Accepted Manuscript Spatial and temporal variations of rainfall-runoff erosivity (R) factor in Kakheti, Georgia M. Tsitsagi, A. Berdzenishvili, M. Gugeshashvili PII:
S1512-1887(18)30095-2
DOI:
10.1016/j.aasci.2018.03.010
Reference:
AASCI 203
To appear in:
Annals of Agrarian Sciences
Received Date: 11 January 2018 Accepted Date: 2 March 2018
Please cite this article as: M. Tsitsagi, A. Berdzenishvili, M. Gugeshashvili, Spatial and temporal variations of rainfall-runoff erosivity (R) factor in Kakheti, Georgia, Annals of Agrarian Sciences (2018), doi: 10.1016/j.aasci.2018.03.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Spatial and temporal variations of rainfall-runoff erosivity (R) factor in Kakheti, Georgia M. Tsitsagia*, A. Berdzenishvilib, M. Gugeshashvilia Ivane Javakhishvili Tbilisi State University, Vakhushti Bagrationi Institute of Geography 6, Tamarashvili Str.,Tbilisi, 0177, Georgia b Ivane Javakhishvili Tbilisi State University 3, Chavchavadze Ave.,Tbilisi, 0179, Georgia Received: 11 January 2018; Accepted: 02 March 2018
*Corresponding author: Mariam Tsitsagi
[email protected]
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ABSTRACT
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Soil erosion is a very complicated process. Rainfall erosivity is one of the main factors affecting on soil erosion. The erosive power of precipitation is accounted for by the rainfall erosivity factor (R-factor). Rainfall erosivity (R-factor) itself is a very important factor in soil erosion modeling. R-factor is a product of rainfall kinetic energy and rainfall intensity. Rainfall intensity change is one of the main indicators of climate change. It has a great influence on agriculture as one of the main factors causing soil erosion. Information of rainfall ersivity is rarely available with good spatial and temporal coverage. Accurate estimation of rainfall erosivity requires continuous rainfall data. Because many parts of the world still do not have detailed rainfall intensity data available, many studies have been performed to estimate R-factor based on available rainfall data. There are several alternative methods cited in science literature. This study aims to evaluate the temporal as well as the spatial distribution of rainfall erosivity and to calculate average annual rainfall erosivity for three study periods (1936-1962; 1963-1989; 19902016) in Kakheti, east Georgia. As far as Kakheti is the agrarian region, frequency and intensity of the rain are very important factors in agriculture point of view. Our study provides the assessment of rainfall erosivity potential with use of modern research methods for five weather stations (Telavi, Gurjaani, Sagarejo, Dedoplistskaro and Lagodekhi) in Kakheti. Rainfall erosivity potential was determined for every weather stations in Kakheti region from literature and records from meteorological stations. Then the same factor was determined by the selected methods (for each method separately), and the outcomes was compared, which allows us to determine the validity of a particular method for the study area. From the three methods used in the study process, method by Loureiro & Cautinho was finally used for the assessment rainfall erosivity during three study periods. Keywords: Soil erosion, Rainfall erosivity, RUSLE R factor, Precipitation intensity, Climate change, Kakheti 1. Introduction Soil erosion is a global problem that tends to become more extreme with the extreme variations in weather [1].Whereas, the consensus of atmospheric scientists is that climate change is occurring, both in terms of global air temperature and precipitation patterns [2]. Soil erosion is the detachment and transport of soil particles by erosive agents, most commonly water and/or wind. It is a natural process but human activities, particularly agricultural production, forestry, mining, and construction, can disturb or destroy vegetation, loosen soil, and greatly increase the 1
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risk of soil erosion losses from subsequent rainfall, runoff, and/or windstorm events [3]. (Figure 1). Herewith, soil erosion is a major agricultural problem.
