016/S0022-1694(02)00138-5
Jothityangkoon C, Hirunteeyakul C, Boonrawd K, Sivapalan M (2013) Assessing the impact of climate and land use changes on extreme floods in a large tropical catchment. J Hydrol 490:88–105. https://doi.org/10.1016/j.jhydrol.2013.03.036
Kasei RA (2009) Modelling impacts of climate change on water resources in the Volta Basin, West Africa, Ph.D. thesis. University of Bonn
Kharel G, Zheng H, Kirilenko A (2016) Can land-use change mitigate long-term flood risks in the Prairie Pothole Region? The case of Devils Lake, North Dakota, USA. Reg Environ Change 16:1–14. https://doi.org/10.1007/s10113-016-0970-y
Kunstmann H, Marx A, Werhahn J, Smiatek G (2006) Early flood warning for alpine catchments through coupled precipitation/river runoff—forecasts. http://www.univie.ac.at/IMG-Wien/meetings/map_d-phase/abstracts/20-floodwarn-marx.pdf. Accessed 4 May 2015
La Marche JL, Lettenmaier DP (2001) Effects of forest roads on flood flows in the Deschutes River, Washington. Earth Surf Process Landforms 26:115–134. https://doi.org/10.1002/1096-9837(200102)26:22005115:AID-ESP166%3e3.0.CO;2-O
Li Z, Liu W, Zhang X, Zheng F (2009) Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. J Hydrol 377:35–42. https://doi.org/10.1016/j.jhydrol.2009.08.007
Mahé G, Paturel J, Servat E et al (2005) The impact of land use change on soil water holding capacity and river flow modelling in the Nakambe River, Burkina-Faso. J Hydrol 300:33–43. https://doi.org/10.1016/j.jhydrol.2004.04.028
McEnery J, Ingram J, Duan Q et al (2005) NOAA’s advanced hydrologic prediction service: building pathways for better science in water forecasting. Bull Am Meteorol Soc 86:375–385. https://doi.org/10.1175/BAMS-86-3-375
McKay MD, Beckman RJ, Conover WJ (2000) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 42:55–61
Mendizabal M, Sepulveda J, Torp P (2014) Climate change impacts on flood events and its consequences on human in Deba River. Int J Environ Res 8:221–230
Menz G, Judex M, Orékan V et al (2010) Land use and land cover modeling in Central Benin. In: Speth P, Christoph M, Diekkrüger B (eds) Impacts of global change on the hydrological cycle in West and Northwest Africa. Springer, Berlin, pp 512–535
Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Am Soc Agric Biol Eng 50:885–900
Orekan V (2007) Implementation of the local land-use and land-cover change model CLUE-s for Central Benin by using socio-economic and remote sensing data, Ph.D. thesis. http://hss.ulb.uni-bonn.de/2007/1084/1084.htm. Accessed 3 May 2015
Refsgaard JC, Knudsen J (1996) Operational validation and intercomparison of different types of hydrological models. Water Resour Res 32:2189–2202. https://doi.org/10.1029/96WR00896
Rice R (1981) A perspective on the cumulative effects of logging on streamflow and sedimentation. In: Cumulative effects of forest management on Californian watersheds. US Department of Agriculture, Forest Service Pacific Southwest Forest and Range Experiment Station, pp 36–46
Richards LA (1931) Capillary conduction of liquids through porous mediums. Physics (College Park Md) 1:318–333
RIVERTWIN (2007) Regional model for integrated water management in Twinned River Basins. Adapted and integrated model for the Ouémé River Basin, Institute for Landscape Planning and Ecology: Stuttgart, Germany, Final Report. http://cordis.europa.eu/publication/rcn/11810_de.html. Accessed 3 Jun 2015
Rohstoffe G, Hannover D (1993) Bewertung von Pedotransferfunktionen zur Schatzung der Wasserspannungskurve. Zeitschrift für Pflanzenernährung und Bodenkunde 455:447–455
Schulla J (2012) Model description WaSiM. Zürich, Switzerland. www.wasim.ch. Accessed 3 Feb 2016
Seidou O, Ramsay A, Nistor I (2012a) Climate change impacts on extreme floods II: improving flood future peaks simulation using non-stationary frequency analysis. Nat Hazards 60:715–726. https://doi.org/10.1007/s11069-011-0047-7
