INTRODUCTION Hazards have different origins and broadly classified as natural (Geological, Hydro-meteorological and Biological) and human induced processes (environmental and technological hazards). Hydro-meteorological hazards deal with the natural processes or phenomenon of meteorological, hydrological or oceanographic nature and climate phenomena origin. Meteorological hazards are related to atmospheric weather patterns or conditions and are generally caused by factors related to temperature, precipitation, wind speed, humidity, or other more complex factors. These hydro-meteorological phenomena cause a wide variety of natural hazards such as floods including flash floods, drought and desertification, forest fires, tropical cyclones (also known as typhoons and hurricanes), coastal storm surges, avalanches, and extreme weather hazards (e.g. heat waves, cold spells). These events are often catastrophic in nature causing loss of life, famine, loss of livelihoods and services, threat to property and the environment. Hydro-meteorological conditions can trigger other hazards viz. landslides, wildfires, epidemics, and in the transport and dispersal of toxic substances and volcanic eruption material (UNISDR, 2009). The terms ‘natural hazard’ and ‘natural disaster’ are frequently used and exchangeable but differ with respect to vulnerability. The impact of natural hazards depends on human presence and the ability of a population to deal with the hazard. Natural disasters occur when hazards meet vulnerability, however If a hazard occurs in an area outside of human settlement, it would not result in a disaster (Wisner et al., 2004). The country whether it more susceptible to a disaster depends on its infrastructure, levels of development, and social stability. Availability and accessibility of healthcare facilities also affect country’s ability to cope with the disasters and their aftermath. Accordingly, strengthening these aspects helps to reduce the prevalence of natural disasters in the region.

A series of major disasters were witnessed in Asia and it is one of the regions of the world most vulnerable to disasters, experiencing a wide variety of natural hazards. During 1980-2013, the frequency of natural disasters increased globally but the sharpest increase witnessed in the Asian and Pacific region because of increasing exposure and vulnerability (ESCAP, 2013). In the past decade (2000-2013), about 0.824 billion people in the Southern Asian region were affected by natural disasters and almost 53,378 (13% of global fatalities) were killed (Emergency Events Database, EM-DAT). Globally about 2.91 billion people were affected by disasters and total number of fatalities was about 425,737, of which Asia accounted for ~57% (Figure 1).

INSERT FIGURE 1

Figure 1. Number of fatalities across continents during 2000-2013 (EM-DAT).

In recent years, the Asian region has been hit by a series of disaster shocks such as droughts, extreme temperature, floods, storms, etc. These natural hazards are expected to rise under the build-up CO2 concentration in the atmosphere under global climate change. These phenomena have widespread impact on society and a prime concern for governments to deal with the social and environmental impacts. In particular, impact due to such hazards predominantly larger in developing nations than that of industrialized countries. The most frequently occurring and routinely observed hazards in developing nations are in the form of natural origin such as floods, flash floods, drought, extreme temperatures, and tropical cyclones. The livelihood of the majority of people in developing countries is predominantly dependant on rainfed agriculture. Start of rainfall season often early or late but it can cause significant losses to farmers as well as country’s food security.

According to the EM-DAT, since 1970 there are 1059 events of natural disaster in Southern Asia. There are 37 droughts, 316 cyclonic storms, 100 extreme temperature events, and 599 flood events (including flash flood). The decadal values indicate the frequencies are increasing in each category, however, flood

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and extreme temperature categories shows a striking increase. The pattern of increases has a correspondence with global climate change and monsoon dynamics. In terms of total damage, largest damages occurred in floods followed by storms and droughts (Table 1). Over the period 1970-2013, India battered by historic 218 flood events, 130 storms, 48 extreme events (heat and cold waves), 9 drought events, and 2 wild fires and total economic damages were ~50 % of the total damages of Southern Asia (~52,507,676).

