Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Mekong River Basin
2.2. Hydrological Models
2.3. Choice of General Circulation Models
2.4. Calibration and Simulation of Streamflows and Water Budget Components
2.5. Study Design
3. Results
3.1. Hydroclimatology of Streamflow
3.2. Historical Peakflow Assessment
3.3. Projected Changes in Flows and Comparison of Models
3.4. Projected Peakflow Estimation
3.5. Basin Scale Water Budget Analysis
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Costa-Cabral, M.C.; Richey, J.E.; Goteti, G.; Lettenmaier, D.P.; Feldkötter, C.; Snidvongs, A. Landscape structure and use, climate, and water movement in the Mekong River basin. Hydrol. Process. 2008, 22, 1731–1746. [Google Scholar] [CrossRef]
- Pokhrel, Y.; Burbano, M.; Roush, J.; Kang, H.; Sridhar, V.; Hyndman, D. A review of the integrated effects of changing climate, land use, and dams on Mekong river hydrology. Water 2018, 10, 266. [Google Scholar] [CrossRef]
- Kiem, A.S.; Ishidaira, H.; Hapuarachchi, H.P.; Zhou, M.C.; Hirabayashi, Y.; Takeuchi, K. Future hydroclimatology of the Mekong River basin simulated using the high-resolution Japan Meteorological Agency (JMA) AGCM. Hydrol. Process. 2008, 22, 1382–1394. [Google Scholar] [CrossRef]
- Thompson, J.R.; Green, A.J.; Kingston, D.G.; Gosling, S.N. Assessment of uncertainty in river flow projections for the Mekong River using multiple GCMs and hydrological models. J. Hydrol. 2013, 486, 1–30. [Google Scholar] [CrossRef]
- Eastham, J.; Mpelasoka, F.; Mainuddin, M.; Ticehurst, C.; Dyce, P.; Hodgson, G.; Ali, R.; Kirby, M. Mekong River Basin Water Resources Assessment: Impacts of Climate Change; CSIRO: Canberra, Australia, 2008.
- Shrestha, B.; Babel, M.S.; Maskey, S.; Van Griensven, A.; Uhlenbrook, S.; Green, A.; Akkharath, I. Impact of climate change on sediment yield in the Mekong River basin: A case study of the Nam Ou basin, Lao PDR. Hydrol. Earth Syst. Sci. 2013, 17, 1–20. [Google Scholar] [CrossRef]
- Thilakarathne, M.; Sridhar, V. Characterization of future drought conditions in the Lower Mekong River Basin. Weather Clim. Extrem. 2017, 17, 47–58. [Google Scholar] [CrossRef]
- Bonnema, M.; Hossain, F. Inferring reservoir operating patterns across the Mekong Basin using only space observations. Water Resour. Res. 2017, 53, 3791–3810. [Google Scholar] [CrossRef]
- Li, D.; Long, D.; Zhao, J.; Lu, H.; Hong, Y. Observed changes in flow regimes in the Mekong river basin. J. Hydrol. 2017, 551, 217–232. [Google Scholar] [CrossRef]
- Kummu, M.; Lu, X.; Wang, J.; Varis, O. Basin-wide sediment trapping efficiency of emerging reservoirs along the mekong. Geomorphology 2010, 119, 181–197. [Google Scholar] [CrossRef]
- Wild, T.B.; Loucks, D.P. Managing flow, sediment, and hydropower regimes in the sre pok, se san, and se kong rivers of the mekong basin. Water Resour. Res. 2014, 50, 5141–5157. [Google Scholar] [CrossRef]
