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Effects of landuse change on the hydrologic regime of
the Mae Chaem river basin, NW Thailand.
P. Rattanaviwatponga, J. Richeyb,* , D. Thomasc, S. Roddab, B. Campbellb , M. Logsdonb
a
b
Chemical Engineering, University of Washington, Seattle, Washington 98195, USA
School of Oceanography, University of Washington, Seattle, Washington 98195, USA
c
World Agroforestry Center ( ICRAF), Southeast Asia Regional Programme,
Chiang Mai, Thailand
Received
Accepted
Abstract
Upland shifting cultivation, upland commercial crops, and lowland irrigated agriculture are
believed to be causing water resource tension in the Mae Chaem watershed in Chiang Mai, Thailand.
In this paper, we assess hydrologic regimes and buffering characteristics of the Mae Chaem River
with land-use change. Three plausible future forest-to-crop expansion scenarios and a scenario of
crop-to-forest reversal were developed based on the landcover transition from 1989 to 2000, with
emphasis on influences of elevation bands, irrigation diversion and soil compaction. Basin hydrologic
responses and river buffering indicators were simulated using the Distributed Hydrology Soil
Vegetation Model (DHSVM). Meteorological data from 6 weather stations inside and adjacent to the
Mae Chaem catchment during the period 1993-2000 were the climate inputs. Computed stream flow
was compared to observed discharge at Kaeng Ob Luang, located downstream from the district town
in Mae Chaem, and to hydrologic simulations by the GenRiver model. We observe altitudinal increase
in evapotranspiration and soil moisture. With current assumptions, expansion of highland crop fields
led to increasing unregulated annual and wet-season water yields and lowered buffering indicators,
compared to similar expansion in the lowland-midland zone. Actual downstream water availability
and stream peak flow were sensitive to irrigation diversion. We did not detect significant difference
after accounting for soil compaction. This modeling approach can be a useful tool for water allocation
*
Corresponding author. Tel.: +206-543-7339; fax: +206-685-3351 Email address: jrichey@u.washington.edu (J.E.Richey)
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and flood forecasting for small catchments undergoing rapid commercialization, because it alerts land
managers to the potential range of water supply in wet and dry seasons, to river buffering
characteristics, and to spatial distribution of basin moisture storage behaviors.
Keywords: Crops; Deforestation; Watershed hydrology; Landuse change; Northern Thailand; Stream
flow; Resource management
1. Introduction
Landscape and water resource management are major challenges for socio-economic
development of upland watersheds in Southeast Asia due to their association with downstream
environmental impacts. During recent decades, concerns about the impact on water resources of
changing patterns of land-use associated with deforestation and agricultural transformation have
created social and political tensions from local to national levels. In response, public policy decisionmaking processes are now seeking both economic and conservation goals. Major concerns focus on
consequences of land-use change for water supply and demand, for local and downstream
hydrological hazards, and for biodiversity conservation. Walker (2002-a) cites numerous sources of
views that logging, shifting cultivation by mountain ethnic minorities, and commercial agriculture in
highland watersheds are believed to cause severe dry-season water supply shortages in northern
Thailand. Ziegler et al. (2004) refers to studies by Sharma in 1992 and by Tuan in 1993, which
conclude that similar types of land-use activities in highlands of Vietnam result in watershed
degradation such as soil and nutrient loss. Controversy over the eight-dam hydropower cascade
system on the Lancang river of the upper Mekong basin in China is an example at a wider scale where
potential effects on downstream river flows and sediment transport are an international issue for the
five countries sharing the lower Mekong river.
Water demand is the other side of the equation, as it also places constraints on water
availability. Dynamics of water use also relate to land-use change, especially through expansion of
lowland cultivation, irrigated upland fields, urban areas, and industrialization. Walker (2002-b) points
out that public debate is mostly centered on consequences of highland activities on water supply, but
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there is little focus on increasing levels of stream water diversion by lowland dry-season irrigated
agriculture.
In this paper, we seek to evaluate seasonal patterns and the composition of hydrologic
components of the Mae Chaem River in northern Thailand. The second objective is to assess effects
of land-use conversion between forest and croplands on the composition of basin hydrology, on water
availability in terms of annual and seasonal water yields, and on the buffering capacity of river
discharge. Specifically, we assess the influence of elevation bands of agricultural fields (highlands
versus lowlands), irrigation diversion, and soil compaction. These goals are achieved through use of
the Distributed Hydrology Soil-Vegetation Model (DHSVM) to simulate water components in the
basin, based on past, current, and hypothetical future land-use scenarios. Scenario analysis eliminates
interpretation problems associated with direct comparison of stream flow in paired watershed analyses
where basins have different underlying geological settings (Brunijnzeel, 2003). Some simulation
results are also compared to those from the Generic River Flow (GenRiver) model.
2. Mae Chaem basin: the study area
The Mae Chaem (Chaem River) watershed is located in Chiang Mai province of northern
Thailand (Fig. 1). It is a major upper tributary sub-basin of the Ping River, which in turn, is the largest
tributary of central Thailand’s Chao Phraya River. The Mae Chaem sub-basin is bounded by
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coordinates 18 06’ - 19 10’ N and 98 04’ - 98 34’ E, and includes a total area of 3,853 km above
the Royal Irrigation Department (RID) river gauge station P.14. Sharp relief and forest vegetation
(and relatively sparse data) characterize the Mae Chaem. The basin has a wide range of elevation,
from 282 m.a.s.l. at its lowest point to 2,535 m.a.s.l. at the summit of its highest peak, Doi Inthanon
(Mount Inthanon) (Kuraji et al., 2001). Altitude variation induces different climatic zones with
distinctive types of natural land cover. Dominant vegetation includes dry dipterocarp and mixed
deciduous forests below 1,000 m.a.s.l., tropical mixed pine forest from 900 – 1,500 m.a.s.l. alternating
with hill evergreen forest that extend up to 2,000 m.a.s.l., and tropical montane cloud forest above
2,000 m.a.s.l. (Dairaku et al., 2000; Kuraji et al., 2001). Steep hillsides with slopes exceeding 25% are
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a common landscape element, resulting in rates of natural soil erosion that prevent advanced soil
development. Thus, soils are relatively shallow and have limited water-holding capacity (Hansen,
2001). Dominant soil textures are sandy clay loam and clay loam.
