Spatial Modeling of Land Cover/Land Use Change and Its Effects on Hydrology Within the Lower Mekong Basin

  • Kel N. Markert
  • Robert E. Griffin
  • Ashutosh S. Limaye
  • Richard T. McNider
Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)


The Lower Mekong Basin is an economically and ecologically important region that is vulnerable to effects of climate variability and land cover changes. To effectively develop long-term plans for addressing these changes, responses to climate variability and land cover change must be evaluated. This research aims to investigate how the land cover change will affect hydrologic parameters both spatially and temporally within the Lower Mekong Basin. The research goal is achieved by (1) modeling land cover change for a baseline land cover change scenario as well as changes in land cover with increases in forest or agriculture and (2) using modeled land cover data as inputs into the Variable Infiltration Capacity (VIC) hydrologic model to simulate the changes to the hydrologic system. The VIC model outputs were analyzed against historic values to understand to what degree land cover changes affect the hydrology of the region and where within the region these changes occur. This study found that increasing forest area will slightly decrease discharge and increase evapotranspiration whereas increasing agriculture area increases discharge and decreases evapotranspiration. These findings will benefit the Lower Mekong Basin by supporting individual country, as well as basin-wide, policy for effective land management for water resources management changes as well as policy for the basin as a whole.


Land cover changes Hydrology changes Lower Mekong Basin 



The authors wish to thank the Mekong River Commission for supplying the observed discharge data used in this study. A special thanks goes to Faisal Hossain for his assistance with setting up the hydrologic model. The authors are grateful to Dan Irwin, Eric Anderson, Africa Flores, Lee Ellenburg, Larry Carey, Maury Estes, and others for their support and valuable comments. This work was funded through the NASA-SERVIR program part of the Capacity Building program of NASA Applied Sciences as part of K.N.M. graduate work.


