Download Assessment of Land Use Change and Climate

Assessment of Land Use Change and Climate
Variability on Hydrological Processes in the
Upper Ma River Basin, Northwest Vietnam for
future land use planning and water resources
management
Ngo Thanh Son1,2, Nguyen Duy Binh1, Sangam Shrestha2, Vo Trong Hoang3, Nguyen
Duc Loc1, Nguyen Anh Tuan1, Nguyen Dinh Cong1,4, Rajendra Prasad Shrestha2
Vietnam National University of Agriculture
Asian Institute of Technology
Vietnam Academy of Science and Technology
Mekong River Commission, Cambodia
Outline
1. Background and Objectives
2. Study area
3. Methodology
4. Results
5. Conclusions
Background

Land use change and climate variability are two main factors
directly affecting regional hydrological conditions.

Changes in land use/cover have affected the water balance and
transformed the water flow pathways in the hydrological
processes. (Chhabra et al., 2006);

Changes in climate are predicted to have severe, direct impacts
of hydrology, ecosystem, and livelihoods, and are the wide
issue (IPCC, 2007)

Understanding the responses of hydrological processes to land
use and climate variability is improtant for land use planning
and water resources management (Dao NK et al., 2013)
Background (cont.)



In the Northwest of Viet Nam (Upper Ma River basin) has
been significantly changing in hydrology and sediment,
mainly due to land use and climate change;
GIS, RS, and hydrological modeling are extremely useful
tools
to analyze land use data ,
 to give valuable insights into processes of land use
change and their underlying causes;
 To assess the impacts of land use and climate change
on water quantity and quality
Soil and Water Assessment Tool (SWAT) is frequently used to
assess hydrology and water quality in the basin because of
user-friendly in handling input data.
Objectives
 Calibrate and validate the SWAT model in term of
stream flow;
 Assess the individual impacts of land use change
and climate variability on hydrological processes;
 Assess the combination impacts of land use change
and climate variability on hydrological processes;
 Provide decision makers with a comprehensive
understanding of the interaction among land use
change, climate change and hydrological processes;
Study area
Area 3501 km2,
Mountain,
Poverty,
Hydrological variations
Soil degradation - Erosion
Methodology
Observed Hydrological Data
Meteorological Data
GIS Data
Observed daily flow
(Precipitation,
temperature,
humidity, wind speed,
solar radiation)
DEM, Soil types, Land
use/Land cover, Slope
SWAT Model
Sensitive Analysis
Calibration and Validation
Assessment of Land Use Change and Climate
Variability Impacts on Hydrological Process
Framework of the study
Input data preparation
Data type
Source
Resolution
Description
Topography Map
MONRE
30m x 30m
Digital Elevation Map
(DEM)
Soil types (FAO)
MONRE and MRC
1km
Soil classification and
physical properties
Land Use
USGS
30m
Land use classification
Climate data
National Climatic
Center of Vietnam
Daily
Daily precipitation, min
and ma temperature at 3
stations
Stream flow
National Climatic
Center of Vietnam
Daily
Xa La station
Figure 2 Soil map of the upper Ma River Basin, Northwest of Vietnam
Fig 3 Land use map in 1993 (left) and 2009 (right) of the upper Ma river basin
Modelling procedure
calibration:
using monthly flow monitoring data from hydrological stations
during 1993-2000;
validation:
using monthly flow monitoring data from hydrological stations 20012009;
applications:
S1: Land use in 1993 and climate data 1993–2000 (Baseline).
S2: Land use in 2009 and climate data 1993–2000 (Land use change).
S3: Land use in 1993 and climate data 2001–2009 (Climate change).
S4: Land use in 2009 and climate data 2001–2009 (Land use and climate
change).
Indicators: Nash-Sutcliffe efficiency (NSE),
determination (R2), and percent bias (PBIAS)

