Hydrological change in the mountainous and downstream regions of Central Asia Alexander I. Shiklomanov Water Systems Analysis Group University of New Hampshire International Workshop on the Northern Eurasia High Mountain Ecosystems Bishkek, Kyrgyzstan, September 8-15, 29
Water resources and water use Tajikistan and Kyrgyzstan ~ 8% of total water resources Uzbekistan and Turkmenistan 75% of total t water use Source: http://www.cawater-info.net and World Water Resources and Their Use, Joint SHI/UNESCO Report
UNH database of hydrometeorological stations for Central Asia Mean Monthly Discharge, m 3 /s First Last Mean Annual Discharge, m 3 /s First Last Water Use First Last Kazakhstan 193 26 193 26 1981 25 Kirgistan 191 26 191 26 1981 199 Tadjikostan 1929 28 1929 28 1981 199 Turkmenistan 1936 1985 193 1991 1981 199 Hydrological Data for Turkmenistan were not updated since collapse of USSR Country Nu mbe r of stati ons Mean monthly air temperature First Last Monthly precipitation Kazakhstan 54 1879 23 1879 23 Kirgistan 62 1879 26 1883 26 Tadjikostan 58 1881 28 1891 28 Turkmenistan 16 1883 23 1883 2 Uzbekistan 1913 26 1913 26 1981 199 Uzbekistan 124 1875 26 1877 26 Total 314 1875 28 1877 28 First Last
Distribution of river gauges with data by countries 8 Turkmenistan Uzbekistan Tadzhikistan Kirgizstan Kazakhstan 7 6 5 4 3 2 1 191 192 193 194 195 196 197 198 199 2 Number of stations Страна Monthly river discharge, Annual discharge, м 3 /s м 3 /s Water use Kazakhstan 383 383 11 Kyrgystan 167 169 3 Tajikistan 128 128 4 Turkmenistan 24 25 6 Uzbekistan 133 133 1 Total 835 838 34
UNH NEESPI website is under development Gidd Gridded dand station ti data Remote sensing and in situ Mapping and analysis of data for river basins
Annual observed air temperature variation from CRU 3 grids and station data Grids of linear slopes over periods are shown 194-22 Station trends over 194-22 Linear Trend Total Significant P<.5 Positive 61 41 Negative 2 1 195-22 Station trends over 195-22 Linear Trend Total Significant P<.5 Positive 61 54 Negative 2 1 196-22 Station trends over 196-22 Linear Trend Total Significant P<.5 Positive 62 4 Negative 1 1
Annual observed precipitation variation from CRU 3 grids and station data Grids of linear slopes over periods are shown 194-22 Station trends over 194-22 Linear Trend Total Significant P<.5 Positive 35 6 Negative 27 7 195-22 Station trends over 195-22 Linear Trend Total Significant P<.5 Positive 27 3 Negative 35 9 196-22 Station trends over 196-22 Linear Trend Total Significant P<.5 Positive 3 2 Negative 32 9
Annual observed precipitation variation from CRU 3 grids and station data Grids of linear slopes over periods are shown 194-22 Station trends over 194-22 Linear Trend Total Significant P<.5 Positive 35 6 Negative 27 7 195-22 Station trends over 195-22 Linear Trend Total Significant P<.5 Positive 27 3 Negative 35 9 196-22 Station trends over 196-22 Linear Trend Total Significant P<.5 Positive 3 2 Negative 32 9
Tian Shan mountains and Syr Daria upstream annual discharge variations M3/S Uchkushoi, DArea=121 km2 M3/S Sokh, DArea=248 km2 Urmaral, DArea=112 km2 Chirchik, DArea=112 km2 Bol. Naryn, DArea=571 km2 Oigaing, DArea=11 km2 Uzunakhmat, DArea=179 km2 Ugam, DArea=866 km2 Talas, DArea=245 km2 Kurshab, DArea=21 km2
Tian Shan mountains and Syr Daria upstream annual discharge variations M3/S Uchkushoi, DArea=121 km2 M3/S Sokh, DArea=248 km2 Urmaral, DArea=112 km2 Chirchik, DArea=112 km2 Bol. Naryn, DArea=571 km2 Oigaing, DArea=11 km2 Uzunakhmat, DArea=179 km2 Ugam, DArea=866 km2 Talas, DArea=245 km2 Kurshab, DArea=21 km2
Discharge variations along Syr Darya river M3/S Syr Darya, DArea=9 km2 Syr Darya m 3 /s 25 2 Syr Darya at Kazalinsk Syr Darya at Kal Syr Darya, DArea=153 km2 Discharge 15 1 5 195 196 197 198 199 2 Syr Darya, DArea=174 km2
Discharge variations along Amu Darya river M3/S Amu Darya, DArea= (39) km2 6 Amu Darya Amu Darya at Samanbai Amu Darya at Kerki Amu Darya, DArea= (34) km2 /s Discharge m 3 5 4 3 2 1 195 1955 196 1965 197 1975 198 1985 199 1995 2 Amu Darya, DArea= (37) km2