The population of Mae Chaem is ethnically diverse and distributed among numerous small
villages. The majority Karen and the Lua ethnic groups live primarily in mid-elevation zones
between 600 to 1,000 m.a.s.l., with some communities extending into higher elevations. Ethnic
northern Thai (khon muang) villages are mostly clustered in lowland areas below 600 m.a.s.l.,
whereas Hmong and Lisu ethnic groups live mostly in highland villages located above 1,000 m.a.s.l.
Land use patterns in Mae Chaem have undergone substantial change during the past several
decades. As recently as the 1960s the agriculture mosaic was comprised of highland (above 1,000
m.a.s.l.) pioneer shifting cultivation that often included opium, mid-elevation (600-1,000 m.a.s.l.)
rotational forest fallow shifting cultivation with a decade long fallow period, and paddy and home
garden-centered cultivation in the lowlands (van Noordwijk et al., 2003; Thomas et al. 2002; Walker
2002-b). In the 1980s, development projects and programs in Mae Chaem began building
infrastructure and promoting commercial agriculture, under programs to reduce rural poverty and
promote alternatives to opium cultivation and shifting agriculture. Results have included significant
increases in production of highland cash crops such as cabbages and carrots, expansion of industrial
field crops such as soybeans and maize up watershed slopes above lowland paddies into mid-elevation
zones, expansion of irrigated paddy fields wherever terrain allows, and planting of fruit orchards in
some areas of all altitude zones (Praneetvatakul et al., 2001; Pinthong et al., 2000; Thomas et al.
2002; Walker, 2002-b).
3. Development of geospatial landscape and hydrology model
3.1 Development of the geospatial model of the Mae Chaem basin
3.1.1. Topography and flow network
Topography for the Mae Chaem basin was initially acquired as a 30-meter digital elevation
model (DEM) constructed by the World Agroforestry Centre (ICRAF), Chiang Mai. This 30-meter
DEM was then aggregated to 150-meter0 resolution (Fig. 2) using a mean calculation. Flow direction,
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flow accumulation, and stream network were derived from the DEM at 150-meter resolution. Soil
depth was generated by DHSVM, based on the DEM, and was adjusted during model calibration.
3.1.2. Soil map and attributes
Soil data in Mae Chaem are very sparse and restricted to the lowlands. Therefore, a soil map
containing physical and chemical properties was constructed using SOILPROGRAM software, which
derives 5-minute resolution (about 10 km.) soil data from the WISE pedon-database developed by the
International Soil Reference and Information Centre (ISRIC) and the FAO-Unesco Digital Soil Map
of the World (FAO-1974 legend). The soil map was re-sampled to 150-meter resolution, with the
number of soil types equal to the number of unique values of physical and chemical soil properties.
Soil texture was assigned based on the percent sand and clay. Porosity and field capacity were
estimated from the soil texture triangle hydraulic properties calculator (Saxton et al., 1986).
Infiltration rates and soil depth were quantified using a local descriptive soil survey (Putivoranart,
1973), and was adjusted during calibration.
3.1.3. Vegetation and land use: 1989, 2000, future
Two landcover datasets (Fig. 3) form the basis for the landcover change scenarios in the
hydrology model. The original classification schemes of these data (Fig. A1 and A2) vary
significantly, so scheme modifications were made to achieve similarity between landcover data.
The first dataset in the land cover time series is a historical 1989 dataset, acquired from the
Land Development Division (LLD), Ministry of Agriculture and Cooperatives, Thailand. This data,
subsequently referred to as Veg 1989, originated as polygons, which were converted to a 150-meter
raster grid representation using a nearest-neighbor assignment algorithm. Data were then generalized
into 11 classes from its original 39. The second dataset represents landcover for the year 2000,
referred to as current landcover or Veg 2000. This dataset, also from the LDD, was prepared for the
model using the same procedure as that of the 1989 data. However, since the original 47 class names
in this dataset differed from those in the 1989 data, class names were reconciled by performing a
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combinatorial analysis between the 1989 reclassified dataset and the 2000 original data. In this way
we were able to establish a correlation between the 11 classes in 1989 and the 47 original classes in
2000. This type of spatial overlay analysis returns not only the frequency of all unique combinations
of land cover types, but also a map product of the spatial commonalities. A plot was made to identify
the frequency of occurrence between a 2000 value (1 to 47) and a 1989 value (1 to 11). Based on this
plot, the 2000 vegetation values were re-assigned a value consistent with the frequency distribution of
shared space with the 1989 dataset.
Veg 2000 is employed as the reference land-use case, and four future scenarios (Fig. 4) were
created based on the transition from Veg 1989 to Veg 2000, with a focus on forest-to-crop conversion.
