landscape structure and use, climate, and water movement...

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Landscape Structure and Use, Climate, and Water Movement in the

Mekong River Basin

Mariza Costa-Cabrala,* , Jeffrey E. Richeyb, Gopalakrishna Gotetia,c,,Dennis P. Lettenmaiera, Christoph Feldkötterd, and Anond Snidvongse

a Department of Civil and Environmental Engineering, Box 35270, University of Washington,Seattle, WA 98195, USA.b School of Oceanography, Box 355351, University of Washington, Seattle, WA 98195, USA.c Department of Earth System Science, University of California, Irvine, CA 92697, USA.d Mekong River Commission Secretariat, Sikhottabong District,Vientiane 01000, Lao PDRe Southeast Asia START Global Change Regional Center, Chulalongkorn University, Bangkok,10330, Thailand.

(Soon to be) in Review: “Advances in Water Resources”

Abstract

This study addresses whether the marked spatial and temporal runoff variability in theMekong basin (795,000 km2) is mostly determined by the variability of precipitation, orwhether significant influence is exerted also by the landscape’s geophysicalcharacteristics, such as topography and soil properties. To investigate these questions,the Variable Infiltration Capacity (VIC) macro-scale hydrologic model was applied to theMekong basin, at 5 arc minutes resolution, and successfully calibrated against streamgauge records. Annual maxima of monthly flows were often under-estimated (andsometimes over-estimated) at the most downstream stations, and we attribute this resultto the sparseness of the network of rain gauges, in addition to low recording reliability;however, the overall reproduction of the records at the 13 stream gauges over the 22years of simulation (1979-2000) was judged adequate. The hydrologic fluxes simulatedby VIC for the period 1979-2000 were then used to analyze the factors controlling runoffgeneration in the model. Distinct differences in the total runoff generated by similar totalmonthly rainfall volumes corresponded to differences in average monthly soil moisture.A series of analyses further revealed that simulated soil moisture plays a decisive role inestablishing both the timing and amount of generated runoff in addition to rainfall. Soilmoisture is a variable with a long memory for antecedent hydrologic fluxes that isinfluenced by soil hydrologic parameters, topography, and vegetation characteristics.This study found evidence that soil moisture may be significantly affected by land cover

* Corresponding author. Address: Department of Civil and Environmental Engineering, Box 35270,University of Washington, Seattle, WA 98105, USA.E-mail address: [email protected] (M. Costa-Cabral), [email protected] (J. E.Richey), [email protected] (G. Goteti), [email protected] (D. P. Lettenmaier),[email protected] (C. Feldkotter), [email protected] (A. Snidvongs)


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type, with the implication that large-scale deforestation for agricultural land may indeedhave important hydrologic consequences.

Keywords: Land use change; runoff variability; streamflow variability; hydrologicfluxes; Southeast Asia; macro-scale hydrologic model; soil moisture


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1. Introduction

One of the most salient contemporary issues in the face of Global Change is howa landscape that is evolving in its land cover and use interacts with a variable andchanging climate, to affect the hydrologic regime of large river basins. A particularlysensitive region is Southeast Asia, where rapid population growth and socio-economicdevelopment over the past several decades has been accompanied by extensivedeforestation, expansion of agriculture and irrigation, and streamflow regulation.

In the largest Southeast Asian basin, the Mekong (795,000 km2; Fig. 1), therainfall released by the northeast and southwest monsoons in May through October (some80-90% of the annual total) varies markedly from year to year, as does streamflow. Whileconsiderable rice production relies on seasonal flooding, floods are in some yearsdevastating to Cambodia’s Phnom Penh, carrying large human and economic costs.Spatial contrasts in rainfall totals are equally marked, with the eastern and northernhighland regions of Laos and the mountains in southwest Cambodia capturing much ofthe monsoonal rain, and imposing occasionally damaging dry conditions acrossThailand’s agriculturally-intensive Khorat Plateau. Periodically severe droughts in thisregion cause crop losses, reservoir depletion, low flows, fish kills, and water qualitydeterioration.

Land cover and land use change has been extensive in the Mekong basin, whichhas a growing population of currently 75 million people. Forests have been replaced byagricultural land, irrigation has expanded and intensified, and a few large river dams havebeen constructed for irrigation and hydropower purposes. The Mekong basin also has oneof the world’s largest freshwater fisheries resources, which is vulnerable to alteredseasonal streamflows, sediment load, and changes in water quality. Conflicting demandsfor freshwater resources are increasing throughout the basin. Climate change predictionscall for a rise in temperature and rainfall in Southeast Asia over the 21st century, in allseasons, with unstudied hydrological consequences [16].

The intent of our work, published in this and a subsequent manuscript (see alsoRichey et al et al., in review) is to gain insight into the phenomena responsible forstreamflow variability in time and space in the Mekong basin, and ultimately to use thatunderstanding to predict the basin’s hydrologic response to land use and land coverchange. We investigate the factors affecting the seasonal and inter-annual variability ofrunoff in the Mekong and its sub-basins. We address the questions of whether thedistribution of rainfall in time and space is the over-riding factor determining streamflowvariability; or whether significant roles are played also by the geophysical characteristicsof the landscape (its topography and soil properties) and by land cover.

An approach based solely on observation-based estimates cannot providesufficient insight into these questions. If we analyze empirical time series data ofprecipitation and runoff, we find that days or months with similar precipitation totalsoften produce quite different daily or monthly runoff volumes. However, theinterpretation of this finding is hindered by the fact that the date of streamflowobservations may differ significantly from the date of runoff generation. For example,flow measured at a streamgauge in a particular day includes runoff generated at differentdates at nearby and remote locations upstream. Time-aggregated records (monthly orannual), on the other hand, have no information on rainfall intensity, hence its role cannot


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be explored. Additionally, the purely empirical approach has no explanatory power as towhich factors other than precipitation might play an important role in runoff generation.

Resolution of these questions calls for the application of a distributed hydrologicmodel. In this study, the Variable Infiltration Capacity (VIC) macro-scale hydrologicmodel [18, 25] is utilized. Because VIC incorporates and is sensitive to climatic andlandscape parameters in addition to hydrology, questions regarding the parameterscontrolling seasonal and interannual variability can be answered. The quality ofmodeling results is contingent on the development of the input surface bio-geophysicaldata layers, which describe the basin’s soil properties, land cover and use, vegetationparameters, topography and climate. The hydrologic responses simulated by the modelcan then be analyzed for those factors which most influence spatial and temporal runoffvariability, and the this information may be utilized to determine how land cover changesalter these factors to affect the basin’s hydrology.

