©Department of Geography. Valahia University of Targoviste Annals of Valahia University of Targoviste. Geographical Series Tome 14/2014 Issues 2: 95-110 http://fsu.valahia.ro/images/avutgs/home.html
ASSESSING THE POTENTIAL FOR THE MAXIMUM DISCHARGE IN COȘUȘTEA HYDROGRAPHIC BASIN WITH G.I.S. TECHNIQUES. A COMPARATIVE ANALYSIS: SCS - CN METHOD VS. TOPOGRAPHIC WETNESS INDEX
Gabriela Adina MOROȘANU University of Bucharest, Faculty of Geography Bd. Nicolae Bălcescu nr. 1, cod poștal 010041,sector 1 București, România, tel 021-315.30.74 Email [email protected]
Abstract
The study was conceived with in view of obtaining the zoning of the maximum volume discharge that may characterize the hidrographic basin of Coșuștea River (449 km2). Because of the fact that we did not have at our disposal enough measured data from the gauging stations in all the main subbasins of a middle surface, the emphasis on the potential of maximum water discharge remains to be calculated using a number of mathematical and hydrological models. Coșuștea river basin is situated in the north-western part of Oltenia Region, being extended over the Getic Plateau in its lower sector and over Mehedinți Plateau and Mehedinți Mountains in its upper sector. The main river (Coșuștea) has a length of approximately 77 km and it is the collector of numerous intermitent rivers and rivulets which may produce flash floods and flow accumulation in their small catchments. In its entirety, Coșuștea basin is composed of 7 main subbasins, which have surfaces from 9,5 km2 (Gârbovăț) to 202 km2 (Coșuștea, which actually accompanies the main river along its riverbed). In relation with the discharge evaluation, we opted for two methods. The first and most complex illustrates the mathematical relation between the direct discharge and precipitations, transposed in an equation where the curve number conventionally represents the maxim potential water retention for each soil type. The second method used was the topographic wetness index, which depicts zones from the river basin most prior to runoff discharge. From the employed analysis, it appears that the maximum volumes correspond to surfaces having clayey texture which are used as pastures or crops along the slope. Also, the largest volumes were calculated for the small river basins, with almost circular shape/form, such being the case of Valea Rea subbasin. As we expected, along Coșuștea river and in its homonymous subbazin, the results show a weak discharge, due to the high percentage of afforestation and complementary, due to its ellongated form/shape, which favors a long period of water concentration.
Keywords: Coșuștea, curve number, maximum discharge, Topographic Wetness Index
1. INTRODUCTION
Over the last years, GIS-based studies for determining the potential for water discharge and wetness persistence within watersheds have undergone significant development, both at the international level and in Romania. An essential component of water cycle is undoubtedly the surface run-off, defined as the water flow over the land surface, which eventuates when rainfall exceeds the maximum saturation soil level and also when land concavities are filled (Bilașco, 2008). Most of the studies (Hawkins et al., 2002; Bilașco, 2008; Stemaiu & Drobot, 2007) were conceived to determine the natural boundary, the areal coverage and the amount of run-off that can
95 be formed in watersheds. Among the most commonly used methods for determining the maximum discharge, while taking into account the physiographic characteristics, one should mention the empirical USDA-SCS-CN model (Walker et al., 2000; and Pandey et al., 2003), due to its simple application and proven efficiency in estimating the runoff. It was first used in 1972 and suffered a string of changes with time, depending on the climatic, hydrographic and land use conditions of the studied areas (Stematiu & Drobot, 2007). Various studies using the SCS-CN method were conducted all over the world, in places such as the Calabarzon Region, Philippines (Adornado & Yoshida, 2010), Ilinois, U.S.A. (Walker et al., 2000) and Hungary (Kovar & Hradek 1988), the last one being applied for the antecedent moisture conditions to model the possible maximum flows. In Romania, SCS-CN approaches were carried out in places such as Pecineaga (Costache, 2014), Ocna Sibiului Lake (Man & Alexe, 2006), or with the aim to achieve a flash flood prediction when having limited or no pluviometric and flow data (Bilașco, 2008). In the majority of the above mentioned examples, the function of hydrologic soil group and land-use/ land cover is represented by the CN. In addition to the CN, rainfall data was used, in order to approximate the areal run-off capacity of the watersheds, expressed numerically and integrated in different formulae (Stematiu & Drobot, 2007). Topography is often one of the major controls on the spatial pattern of saturated