AmuDaria upstream annual discharge variations 75 Varzob At Dagana, F=127 km2 65 55 45 35 5 45 4 35 3 25 Yagnob At Takfon, F=149 km2 25 193 194 195 196 197 198 199 2 21 2 193 194 195 196 197 198 199 2 21 18 16 14 12 1 8 6 4 2 Magiyandar'ya At Sudzhina, F=11 km2 193 194 195 196 197 198 199 2 21 7 6 5 4 3 2 1 Yakhsu At Karboztonak, F=144 km2 194 195 196 197 198 199 2 21 24 Kafirnigan At Tartki, F=978 km2 2 16 12 8 193 194 195 196 197 198 199 2 21 26 24 22 2 18 16 14 12 1 Zeravshan At Dupuli, F=12 km2 192 193 194 195 196 197 198 199 2 21
AmuDaria upstream annual discharge variations 75 65 55 Varzob At Dagana, F=127 km2 5 45 4 35 Yagnob At Takfon, F=149 km2 45 3 35 25 25 193 194 195 196 197 198 199 2 21 2 193 194 195 196 197 198 199 2 21 18 16 14 12 1 8 6 4 2 Magiyandar'ya At Sudzhina, F=11 km2 193 194 195 196 197 198 199 2 21 7 6 5 4 3 2 1 Yakhsu At Karboztonak, F=144 km2 194 195 196 197 198 199 2 21 24 2 16 12 8 Kafirnigan At Tartki, F=978 km2 Zeravshan At Dupuli, F=12 km2 193 194 195 196 197 198 199 2 21 26 24 22 2 18 16 14 12 1 192 193 194 195 196 197 198 199 2 21
GCM air temperature projections for Central Asia Syr Darya Amu Darya
GCM precipitation projections for Central Asia GCMs have wide variability for both 2C and future simulations. Most downscaling methods will not change this trend.
WBM/WTM... WBMPlus WBM/WTM 1-D physically based macroscale hydrological model (Vörösmarty, 1998) WTM Routing based on river network (STN) WBMPlus WBM + irrigation + reservoirs; daily time step (real time routing, irrigation, reservoirs) Flow routing model Q t+1 = C I t+1 + C 1 I t + C 2 Q t Coefficients C, C 1, C 2, = f(river Geometry) Q = river discharge from grid cell I = locally generated inflow to river (less irrigation) I t Q t Grid Cell
22-24 Runoff simulations for Central Asia with WBMPlus for GCM ECHAM5, sres A1b (deviations from 2c3m) 3 grid for GCM outputs not adjusted 24-26 6 grid for GCM outputs adjusted based on Udel monthly gridded climatology 28-21 28-21
Simulations of WBMPlus with ECHAM-5 A1b and B1 scenarios 3 m 3 /s R.URAL - G.GUR'EV, F=23 km2 25 2 6 4 Cont emporar A1b 271- B1 Large 271- river basins in Central Asia 15 2 1 1 2 3 5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Contemporary A1b (271-21) B1 (271-21) 3 m 3 /s 25 2 R.ILI - S.USHZHARMA, F=129km2 12 1 8 6 4 Contemporar 45 m 3 /s 4 35 3 IR.AMUDAR'YA - G.KERKI, F=39 km2 25 Cont emporar 2 15 1 A1b 271- B1 271- A1b 271- B1 271-15 2 25 5 1 2 15 5 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Contemporary A1b (271-21) B1 (271-21) 5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Contemporary A1b (271-21) B1 (271-21) 2 m 3 /s 18 16 14 12 1 8 6 4 2 R.SYRDAR'YA - ZH.D.ST.TUMEN'-ARYK, F=219 km2 1 Cont empora 9 8 7 6 5 4 3 2 1 A1b 271- B1 271-21 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Contemporary A1b (271-21) B1 (271-21) Irtysh at Khanty Mansiisk F=1 65 km2 m 3 /s 14 4 12 3 1 2 8 1 6 Contemporary A1b_27-21 B1_27-21 4 2 "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec" Contemporary A1b (271-21) B1 (271-21)
PWBM simulations (cold regions) Runoff shift, peak higher, annual discharge is increasing; 88 different models from 6 different countries show remarkable consistencies; Evapotranspiration p p is going g up; More water in the soil; Less snow in the spring shorter period Less snow in the spring, shorter period with snow.
UZHYDROMET DISCHARGE SIMULATIONS Runoff anomalies over the growing season in % from contemporary long-term mean for the A2 emission scenario. The results from our local collaborators from Central Asia. They used regional hydrological model with computation of glacier runoff and snow melt in the mountains. The projected a significant decline in runoff over the vegetative period might significantly effect on sustainable agriculture in the region (Agaltseva and Pak, 27).
Summary Contemporary gridded fields are not sufficient to simulate Central Asian water balance UNH group is working to produce improved climate drivers based on better observational coverage IPCC scenarios have wide variability for the region But there are consistent patterns between models Existing data bases of dams contain mostly major dams Models can be used to identify areas with smaller dams to improve simulations of water management Remote sensing is a powerful tool for monitoring irrigation The Land Surface Water Index (LSWI) along with other products can help in mapping regional irrigation