The first scenario represents reversal of all croplands back to evergreen needleleaf forests in zones
above 1,000 m.a.s.l., and to deciduous broadleaf forests below 1,000 m.a.s.l. Selected forest types
were generally in accord with actual dominant vegetation in the respective elevation zones. The
second scenario forecasts the doubling of cropland area in Veg 2000 by growing a buffer of new crop
cells around all existing crop patches. This ultimately increased the cropland share of total basin area
from 10.4% in 2000 to 19.9%. Finally, the third and fourth scenarios depict a doubling of cropland
that is limited to either highland zones of the basin (above 1,000 m.a.s.l.), or to lowland and midland
basin zones (below 1,000 m.a.s.l.). Growth of croplands limited to highland and to lowland-midland
basin zones increased cropland shares of total basin area to 18.0% and 19.1%, respectively. In both
cases, a buffer was grown around existing crop patches in the selected elevation range, while crop cell
area outside the selection remained the same as in 2000.
3.2. Climate Forcing and Hydrology
The climate of this mountainous basin is defined by large variations in seasonal and annual
rainfall that are influenced by tropical monsoon and maritime patterns (Walker, 2002-a), whereas the
orographic effect induces an altitudinal increase of spatial rainfall distribution (Dairaku et al., 2000;
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Kuraji, et al., 2001). The average annual temperature ranges from 20 to 34 C and the rainy season is
from May to October.
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To best represent climatic variation within the catchment, we used daily rainfall, and
maximum and minimum air temperature records for the period of 1993-2000, which were obtained
from five meteorological stations and one agro-meteorological station (Fig. 5). Doi Inthanon (DO)
and Wat Chan (WA) stations are operated by the Royal Project Foundation, and their recorded values
were obtained from both ICRAF and the Royal Project Foundation. The Research Station (RE)
belongs to the Asian Monsoon Experiment-Tropics (GAME-T), led by the University of Tokyo in
Japan under the Global Energy and Water Cycle Experiment (GEWEX). Mae Jo Agromet
(TMD327301), Mae Hong Son (TMD300201), and Mae Sariang (TMD300202) stations are
managed by the Thai Meteorological Department (TMD). Data for these stations were acquired
directly from the respective agencies.
The hydrology model runs on a sub-daily time step. Thus, disaggregated temperature,
radiation, and relative humidity are generated from daily records using a diurnal interpolation
scheme from the Variable Infiltration Capacity (VIC) model (Liang et al., 1994; Maurer et al., 2002).
Total daily rainfall was evenly distributed through sub-daily intervals. Calculated 3-hour
precipitation values in 1998 – 2000 were then replaced by observed records for all TMD stations.
Wind speed was assumed to be 2 m/s for all stations except RE and TMD327301, where actual daily
wind speed records are available.
The Mae Chaem hydrologic regime consists of high flow from May to October and base-flow
from November to April (Fig. 6), contributing 70% of the total flow and an average annual water
yield of 270 mm (we consider the water year to begin in November of the year previous to the year
cited). Due to the strong orographic effect on precipitation (Fig. 7), the surface runoff ratio could be
between 12 – 25%, depending on selection of reference rainfall stations and the interpolation scheme.
Van Noordwijk et al. (2003) and Walker (2002-a) provide thorough discussions on the long-term
rainfall-discharge relationship in Mae Chaem.
For model calibration and testing, 1989 – 1999 daily average stream flow measurements were
acquired from GAME-T for the basin outlet at Kaeng Ob Luang (gage P.14), operated by the RID.
The estimated daily discharge from the stage height observation in 2000 was obtained from the RID
Hydrology and Water Management Center for the Upper Northern Region.
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3.3. DHSVM: the hydrology model
To examine problems at this spatial scale, we use the Distributed Hydrology Soil Vegetation
Model (DHSVM) (Wigmosta et al., 1994), a regional-scale hydrologic model that can recognize the
spatial heterogeneity of the watershed. The model can be seen as a multi-layered representation of
characteristics and processes in a drainage basin that allows us to examine intersections of the data
required for describing both the basin’s topographic features and its climatology patterns. DHSVM is
intended for small to moderate drainage areas (typically less than about 10,000 km2), over which
digital topographic data allows explicit representation of surface and subsurface flows. DHSVM is
utilized for stream flow forecasting and for addressing hydrologic effects of land management or of
climate change. It is tested on several basins in the USA (Bowling et al., 2000; Bowling and
Lettenmaier, 2001; Storck, 2000; VanShaar et al., 2002) and in British Columbia (Schnorbus and
Alila, 2004).
The model simulates soil moisture, snow cover, runoff, and evapo-transpiration on a sub-daily
time scale. It accounts for topographic and vegetation effects on a pixel-by-pixel basis, with a typical
resolution of 30 to 150 m. Snow accumulation and snow melt are calculated by a two-layer energybalance model. Evapo-transpiration follows the Penman-Monteith equation. The multi-layer soil
column in each pixel is a series of soil moisture reservoirs, and saturated sub-surface flow exists only
in the deepest soil layer. Runoff generation is represented by saturation excess and infiltration excess
mechanisms. Stream water balance was derived using Linear-reservoir routing.
The hydrology model sets requirements for the climate data model and the landscape model
based on basin characteristics.
3.3.1. Simulation conditions
The spatial domain was partitioned into 150-meter grid cells, and 3-hourly-timestep
simulations were run on the current landcover (Veg 2000), using climate data from June 1993 - May
1995 for calibration, and from June 1995 - October 2000 for model testing. Climate data across the
basin was computed from data of the 6 meteorological stations using a nearest-station interpolation.
The soil profile is divided into 3 root zones, 0-30 cm, 30-60 cm, and 60-100 cm. Lateral subsurface
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flow was calculated using a topographic gradient. In the routing scheme, roads were not included, and
stream classification was based on Strahler stream order and segment slope, derived from the DEM.
Temperature and precipitation lapse rates were assumed constant for the entire catchment. After the
initial run, the approximate amount of irrigation diversion was subtracted from simulated stream flow
at the basin outlet, and then calibrated and tested against daily discharge observed at station P.14.
