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Rainfall-Runoff event-based studies

Among the forty nine flood studies, twenty one includes useful information for quantative on 136 catchments and 191 events (Fig. 1.1c and Table 1.2).
The analysis was only conducted on hydrological studies at the catchment scale. Hence, studies focusing on the hydraulic aspects of the catchment response at the reach scale were excluded. These studies are not limited to extreme flood events (such as Marchi et al. 2010 and Tarolli et al. 2012) but comprise any runoff-generating rainfall event study that contains substantial event-related information. However, this information is not equally available to all. Details on each study and event-related data are presented in Table 1.2. Here, one must clarify that a single event can affect more than one catchment and that more than one event could be reported for the same catchment. Among these studies, 13 were conducted in NWM, 5 in EM, and 3 in SM. One study (Tarolli et al. 2012) was an analysis of flash flood regimes in the northwestern Mediterranean (France, Spain, Italy) and the southeastern Mediterranean region (Israel). Moreover, 69 catchments out of a total of 136 are located in the NWM. The rest of the catchments are divided between the EM (22) and SM (45). The catchments areas range from 3.83 km² for the Valsecure catchment (a headwater catchment of the Gardon d’Anduze, Southern France; Tramblay et al. 2010) to 16,330 km² for the Negada catchment at Lassoud in Tunisia (Abida and Ellouze 2008).

hydrological response characteristics

In this section we analyse the hydrological response characteristics of the studied Mediterranean catchments based on the collected data described above, in terms of annual water balance, rainfall-runoff events and drought.

Annual water balance studies

The mean annual precipitation (MAP), reference evapotranspiration (ET0), and mean annual runoff (MAQ) are highly variable across the three Mediterranean sub-regions. The median values show decreasing MAP, MAQ, MAQ/MAP and an increasing ET0 from NWM to EM and SM (Table 1.4).
The relationship between MAQ and MAP is plotted whereeach point represents a studied catchment (Figure 1.2). The results show a significant correlation (R²=0.745) between MAQ and MAP reported in all studies. Most low-yielding catchments with MAP values lower than 400 mm are located in the SM. For a larger MAP, the graph shows important scattering. The graph also shows that even for large MAP values (800 – 900 mm), the runoff yield can be very low (approximately 30 mm); this is true for catchments in Crete (Koutroulis et al. 2013). Thus, the catchment yield is highly variable. The trend for the EM and the NWM catchments is quite similar, with an intercept close to 500-550 mm and comparable slopes. The trend is different for the SM catchments with an intercept close to 250-300 mm and a lower slope. For a few catchments (especially in NWM and EM), the MAQ is larger than the MAP. Here, two factors may explain this phenomenon: the contribution of snow amounts in mountainous catchments that is underestimated and the presence of karsts that may greatly increase the effective catchment area.
A similar analysis (not shown) that accounted for catchment area and elevation was undertaken. Catchment area does not appear to influence catchment response, expressed as the water depth (mm per time step). The impact of the elevation, however, could not be neglected because both the MAP and catchment runoff increased with elevation.

Runoff ratio

A very important concept for assessing the catchment hydrological response is the event runoff ratio, which is defined as the ratio of the event runoff volume to the event rainfall volume. Figure 1.7 presents the runoff depth (Fig. 1.7a) and the runoff ratio (Fig. 1.7b) as a function of the cumulative event rainfall. There is clear scattering in the response. Thus, for a given rainfall depth, both the runoff and runoff ratio may greatly vary.
In Mediterranean catchments, event runoff ratios vary over a large range (Fig. 1.7b). For instance, the ratio varies from 0.01 in the Rafina catchment (Greece; Massari et al. 2014) to 1.2 in the Lez catchment (France; Coustau et al. 2012). The mean value of the sample we studied was 0.37, with a standard deviation of 0.27. The median was 0.30, and the interquartile range was 0.14 – 0.51. There are geographical discrepancies in the catchment responses to rainfall events. Figure 1.7 shows that the highest runoff depth and runoff ratios appear in the NWM catchments. In fact, the median runoff ratios vary between regions, e.g., from 0.40 in the NWM (similar to the values found by Marchi et al. 2010) to 0.36 in the SM and only 0.12 for EM catchments. It is also obvious that for low rainfall depths (particularly below 50 mm), all events are from the EM and SM regions, which may exhibit very high runoff ratios despite low rainfall depths.

Can we identify regional patterns in the Mediterranean?

