Modeling Kenya's Vulnerability to Climate Change – A Multifactor Approach

Abstract

Kenyan landscape has experienced in extremes the impacts of climate change. This study assesses climate change vulnerability and shows its spatial distribution in Kenya in the last thirty years by using geospatial techniques to integrate climate data, land use data and socioeconomic data. The measure of vulnerability (vulnerability index) has been used in this study to indicate the combined effects of exposure, sensitivity and adaptive capacity to climate change. Various factors representing exposure, sensitivity, impacts and adaptive capacity were derived from spatial data. Primary data obtained from official sources was used in the assessment. The climate change vulnerability map developed highlights how different regions are vulnerable to climate change with 5.01% of the country was categorized as highest vulnerability, 79.9% as high vulnerable, 14.82% as moderately vulnerable and 0.28% being least vulnerable. These figures are obtained after weighted analysis of exposure, sensitivity and adaptive capacity. Vulnerability index map obtained can be a useful tool to guide decision making in Kenya climate change response planning and the implementation of mitigation measures to the effects of climate change.

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International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Impact Factor (2012): 3.358
Volume 3 Issue 9, September 2014
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Modeling Kenya’s Vulnerability to Climate Change
– A Multifactor Approach
Kenneth Kemucie Mwangi1, Felix Mutua2
1Jomo Kenyatta University of Agriculture and Technology, Department of Geomatic Eng. & Geospatial Information Systems,
P.O. Box 62000, 00100 Nairobi, Kenya
kemucie@gmail.com
2 Jomo Kenyatta University of Agriculture and Technology, Department of Geomatic Eng. & Geospatial Information Systems,
P.O. Box 62000, 00100 Nairobi, Kenya
fnmutua@jkuat.ac.ke
Abstract: Kenyan landscape has experienced in extremes the impacts of climate change. This study assesses climate change
vulnerability and shows its spatial distribution in Kenya in the last thirty years by using geospatial techniques to integrate climate data,
land use data and socio-economic data. The measure of vulnerability (vulnerability index) has been used in this study to indicate the
combined effects of exposure, sensitivity and adaptive capacity to climate change. Various factors representing exposure, sensitivity,
impacts and adaptive capacity were derived from spatial data. Primary data obtained from official sources was used in the assessment.
The climate change vulnerability map developed highlights how different regions are vulnerable to climate change with 5.01% of the
country was categorized as highest vulnerability, 79.9% as high vulnerable, 14.82% as moderately vulnerable and 0.28% being least
vulnerable. These figures are obtained after weighted analysis of exposure, sensitivity and adaptive capacity. Vulnerability index map
obtained can be a useful tool to guide decision making in Kenya climate change response planning and the implementation of mitigation
measures to the effects of climate change.
Keywords: Climate Change, GIS, Vulnerability Index.
INTRODUCTION
Climate change is a change in the state of the climate that
can be identified by changes in the mean and/or the
variability of its properties and that persists for an extended
period, typically decades or longer [1]. It has been attributed
to natural causes and thought to be accelerated by manmade
causes. Among the most manmade cause is the burning of
fossil fuel and land use changes which lead to increase in
quantities of green house gases; carbon dioxide (CO2),
methane (CH4) and nitrogen dioxide (N2O). The rise in these
gases has caused a rise in the amount of heat from the sun
withheld in the earth’s atmosphere which would normally be
radiated back to space. This is the green house effect which
results in climate change [2]. According to [1], over the last
century atmospheric concentration of carbon dioxide
increased from a pre-industrial value of 278 parts per million
to 379 parts per million in 2005 and the average global
temperature rose by 0.74oC. This according to scientists is
the largest and fastest warming trend.
For proper observation of climate change, the observations
must be based on long term data to eliminate the effect of
climate variability. The climate variability is natural and
hence is expected. Over the next decades billions of people
are predicted to face shortages of water and food and greater
risks to health and life as a result of climate change.
Concerted global action is needed to enable developing
countries to adapt to the effects of climate change that are
happening now [1].