To observe seasonal and inter-annual variability of hydrologic responses, and to study effects
of land-use change, the same set of climate data and parameters were used for all vegetation
scenarios, both with and without irrigation. For future scenarios II, III, and IV, two sets of soil
conditions were applied. The first condition was consistent with soil properties of other scenarios and
the second reflected soil compaction by altering properties of the top-soil layer. In the latter, bulk
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density was assumed to be 1800 kg/m , the typical value that interferes with root growth for crops
grown in clay loam, and soil porosity was adjusted accordingly. The soil infiltration rate and vertical
hydraulic conductivity were reduced to 50% of calibrated values. In all cases, the soil matrix was
considered uniform and did not have a preferential flow path.
When irrigation was considered, daily irrigation consumption was calculated and subtracted
from computed daily discharge to account for water diversion to irrigated area. Crops were divided
into 3 categories based on their water demand: wet season rice, dry season rice, and cash crops (Table
1). Irrigated area was approximated from the number of pixels of each crop type in the original 1989
and 2000 landcover data sets (Fig. A1 and A2). Percentages of total irrigated areas in the basin in
1989 and 2000 were used to project a range of potential irrigated areas in the future scenarios. The
following assumptions were made in calculating irrigation diversion: First, only 1/8 of the area
designated as swidden cultivation in the original classification scheme was used for irrigated
cropping. Second, for general field-crop classes, half of the area was wet-season rice and the other
half was cash-crop; the composition of incremental cropland in future scenarios was divided in the
same manner. Diverted water was set equal to crop water demand, and this amount was then added to
simulated evapo-transpiration to maintain the water balance. Table 2 summarizes all simulation
conditions.
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3.3.2. Calibration and validation
Calibration parameters were total soil depth, soil lateral and vertical hydraulic conductivities,
and vegetation parameters. Values of vegetation parameters were initially set to correspond with
Global Land Data Assimilation System (GLDAS) vegetation parameters mapped to the University of
Maryland (UMD) classification scheme, and were then tuned to northern Thailand based on forest
description by Gardner et al. (2000). Precipitation and temperature lapse rates were approximated
from linear regression of daily precipitation and temperature records and elevation gain. Final
calibration parameters are listed in Table 3.
Simulated hydrographs from May 1994 – October 2000, before subtracting irrigation
diversion, using Veg 2000 were compared to observed stream flows at gage P.14 (Fig. 8-a). Data from
May 1993 – April 1994 were excluded from the comparison because they were used in the start up
period. The model captures the on-set of the storm season, and estimated average wet-season flows
were relatively close to recorded values (Fig. 8-b). The average natural base flow was underestimated,
and the difference was higher for dry-seasons that follow a wetter storm season (Fig. 8-c). This is not
surprising since the model does not include deep groundwater components. In terms of percentage
deviation of prediction from observation within a season, the model is more consistent in estimating
daily dry-season flows, compared to wider-scattering in values of wet-season flows (Fig. 8-d). Thus,
we can infer that prediction accuracy is governed by climate inputs during the storm period, whereas
factors describing soil moisture storage and water transmission behavior dominate during the dry
season.
In other words, model performance depends on input data, operational parameters and
operational procedure. Rainfall measurement and appropriate basin-wide meteorological data
interpolation from weather station records are critical due to strong orographic effects. Estimation of
sub-daily climate input functions from daily data records is another source of uncertainty. In terms of
operational parameters, the simulations were sensitive to soil lateral hydraulic conductivity and soil
depth.
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4. Hydrologic flow paths: current conditions and scenarios
Highland watersheds play an important role on controlling the streamflow regime (Tangtham,
1998) and its hydrologic behaviors, such as water yield and runoff generation, depend on the
vegetation cover, soil and geologic setting. We will evaluate the hydrologic response to the current
land cover and to the effect of forest-to-crop conversion in terms of the water yields and spatial
variation of soil moisture and evapotranspiration inside the basin. In addition, we analyze the
evenness of stream flow, represented by the buffering capacity of stream discharge relative to the
rainfall peaks, and the indicators of high peak discharge (van Noordwijk et al., 2003). These two
factors tell us how a river will respond to heavy storms regarding flooding potential.
4.1. Hydrologic dynamics under current conditions
For current conditions, the predicted values of basin hydrology were also compared to those
computed from the physically-based GenRiver model on Mixed-Landuse Scenario. The model
description, input data and landcover data set were described in the paper by van Noordwijk et al.
(2003).
4.1.1. Water yields and river buffering indicators
The runoff mechanism was mainly the sub-surface flow and no overland flow was produced.
High flow comprises 70% of total flow (Table 4) the surface runoff makes up about 20% of total
rainfall (Table 5). Predicted annual yields from DHSVM, accounting for irrigation, were 20% and
10% lower than observed and calculated values from GenRiver, respectively, due to lower estimate
for base flow. However, the magnitude of forecasted low flow was sensitive to the approximation of
irrigation consumption, and will be discussed in section 4.2.
In terms of river buffering capacity, the buffering of Mae Chaem river decreases as the basin
water yield increases (Fig. 9-a). However, we do not observe significant correlation of high peak
discharge to the surface runoff ratio over 6-year period (Fig. 9-b).
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4.1.2. Evapotranspiration and soil moisture
Simulated average annual evapotranspiration was 760 mm, corresponding to 80% of basinwide estimated precipitation. Evapotranspiration was highest during June to September and reached
minimum values in January and February (Fig. 10-a). The seasonal trend is positively correlated with
rainfall seasonality.
Spatially, soil moisture was high near ridge areas and values were lower in lowlands (Fig.