areas, which in turn is key to understand much of the variability in soils, hydrological processes, and stream water quality (Man & Alexe, 2006; Grabs et al., 2009). In this outlook, the topographic wetness index (TWI) has become a widely used tool to describe wetness conditions at the catchment scale. Numerous authors have attempted to correlate topographic wetness index with other indexes based on the topographical features of the study area, in order to compare their efficiency in showing the intensity and the paths of the water flow on the slopes and along the valleys. Some relevant examples in this sense would be the use of topographic wetness index, by the measurement of the upslope area, slope and creeks representation, along with the valid representation of groundwater level, moisture and pH of the soil (Sørensen et al., 2006), the estimation of the downslope gradient in four artificial constructed terrains (Cheng-Zhi Qin et al., 2011), in order to reflect the local terrain conditions for the maximum flow generation, or finally, a comparison between topographic wetness index and model-based wetness indices (Grabs et al., 2009). The advantage of estimating the extent of the areas subjected to maximum flow by the two above mentioned methods consists in their applicability to any type of slope, land cover and soil condition (Western et al., 1999), one referring to the inner property of an area to generate an elevated flow or to maintain the moisture in the soil (Topographic Wetness Index), while the other visually predicts the soil humidity and land use patterns and thresholds (SCS-CN Curve Number).
2. PARTICULARITIES OF THE STUDY AREA
Coşuştea Basin is located in south-western Romania, more precisely in the north-western portion of Oltenia, bordered in the North by the 45o Northern latitude parallel, in the South by the 44o10’ Northern latitude parallel, in the West by the 22o35’ Eastern longitude meridian and in the west by the 23o 20’ Eastern longitude meridian (Figure 1). The north-western extremity of the basin is represented by a portion of the Mehedinţi Mountains and is surrounded by the summits of the Domogled Massif, which reach altitudes of more than 1200 meters along the drainage divide (Badea & Sandu, 2010). The upper sector ends with the cliff/escarpment/steep slope between the mountains and the Mehedinţi Plateau, and the plateau’s highest elevations can be found within the basin, such as Paharnicului Peak (885.4 metres) or Cornetul Babelor (759.9 m). Most of the basin coincides with the Coşuştea Hills, a part of the Getic Piedmont, with altitudes that rarely exceed 400 m (Badea & Sandu, 2010; Șchiopoiu, 1982). From a hydrographical standpoint, the Coșuștea Basin is located to the west of the Motru Basin, for whom Coșuștea is a first degree tributary. In relation with the Danube, Coșuștea is a third
96 degree tributary, through the Jiu hydrographic system (Savin, 2008). It is surrounded by numerous smaller basins, tributaries of the Motru and Cerna rivers, such as: Due to its position (Figure 1), the Coșuștea Basin has a temperate-continental climate, with Mediterranean influences in its lower and middle sectors and a temperate-mountainous climate which passes through various climate zones, as one advances upstream (Badea & Sandu, 2010). These climatic particularities determine a distinct discharge regime, with fluctuating flows in the sector represented by the Coșuștea Hills, generated by the pluviometric regime and with permanent water resources in the upper mountainous and sub-mountainous sector, which is nevertheless affected by significant water losses because of the geology of the Mehedinți Plateau and Mehedinți Mountains, which consists mostly of limestone (Romanian Geological Map; 1:200.000).
Figure 1. Location of the study area
The physical characteristics of the study area that mostly interested were the soils, the land cover and the slope. First, the soils of the Coșuștea Basin fall within the following zones and domains (Șchiopoiu, 1982): a. The cold climate soil zone – moist, with the cambisol domain; b. The cool climate soil zone– moist and cool – partly moist, with the cambisol, luvisol and faeoziom soil region. The soil texture provides the best connection between the possibilities of flowing waters to circulate, stagnate or infiltrate. In Coșuștea river basin, in addition to the fine textures represented by the loamy-clayey and clayey classes, one can also find overlapping areas with a moderately intense level of stagnogleic and with a loam texture (most of the interfluves). The mixed textures (loam-sandy or loam-clayey) are the most favorable for triggering hydromorphic processes and are the dominant textures in our basin. The low percentage of soils with a sandy texture (2,4%) prevents the infiltration of a significant amount of water into the soil cover during heavy rains and thus favors slope flow.