Vulnerability is the inability to withstand the effects of a
hostile environment in this case climate change. It is the
degree to which a system is susceptible to, and unable to
cope with adverse effects of climate change, including
climate variability and extremes [3]. Several factors make a
community vulnerable to the effects of climate change and
this study spatially assessed field data and came up with
individual maps for the factors. These were then combined to
a climate change vulnerability map for Kenya.
STUDY AREA
The study area was selected as Kenya, a country in East
Africa lying between 34o E and 42o E and 5oN and 4oS with a
landmass of about 582,350 km2 and population of 38.6
million Figure 1. 17% of the land in Kenya is arable while
the remaining 83% consists of arid and semi-arid land
(ASAL). There are indications according to [4] study that the
ASAL is increasing and the country is losing valuable
natural assets due to climate change.

International Journal of Science and Research (IJSR)
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Figure 1: Map of the Study Area
MATERIALS AND METHODOLOGY
3.1 Vulnerability Assessment
The IPCC’s assessment technique that vulnerability
depends on exposure, sensitivity and adaptive capacity was
adopted [5]. Their interaction can be seen in Figure 2. This
vulnerability assessment sought to geographically portray
each of the factors by looking at the sub-factors that drive
exposure, sensitivity and adaptive capacity.
Figure 2: How Exposure, Sensitivity and Impacts, and Adaptive
Capacity Interact
Vulnerability index (VI) indicates the combined effects of
exposure (e), impacts (i) and sensitivity (s) and adaptive
capacity (a) by the algebraic combination;
         (1)
Such an index of vulnerability allows the emphasis on
some factors more than others. To illustrate, most regions are
exposed to climate change equally in terms of magnitude of
temperature and rainfall variation; but some communities are
able to cope better than others due to their socio-economic
setting.
Empirical data of the various factors was represented in
raster format such that each attribute was recorded in a
separate overlay so that any mathematical operation
performed on one or more attributes for the same cell can
easily be applied to all cells in the overlay. Map algebra
method was used to build a vulnerability model for spatial
analysis [6].
3.2 Exposure to Climate Change
Climate change is manifested in the changes in average
temperature and rainfall amounts. Exposure to climate
change was assessed by the change over-time t of annual-
mean temperature ∆T (t) at a country level and change of
annual mean rainfall ∆P (t) [7].
3.1.1 Climate Data Gap Filling
Temperature and rainfall were acquired from the Kenya
Meteorological Department (KMD). The study period
concentrated on the latest 30yrs (1981-2011) due to the fact
that it is the period under which satellite records can be
obtained and World Metrological Organization (WMO)
recommends 30yrs as a reasonable climate normal.
Both minimum temperature and rainfall data had missing
data in some months. To fill the data gaps, two techniques
were used. In the case of one data gap missing, a technique
of moving averages was applied [10] within the particular
station.
In some cases, missing data was covering more than one
month. In such instances data from several years before and
years after was used to fill the gaps in a non-linear
regression. Each data gap was treated as a specific case with
identification of the best set of stations and the period that
minimizes the estimated reconstruction error for the gap. The
procedure used was similar to that in [11] which entailed the
following procedures;
1. Analysis of target station by identification of a
period without gaps of sufficient length contiguous
to the gap.
2. Identification of stations that can be used for data
reconstruction in the neighbourhood of target
station through the coefficient of determination R2.
Stations with the highest R2 were selected in each
case.
3. Selection of the period to be considered (before or
after the gap)
4. Identification of the station giving the best
correlation with the target station for the specific
gap to be filled.
5. Identification of the best sampling size or length of
period to be used for data coupling
6. Reconstruction of the gap
Exposure to climate change was obtained by mapping
variability of current minimum mean temperature from a
long term (30 years) mean. Historical annual temperature
data was used to derive statistical variation. The minimum

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temperature was picked for analysis of vulnerability of
climate change in Kenya. This is because the minimum
temperature (Tmin) as compared to maximum temperature
(Tmax) is not sensitive to cloud cover and down dwelling
radiation which is observed most during summer months.
This was from a study by [14] that investigated the
uncertainties in climate sensitivity to model differences in
cloud behavior.
The same approach was applied for annual-mean rainfall
variation for the different rainfall stations in Kenya. In Both
cases rasterization was done by using inverse distance
weighting in GIS spatial analysis. Each of the variation raster
was classified into 4 classes to represent; High variation,
medium variation, low variation and no variation.