11). Vertically, the deeper soil layer (60 - 100 cm) was moister and moisture storage was less
sensitive to precipitation level than in the upper layer (30 - 60 cm). The spatial pattern of
evapotranspiration also shows higher values on watershed boundaries and less along the main channel
path.
Direct observation indicates that soil moisture dynamics may follow spatial variation of
rainfall across the basin. To analyze if spatial relationships exist, basin elevation data was categorized
into 5 zones, and zonal means of soil moisture and evapotranspiration were computed. Results
showed an altitudinal increase in soil moisture (Fig. 12) and evapotranspiration (Fig. 13). Gaps in soil
moisture differences between lower and higher elevation zones were more significant during heavy
rain, but the reverse was true for the evapotranspiration. Seasonally, soil moisture was more static in
the lowland-midland zone, whereas in the highlands it followed the precipitation profile. In contrast,
evapotranspiration was dynamic in all elevation bands.
In summary, sub-surface flow was the main source of Mae Chaem river discharge, and
sensitivity to individual storms is due to short travel times caused by steep topography. The soil
column is relatively dryer in the lowlands, with less precipitation and less evapotranspiration. High
interannual variability during 1995-2000 resulted in simulated runoff ratios ranging from 0.15-0.25,
and buffering indicators decreased with increasing discharge fractions.
4.2. Effects of land-use change on hydrologic responses
The most important concerns regarding forest-to-crop land-use change relate to water
availability during the dry season and to potential downstream flooding hazard. Thus, we compared
buffering indicators and high peak flow responses among scenarios, as well as change in basin
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moisture storage behavior. Scenario analysis based on the same climate forcing data limits the focus
of hydrologic change to only the influence from long term land-use change and excludes short-term
variation due to seasonal fluctuation.
4.2.1. Conversion from forests to croplands and influence of irrigation diversion.
Under the current set of model parameters, we demonstrated that increased croplands caused
greater natural (unregulated) seasonal and annual water yields, and lower evapotranspiration (Table 6)
compared to simulation using Veg 2000. The opposite trend was true when croplands were converted
to forests as in Scenario I. Highland crop expansion (Scenario III) yielded the highest annual and wetseason water yields, while doubling of crop areas in lowland-midland zones (Scenario IV) resulted in
approximately the same water yields as under Veg 2000. If crops were converted back to forests
(Scenario I), the basin will provide less unregulated stream flow, and lose more water through
evapotranspiration. More forest increases river buffering capacity (Fig. 14-b) and yields a higher
range of discharge fractions. On average, expanded lowland-midland cultivation results in higher
buffering indicators and lower peak flows than the highland counterpart (Fig. 14-b, Fig. 14-d).
The magnitude of differences in stream flow behavior among scenarios depends on the
approximation of irrigation diversion. Low-season flow is a volatile component and available yield at
the basin outlet varies from 62% of regulated flow under Veg 2000, to 55% under Scenario III, and to
46% on Scenarios II and IV. Wet-season discharge is less sensitive and the flow remaining after
diversion is about 90% in each case. Evapotranspiration is 5% higher than the non-irrigated case for
Veg 2000, and about 8% higher for Scenarios II - IV. Crop-to-forest conversion clearly has the
highest buffering indicators, and lowest peak flow indicators. Overall, irrigation diversion decreases
by 20% the rate of reduction in natural flow-buffering indicators with incremental discharge fractions.
Among crop expansion scenarios (Fig. 14-a), Scenario IV has, on average, higher buffering indicators
on a lower range of discharge fractions, and has a 13% greater rate of decrease in buffering indicators
than does Scenario III. Scenario II provides results that fall in the middle of those from Scenario III
and IV. Trends among forest-to-crop scenarios are similar for relationships of highest flow fractions
and runoff ratios (Fig. 14-c).
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We calculated zonal means of soil moisture in the upper soil column (30-60 cm depth) in each
elevation band in Scenario III and IV and compared the results to those of Veg 2000, on a 3-hour
period of dry (Table 7), and wet (Table 8) days, to analyze if the signal of vegetation composition
change stands out. No significant differences were observed between scenarios on both time periods.
The zonal means based on vegetation types, limited to each single zone of elevation band were also
compared. The results were indifferent and were not reported here.
4.2.2. Soil compaction
Forecasted natural annual and seasonal water yields increase by an average of 3% for all
scenarios (Table 9) when soil infiltration rate and top-soil hydraulic conductivity are reduced to half
of calibrated values. Estimated evapotranspiration, runoff ratio, buffering indicator, and highest
monthly flow remain about the same as for non-compacted cases. Again, Scenario III provides the
highest annual and seasonal yields.
5. Discussion
5.1. Effects of topography and vegetation types on hydrologic patterns
Mae Chaem’s simulated runoff mechanism is primarily sub-surface driven, so that the basin
acts like a pump, with about 75-80% of input rainfall returning to the atmosphere as
evapotranspiration. Ziegler et al. (2000) conducted experiments to generate runoff and sediment flow
on unpaved roads, foot paths and agricultural land surfaces in a nearby sub-basin, and found that
overland runoff was rarely generated from hoed fields and none from fallow fields. Runoff production
depends on infiltration characteristics (Ziegler et al., 2004), antecedent soil moisture, the conductivity
through the soil, and rainfall depth. According to a descriptive soil survey at a site in Mae Chaem
district (Putivoranart, 1973), the soil is relatively well-drained and has moderate water holding
capacity. Tangtham (1998) also found that tropical mountainous soils usually have infiltration rates
above 130 mm/hr and high permeability. This may explain why overland flow was not generated.
Estimated runoff ratios were consistent with the 15-25% runoff ratio published in Alford’s study of
annual runoff in mountainous regions of northern Thailand (1992).
