Georgian Geographical Journal The Scale of the Expected Largest Maximum High Water Discharges on the Rivers of Eastern Georgia Tsisana Basilashvili1,* , Khatuna Basilashvili2, Magda Janelidze2 1 Institute of Hydrometeorology, Georgian Technical University, Tbilisi, Georgia 2 Caucasus University, Tbilisi, Georgia * Corresponding author: ts.basilashvili@gtu.ge Introduction Since the second half of the 20th century, climate warming and increasing anthropogenic pressure on the environment have contributed to the intensification of elemental processes, accompanied by a clear upward trend and the emergence of critical ecological challenges. The complex mountainous landscape of Georgia—with its diverse climatic, relief, and soil conditions, and numerous deep river valleys—creates a favourable environment for the occurrence of destructive floods. Such events have frequently resulted in the destruction of residential buildings and infrastructure, damage to agricultural land, and significant losses to the land fund. The presented flood photograph serves as documentary evidence of the urgent need for an objective and impartial assessment of flash floods, which is essential for developing recommendations to prevent catastrophic events and mitigate associated damages. Floods and high-water events in the rivers of Eastern Georgia (fig. 1), along with the damage they have caused, are discussed in previous studies (Basilashvili et al., 2011; Basilashvili et al., 2012), which are based on observational data prior to 1991 and include corresponding estimates of maximum river discharges and their probable values. More recently, under the conditions of ongoing global warming, Basilashvili (2024) extended this work by incorporating data from 1991–2021, updating the multi-year characteristic parameters of annual maximum flash flood discharges, and providing an appropriate statistical analysis of observational series spanning 70–100 years. Looking ahead, continued climate warming is expected to further intensify natural hazards such as floods, avalanches, landslides, and erosion, often occurring in conjunction with frequent flooding events. Given the increasing scale of damage and casualties associated with such phenomena, determining the magnitude of expected maximum river discharges and refining their numerical estimates has become essential. These parameters are critical for the effective planning and management Georgian Geographical Journal, 2025, 5(2) 17-21 © The Author(s) 2025 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). DOI: https://journals.4science.ge/index.php/GGJ Abstract Based on a 70–100-year empirical series of stationary observations from 27 principal hydrological stations on the rivers of Eastern Georgia up to 2023, the probable maximum discharges of high waters have been estimated for exceedance probabilities of 0.01%, 0.1%, 1%, 2%, 5%, and 10%. These correspond to recurrence intervals of approximately 10,000, 1,000, 100, 60, 20, 10, and 5 years, respectively. The derived data provide a valuable resource for scientific, economic, and engineering applications, and should be utilised by relevant organisations for water management calculations. They are essential for the planning and design of hydraulic, civil, and industrial infrastructure—including roads, bridges, pipelines, and communication facilities—located along rivers and within their coastal zones. Moreover, these data underpin the substantiation of technical and economic parameters of such projects. The application of these findings will contribute significantly to enhancing the safety of populations, infrastructure, and the environment, thereby reducing the risks of structural damage, environmental degradation, and potential casualties associated with high-water events. Keywords: probabilistic value, repeatability, provision, safety Citation: Basilashvili, T.; Basilashvili, K.; Janelidze, M. The Scale of the Expected Largest Maximum High Water Discharges on the Rivers of Eastern Georgia. Georgian Geographical Journal 2025, 5(2), 17-21. https://doi.org/10.52340/ggj.2025.05.02.03 Basilashvili et al. 2025 5(2) 18 of hydroeconomic calculations, and for the development of strategies to mitigate the adverse effects of hydrological disasters. Methods and Materials To estimate the expected changes in maximum river discharges, probability theory provision curves were applied to determine the scale of anticipated largest flash flood discharges (Ukleba, 1967; Luchsheva, 1976). This study utilised long-term observational data from 27 hydrological stations in Eastern Georgia. Data prior to 1981 were sourced from published references, including Fundamental Characteristics (1967, 1977, 1978) and the State Water Cadastre (1987). More recent observations, up