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Assessing land use /cover variation effects on flood intensity via hydraulic simulations

Assessing land use /cover variation effects on flood intensity via hydraulic simulations GeoloGy, ecoloGy, and landscapes, 2018 Vol . 2, no . 2, 73–80 https://doi.org/10.1080/24749508.2018.1452461 INWASCON OPEN ACCESS Assessing land use /cover variation effects on flood intensity via hydraulic simulations A case study of Oued El Abid watershed (Morocco) a a b a c Ismail Karaoui , Abdelkrim Arioua , Abdelkhalek El Amrani Idrissi , Mohammed Hssaisoune , Wafae Nouaim , a a kamal Ait ouhamchich and Driss Elhamdouni a b Water Resources Management and Valorization and Remote s ensing Team, sultan Moulay slimane University, Beni Mellal, Morocco; Water Quality d epartment, o um er-biaa Hydraulic Basin a gency, Beni Mellal, Morocco; d epartment of earth s ciences, Ibn Tofail University, Kenitra, Morocco ARTICLE HISTORY ABSTRACT Received 3 s eptember 2017 Despite the semi-arid to arid nature of its climate, Morocco is exposed, like all the Mediterranean a ccepted 20 January 2018 countries to inundations, which can be very damaging for public and private infrastructure, and cause many victims. On Monday 10 August 2015, one of Oued El Abid tributary raised in a KEYWORDS remarkable way, causing five deaths and serious material damage in Iminwargue village (Azilal land use/cover; inundation; province). This study comes to assess the impact of land use/cover variation in the multiplication o ued el abid watershed; of floods frequency via the hydraulic simulations. For this purpose, relying on land use/cover hydraulic simulation; hydrological response variation from 1987 to 2017, flow and precipitation database, we made a hydraulic simulation of the 2015 event and the 100 years recurrence inundation. As results, the land use/cover decrease is always followed by an increase in flows peak even that all recorded precipitations are more or less similar. The case study hundred years inundation prediction shows a 5.03 m increase in the river and all population near banks, agricultural land and other useful infrastructure such as roads and bridges are invaded. These results show that more catastrophic events could be reproduced and oblige the decision makers in Oued El Abid watershed to be aware of the critical situation. 1. Introduction risk management approach (Sevruk,  Ondrás, & Chvíla, 2009). Identifying prone areas can therefore be one of In the last decades many Inundations have occurred in the key solutions in inundation mitigation (Achleitner the world and claiming more than 20,000 lives per year et al., 2016). Predicting susceptible inundation plains and and adversely ae ff cting about 75 million people world- keys factors of its development can help authorities in wide, mostly through homelessness (Sarhadi, Soltani, & planning management strategies for inundation mitiga- Modarres, 2012). This phenomenon have been aggra- tion (Cundill & Fabricius, 2010; Sanders, 2007). Hydro- vated, in several cases, by the land cover use changes meteorological catastrophes cannot be totally evaded, but especially in the intense urbanisation of inundation the impacts and triggering factors can be handled by devel- prone areas and deforestation (Alexander et al., 2006; oping effective risk reduction strategies through applica- Brath, Montanari, & Moretti, 2006; Bronstert, 2003; tion of latest geospatial tools and decision support systems Esteban-Parra, Rodrigo, & Castro-Diez, 1998; Meehl (Khattak et al., 2016). The application of full modelling et al., 2000; Ntegeka & Willems, 2008; Oudin, steps from rainfall-runoff to test different scenarios of land Andréassian, Perrin, Michel, & Le Moine, 2008). use variation is a paramount necessity tool to gain spatial Projected future climate change and extreme weather coherence when estimating losses (Al-Zahrani, Al-Areeq, events are expected to get worse than the actual situation & Sharif, 2016; Booij, 2005; De Bruijn,  Diermanse, & (Pachauri et al., 2014). As the severity and inundations fre- Beckers, 2014; Gems, Achleitner, Huttenlau, Thieken, & quency considerably increase, the human societies realized Aufleger, 2009; Golshan, Jahanshahi, & Afzali, 2016). that the traditional paradigm of protecting themselves are In Morocco, the frequency and intensity of inun- not a sufficient option for the future (Kuks & Kissling- dations in recent decades have grown in a remarkable Näf, 2004; Pahl-Wostl et al., 2013; Smits,  Nienhuis, & way, especially in Oued El Abid watershed, due to heavy Leuven, 2000). Instead, the practitioners agree that inun- rain intensity, land use variation and rugged topography dation management should be based on an integrated characterize by high slope. CONTACT Ismail Karaoui I.karaoui@usms.ma © 2018 The a uthor(s). published by Informa UK limited, trading as Taylor & Francis Group. This is an open a ccess article distributed under the terms of the creative c ommons a ttribution license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 74 I. KARAOUI ET AL. In light of this repetitive catastrophic event, the pres- 3.1. Analysis of the land