Water Tales from Turkistan: Challenges and Opportunities for the Badam-Sayram Water System under a Changing Climate

Aidar Zhumabaev ^a, Hannah Schwedhelm ^b, Beatrice Marti ^a, Silvan Ragettli ^a, Tobias Siegfried ^a*

^a Hydrosolutions GmbH, Venusstrasse 29, 8050 Zürich, Switzerland

^b Technical University Munich, Arcisstraße 21, 80333 Munich, Germany

https://doi.org/10.29258/CAJWR/2024-R1.v10-2/1-25.eng

Corresponding author e-mail: siegfried@hydrosolutions.ch

Aidar Zhumabaev: zhumabaev@hydrosolutions.ch; Hannah Schwedhelm: hannah.schwedhelm@tum.de; Beatrice Marti: marti@hydrosolutions.ch; Silvan Ragettli: ragettli@hydrosolutions.ch

August 7, 2024

Abstract

The Badam River, a tributary to the Arys River located in the Syr Darya basin, is a crucial natural resource for ecological, social, and economic activities in the semi-arid region of southern Kazakhstan. The river basin is heavily influenced by manmade water infrastructure and faces water scarcity, particularly during summer, highlighting the importance of understanding its hydrological processes for effective water resource management. In this study, a semi-distributed conceptual hydrological model of the Badam River was implemented using the RS MINERVE hydrological software to evaluate the impacts of climate change on hydrology and to test the resilience of the water system. Connected HBV models were implemented for each of the hydrological response units that were defined as altitudinal zones. The hydrological model was calibrated using daily time steps between 1979 and 2011, and the resulting flow exceedance curves and hydrographs were used to assess the potential impacts of climate change on the basin, using CMIP6 precipitation and temperature scenarios. Future climate scenarios for the 2054 – 2064 period demonstrate that the peak discharge will be shifted to spring/late spring compared to the current early summer with no significant decrease in average discharge per day of the year. The insights gained from this hydrological-hydraulic model can be used to effectively manage the water system and inform future hydropower design decisions and serve as a blueprint for similar studies in the region and elsewhere.

Available in English

Download the article (eng)

For citation: Zhumabaev, A., Schwedhelm, H., Marti, B., Ragettli, S., Siegfried, T. (2024). Water Tales from Turkistan: Challenges and Opportunities for the Badam-Sayram Water System under a Changing Climate. Central Asian Journal of Water Research,  10(2), 1-25. https://doi.org/10.29258/CAJWR/2024-R1.v10-2/1-25.eng

References

Azimov, U., & Avezova, N. (2022). Sustainable small-scale hydropower solutions in Central Asian countries for local and cross-border energy/water supply. Renewable and Sustainable Energy Reviews, 167, 112726. https://doi.org/10.1016/j.rser.2022.112726

Bedient, P. B., Huber, W. C., & Vieux, B. E. (2013). Hydrology and Floodplain Analysis. Prentice Hall. https://books.google.ch/books?id=53JBpwAACAAJ

Bergström, S. (1976). Development and application of a conceptual runoff model for Scandinavian catchments (RHO, Hydrology and Oceanography, ISSN 0347-7827 ; 7, p. p.162).

Bernauer, T., & Siegfried, T. (2012). Climate change and international water conflict in Central Asia. Journal of Peace Research, 49(1), 227–239. https://doi.org/10.1177/0022343311425843

De Keyser, J., Hayes, D. S., Marti, B., Siegfried, T., Seliger, C., Schwedhelm, H., Anarbekov, O., Gafurov, Z., López Fernández, R. M., Ramos Diez, I., Alapfy, B., Carey, J., Karimov, B., Karimov, E., Wagner, B., & Habersack, H. (2023). Integrating Open-Source Datasets to Analyze the Transboundary Water- Food-Energy-Climate Nexus in Central Asia. Water, 15(19). https://doi.org/10.3390/w15193482

Didovets, I., Lobanova, A., Krysanova, V., Menz, C., Babagalieva, Z., Nurbatsina, A., Gavrilenko, N., Khamidov, V., Umirbekov, A., Qodirov, S., Muhyyew, D., & Hattermann, F. F. (2021). Central Asian rivers under climate change: Impacts assessment in eight representative catchments. Journal of Hydrology: Regional Studies, 34, 100779. https://doi.org/10.1016/j.ejrh.2021.100779

Duan, Q., Sorooshian, S., & Gupta, V. (1992). Effective and efficient global optimization for conceptual rainfall‐runoff models. Water Resources Research, 28(4), 1015–1031. https://doi. org/10.1029/91wr02985

Erasov, N. V. (1986). Method for determining of volume of mountain glaciers. Mater. Glyatsiol., 14, 307–308.