Seidou O, Ramsay A, Nistor I (2012b) Climate change impacts on extreme floods I: combining imperfect deterministic simulations and non-stationary frequency analysis. Nat Hazards 61:647–659. https://doi.org/10.1007/s11069-011-0052-x
Sintondji LOC (2005) Modelling the rainfall-runoff process in the Upper Ouémé catchment (Terou in Bénin Republic) in a context of global change : extrapolation from the local to the regional scale, Ph.D. thesis. University of Bonn
Teng J, Chiew FHS, Timbal B et al (2012) Assessment of an analogue downscaling method for modelling climate change impacts on runoff. J Hydrol 472–473:111–125. https://doi.org/10.1016/j.jhydrol.2012.09.024
Thanapakpawin P, Richey J, Thomas D et al (2006) Effects of landuse change on the hydrologic regime of the Mae Chaem river basin, NW Thailand. J Hydrol 334:215–230. https://doi.org/10.1016/j.jhydrol.2006.10.012
Vaze J, Teng J (2011) Future climate and runoff projections across New South Wales, Australia: results and practical applications. Hydrol Process 25:18–35. https://doi.org/10.1002/hyp.7812
Verry ES, Lewis JR, Brooks KN (1983) Aspen clearcutting increases snowmelt and storm flow peaks in north central Minnesota. Water Resour Bull 19:59–67
Vertessy Ra, Zhang L, Dawes WR (2002) Plantations, river flows and river salinity. Prospect Aust For Plant 2002(66):55–61. https://doi.org/10.1080/00049158.2003.10674890
Viney NR, Bormann H, Breuer L et al (2009) Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: ensemble combinations and predictions. Adv Water Resour 32:147–158. https://doi.org/10.1016/j.advwatres.2008.05.006
Vrugt JA, Gupta HV, Bouten W, Sorooshian S (2003) A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour Res 39:1201. https://doi.org/10.1029/2002WR001642
Wagner S (2008) Water Balance in a Poorly Gauged Basin in West Africa Using Atmospheric Modelling and Remote Sensing Information. Institut für Wasserbau der, Universität Stuttgart. https://elib.unistuttgart.de/bitstream/11682/301/1/wagner_173_online_UB.pdf. Accessed 13 Oct 2016
Willems P (2014) WETSPRO: water engineering time series processing tool. http://www.kuleuven.be/hydr/pwtools.htm. Accessed 16 Jul 2015
Yira Y, Diekkrüger B, Steup G, Bossa aY (2016) Modeling land use change impacts on water resources in a tropical West African catchment (Dano, Burkina Faso). J Hydrol 537:187–199. https://doi.org/10.1016/j.jhydrol.2016.03.052
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This work was supported by the German Ministry of Education and Research (BMBF) through the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL; www.wascal.org).
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Appendix
Appendix
See Table 7 and Figs. 9 and 10.
Percentage changes in x-year quantile and the corresponding new return periods as computed from WaSiM for the different scenarios. Three time windows have been chosen for the two land use scenarios: 2015–2019 (LU2017A, LU2017B); 2020–2024 (LU2022A, LU2022B); and 2025–2029 (LU2027A, LU2027B)
Percentage changes in x-year quantile and the corresponding new return periods as computed from SWAT for the different scenarios. Three time windows have been chosen for each land use scenario: 2015–2019 (LU2017A, LU2017B); 2020–2024 (LU2022A, LU2022B); and 2025–2029 (LU2027A, LU2027B)
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Hounkpè, J., Diekkrüger, B., Afouda, A.A. et al. Land use change increases flood hazard: a multi-modelling approach to assess change in flood characteristics driven by socio-economic land use change scenarios. Nat Hazards 98, 1021–1050 (2019). https://doi.org/10.1007/s11069-018-3557-8
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DOI: https://doi.org/10.1007/s11069-018-3557-8