The two raster were then overlay mathematically in GIS to
come up with a climate change exposure map for Kenya.
3.3 Sensitivity to Climate Change
In climate change setting sensitivity is the degree to which
a system is affected either adversely or beneficially, by
climate-related stimuli. Climate-related stimuli encompass
all the elements of climate change, including mean climate
characteristics, climate variability, and the frequency and
magnitude of extremes [5]. Sensitivity to climate change is
determined by the bio-physical factors of an area and hence
the uses of land use map and agro-climatic zones.
Kenya’s land use was extracted from GlobCover 2009
land use and 26 classes merged to march the IPCC land use
classification system for analysis [8]. Using literature and
similar assessments the various land uses were ranked into 4
classes according to their sensitivity to climate change. The
classes are; most sensitive, moderately sensitive, least
sensitive and not sensitive.
Impacts of climate change are often manifested on the bio-
physical environment. Thus to be included in the mapping of
vulnerability of climate change impacts will be assessed on
the land use types. Different Kenya land uses were ranked
according to the observed impacts of climate change over the
years. The ranking was based on supporting literature and
expert-based opinion. As an example, deciduous forest cover
is less severely affected by climate change as compared to
forests occurring in the ASALS. Thus impacts were ranked
from 1 to 4 with 1 being the least impacted by climate
change and 4 most impacted.
According to [9], the current climate in East Africa is
characterized by large variability in rainfall with occurrence
of extreme events in terms of droughts and floods. These are
the most visible impacts of climate change in Kenya. Certain
sectors are most impacted by climate change with the most
impact in Kenya being on the rain-fed agriculture sector.
These means areas where rain-fed agriculture is practiced
suffer most when drought or floods occur. In the Kenya
vulnerability index model, rain-fed agriculture land use gets
the highest ranking 4, since the impact of climate change are
most/highest in that. Other land uses are assessed in the same
approach based on how drought and floods affect them.
3.4 Impacts of Climate Change in Kenya
3.4.1 Drought Occurrence
For this study, monthly rainfall datasets were acquired
from the Kenya Meteorological Department (KMD) for the
period 1981-2011. This data was used to compute
Standardized Precipitation Index (SPI).
Standardized Precipitation Index (SPI) was developed by
[12] for the purpose of defining and monitoring drought. It is
based in cumulative probability of a given rainfall event
occurring at a station. Historic rainfall data is fitted in a
Gamma distribution and transformed into a standard normal
variable Z with a mean of zero and standard deviation of one.
The SPI is thus a representation of the number of standard
deviations from the mean at which an event occurs often
called a ‘z-score’ [12].
SPI was used in drought assessment by identifying the
drought occurrence and severity by how much the value
varies from the historical mean. A threshold value indicating
drought severity of SPI values between -3 to -1.5
representing severe dry to extreme dry was used derive a
frequency of drought occurrence of each station in Kenya.
The droughts frequency SPI was then interpolated using
Inverse Distant Weighting (IDW) to obtain a drought
frequency distribution map. For the stations, the number of
drought occurrences range from 3 to 25 occurrences. The
raster values obtained were then classified into four classes
representing areas of different drought frequency in 30 year
period to result in Figure 3 below.
Figure 3: Drought Frequency in Kenya 1981-2011 based on SPI
3.4.2 Flood Frequency
As an impact of climate change, floods have come as El-
Nino in Kenya and in the East African region [9]. Due to the
variation in climate zones influenced by altitude and land
formation, floods impact different areas differently in terms
of magnitude and frequency.
For this study, existing analysis in frequency and
distribution of floods as a hazard was used. The data was
obtained from [13]. The analysis period was 1985 and 2003
which slightly varies from the study period of the other
factors considered in this vulnerability assessment. The flood
frequency raster was reclassified into four classes ranked

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according to increasing severity in flood as shown in Figure
4 below.
Figure 4: Flood Frequency in Kenya
Floods and Droughts in Kenya cause different impacts
according to their extents and frequency of occurrence. Thus
to develop an impact map, the recorded frequency of
occurrence was used to assign weights. Data from the
International Disaster Database published by [16] was
obtained for Kenya. The data enumerates disasters
occurrence, numbers affected and estimated economic
damage. From the data, flood events in Kenya for the period
of study number at 13 while flood events at 33 events [16].