97 Second, in terms of land use, it is important to mention the significant proportion of land covered by forests and the moderate spread of secondary pastures, mixed farmland areas and orchards. Finally, the slope map has played a significant role in implementing the SCS-CN Curve Number and the Wetness Topographic Index methodologies. Most of the slopes are small or average (between 3 and 20 degrees); very small slopes (below 3o) appear only in alluvial plains and large or very large slopes can only be found on some mountain sides. Nonetheless, the hydrological regime of the rivers, which is strongly influenced by climatic particularities (two precipitation peaks separated by a long period of dry weather with intermittent downpours), has obvious effects in modeling the entire basin. Additionally, the geological make-up is highly favorable for an intense modeling of the landscape and the appearance of denudation processes.
3. METHODOLOGY 3.1. General Considerations On The Methods And Data Used
The aim of this study is to calculate the weighted Curve Number within Coșuștea river basin, by SCS-CN method and to correlate the results with the extension of the area prone to hydrological risk, according to the wetness topographic index map. • In order to achieve that, we estimate the punctual value of lag time and concentration time for the closure section of Coșuștea river basin. • The same two steps of the research are repeated for the six main subbasins: Coșuștea Mică, Valea Verde, Valea Rea, Gârbovăț, Valea Găinii, Govadarva. • In the end, we intend to correlate the topographic wetness index with the values of Curve Number for the whole basin and its subbasins. The cartographic data and the software needed to implement the methdology are synthesized in the figure 2:
Figure 2. Research execution procedure
To reach our objectives, we relied upon two different hydrological approaches: SCS-CN method and Topographic Wetness Index from a steady-state. The first model took in consideration the slope and hydrological type of soil, as primordial factors for the maximum discharge, whereas the second one operated on the following assumption: the depth of groundwater tables and flowpaths are largely controlled by the surface topography, computed as grid cells (Grabs et al., 2009).
3.2. SCS – CN Method Implementation
From the smallest catchments to the most extended river basins, a hydrographic system is affected by its environmental conditions that make it more or less vulnerable. For this reason, a link between environmental or physiographical factors (Stematiu & Drobot, 2007) and the prevention of hydrological extremes needs to be established. There is a growing awareness about reducing the hydrological risks by taking into account the environmental factors that may change within a watershed and might certainly influence the distribution of the maximum flow on the slopes and
98 along the hydrographic network. In an attempt to analyze the possibility for the formation of the maximum flow in Coșuștea river basin, we took advantage of the widely used SCS-CN method, developed by Natural Resources Conservation Service (NRCS). Evidently, far from being a complete and sufficient approach to flood risk prevention, the methodology provides useful procedures to determine the maximum water discharge for a certain area. Disposing of the altitudinal distribution of the main watercourse and its tributaries, along with the important morphological parameteres’ variation (slope, profile curvature, flow direction) and physiographical characteristics (soil texture, land use), factors that can compete with geological features (limestone and metamorphic rocks in the upper basin and sedimentary rocks in the lower basin (***, Romanian geological map, scale 1:200.000) for the potential formation of floods and high waters, we considered two main variables in our study. Thus, as recommended in the literature (Man & Alexe, 2006; Stematiu & Drobot, 2007; Adornado & Yoshida, 2010) the database structure included hydrological soil types and Corine Land Cover database for land use, the key vectors fixing what the authors called "Curve Number". As manual mapping of soil moisture patterns is expensive and time-consuming, especially for small catchments with great spatial variation, and land use design from the field surveys or ortophotomaps visualisation may be risky and labourous, we used as entrance data the digital map of soil (www.geo-spatial.org) and the basin zonation, according to Corine Land Cover database (Bossard et al., 2000; www.geo-spatial.org). The land cover/ land use map (for 2006), as well as the soil texture map, were pre-processed for the reclassification and superposing, according to the standard classes of the SCS-CN methodology (Bossard et al., 2000; Stematiu & Drobot, 2007). The follow-up process was the rectification of the two reference maps – soil map and land use map of the watershed -, as well as the re-creation of vector layers used in the final table associated to the spatial distribution of Curve Number values. Though all necessary software tools were provided, knowledge on existing soil textures needed to be gained. For this, the soil texture map, reclassified according to SCS-CN intervals, represented the first output and at the same time input of the final results, in terms of cartography (Figure 3).