to 2022, were obtained from the National Environment Agency of Georgia (NEA). Due to the inherent difficulties in measuring water discharges of mountain rivers during flash flood events, some observational records were incomplete. To address these gaps, correlation analyses of discharge relationships between analogous rivers were conducted, complemented by graphical interpolation methods. This approach enabled the reconstruction of missing data points. Correlation analysis was performed to establish relationships among maximum river discharges, followed by the formation of descending ranks of multi-year data and the determination of their corresponding exceedance probabilities, in accordance with the methodology proposed by Basilashvili (1977), implemented using computer software tools. As a result of this comprehensive research, a robust database of multi-year (70–100 years) empirical series of annual maximum river flood discharges was compiled. The hydrographic and hypsometric characteristics of the river basins monitored at the 27 hydrological stations are presented in Table 1. Table 1. The hydrographic and hypsometric characteristics of the river basins at the 27 hydrological stations № River - Point A re a o f B as in L en g th o f R iv er H ei g h t o f B as in W o o d in es s o f B as in H ei g h t o f S o u rc e H ei g h t o f P o in t F al l o f R iv er S lo p e o f B as in S lo p e o f R iv er F km2 L km H m W % HS m HP m H m SB ‰ SR ‰ 1 Mtkvari - Khertvisi 4980 223 2720 1120 1600 2 Mtkvari - Minadze 8010 265 2720 944 1776 3 Mtkvari - Borjomi 10500 315 2720 781 1939 4 Mtkvari - Dzegvi 18000 444 2720 456 2264 5 Mtkvari - Tbilisi 21100 474 2720 391 2329 6 Faravani - Khertvisi 2350 73 2120 0 2080 1120 960 91 13.8 Figure 1. Gori flooded by flash flood on June 11, 1983 Basilashvili et al. 2025 5(2) 19 7 Potskhovi - Skhvilisi 1730 54 1870 11 1231 969 262 32.2 8 Kvabliani -Mlashe 468 19 1940 38 2540 1162 1378 132 35.0 9 Abastumani - Abastumani 99 10 1830 32 1373 1272 101 360 94.0 10 Borjomula - Borjomi 165 18 1810 65 2400 781 1619 256 50.7 11 Didi Liakhvi - Kekhvi 924 59 2100 25 3032 960 2072 373 38.2 12 Patara Liakhvi - Vanati 422 41 1940 35 2966 1015 1951 373 46.2 13 Ksani – Korinta 461 46 1830 50 2820 909 1911 260 45.0 14 Aragvi - Zhinvali 1900 28 1890 45 3126 718 2408 380 35.5 15 White Aragvi - Fasanauri 335 41 2140 22 3126 1035 2091 362 51.2 16 Black Aragvi - Shesartavi 235 29 2030 27 3392 1070 2322 416 66.4 17 Pshavis Aragvi - Magaroskari 736 38 2060 40 2731 920 1811 452 40.6 18 Yori - Lelovani 484 43 1640 59 2827 1090 1731 262 31.3 19 Alazani - Birkiani 282 9 2200 42 2750 758 1992 469 61.7 20 Alazani - Shakriani 2190 72 1260 61 2750 340 2410 270 26.2 21 Ktsia Khrami - Edikilisa 544 51 2040 0 2422 1516 906 135 18.9 22 Ktsia Khrami - Dagetkhachini 2150 136 1720 17 2422 526 1896 142 14.0 23 Ktsia Khrami - Imiri 3840 171 1510 29 2422 345 2077 147 12.6 24 Ktsia Khrami - Red bridge 8260 196 1530 33 2422 265 2157 179 11.3 25 Algeti - Partskhisi 359 40 1320 50 1900 672 1228 191 23.4 26 Mashavera - Dmanisi 570 25 1660 19 1358 795 623 155 43.0 27 Debeda - Sadakhlo 3790 150 1680 18 480 413 67 174 12.0 Results To determine the probabilistic values of maximum river discharges, the annual peak discharges from all individual hydro-catchments were arranged in descending order, from largest to smallest. This approach, representing the simplest form of their distribution, demonstrates that a given maximum discharge on a river can occur multiple times during the observation period, corresponding to the number of values exceeding it in the descending sequence. The integration of values arranged in descending order produces the provision curve, with provision typically expressed as a percentage. According to Luchsheva (1976), the corresponding percentage value for each member of the descending sequence is calculated using the following formula: P % = ((m – 0.3) / (n + 0.4)) • 100, where 𝑚 represents the sequence number in the descending order of maximum discharges, and 𝑛 denotes the total number of members in the sequence. Based on the parameters reported by Basilashvili (2024), the coefficients of variation for the maximum discharges of the rivers under study are notably high (𝐶𝑣≥0.5and asymmetry 𝐶𝑠≥2.0). Therefore, in accordance with Luchsheva (1976), provision curves were constructed using high-asymmetry cells rather than moderate-asymmetry cells to better reflect the statistical characteristics of the data. Using the ordinates of the provision curves and relevant calculations, the probable values of the largest maximum discharges at different provision levels are presented in Table 2. Each probability value corresponds to a specific recurrence interval, indicating the average number of years in which the corresponding