- Perra, E.; Piras, M.; Deidda, R.; Paniconi, C.; Mascaro, G.; Vivoni, E.R.; Cau, P.; Marras, P.A.; Ludwig, R.; Meyer, S. Multimodel assessment of climate change-induced hydrologic impacts for a Mediterranean catchment. Hydrol. Earth Syst. Sci. 2018, 22, 4125–4143. [Google Scholar] [CrossRef] [Green Version]
- Mendoza, P.A.; Clark, M.P.; Mizukami, N.; Newman, A.J.; Barlage, M.; Gutmann, E.D.; Rasmussen, R.M.; Rajagopalan, B.; Brekke, L.D.; Arnold, J.R. Effects of hydrologic model choice and calibration on the portrayal of climate change impacts. J. Hydrometeorol. 2015, 16, 762–780. [Google Scholar] [CrossRef]
- Al-Safi, H.I.J.; Sarukkalige, P.R. The application of conceptual modelling to assess the impacts of future climate change on the hydrological response of the Harvey River catchment. J. Hydro-Environ. Res 2018. [Google Scholar] [CrossRef]
- Al-Safi, H.I.J.; Sarukkalige, P.R. Evaluation of the impacts of future hydrological changes on the sustainable water resources management of the Richmond River catchment. J. Water Clim. Chang. 2018, 9, 137–155. [Google Scholar] [CrossRef]
- Al-Safi, H.I.J.; Kazemi, H.; Sarukkalige, P.R. Comparative study of conceptual versus distributed hydrologic modelling to evaluate the impact of climate change on future runoff in unregulated catchments. J. Water Clim. Chang. 2019. [Google Scholar] [CrossRef]
- Piman, T.; Lennaerts, T.; Southalack, P. Assessment of hydrological changes in the lower Mekong basin from basin-wide development scenarios. Hydrol. Process. 2013, 27, 2115–2125. [Google Scholar] [CrossRef]
- Gosling, S.; Taylor, R.G.; Arnell, N.; Todd, M.C. A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrol. Earth Syst. Sci. 2011, 15, 279–294. [Google Scholar] [CrossRef] [Green Version]
- Kingston, D.G.; Thompson, J.R.; Kite, G. Uncertainty in climate change projections of discharge for the Mekong River Basin. Hydrol. Earth Syst. Sci. 2011, 15, 1459–1471. [Google Scholar] [CrossRef] [Green Version]
- Homdee, T.; Pongput, K.; Kanae, S. Impacts of land cover changes on hydrologic responses: A case study of chi river basin, Thailand. J. Jpn. Soc. Civ. Eng. Ser. B1 2011, 67, I31–I36. [Google Scholar] [CrossRef]
- Thanapakpawin, P.; Richey, J.; Thomas, D.; Rodda, S.; Campbell, B.; Logsdon, M. Effects of landuse change on the hydrologic regime of the Mae Chaem river basin, NW Thailand. J. Hydrol. 2007, 334, 215–230. [Google Scholar] [CrossRef]
- Tatsumi, K.; Yamashiki, Y. Effect of irrigation water withdrawals on water and energy balance in the Mekong River Basin using an improved VIC land surface model with fewer calibration parameters. Agric. Water Manag. 2015, 159, 92–106. [Google Scholar] [CrossRef]
- Introduction to SWAT and the Instructional Videos. Available online: https://swat.tamu.edu/workshops/instructional-videos/ (accessed on 24 June 2019).
- VIC Model Overview. Available online: https://vic.readthedocs.io/en/master/Overview/ModelOverview/ (accessed on 24 June 2019).