Figure 3. Map of soil textures, based on the hydrological groups of SCS-CN
99 The first group designated the areas with low runoff potential, being present in the upper basin, on limestone and metamorphic rocks. The soils are characterized by a high infiltration rate even when intensively wetted, having a high rate of water transmission (Pandey et al., 2003). The second group occupies limited areas accompanying the main rivers and chiefly consisting of soils with a moderate infiltration rate. Moderation is the key term to describe the hydrological behavior in this case, presenting tolerable draining, textures and water transmission (Pandey et al., 2003; Walker et al., 2000). The third hydrological soil group has a moderate spreading, on broader areas than the latter, as well as a slow rate of water movement. This group appears in most of the cases in the lower sector of the river basin, being related to the loamy and loamy-clayey textures of the soil, which impedes the descending of the water, due to the moderately fine texture. Lastly, the forth group concerns the soils with a very slow infiltration rate and thus a high runoff potential. Generally, the impervious soils with clayey texture form part of this category, having a developed swelling potential. In our study area, they appear both in the lower sector and in the upper one, due to the fact that they can be association to soils with a permanent high water table and fine texture, such as clays, but also to thin and shallow layers of soils over nearly impervious material, at high altitudes (Man & Alexe, 2006; Adornado & Yoshida, 2010). Not only did the hydrological soil groups account for the computation of SCS-CN algorithms, but also for the land cover map (Figure 4) of the river basin, classified by the recommendations of the technical guide available for CORINE Land Cover 2006.
Figure 4. Land use and Land cover map of the study area
After a brief analysis, we can say that the broad-leaved forests are predominant, having from the start a favorable score contrary to the high values used in the calculation formula of the curve number, thus keeping a balance between maximum flow and heavy rains on most of the area. Local discontinuities, such as bare rocks, natural grassland, urban fabric or agricultural land, add a small percentage of hydrological risk from the point of view of land use. In general, the most crumbled part of the river basin, and at the same time the one prone to hydrological risk from this perspective of land use/ land cover, is certainly the lower sector.
100 On the basis of these two maps, a new combined and composite layer, resulted from their intersection, was generated in ArcGIS and introduced as cartographical input in order to derive the modified SCS – Curve Number values for every cell of the map. With the help of this technique, runoff studies can be undertaken anytime as long as new information is available and updated spatially and from the temporal point of view. The set of equations, in which the CN is presumed to emphasize the potential maximum retention of soil equal to the ratio of direct rainfall (Stematiu & Drobot, 2007), are as follows: S = 25.4* (1000-CN-10) CNi = 25400/ (S+254) TL = (3.28084 * L)^0.8* [(S+1)^0.7]/(1900* ) Tc = 1.67 * TL
The notation S is for the retention capacity of the spatial cell, wherein the curve number (0 < CN <100) shows a convenient representation of the potential maximum soil retention (S). In addition, lag time (TL) and concentration time (Lc) are obtained by also integrating the hydraulic length (L) and the average slope of the catchment taken into consideration (IB) – Appendix 1 and Appendix 2. Also, CNi (the curve number for each land use polygon) and CNaw could be computed to obtain the area-weighted for the entire river basin or for its sub-catchments (Costache, 2014). As a function of the concentration time, defined as the maximum period between the moment when a drop of water reaches a point on the surface of the watershed and the moment when it gets to the measuring point (Stematiu & Drobot, 2007), the concentration time remains in the leading edge to enhance the sub-catchments with values of less than 6 hours, considered as torrential ones (Costache, 2014).