2. Methodology

In this section, the biophysical characteristics of the Mekong Basin are described.Next, the VIC model and various landscape attributes required by VIC as input datasetsare presented, including soil and vegetation parameters. These input datasets define theview of the landscape and determine the results provided by VIC. After defining theinput datasets, the VIC model is run at a daily time step for the period of 1979-2000. Theperiod used for calibration is 1979-1988, and the model performance is verified bytransferring the calibrated parameters for 1989-2000.

2.1. Modeling study site: The Mekong Basin

The Mekong has the world’s 8th largest discharge (ca. 470 million m3yr-1), 12th largestlength (ca. 4,800 km), and 21st largest drainage area (ca. 795,000 km2; MRC, 1998). Theheadwaters of the Mekong River (Fig. 1) are in the Tibetan Highlands, at nearly 5,000 m.Fed by the melting snow, the Mekong rushes down the steep Tibetan slopes and througha narrow gorge in the Yunnan province of China. At the latitude 23˚30’N, still in Chineseterritory, the river enters the torrid climatic zone. After passing the joint borders ofChina, Burma, and Laos (latitude 22˚15’N), the basin gradually widens. Its flow velocityprogressively decreases, until it reaches a very slow pace at its estuary. The basin drains aportion of China (containing about 21% of the basin area), Burma (3%), Laos (25%),Thailand (23%), Cambodia (20%), and Vietnam (8%). The portion of the basin lyingwithin China, Burma, and the northern part of Laos, consists of mountainous terrainbetween 400 and 5,000 m elevation, is know as the “Upper Mekong Basin” (189,000km2). The remainder 606,000 km2 territory make up the “Lower Mekong Basin”.

The “Northern Highlands” include the region from southern Yunnan throughBurma, Laos, and Northern Thailand, and eastward into the northern end of the AnnamiteCordillera in Vietnam. The “Eastern Highlands” form a southern extension of thismountainous landscape, about 700 km from Laos through Vietnam. Next to westernCambodia, which often receives over 3,000 mm of rainfall annually (according to ourestimates obtained by the interpolation of raingauge data), the Northern and EasternHighlands, with elevations of up to about 2,800 meters, are the wettest regions in the


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Mekong basin. The high mountains have deep-cut valleys and topsoil consisting of a thindeposit of sandstone and igneous rocks. In contrast, the Khorat Plateau, composing thenortheastern region of Thailand, is a dry region, with annual rainfall between about 1,000and 1,600 mm and intense evapo-transpiration. The Plateau is mainly drained by the Chiand Mun rivers and has sandy and saline soils. The Mekong river and the Tonle SapLake (the largest freshwater body in Southeast Asia) are the main features of theCambodian plains, whose average elevation is less than 10 m. The Tonle Sap Lakeconnects to the mainstem Mekong via the Tonle Sap river, where the flow directionreverses with the season, bringing excess flow from the mainstem into the lake in July-September, which may then be released in the dry season (October-April). At the PhnomPenh confluence, the mainstem splits into the Mekong (to the east, with ca. 80% of theflow), and the Bassac.

2.2 Variable Infiltration Capacity (VIC) model

VIC is a distributed macro-scale hydrologic model [18, 25], which incorporatessoil and vegetation parameters. The VIC model has been applied to all river basins in theUS [1, 8, 21, 24]; the entire country of China [34]; the pan-Arctic region (Su et al.,submitted); and the entire Earth (at 2° resolution; [25]). VIC’s major distinguishingcharacteristic among macro-scale hydrologic models is that it represents soil moisturestorage capacity as variable within each model grid cell, having a spatial probabilitydistribution (following the Xinanjiang model of Zhao et al. [36], described in [35]). Inaddition, it represents base flow as a nonlinear recession [10], and water and energybalance equations are solved simultaneously. In this study, the resolution used is 1/12°,or 5 arc minutes.

The subsurface is characterized in the vertical direction by three soil layers in thisstudy. The top soil layer contributes to runoff via fast response mechanisms, whereas thedeepest soil layer produces base flow. Drainage between the soil layers is modeled asgravity driven. Controls of vegetation on evapo-transpiration are represented explicitlyusing a Penman-Monteith formulation [18]. Sub-grid variations in precipitation rate andtemperature, due to variations in elevation, are represented by sub-dividing each grid cellinto a number of elevation bands. The effects of snow accumulation and melt arerepresented using an internal coupled snow model described by Storck and Lettenmaier[33].

The VIC model can be operated in one of two modes: an energy balance modeand a water balance mode. In the energy balance mode, all the water and energy fluxesnear the land surface are calculated, and the surface energy budget is closed by iteratingover an effective surface temperature. In the water balance mode, the effective surfacetemperature is assumed to equal the air temperature and only the surface water balancefluxes are calculated. In this study, the VIC model is operated in the water balance mode,similar to most operational hydrological models. Precipitation, maximum and minimumtemperature, and wind speed are the meteorological variables that drive the model in thewater balance mode. Hourly temperatures are estimated by fitting a spline function to thetime series of daily minimum and maximum temperatures. Daily precipitation inputs aredistributed uniformly in time throughout the day.

The model used to simulate the routing of streamflow along the stream network isdescribed by Lohmann et al. [19, 20]. This model uses a triangular unit hydrograph and


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linearized St. Venant’s equations to route the streamflow from each individual grid cellseparately to the basin outlet through the channel network. This model does not accountfor channel losses, extractions, diversions, or reservoir operations. The Mekong WaterManagement Model and the Irrigation Consumption Model account for changes in thehydrological output due to reservoir operation and irrigation losses, respectively. Thesemodels are described in detail in Richey et al. (in review).

2.3 Basin topography and river network inputs

Runoff simulated by VIC is routed from each model grid cell to one of its eightneighboring cells, according to the local “flow direction” [19, 20]. Flow directions aredetermined by terrain topography, and a variety of computerized methods are availablefor deriving a stream network from a digital elevation model (DEM). Our model gridcells are defined by meridian and parallel lines with a spacing of 5 arc minutes (1/12 of adegree) of latitude and longitude, roughly, 10 x 10 km at low latitudes. A DEM with thefiner resolution of 30 arc seconds, or about 1 x 1 km from the GTOPO30 dataset(http://edcdaac.usgs.gov/gtopo30/gtopo30.html), was used to produce the base network.This 1-km network was then “upscaled” to 10-km resolution (Fig. 2). This approach issimilar to that used in most of the previous applications of the VIC model cited above.