Table 1. Frequency of disasters occurred over Southern Asia during 1970-2013 (EM-DAT). Disaster Types 1970’s 1980’s 1990’s 2000’s 2010-13 Total

(Col)Total damage

('000 USD)

Flood 37 93 152 245 72 599 78,164,500

Drought 9 10 4 12 2 37 6,040,172

Storm 50 64 87 86 29 316 19,745,843

Extreme TEMP 7 15 23 39 16 100 544,133

Wild fire 0 1 5 1 0 7 11,700

Total (Row) 103 183 271 383 119 1059 104,506,348

Over the last few decades, geospatial approaches deals with the procedures to acquire, analyze and evaluate spatial information for risk assessment from natural and human-induced hazards viz. geological hazard, hydro-meteorological hazards, environmental hazards and technological hazards. Geographic Information System (GIS) is commonly used as a mapping and analytical tool that support hazard risk and assessment, mitigation, and emergency response planning. Thereby, geospatial technologies have raised pronounced expectations in recent years as potential means of prevention and mitigation of natural disasters, including hydro-meteorological hazards. GIS has the capability to evaluate spatial information from various sources like remote sensing imagery, digital photogrammetry, meteorological radar, etc. and can deliver cost-effective geospatial solutions to policy makers.

This chapter introduces the use of space technology and geographic information systems in Hydro-meteorological hazards for detection, monitoring, and developing early warning systems for disasters. The major disasters and existing assessment methods and approaches were introduced and further selected case studies were presented.

HYDRO-METEOROLOGICAL DISASTERSFloodsFloods are most common natural hazards resulting from meteorological processes such as prolonged rainfall, intense thunderstorms, onshore winds. Other processes viz. landslides, avalanches, levee breakage, and dam failure can cause rapid and widespread flooding. Floods cause more fatalities and loss of property and livelihoods of people than any other type of hazards. It can disrupt water purification and sewage disposal system, and cause toxic waste sites to overflow. Flash flooding is the result of intense

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rainstorms within a brief period of time and it occurs with little or no warning. It is often the result of rapid, unplanned urbanization, which can greatly reduce the land’s ability to absorb rainfall. The resulting runoff has nowhere to go and accumulates as quickly as the rain can fall. Drainage systems can be built to alleviate some of this problem, but very heavy rains will often exceed the capacity of even the best-designed systems of the developed countries. Floods have been occurring almost every year in India sub-continent and prevalent during monsoon season June to September. Flood problems are confined to the states located in the Indo-Ganges plains, northeast India and central India (Dhar and Nandargi, 2003) caused by tropical disturbances like cyclonic storms, depressions, low pressure, and thunderstorms.

To identify flood prone areas satellite images with high resolution data and GIS data can be used to create flooded maps. Since flood is a dynamic phenomenon, the period of submergence may vary greatly at different place and take times from hours to weeks. This leads to incapability of mapping the widest spread of flooding (time delay between the peak flood phase and that of satellite observation). Besides, floods prevalence during monsoon season (cloudy condition) cause a problem to acquire information as optical satellite sensors has a limitation which makes it difficult to map the spatial extent of inundation. On the other hand, microwave data/radar image have a limitation in difficult classification of acquired signal because of influence of complex ground and system variables. So an integrated approach of combining all data from different remote sensing based sensors and historical data (e.g. river discharge, past flood events) in a GIS platform could provide firsthand information for flood prevention and decision making.

Droughts Drought is defined as “a period of abnormally dry weather sufficiently prolonged for the lack of water to cause serious hydrologic imbalance in the affected area” (Glossary of Meteorology, 1959). In a simpler form it is a period of unusually dry weather that has a prolonged existence to cause environmental or economic problems. Droughts are recurring climatic events, episodic in nature and recognized as slow creeping natural hazard. It is a major limiting factor to the region’s economic development by affecting the development of agricultural and water resources and food production. It produces complex impacts especially on agriculture sector by declining food grain production depending upon the intensity, duration, and spatial coverage of drought stress. It can also lead to increased migration from rural to urban areas, posing additional pressures on diminishing food production. Frequent droughts and more erratic nature of rains combined with underlying economic, social and environmental vulnerabilities may increase as a result of climate change and can have an increasingly destructive impact on at risk populations living in poor countries. Drought severity depends on duration, moisture deficiency, and the size of the affected area. It could be widespread and devastating which can cause large agro-ecological damage and disrupt socio-economic life. It often hits South Asia, causing massive water shortages, financial losses and adverse social consequences. In the history of British Rule, The Great Famine of 1876–78 affected severely entire Southern peninsula of India and heads to Central and Northern parts of India. The famine due to intense drought was spread over 16.7 million ha and the mortality was estimated as 5.5 million people. In Late Victorian Holocausts, Davis, M. (2001) explores the impact of colonialism and capitalism during the extreme climactic conditions "El Niño-Southern Oscillation (ENSO)" droughts related famines of 1876–1878, 1896–1897, and 1899–1902 in India. In the second half of the 19th century, India subcontinents witnessed a near-permanent cycle of droughts, bad harvests and subsequent famine. Subsequently, The Bengal famine in 1943 occurred due to crop failures and food shortages and it is estimated that at least 3 million people were died from starvation, malnutrition and related illnesses during the famine.