use/cover variation in ent work aims to propose a methodology for the land Oued El Abid watershed use variation effect on the hydrological response in the In recent decades, considerable changes in land use upper part of Oued EL Abid watershed, by developing a and vegetation cover have occurred in the different case study that refers to Alfet River basin (Oued El Abid regions of Morocco. As a quite general rule, from the tributary). This sub-basin in 10 August 2015 has experi - early 1970s meadows and pastures characterized the enced one of the most violent event in which the water most mountainous region at the time, decreased as level raised by 10 m and caused the death of 5 peoples, deforestation and urbanization that led to the aban- the destruction of 5 houses and the road No. 302 linking donment of cattle rearing. As a result, these moun- the two villages Tillouguite and Aminouak (Figure 1). tainous areas have been left fallow and unproductive. A similar behaviour occurred in the considered basin 2. The study area of Oued El Abid. To quantify the Land use  /cover variation rate in e u Th pper part of Oued El Abid watershed is located in Oued El Abid watershed and its effects on the hydro- the centre of northern part in Morocco, in Khenifra- logical response. Satellite images from the Landsat TM5 Beni Mellal region and Azilal province. It extends over and Oli 8 sensors were used to monitor the annual rate an area of 7686 km . The study area is characterized by evolution since October 1987 until 2017. a semi-arid climate, and concentration of rainfall in In this study, we used satellite images captured in autumn and winter, high evaporation and high average October month of every year to homogenize the land temperatures with high monthly and daily amplitude. use /cover obtained results, to avoid intense cloud rate, e minim Th um temperature is −5 °C recorded in January and get a better results from the vegetation cover. while the maximum is 34.9 °C in August. e c Th ase study (Iminwargue village), is located in a distance of 30 km from Azilal City, in Alfet River (Oued 3.2. Hydrological study El Abid tributary). The Alfet River watershed originates Precipitation and flow data adopted in our study are in 2527 m at Morocco High Atlas and extends over an those of Tillouguite station, located in the south-east of area of 2.04 km in the middle of Oued El Abid water- Oued El Abid watershed. The Tillouguite information is shed (Figure 2). summarized in Table 1. e s Th tatistical adjustment was carried out through 3. Materials and methods HYfran software to predicted precipitation and flow peak in different returns periods (Alzate, Guyumus, Quijano, Our work methodology has been initiated by studying & Díaz-Granados, 2016; Sami, Hadda, & Mahdi, 2016). the land use variation at Oued El Abid watershed during e c Th hosen law should take into consideration a lowest a period of 1987–2017, then the effects of this variation AIC and BIC coefficients and greatest posterior proba- on the hydrological response in the study area water- bility criteria (Banner & Higgs, 2017). courses. e Th obtained results in this analysis are used To estimate the peak flow in ungauged rivers (as Alfet later in a case study of Tillouguite village to indicate River), deterministic and regionalization formulas are the inundation event extension, and then estimating the used. and the following approaches are adopted in this damages that could be caused in the future inundations estimation (D’Ambrosio, De Girolamo, Barca, Ielpo, & events via the hydraulic simulation. Rulli, 2016): e Th topographic maps and last inundations damage statistics were given by the Authority of Tillouguite. • Empirical approach: when there is no rainfall or While hydrological and climatic data provided by hydrometric data or the recorded data are not the Meteorological Department in the Oum Er-Rbia sufficient, then the flow peak prediction is based Hydraulic Basin Agency of Beni Mellal city. on the geometric parameters (length of the river, Figure 1. Field photographs (a ) destroyed house (B) flood lines inside a house. GEOLOGY, ECOLOGY, AND LANDSCAPES 75 Figure 2. s tudy area location. s ource: a uthor. Table 1. Tillouguite station’ description. 4. Results Coordinates e l Th and use /cover variation is more or less variable in Station Measuring name No IRE X Y Z period every year, we notice that since the year 1987 its gener- Tillouguite 138/46 422,674 158,474 1100 1977–2017 ally varying between 9.9% and 44.4%, and most covered part in the study area is the lower one (Figure 3). These results were superimposed with flow peak and maxi- average slope, vegetation cover, rainfall annual) and mum annual precipitations values (Figure 4). using many formulas as Mallet-Gauthier, Hazan e m Th orphological characteristics (Table 2) of the Lazarevic, Fuller II … case study (Alfet catchment) are determined using GIS • Hydro meteorological approach: in which we techniques. employ close climatic station data to estimate flow e p Th recipitation adjustment showed (Figure 5 and peak in the watercourse. Table 3) that Normal distribution law fits to our mete- orological data. Table 4 gives