2.4 Soil texture and hydrologic parameters

The soil parameters required by the VIC model for each soil layer are thesaturated hydraulic conductivity (Ks), porosity (θs), field capacity (θc), wilting point (θw),and parameter n in the Brooks-Corey equation for unsaturated conductivity [5]. Weutilize 3 soil layers, where the top layer is arbitrarily set to a depth of 10 cm, and thedepths of the 2nd and 3rd layers are established by calibration against the observedhydrographs. The soil parameters were estimated based on United States Department ofAgriculture (USDA) soil texture classes, using the table published by Schaake [28].Brooks-Corey’s n was obtained from the b parameter in Schaake’s table [28], whichrepresents the slope of the moisture retention curve, in log space, using the relation n = 3+ 2b (see, e.g., [27], Table 5.1.1). Soil texture class was obtained from soil type asdescribed below.

For soil type, we used primarily a map of soil types developed by the MekongRiver Commission (MRC) [22], covering the Lower Mekong Basin, and utilizing theFAO-1988 classification. This map is in polygon format, and was converted to griddedformat at 5 arc-minutes resolution in order to match our model’s grid. Where re-classification of the MRC data into a FAO-1979 (required to use the soilprogram, below)wasn’t feasible, the FAO/UNESCO digital soil map of the world [12], was used. Soiltexture (%clay and %sand) and bulk density (ρb) were derived from the soil type mapusing the World Inventory of Soil Emission Potentials (WISE) pedon data base [3] withthe aid of the soilprogram of [7]. From %clay and %sand, each of our 5 arc-minutespixels was assigned one of the twelve USDA soil texture classes for the top soil layer(<10 cm) and bottom soil layer (10-100 cm) (Fig. 3).

2.5 Land cover


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While there have been land use changes in the Mekong basin in our simulationperiod of 1979-2000, their quantification is difficult. No reliable estimates ofdeforestation rates are available for the Mekong basin. Different data sources, includingsatellite sources, are generally not mutually compatible, and sometimes show frankdisagreement due to image processing that has been conducted by different interpretationmethods. The exception is the data set for 1993-1997 by the Forest Cover MonitoringProject ([13], cited by [22]) which yielded a rate of net loss of forest of 0.53% per yearover the basin. However, net deforestation rates for this period cannot be extrapolated toany other period, and hence have limited value to our study.

Satellite-based data sets were compared with the intent of deriving land coverchanges over the interim period of the simulation. The data sets compared were thefollowing: The data set based on Moderate Resolution Imaging Spectroradiometer(MODIS) 2000-2001 readings, which uses the International Geosphere BiosphereProgram (IGBP) 16-class legend; three different data sets based on Advanced Very HighResolution Radiometer (AVHRR) 1992 readings: the first using the University ofMaryland (UMD) 13-class legend, the second using the Global Ecosystems (OGE) 96-class legend, and the third using the Global Land Cover-Asia (GLC-Asia) 30-classlegend; the data set GLC-Southeast Asia which uses a 16-class legend and is based onSPOT VEGETATION data for 2000; and the Michigan State University Tropical RainForest Information Center (TRFIC) data set based on Multi-Spectral Scanner (MSS) andThematic Matter (TM) satellite data, distinguishing forest from non-forest for threedifferent years. The different land cover maps showed little correspondence between anyof their land cover classes, even when they include classes with the exact samedesignation.

Unable to find reliable means for estimating deforestation rates for the Mekongbasin during the course of our simulation period of 1979-2000, and considering that muchof the large-scale deforestation and establishment of permanent agricultural fields in thebasin had occurred prior to this period, we opted for using an unchanging land cover mapfor these 22 years. We chose the most reliable data set available for this map: the data setprepared by the MRC [23] for the Lower Mekong Basin, representing 1997, combinedwith GLC-2000 products for the Upper Mekong Basin.

Fig. 4 shows the land cover map used in this study to represent conditions in1979-2000. This map was obtained as a composite of the four maps (a)-(d), hence dataresolution differs for the Upper Mekong Basin (30 arc-seconds, or roughly 1 km) and theLower Mekong Basin (0.0012°, or about 250 meters).

a) For the Lower Mekong Basin, we used the 1997 land cover map, with a resolutionof 0.0012°, produced by the MRC [23] in co-operation with the GermanGesellschaft für Technische Zusammenarbeit (GTZ). The methods used toproduce the MRC/GTZ map of the Lower Mekong Basin were described byStibig [32]. This map is henceforth designated the “MRC/GTZ map” andrepresents the year 1997. The methods used to produce this map combined fieldobservations, aerial photographs, and multi-seasonal satellite images at a1:250,000 scale [32]. In the legend of Fig. 4, classes numbered 1-26, and 43, aretaken from the legend of the MRC/GTZ map.


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b) To locate the irrigated areas in the Lower Mekong Basin, we used the “irrigatedlands 2001” data set of the same Atlas [22]. Class 42 in the legend of Fig. 4represents this map.

c) For the Chinese portion of the Upper Mekong Basin, we used the 20 arc-secondresolution land cover map of the Global Land Cover 2000 (GLC2000) map forChina [11], which was produced from satellite data collected from January toDecember of 2000, by SPOT Vegetation S10. The GLC2000 maps use the UnitedNations Food and Agriculture Organization Land Cover Classification System(FAO-LCCS) (http://www.gvm.jrc.it/glc2000/legend.htm). Classes numbered 27-41 in Fig. 4 are taken from this map.

d) For the Burmese portion, we used the 30 arc-second resolution land cover map ofthe GLC2000 map for Southeast Asia [11], which was produced from the satelliteSPOT Vegetation S10 data using dry-season images (January to March) for1999/2000. Similarly to (c), it uses the FAO-LCCS. This map was chosen overthe MODIS 2000 satellite-derived map because, judged visually, it is in bestagreement with the MRC/GTZ map. Only two classes are represented in theBurmese portion of the Mekong: the class designated “tree cover, broadleaved,evergreen, closed and closed to open” was assigned to MRC/GTZ class 1,“Evergreen forest, high cover density”; and the class designated “cultivated andmanaged, non irrigated (mixed)” was assigned to MRC/GTZ class 23,“agricultural land”.

2.6 Vegetation parameters

Leaf Area Index

The leaf area index (LAI), a non-dimensional variable, represents the average leafsurface area covering each unit ground area. The LAI is one of the vegetation parametersto which VIC is most sensitive, having important impacts on the computed fraction ofintercepted precipitation, evaporation from the canopy (and snow ablation), and planttranspiration.