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Seasonal and annual patterns of evapotranspiration integrate effects of air temperature,
relative humidity, and vegetation. Each vegetation type differs in root depth, surface albedo, leaf area
index, stomatal and aerodynamic behavior (Giambelluca, 2002; Giambelluca et al., 2003), and these
factors govern potential evapotranspiration. Estimated monthly evapotranspiration was comparable to
literature values (Table 4) and the trend was similar to simulations by the GenRiver model (van
Noordwijk et al., 2003) (Fig. 10). The difference in magnitudes was partly due to the discrepancy in
estimated basin-wide rainfall.
Spatial variation of soil moisture and evapotranspiration follows altitudinal patterns and is
dynamic. During the wet season, topographic effects dominate and homogeneous profiles of both
hydrologic components were observed. On the other hand, the influence of local plants and soil
characteristics are clearer during the dry season.
5.2. Impacts of forest-to-crop conversion
Based on future scenario analysis, effects of land-use change on seasonal and annual water
yields are a net balance of change in basin moisture storage size, vegetation pumping effects, and flow
regulation. Tropical forests have higher evaporation from rainfall interception and transpiration than
other landcover types, especially in northern Thailand where forests are referred to as ‘pumps’ rather
than ‘sponges’ (Bruijnzeel, 2004). Consequently, forest-to-crop conversion reduces pumping effects
due to lower evapotranspiration rates, releasing more water out into streams. This assumes that
agricultural practices do not cause significant soil compaction, which lowers infiltration rate and
hydraulic conductivity. Soil compaction typically increases infiltration-excess overland flow, reduces
the size of basin moisture storage, and induces slower travel time of sub-surface water to a river
channel. Since overland runoff rarely occurs on simulations and observations, soil compaction
appears to be the least important factor in water yield dynamics. We find no evidence to support the
notion that selected soil compaction conditions alter hydrologic behavior at the whole-catchment
scale.
Irrigation diversion is the most direct influence on discharge magnitude, and it causes
significant difference on potential ranges of water yields among vegetation scenarios. Thus, discharge
16
magnitude is sensitive to assumptions on the percentage of area irrigated, crop types, and crop water
needs.
Crop field expansion in the highlands (> 1,000 m.a.s.l.) transmitted more natural sub-surface
lateral flow than lowland-midland expansion. Changes in spatial soil moisture distribution were not
detected, however, due to orographic effects that produce more excess soil moisture in the highlands,
which percolates through well-drained soils. Accumulation of water as it moves downslope toward
the river channel is observable, whereas soil moisture storage is a state of the basin and change is
more difficult to capture over snap shots in time. Furthermore, the fraction of landuse-change parcels
is so small compared to the total area in an elevation band that change in soil moisture did not affect
the zonal mean. The implications of increased peak flow are for potential flooding from the main
channel rather than from overland flow. Lowland-midland expansion provided more even stream
flow, both seasonally and annually, in this setting.
5.3. Model performance: comparison with GenRiver and IHACRES models
Previous hydrologic modeling and scenario analyses in the Mae Chaem basin includes
simulation on gauged and ungauged sub-basins using the IHACRES metric-conceptual rainfall-runoff
model by Schreider (2002) and Croke et al. (2004), and simulation of the whole basin by van
Noordwijk et al. (2003) using the physically-based GenRiver model. While the conceptual model uses
only a few input parameters, the parameters for each sub-basin need to be calibrated individually and
spatial variability of the specified land cover change was not testable. Flow prediction at the Mae
Chaem basin outlet was not executed due to sparse input data, and Croke pointed that this limitation
makes use of a physically-based model inapplicable. In contrast, we believe the physically-based
approach is appropriate for this scale, and the distributed feature allows us to adjust calculation based
on the availability of data and level of complexity.
Simulated total basin rainfall and results from DHSVM were compared to those from
GenRiver as another test of model reliability. The dynamics of rainfall and evapotranspiration (Fig.
10) were relatively well-correlated (Pearson’s r = 0.94 and 0.88 respectively). Water yields and river
buffering characteristics were in the same magnitude (Table 5 and Fig. 9), except for dry-season yield.
17
Estimation of basin-wide rainfall in each model appears to be an important factor in differences
between simulation results, as seen in the amount of monthly rain used in GenRiver versus in
DHSVM (Fig. 10-a). The SpatRain model which generates rainfall for the GenRiver model accounts
for local patchiness of rainfall events, spatial rainfall intensity based on the inverse distance from the
core of the storm, and elevation effects. Consequently, its accuracy relies on long-term statistics of
rainfall patterns from reference stations. On the other hand, the current setting of DHSVM depends on
orographic effects and interpolation from weather stations. Performance of both models should be
similar on seasonal and annual scales, due to the highly fluctuating nature of basin rainfall behavior. If
the purpose is to evaluate basin response to individual storm events, however, we would expect to see
more dramatic differences in prediction. Another factor contributing to differences in model
performance is the lack of slow subsurface flow in DHSVM. Spatially distributed soil moisture or
other soil hydraulic field measurements are necessary to check the accuracy of model predictions. On
the other hand, once the model is calibrated, the advantage of a physically-based approach is that the
final parameter set is transferable, and little adjustment is required to start a new application in similar
geological settings.
The strength of a distributed model is its ability to identify if spatial or spatially-explicit
relations exist among model input and output parameters. Examples of applications include
examination of effects of elevation bands in conjunction with connectivity among same-land-use
patches, proximity of agricultural fields to streams, or adjacency of certain landcover types. In this
paper, we identified altitudinal increases in soil moisture and evapotranspiration.