the results of this adjust- ment where the precipitation measurements distribution 3.3. Hydraulic study is represented in 99% confidence interval and AIC and e h Th ydraulic simulation has been performed in one-di - BIC criteria are the lowest. mensional (1D) software to define the inundation e Th case study flow peak calculation is made through spread. The required data include cross-sections geom- various formulas for the ungauged river (Table 5). etry, Manning’s n values (land use/cover nature), flow e sim Th ulation results for the 100  year recurrence peak and boundary conditions. period (Table 6 and Figure 6) showed that flow regime e Th geometric data has been provided by Tillouguite is usually torrential, characterized by Froude numbers municipality and covers urbanized of 2.1  km in greater than unity (Fr > 1) in 78.1% of the simulated sec- Iminwargue centre as contour map in Auto CAD (.dwg tions. The average speed varies between 0.36 m/s (V ) min file). Thirty-one cross-sections at various important and 6.69 m/s (V ) while the average velocity (V ) max mean locations on the river have been used. is 3.53 m/s. e o Th ne-dimensional (1D) software, such as Mike 11, e Th inundation zones limits for the predicted 100 year ISIS or HEC-RAS, are based on water Equations varia- and the 2015 events via the hydraulic simulation are rep- tions, and still form the majority of traditional numerical resented in Google Earth image background (Figure 7). hydraulic models used in practical river engineering. It might be explained not only by the fact that 1D models 5. Discussion are (in comparison to higher dimensional models) sim- pler and required a minimum input data, but also because By observing the Figure 4, it can be seen that during the basic concepts and programs have already been the 1994–2007 period, the study area undergone a very around for several decades and improved its accuracy significant decrease in the land use /cover rate (0.4%), (Goodell & W arren, 2006; Pappenberger,  Beven,  Horritt, &  while the two periods of 1991–1994 and 2007–2013 Blazkova, 2005). have generally a fluctuation between 8.2 and 17.3%. 76 I. KARAOUI ET AL. Figure 3. land use/cover annual evolution since o ctober 1987 to 2017. s ource: Geographic information system (QGIs) open source. 40.0 0.0 38.0 20.0 36.0 40.0 34.0 32.0 60.0 30.0 80.0 28.0 100.0 26.0 24.0 120.0 22.0 140.0 20.0 160.0 18.0 16.0 180.0 14.0 200.0 12.0 220.0 10.0 8.0 240.0 6.0 260.0 4.0 280.0 2.0 0.0 300.0 Maximum monthly rainfall in year (mm) Land cover (%) Instantaneous flow maximum annual (m3/s) Figure 4. land use /cover rate evolution according to instantaneous flow peak and maximum monthly rainfall per year since 1987. Table 2. alfet watershed general characteristics. in the watershed flow peak until 94.7  m /s. The land use  /cover presence can play the role of braking flow General parameters peaks in the intense precipitations. A similar result was area (km²) 66.81 difference in height (m) 1404 perimeter (km) 39.29 slope (%) 6.77 found by Zhang where he determined that land use/ l ength (km) 20.73 equivalent length (km) 15.48 cover (vegetation rate) can import a significant influ- Maximum elevation 2527 equivalent length (km) 4.32 Minimum elevation 1123 Ic Gravelius 1.36 ence in recorded flow peaks (Zhang, Zhang, Zhao, X, y outlet (m) 306 786 c oncentration time Tc 274.37 Rustomji, & Hairsine, 2008). In its low rate presence, (min)   147 382     the precipitation impact becomes very important in mountainous area, where the surface flow became major due to infiltration and stagnation lack (DeFries & Eshleman, 2004; Hundecha & Bárdossy, 2004; From the year 2013, the Land use  /cover began to Matheussen, Kirschbaum, Goodman, O’Donnell, & deteriorate to a value of 4.5% in 2017. This degrada- Lettenmaier, 2000). tion is generally followed by a very significant increase Land cover % Flow peak (m3/s) and Maximum monthly rainfall in the yeay (mm) GEOLOGY, ECOLOGY, AND LANDSCAPES 77 Figure 5. Monthly rainfall graphic representation according to normal low. Table 3. distributions low comparison criteria. Model Nb param XT P(Mi) P(Mi|x) BIC AIC normal (maximum likelihood) 2 619.14 12.50 39.35 356.17 353.51 Weibull (maximum likelihood) 2 636.25 12.50 34.19 356.45 353.79 pearson type 3 (maximum likelihood) 3 638.22 12.50 10.95 358.73 354.73 Gamma (method of moments) 2 688.73 12.50 10.33 358.85 356.18 Gumbel (method of moments) 2 721.58 12.50 4.20 360.65 357.98 l ognormal (maximum likelihood) 2 920.79 12.50 0.97 363.58 360.91 Inverse gamma (maximum likelihood) 2 1449.74 12.50 0.01 372.72 370.06 exponential (maximum likelihood) 2 1326.10 12.50 0.00 377.75 375.09 In 1993, 2006 and 2012 years, we have an exceptional Table 4. precipitation statistical adjustment results by normal low. cases, where the land use /cover rate is very important but the watershed hydrological response is entirely not Estimated Return precipita- Confidence normal (a significant flow peak). This difference could period Probability tion Standard interval be due to satellite images used for land use/cover deter- 100 0.9900 619.14 46.644 498.97–739.30 mination, which are collected during the October month 