LAI values derived from the MODIS satellite readings were used, taken every 8days at 1-km resolution, from 03/01/2000 through 02/28/2001(http://modis.gsfc.nasa.gov). MODIS LAI values range from zero to a theoreticalmaximum value of 8.0. The MODIS LAI data were projected onto a geographical grid of30 arc-seconds resolution (1/120o; about 1 km), and were then aggregated by spatialaveraging to the coarser model grid of resolution 5 arc-minutes (1/12o). Data were alsoaggregated in time to monthly values, by averaging the 8-day readings. A data set of 12monthly mean LAI values was thus generated for each model grid pixel (Fig. 5). Thespatial patterns of LAI in Fig. 5 show good correspondence with the land cover map inFig. 4, with forest having the highest LAI values, and agricultural land having the lowest.The regions with highest LAI are Laos and Vietnam (with the exception of the MekongDelta), and some regions of Cambodia far from the Tonle Sap Lake. The Thai portion ofthe basin has a LAI mostly below 2.0 throughout the year, due to the strong dominance ofagriculture there. The high-elevation areas of China’s Yunnan province also have a LAImostly under 2.0 year-round. However, we determined that there was a problem with the


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MODIS values during the May through October rainy season, probably due to difficultiesin obtaining valid satellite readings in the cloudy conditions of this season. Therefore, wedecided to use the MODIS LAI values for November 2001 to represent the months fromJune through October, and the MODIS LAI values for April 2000 to represent the monthof May.

Albedo

The VIC model is also sensitive to albedo, which has a large impact on thecomputed evaporation from the canopy, and plant transpiration. In tropical regions,albedo is generally observed to be highest during the dry season because of reduced LAI([26] and [2], cited by [14]), but seasonal variations due to changes in sun angle arerelatively unimportant. Therefore, we estimated the seasonal variability of albedo valuesto follow that of the measured LAI values. We assigned seasonal albedo values to eachland cover class, based on observed values available from Giambelluca et al. [14].Annual minimum and maximum albedo values were established for each of the landcover classes in Fig. 4, corresponding to the dry season (November-May) and the wetseason (June-October), respectively (see Table 1). Lacking spatially extensive albedoobservations, we based our assignment of albedo values in great part to previous relevantobservations elsewhere, and attempted to correctly represent the albedo values of thevegetation classes relative to each other. The spectrum of values for albedo utilizedranged from a low of .085 for irrigated cropland (class 41) to values ranging from 0.8-0.9for new snow and 0.6-0.8 for old snow (e.g. [15]).

Vegetation height, displacement height, roughness length, and architectural resistance

Vegetation height is the variable based through which the VIC parametersroughness length and displacement height are estimated. We were unable to identifystudies reporting vegetation height for different land cover classes in Mekong regions,except for the approximate values mentioned in [32] for classes 7, 9, 10, 16, 19 and 20.Values from Sellers et al. [29] were used for classes 1, 2, 27 and 28. For all other classes,reasonable values were utilized; however, these values have not been validated.

Roughness length is defined as the height above the ground where wind speed isreduced to zero due to surface resistance. This parameter is used to determine the windprofile in VIC using a logarithmic approximation. Displacement height is defined as theheight above the ground were wind speed is not significantly affected by surfaceroughness. Roughness length and displacement height were estimated by multiplying thevegetation height in Table 3 by the factors 0.123 and 0.67, respectively (followingBrutsaert [6], cited in [31], p. 4.12). The values used for architectural resistance (Rarc,Table 3) are based on Ducoudre et al. [9].

Minimum stomatal resistance, RGL, and solar radiation attenuation

The minimum stomatal resistance (Rmin) is defined as that occurring with fullsunlight and at saturation leaf water potential. VIC uses Rmin, together with LAI andcurrent soil moisture, to calculate the canopy resistance to transpiration (based on the


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formulations of Blondin [4] and Ducoudre et al. [9], as described in [18]). The values ofRmin used in our simulations are listed in Table 3.

RGL is the minimum incoming shortwave radiation at which there will betranspiration. For forest cover classes we used the value 30 Wm-2, and for agriculture weused 100 Wm-2. Intermediate values were used for other land cover classes, as specifiedin Table 3. Solar radiation attenuation was approximated at 50%.

Wind height and wind attenuation

The VIC parameter wind height is the height above ground at which windobservations were recorded. VIC expects this measurement to have been made at highenough elevation that the vegetation effects on wind speed are negligible. The model thenestimates wind speed through and below the canopy using typical logarithmic windprofiles. We used wind speed data from the NCEP-NCAR Reanalysis [17] and assumedthe wind measurement height to be equal to 2 meters above the vegetation height in thecase of short vegetation classes (of grassland, shrubland, and agricultural land), and equalto 10 meters above the vegetation height in the case of tall vegetation classes (forestclasses). Wind speed attenuation through the overstory was approximated at 50% when atree canopy is present and at 10% when it is not.

Maximum rooting depth, and distribution of root mass with depth

Maximum rooting depth affects the ability of the vegetation to extract moisturefrom the three soil layes, and hence affects evapotranspiration, and the partitioning ofprecipitation into runoff and evapotranspiration. The values of maximum rooting depthused in our simulations are listed in Table 1. The bulk of root mass was allocated to thetop two soil layers.

2.7 Meteorological data

The meteorological data used to drive VIC are daily precipitation, maximum andminimum temperatures, and mean wind speed. Other forcing data (e.g., downward solarand longwave radiation, humidity) are derived from the daily temperature or temperaturerange. Station observations of daily precipitation and temperature were obtained from theNOAA Climate Prediction Center Summary of the Day data archived at the NationalCenter for Atmospheric Research (NCAR) for the period January 1979 throughDecember 2000. Data from 279 stations were interpolated to the 5 arc-minutes modelgrid cells using the SYMAP algorithm [30] to obtain daily time series of precipitation andmaximum and minimum temperature for each grid cell. Temperature data wereinterpolated using a lapse rate of -6.5oC/km to adjust temperature from the station to thegrid cell elevations. Wind speed data was interpolated from the NCEP-NCAR Reanalysisdata set [17] to the 5’ model grid cells.

2.8 Model calibration and verification

Some of the soil parameters in VIC generally cannot be estimated from any datasources, and must be adjusted by calibration. These calibration parameters are: the depth


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of each soil layer (L1, L2, L3 ; L1 was arbitrarily set to 10 cm and only L2 and L3 werecalibrated); the maximum velocity of baseflow (Dsmax); the fraction of Dsmax wherenonlinear baseflow begins (Ds); and the parameter of the variable infiltration curve (binf).The sensitivity of model results to each of these parameters is briefly discussed byNijssen et al. [25].

Calibration was performed for each sub-basin separately, using the mean monthlyflow recorded by each stream gauge (for location of the stream gauges, see Fig. 1). Forevery gauge located downstream of another, an additional portion of the basin is drained.This additional area was calibrated separately, using the difference in flow rates recordedby the two stream gauges. Hence, the flow observations from all 13 stream gauges wereused in model calibration. The calibration period was the first 10 years of simulation(1979-1988), and the verification period was the remaining 12 years (1989-2000). Thecalibrated VIC model was then run in water balance mode at a daily time step for theentire 1979-2000 period. The daily surface runoff and baseflow from each grid cell wereadded together, and used as the input to the routing model.