Fig. 1. Irrigation erosion on the agriculture plot, near village Erisimedi (photo by M. Gongadze)
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In the context of climate change the effect of altered rainfall characteristics on soil erosion is one of the main concerns of soil conservation studies [4]. Soil erosion rates may be expected to change in response to change in climate for a variety of reasons, the most direct of which is the change in the erosive power of rainfall [1]. Furthermore, during the last 60 years wet periods have become longer over most of Europe by about 15– 20%. The lengthening of wet periods was not caused by an increase of the total number of wet days. Becoming longer, wet periods in Europe are now characterized by more abundant precipitation [5]. The same tendency has been observed in some regions of Georgia [6]. On the background of the above mentioned, previous studies confirm that the temperature and precipitation change on the territory of Georgia is heterogeneous in nature, due to the complex physicalgeographical mainly orographic and landscape-climatic conditions. In the large parts of eastern Georgia, annual precipitation decreases, decadal trend composes 1-3%. The largest decreasing in rainfall trend is observed in Kvemo Kartli, south of Tbilisi, and is more than 5%. In extreme eastern part of eastern Georgia, characterized by steppe and semi-desert landscapes, as well as in large parts of western Georgia, significant change in precipitation wasn't observed [7]. However, it is interesting to see whether the nature of rainfall is changed, which in turn influences the rainfall erosivity. A knowledgeable forecast of how erosion will change in the coming decades is thus necessary to plan for land stewardship and ecosystem preservation and it is made more urgent and complex by the general expectation that increases in rainfall intensity under global warming [8], which is why, estimating the soil loss risk and its spatial distribution are the one of the key factors for successful erosion assessment [9]. Since soil erosion is difficult to measure at large scales, soil erosion models are crucial estimation tools at regional, national and European levels [10]. To solve this task, soil erosion prediction models are effective tools for helping to guide and inform soil conservation planning and practice [11]. Many similar or different factors are connected in these models but they have in common a rainfall erosivity factor (R) which reflects the potential capability of rainfall to cause soil loss from hillslopes, and which is one of the most important basic factors for estimating soil erosion. Of all the erosion factors, rainfall erosivity and land cover/management factors are considered to be the most dynamic [12]. Climate change may lead to changes in rainfall characteristics and is thus a major concern to soil conservation [13]. However, to obtain an average measure of long-term rainfall erosivity according to the RUSLE methodology requires high-resolution of rainfall measurements and an accurate computation of each storm erosivity index: a very onerous procedure [14]. Only few studies exist that determine R-factor directly from high temporal resolution data in mountain areas of Europe [13]. Similar situation is in Georgia. Researchers have studies on soil erosion and rainfall erosivity in different regions of Georgia. Based on the research conducted in the past, it is clear that in eastern Georgia, at the boundary between steppe and forest zones, rainfall erosivity in 1963-1990 was 20-75% higher than that in 1936-1963 [15]. It is interesting to see whether the situation changed 2
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in recent decades. One of the main disadvantages in seeking to employ the RUSLE R factor is the need for a relatively continuous rainfall data series, with a time resolution of at least 15 min (pluviograph data). In its simplest form,the R factor is as an average annual value, calculated as a summation of event-based energy-intensity values, EI30, for a location divided by the number of years over which the data was collected. The calculation of EI30 requires high-temporal resolution rainfall data, typically breakpoint data, which are often unavailable in many regions of the world where rainfall is recorded only at a daily resolution [11]. Information of this nature is rarely available with good spatial and temporal coverage. Other attempts to predict rainfall erosivity from mean annual rainfall and/or mean monthly rainfall have provided results that are quite coarse, but these have been extensively cited in the scientific literature [4]. The main goal of this study is to evaluate the temporal as well as the spatial distribution of rainfall erosivity and to calculate average annual rainfall erosivity for three study periods in Kakheti.
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2. Methods and materials 2.1 Study Area
Fig. 2. Study Area
Kakheti region is located in the extreme eastern part of Georgia and between the regions of Georgia it occupies the biggest territory, it covers about 12 thousand km2 (Figure 2). Population is more than 400 thousand. Alazani Valley is placed between the ranges, from which the region's two major rivers flow, one is - Alazani and the second is – Iori. Iori River flows from the west to the east and creates Iori lowland. These two ranges, two rivers and two valleys causes the Kakheti region's agricultural production characteristics and potential, distinguishes Kakheti region from other areas of Georgia. The favorable local soil and climatic conditions allow gaining rich harvest of cereals, as well as autumn and spring wheat, barley, maize, etc [16]. Kakheti is an agricultural region, its main activity is viticulture. There are about 80 species of grape-vine in Kakheti. There are some oil reserves in Sagarejo and Dedoplistskaro municipalities. 3