3.3. Topographic Wetness Index Implementation
Topography is certainly the most important control factor that dictates the pathways of water and predicts the location of accumulation and runoff areas (Qin et al., 2011). Generally, water flows according to the topographical predisposition, making thus possible for the spatial variations of hydrological processes to be estimated. Therefore, the products generated from the topographic treatment, by means of spatial geometrics applied to the digital elevation models (DEMs), play a significant role in measuring the topographic control on the water flow. As an introductory framework, the topographic wetness index (TWI) was developed by Beven and Kirkby (1979) within the runoff model TOPMODEL (Qin et al., 2011). The formula used by the topographic wetness index is ln(α/tanβ), where α represents the specific upslope area, while β is the local (the pixel’s) surface slope (Sørensen et al., 2006; Grabs et al., 2009). For our study, a medium-resolution digital elevation model of 30 m grid size, derived from SRTM data was used, in order to investigate the influence of the topography in the water discharge. The most important dataset in this part of the research is the elevation information. Thus, prior to any other work, we resorted to a numeric model, which was then converted into a DEM and became the primary input for the wetness index computation in SAGA GIS software. Geospatial processing of the topographical map, as well as the subsequent topographical products, enhanced the consisting subcatchments/watersheds. The steps towards the generation of the final map representing the spatial distribution of the topographic wetness index used the tools for filling (the removal of sinks in the relief), sink (removal the pixels showing areas of internal drainage developed during the process of DEM creation), designing the flow direction map (Figure 5) and the flow accumulation map. As a first remark, we can assert that the flow direction to the southeast is predominant, while the northeast and northwest flow directions are the least common. The topographical treatment provided the spatial patterns of catchments, starting from the assumption that the water flow is rendered by topography convexities, concavities and slope
101 orientation. If we were to compare the concepts which lies behind the TWI map generation with the hydrological meaning of soil textures for SCS-CN, the subsurface lateral transmissivity is the key parameter that ties them, while TWI offeres a natural predictibility for the downslope hydraulic gradient, not one depending of the man influence on the soil. However, the TWI method is less suitable for flat surfaces, because of rather indeterminate paths of flow as the direction vector regards/in terms of direction vectors. This is due to the fact that, even if the topography is usually a constant factor to be relied upon, it is not so dynamic and may be overcome by situations when meteorological and hydrological data change the water behavior on the slopes (Western et al., 1999).
Figure 5. Flow direction map of the study area
To avoid undesirable results, apart from a simple comparison between the two methods, we automatically built a model out of their combination. In figure 6, the process of comparison of the two methods is represented through a diagram operated in ArcGIS Model Builder. The first two products were reclassified according to a scale from low to very high potential for water discharge, and then superposed with the aim to show the pixels with the highest scores in both methodologies applied. The scores of the reclassified WTI and CN rasters were taken into consideration distinctly, the first one accounting for 75% of the final runoff map, while the other for 25%. The percentage contribution of the two rasters in the final map was decided in concordance with the weight each one has in reality in forming the maximum discharge. The Curve Number indicator calculated on the land use and soil hydrological condition received values ranging from 52 to 95, increasing directly proportional with the hydrological risk, while the wetness topographic index had values ranging from 4.4 to approximately 22. It would have been obvious that, in the flat areas with bare rock or impermeable edaphic layer (karst zones, mountain slopes higher than 1000 m altitude), even if the In the curve number received big scores, generally surpassing 80 units, the maximum discharge would not have been so intense in reality. Conversely, areas with permeable soil, belonging to hydrologic soil group A or B, would have been showed as less risky (with low values) in the curve number map, but they frequently are prone to risk in reality. Keeping all this facts in mind, the process of numeric modeling of the runoff potential for each sub-catchment (whose individual rasters were obtained through a process of reiteration in Model Builder, to hasten the workflow), introduced all the pixels’ values for WTI and Curve Number in a database table.
102 Therefore, the model tried all the possible combinations between the two variables in raster calculator, so that the final result would reflect as close as it could the real discharge potential.
Figure 6. Model of the workflow for the process of Runoff final map
4. RESULTS AND DISCUSSIONS
Essentially, the results of our research will focus on the fact that the study was conducted in the hydrological basin of Coșuștea river, to determine the extent of watershed areas fron the angle of the maximum run-off generated. To achieve this goal, geospatial processing for implementing SCS-CN and WTI methods was done. Firstly, the resulting Curve Number map was accurately drafted (Figure 7), showing the areas prone to increased humidity risk (because of inondation or water stagnation) through a complex GIS-based analysis. With this technique, the CN map retrieved the areas with a poor hydrological quality of the soil (clayey texture or bare rock, being highly impermeable) and also lacking vigorous and dense vegetation, such as deciduous forests and plantations. This information is significant if taken into account with precaution, being aware of the fact that the spread of high CN number values can sometimes be very large, while only a small part of them are in fact submitted to hydrological risks. In an attempt to characterize the drainage pattern of the sub-catchments, the seven sub- basins were individualized (Table 1), with surfaces ranging from 9,6 km2 (Gârbovăț sub-basin) to 300,9 km2 (Coșuștea sub-basin, located along the river with the same name and more developed on the left bank due to the low density of the hydrographical network and the absence of major tributaries that can have their own sub-basins).