3. Analysis of Model Calibration

Simulated monthly average flows are compared to observed flows in Fig. 6 foreach gauge station used for calibration. Given the sparseness of rain gauges available, theagreement between the calibrated simulation results, referred to as the “historicalsimulation” (solid lines in Fig. 6), and the observation-based estimates (dotted lines)appears reasonable, for both the calibration period (1979-1988) and the verificationperiod (1989-2000). Significant disagreements in predicted and observed year-to-yearvariability can be seen in the smaller sub-basins, especially Ban Chot, where agreementcould not be improved by any further adjustment of the calibration parameters. Smallscale rainfall variability is reflected most in such small basins, and is not captured by therain gauge network. Consequently, the overall reproduction of the records at the 13stream gauges over the 22 years of simulation (1979-2000) was judged adequate.

Two simulated hydrographs were created for locations where recorded data arenot available: Phnom Penh (just above the confluence with the Tonle Sap River), and theTonle Sap Lake, where total runoff inputs (direct runoff and baseflow) were notcalibrated. In these areas for which calibration was not possible due to lack ofobservations, soil parameters calibrated for neighboring areas were used.

4. Analysis of Hydrologic Flow Patterns

In this section, the spatial and seasonal patterns of precipitation, simulated runoffand soil moisture are analyzed from 1979-2000, and the influence of soil moistureconditions on runoff generation in the VIC model is then investigated. Upon establishingthis relationship, the effects of land cover on soil moisture is explored.

4.1 Spatial and seasonal patterns of precipitation, soil moisture and runoff

Estimated monthly mean precipitation, runoff, and soil moisture are shown infigures 7, 8, and 9 respectively. Fig. 7 reveals a distinct rainy season due to the passage


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of the southwest and the northeast monsoons during May through October, with thehighest rainfall in Southwestern Cambodia (over 3,000 mm yr-1) and the EasternHighlands of Laos (2,500-3,000 mm yr-1). Precipitation over the Northern Highlandsregion is also considerable in June-August. The lowest precipitation volumes year-roundare found in the Himalayan Highlands (roughly 500 mm yr-1), falling largely as snow.The Mun-Chi basin in Thailand’s Khorat Plateau (100-200 m elevation) is also notablydry, receiving on average an estimated ca. 1,300 mm yr-1 of rainfall. The mountain rangesto the south and west of the plateau capture much of the moisture arriving with thesouthwest monsoon, while the Annam range in Vietnam depletes the moisture from thenortheast monsoon.

The spatial distribution of soil moisture in the upper two layers shows theinfluence of precipitation patterns, as well as topography, and soil texture (Fig. 9). Thedeepest soil layer, L3, however, is more influenced by baseflow rates and evapo-transpiration, such as soil depth, soil hydrologic parameters, vegetation parameters, andterrain slope. The peak soil moisture in the upper two layers closely follow the seasonalmaximum of precipitation. The peak is somewhat delayed for L3 (Fig. 9), in which soilmoisture remains high through November, long after the decline in precipitation.

The spatial patterns of simulated runoff peaks (Fig. 8) roughly follow those ofprecipitation (Fig. 7). On the other hand, precipitation minima occur in January at mostlocations, but the minimum of local runoff generation (before any stream routing) persiststhrough February and March. This reflects the influence of antecedent soil moisture, aswell as precipitation, on runoff – especially during the low flow period. Low rainfallrates in February-March generally influence runoff relatively little, because much of thisrainfall is intercepted by the vegetation canopy, and evaporated. Simulated losses byinterception and evaporation have a minimum in January but rise in February and March(not shown). The spatial and temporal patterns in figures 7, 8, and 9 do not reflectcompletely the role of antecedent soil moisture in controlling runoff generation

4.2 The influence of rainfall and soil moisture on runoff generation

The calibrated VIC simulations will now be used to investigate the influence ofthe major time-varying factors on runoff rate and its two components: quickflow andbaseflow. These major factors are: a) current rainfall rate; and b) current soil moisture.

Rainfall, in most cases, is only modestly autocorrelated. Soil moisture, incontrast, strongly reflects accumulated precipitation of several weeks or months, andhence is strongly autocorrelated with characteristic time scales ranging from a fewmonths to a few years. This is a consequence of the fact the soil moisture is a storage, andrepresents the balance between accumulated infiltration, evapotranspiration, and baseflow (the latter for the lowest VIC layer only).

In Fig. 10, the weekly mean soil moisture (in all three soil layers) during thesimulated period, 1979-2000, is plotted against that week’s precipitation for the followingareas (see Fig. 1): the high elevation sub-basin above Chiang Saen, which has thin soils(calibrated soil depths are no larger than 1 meter) and steep slopes; the Mun-Chi riversub-basin in Thailand’s Khorat Plateau, above Ubon, for which calibrated soil depths arelarge (up to 3 meters) and slopes are minimal; the territory of Laos lying within theMekong basin, a largely mountainous area with calibrated soil depths of about 1 meter;and the entire catchment area above Stung Treng. Vertical color separation lines in Fig.


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10 would indicate that runoff rates are determined solely by precipitation (represented inthe x axis), whereas horizontal slopes indicate runoff rates are determined by soilmoisture.

In Fig. 10, color-separation lines are slanted but closer to vertical than horizontalat all sites, reflecting a dominant dependence of quickflow on recent precipitation. Theopposite is true in the case of baseflow, where boundaries between colors are almosthorizontal at all sites, reflecting the dominance of soil moisture in determining baseflowrates. An examination of the nature of the green lines that separate runoff rates lower andhigher than 10 mm wk-1 (an intermediate runoff rate) is instructive. In the case ofquickflow, Laos has the steepest green line and the basin above Ubon has the least steepline. This difference indicates a stronger dependence of quickflow on current rainfall inthe Laotian portion of the basin, where steep slopes with thin soils retain less moisture,than in the Thai portion, where thicker soils with little slope retain more soil moisture.The topography and soil parameters of Laos allow for faster drainage of the groundwaterreservoir and thereby limit the memory of the soil moisture to antecedent precipitation.Baseflow responses are more controlled by soil moisture than runoff (less steep greenlines) as expected. For total runoff, i.e., the sum of quickflow and baseflow, theboundary between colors representing different weekly runoff volumes are diagonal,reflecting the dependence of runoff on both precipitation and soil moisture.