FAO. (2012). AQUASTAT Transboundary River Basin Overview – Aral Sea. Food and Agricultural Organization of the United Nations (FAO).

Farinotti, D., Huss, M., Fürst, J. J., Landmann, J., Machguth, H., Maussion, F., & Pandit, A. (2019). A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nature Geoscience, 12(3), 168–173. https://doi.org/10.1038/s41561-019-0300-3

Foehn, A., Garcia Hernandez, J., Roquier, B., Fluixa-Sanmartin, J., Brauchli, T., Paredes Arquiola, J., & De Cesare, G. (2020). RS MINERVE – User Manual, V2.15 (ISSN 2673-2653). Ed. CREALP.

Garcia Hernandez, J., Foehn, A., Fluixa-Sanmartin, J., Roquier, B., Brauchli, T., Paredes Arquiola, J., & G, D. C. (2020). RS MINERVE – Technical manual, v2.25 (ISSN 2673-2661). Ed. CREALP.

Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012). Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods. Hydrology and Earth System Sciences, 16(9), 3383–3390. https://doi.org/10.5194/ hess-16-3383-2012

Hartmann, J., & Moosdorf, N. (2012). The new global lithological map database GLiM: A representation of rock properties at the Earth surface. Geochemistry, Geophysics, Geosystems, 13(12). https:// doi.org/10.1029/2012GC004370

Hock, R. (2003). Temperature index melt modelling in mountain areas. Journal of Hydrology, 282(1– 4), 104–115. https://doi.org/10.1016/S0022-1694(03)00257-9

Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., & Kääb, A. (2021). Accelerated global glacier mass loss in the early twenty-first century. Nature, 592(7856), 726–731. https://doi.org/10.1038/s41586-021-03436-z

Karger, D. N., Schmatz, D. R., Dettling, G., & Zimmermann, N. E. (2020). High-resolution monthly precipitation and temperature time series from 2006 to 2100. Scientific Data, 7(1), 248. https:// doi.org/10.1038/s41597-020-00587-y

Karger, D. N., Wilson, A. M., Mahony, C., Zimmermann, N. E., & Jetz, W. (2021). Global daily 1 km land surface precipitation based on cloud cover-informed downscaling. Scientific Data, 8(1), 307. https://doi.org/10.1038/s41597-021-01084-6

KazGidromet [KazHydromet]. (2006). Gosudarstvennyj Vodnyj Kadastr Respubliki Kazahstan: Mnogoletnie dannye o rezhime i resursah poverhnostnyh vod sushi. Vypusk 3 [State Water Cadastre of the Republic of Kazakhstan: Long-term data on the regime and resources of land surface waters. Issue 3]. Ministersvo Ohrany Okruzhajushhej Sredy [Ministry of Environmental Protection]. (in Russian)

Krasting, J. P., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Dunne, K. A., Gauthier, P. P., Ginoux, P., Griffies, S. M., … Zhao, M. (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Earth System Grid Federation. https://doi. org/10.22033/ESGF/CMIP6.1407

Lange, S. (2021). ISIMIP3b bias adjustment fact sheet. https://www.isimip.org/documents/413/ ISIMIP3b_bias_adjustment_fact_sheet_Gnsz7CO.pdf

Liu, Y., Fang, Y., & Margulis, S. (2021). High Mountain Asia UCLA Daily Snow Reanalysis [dataset]. NASA National Snow and Ice Data Center DAAC. https://doi.org/10.5067/HNAUGJQXSCVU