50 0.9800 584.66 42.682 474.70–694.62 20 0.950 532.93 37.060 437.45–628.40 of each year. And sometimes we can have intense precip- 10 0.9000 486.95 32.530 403.14–570.75 itations and high flow peak before October. Thereaer ft , 5 0.8000 431.26 27.951 359.25–503.26 2 0.5000 324.82 23.905 263.23–386.40 the cover is that of the previous year. By analysing land use/cover of 1993, 2006 and 2016 we can see that before those events it has a less rate cover compared to the event itself. These exceptional cases are generally obtained when the precipitation during the period between June Table 5. Flow results according to different recurrences times. and September is important. Flow point m /s used in simulation With the same amount of precipitation, sometimes Recurrenc- Q(02) Q(05) Q(10) Q(20) Q(50) Q(100) es time the hydrological response of the watershed is differ - alfet river 72.84 96.46 112.05 116.46 143.51 166.72 ent, this can be explained by the influence of several parameters, not only the land use  /cover rate. Several researchers have found that the watershed hydrological Table 6. extract from the simulation table results. Year of Q total Min Ch. El. W.S. Elev. Water Elev. Water speed Flow area Top width 3 2 Profil return (m /s) (m) (m) (m) (m/s) (m ) (m) Froude Chl. 1 100 year 166.72 1181.7 1183.66 1.96 3.34 49.92 44.35 1 2 100 year 166.72 1179.99 1181.21 1.22 5.72 29.15 44.1 2.25 3 100 year 166.72 1179.18 1180.68 1.5 3.94 42.35 53.02 1.41 4 100 year 166.72 1176.11 1177.15 1.04 6.69 24.93 43.9 2.83 5 100 year 166.72 1172.62 1174.63 2.01 5.72 29.16 26.59 1.74 6 100 year 166.72 1172.82 1175.3 2.48 2.52 66.21 41.63 0.64 7 100 year 166.72 1173.18 1174.66 1.48 3.31 50.46 46.01 1 notes: Q-total: total discharge, Min ch el: min channel elevation (is elevation of each station, W.s elev: elevation of water surface, Water elev: water eleva- tion, Flow area: area that flood covers, Top Width: Maximum of elevation of flood surface, Froude chl: Froude number that can be critical, sub-critical of hyper-critical. 78 I. KARAOUI ET AL. Figure 6. (a ) Flooding zones in 3d schema (B) water level in a cross section. Figure 7. Inundation zones limits in a Google earth background. s ource: a uthor. response is also influenced by precipitation intensity, with the predicted 100 years recurrence inundation via slope and geology … (Lavabre, Torres, & Cernesson, the hydraulic simulation. 1993; Vicente-Serrano & López-Moreno, 2005). In our e Th 100 years inundation simulation results (Figure 7 ) case, unfortunately we don’t have precipitation intensity showed that the extents of predicted flood can touch many data, for this reason the absolute explanation is hard to buildings on the right bank, the school near the right bank be done. But the important thing in our results is that of the road, and invade almost the entire road that through flow peaks generally knows an increase when the land the centre and contain an hydraulic structure (double box use /cover decrease. culverts) due to its insuc ffi ient capacity. These results pro - Regarding the case study and prediction, the results vide a scientic fi basis for decision makers to choose a better found are estimates and statistical prediction. its valida- way to protect people and their belongings. This protec- tion remains difficult to complete and achieve (Horritt & tion can be achieved by the use of hydraulic protection Bates, 2002) to bring out, some researchers use recorded structures or by protection of plant covers and implan- historical inundations events to compare its extents tation of new trees to reduce the process of flow peaks with the predicted results. This means that hydraulic increase (Harada et al., 2015; Henderson, 1986; Khosru simulation can give very exploitable results in terms of Shahi, Abrishami, Asghar, & Sheybani Zadeh, 2009; delimitation of the inundation zones but remain with a Yang, Wang, Voisin, & Copping, 2015; Zając, Solarz, & margin of error extremely small view that no one can Bielański, 2006; Zelo, Shipman, & Brennan, 2000). validate them surely (Faghih, Mirzaei, Adamowski, Lee, & El-Shafie, 2017). This margin can be neglected in the 6. Conclusions majority of the cases, because this constraint can be e o Th btained results during this study have shown that avoided by adding more distance on the inundation zone the evolution of the land use /cover has a very impor- boundaries to avoid any repercussion. In our case study, tant impact on the watershed’s response and can increase we used the August 10, 2015 inundation limits as one of inundation intensity. 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Assessing land use /cover variation effects on flood intensity via hydraulic simulations