The importance of soil moisture in runoff generation is apparent even when thehydrologic response is aggregated to monthly values for a large basin (Fig. 11, wheremonthly means of precipitation, simulated runoff, and simulated soil moisture aggregatedover the large basin area upstream of Stung Treng). The monthly means (the central blackline) illustrate that the peak in average monthly soil moisture occurs in September ratherthan during the peak precipitation month of August. Soil moisture also maintains highvalues in October and November, despite the steep drop in average monthly precipitationduring these months. The soil moisture minimum is particularly lagged, occurring inMarch-May, with respect to the month of lowest precipitation (January).

The red lines in Fig. 11 correspond to 1980, a flood year. In 1980, there were twopeaks in monthly precipitation, the most extreme of which was in June. A secondary peakoccurred in September, where it was the second precipitation peak that generated thehighest simulated levels of monthly runoff. Why was monthly runoff in June 1980 lowerthan September 1980, and why was the September 1980 runoff so extreme? The answerlies in the soil moisture levels. High runoff in September 1980 occurred because of theintense rainfall during that month; however previous rainfall caused the soils to becomemore highly saturated following the second precipitation peak (547 mm) as compared tothe June peak (439 mm), resulting in the extreme increase in runoff during September.

The monthly values for 1987 (blue lines), a drought year, are also shown in Fig.11. After four months of below-average precipitation (April-July), above-averageprecipitation occurred in August and September. Yet the runoff response in those twomonths was modest compared with similar amounts in other years. In September 1987,runoff was only about average despite the high precipitation (254 mm, a value not farbelow that of September 1980). This is likely a consequence of below-average soilmoisture level in September of 1987, which resulted from the low precipitation in April-July.

In addition to soil moisture, the distribution of rainfall in time and in space alsoinfluences runoff response. In Fig. 12, the precipitation above Stung Treng in each day of


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the month of September during 1980, 1987, and 1996 is shown. The figure indicates thatrainfall intensities (aggregated at this spatial scale) were roughly similar in all three years.Hence, they can hardly explain the differences in runoff.

The runoff response will also be different if most rainfall is concentrated in a fewspatial locations with high intensity, rather than over a large portion of the basin withmoderate local intensity. An analysis of these spatial factors (not shown) indicates thatspatial variability partially explains the differences in simulated runoff in September of1980, 1982 and 1996, but spatial variability at the scale of the Mekong basin appears tobe secondary to the effect of (basin average) soil moisture,

4.3 Influence of landcover type on soil moisture and runoff generation

Given the influence of soil moisture on runoff generation and the difference invegetation parameters among the land cover classes, land cover is expected to influencesoil moisture and runoff generation as well. To investigate this question, the territory ofLaos was examined, which has a variety of land cover classes. Despite the mountainousnature of this area, the calibrated soil moisture depths are uniform over this area,therefore model differences will be due primarily to prescribed differences in vegetation.Fig. 13 shows the mean weekly soil moisture as a function of the total precipitation in thepreceding 12-week period, for each week of the simulation period. Soil moisture andantecedent precipitation are positively correlated. For any given value of antecedentprecipitation, soil moisture is generally highest in the portion of Laos where agriculturepredominates. This result is explained by the higher rainfall interception and evapo-transpiration rates for forests, aided by higher leaf area index, lower albedo, higher aero-dynamic roughness, and deeper roots. When antecedent precipitation is relatively low,the lowest soil moisture generally occurs where grassland or woodland predominates; andwhen antecedent precipitation is higher, the lowest soil moisture occurs where forestpredominates – which reflects the ability of deeper (relative to other vegetation types)tree roots to extract deep soil moisture.

Interpretation of Fig. 13 is however not straightforward because, within Laos,land cover classes, like rainfall and temperature, are correlated with terrain elevation andslope orientation. However, VIC does not represent slope effects directly – they are onlyrepresented indirectly through the VIC b parameter (which controls partitioning ofprecipitation into quickflow and infiltration into the upper layer, but which also reflectsvarious other heterogeneities besides topography). Hence, differences in VIC simulatedrunoff across vegetation types are likely to be primarily a result of the influence ofvegetation on evapotranspiration.

5. Summary and Conclusions

This study was intended to address the relative influence of space-time variabilityof rainfall and vegetation differences in the Mekong basin on runoff generation Toresolve this question we applied the VIC hydrologic model, and diagnosed variousaspects of simulated runoff and streamflow over the Mekong basin. Our analysis showsthat:


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• While much of the runoff variability is explained by the monsoonalprecipitation regime and topography, a significant proportion of the runoff variability isexplained by the simulated spatial patterns of soil moisture, which do not followprecipitation patterns – especially for the lowest soil moisture zone. Soil moisture peakslag precipitation especially in the deepest soil moisture layer, the dynamics of which arecontrolled primarily by evapotranspiration extraction from deep roots (mostly forest),infiltration from above, and baseflow. The upper layers show much less lag relative toprecipitation.

• Three sub-regions of contrasting geophysical and climatic characteristicswere examined to investigate the relative influence of soil moisture and currentprecipitation on runoff generation, ranging from mountainous areas with steep slopes andthin soils to flat, agricultural areas with deep soils. At all sites, quickflow was stronglydependent on precipitation, whereas baseflow was more dependent on soil moisture.However, the relative dependence was influenced by the slope and soil depth of a givenlocation. Soil moisture was shown to be a variable with a long memory for antecedenthydrologic fluxes, especially in the Thai portion of the basin with deeper soils and lowterrain slopes, which retard baseflow recessions and maximize the groundwater’smemory capacity.

• Soil moisture shows a strong relationship to vegetation type. Soilmoisture in general was highest for agricultural areas; and lowest for grassland andwoodland areas – except when antecedent precipitation was high (as is the caseimmediately following the rain season), when soil moisture was the lowest for forestedareas.

Rapid population growth and economic development over the past severaldecades in the Mekong Basin has placed increasing demands on the region’s freshwaterresources. Consequently, it is essential to determine how soil moisture will be impactedthrough land use change, potentially altering streamflow variability. In a separatemanuscript (Richey et al., in review) we explore the magnitude of the effects of land usechange on runoff generation by using the calibrated VIC model to simulate thehydrologic response of hypothetical land use change scenarios. However, furtherresearch must be conducted to fully understand how streamflow variability will respondin the face of land use change, increasing demands on freshwater resources, and climatechange.