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There is a transitional climate between the moist subtropical and continental climate in Kakheti. The most sediment periods are spring and autumn, while the dry periods are - winter and summer seasons. Heavy rain, especially with hail, is typical during the summer period, which is very harmful for agriculture. Analysis of the meteorological data of the 1961-2012 period shows that seven from eight cases with heavy hailstorms in Georgia have been reported in Kakheti and consequently inflicted financial loss [17]. On the other hand, the territory of Kakheti is not well provided with the atmospheric precipitations in the vegetation period. The lack of precipitations is even severer in the plant active period (VI-VII-VIII), when the harvest is formed [16]. According to the administrative-territorial division, Kakheti includes: Akhmeta, Gurjaani, Dedoplistskharo, Telavi, Lagodekhi, Sagarejo, Sighnaghi and Kvareli municipalities. Much study in recent years is addressed to the vital aspects of the data infrastructures hosting climatic data and point out that their contributions are becoming more valuable to policy making [10]. Pluviograph data were created by the meteorological stations in the study area in the 60-80s of last century. Scientific research on the study area provides data [15] on rainfall erosivity potential but it should be noted that these studies were implemented until 1989. According to the results of above mentioned research soil erosion rate is quite high within River Alazani and Iori basins. Annual soil loss is 28 t in Alazani basin and 20 t in Iori basin which is the output of the precipitation with high rainfall erosivity potential [18]. From Table 1 we can see soil erosion from arable land within the watersheds of main rivers in Kakheti [19]. Table 1. Soil erosion from arable land within the watersheds
Riverpoint
Eroded arable soils, ha
494
10.0
282
21.5
4530
20.5
203
13.5
78
17.0
41.3
15.0
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IoriLelovani AlazaniBirkiani AlazaniChiaura StoriLechuri DidkheviArtana IntsobaSabue
Total area of watershed, km2
As far as Kakheti is the agrarian region, frequency and intensity of the rain are very important factors, especially when the soil is loosen [20]. According to Gogichaishvili (2016) country’s territory is broken down into zones according to the soil erosion on the arable land in river basins. From these zones it becomes clear that the largest part of Kakheti is in 2nd and 5th zones, which in turn means 1-5 and 15-20 t/ha year soil loss [19].
2.2 Methods and Materials The erosive power of precipitation is accounted for by the rainfall erosivity factor (R-factor), which gives the combined effect of the duration, magnitude and intensity of each rainfall event [10]. 4
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The R-factor is the sum of individual storm EI-values for a year averaged over long time periods (> 20 years) to accommodate apparent cyclical rainfall patterns. The EI term is an abbreviation for energy multiplied by the maximum intensity in 30 min [21]. Soil loss in agricultural fields is associated with the product of the total storm energy-E (MJ ha−1) and the maximum intensity in 30 min -I30, (mmh−1). The result of this product is the EI30 index or storm erosivity index (MJ mm ha−1 h−1) that reflects the combined effect of soil detachment and runoff transport capacity to produce net soil erosion. After determining E and I30 values for each individual storm over the period of record, they are to be multiplied by each other and then summed on a per-year basis. The average of these annual sums over the period of record is the R-factor. Renard and et al, [21] has defined the rainfall factor R (MJ mm ha−1 h−1yr-1) as the sum of the EI30 values for the whole year according to the equations: = ∑ ∑ (1)
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Where: R factor is average annual rainfall erosivity (MJ mm ha−1 h−1yr-1), n is the number of years covered by the data records, is the number of erosive events of a given year j, E is total storm kinetic energy (MJ/ha), I30 is maximum 30-min rainfall intensity (mm/ha), k is the index of number of storm in a year. = = ∑ Δ (2) -1 -1 ℎ -unit rainfall energy (MJ ha mm ) -rainfall volume (mm) in a period of r time = 0.29 1 − 0.72#$% −0.05' ( (3) =
)*+ ),+
(4)
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ℎ ' -rainfall intensity during the time interval (mm h-1) According to abovementioned equations EI30 is the crucial part because most of the time rainfall intensity and storm kinetic energy data are not available at national meteorological stations. When detailed information about rainfall each 15 or 30 min is not available the EI30 index can be estimated from daily and monthly values of precipitation. In response, many approximations to the original formula have been developed in the literature. Typically, point-wise erosivity calculated from the original definition is used to derive an approximate formula that uses temporally averaged rainfall data (from 30 min, to annual averages), and the approximation is used for locations with sparser data. A field of erosivity values is then extrapolated from the available data points using other physical variables as added predictors (e.g., topography, annual mean rainfall, average rainfall intensity, or more derived quantities such as the modified Fournier index) [8]. Loureiro and Coutinho [22] developed a new model through multiple linear regressions for estimation of RUSLE EI30 parameter using monthly rainfall data of twenty-eight years from thirty-two daily-reading of rain guage stations in Algarve region, Portugal. The model has been used in many other similar studies owing to its high predicting power. (5) - ,. = 7.05 /'0 − 88.922/34 Where: rain10 – monthly rainfall for days ≥10 mm, otherwise set to zero, days10 – monthly number of days with rainfall ≥10 mm. From this equation: = 5 ∑ 7 7.05 ∗ /'0 − 88.92 ∗ 2/34 (6) Except of abovementioned equation several other attempts are widely cited in science literature. D’Asaro and Santoro, 1983 is one of them = 0.21 ∗ 8 9 . :; ∗ < 97 (7) 5
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Where q is the elevation of the station, P is average annual precipitation in mm and NGP is average annual number of rainy days. Another well-known method is developed by Renard and Freimund (1994). = 0.0483 ∗ < .; (8) Where P is average annual precipitation in mm.