- Kang, H.; Sridhar, V. Combined statistical and spatially distributed hydrological model for evaluating future drought indices in Virginia. J. Hydrol. Reg. Stud. 2017, 12, 253–272. [Google Scholar] [CrossRef]
- Kang, H.; Sridhar, V. Assessment of Future Drought Conditions in the Chesapeake Bay Watershed. JAWRA J. Am. Water Res. Assoc. 2018, 54, 160–183. [Google Scholar] [CrossRef]
- Kang, H.W.; Sridhar, V. Improved drought prediction using near real-time climate forecasts and simulated hydrologic conditions. Sustainability 2018, 10, 1799. [Google Scholar] [CrossRef]
- Sehgal, V.; Sridhar, V. Watershed-scale retrospective drought analysis and seasonal forecasting using multi-layer, high-resolution simulated soil moisture for Southeastern U.S. Weather Clim. Extrem. 2019, 23, 100191. [Google Scholar] [CrossRef]
- Hoekema, D.J.; Sridhar, V. A system dynamics model for conjunctive management of water resources in the Snake River basin. JAWRA J. Am. Water Res. Assoc. 2013, 49, 1327–1350. [Google Scholar] [CrossRef]
- Sridhar, V.; Ali, S.; Modi, P.; Kang, H.; Quan, N.; Dat, N.D.; Kansal, M.D. Can we quantify the resilience of the Lower Mekong Basin in the face of dams and agricultural expansion? In Proceedings of the EWRI World Environmental & Water Resource Congress, Pittsburgh, PA, USA, 19–23 May 2019. [Google Scholar]
- Kang, H.; Sridhar, V.; Mills, B.F.; Hession, W.C.; Ogejo, J.A. Economy-wide climate change impacts on green water droughts based on the hydrologic simulations. Agric. Syst. 2019, 171, 76–88. [Google Scholar] [CrossRef]
- Sehgal, V.; Sridhar, V.; Juran, L.; Ogejo, J. Integrating Climate Forecasts with the Soil and Water Assessment Tool (SWAT) for High-Resolution Hydrologic Simulations and Forecasts in the Southeastern US. Sustainability 2018, 10, 3079. [Google Scholar] [CrossRef]
- Jin, X.; Sridhar, V. Impacts of climate change on hydrology and water resources in the Boise and Spokane River Basins. JAWRA J. Am. Water Res. Assoc. 2012, 48, 197–220. [Google Scholar] [CrossRef]
- Sridhar, V.; Jin, X.; Jaksa, W.T.A. Explaining the hydroclimatic variability and change in the Salmon River basin. Clim. Dyn. 2013, 40, 1921–1937. [Google Scholar] [CrossRef]
- Arnold, J.G.; Srinivasan, R.; Muttiah, R.S.; Williams, J.R. Large area hydrologic modeling and assessment part I: Model development1. JAWRA J. Am. Soc. Water Resour. Assoc. 1998, 34, 73–89. [Google Scholar] [CrossRef]
- Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Williams, J.R. Soil and Water Assessment Tool Theoretical Documentation Version 2009; Texas Water Resources Institute: Temple, TX, USA, 2011. [Google Scholar]
- Arnold, J.G.; Moriasi, D.N.; Gassman, P.W.; Abbaspour, K.C.; White, M.J.; Srinivasan, R.; Santhi, C.; Harmel, R.D.; Van Griensven, A.; Van Liew, M.W.; et al. SWAT: Model use, calibration, and validation. Trans. ASABE 2012, 55, 1491–1508. [Google Scholar] [CrossRef]
- Jha, M.; Arnold, J.G.; Gassman, P.W.; Giorgi, F.; Gu, R.R. Climate change sensitivity assessment on upper Mississippi River Basin streamflow using SWAT. JAWRA J. Am. Water Resour. Assoc. 2006, 42, 997–1015. [Google Scholar] [CrossRef]
- Githui, F.; Gitau, W.; Mutua, F.; Bauwens, W. Climate change impact on SWAT simulated streamflow in western Kenya. Int. J. Climatol. 2009, 29, 1823–1834. [Google Scholar] [CrossRef]
- Wu, Y.; Liu, S.; Abdul-Aziz, O.I. Hydrological effects of the increased CO2 and climate change in the Upper Mississippi River Basin using a modified SWAT. Clim. Chang. 2012, 110, 977–1003. [Google Scholar] [CrossRef]
- Ficklin, D.L.; Luo, Y.; Luedeling, E.; Zhang, M. Climate change sensitivity assessment of a highly agricultural watershed using SWAT. J. Hydrol. 2009, 374, 16–29. [Google Scholar] [CrossRef]
- Taye, M.T.; Ntegeka, V.; Ogiramoi, N.P.; Willems, P. Assessment of climate change impact on hydrological extremes in two source regions of the Nile River Basin. Hydrol. Earth Syst. Sci. 2011, 15, 209–222. [Google Scholar] [CrossRef] [Green Version]
- Ashraf Vaghefi, S.; Mousavi, S.J.; Abbaspour, K.C.; Srinivasan, R.; Yang, H. Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran. Hydrol. Process. 2014, 28, 2018–2032. [Google Scholar] [CrossRef]
- Yatagai, A.; Kamiguchi, K.; Arakawa, O.; Hamada, A.; Yasutomi, N.; Kitoh, A. APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Am. Meteorol. Soc. 2012, 93, 1401–1415. [Google Scholar] [CrossRef]
- Sheffield, J.; Goteti, G.; Wood, E.F. Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling. J. Clim. 2006, 19, 3088–3111. [Google Scholar] [CrossRef] [Green Version]
- Danielson, J.J.; Gesch, D.B. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010); No. 2011-1073; US Geological Survey (USGS): Sioux Falls, SD, USA, 2011.