Acknowledgements

Harvey Greenberg (U.W.) downloaded and processed the meteorological data,and the MODIS LAI data. Tom Giambelluca (U. Hawaii) and Kristiina Vogt (U.W.)graciously assisted in revising some of our vegetation parameters in Table 1. MarkJohnson and Johannes Lehmann (Cornell U.) provided crucial information that allowedus to convert between soil type legends. Chunmei Zhu (U.W.), Alan B. Adams (U.W.)and Erick Fernandes (Cornell U.) assisted with the “soilprogram”. PorraneeRattanaviwatpong (U.W.) helped with discussions and literature references. MilesLogsdon (U.W.) re-projected the MRC land cover map. Lauren McGeoch (U.W.) helped


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prepare Fig. 1. Financial support for this work came from the US National ScienceFoundation Carbon program, and in part from the Functional Value of BiodiversityProgram, funded by the World Bank-Netherlands Partnership Program. All findings andinterpretations belong to the authors alone and should not be attributed to the Bank, itsExecutive Board of Directors, or the countries they represent.

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[3] Batjes NH. A homogenized soil data file for global environmental research: A subset of FAO,ISRIC and NRCS profiles (Version 1.0), Working Paper 95/10, ISRIC, Wageningen, TheNetherlands, 1995.

[4] Blondin, C. Parameterization of land-surface processes in numerical weather prediction. In:Schmugge TJ, Andre JC, editors. Land Surface Evaporation: Measurements andParameterization. New York: Springer Verlag, 1991. pp. 31-54.

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[8] Cherkauer KA, Lettenmaier DP. Hydrologic effects of frozen soils in the upper MississippiRiver basin. J Geophys Res 1999;104(D16): 19,599-19,610.

[9] Ducoudre NI, Laval K, Perrier A. SECHIBA, a new set of parameterizations of the hydrologicexchanges at the land-atmosphere interface with the LMD atmospheric general circulation model.J Climate 1993;6: 248-273.

[10] Dümenil L, Todini E. A rainfall-runoff scheme for use in the Hamburg climate model. In:O’Kane JP, editor. Advances in Theoretical Hydrology, A Tribute to James Dooge, EuropeanGeophysical Society Series on Hydrological Sciences, 1. New York: Elsevier, 1992. pp. 129-157.

[11] European Commission. South East Asia Regional Land Cover 2000 data set of the GlobalLand Cover 2000 database (GLC2000), European Commission, Joint Research Centre,http://www.gvm.jrc.it/glc2000, 2003.NOT SURE HOW TO FORMAT THIS…

[12] Food and Agricultural Organization of the United Nations. Forest Resource Assessment1990: Global synthesis. Rome: Food and Agricultural Organization of the United Nations, 1995.


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[13] Forest Cover Monitoring Project. Forest cover monitoring project MRC/GTZ: technicalnotes 2 – interpretation and delineation from satellite images, Vientiane, Lao PDR, 1997.

[14] Giambelluca TW, Fox J, Yarnasarn S, Onibutr P, Nullet MA. Dry-season radiation balanceof land covers replacing forest in northern Thailand. Agricultural and Forest Meteorology 1999;95:53-65.

[15] Gray DM, Prowse TD. Snow and Floating Ice. In: Maidment DR, editor. Handbook ofHydrology, PUBLISHING LOCATION: PUBLISHER, 1993. pp. 7.1-7.58.

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[17] Kalnay E et al. The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc1996;77: 437-471.

[18] Liang X, Lettenmaier DP, Wood EF, Burges SJ. A simple hydrologically based model ofland surface, water, and energy fluxes for general circulation models. J Geophys Res 1994;99(D7): 14,415-14,428.

[19] Lohmann D, Nolte-Holube R, Raschk E. A large-scale horizontal routing model to becoupled to land surface parameterization schemes. Tellus 1996;48A: 708-721.

[20] Lohmann D, Raschke E, Nijssen B, Lettenmaier DP. Regional scale hydrology: I.Formulation of the VIC-2L model coupled to a routing model. Hydrol Sci J 1998; 43: 131-141.

[21] Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B. A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States. J Climate2002;15: 3237-3251.

[22] Mekong River Commission and World Wildlife Fund. People and the Environment Atlas ofthe Lower Mekong Basin. Phnom Penh, Kingdom of Cambodia: MRC Secretariat, 2003.

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[24] Nijssen B, Lettenmaier DP, Liang X, Wetzel SW, Wood EF. Streamflow simulation forcontinental-scale river basins. Water Resour Res 1997;33: 711-724.

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[28] Schaake J. Table published on the following web page:http://www.hydro.washington.edu/Lettenmaier/Models/VIC/Documentation/Info/soiltext.html,2000.NOT SURE HOW TO FORMAT THIS…

[29] Sellers PJ, Mintz Y, Sud YC, Dalcher A. A simple biosphere model (SiB) for use withingeneral circulation models. J Atmos Sci 1986;43(6): 505-531.

[30] Shepard DS. Computer mapping: The SYMAP interpolation algorithm. In: Gaile GL,Willmott CJ, editors. Spatial Statistics and Models. PUBLISHING LOC: PUBLISHER, 1984. pp.133-145.

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[32] Stibig HJ. Interpretation and Delineation from Satellite Images, Technical Note 2 of theForest Cover Monitoring Project, a Co-operation project between the Mekong River Commissionand the Gesellschaft für Technische Zusammenarbeit (GTZ), 1999.

[33] Storck P, Lettenmaier DP. Predicting the effect of a forest canopy on ground snowaccumulation and ablation in maritime climates. In: Proc. 67th Western Snow Conf. Fort Collins,CO: PUBLISHER, 1999. pp. 1-12.

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Table 1: Vegetation parameter values adopted in the simulation runs for each land cover class inFig. 4.

Albedo

Land cover class

Wet

seas

on

Dry

sea

son

Veg.height

(m)

Rarc

(s m-1)Rmin

(s m-1)RGL

(Wm-2)

Max.rootingdepth(m)

Forest cover in Lower Mekong Basin and Burma:1 Evergreen forest, high cover density 0.120 0.120 35 25 80 30 3.02 Evergreen forest, medium to low cover

density0.125 0.125 20 25 80 30 3.0

3 Evergreen forest mosaic 0.125 0.125 15 25 80 30 3.04 Mixed (evergreen & decid.), high cover

density0.120 0.125 20 40 100 30 2.5

5 Mixed (evergreen & decid.), low coverdensity

0.125 0.130 15 35 95 30 2.5

6 Mixed forest mosaic 0.130 0.140 10 30 90 30 2.57 Deciduous forest 0.125 0.135 7 40 80 30 2.08 Deciduous forest mosaic 0.135 0.140 15 40 80 30 2.09 Regrowth forest 0.150 0.153 8 30 90 30 1.010 Regrowth forest, inundated 0.120 0.120 8 20 90 30 1.011 Inundated forest 0.120 0.120 1 20 80 30 1.012 Mangrove forest 0.120 0.120 8 30 90 30 1.013 Plantation forest 0.130 0.135 5 2 80 30 1.014 Other forest cover 0.125 0.130 8 30 90 30 2.015 Inundated forest mosaic 0.120 0.120 1 2 80 30 1.0Non-forest cover in Lower Mekong Basin and Burma:16 Woodland & shrubland, evergreen 0.150 0.155 5 25 110 50 1.017 Grassland 0.168 0.171 1 2 80 100 0.518 Bamboo 0.147 0.150 3 5 80 100 0.519 Woodland & shrubland, dry 0.157 0.160 5 3 110 75 5.020 Woodland & shrubland, inundated 0.120 0.120 5 3 90 75 0.521 Cropping mosaic (crop < 30%) 0.157 0.160(*) 4 2 80 100 0.522 Cropping mosaic (crop > 30%) 0.162 0.165(*) 3 2 80 100 0.523 Agricultural land 0.170 0.170(*) 1 2 80 100 0.524 Baren land25 Rocks26 Urban or built up