103
Figure 7. Distribution of CN values
Table 1. Curve Number and its derived parameters Results of Time of concentration on Coșuștea basin and its subbasins Flow length (Km) CN Slope (%) Tlag (h) Whole catchment 57 69.4 14.7 7.38 Subb. Govadarva 9.4 78.3 12.7 1.46 Subb. Coșuștea Mică 15.3 74.5 12 2.48 Subb. Valea Rea 3.2 57.8 12.3 1.09 Subb. Gârbovăț 6.1 79.2 10.6 1.10 Subb. Valea Verde 7.4 61 26.6 1.33 Subb. Valea Găinii 3.7 55.5 15 1.17
An overview of the values of CN and its derived parameters, averaged for the main sub- catchements, highlights Gârbovăț, Govadarva and Coșuștea Mică subcatchments, with CN values over 70, although their slopes are not the highest ones. The Lag Time, considered as the time for the rain drop to travel from the point where it hits the ground and the point where the maximum discharge is measured, is in generally less than 6 hours (apart from the whole catchment, which has a longitudinal shape and a complex composition in terms of slope, soils and land cover). This means that in all the sub-catchments, the lag time confirms an inner runoff potential which can be transformed in flash floods in the eventuality of a long and intense rainstorm. Forwards, some simple but suggestive correlations have been made. The first analysis undertaken regards the correlation between the potential of water retention (S[mm]) and the concentration time (Tc[h]) in each sub-basin (Figures 8a and 8b).
104
Figure 8. a. Correlation graph S – Tc; b. Corresponding table with Tc(h), Tc(min) and S(mm) values
Thus, an indirect relation was established, explained through the fact that when runoff potential increases, water retention potential, which can possibly lead to water stagnation risk, decreases. In the up-top positions, there are Valea Găinii and Valea Rea sub-catchments, with high potential of water retention and low concentration time. On the contrary, Coșuștea Mică subcatchment has the highest concentration time, but also a medium water retention value. While still keeping the CN results in the heart of our study, a second GIS analysis was needed. Therefore, the wetness topographic index map (Figure 9) was thought to provide a more consistent input for a GIS decision making. It could also provide input for policy and decision making for the benefit of the general public. Lastly, further detailed soil study as a basis for hydrologic soil group determination and evenly collected precipitation information is encouraged to obtain a more detailed and reliable output.
Figure 9. Topographic Wetness Index map
105 Compared to the aspect of the Curve Number map, in this case, WTI values are intimately tied to the confluence or greater hydrographic density zones. For instance, two major high value areas are highlighted in the upper sector of Coșuștea basin and along the lower part of Coșuștea River and along its main tributary, Coșuștița. The map demonstrated that the hydraulic length (appendix 1) weights the most in the influence of the topography on runoff discharge. These findings are significant, especially when there is limited discharge and rainfall data, as it is the case of Coșuștea sub-catchments, with agricultural lands and settlements situated along the rivers or on unprotected slopes and subjected to hydrological of fluvial-geomorphological risks. Our empirical research also indicated that a relationship between Curve Number and Wetness Topographic Index can be established, in terms of values associated for every sub- catchment. Thus, in Figure 10, a graphical representation of the direct connection between them was created. Although not presenting a high correlation coefficient, the results validate one methodology through the other. Nonetheless, optimal average values could be obtained, despite the scale representation values of the two indices. Gârbovăț, Coșuștea Mică and Govadarva sub-catchments present the most significant correlation, while the others are more detached from the trend. As we have expected, Valea Verde sub-catchment, as its name suggests (hydronym – Green Valley) is the area less subjected to hydrological risks, due to the permeable soils and the large extent of the forests within it, but also due to a smaller hydraulic length, slope and, consequently, lower WTI average value. The sub-catchment that basically represents an exception from the rule is Valea Rea (hydronium – Bad Valley), which has a meaningless potential for runoff discharge from the point of view of the CN values, but a high potential for extreme hydrological phenomena, because of its topography (high slopes, high/significant density of torrential organisms).