- FAO (Food and Agriculture Organization). Digital Soil Map of the World and Derived Soil Properties; Food and Agriculture Organization of the United Nations: Rome, Italy, 1995. [Google Scholar]
- Global Land Cover Characterization (GLCC). Available online: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-land-cover-products-global-land-cover-characterization-glcc?qt-science_center_objects=0#qt-science_center_objects (accessed on 24 June 2019).
- Cosby, B.J.; Hornberger, G.M.; Clapp, R.B.; Ginn, T.R. A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils. Water Resour. Res. 1984, 20, 682–690. [Google Scholar] [CrossRef] [Green Version]
- Ren-Jun, Z. The Xinanjiang model applied in China. J. Hydrol. 1992, 135, 371–381. [Google Scholar] [CrossRef]
- Ahirwar, A.; Jain, M.K.; Perumal, M. Performance of the Xinanjiang model. In Hydrologic Modeling; Springer: Singapore, Singapore, 2018; pp. 715–731. [Google Scholar]
- Sahoo, B. The Xinanjiang model and its derivatives for modeling soil moisture variability in the land-surface schemes of the climate change models: An overview. In Proceedings of the International Conference on Hydrological Perspectives for Sustainable Development, New Delhi, India, 23–25 February 2005. [Google Scholar]
- Liang, X.; Lettenmaier, D.P.; Wood, E.F.; Burges, S.J. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmos. 1994, 99, 14415–14428. [Google Scholar]
- Franchini, M.; Pacciani, M. Comparative analysis of several conceptual rainfall-runoff models. J. Hydrol. 1991, 122, 161–219. [Google Scholar] [CrossRef]
- Wood, E.F.; Lettenmaier, D.P.; Zartarian, V.G. A land-surface hydrology parameterization with subgrid variability for general circulation models. J. Geophys. Res. Atmos. 1992, 97, 2717–2728. [Google Scholar] [CrossRef]
- Lohmann, D.; Nolte-Holube, R.; Raschke, E. A large-scale horizontal routing model to be coupled to land surface parametrization schemes. Tellus A 1996, 48, 708–721. [Google Scholar] [CrossRef]
- Lohmann, D.; Raschke, E.; Nijssen, B.; Lettenmaier, D.P. Regional scale hydrology: II. Application of the VIC-2L model to the Weser River, Germany. Hydrol. Sci. J. 1998, 43, 143–158. [Google Scholar] [CrossRef] [Green Version]
- Haddeland, I.; Lettenmaier, D.P.; Skaugen, T. Effects of irrigation on the water and energy balances of the Colorado and Mekong river basins. J. Hydrol. 2006, 324, 210–223. [Google Scholar] [CrossRef]
- Västilä, K.; Kummu, M.; Sangmanee, C.; Chinvanno, S. Modelling climate change impacts on the flood pulse in the Lower Mekong floodplains. J. Water Clim. Chang. 2010, 1, 67–86. [Google Scholar] [CrossRef]