6. Conclusions
Land-use change in Mae Chaem has largely featured agricultural transformations in different
altitude zones. Highland pioneer shifting cultivation has been replaced by expanded permanent fields
producing commercial horticultural crops, often with seasonal sprinkler irrigation. While some
midland rotational forest fallow shifting cultivation systems remain, others have been replaced by
rainfed permanent plots producing subsistence and commercial field crops. Irrigated paddy has
expanded where terrain allows, and lowland agriculture has increased dry season water use for
18
irrigated rice, cash crops and fruit orchards. The DHSVM hydrology model was used as a tool for
analyzing impacts of forest-to-crop conversion, and vice versa, on basin hydrology and water
availability at the basin outlet, resulting in the following conclusions:
1. The utility of a spatially-explicit, process-based analytical modeling environment is demonstrated
by its ability to reproduce hydrographs across a range of conditions, in a basin where data are
relatively sparse. At a minimum, the efficacy of the model as an intelligent data-interpolation engine
is clear. That the model does as well as it does implies that the constituent dynamics are relatively
well-understood and some confidence can be placed in the quantitative implications of scenarios. This
modeling approach can be useful in assessing the influence of spatial configuration or fragmentation
of landcovers.
2. Topography is the primary factor controlling climatic, vegetation, and, consequently, spatial
variation of Mae Chaem’s hydrologic components. We found altitudinal increase in soil moisture and
evapotranspiration.
3. The basin hydrology is sensitive to changes in landcover attributes, with a general pattern of
increasing runoff with migration from trees to crops due to decreasing evapotranspiration. As there is
no evidence that soil compaction in agricultural fields has significantly altered overall infiltration
rates, rainfed upland agriculture, especially in the midland zone, does not appear to result in lower
water availabilities downstream.
4. Irrigation has a counter effect by withdrawing water, and its overall impact will relate to its
seasonality, and to water use efficiencies of the crops, irrigation systems, and management regimes
employed.
5. Highland crop expansion (>1,000 m.a.s.l.) may lead to lower buffering indicators, higher peak
flow, and higher seasonal and annual yields. This is due to increased water yields resulting from
reduced evapotranspiration. Since the increased water percolates through well-drained soils, primary
implications are for downstream main channel flooding rather than increased overland flow, and
extractions for irrigation may reduce downstream impacts.
19
Acknowledgements
This work was supported in part by the Bank Netherlands Partnership Program. Findings and
interpretations are those of the authors and do not necessarily reflect the views of the World Bank, its
Executive Board of Directors, or the countries they represent. The work was further supported by the
U.S. National Science Foundation and the SEA/BASINS Program of the University of Washington
and Chulalongkorn University, the Mekong River Commission, and the Puget Sound Regional
Synthesis Model. Meteorological and stream flow data were provided by the Thai Meteorological
Department, the Royal Irrigation Department, the Royal Project Foundation, and the GEWEX-Asian
Monsoon Experiments (GAME)-Tropics. The soil descriptive survey was available from the Land
Development Department of Thailand. The authors thank Meine van Noordwijk for sharing input data
and simulation results from GenRiver model and supporting documents of SpatRain and GenRiver
models.
20
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Fig. 1. Location and river network alignment of Mae Chaem basin in northern Thailand
24
25
Fig. 2. DEM, soil depth, and stream network grids (left to right) represented by the 150-m resolution.
Fig. 3. Mae Chaem landcover (Veg 1989) re-processed 1989, (Veg 2000) re-processed 2000 data sets
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Fig. 4. Landuse conversion (Scenario I) crops to forest, (Scenario II) Double crops,
(Scenario III) More upland crops, (Scenario IV) More lowland crops
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27
Fig. 5. Location of climate stations with elevation (m. a. s. l.)
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28
Fig. 6. Historic records of stream flow at gage P.14 and precipitation from Research Station (RE)
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29
Fig. 7. Orographic effects on average annual rainfall (1989-2000)
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30
Fig. 8. Comparison of observed and predicted hydrographs on Veg 2000 as (a) daily discharge, (b)
average wet-season flow, (c) average dry-season flow, and (d) % deviation of simulated from
observed values. Error bars represent the standard deviation.