maximum discharge is expected to occur at least once. Table 2. Probable values of the largest maximum discharges (Q m3/s) of the rivers of Eastern Georgia with different provision (%) and frequency (years) № Provision (%) 0,01 0,1 1 2 5 10 20 Recurrence (years) 10000 1000 100 80 20 10 5 Characterization of waterfalls Disastrous Very strong Strong High Moderate Average 1 Mtkvari - Khertvisi 930 770 610 560 495 445 390 2 Mtkvari - Minadze 1900 1430 1040 920 770 660 540 3 Mtkvari - Borjomi 2280 1780 1350 1220 1050 920 780 4 Mtkvari - Dzegvi 3880 2960 2180 1950 1700 1440 1200 5 Mtkvari - Tbilisi 4080 3220 2460 2220 1940 1700 1450 6 Faravani - Khertvisi 330 255 198 179 154 135 114 7 Potskhovi - Skhvilisi 960 700 490 425 350 298 240 Basilashvili et al. 2025 5(2) 20 8 Kvabliani -Mlashe 450 328 230 204 168 142 115 9 Abastumani - Abastumani 92 64 42 36 28 24 19 10 Borjomula - Borjomi 187 100 64 56 47 43 37 11 Didi Liakhvi - Kekhvi 550 432 330 300 260 228 192 12 Patara Liakhvi - Vanati 484 300 173 142 105 80 60 13 Ksani – Korinta 523 376 245 210 158 136 105 14 Aragvi - Zhinvali 1005 960 840 780 660 530 360 15 White Aragvi - Fasanauri 290 235 178 158 133 110 76 16 Black Aragvi - Shesartavi 600 420 260 211 155 114 60 17 Pshavi Aragvi - Magaroskari 1450 1100 685 540 390 270 160 18 Yori - Lelovani 556 522 470 430 400 350 250 19 Alazani - Birkiani 820 560 340 280 205 150 90 20 Alazani - Shakriani 1560 1100 890 770 623 504 360 21 Ktsia Khrami - Edikilisa 200 160 126 117 104 98 92 22 Ktsia Khrami - Dagetkhachini 790 594 420 360 285 230 155 23 Ktsia Khrami - Imiri 1200 840 580 490 388 306 280 24 Ktsia Khrami - Red bridge 2660 1860 1240 1030 790 600 420 25 Algeti - Partskhisi 490 360 240 203 256 123 84 26 Mashavera - Dmanisi 790 540 335 274 200 147 88 27 Debeda - Sadakhlo 880 744 600 555 485 426 346 It should be emphasised that any probable value of maximum discharge derived at a given provision level provides critical information for assessing the safety of structures and other engineering measures under specific hydrological conditions. Such estimates are therefore essential for informed decision- making in hydraulic engineering and water resource management. Discussions Probable values of the largest maximum river discharges, based on observational data available before 1981 and 1991, have been published in earlier studies (Water Resources, 1988; Basilashvili, 2017). A comparison of these historical estimates with the values presented in Table 2, derived from data up to 2022, reveals a general reduction in the largest maximum discharges of flash floods across nearly all hydro-catchments in Eastern Georgia. This reduction is particularly pronounced for high-recurrence discharge events. This trend is likely attributable to changes in the precipitation regime in Northeastern Georgia, notably a reduction in snow cover. In mountainous terrain, snowmelt during melting periods typically contributes meltwater directly to river channels, and under conditions of heavy rainfall, this process can trigger substantial floods. However, climate warming has extended the warm season, and liquid precipitation is increasingly lost to evaporation and infiltration into dry soils within river basins due to higher air temperatures. Consequently, the magnitude of flood discharges has diminished. Furthermore, in certain river basins, the area of existing glaciers has already decreased, leading to a corresponding decline in glacial runoff, which further contributes to the observed reduction in maximum flood discharges. Conclusion Thus, based on stationary observation data up to 2022 from 27 hydro-catchments of rivers in Eastern Georgia, the projected development of the largest maximum flood discharges has been analysed. Through rigorous statistical analysis of a 70–100-year empirical dataset of river discharges, updated and refined probabilistic estimates of the largest maximum flood discharges have been obtained for various provision levels (0.01%, 0.1%, 1%, 2%, 5%, 10%, 20%), corresponding to specific recurrence intervals of 10,000, 1,000, 100, 80, 20, 10, and 5 years, respectively. These results are of critical importance for hydroeconomic planning, as they provide the basis for justifying the technical and economic parameters of hydraulic structures and other measures planned along rivers and within their floodplains. Moreover, such data support informed decision-making in water resource management and help mitigate potential damages from future flood events. Competing interests The authors declare that they have no competing interests. Basilashvili et al. 2025 5(2) 21 Authors’ contribution T.B. and K.B. performed the analytic calculations. M.J. took the lead in writing the manuscript. 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