This gives a total occurrence of 46. From this, the combine
impact of climate change was derived to give the formula of;
Combined Impacts = 0.3Drought + 0.7Flood (2)
3.5 Adaptive Capacity
Adaptation refers to an adjustment in natural or human
systems in response to actual or expected climatic stimuli or
their effects that moderate, harm or exploit beneficial
opportunities [7].
The challenge of using secondary data to develop socio-
economic development indices is that the data comes at
varied and course resolution. For example in this study, some
data were obtained at a provincial scale and others at a
district administrative level. Such data was re-sampled to a
common spatial resolution to allow integration with other
datasets and represented in a raster format as seen in Figure 5
Figure 5: Classified adult literacy rates in Kenya
Each of the socio-economic indicators was ranked into
four classes according to their adaptive capacity to climate
change. The classes are; highly adaptive, moderately
adaptive, least adaptive and not adaptive. An overlay in GIS
was done such that some factors received higher weights
according to their inability to adapt to climate change.
RESULTS
From the minimum temperature analysis in the years 1981
to 2011 all stations reported a positive slope trend except
fours stations. This indicates an overall increase in the
average monthly minimum temperature for the period under
study as seen in Figure 6. This correlates with the studies
done in [3].
Figure 6: Minimum temperature trend in Kenya 1981-2011

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The monthly rainfall amounts analyzed using trend
analysis slope function yielded a raster where negative slope
meant a decrease in long term rainfall while a positive slope
an increase in long term rainfall. The magnitude of the slope
represents a magnitude of change in monthly precipitation
over the 30years analyzed as seen in Figure 7 below.
Figure 7: Precipitation trend in Kenya for the years 1981-2011
Long term minimum temperature and rainfall trend
combined yielded the climate change exposure indicator.
According to the results obtained least exposure to climate
change happened in the north eastern area in a unique zone.
This area had the least changes in temperature and rainfall
amounts in the 30year period analyzed. Highest exposure
happened in a zone near Kenya’s capital and a curved stretch
as seen in the marked red class in Figure 8.
Figure 8: Climate change exposure map of Kenya
Using this analysis, majority of Kenya falls under low
exposure and high exposure. High exposure is
characteristically around the agricultural areas of Kenya.
The results of the land cover sensitivity assessment shows
the areas of Northwestern as having the highest sensitivity to
climate change Figure 9. This is mainly due to their having
sparse vegetation which is easily affected by a change in
climate. Forests and irrigated croplands are some of the land
cover classes that are least affected by climate change. This
is because of their ability to withstand a high threshold of
climate change effects namely floods and droughts as
compared to other land cover types.
The idea of thresholds to climate change has been
discussed in which some levels are tolerable and must be
exceeded before significant impacts occur [15]. In the same
way the idea that some land cover classes are able to
withstand a higher level of climate change that others has
been accounted for in this study.
Figure 9: Land cover sensitivity classfication derived form
Globcover classes for Kenya
The classification of Agro-Climatic zones yielded Figure
10 below. Notably, the largest area of Kenya has been
classified as highest risk zones and falling in the classes 3
and 4 as seen in Table 1. This is because majority of the
country is classified as ASAL-Arid and Semi Arid Land.
Table 1: Agro Climatic Zones of Kenya reclassification
according to climate change risk
ACZ Description
Ranking/Classification
Humid
1
Semi-Humid to Semi-Arid,
Semi-Humid
2
Semi Arid
3
Very Arid, Arid
4

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Figure 10: ACZ of Kenya classified to show climate change
effects risk
The result of the climate change sensitivity index map
obtained by combining land cover sensitivity and agro-
climatic zone risk is shown in Figure 11.
Figure 11: Climate change sensitivity map of Kenya
Due to the factors considered in the analysis, areas in the
high moisture climatic zones and have land covers that are
forests, urban or irrigated agriculture have the sensitivity to
the effects of climate change.