Luo, T., Young, R., & Paul, R. (2013). Aqueduct country and river basin rankings: A weighted aggregation of spatially distinct hydrological indicators. Washington, DC: World Resources Institute. https:// www.wri.org/research/aqueduct-country-and-river-basin-rankings

Marti, B., Yakovlev, A., Karger, D. N., Ragettli, S., Zhumabaev, A., Wakil, A. W., & Siegfried, T. (2023). CA-discharge: Geo-Located Discharge Time Series for Mountainous Rivers in Central Asia. Scientific Data, 10(1), 579. https://doi.org/10.1038/s41597-023-02474-8

Miles, E., McCarthy, M., Dehecq, A., Kneib, M., Fugger, S., & Pellicciotti, F. (2021). Health and sustainability of glaciers in High Mountain Asia. Nature Communications, 12(1), 2868. https://doi. org/10.1038/s41467-021-23073-4

National Centers for Environmental Information. Daily Observational Data. (https://www.ncei.noaa. gov/maps/daily/)

NASA JPL. (2013). NASA Shuttle Radar Topography Mission Global 1 arc second [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003

Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022- 1694(70)90255-6

Peña‐Guerrero, M. D., Umirbekov, A., Tarasova, L., & Müller, D. (2022). Comparing the performance of high‐resolution global precipitation products across topographic and climatic gradients of Central Asia. International Journal of Climatology, 42(11), 5554–5569. https://doi.org/10.1002/joc.7548

QGIS Association. (2022). QGIS Geographic Information System [Computer software]. QGIS.org. http:// www.qgis.org

Ragettli, S., Herberz, T., & Siegfried, T. (2018). An Unsupervised Classification Algorithm for Multi- Temporal Irrigated Area Mapping in Central Asia. Remote Sensing, 10(11), 1823. https://doi. org/10.3390/rs10111823

RGI Consortium. (2017). Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 6.0 [dataset]. https://doi.org/che

Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009

RS Minerve (2.9.1). (2021). [Computer software]. CREALP. https://crealp.ch/rs-minerve/

Saspugaeva, G. E., Mahambetova, N. M., Ramazanova, N. E., & Tulebekova, A. S. (2019). Jekologicheskie problemy vodnyh resursov Juzhno-Kazahstanskoj oblasti [Ecological problems of water resources of South Kazakhstan region]. Vestnik Vostochno-Kazahstanskogo Gosudarstvennogo Tehnicheskogo Universiteta Im. D. Serikbaeva [Bulletin of the East Kazakhstan State Technical University Named after D. Serikbaev], № 2, С. 51-55. (in Russian)

Schwedhelm, H. (2023). Feasibility Study Badam Reservoir.

Scientific-Information Center of the Interstate Commission for Water Coordination of Central Asia (SIC ICWC). (n.d.). Portal of Knowledge for Water and Environmental Issues in Central Asia. www. Cawater-Info.Net. http://www.cawater-info.net/bk/1-1-1-1-3-kz_e.htm

Siegfried, T., & Marti, B. (2022). Modeling of Hydrological Systems in Semi-Arid Central Asia. GitHub Pages. https://doi.org/10.5281/zenodo.4666499

Siegfried, T., Mujahid, A. U. H., Marti, B., Molnar, P., Karger, D. N., & Yakovlev, A. (2023). Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling. https://doi.org/10.21203/rs.3.rs-3611140/v1

Silvan Ragettli. (2022). Remote Sensing and Geospatial Analysis Applied to Irrigation Performance Assessment, CropMapper Methodology (01.03.2022).

Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., & Jones, C. (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP esm-piControl. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.5953

Tleuova, Z., Snow, D. D., Mukhamedzhanov, M., & Ermenbay, A. (2023). Relation of Hydrogeology and Contaminant Sources to Drinking Water Quality in Southern Kazakhstan. Water, 15(24), 4240. https://doi.org/10.3390/w15244240

United Nations Department of Economic and Social Affairs Population Division. (2022). World Population Prospects 2022 Demographic indicators by region, subregion and country, annually for 1950-2100 (27). United Nations Department of Economic and Social Affairs, Population Division.

Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., & Adachi, Y. (2019). MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.6842