Figure 10. WTI – CN correlation graph
Furthermore, the comparison would not have been complete without the combination of the two methods and the decreasing of the influence of some contributing factors in the formation of runoff discharge. The correlation between the curve number and some of its derived parameters, but also between CN and TWI values, effectively demonstrated that there is not one single index that can be considered optimal to estimate and represent the areas prone to hydrological risk. Even if we are aware that a multiplication of the indices used will inevitably lead to a variation in correlation strengths between them, it is also well know that a GIS study is sometimes useless without any field measurements and observations, or risk mapping. Hence, to improve our results, we conceived a combined TWI-CN runoff potential map (Figure 11).
106
Figure 11. CN – TWI runoff discharge map
Overall, the areas with high and very high runoff potential are limited to the river valleys, confluence zones and high density of torrential organisms and rivers. As far as the sub-catchments are concerned (Figure 12), there are three of them (Valea Rea, Valea Verde and Valea Găinii) which do not present high runoff potential, contrary on what we expected from the topographic wetness index analysis (at least for the first mentioned).
Figure 12. Percentages of runoff potential level for the sub-catchments
The map’s statistics also yield a general 2.11% (9.44 km2) very high runoff potential, 8.29% (37.1 km2) - high potential, while the medium and low runoff potential occupy the major part of
107 Coșuștea Basin (20.75%, respectively 68%). In this final map too, Coșuștea Mică, followed by Govadarva and Gârbovăț are the forefront sub-catchments with the highest runoff potential.
5. CONCLUSIONS
The parallel between the two methodologies proved its effectiveness, one methodology supplying what the other lacked and together leading to a more reliable runoff map, by their weighted combination. In general, the Curve Number scores around 70, which means that Coșuștea river basin is characterized by a moderate to high potential for surface runoff, a precursor factor of flash-floods, due to the moderate slope values and the predominance of clayey texture of the soil, which are favorable to accelerated flow and water retaining. The Wetness topographic Index demonstrated its suppleness in showing a zoom-in of the most risky areas prone to runoff discharge. Water time lag and time of concentration values (most of the times less than 6 hours) are specific for such a basin with wide development in the plateau (Mehedinți and Getic Pleateau). The correlations between the Curve Number and the mean altitudine and mean slope were successful and relevant to define the behaviour of flow concentration on the slopes. The correlation between water concentration time and water potential for stagnation had a low dependency coefficient, although valuable lessons could be drawn from this generally indirect connection. The correlation between topographic wetness index and the Curve Number did not show a high dependence within the whole basin and its six subbasins. The low values of these parameters demonstrate that, in case of torrential rainfall, accelerated flow and water concentration may be at the origin of a rapid transmissivity of the flood wave, and thus lead to an exponential growth of the discharge. There are major differences withing the subbasins, given their form (from almost circular to elongated ones), their slopes, their topography, their soil textures and most importantly, their land use. In support of this statement came the final runoff map, in which it could be clearly seen that river confluences in the lower sector of Coșuștea Basin are mostly prone to hydrological risk. In addition to these findings, the final runoff map, created from the two reclassified and further processed indices, distinghuished even better the areas presenting the highest maximum discharge, in this sense Coșuștea Mică sub-basin being re-confirmed as an area subjected to hydrological risks. Conclusively, this research has provided empirical insights on how a maximum water discharge could be estimated and integrated in further, more detailed and extended studies on hydrological risk potential and even prognosis. In the end, the research fulfils an identified need to bring about two GIS supportive methods and one new resulting combination of the two indexes that can substitute, when necessary, the punctual and scattered hydrometrical measurements along the rivers.
ACKNOWLEDGEMENTS The author expresses her gratitude to Mihail Andreas Mitoșeriu, for the support provided with the translation of the paper.
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109 APPENDICES
Appendix 1. Map of the hydraulic length
Appendix 2. Geodeclivity map
110