- Zhou, T.; Nijssen, B.; Gao, H.; Lettenmaier, D.P. The contribution of reservoirs to global land surface water storage variations. J. Hydrometeorol. 2016, 17, 309–325. [Google Scholar] [CrossRef]
- Hempel, S.; Frieler, K.; Warszawski, L.; Schewe, J.; Piontek, F. A trend-preserving bias correction–the ISI-MIP approach. Earth Syst. Dyn. 2013, 4, 219–236. [Google Scholar] [CrossRef]
- Burhan, A.; Waheed, I.; Syed, A.A.B.; Rasul, G.; Shreshtha, A.B.; Shea, J.M. Generation of high-resolution gridded climate fields for the upper Indus River Basin by downscaling CMIP5 outputs. J. Earth Sci. Clim. Chang. 2015, 6, 1. [Google Scholar]
- Ruan, Y.; Liu, Z.; Wang, R.; Yao, Z. Assessing the Performance of CMIP5 GCMs for Projection of Future Temperature Change over the Lower Mekong Basin. Atmosphere 2019, 10, 93. [Google Scholar] [CrossRef]
- Abbaspour, K.C. User Manual for SWAT-CUP: SWAT Calibration and Uncertainty Analysis Programs; Swiss Federal Institute Aquatic Science and Technology: Dübendorf, Switzerland, 2011. [Google Scholar]
- Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 1970, 10, 282–290. [Google Scholar] [CrossRef]
- Sridhar, V.; Hubbard, K.G.; Wedin, D.A. Assessment of soil moisture dynamics of the Nebraska Sandhills using long-term measurements and a hydrology model. J. Irrig. Drain. Eng. 2006, 132, 463–473. [Google Scholar] [CrossRef]
- Sridhar, V.; Wedin, D.A. Hydrological behaviour of grasslands of the Sandhills of Nebraska: Water and energy-balance assessment from measurements, treatments, and modelling. Ecohydrol. Ecosyst. Land Water Process Interact. Ecohydrogeomorphol. 2009, 2, 195–212. [Google Scholar] [CrossRef]
- USDA (U.S. Department of Agriculture). Soil Conservation Service, National Engineering Handbook; Hydrology (Section 4, Chapters 4–10); GPO: Washington, DC, USA, 1972. [Google Scholar]
- Boughton, W.C. A review of the USDA SCS curve number method. Soil Res. 1989, 27, 511–523. [Google Scholar] [CrossRef]
Parameter | Description | Min | Max | Best Parameters |
---|---|---|---|---|
r_CN2.mgt | Curve number for moisture condition II | −0.2 | 0.2 | 0.06 |
v_ALPHA_BF.gw | Baseflow alpha factor | 0 | 1 | 0.35 |
v_GW_DELAY.gw | Groundwater delay time | 30 | 450 | 177 |
v_GWQMN.gw | Threshold water depth in shallow aquifer for back discharge | 0 | 2000 | 1500 |
S. No. | Parameter | Description | Allowable Range | |
---|---|---|---|---|
Lower | Upper | |||
1 | bi | variable infiltration curve parameter | 0.1 | 0.5 |
2 | D | the depth of soil layers | 0.1 | 1.5 |
3 | Ds | fraction of maximum velocity of baseflow where non-linear baseflow begins | 0 | 0.4 |