coefficient

2
n i
2  n  i
i  
i
NSE  1   Qobs  Qsim  /   Qobs  Q sim  
 
 i 1
  i 1 





i
i
 Qobs  Qobs Qsim  Qsim 

R 2  n i 1
n
2
2
i
i
 Qobs  Qobs  Qsim  Qsim
n

 
i 1
2

i 1
n
PBIAS  
i 1

i
Qobs
i
 Qsim
100 /  Q
n
i 1
i
obs
where, n is the number of events, Qiobs, and Qisim are the observed and
simulated data on the ith time events, 𝑄𝑜𝑏𝑠 is the mean of observed data across
the n evaluation time steps
of
Results
Calibration period (1993-2000) and validation period (2001-2009)
Indicator
 SWAT model can simulate well for
stream flow for the upper Ma river
basin using calibrated parameters
Runoff
Calibration
(1993-2000)
Validation
(2001-2009)
NSE
0.94
0.74
R2
0.96
0.75
PBIAS (%)
12.27
-6.04
Land Use Change (1993-2009)
Land use
types
1993
Area
(km2)
2363.44
Forest
Perennial crop 101.21
19.64
Paddy field
1.02
Miscellaneous
38.85
Water body
Field crop
978.63
Total
3502.79
2009
(%)
67.47
2.89
0.56
0.03
1.11
27.94
100
Area
(km2)
1615.01
343.39
49.55
5.68
67.25
1421.91
3502.79
Change
(%)
46.11
9.80
1.41
0.16
1.92
40.59
100
Area
(km2)
-748.43
242.18
29.92
4.66
28.39
443.28
(%)
-21.36
6.91
0.85
0.13
0.81
12.65
Impact of land use change on hydrological processes
Surface
runoff
(mm)
Items
Lateral
Flow
(mm)
Base Flow Percolation
ET
(mm)
(mm)
(mm)
Water
yield
(mm)
S1
164.9
74.5
520.8
568.4
482.2
758.4
S2
182.2
68.0
447.3
489.9
556.3
695.6
Change (mm)
17.2
-6.5
-73.5
-78.5
74.1
-62.9
Percent (%)
10.4
-8.7
-14.1
-13.8
15.4
-8.3
%
20
15
10
5
0
-5
-10
-15
-20
S2/S1
S2/S1
Surface
runoff
10.4
Lateral Flow
Base Flow
Percolation
ET
Water Yield
-8.7
-14.1
-13.8
15.4
-8.3
Impact of Climate Variability on hydrological processes
Surface
runoff
(mm)
Items
Lateral
Flow
(mm)
Base Flow Percolation
ET
(mm)
(mm)
(mm)
Water
yield
(mm)
S1
164.9
74.5
520.8
568.4
482.2
758.4
S3
Change (mm)
Percent (%)
231.3
66.4
40.2
84.3
9.9
13.3
620.5
99.7
19.1
675.3
106.8
18.8
413.0
-69.2
-14.4
934.4
176.0
23.2
50
40
30
20
%
10
S3/S1
0
-10
-20
S3/S1
Surface
runoff
40.2
Lateral Flow
Base Flow
Percolation
ET
Water Yield
13.3
19.1
18.8
-14.4
23.2
Assessment of combined impacts of Land use change
and climate variability on hydrological processes
Items
Surface runoff
(mm)
S1
164.9
241.2
76.3
46.3
S4
Change (mm)
Percent (%)
%
50
45
40
35
30
25
20
15
10
5
0
S4/S1
Lateral
Flow
(mm)
74.5
76.1
1.6
2.2
Base Flow
(mm)
Percolation
(mm)
520.8
522.0
1.2
0.2
568.4
570.0
1.5
0.3
ET
(mm)
482.2
521.9
39.7
8.2
Water
yield
(mm)
758.4
837.5
79.1
10.4
S4/S1
Surface
runoff
46.3
Lateral Flow
Base Flow
Percolation
ET
Water Yield
2.2
0.2
0.3
8.2
10.4
Simulated average-annual stream flow under the
impacts of land use and climate change
Scenarios
Land use
Climate
S1
S2
S3
S4
1993
2009
1993
2009
1993-2000
1993-2000
2001-2009
2001-2009
Measured
(mm)
164.1
164.1
224.7
224.7
Simulation
(mm)
164.9
182.2
231.3
241.2
Simulated
changes
(mm)
17.3
66.4
76.3
Percent
(%)
10.5
40.3
46.3
50.0
40.0
30.0
S2
20.0
S3
S4
10.0
0.0
-10.0
Wet
Dry
Annual
Fig 4 Changes in annual and seasonal stream flow under the impact of land
use and climate changes
Conclusions
• SWAT model was applied successfully to simulate stream
flow in the upper Ma River Basin
• Rapid land use change occurred from 1993 to 2009,
especially dramatically decreased forest from 67.47% to
46.11% and increased field crop from 27.94% to 40.59%.
• individual land use change in the study increased surface
runoff and ET considerably while decreased lateral flow,
base flows, percolation, and water.
• individual climate variability led to significantly increased
all hydrological processes (percent contribution were
surface runoff (40.2%), lateral flow (13.3%), base flow
(19.1%) percolation (18.8%), and water yield (23.2%)
• combined impacts of land use and climate variability caused
all hydrological components, especially surface water.
• climate variability influenced hydrological processes more
significantly and strongly than the land-use change
Limitations
Model data requirements proved to be the main issue for
the study
huge amount of data requires skills in GIS ( ArcGIS, DEM, dbf,
etc.), in programming, in hydrology and soil science (to
assess reliability of data), etc.
dealing with changes in time of land use and infrastructures
have impacts on monitored flow data.
Future research
• Developing website to disseminate study
results to the public
• allowing users to investigate land use and
climate change impacts by modification of
climate data, running models and displaying
modeling results,
• expecting to raise land use and climate change
awareness among the public.
Acknowledgements
The authors would like to thank IDRC-CRDI for funding
this project. The author is also grateful to SEARCA for
funding the travel grants and giving me a chance to be
here to present my paper.
Thank you for your attention