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GeoloGy, ecoloGy, and landscapes, 2018 Vol . 2, no . 2, 73–80 https://doi.org/10.1080/24749508.2018.1452461 INWASCON OPEN ACCESS Assessing land use /cover variation effects on flood intensity via hydraulic simulations A case study of Oued El Abid watershed (Morocco) a a b a c Ismail Karaoui , Abdelkrim Arioua , Abdelkhalek El Amrani Idrissi , Mohammed Hssaisoune , Wafae Nouaim , a a kamal Ait ouhamchich and Driss Elhamdouni a b Water Resources Management and Valorization and Remote s ensing Team, sultan Moulay slimane University, Beni Mellal, Morocco; Water Quality d epartment, o um er-biaa Hydraulic Basin a gency, Beni Mellal, Morocco; d epartment of earth s ciences, Ibn Tofail University, Kenitra, Morocco ARTICLE HISTORY ABSTRACT Received 3 s eptember 2017 Despite the semi-arid to arid nature of its climate, Morocco is exposed, like all the Mediterranean a ccepted 20 January 2018 countries to inundations, which can be very damaging for public and private infrastructure, and cause many victims. On Monday 10 August 2015, one of Oued El Abid tributary raised in a KEYWORDS remarkable way, causing five deaths and serious material damage in Iminwargue village (Azilal land use/cover; inundation; province). This study comes to assess the impact of land use/cover variation in the multiplication o ued el abid watershed; of floods frequency via the hydraulic simulations. For this purpose, relying on land use/cover hydraulic simulation; hydrological response variation from 1987 to 2017, flow and precipitation database, we made a hydraulic simulation of the 2015 event and the 100 years recurrence inundation. As results, the land use/cover decrease is always followed by an increase in flows peak even that all recorded precipitations are more or less similar. The case study hundred years inundation prediction shows a 5.03 m increase in the river and all population near banks, agricultural land and other useful infrastructure such as roads and bridges are invaded. These results show that more catastrophic events could be reproduced and oblige the decision makers in Oued El Abid watershed to be aware of the critical situation. 1. Introduction risk management approach (Sevruk,  Ondrás, & Chvíla, 2009). Identifying prone areas can therefore be one of In the last decades many Inundations have occurred in the key solutions in inundation mitigation (Achleitner the world and claiming more than 20,000 lives per year et al., 2016). Predicting susceptible inundation plains and and adversely ae ff cting about 75 million people world- keys factors of its development can help authorities in wide, mostly through homelessness (Sarhadi, Soltani, & planning management strategies for inundation mitiga- Modarres, 2012). This phenomenon have been aggra- tion (Cundill & Fabricius, 2010; Sanders, 2007). Hydro- vated, in several cases, by the land cover use changes meteorological catastrophes cannot be totally evaded, but especially in the intense urbanisation of inundation the impacts and triggering factors can be handled by devel- prone areas and deforestation (Alexander et al., 2006; oping effective risk reduction strategies through applica- Brath, Montanari, & Moretti, 2006; Bronstert, 2003; tion of latest geospatial tools and decision support systems Esteban-Parra, Rodrigo, & Castro-Diez, 1998; Meehl (Khattak et al., 2016). The application of full modelling et al., 2000; Ntegeka & Willems, 2008; Oudin, steps from rainfall-runoff to test different scenarios of land Andréassian, Perrin, Michel, & Le Moine, 2008). use variation is a paramount necessity tool to gain spatial Projected future climate change and extreme weather coherence when estimating losses (Al-Zahrani, Al-Areeq, events are expected to get worse than the actual situation & Sharif, 2016; Booij, 2005; De Bruijn,  Diermanse, & (Pachauri et al., 2014). As the severity and inundations fre- Beckers, 2014; Gems, Achleitner, Huttenlau, Thieken, & quency considerably increase, the human societies realized Aufleger, 2009; Golshan, Jahanshahi, & Afzali, 2016). that the traditional paradigm of protecting themselves are In Morocco, the frequency and intensity of inun- not a sufficient option for the future (Kuks & Kissling- dations in recent decades have grown in a remarkable Näf, 2004; Pahl-Wostl et al., 2013; Smits,  Nienhuis, & way, especially in Oued El Abid watershed, due to heavy Leuven, 2000). Instead, the practitioners agree that inun- rain intensity, land use variation and rugged topography dation management should be based on an integrated characterize by high slope. CONTACT Ismail Karaoui I.karaoui@usms.ma © 2018 The a uthor(s). published by Informa UK limited, trading as Taylor & Francis Group. This is an open a ccess article distributed under the terms of the creative c ommons a ttribution license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 74 I. KARAOUI ET AL. In light of this repetitive catastrophic event, the pres- 3.1. Analysis of the land use/cover variation in ent work aims to propose a methodology for the land Oued El Abid watershed use variation effect on the hydrological response in the In recent decades, considerable changes in land use upper part of Oued EL Abid watershed, by developing a and vegetation cover have occurred in the different case study that refers to Alfet River basin (Oued El Abid regions of Morocco. As a quite general rule, from the tributary). This sub-basin in 10 August 2015 has experi - early 1970s meadows and pastures characterized the enced one of the most violent event in which the water most mountainous region at the time, decreased as level