  1. Abdulla FA, Lettenmaier DP, Wood EF, Smith JA (1996) Application of a macroscale hydrologic model to estimate the water balance of the Arkansas–Red River Basin. J Geophys Res 101:7449–7459CrossRefGoogle Scholar
  2. Al-Hamdan MZ et al (2017) Evaluating land cover changes in Eastern and Southern Africa using validated Landsat and MODIS data. Int J Appl Earth Observ Geoinf 62:8–26. CrossRefGoogle Scholar
  3. Berrisford P et al (2011) Atmospheric conservation properties in ERA-Interim. Q J R Meteorol Soc 137:1381–1399CrossRefGoogle Scholar
  4. Bowling LC, Lettenmaier DP (2010) Modeling the effects of lakes and wetlands on the water balance of arctic environments. J Hydrometeorol 11(2):276–295CrossRefGoogle Scholar
  5. Bowling LC, Pomeroy JW, Lettenmaier DP (2004) Parameterization of blowing-snow sublimation in a macroscale hydrology model. J Hydrometeorol 5(5):745–762CrossRefGoogle Scholar
  6. Brovkin V, Sitch S, von Bloh W, Claussen M, Bauer E, Cramer W (2004) Role of land cover change for atmospheric CO2 increase and climate change during the last 150 years. Glob Chang Biol 10:1253–1266CrossRefGoogle Scholar
  7. Burn DH (1997) Catchment similarity for regional flood frequency analysis using seasonality measures. J Hydrol 202:212–230CrossRefGoogle Scholar
  8. Clay DE et al (2016) Does the U.S. cropland data layer provide and accurate benchmark for land-use change estimates? Agron J 108:266–272CrossRefGoogle Scholar
  9. Cong Thanh N, Singh B (2006) Trend in rice production and export in Vietnam. Omonrice 14:11–123Google Scholar
  10. Costa-Cabral MC et al (2008) Landscape structure and use, climate, and water movement in the Mekong River Basin. Hydrol Process 22:1731–1746CrossRefGoogle Scholar
  11. Costenbader J, Broadhead JS, Yasmi Y, Durst PB (2015a) Drivers affecting forest change in the GMS: an overview. FAO/USAID LEAF, Sept 2015Google Scholar
  12. Costenbader J, Varns T, Vidal A, Stanley L, Broadhead JS (2015b) Drivers of forest change in the greater Mekong subregion. Regional Report, FAO/USAID LEAF, Sept 2015Google Scholar
  13. Cuo L et al (2011) Effects of mid-twenty-first century climate and land cover change on the hydrology of the Puget Sound basin, Washington. Hydrol Process 25:1729–1753CrossRefGoogle Scholar
  14. Danielson JJ, Gesch DB (2011) Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). Open File Report, US Geological Survey, RestonGoogle Scholar
  15. Decker M et al (2012) Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations. J Clim 25:1916–1943CrossRefGoogle Scholar
  16. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  17. Dobson JE et al (2000) A global population database for estimating populations at risk. Photogramm Eng Remote Sens 66(7):849–857Google Scholar
  18. Dudgeon D (2000) Large-scale hydrological changes in tropical Asia: prospects for riverine biodiversity. Bioscience 50:793–806CrossRefGoogle Scholar
  19. Dwarakish GS, Ganasri BP (2015) Impact of land use change on hydrologic systems: a review of current modeling approaches. Cogent Geosci 1:1115691CrossRefGoogle Scholar
  20. Eastham J et al (2008) Mekong river basin water resource assessment: impacts of climate change. Technical Report, CSIROGoogle Scholar
  21. ECMWF (European Centre for Medium-Range Weather Forecasts) (2009) (updated monthly) ERA-Interim Project. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Accessed 31 Mar 2016
  22. Fernandes R, Leblanc SG (2005) Parametric (modified least squares) and non-parametric (Theil-Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors. Remote Sens Environ 95:303–316CrossRefGoogle Scholar
  23. Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201CrossRefGoogle Scholar
  24. Francisco HA (2008) Adaptation to climate change—needs and opportunities in Southeast Asia. ASEAN Econ Bull 25(1):7–19CrossRefGoogle Scholar
  25. Friedl MA et al (2010) MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ 114:168–182CrossRefGoogle Scholar
  26. Funk C et al (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2:150066CrossRefGoogle Scholar
  27. Godin R (2014) Joint Polar Satellite System (JPSS) VIIRS Surface Type Algorithm Theoretical Basis Document (ATBD). Algorithm Theoretical Basis Document (last access: 3 January 2017). Scholar
  28. Haddeland I, Lettenmaier DP, Skaugen T (2006a) Effects of irrigation on the water and energy balances of the Colorado and Mekong river basin. J Hydrol 324:210–223CrossRefGoogle Scholar
  29. Haddeland I, Skaugen T, Lettenmaier DP (2006b) Anthropogenic impacts on continental surface water fluxes. Geophys Res Lett 33(8):L08406CrossRefGoogle Scholar