The comparison of catchments in different Mediterranean areas shows that some regional tendencies exist. In terms of the annual water balance, catchments from the NWM have a higher humidity, a relatively low dryness index and higher annual runoff yields. This finding could be due to the influence of the humidity from the Atlantic Ocean, which modifies the seasonal pattern of rainfall, i.e., less precipitation in winter and a rainfall peak in autumn and/or spring (McNeill 1992). SM catchments are the driest, with the highest aridity index and lowest annual runoff yields. The EM proves to be more heterogeneous, with a relatively wide range of values in terms of both the aridity index and runoff yields (e.g., Rimmer and Salingar 2006, Tzoraki and Nikolaidis 2007, Kourgialas et al. 2010).
Regional tendencies also exist in the seasonal distribution and severity of extreme rainfall events. In fact, rainfall events in the NWM mostly occur in autumn, with a peak in September. Rainfall in the EM region mostly occurs in winter, with a peak in January and February. In the SM, the sample is too small to generalize. Moreover, in terms of the event rainfall depth, peak discharges and runoff ratios, the highest values occur in the NWM. These findings were reported by other authors who studied floods in the Mediterranean (Marchi et al. 2010, Tarolli et al. 2012, and Llasat et al. 2013). In fact, the NWM region exhibits extreme rainfall regimes, with rainfall commonly exceeding 200 mm in 24 hrs. Cortesi et al. (2012) studied the distribution of the daily precipitation concentration index across Europe. The highest values were computed for the coastal arc that extends from southeastern Spain to Sicily (Italy). Moreover, Reiser and Kutiel (2011) compared the rainfall regimes in Valencia (Spain) and Larnaca (Cyprus). The authors found that in Valencia, the rainfall regimes are more extreme than in Larnaca. Hence, in the NWM, daily rainfall values that exceed 600 mm have been recorded.
However, there is also overlap between morphometric and hydrological characteristics of particular catchments located in different regions, particularly between the NWM and EM. These similarities between catchments can highlight twin basins (e.g., basins with similar physiographic features and/or hydrological responses), for which hydrological responses can be transferred from gauged to ungauged basins.

What is required to model Mediterranean catchments?

A difference can be made between continuous streamflow simulations and event-based simulations. The former is usually applied to quantify water resources in the catchment of interest, assess land cover and/or climate change impacts, or test new modelling approaches. In event-based studies, the objectives may vary from flood risk mitigation to understanding flood-triggering characteristics. For continuous streamflow simulations, daily climatic and hydrological data are mostly used (sometimes monthly data). The applied models are often lumped as conceptual models with relatively good results. Nevertheless, modelling the catchment response at the event scale is a dilemma. The high rainfall intensity and spatial variability within a storm, along with a catchment’s initial wetness conditions, complicate this task. Classical hydrological models are not well suited to the Mediterranean area. In fact, many of these models assume precipitation abstraction (such as the widely used SCS CN) or saturated excess mechanisms (such as the TOPMODEL family), which is not necessarily the case in the Mediterranean. Moreover, any suitable modelling approach for these catchments is highly demanding in terms of data. Hence, to account for the variability in the rainfall intensity in a short time, the model should be applied at the hourly (or shorter) scale for flood studies. Furthermore, this model should be able to account for any rainfall spatial variability and catchment wetness conditions. Therefore, information on soil properties and soil moisture conditions is needed.
The specificities of the Mediterranean catchment responses explain the large response heterogeneity in the region and outline the fact that modelling the hydrological behaviour of Mediterranean catchments is difficult (Oudin et al., 2008). In regional studies that involve catchments from Mediterranean and non-Mediterranean (humid) regions, such as in Goswami et al. (2007) and Oudin et al. (2008, 2010), the performance of the model-dependent regional approaches is worse in a Mediterranean climate. High-resolution spatial and temporal rainfall and soil properties and moisture data may be necessary to accurately simulate the hydrological behaviour of Mediterranean catchments. However, such data are rarely available. Consequently, detailed flood studies are usually performed in small, research catchments, with results often difficult to generalize in space and time (Gallart et al. 2007, Latron and Gallart 2007, Lana-Renault et al. 2007; Manus et al. 2009, Rozalis et al. 2010, Molina et al. 2014). This finding also explains why studies in large catchments are usually limited to flood risk mitigation or peak flow estimation, sometimes using lumped methods only.
To overcome the difficulties in modelling hydrological responses of Mediterranean catchments, particularly in terms of accounting for the high spatial variability in model parameters, radar rainfall, spatial soil moisture information and remote sensing data are considered (Massari et al. 2015; Tramblay et al. 2010, 2012, Rozalis et al. 2010). New approaches that couple observations (that are usually obtained on small catchments) and modelling are used to improve our understanding of flood-triggering processes. For example, recent work was undertaken within the HyMeX project on two French catchments (Braud et al. 2014). This is a multi-scale approach that assesses the runoff-generation processes from observations at a small hillslope scale; the rainfall variability and soil moisture, along with the network organization, were studied for medium-sized catchments (1 – 100 km²) and for river rooting and flooding at a large scale (100 – 1000 km²). Data analyses were coupled to modelling techniques, and the results are promising. However, these approaches are very demanding in terms of instrumentation, data, and computational efforts; thus, they may be very expensive. Thus, the majority of flood simulation studies in the Mediterranean are concentrated in the developed countries of the Euro-Mediterranean region and Israel.