4 | Ws | fraction of maximum soil moisture where non-linear baseflow occurs | 0.5 | 1 |
Station | Calibration Period | Validation Period | Calibration | Validation | ||||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | NS | R2 | NS | |||||||
SWAT | VIC | SWAT | VIC | SWAT | VIC | SWAT | VIC | |||
Chiang Saen | 1984–1990 | 1991–1996 | 0.92 | 0.93 | 0.86 | 0.83 | 0.93 | 0.91 | 0.85 | 0.81 |
Luang Prabang | 1984–1990 | 1991–1997 | 0.93 | 0.93 | 0.81 | 0.73 | 0.94 | 0.89 | 0.86 | 0.67 |
Vientiane | 1984–1990 | 1991–1996 | 0.92 | 0.93 | 0.83 | 0.91 | 0.95 | 0.94 | 0.88 | 0.92 |
Nakhon Phanom | 1984–1990 | 1991–1995 | 0.93 | 0.93 | 0.87 | 0.90 | 0.92 | 0.92 | 0.86 | 0.79 |
Mukdahan | 1984–1990 | 1991–1995 | 0.93 | 0.94 | 0.89 | 0.86 | 0.93 | 0.94 | 0.88 | 0.83 |
Pakse | 1984–1990 | 1991–1998 | 0.90 | 0.91 | 0.84 | 0.86 | 0.90 | 0.93 | 0.85 | 0.87 |
Kratie | 1984–1990 | 1991–1998 | 0.90 | 0.90 | 0.85 | 0.85 | 0.91 | 0.93 | 0.86 | 0.86 |
Station | Season | SWAT | VIC | ||||||
---|---|---|---|---|---|---|---|---|---|
RCP4.5 | RCP8.5 | RCP4.5 | RCP8.5 | ||||||
2020–2059 | 2060–2099 | 2020–2059 | 2060–2099 | 2020–2059 | 2060–2099 | 2020–2059 | 2060–2099 | ||
Chiang Saen | Wet | 10.5 | 21.9 | 10.7 | 25.1 | 31.4 | 41.0 | 31.7 | 49.1 |
Dry | 17.6 | 25.7 | 14.7 | 21.3 | 33.6 | 41.3 | 32.2 | 36.6 | |
Luang Prabang | Wet | 11.3 | 23.5 | 11.9 | 25.8 | 56.1 | 68.7 | 57.1 | 77.7 |
Dry | 17.7 | 25.5 | 14.1 | 19.4 | 26.8 | 34.2 | 25.2 | 29.7 | |
Vientiane | Wet | 14.3 | 27.6 | 15.6 | 29.3 | 12.1 | 22.3 | 13.8 | 26.8 |
Dry | 20.4 | 28.6 | 16.5 | 22.0 | −11.3 | −5.3 | −12.4 | −8.6 | |
Nakhon Phanom | Wet | 18.8 | 32.2 | 21.0 | 32.7 | 28.0 | 39.4 | 30.1 | 42.7 |
Dry | 26.0 | 33.5 | 21.4 | 26.9 | −8.7 | −3.8 | −10.8 | −6.5 | |
Mukdahan | Wet | 19.2 | 32.4 | 21.4 | 32.3 | 38.2 | 50.5 | 40.4 | 53.5 |
Dry | 27.6 | 35.0 | 22.8 | 28.5 | −3.3 | 1.9 | -5.5 | −0.7 | |
Pakse | Wet | 18.6 | 30.4 | 20.2 | 29.3 | 22.6 | 32.0 | 23.5 | 33.2 |
Dry | 31.7 | 39.0 | 26.3 | 33.4 | −31.2 | −27.0 | −32.5 | −28.2 | |
Kratie | Wet | 21.0 | 30.2 | 21.5 | 29.4 | 32.1 | 39.7 | 32.0 | 40.6 |
Dry | 37.2 | 42.8 | 31.4 | 39.4 | −22.7 | −19.0 | −24.3 | −18.8 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sridhar, V.; Kang, H.; Ali, S.A. Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin. Water 2019, 11, 1307. https://doi.org/10.3390/w11061307
Sridhar V, Kang H, Ali SA. Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin. Water. 2019; 11(6):1307. https://doi.org/10.3390/w11061307
Chicago/Turabian StyleSridhar, Venkataramana, Hyunwoo Kang, and Syed A. Ali. 2019. "Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin" Water 11, no. 6: 1307. https://doi.org/10.3390/w11061307