Fig. 3. Annual precipitation and R-factor for three study periods in Telavi
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In this paper the approach of Loureiro and Coutinho (2001) has been used to assess the R factor for the 5 weather stations as well as to determine the spatial variability of R between these weather stations. For our analysis we used meteorological data series provided by National Environmental Agency of Georgia, for 1936-2016 period from five weather stations in Kakheti. Each station provides daily precipitation data. Daily weather records are now commonly available, providing good coverage that better represents rainfall intensity behavior than do more aggregated rainfall data. We used all available data series for the study period and with use of (6) equation R-factor was identified for each year of study period and then the average annual data was calculated. A monthly tipping-bucket rain gauge database was created for the period January 1936-December 2016 for Telavi, January 1936-December 1989 for Gurjaani, Lagodekhi and Sagarejo, January 1963-December 2016 for this Dedoplistskaro. It includes five stations and total 2916 months. 3. Results and Analysis Beforehand, daily precipitation data were analyzed for five meteorological stations. 81-year study period was divided into three 27-year periods. I period-1936-1962; II period-1963-1989 and III period-1990-2016. To summarize emerged from this outcome, as can be seen in Table 2, there are three study periods only in case of Telavi. As for the second period, we have precipitation data in second period for all weather stations. It should also be noted that in this period the most detailed meteorological data base, where almost every meteorological element was observed, was created for all of Georgia. Our intention was to analyze of R factor for each period using the approach of Lureiro & Cautinho. 6
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Table 2. Characteristics of the weather stations used during data processing
560 415.4 802 700 456
I period (19361962) + + + +
II period (19631989) + + + + +
III period (19902016) + + -
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Telavi Gurjaani Sagarejo Dedoflistskaro Lagodekhi
Elevation (m)
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Weather Station
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3.1. Telavi At the beginning, according to the results, for the 1st period, the highest value of R-factor 3079.2 MJ mm ha−1 h−1yr-1 is found in 1936 when the annual precipitation was 1118.1 mm and the lowest value was found in 1945-1206.135 MJ mm ha−1 h−1yr-1 when the annual precipitation was 608.2 mm (Fig. 3). It should be noted that R-factor doesn’t change directly to the annual precipitation, as discussed previously, it is of paramount importance of the rainfall intensity. It is possible that the same erosive potential can be obtained in case of different annual precipitation and vice versa the different erosive potential in case of similar annual precipitation. For example, rainfall erosivity is 1611.495 MJ mm ha−1 h−1yr-1 in 1943 when the annual precipitation is 1038.5 mm and 1626.585 MJ mm ha−1 h−1yr-1 in 1956 with the annual precipitation 769.1 mm. The difference is explained by different intensity of precipitation, e.g. in 1943, annual precipitation with ≥10 mm was 543.9 mm and the number of days with ≥10 mm was 23. In 1956, annual precipitation with ≥10 mm was 407.3 mm while the number of such a days was 14. Then, Figure 3 demonstrates that the highest value of R-factor-2683.71 MJ mm ha−1 h−1yr-1 is found in 1964 when the annual precipitation was 1006.7 mm and the lowest value-661.515 MJ mm ha−1 h−1yr-1 was found in 1980 when the annual precipitation was 636.5 mm in the second period. In 1964, annual precipitation with ≥10 mm was 709 mm and the number of days with ≥10 mm was 26. In 1980, annual precipitation with ≥10 mm was 358.7 mm while the number of such a days was 21. It is evident from the results that, the highest value of R-factor-2561.82 MJ mm ha−1 h−1yr-1 is found in 2005 when the annual precipitation was 1006 mm and the lowest value-428.67 MJ mm ha−1 h−1yr-1 was found in 2016 when the annual precipitation was 702.2 mm in third period . In 2005, annual precipitation with ≥10 mm was 779.6 mm and the number of days with ≥10 mm was 33. In 2016, annual precipitation with ≥10 mm was 451.8 mm while the number of such a days was 31, evidence of this is in Figure 3.