In the last three decades, rapidly ballooning populations has added to the growing demand for water, food and other natural resources in the region. The drought in 2000-2004 across Southern Asia affected more than 462 million people, with severe impacts felt in western India (Gujarat and Rajasthan States), in

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Pakistan’s Sind and Baluchistan provinces, as well as in parts of Iran and Afghanistan (Thenkabail et al., 2004). During the last three decades (1980-2013), more than 833 millions of people across Southern Asia affected due to the consequences of drought, and millions were forced to abandon their land (EM-DAT). Assessment and monitoring drought development becomes critical issue in most parts of South Asian countries as droughts are expected to rise in these regions under climate change.

Desertification Desertification is land degradation in arid, semi-arid, and dry sub humid areas resulting from various factors including climatic variations and human activities and has several definitions. It is a process leading to reduced biological productivity, with consequent reduction in plant biomass, which leading to the intensification or extension of desert conditions (UNCOD, 1977). The degradation types as described by Oldeman (1988) are:

a) by displacement of soil material through water erosion (e.g., loss of topsoil, flooding) and wind erosion (top-soil loss, terrain deformation/overblowing) and

b) by internal soil deterioration through chemical (e.g., pollution, salinization), physical (e.g., soil compaction ) and biological deterioration (biological imbalance in the topsoil).

There are several factors, causes and processes are involved in the complex process of desertification and to assess desertification all factors need to consider. Broadly, the causes can be classified as Environmental (climate, geomorphologic, quality of soil water sources) and Anthropogenic (vegetation, water, land/soil resources degradation) (Sepehr et al., 2007). The factors responsible for soil degradation are wind and water erosion and salinity. Desertification in the arid regions of Southern Asia is characterized by frequent droughts, intensive agriculture, overgrazing, deforestation, soil erosion, and salt damage to irrigated land.

Tropical cyclones The Tropical cyclone occurs between Tropics of Cancer and Capricorn. The tropical cyclones develop over the warm waters of the North Indian Ocean including Bay of Bengal and the Arabian Sea. Tropical cyclone is a rotational low-pressure system in tropics when the central pressure falls by 5 to 6 hPa from the surrounding and maximum sustained wind speed reaches 34 knots (~62 kmph). Tropical cyclones are severe storms that form over Asia is called ‘typhoons’. Tropical cyclones are called hurricane over the Atlantic Ocean. The low-pressure systems over Indian region are classified as Depression, Deep Depression (DD), Cyclonic Storm (CS), Severe Cyclonic Storm (SCS), Very Severe Cyclonic Storm (VSCS), and Super Cyclonic Storm (SuCS). The associated maximum wind speed (kmph) are 60-90 for CS, 90-119 for SCS, 119-220 for VSCS and >220 for SuCS (RSMC, 2013).

Tropical cyclone significantly affects coastal zones but can travel far inland under certain conditions. These storms are marked by a combination of high winds, heavy rainfall, and coastal storm surges. Climatologically, cyclones are noticed in the month of May-June and October-November but higher frequency of dissipation of cyclones occurred in the month of October in the Bay of Bengal, India because of strong easterly winds aloft. The frequency of tropical cyclones in the north Indian ocean revealed a significant increasing trends during November and May over the past decades (Singh et al., 2000). Based on EM-DAT, about 69 million people were affected by cyclones in India over the period 1980-2013. There were 62 events of tropical cyclones that caused 20,465 causalities with total economic damage of 9,051,375 ('000 USD). The most affected states are located in the east coast such as Andhra Pradesh, Odisha, TN, and WB. The damage and destruction due to storms are increasing however loss of

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life has been minimized as a consequence of better weather forecasts and warnings, and disaster management strategies.

