Our formulae were grouped based on areal/geometric properties, linearity, relief and land use presented in former literature (Srinivasa Vittala et al. 2004; Farhan et al. 2016; Fenta et al. 2017; Daipan 2020). Although we aimed at adjusting our analyses to the formerly published systems, some minimal subjectivity was unavoidable during the classifications. Table 2 shows the investigated morphometric parameters employed in the current study.
Table 2 The fundamental descriptive statistics for all calculated parameters with the exception of land use ( Fa, Ga, Aa ).
|
n
|
Mean
|
Median
|
Std.Dev.
|
Kurtosis
|
Skewness
|
Min
|
Max
|
Areal/geometry parameters
|
P
|
53
|
24.10
|
24.46
|
7.00
|
3.28
|
0.72
|
8.56
|
51.36
|
A
|
53
|
12.35
|
12.49
|
6.62
|
3.66
|
1.43
|
1.45
|
37.30
|
Rt
|
53
|
0.22
|
0.18
|
0.18
|
3.32
|
1.65
|
0.03
|
0.84
|
L
|
53
|
7.04
|
7.00
|
2.32
|
5.82
|
1.45
|
2.60
|
17.11
|
C
|
53
|
1.70
|
1.67
|
0.58
|
0.23
|
0.65
|
0.56
|
3.25
|
Dd
|
53
|
0.90
|
0.91
|
0.33
|
-0.42
|
-0.25
|
0.16
|
1.57
|
Re
|
53
|
0.56
|
0.55
|
0.10
|
0.18
|
0.32
|
0.32
|
0.82
|
Ff
|
53
|
0.26
|
0.24
|
0.09
|
0.64
|
0.83
|
0.08
|
0.53
|
Rc
|
53
|
0.26
|
0.26
|
0.06
|
0.28
|
0.34
|
0.11
|
0.43
|
k
|
53
|
3.52
|
3.25
|
1.45
|
5.2
|
1.79
|
1.47
|
9.59
|
GC
|
53
|
2.00
|
1.97
|
0.26
|
2.33
|
1.00
|
1.53
|
2.97
|
Rf
|
53
|
0.25
|
0.02
|
0.12
|
5.42
|
1.02
|
0.00
|
0.77
|
Linear parameters
|
Nu
|
53
|
5.62
|
4.00
|
5.00
|
3.53
|
1.78
|
1.00
|
23.00
|
ΣL
|
53
|
11.13
|
9.78
|
7.19
|
1.81
|
1.18
|
1.05
|
36.27
|
u max
|
53
|
2.30
|
2.00
|
0.85
|
-0.51
|
1.16
|
1.00
|
4.00
|
Lu
|
53
|
2.59
|
2.15
|
1.63
|
9.73
|
2.70
|
1.05
|
10.43
|
Cl
|
53
|
6.2
|
6.39
|
2.9
|
-0.5
|
0.15
|
1.05
|
13.15
|
Relief parameters
|
H
|
53
|
473.83
|
488.62
|
130.33
|
-1.06
|
-0.31
|
200.58
|
679.92
|
h
|
53
|
154.20
|
151.54
|
33.91
|
-0.24
|
0.63
|
105.13
|
224.29
|
r
|
53
|
319.63
|
332.38
|
123.47
|
-1.15
|
-0.20
|
93.70
|
541.90
|
Rr
|
53
|
48.39
|
46.0
|
20.53
|
-0.15
|
0.39
|
9.46
|
96.9
|
4.1 Areal and geometric parameters
At this level of detail areas of the studied watersheds ranged from 1.45 to 37.3 km2. According to the classification scheme proposed by Daipan (2020) they belong to the micro (A ≤ 10 km2, n = 20) and small (A = 10 to 100 km2, n = 33) watershed categories. In accordance with the findings of Zavoianu (1985), performed on watersheds of hilly and mountainous terrains, we found a close linear correlation between catchment perimeters and catchment area with an R2 = 0.78 (Fig. 8). The general inverse relationship between watershed size and flash flood susceptibility, therefore, suggests high levels of flash flood hazard for the study area (Daipan 2020).