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Fig. 4. Rainfall erosivity change during three study periods in Telavi
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As illustrated by Figure 4, rainfall erosivity increased by 11.5% in 2nd period compare with 1st period, in 3rd period 7.9 % decline is found in comparison with 2nd period, but in comparison with 1st period there is 2.6% increase in Telavi.
Fig. 5. Annual precipitation and R-factor for two study periods in Gurjaani
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3.2. Gurjaani We have two study periods in case of Gurjaani (see Table 2). Using the method described above, we obtained the highest value of R-factor-2371.1055 MJ mm ha−1 h−1yr-1 in 1948 when the annual precipitation was 940.1 mm in the first period. In 1948, annual precipitation with ≥10 mm was 714.71 mm and the number of days with ≥10 mm was 34, as indicated in Figure 5. The lowest value-657.3 MJ mm ha−1 h−1yr-1 was found in 1954 when the annual precipitation was 511.6 mm. In 1954, annual precipitation with ≥10 mm was 257.2 mm while the number of such a days was 13. It is evident from the results that, the highest value of R-factor-4121.715 MJ mm ha−1 h−1yr-1 is found in 1983 when the annual precipitation was 1288.1 mm. In 1983, annual precipitation with ≥10 mm was 1038.7 mm and the number of days with ≥10 mm was 36. We observe from Figure 5 that, the lowest value-486.15 MJ mm ha−1 h−1yr-1 was found in 1970 when the annual precipitation was 534.6 mm. In 1970, annual precipitation with ≥10 mm was 308.6 mm while the number of such a days was 19 (Figure 5).
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Fig. 6. Rainfall erosivity change during two study periods in Gurjaani
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Data in Figure 6 shows that R-factor is 8% higher in 2nd period compare with 1st period in Gurjaani. 3.3. Sagarejo As mentioned previously, there are 2 study periods in case of Sagarejo. According to the results, for the 1st period, the highest value of R-factor-3051 MJ mm ha−1 h−1yr-1 is found in 1936 when the annual precipitation was 113.2 mm. In 1936, annual precipitation with ≥10 mm was 938.1 mm and the number of days with ≥10 mm was 34. The lowest value-371.595 MJ mm ha−1 h−1yr-1 was found in 1941 when the annual precipitation was 574.6 mm. In 1941, annual precipitation with ≥10 mm was 254.7 mm while the number of such a days was 15 (Figure 7).
Fig. 7. Annual precipitation and R-factor for two study periods in Sagarejo
According to the results displayed in Figure 7, for the 2nd period, the highest value of R-factor3729.105 MJ mm ha−1 h−1yr-1 is found in 1963 when the annual precipitation was 1368.7 mm. In 1963, annual precipitation with ≥10 mm was 1071.3 mm and the number of days with ≥10 mm was 43. The lowest value-597.705 MJ mm ha−1 h−1yr-1 was found in 1986 when the annual precipitation was 637.3 mm. In 1986, annual precipitation with ≥10 mm was 400.1 mm while the number of such a days was 25.
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Fig. 8. Rainfall erosivity change during two study periods in Sagarejo
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Figure 8 illustrates that R-factor is 17.9% higher in 2nd period compare with 1st period in Sagarejo. 3.4. Dedoplistskaro Conversely from previous stations there are only 2nd and 3rd periods in case of Dedoplistskaro. It is evident from the results that the highest value of R-factor-2265.03 MJ mm ha−1 h−1yr-1 is found in 1983 when the annual precipitation was 893.7 mm in the second period. In 1983, annual precipitation with ≥10 mm was 636.6 mm and the number of days with ≥10 mm was 25. We observe from figure 9 that the lowest value-227.61 MJ mm ha−1 h−1yr-1 was found in 1980 when the annual precipitation was 404.8 mm. In 1980, annual precipitation with ≥10 mm was 145.8 mm while the number of such a days was 9.
Fig. 9. Annual precipitation and R-factor for two study periods in Dedoplistskaro
According to the results reported in Figure 9 the highest value of R-factor-2850.87 MJ mm ha−1 h−1yr-1 is found in 2012 when the annual precipitation was 1001.2 mm. In 2012, annual precipitation with ≥10 mm was 820.6 mm and the number of days with ≥10 mm was 33. As we can see in Figure 9 the lowest value-422.1 MJ mm ha−1 h−1yr-1 was found in 2014 when the annual precipitation was 406.8 mm. In 2014, annual precipitation with ≥10 mm was 186 mm while the number of such a days was 10 (Figure 9). 10
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Figure 10 demonstrates that R-factor is 3.8% higher in 3rd period compare with 2nd period in Dedoplistskaro.