  30. Hijmans RJ et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  31. Ito A (2007) Simulated impacts of climate and land-cover change on soil erosion and implications for the carbon cycle, 1901 to 2100. Geophys Res Lett 34:L09403Google Scholar
  32. Justice CO et al (2013) Land and cryosphere products from Suomi NPP VIIRS: overview and status. Geophys Res Lett 118:9753–9765Google Scholar
  33. Keskinen M et al (2010) Climate change and water resources in the Lower Mekong River Basin: putting adaptation into the context. J Water Clim Chang 1:103–117CrossRefGoogle Scholar
  34. Kibria KN, Ahiablame L, Hay C, Djira G (2016) Streamflow trends and responses to climate variability and land cover change in South Dakota. Hydrology 3Google Scholar
  35. Kingston DG, Thompson JR, Kite G (2011) Uncertainty in climate change projections of discharge for the Mekong River Basin. Hydrol Earth Syst Sci 15:1459–1471CrossRefGoogle Scholar
  36. Kite G (2001) Modelling the Mekong: hydrological simulation for environmental impact studies. J Hydrol 253:1–13CrossRefGoogle Scholar
  37. Kityuttachai K, Heng S, Sou V (2016) Land cover map of the Lower Mekong Basin, MRC Technical Paper No. 59, Information and Knowledge Management Programme. Mekong River Commission, Phnom Penh, CambodiaGoogle Scholar
  38. Kummu M, Sarkkula J (2008) Impact of the Mekong river flow alteration on the Tonle Sap flood pulse. Ambio 37(3):185–192CrossRefGoogle Scholar
  39. Kummu M, Sarkkula J, Koponen J, Nikula J (2006) Ecosystem management of Tonle Sap Lake: integrated modelling approach. Int J Water Resour Dev 22(3):497–519CrossRefGoogle Scholar
  40. Lamberts D (2008) Little impact, much damage: the consequences of Mekong River flow alterations for the Tonle Sap ecosystem. In: Kummu M, Keskinen M, Varis O (eds) Modern myths of the Mekong—a critical review of water and development concepts, principles and policies. Water & Development Publications—Helsinki University of Technology, Espoo, pp 3–18Google Scholar
  41. Lauri H et al (2012) Future changes in Mekong River hydrology: impact of climate change and reservoir operation on discharge. Hydrol Earth Syst Sci 16:4603–4619CrossRefGoogle Scholar
  42. Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res 99:14415–14428CrossRefGoogle Scholar
  43. Liu M, Adam JC, Hanlet AF (2013a) Spatial-temporal variations of evapotranspiration and runoff/precipitation ratios responding to the changing climate in the Pacific Northwest during 1921-2006. J Geophys Res Atmos 118:380–394. CrossRefGoogle Scholar
  44. Liu X, Liu W, Xia J (2013b) Comparison of the streamflow sensitivity to aridity index between the Danjiangkou Reservoir basin and Miyun Reservoir basin, China. Theor Appl Climatol 111:683–691. CrossRefGoogle Scholar
  45. Liu Y, Li Y, Li S, Motesharrei S (2015) Spatial and temporal patterns of global NDVI trends: correlations with climate and human factors. Remote Sens 7:13233–13250. CrossRefGoogle Scholar
  46. Lohmann D, Nolte-Holube R, Raschke E (1996) A large-scale horizontal routing model to be coupled to land surface parametrization schemes. Tellus 48:708–721CrossRefGoogle Scholar
  47. Lohmann D, Raschke E, Nijssen B, Lettenmaier DP (1998) Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrol Sci J 43:131–141CrossRefGoogle Scholar
  48. Loveland TR, Belward AS (1997) The IGBP-DIS global 1 km land cover data set, DISCover: first results. Int J Remote Sens 18:3289–3295CrossRefGoogle Scholar
  49. LP DAAC (Land Processes Distributed Active Archive Center) (2010) MCD12Q1. V051, NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls. Accessed 14 Feb 2016
  50. Matheussen B et al (2000) Effects of land cover change on streamflow in the interior Columbia basin. Hydrol Process 14:867–885CrossRefGoogle Scholar
  51. Milne R, Jallow BP (2003) Basis for consistent representation of land areas. In: Apps M, Miguez JD (eds) IPCC good practice guidance for LULUCF. pp 2.1–2.29Google Scholar
  52. Moriasi DN et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900CrossRefGoogle Scholar
  53. MRC (Mekong River Commission) (2010) State of the Basin Report: 2010. Mekong River Commission, Vientiane Lao PDRGoogle Scholar
  54. MRC (Mekong River Commission) (2011) Hydrological database. Mekong River Commission, Vientiane Lao PDRGoogle Scholar
  55. Nachergaele F, van Vilthuizen H, Verelst L, Wiberg D (2012) Harmonized World Soil Database. Technical Document, United Nations FAOGoogle Scholar