Majority of Kenya (47.36%) has been found to be high
risk and 36.11% very high risk sensitivity to climate change
as shown in Figure 12. This means that these areas have a
low threshold to the effects of climate change and would
suffer most to the effects if no adaptation mechanisms are in
place. Areas with lowest risk would have a higher threshold
to withstand or cope with the effects of climate change and
compose the smallest area of 1.65% in Kenya.
Figure 12: Percentage of land by area representing sensitivity to
climate change in Kenya
In the analysis of impacts of climate change the results
showed that areas with low drought occurrence based on SPI
analysis and low floods frequency based on the floods
occurrence experienced the lowest impacts of climate
change, and vice versa. This is observed in the Northeastern
and Northwestern areas of the country as shown in Figure
13.
Figure 13: Impacts of climate change showing combined effects
of drought and flood frequencies
For this vulnerability study, wealth levels in Kenya were
derived from level of poverty as studied in [17]. The poverty
figures used were at District level for the period 2005-2006.
Four classes were used that ranks poverty percentages into
quartiles (0-25%, 25-50%, 50-75% and Greater than 75%) as
seen in Figure 14.

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Figure 14: Poverty rates in Kenya
The adaptive capacity developed showed that the areas
with high adult literacy and low poverty rates are considered
to be the ones with the highest adaptive capacity as seen in
Figure 15. This means they have best coping mechanisms.
Figure 15: Combined socio economic indicators adult literacy
and poverty rates representing climate change adaptation in
Kenya
The results of vulnerability index map Figure 16 show
different vulnerability classes from low vulnerable to highest
vulnerable.
Figure 16: Developed climate change vulnerability map of
Kenya
In addition, when the area of classes were computed over
Kenya’s total area, 5.01% was found to be highest
vulnerability, 79.9% high vulnerable, 14.82% moderately
vulnerable and 0.28% being least vulnerable as seen in the
chart in Figure 17 .
Figure 17: Chart showing percent of land area in the different
vulnerability to climate change classes
CONCLUSION
No particular pattern or trend was found in the occurrence
of different vulnerability classification in this case. Majority
of the country falls under the high vulnerability class
(79.9%). This means that when the allowable threshold of
climate change has occurred, majority of Kenya’s
communities would suffer the effects of climate change.
Global studies of a similar approach of integrating
information about climate-change exposure, sensitivity and
adaptive capacity have classified countries according to the
impacts and their ability to adapt to the effects of climate
change. Such a study has been done is the Global
Distributions of Vulnerability to Climate Change [7] Kenya
was classified as extremely vulnerable in the different
emissions scenarios and moderately vulnerable when

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mitigation measures are considered.
RECOMMENDATIONS
Interventions measures to combat climate change can be
based on such a systematic study to know where exactly and
which interventions would enhance adaption to climate
change. Scaling down such a study to county level would
enable interventions to be community specific. In turn, the
overall effect would be effective intervention with a
contribution to the overall climate change adaption strategy
for Kenya
Most of the factors are obtained from data from different
sources, spatial and temporal resolutions. This posed the
challenge on accuracy of the final results since error is
propagated through the climate change vulnerability model.
To minimize on this in future studies, primary studies or
pilot studies can be done in smaller scale representative of
larger areas and all the data collected for the specific area.
This particular vulnerability study due to lack of specific
socio-economic data, considered limited adaptation
measures. While literacy levels and poverty index were taken
as a good indication of adaptive capacity, community
specific climate adaption data could give a more accurate
indication of vulnerability to climate change.
References
[1] Intergovernmental Panel on Climate Change, ‘’Climate
Change 2007: Synthesis Report,’’: Intergovernmental
Panel on Climate Change, Valencia, 2007.
[2] UNFCCC, Climate Change: Impacts, Vulnerabilities
and Adaptation in Developing Countries, Climate
Change Secretariat, Bonn, 2007.
[3] Government of Kenya, National Climate Change
Action Plan 2013-2017, Government of Kenya Press,
Nairobi, 2013.
[4] Government of Kenya, National Climate Change
Response Strategy, Government of Kenya, Nairobi,
2010.