Fig. 10. Rainfall erosivity change during two study periods in Dedoplistskaro
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3.5. Lagodekhi Like Gurjaani and Sagarejo there only two periods-1st and 2nd in Case of Lagodekhi. Inspection of Figure 11, the highest value of R-factor-3808.605 MJ mm ha−1 h−1yr-1 is found in 1937 when the annual precipitation was 1270.2 mm in first period. In 1937, annual precipitation with ≥10 mm was 1006.9 mm and the number of days with ≥10 mm was 37. Results given in Figure 11 represent that the lowest value-1003.17 MJ mm ha−1 h−1yr-1 was found in 1952 when the annual precipitation was 694.1 mm. In 1952, annual precipitation with ≥10 mm was 445 mm while the number of such a days was 24.
Fig. 11. Annual precipitation and R-factor for two study periods in Lagodekhi
According to the results, for 2nd period, the highest value of R-factor-5831.685 MJ mm ha−1 h yr-1 is found in 1983 when the annual precipitation was 1548.2 mm. In 1983, annual precipitation with ≥10 mm was 1331.7 mm and the number of days with ≥10 mm was 40. The lowest value-504.825 MJ mm ha−1 h−1yr-1 was found in 1985 when the annual precipitation was 664.2 mm. In 1985, annual precipitation with ≥10 mm was 361.7 mm while the number of such a days was 23 (Figure 11). −1
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Fig. 12. Rainfall erosivity change during two study periods in Lagodekhi
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Figure 12 shows that R-factor is 4.6 % lower in 2nd period compare with 1st period in Lagodekhi. Eventually, as described previously, rainfall erosivity was maximum in Lagodekhi while the minimum in Sagarejo during the 1st period. The highest value was in Lagodekhi during the 2nd period too and the lowest value in Dedoflistskaro. At the end, Figure 13 summarizes the detailed spatial distribution of rainfall erosivity in the study area. Overall, the spatial distribution of the R-factor in the study area could be explained by the interaction of climate and terrain.
Fig. 13. Spatial and temporal distribution of rainfall erosivity in Kakheti
Afterwards, a more quantitate assessment will remain a challenge hindered both by model deficiencies and by lack of complete rainfall intensity records. 4. Conclusion Estimation of rainfall erosivity is of great importance for soil erosion assessment and has great influence on agriculture. It is well known that several very intense rainfall events are responsible for the largest proportion of soil erosion and sediment delivery. Rainfall erosivity is an indicator of precipitation aggressiveness and depends on rainfall kinetic energy and the intensity of the 12
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storm event. The purpose of this study was to provide assessment of the spatial and temporal variations of rainfall erosivity in Kakheti. We have used daily precipitation data from five weather stations: Telavi, Gurjaani, Sagarejo, Dedoplistskaro and Lagodekhi. Due to lack of rainfall intensity, data alternative method by Lureiro and Cautinho was used for data analysis. Daily weather records with good spatial and temporal coverage that adequately represent rainfall characteristics are usually available for most locations Results show that rainfall erosivity increased by 11.5% in 2nd period compare with 1st period, in 3rd period 7.9 % decline is found in comparison with 2nd period, but in comparison with 1st period there is 2.6% increase in Telavi. In case of Gurjaani data of 1st and 2nd period were analyzed. According to the results R-factor is 8% higher in 2nd period compare with 1st period in Gurjaani. In case of Sagarejo the tendency is the same. R-factor is 17.9% higher in 2nd period compare with 1st period in Sagarejo. In case of Dedoplistskaro precipitation data of 2nd and 3rd periods were analyzed. Here R-factor is 3.8% higher in 3rd period compare with 2nd period in Dedoplistskaro. As for Lagodekhi, we have defferent tendency here, R-factor is 4.6 % lower in 2nd period compare with 1st period in Lagodekhi. Although our research included all recently available meteorological data, the results are quite coarse. Future work should therefore include more data from previous research for better accuracy assessment. Acknowledgements The authors would like to thank to the Shota Rustaveli National Science Foundation (SRNSF) for the financial support (grant MR_2016_7_208) and the National Environmental Agency for providing the data used in this study.
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