DEVELOPMENT OF NEW DROUGHT INDEX THAT CONSIDERS JOINT EFFECTS OF CLIMATE AND LAND SURFACE CHANGE

1. SEMINAR PRESENTATION
ON
“A new drought index that considers the joint
effects of climate and land surface change”
Liu, M., X. Xu, C. Xu, A. Y. Sun, K. Wang, B. R. Scanlon, and L. Zhang (2017)
SHYAM MOHAN CHAUDHARY
17AG62R13
Land and Water Resources Engineering
Agricultural and Food Engineering Department
IIT KHARAGPUR

2. CONTENTS
• INTRODUCTION
• REVIEW OF LITERATURE
• TYPES OF DROUGHT AND DROUGHT INDICES
• PROBLEM STATEMENT• PROBLEM STATEMENT
• OBJECTIVE
• METHODOLOGY
• RESULTS AND DISCUSSION
• CONCLUSION

3. INTRODUCTION
• Drought is a natural phenomenon which results
in a temporary but significant decrease in water
availability in a region.
• Some causes of drought may be deficiency of rain• Some causes of drought may be deficiency of rain
water, over exploitation of water resources,
deforestation etc.
• World’s longest drought was observed from Oct
1903 to Jan 1918 in Africa and Chile with almost
no rain for 14 years.

4. REVIEW OF LITERATURE
AUTHORS YEAR STUDY
Sergio et al. 2009 Introduced a new climate index
SPEI (Standardized Precipitation
Evapotranspiration Index) which is
based on precipitation and alsobased on precipitation and also
include effects of temperature
variability on droughts.
Zargar et al. 2011 Provided a comprehensive
overview of 74 operational
drought indices and highlighted
their advantages and limitations.

5. TYPES OF DROUGHT
• HYDROLOGICAL DROUGHT
• METEOROLOGICAL DROUGHT
• AGRICULTURAL DROUGHT
• FAMINE DROUGHT
• SOCIO ECONOMIC DROUGHT

6. SOURCE : Modified from NDMC (2006b)

7. DROUGHT INDICES
To check and estimate the severity of drought
over a region for a given period of time
• Percent of normal
It is determined by dividing actual precipitation by
normal precipitation for a location.normal precipitation for a location.
Does not considers frequencies of deviations from
normal rainfall.
• Decile
Scale with values ranging from 1 to 10.
Low values on this scale represent conditions that
are drier and high values represent conditions that
are wetter than normal.

8. continued…
• Palmer Drought Severity Index (PDSI)
It is an agricultural drought index as it deals with the
soil moisture data along with precipitation and
temperature.
• Normalized Difference Vegetation Index (NDVI)• Normalized Difference Vegetation Index (NDVI)
It is based on remote sensing
NDVI = (NIR – R) / (NIR + R)
Where, NIR is digital number in near infrared band and R is digital number
in red band.
Values of NDVI range from -1 to +1.

9. continued…
• Standardized Precipitation Index (SPI)
Negative values indicate drier and drought like conditions
and positive values indicate water availability.
It is obtained by fitting the precipitation values in a
gamma function then standardizing it.gamma function then standardizing it.
• Standardized Precipitation Evapotranspiration Index
(SPEI)
Considers evapotranspiration along with precipitation.
It is of great use to detect and monitor drought
conditions and also analyze the impacts of global
warming on droughts

10. PROBLEM STATEMENT
• Climate Change and land surface changes such as
re-vegetation and deforestation can consequently
influence the regional water balance.
• All existing drought indices generally have
limitations in reflecting the effects these changeslimitations in reflecting the effects these changes
on dryness/wetness.
• Therefore, a new drought index has to be
generated which considers the joint effects of
climate and land surface change on
dryness/wetness of a region.

11. OBJECTIVE
To develop a new hydrological
drought index, named the
Standardized Wetness IndexStandardized Wetness Index
(SWI) based on the structure of
the SPEI.

12. METHODOLOGY
DEVELOPMENT OF THE SWI
• A drought index that considers land surface
changes should use actual evapotranspiration as
an input variable, because land surface changes
can directly affect the water-energy balance and
then actual ET.