[5] Intergovernmental Panel on Climate Change, Climate
Change 2001: Impacts, Adaptation, and Vulnerability,
Cambridge University Press, New York, 2001
[6] P. Burrough, & R. McDonnell, Principles of
Geographic Information Systems, Oxford University
Press, New York, 2002.
[7] G. Yohe, E. Malone., A. Brenkert, M. Schlesinger, H.
Meij, & Z. Xing, ‘’Global Distributions of
Vulnerability to Climate Change,’’ Integrated
Assessment Journal, 35-44, 2006.
[8] R. Milne,B. J. Jallow, D. Arrouays, P. Beets, P. Drichi,
I. B. Harun, . . . E. Viglizzo, Good Practice Guidance
for Land Use, Land-Use Change and Forestry, Institute
for Global Environmental Strategies (IGES),
Kamiyamaguchi Hayama, 2003.
[9] J.K. Nganga, J. K, ‘’Change Impacts, Vulnerability and
Adaptation Assessment in East Africa,’’ In Proceedings
of the African Regional Workshop on Adaptation,
UNFCCC, Accra, 2006.
[10] W.P. Kemp, D.G. Burnell, D.O. Everson, & A.J.
Thomson, ‘’Estimating Missing Daily Maximum and
Minimum Temperatures,’’ Journal of Climate Applied
Meteorology, 1587-1593, 1983.
[11] G. Tardivo, & A. Berti, ‘’A Dynamic Method for Gap
Filling in Daily Temperature Datasets,’’ Journal of
Applied Meteorology and Climatology, 1079-1086,
2012.
[12] T.B. McKee, N.J. Doesken, & J. Kleist, ‘’the
Relationship of Drought Frequency and Duration to
Time Scales,’’ In Proceedings of the Eighth Conference
on Applied Climatology, pp. 179-184, California, 1993.
[13] M. Dilley, R.S. Chen, U. Deichmann, A. L. Lerner-
Lam, M. Arnold, J. Agwe, . . .G. Yetman, Retrieved
from Global Flood Hazard Frequency and Distribution
Centre for Hazards & Risk Research, 2006, April 26.
Columbia University. [Online]
http://www.ldeo.columbia.edu/chrr/research/hotspots/.
[Accessed: Nov.15, 2014]. (General Internet Site)
[14] D. B. Lobell, C. Bonfils, & P.B. Duffy, ‘’Climate
Change Uncertainty for Daily Minimum and Maximum
Temperatures,’’ A Model Inter-comparison.
Geophysical Research Letters Vol. 34, 2007, March 15.
[15] C. Hope, J. Anderson, & P. Wenman., ‘’Policy
Analysis of the Greenhouse Effect: An Application of
the PAGE Model,’’ Energy Policy 21, 327-338, 1993.
[16] D. Guha-Sapir, R. Below, & P. Hoyois, 2009, ‘’EM-
DAT: International Disasters Database,’’ Retrieved
from Université Catholique de Louvain, 2009. [Online]
www.emdat.be. [Accessed: Nov.15, 2014]. (General
Internet Site)
[17] Kenya Institute for Public Policy Research and
Analysis, ‘’Kenya Economic Report 2013,’’ Kenya
Institute for Public Policy Research and Analysis,
Nairobi, 2013.
Author Profile
Kenneth Kemucie Mwangi received his BSc. in
Soil, Water and Environmental Engineering from
Jomo Kenyatta University of Agriculture and
Technology in 2010 and is currently pursuing his
MSc. Degree in the same university. He has
worked in Regional Centre for Mapping of
Resources for Development, Nokia-Navteq and
now works for IGAD Climate Prediction and Applications Centre.
Kenneth’s interest is in development and use of satellite products
and geo-information for environment applications, agriculture and
climate change assessment.
Felix Mutua received his BSc. in Geomatic
Engineering from Jomo Kenyatta University
of Agriculture and Technology, Kenya with
honors in 2006. He continued his studies at
the same university where he obtained his
MSc. in Environmental Information Systems
in 2009. He acquired a Ph.D. in Civil
Engineering from The University of Tokyo, Japan in 2013. Felix
Mutua’s current interests involve GIS and applications of satellite
products in monitoring water resources, land use and land cover as
well as applications of microwave remote sensing for extreme
weather and climate change impact assessment.