30
31
Fig. 9. Quantitative indicators of (a) river buffering capacity and (b) transmission of high peak
discharge in relation to annual (Jan - Dec) basin water yield indicators
31
32
Fig.10. Seasonal patterns of monthly simulated basin-wide evapotranspiration (ET) and rainfall
simulated by DHSVM on Veg 2000 with irrigation and compared to values from GenRiver model
32
33
Fig. 11. Illustration of the underlying dynamics changes in hydrographs, with soil moisture in the root
zones at 30-60 cm, 60-100 cm, evapotranspiration, and precipitation (top to bottom). Values are at
time = 0:00-3:00 and simulated on Veg 2000
33
34
Fig. 12. Soil moisture temporal dynamics in the 30-60 cm root depth and the correlation with
elevation zone, simulated on Veg 2000
34
35
Fig. 13. Evapotranspiration temporal dynamics in correlation with elevation zone, simulated on Veg
2000
35
36
Fig. 14. Comparison of (a) - (b) river buffering indicators and (c) - (d) transmission of high peak
discharge in relation to annual (Jan - Dec) basin water yield indicators among landcover scenarios
36
37
Fig. A1. Original landcover in 1989
37
38
Fig. A2. Original landcover in 2000
38
39
Table 1
Monthly irrigation water demand (in mm) of northern agricultural crops (Schreider et al., 2002)
Month
Crop type
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Wet season rice
0
0
0
0
250
300
350
150
50
50
0
0
Dry season rice
250
200
200
0
0
0
0
0
0
0
300
500
Cash crops
150
150
100
0
0
0
0
0
0
0
300
100
Table 2
Simulation scenarios to look at effects of land-use type, irrigation, and soil compaction
Primary factor : Land-use change
Secondary factors
Veg 1989
Veg 2000 Scenario I
Scenario II
Scenario III Scenario IV
Irrigated areas
0%
X
20%a
X
35%b
X
X
Soil compaction, no irrigation
a
b
X
X
X
X
X
X
X
Approximate maximum percentage of croplands being irrigated based on Veg 1989
Approximate maximum percentage of croplands being irrigated based on Veg 2000
39
X
X
X
X
40
Table 3
Final values for DHSVM model parameters
Parameter
Values
Ground roughness, m
0.02
Reference height, m
40
Rain LAI multiplier
0.0005
Temperature lapse rate, oC/m
-0.0015
Precipitation lapse rate, m/m
0.002
Overstory height, m
20-30
Understory height, m
0.2-5
Vegetation vapor pressure deficit threshold, kPa
4000
Maximum stomatal resistance for overstory, s/m
4000-5000
Minimum stomatal resistance for overstory, s/m
200-400
Maximum stomatal resistance for understory, s/m
600-3000
Minimum stomatal resistance for understory, s/m
120-175
2.3-10.8 (broadleaf)
Overstory LAI
4.8-12.5 (needleleaf)
Understory LAI
0.8-10
Overstory albedo
0.2
Understory albedo
0.2
Lateral soil hydraulic conductivity, m/s
0.0005 - 0.005
Vertical hydraulic conductivity, m/s
10-6 – 10-3
40
41
Table 4
Comparison of hydrologic compositions between observation, simulation by DHSVM on Veg 2000 with
irrigation diversion, and simulation by GenRiver on Mixed-landuse, based on water year
Average values of hydrologic components (1995 – 2000)
Source of computed
Annual yield
High flow a
Low flow
Annual evapotranspiration
mm (m3/s)
m3/s
m3/s
mm
Observed
281 (34.3)
50.7
18.5
750 b 1230 c
DHSVM
215 (26.2)
54.7
7.6
762
GenRiver
247 (30.3)
43.7
18.0
968
hydrologic components
a
Exception: high flow is an average from 1994-2000
b
Hill evergreen forest in Chiang Mai (Tangtham,1998)
c
Typical mountainous watershed, excluding cloud forests (Tangtham,1998)
Table 5
Comparison of average observed and simulated annual (January to December) runoff ratios, buffering
indicators, and highest-month flow fraction from DHSVM (Veg 2000 with irrigation) and GenRiver. For
observed and DHSVM values, basin-wide DHSVM-generated rainfall was used in the calculation. Rainfall data
in GenRiver was generated from SpatRain model (van Noordwijk et al., 2003)
Average values of hydrologic components (1995 – 1999)
Source of computed
Highest monthly discharge
hydrologic components
Runoff ratio
Buffering indicator
relative to mean rainfall
Observed
0.26
0.832
3.13
DHSVM
0.21
0.815
4.22
GenRiver
0.18
0.893
3.91
41
42
Table 6
Potential rangesa of basin hydrology simulated on different landcover scenarios, with and without irrigation
based on water year (November – October)
Average hydrologic components (1995 – 2000)
Landcover scenarios
Annual yield,
High flow,
Low flow,
Annual evapotranspiration,
mm (m3/s)
m3/s
m3/s
mm
I
215 (26.2)
54.7
7.6
762
NI
249 (30.5)
58.6
12.0
727
NI
223 (27.2)
53.3
11.1
752
I
202 (24.7)
53.6
5.8
781
NI
261 (31.8)
61.2
12.5
715
I
220 (25.6)
56.8
7.0
759
NI
269 (32.8)
63.1
12.7
707
I
193 (23.6)
51.6
5.6
786
NI
251 (30.7)
59.1
12.2
724
Veg 2000
Scenario I
Scenario II
Scenario III
Scenario IV
a
I
Based on percentage of irrigated croplands in Table 2
= With irrigation
NI = No irrigation
42
43
Table 7
Comparison of soil moisture in the 30-60 cm depth in all elevation bands among land-use scenarios for
simulations on March 9, 1999 time step 0:00 – 3:00
Landcover scenario
Elevation range, m
Veg 2000
Scenario III
Scenario IV
> 1600
0.227
0.229
0.227
1200 - 1600
0.180
0.186
0.180
800 - 1200
0.163
0.166
0.164
500 - 800
0.151
0.151
0.151
< 500
0.147
0.147
0.149
Table 8
Comparison of soil moisture in the 30-60 cm depth in all elevation bands among land-use scenarios for
simulations on October 10, 1999 time step 0:00 – 3:00
Landcover scenario
Elevation range, m
Veg 2000
Scenario III
Scenario IV
> 1600
0.366
0.366
0.366
1200 - 1600
0.331
0.331
0.331
800 - 1200
0.298
0.299
0.299
500 - 800
0.185
0.185
0.186
< 500
0.147
0.147
0.149
43
44
Table 9
Comparison of simulated hydrologic components among future forest-to-crop scenarios with soil compaction
(no irrigation)
Average hydrologic components (1995 – 2000)
Landcover
Annual
Annual yield a,
High flow a,
Low flow a,
scenarios
Runoff
Evapotranspiration a,
3
mm (m /s)
3
a
m /s
3
m /s
Buffering indicator b
ratio
b
mm
Scenario II
269 (32.8)
62.9
12.8
708
0.26
0.81
Scenario III
277 (33.8)
64.8
12.9
700
0.27
0.80
Scenario IV
259 (31.6)
60.9
12.6
717
0.25
0.81
a
Calculation based on water year (November – October)
b
Calculation based on Julian year (January – December)
44