13. RESIDUAL WATER ENERGY RATIO
• This ratio is defined as
where P, E and ET0 represent precipitation,
actual evapotranspiration, and potential
evapotranspiration, respectively

14. Calculation of actual E using Budyko equation
Where n represents the joint effects of climate and
land surface.
This value of E is substituted in residual WER ratio
and then WER series is generated on any month
scale

15. Fitting of WER series in a probability
distribution
The three-parameter log logistic distribution (TLPD) is used to
fit the WER series. The probability density function f(x) and
cumulative probability F(x) of this TLPD are given as:
where α, β and γ are the scale, shape and origin parameters

16. One method of standardizing WER series using TLPD
Values of α, β and γ are obtained using following equations
w1, w2, w0 represents probability weighted moments PWMs

17. Calculation of PWMs
ws is probability weighted moment of sth order, N is
the number of data points.the number of data points.
where Fi is an estimator

18. Two features of SWI
Parameter n in Budyko equation represents the joint
effects of climate and land surface properties and reflects
the interactions between climate and land surface.
• Dynamic (temporally changing) n to derive SWI, considers
the joint effects of climate and land surface changes.the joint effects of climate and land surface changes.
(SWIrm)
• Fixed n to estimate the SWI, considers effects of only
climate change. (SWIr)

19. Estimation of dynamic n at catchment scale
Concept of moving windows (5-10 years) is used to
estimate dynamic n
Mean annual P, ET0 and R in the period of 1960-1964 are
used to derive n5y by solving
Note that n5y means that n is derived using 5 year mean
P, ET0 and R based on 5 year moving windows
Similarly n6y, n7y etc. are used.
The aim of using moving windows is to evaluate
uncertainties of SWIrm based on different moving
windows.

20. Estimation of fixed n
• A reference period is required to estimate SWIr
• The reference period would be the entire period or
specific period of the input data.
• Based on the mean annual P, ET0 and R in the• Based on the mean annual P, ET0 and R in the
reference period the fixed n (nr) is calculated by
solving

21. Validation of SWI in drought detection
• Study area
Wuding and Poyang Catchments (China) with considerable land surface
changes

22. Reasons for choosing these two catchments
• The climate in the Wuding catchment is temperate
semiarid and that of Poyang is humid monsoon.
• In the Wuding catchment, ecological restoration
measures, including terraces, check dams, to control
water loss and soil erosion since the 1950s have beenwater loss and soil erosion since the 1950s have been
adopted.
• In the Poyang catchment, the Mountain-River-Lake
Program was launched in 1983 to address severe soil
and water losses and Green project, was implemented
by converting farmland into forest and grassland in
the two catchments

23. Data collection
• Long-term annual runoff (R) (1961–2009) were
obtained from the Yellow River Conservancy
Commission. Daily meteorological data from 1961 to
2009 at 6 (Wuding) and 19 (Poyang) meteorological
stations were obtained from the China Meteorologicalstations were obtained from the China Meteorological
Data Service Network.
• The ET0 was computed using the Penman method
• Basin-scale monthly P and ET0 were obtained by
arithmetically averaging the monthly data (aggregated
from daily values) at all the stations.

24. Validation of SWI
• Since the parameter n is the key component in
determination of SWI, therefore its
relationship to land surface (ecological
restoration) was assessed.restoration) was assessed.
• To assess SWI for drought detection, globally
reported droughts by (NCDC, NOAA) during
2000-2011 were used.

25. Results and discussion
Total percentage of the area that
affected by ecological restoration
and parameter n in Wuding
catchment
Forest coverage and stand
volume and parameter n in
Poyang catchment

26. continued….
• Parameter n showed overall increasing trends.
• In the Wuding catchment, results showed that the
parameter n was closely related to land surface, high
correlations
n = 0.006 x affected area (%) + 1.323n = 0.006 x affected area (%) + 1.323
R2 = 0.86
• In the Poyang catchment
n = 0.0038 x forest coverage (%) + 0.0027 x
stand volume (m3/ha) + 0.95
R2 = 0.63

27. continued…

28. Global reports stated that:
• Many areas in Asia experienced drought during 2000–2011.
Particularly, the drought during 2000–2003 in Pakistan and
northwestern India and the 2004 drought in Thailand.
• In China, major droughts occurred in 2004 (southern China), 2007
(large part of China), 2008–2009 (northern and southern China,
2009–2010 (southwestern China) and 2011 (large part of China).
• Droughts in North America, such as the 1998–2004 drought
• In Australia, droughts during most of the years during 2001–2009.
• In Europe, a 4 year (2004–2007) drought in Western Europe and
that affected most of Western Europe in 2004 and 2005.
SWI could efficiently capture these droughts on global scale

29. CONCLUSIONS
• Newly developed drought index SWI considers the joint
effects of climate and land surface change.
• If the interactions between climate and the land surface
are assumed to remain constant i.e. n remains constant,
although this situation is nonexistent, then SWI
estimates the dryness/wetness resulting from climateestimates the dryness/wetness resulting from climate
change solely (SWIr).
• If using dynamic n to derive SWI, then this SWI
represents the dryness/wetness resulting from joint
effects of climate and land surface changes (SWIrm).
• Land surface change has larger impacts on drought
conditions in Wuding catchment than Poyang
catchment.