raised by 10 m and caused the death of 5 peoples, deforestation and urbanization that led to the aban- the destruction of 5 houses and the road No. 302 linking donment of cattle rearing. As a result, these moun- the two villages Tillouguite and Aminouak (Figure 1). tainous areas have been left fallow and unproductive. A similar behaviour occurred in the considered basin 2. The study area of Oued El Abid. To quantify the Land use  /cover variation rate in e u Th pper part of Oued El Abid watershed is located in Oued El Abid watershed and its effects on the hydro- the centre of northern part in Morocco, in Khenifra- logical response. Satellite images from the Landsat TM5 Beni Mellal region and Azilal province. It extends over and Oli 8 sensors were used to monitor the annual rate an area of 7686 km . The study area is characterized by evolution since October 1987 until 2017. a semi-arid climate, and concentration of rainfall in In this study, we used satellite images captured in autumn and winter, high evaporation and high average October month of every year to homogenize the land temperatures with high monthly and daily amplitude. use /cover obtained results, to avoid intense cloud rate, e minim Th um temperature is −5 °C recorded in January and get a better results from the vegetation cover. while the maximum is 34.9 °C in August. e c Th ase study (Iminwargue village), is located in a distance of 30 km from Azilal City, in Alfet River (Oued 3.2. Hydrological study El Abid tributary). The Alfet River watershed originates Precipitation and flow data adopted in our study are in 2527 m at Morocco High Atlas and extends over an those of Tillouguite station, located in the south-east of area of 2.04 km in the middle of Oued El Abid water- Oued El Abid watershed. The Tillouguite information is shed (Figure 2). summarized in Table 1. e s Th tatistical adjustment was carried out through 3. Materials and methods HYfran software to predicted precipitation and flow peak in different returns periods (Alzate, Guyumus, Quijano, Our work methodology has been initiated by studying & Díaz-Granados, 2016; Sami, Hadda, & Mahdi, 2016). the land use variation at Oued El Abid watershed during e c Th hosen law should take into consideration a lowest a period of 1987–2017, then the effects of this variation AIC and BIC coefficients and greatest posterior proba- on the hydrological response in the study area water- bility criteria (Banner & Higgs, 2017). courses. e Th obtained results in this analysis are used To estimate the peak flow in ungauged rivers (as Alfet later in a case study of Tillouguite village to indicate River), deterministic and regionalization formulas are the inundation event extension, and then estimating the used. and the following approaches are adopted in this damages that could be caused in the future inundations estimation (D’Ambrosio, De Girolamo, Barca, Ielpo, & events via the hydraulic simulation. Rulli, 2016): e Th topographic maps and last inundations damage statistics were given by the Authority of Tillouguite. • Empirical approach: when there is no rainfall or While hydrological and climatic data provided by hydrometric data or the recorded data are not the Meteorological Department in the Oum Er-Rbia sufficient, then the flow peak prediction is based Hydraulic Basin Agency of Beni Mellal city. on the geometric parameters (length of the river, Figure 1. Field photographs (a ) destroyed house (B) flood lines inside a house. GEOLOGY, ECOLOGY, AND LANDSCAPES 75 Figure 2. s tudy area location. s ource: a uthor. Table 1. Tillouguite station’ description. 4. Results Coordinates e l Th and use /cover variation is more or less variable in Station Measuring name No IRE X Y Z period every year, we notice that since the year 1987 its gener- Tillouguite 138/46 422,674 158,474 1100 1977–2017 ally varying between 9.9% and 44.4%, and most covered part in the study area is the lower one (Figure 3). These results were superimposed with flow peak and maxi- average slope, vegetation cover, rainfall annual) and mum annual precipitations values (Figure 4). using many formulas as Mallet-Gauthier, Hazan e m Th orphological characteristics (Table 2) of the Lazarevic, Fuller II … case study (Alfet catchment) are determined using GIS • Hydro meteorological approach: in which we techniques. employ close climatic station data to estimate flow e p Th recipitation adjustment showed (Figure 5 and peak in the watercourse. Table 3) that Normal distribution law fits to our mete- orological data. Table 4 gives the results of this adjust- ment where the precipitation measurements distribution 3.3. Hydraulic study is represented in 99% confidence interval and AIC and e h Th ydraulic simulation has been performed in one-di - BIC criteria are the lowest. mensional (1D) software to define the inundation e Th case study flow peak calculation is made through spread. The required data include cross-sections geom- various formulas for the ungauged river (Table 5). etry, Manning’s n values (land use/cover nature), flow e sim Th ulation results for the 100  year recurrence peak and boundary conditions. period (Table 6 and Figure 6) showed that flow