This is explained by the rapid hydrologic response due to the steepness of slopes of small headwater catchments and the surplus precipitation of orographic origin. In terms of runoff intensity, the short times of concentration have already been documented for the small headwater catchments of the Mecsek Mountains (Czigány et al. 2008).
For drainage texture (Rt) all studied watershed belong to the very coarse class (low drainage density) as its value was found to be less than 2 km km−1 in all cases. The low surface density of drainage is likely explained by the jointing of limestone and the relatively dense vegetation cover in the majority of the area (mean forest cover: 46.5%). These properties reduce flash flood susceptibility.
Maximum catchment lengths (L) varied broadly, spanning between 2.60 and 17.11 km. Maximum basin length, in accordance with our presumptions, showed a close relationship with watershed areas and perimeters (R2 = 0.62 and 0.85, respectively) (Fig. 8). The L value is assumed to be inversely proportional with flash flood susceptibility.
Drainage density (Dd) is supposed to be closely correlated with lithology and the tectonic evolution of the catchment (Tucker et al. 2001). Others found correlation between relative relief and Dd value (Nag 1998). Our results revealed no correlation with basin relief (R2 = 0.003) (Fig. 8). Calculated Dd varied between 0.16 and 1.57 with a standard deviation of 0.33. Based on the available classification of the aforementioned two literary sources, the studied watersheds belong to the group of very low drainage density catchments (Dd < 2 km km−1). Adopting the results of Melton (1957) to our study site, we found different Dd values for watersheds of different area and identical lengths. Low Dd values indicate basins of relatively low surface stream density and a prolonged hydrologic response and refer to permeable near-surface rocks, dense vegetation and low relief (Sukristiyanti et al. 2018).
Computed low elongation ratios (x̅ = 0.56) demonstrate that the majority of the analysed watersheds are elongated (0.5–0.7, n = 33) or highly elongated (< 0.5, n = 13). Only a minority of them are less elongated (0.7–0.8, n = 6) and oval (0.8–0.9, n = 1). The large number of elongated and highly elongated basins suggests younger and neotectonic evolution, whereas oval and circular basins show higher runoff intensity. This finding corroborates the results of Daipan (2020); Elsadek et al. (2019) and Sukristiyanti et al. (2018). Furthermore, the low Re values indicate low flood hazard in basins 5, 48, 51 and 53 (Re = 0.44, 0.32, 0.37 and 0.47, respectively). In contrast, the high Re values of basins 12, 19, 21 and 35 (0.71, 0.76, 0.75 and 0.82, respectively) likely demonstrate intense erosion and high flash flood hazard. Elongated watersheds are usually characterized by longer distances between the adjacent confluences (hydrographic nodes), low peak flows and broader hydrographs. All studied watersheds have low form factors. Therefore, are considered to be elongated rather than circular (0.08–0.53). This parameter did not provide any additional information on flood hazard.
Circularity ratio (Rc), essentially the same as elongation ratio and form factor, of less than 0.5 were found for all studied watersheds of the current study, again indicating low runoff. The calculated lemniscate (k) values (1.47 to 9.59) in our study are dominantly high, hence, similarly to the circularity ratio, indicate elongated catchments. This finding also confirms the fact that reduced k values are coupled with increasing stream orders. Gravelius coefficients (GC) of 1.53 to 2.97 were found for the watersheds from which GC values of 1.7 to 2.1 covered 68% of the total area. According to the classification of Sassolas-Serrayet et al. (2018), 25 catchments (47.1%) with a total area of 295.7 km2 (45.4% of the mountainous area) belong to the group of elongated (GC ≥ 2) catchments.
In correspondence to previous literature, maximum basin length showed a markedly higher correlation with catchment area (R2 = 0.67) than with stream length (Fig. 8). It was due to the inaccuracy of the drainage network database, where streams with order = 1 were not included (Fig. 3).
4.2 Linear parameters
For the stream order we used the traditional hierarchical ranking by Strahler (1957). The lowest and the highest orders and the number of stream segments in the studied basins are shown in Table 2. The maximum Strahler orders (umax) in the studied catchments were found to be dominantly between 1 and 3. Watercourses of Strahler order of 3 (n = 4) were only found in the periphery of the studied area (Figs. 4 and 5 and 6). Total stream lengths (ΣL) varied between 1.05 and 36.27 km. We found a correlation of R2 = 0.75 between the two parameters which points out that number of streams is associated with greater total length (Fig. 8).