VIC’s treatment of “barren land” was used.

Forest cover in Chinese part of Upper Mekong Basin:27 Needleleaf deciduous forest 0.125 0.350 17 25 80 30 3.028 Needleleaf evergreen forest 0.125 0.300 20 25 80 30 3.029 Broadleaf evergreen forest 0.125 0.300 35 25 80 30 3.030 Broadleaf deciduous forest 0.125 0.350 20 25 80 30 3.0Non-forest cover in Chinese part of Upper Mekong Basin:31 Bush 0.155 0.350 2 3 100 75 5.032 Sparse woods 0.150 0.350 2 10 100 50 2.533 Alpine and sub-alpine meadow 0.200 0.500 0.5 2 80 100 0.534 Slope grassland 0.200 0.400 0.5 2 80 100 0.535 Plain grassland 0.200 0.400 0.5 2 80 100 0.536 Desert grassland 0.200 0.400 0.5 2 80 100 0.537 Meadow 0.200 0.400 0.5 2 80 100 0.538 Gravel39 Desert

VIC’s treatment of “barren land” was used.

40 Alpine and sub-alpine grassland 0.200 0.500 0.5 2 80 100 0.5Water:41 Irrigated cropland 0.085 0.085 0.5 2 60 100 0.542 Water body VIC’s treatment of “barren land” was used.(*) In grid cells receiving snow in winter, Winter albedo was set to 0.4 for cropping mosaic classes (21 and 22) and 0.5for the agricultural land class (23).


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Fig. 1. The Mekong river basin: topography, country borders, stream gauges, and damsin operation within 1979-2000.


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Fig. 2. Mekong 10 km resolution stream network obtained from using an “upscaling”technique, from 1-km USGS GTOPO30).


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Fig. 3. USDA texture class. Panel A: top layer (0-10 cm). Panel B: deeper layer (10-100cm). Classes are the following. c: clay; cl: clay loam; scl: sandy clay loam; l: loam; sl:sandy loam; s: sand.


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Fig. 4. Composite land cover map of the Mekong basin, combining maps (a), (b), (c), and(d) (see text). Classes 21, 22 and 23 (cropping mosaics and agricultural land) are alsopresent in the Chinese section.


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Fig. 5. Maps of mean monthly LAI for the Mekong basin obtained by spatial andtemporal aggregation of MODIS data, from March of 2000 to February of 2001.

Figure 9. Map images of the dataset of 12 monthly mean LAI values obtained from MODIS 2000/01.Figure 9. Map images of the dataset of 12 monthly mean LAI values obtained from MODIS 2000/01.


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Fig. 6. Observed and simulated hydrographs for recording gauges in the Mekong basin,in 1979-2000. The observed hydrographs were calculated by the Mekong RiverCommission (personal communication, 2003) using rating curves. (a) mainstem andTonle Sap inputs (b) tributaries YES I KNOW CAN’T BE READ HAS TO BE FIXED -jeff


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Fig. 7. Average monthly precipitation over the Mekong basin, obtained by interpolationfrom the rain gauge network


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Fig. 8. Average monthly runoff (millimeters per month) in 1979-2000 in the calibratedsimulation.


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Fig. 9. Average monthly soil moisture saturation (expressed as a fraction of the maximumwater content, established by porosity) in 1979-2000 in the calibrated simulation, for thesurface soil layer L1, L2, and L3.

L1 L2 L3L1L1 L2 L3


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Fig. 10. Dependence of quick flow (QF), base flow (BF), and total flow (TF) on weeklymean precipitation total (x axis) and soil moisture (y axis), for the sub-basins of upstreamof Chiang Saen, Mun-Chi basin in Thailand, draining through Ubon, the Laos territorywithin the basin, and the sub-basin encompasses upstream of Stung Treng (includingChiang Saern, Laos, and Ubon). Different value intervals of weekly flow aredistinguished in color: blue: flow<5 mm wk-1; green: 5<flow<10 m wk-1; orange:10<flow<20 mm wk-1; red: flow>20 mm wk-1. ). Visually-fitted dashed linesapproximately separate regions where different colors dominate.

Chiang Saen-QF Laos QF Ubon QF Stung Treng QF

UbonLaos Chiang Saen-TF

Chiang Saen-BF

Laos TF

Laos BF

Ubon TF

Ubon BF

Stung Treng TF

Stung Treng BF

Chiang Saen-QF Laos QF Ubon QF Stung Treng QF

UbonLaos Chiang Saen-TF

Chiang Saen-BF

Laos TF

Laos BF

Ubon TF

Ubon BF

Stung Treng TF

Stung Treng BF


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Fig. 11. Precipitation , total runoff generated, without stream routing, and total soilmoisture above Stung Treng. For each month, the range from minimum to maximumsimulated monthly values in 1979-2000 is represented by the shaded area, and themonthly average is plotted in black. The red and blue curves represent years 1980 and1987, respectively. Reasons for the different runoff responses to the two monthlyprecipitation peaks of June and September of 1980, and reasons for the different runoffresponses to the two September peaks of 1980 and 1987, are explored in the text.

precipitation total runoff total soil moisture precipitation precipitation total runoff total runoff total soil moisture total soil moisture


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Fig. 12. Daily precipitation in the month of September, aggregated over the sub-basinupstream of Stung Treng, in the three different years discussed in the text: 1980, 1987,and 1996. All three years had above-average rainfall volumes in the month of September,and the range of daily rainfall intensities throughout the month is roughly comparableamong the three years.


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Fig. 13. Dependency of soil moisture in Laos on the total precipitation falling in the 12anteceding weeks, for forest classes (green), grassland and woodland classes (blue), andagricultural classes (red). Each plotted point corresponds to one week in the simulationperiod.

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