regime e Th geometric data has been provided by Tillouguite is usually torrential, characterized by Froude numbers municipality and covers urbanized of 2.1  km in greater than unity (Fr > 1) in 78.1% of the simulated sec- Iminwargue centre as contour map in Auto CAD (.dwg tions. The average speed varies between 0.36 m/s (V ) min file). Thirty-one cross-sections at various important and 6.69 m/s (V ) while the average velocity (V ) max mean locations on the river have been used. is 3.53 m/s. e o Th ne-dimensional (1D) software, such as Mike 11, e Th inundation zones limits for the predicted 100 year ISIS or HEC-RAS, are based on water Equations varia- and the 2015 events via the hydraulic simulation are rep- tions, and still form the majority of traditional numerical resented in Google Earth image background (Figure 7). hydraulic models used in practical river engineering. It might be explained not only by the fact that 1D models 5. Discussion are (in comparison to higher dimensional models) sim- pler and required a minimum input data, but also because By observing the Figure 4, it can be seen that during the basic concepts and programs have already been the 1994–2007 period, the study area undergone a very around for several decades and improved its accuracy significant decrease in the land use /cover rate (0.4%), (Goodell & W arren, 2006; Pappenberger,  Beven,  Horritt, &  while the two periods of 1991–1994 and 2007–2013 Blazkova, 2005). have generally a fluctuation between 8.2 and 17.3%. 76 I. KARAOUI ET AL. Figure 3. land use/cover annual evolution since o ctober 1987 to 2017. s ource: Geographic information system (QGIs) open source. 40.0 0.0 38.0 20.0 36.0 40.0 34.0 32.0 60.0 30.0 80.0 28.0 100.0 26.0 24.0 120.0 22.0 140.0 20.0 160.0 18.0 16.0 180.0 14.0 200.0 12.0 220.0 10.0 8.0 240.0 6.0 260.0 4.0 280.0 2.0 0.0 300.0 Maximum monthly rainfall in year (mm) Land cover (%) Instantaneous flow maximum annual (m3/s) Figure 4. land use /cover rate evolution according to instantaneous flow peak and maximum monthly rainfall per year since 1987. Table 2. alfet watershed general characteristics. in the watershed flow peak until 94.7  m /s. The land use  /cover presence can play the role of braking flow General parameters peaks in the intense precipitations. A similar result was area (km²) 66.81 difference in height (m) 1404 perimeter (km) 39.29 slope (%) 6.77 found by Zhang where he determined that land use/ l ength (km) 20.73 equivalent length (km) 15.48 cover (vegetation rate) can import a significant influ- Maximum elevation 2527 equivalent length (km) 4.32 Minimum elevation 1123 Ic Gravelius 1.36 ence in recorded flow peaks (Zhang, Zhang, Zhao, X, y outlet (m) 306 786 c oncentration time Tc 274.37 Rustomji, & Hairsine, 2008). In its low rate presence, (min)   147 382     the precipitation impact becomes very important in mountainous area, where the surface flow became major due to infiltration and stagnation lack (DeFries & Eshleman, 2004; Hundecha & Bárdossy, 2004; From the year 2013, the Land use  /cover began to Matheussen, Kirschbaum, Goodman, O’Donnell, & deteriorate to a value of 4.5% in 2017. This degrada- Lettenmaier, 2000). tion is generally followed by a very significant increase Land cover % Flow peak (m3/s) and Maximum monthly rainfall in the yeay (mm) GEOLOGY, ECOLOGY, AND LANDSCAPES 77 Figure 5. Monthly rainfall graphic representation according to normal low. Table 3. distributions low comparison criteria. Model Nb param XT P(Mi) P(Mi|x) BIC AIC normal (maximum likelihood) 2 619.14 12.50 39.35 356.17 353.51 Weibull (maximum likelihood) 2 636.25 12.50 34.19 356.45 353.79 pearson type 3 (maximum likelihood) 3 638.22 12.50 10.95 358.73 354.73 Gamma (method of moments) 2 688.73 12.50 10.33 358.85 356.18 Gumbel (method of moments) 2 721.58 12.50 4.20 360.65 357.98 l ognormal (maximum likelihood) 2 920.79 12.50 0.97 363.58 360.91 Inverse gamma (maximum likelihood) 2 1449.74 12.50 0.01 372.72 370.06 exponential (maximum likelihood) 2 1326.10 12.50 0.00 377.75 375.09 In 1993, 2006 and 2012 years, we have an exceptional Table 4. precipitation statistical adjustment results by normal low. cases, where the land use /cover rate is very important but the watershed hydrological response is entirely not Estimated Return precipita- Confidence normal (a significant flow peak). This difference could period Probability tion Standard interval be due to satellite images used for land use/cover deter- 100 0.9900 619.14 46.644 498.97–739.30 mination, which are collected during the October month 50 0.9800 584.66 42.682 474.70–694.62 20 0.950 532.93 37.060 437.45–628.40 of each year. And sometimes we can have intense precip- 10 0.9000 486.95 32.530 403.14–570.75 itations and high flow peak before October. Thereaer ft , 5 0.8000 431.26 27.951 359.25–503.26 2 0.5000 324.82 23.905 263.23–386.40 the cover is that of the previous year. By analysing land use/cover of 1993, 2006 and 2016 we can see that before those events it has a less rate cover compared to the event itself. These exceptional cases are generally obtained when the precipitation during the period between June Table 5. Flow results according to different recurrences