4.3 Topographic parameters
Properties associated with relief were characterized by the parameters of maximum height (H), minimum height (h), basin relief (r) and relief ratio (Rr). The highest (H) and the lowest (h) points of the studied catchments are located at elevations of 201 to 680 m and 105 to 224 m, respectively (Fig. 6). Basin relief changes in close correlation with H (R2 = 0.93) (Fig. 8). In accordance with former findings (Daipan 2020), relief ratio was also found higher for watersheds of smaller area.
4.4 Land use parameters
In accordance with the findings of Kamykowska et al. (1999), we revealed that response times of flash floods in the studied area are significantly controlled by land use, i.e. the extent of forests (Fa), grasslands (Ga) and arable land (Aa). Percentage forest cover in the headwaters was found especially influential on delaying and mitigating runoff and changing the proportions of evaporation, infiltration, and surface runoff. Percentage of forests, strongly correlated with high relief and higher elevation, exceeds 70% in the studied subwatersheds. (The average ratio of forests is 23.4% for the Mecsek Hills.) However, the higher percentage of forested areas contributes to an intense production of woody debris contributing to flash flooding in the area through hindering runoff. In the foothill areas of lower elevation arable lands dominate the subwatersheds, while the total area of natural pastures is negligible (Fig. 7).
4.5 Flash flood susceptibility
Among the topographical factors discussed by former literature (Esper Angillieri 2008; Singh et al. 2013; Abdel-Fattah et al. 2017; Puno and Puno 2019; Alam et al. 2020; Obeidat et al. 2020) the following parameters have been selected for analysis: area (A), drainage texture (Rt), drainage density (Dd), elongation ratio (Re), form factor (Ff), lemniscate index (k), Gravelius coefficient (GC), forested area (Fa), relief ratio (Rr). Among them A, Dd and Rr were in direct relationship with the probability of flash flood generation, while Rt, Re, Fa, Ff, k and GC are in inverse relationship with flash floods. All selected factors are related to runoff intensity and flash flood generation; hence they are applicable for the evaluation of flood susceptibility at watershed levels.
On the flood susceptibility map based on morphometric parameters subwatersheds were ranked on a linear scale of 0 to 9 (0 = lowest, 9 = highest) (Fig. 9). We revealed that the parameters most significantly contributed to flash flood generation were subwatershed size (small or medium) and compactness. Runoff was further intensified by high relief and low forest cover. The subwatersheds of the highest flood susceptibility (7 to 9) were 2, 10, 12, 16, 19, 29, 35, 49, while the lowest susceptibility (0 to 4) was found for watersheds of large area, elongated shape, low relief and extensive forest cover (5, 13, 15, 17, 18, 31, 32, 38).
Our results partly corroborated the findings of previous modelling which had been based on the spatial distribution of precipitation totals and intensities (e.g., Lóczy et al. 2012b). The spatial distribution of flash flood susceptibility showed a good spatial correspondence with the documented locations of flash flood events in the Mecsek Mountains (subwatersheds 2, 7, 12, 19, 16, 41, 46, 47, 49) (Pirkhoffer et al. 2009).
In contrast, ranking purely based on morphometric parameters performed somewhat poorly in the south-central part of the studied area (downtown Pécs, subwatersheds 36, 39, 40, 45, 48). Here, urban development, improper maintenance of hydrologic structures used for flood mitigation and smooth conveyance of stormwater and large extent of impervious surfaces may significantly increase runoff and may alter flow directions. Such factors increase the actual flood potential of these subwatersheds (Czigány et al. 2010).
From the comparison of watershed ranking for flash flood susceptibility with discharge data of official stream gauges, the following conclusions have been drawn. The number of events undoubtedly qualified as flash floods and the susceptibility rank established from shape, morphological, relief and land use variables do not correlate (R2=0.28) (Fig. 10).
It should be noted that this statistical result is essentially distorted by a range of factors. Out of the 53 studied water discharge data are available in ten cases and they are asymmetrically distributed. Furthermore, there are four additional subwatersheds with two gauges. The outlets of the delineated subwatershed do not coincide with the gauges and the available date series do not cover the same period.