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Climatic change impact over Lebanon

For the Middle East region, studies dealing with climate change impact indicate a trend towards decreasing precipitation and a more extreme rainfall regime (Philandras et al. 2011). Hence, Global Climatic Model projections for the region at the end of 21th century, predict a decrease in precipitation, an increase in temperature (Ragab 2002; Hemming et al. 2010, IPCC, 2007, 2011, 2014) and a tendency towards a more extreme climate. Nonetheless, Bou-Zeid and El-Fadel (2002), and Hemming et al. (2010) pointed to the large uncertainties and discrepancies between different GCM projections. Moreover, as an example of the uncertainty related to the GCM projections, Ragab (2005) used a GCM with 2.5 x 3.75 ˚ grid squares for precipitation prediction, and 0.5 x 0.5 ˚ grid squares for temperature prediction. Such a resolution is way too coarse to account for the very highly spatial variability in rainfall and temperature in mountainous countries such as Lebanon.
Nevertheless, Regional Climate Models (RCM) -with a more finite resolution- were also used for climatic predictions in the region (Alpert et al. 2008; Black 2009; Hemming et al. 2010) and the results were constituent with the above-mentioned GCM predictions.
In Lebanon many authors point towards signs of increased hydrological droughts that could be partly –anthropogenic induced droughts such as changes in the land cover/use, the over-exploitation of groundwater resources, and the excessive use of surface water for irrigation, etc. were not taken into account- attributed to climate change. Shaban (2009, 2011) suggests that over the last few decades (1967 – 2006), Lebanon has witnessed a decrease in the annual discharges of river a drop down in groundwater piezometric level, and a decrease in the extent of the snow covered area. In addition, Ramadan et al. (2012), studied changes in runoff of the Litani River in the upper and lower Litani basins over the last century (1900 – 2008); they found that the Litani exhibits a drying trend with a reduction rate of 0.1-0.8 m3/s per decade.
Even though, there is no decisive evidence on the influence of climate change on water availability in Lebanon (UNDP 2011). Signs of hydrological droughts are discernable in the country (Shaban 2009, 2011). Moreover, with increasing anthropogenic pressure from urbanization, land cover/use changes and extensive irrigation, water resources in Lebanon becomes more and more vulnerable quantitatively and qualitatively, and the available resources become unable to fulfill the water demand for the population. However, while many studies tried to quantify the impact of climate change on water resources in the country, the impact of land cover/use changes need profound studies.

Table of contents :

Part I: State of the art
1.1 Introduction
1.1.1 The Mediterranean as a focus of research
1.1.2 Objectives of the review
1.2 The Mediterranean region
1.2.1 Boundary of the Mediterranean region
1.2.2 Physical characteristics of the Mediterranean region
1.3 Review extent and database
1.3.1 Review information
1.3.2 Annual water balance studies
1.3.3 Rainfall-Runoff event-based studies
1.3.4 Drought studies
1.4 hydrological response characteristics
1.4.1 Annual water balance studies
1.4.2 Rainfall-runoff event-based studies
1.4.3 Drought studies
1.5 Objectives and methods of hydrological studies
1.5.1 Annual water balance studies
1.5.2 Rainfall-runoff event-based studies
1.5.3 Drought studies
1.6 Discussion and perspectives
1.6.1 Can we identify regional patterns in the Mediterranean?
1.6.2 What is required to model Mediterranean catchments?
1.6.3 What are the main challenges for future research in the Mediterranean?
Part II: Lebanese catchments characteristics and data analysis
2.1 Introduction
2.2 Study area and data
2.2.1 Land cover/use
2.3 Methodology
2.4 Results and Discussions
2.4.1 Morphometry
2.4.2 Geology and Karst
2.4.3 Soils
2.4.4 Land cover/use
2.5 Conclusion
3.1 Introduction
3.2 Climatic change impact over Lebanon
3.3 Dataset
3.4 Climatic characteristics of Lebanon
3.4.1 Precipitations
3.4.2 Temperatures and reference evapotranspiration
3.5 Spatial interpolation of precipitation
3.5.1 Methodology
3.5.2 Results and Discussions
3.6 Conclusion
4.1 Introduction
4.2 Dataset
4.3 Methodology
4.4 Results and discussions
4.4.1 Mean annual discharge, runoff and runoff ratio
4.4.2 Distribution of monthly runoff
4.4.3 Distribution of daily discharge
4.4.4 Maximum specific daily discharges
4.5 Conclusion
5.1 Introduction
5.2 Materials and Methods
5.3 Annual water balance
5.4 Rainfall-runoff events characterization: Rainfall and flood response
5.4.1 Selected Rainfall-Runoff events
5.4.2 Rainfall amount and duration
5.4.3 Unit maximum daily discharge
5.4.4 Runoff ratio
5.5 Conclusion
Part III: Classification and Modeling
6.1 Introduction
6.2 Materials and Methods
6.3 Results and Discussions
6.3.1 Catchment classification
6.3.2 Physically similar catchments
6.3.3 Hydrologically similar catchments
6.3.4 Physical similarity Vs hydrological similarity
6.4 Conclusions
7.1 Introduction
7.2 Methodology
7.3 Results and Discussions
7.3.1 GR2M modeling Results
7.3.2 Regionalization
7.3.3 GR2M Results by group of similar catchments
7.3.4 GR2M simulation analysis
7.4 Conclusions


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