times. and September is important. Flow point m /s used in simulation With the same amount of precipitation, sometimes Recurrenc- Q(02) Q(05) Q(10) Q(20) Q(50) Q(100) es time the hydrological response of the watershed is differ - alfet river 72.84 96.46 112.05 116.46 143.51 166.72 ent, this can be explained by the influence of several parameters, not only the land use  /cover rate. Several researchers have found that the watershed hydrological Table 6. extract from the simulation table results. Year of Q total Min Ch. El. W.S. Elev. Water Elev. Water speed Flow area Top width 3 2 Profil return (m /s) (m) (m) (m) (m/s) (m ) (m) Froude Chl. 1 100 year 166.72 1181.7 1183.66 1.96 3.34 49.92 44.35 1 2 100 year 166.72 1179.99 1181.21 1.22 5.72 29.15 44.1 2.25 3 100 year 166.72 1179.18 1180.68 1.5 3.94 42.35 53.02 1.41 4 100 year 166.72 1176.11 1177.15 1.04 6.69 24.93 43.9 2.83 5 100 year 166.72 1172.62 1174.63 2.01 5.72 29.16 26.59 1.74 6 100 year 166.72 1172.82 1175.3 2.48 2.52 66.21 41.63 0.64 7 100 year 166.72 1173.18 1174.66 1.48 3.31 50.46 46.01 1 notes: Q-total: total discharge, Min ch el: min channel elevation (is elevation of each station, W.s elev: elevation of water surface, Water elev: water eleva- tion, Flow area: area that flood covers, Top Width: Maximum of elevation of flood surface, Froude chl: Froude number that can be critical, sub-critical of hyper-critical. 78 I. KARAOUI ET AL. Figure 6. (a ) Flooding zones in 3d schema (B) water level in a cross section. Figure 7. Inundation zones limits in a Google earth background. s ource: a uthor. response is also influenced by precipitation intensity, with the predicted 100 years recurrence inundation via slope and geology … (Lavabre, Torres, & Cernesson, the hydraulic simulation. 1993; Vicente-Serrano & López-Moreno, 2005). In our e Th 100 years inundation simulation results (Figure 7 ) case, unfortunately we don’t have precipitation intensity showed that the extents of predicted flood can touch many data, for this reason the absolute explanation is hard to buildings on the right bank, the school near the right bank be done. But the important thing in our results is that of the road, and invade almost the entire road that through flow peaks generally knows an increase when the land the centre and contain an hydraulic structure (double box use /cover decrease. culverts) due to its insuc ffi ient capacity. These results pro - Regarding the case study and prediction, the results vide a scientic fi basis for decision makers to choose a better found are estimates and statistical prediction. its valida- way to protect people and their belongings. This protec- tion remains difficult to complete and achieve (Horritt & tion can be achieved by the use of hydraulic protection Bates, 2002) to bring out, some researchers use recorded structures or by protection of plant covers and implan- historical inundations events to compare its extents tation of new trees to reduce the process of flow peaks with the predicted results. This means that hydraulic increase (Harada et al., 2015; Henderson, 1986; Khosru simulation can give very exploitable results in terms of Shahi, Abrishami, Asghar, & Sheybani Zadeh, 2009; delimitation of the inundation zones but remain with a Yang, Wang, Voisin, & Copping, 2015; Zając, Solarz, & margin of error extremely small view that no one can Bielański, 2006; Zelo, Shipman, & Brennan, 2000). validate them surely (Faghih, Mirzaei, Adamowski, Lee, & El-Shafie, 2017). This margin can be neglected in the 6. Conclusions majority of the cases, because this constraint can be e o Th btained results during this study have shown that avoided by adding more distance on the inundation zone the evolution of the land use /cover has a very impor- boundaries to avoid any repercussion. In our case study, tant impact on the watershed’s response and can increase we used the August 10, 2015 inundation limits as one of inundation intensity. This result has been proven by the most destructive event, and we compare its results GEOLOGY, ECOLOGY, AND LANDSCAPES 79 De Bruijn, K. M., Diermanse, F. L. M., & Beckers, J. V. L. the 2015 event analysis, which is due to land use/cover (2014). An advanced method for flood risk analysis in decrease followed by an increase in flow peak and subse - river deltas, applied to societal flood fatality risk in the quently the fatal damages. Predictions of precipitations, Netherlands. Natural Hazards and Earth System Science, flow peaks and the 2013–2017 land use/cover degrada- 14(10), 2767–2781. tion trend have shown that fatal events will be repeated DeFries, R., & Eshleman, K. N. (2004). Land-use change over and over again if we do not have any intervention of and hydrologic processes: A major focus for the future. Hydrological Processes, 18(11), 2183–2186. decision-makers, in order to protect riparian and vegeta- Esteban-Parra, M. J., Rodrigo, F. 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Journal

Geology Ecology and Landscapes – Taylor & Francis

Published: Apr 3, 2018

Keywords: Land use/cover; inundation; Oued El Abid watershed; hydraulic simulation; hydrological response

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