  56. Nagaraj MK, Yaragal SC (2008) Sensitivity of land cover parameter in runoff estimation using GIS. ISH J Hydraul Eng 14:41–51. CrossRefGoogle Scholar
  57. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  58. Nijssen B, O’Donnell GM, Hamlet AF, LettenMaier DP (2001a) Hydrologic sensitivity of global rivers to climate change. Clim Chang 50:143–175CrossRefGoogle Scholar
  59. Nijssen B, Schnur R, Lettenmaier DP (2001b) Global retrospective estimation of soil moisture using the variable infiltration capacity land surface model, 1980–93. J Clim 14:1790–1808CrossRefGoogle Scholar
  60. Nijssen B et al (2001c) Predicting the discharge of global rivers. J Clim 14:3307–3323CrossRefGoogle Scholar
  61. NOAA CLASS (National Oceanic and Atmospheric Administration Comprehensive Large Array-Data Stewardship System) (2013) VIIRS Surface Type EDR (VSTYO), NOAA CLASS, NOAA Center for Satellite Applications and Research, College Park, Maryland, Accessed 18 July 2016Google Scholar
  62. Olofsson P et al (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ 148:42–57. CrossRefGoogle Scholar
  63. Parajka J et al (2009) Comparative analysis of the seasonality of hydrological characteristics in Slovakia and Austria. Hydrol Sci J 54:456–473CrossRefGoogle Scholar
  64. Pech S, Sunada K (2008) Population growth and natural-resources pressure in the Mekong River Basin. Ambio 37:219–224CrossRefGoogle Scholar
  65. Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Koppen-Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644. CrossRefGoogle Scholar
  66. Pontius RG Jr, Schneider LC (2001) Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85:239–248CrossRefGoogle Scholar
  67. Prathumratana L, Sthiannopkao S, Kim KW (2008) The relationship of climate and hydrologic parameters to surface water quality in the lower Mekong River. Environ Int 34:860–866. CrossRefGoogle Scholar
  68. Ratnam J et al (2011) When is a ‘forest’ a savanna, and why does it matter? Glob Ecol Biogeogr 20:1–8. CrossRefGoogle Scholar
  69. Romanic D, Curic M, Jovicic I, Lompar M (2015) Long-term trends of the ‘Koshava’ wind during the period 1949-2010. Int J Climatol 35:288–302. CrossRefGoogle Scholar
  70. Sakamoto T et al (2006) Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens Environ 100:1–16CrossRefGoogle Scholar
  71. Schaake JC (1990) From climate to flow. In: Waggoner PE (ed) Climate change and U.S. water resources. Wiley, New York, pp 177–206Google Scholar
  72. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389CrossRefGoogle Scholar
  73. Stehman SV (1996) Estimating the Kappa coefficient and its variance under stratified random sampling. Photogramm Eng Remote Sens 62:401–407Google Scholar
  74. Strahler A et al (1999) MODIS land cover product algorithm theoretical basis document (ATBD) Version 5.0. Technical Document, NASA.
  75. Tatsumi K, Yamashiki Y (2015) 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 159:92–106. CrossRefGoogle Scholar
  76. Thompson JR, Green AJ, Kingston DG, Gosling SN (2013) Assessment of uncertainty in river flow projections for the Mekong River using multiple GCMs and hydrological models. J Hydrol 486:1–30. CrossRefGoogle Scholar
  77. Tote C et al (2015) Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique. Remote Sens 7:1758–1776. CrossRefGoogle Scholar
  78. Trenberth KE, Fasullo JT, Mackaro J (2011) Atmospheric moisture transports from Ocean to land and global energy flows in reanalyses. J Clim 24:4907–4924. CrossRefGoogle Scholar
  79. Verburg PH et al (2002) Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manag 30:391–405. CrossRefGoogle Scholar
  80. Wijesekara GN et al (2012) Assessing the impact of future land-use changes on hydrological processes in the Elbow River watershed in southern Alberta, Canada. J Hydrol 412:220–232CrossRefGoogle Scholar
  81. Zheng J, Yu X, Deng W, Wang H, Wang Y (2013) Sensitivity of land-use change to streamflow in Chaobai river basin. J Hydrol Eng 18:457–464. CrossRefGoogle Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2018

Authors and Affiliations

  • Kel N. Markert
    • 1
    • 2
  • Robert E. Griffin
    • 3
  • Ashutosh S. Limaye
    • 4
  • Richard T. McNider
    • 3
  1. 1.NASA SERVIR Science Coordination OfficeMarshall Space Flight CenterHuntsvilleUSA
  2. 2.Earth System Science CenterUniversity of Alabama in HuntsvilleHuntsvilleUSA
  3. 3.Department of Atmospheric ScienceUniversity of Alabama in HuntsvilleHuntsvilleUSA
  4. 4.NASA Marshall Space Flight CenterHuntsvilleUSA

Personalised recommendations