Weather Hazards: Heat Waves and cold Waves Heat waves or extreme heat is a temperature-related hazard connected with a significant deviation above normal high temperatures for a given geographical region. Heat waves are global phenomena and most significantly affect human beings, livestock, Wildlife and habitats, agriculture, infrastructure and water resources. This phenomenon occurs during the period March–July across the Indian subcontinent, and causes fatalities attributed to sun stroke. The duration of the heat wave generally varies from 5 to 6 days but may go up to 15 days (Chaudhury et al., 2000). According to the India Meteorological Department (IMD), criteria for heat waves in India are defined as:

a) When normal maximum temperature of a station is less than or equal to 40° C● Heat wave departure from normal is 5° C to 6° C whereas severe heat wave departure from

normal is 7° C or more. b) When normal maximum temperature of a station is more than 40° C

● Heat wave departure from normal is 4° C to 5° C whereas severe heat wave departure from normal is 6° C or more.

c) When actual maximum temperature remains 45° C or more irrespective of normal maximum temperature, heat wave should be declared.

In the last century (1901-1999), the maximum number of heat waves occurred over Uttar Pradesh (134), Bihar (113), Maharashtra (99), and Rajasthan whereas the minimum number of heat waves reported in Delhi NCR (3), Gujarat (2) and Punjab (1) (De et al., 2005). With respect to fatalities, highest number reported from Rajasthan (1625), followed by Bihar, Uttar Pradesh, Odisha during the period 1978-99 (De and Sinha Ray, 2000). Notably, a significant increase in the frequency and spatial coverage of heat wave has been observed during the decade 1991-2000 as compared to the two earlier decades 1971-80 and 1981-90 (Pai et al., 2004). More frequent and extreme heat waves witnessed during 1991-2000 coincided with highest temperatures increase as a result of global warming, which is also the warmest decade during the past 140 years (WMO, 2001). Global warming induced heat waves might lead to serious implications in the developing countries. According to EM-DAT, human and economic losses in India from heat wave disasters that have occurred between 1980 and 2013 revealed that there were 18 events with 7,725 number of fatalities and total damage was 400,000 ('000 USD). On the other hand, number of cold waves reported were 24 events with 4,567 number of fatalities and total damage was 144,000 ('000 USD). In terms of death toll and total economic damages, cold waves have lesser repercussion against heat waves in India.

Cold waves or extreme cold temperatures are short-lived or may persist for days or weeks which can have severe negative consequences. According to World Meteorological Organization (WMO) report, wind chill factor brings down the actual minimum temperature that depends on wind speed. The actual minimum temperature of a station is reduced to “wind chill effective minimum temperature (WCTn)” based on wind chill factor. For declaring cold wave, IMD uses WCTn indicator and If WCTn is 10°C or less, then only the conditions for cold wave are considered. Criteria for cold waves as defined by IMD:

a) When normal minimum temperature is equal to 10°C or more.

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● Cold wave departure from normal is -5°C to -6°C whereas Severe cold wave departure from normal is -7°C or less

b) When normal minimum temperature is less than 10°C.● Cold wave departure from normal is -4°C to -5°C whereas Severe cold wave departure from

normal is -6°C or lessc) When WCTn is 0°C or less, cold wave is declared irrespective of normal minimum temperature

of the station. However, this criteria is not applicable for those stations whose normal minimum temperature is below 0°C.

In India, western disturbances cause cold waves mainly affect the areas to the north of 20° but in association with large amplitude troughs, cold wave conditions are reported in different parts of the country except Southern India. In the last century (1901-1999), the maximum number of cold waves occurred over Jammu & Kashmir (JK), Rajasthan, Uttar Pradesh, and Madhya Pradesh whereas the minimum number of heat waves reported in Assam and Andhra Pradesh (De et al., 2005). In terms of causalities, highest number reported from Uttar Pradesh and Bihar during the period 1978-99 due to lack of shelters to the workers and homeless people (De and Sinha Ray, 2000). People lacking shelter are the most vulnerable and IFRC (2013) reported that about 78 million people are homeless and are unprepared to cope with the extreme cold weather conditions.

CASE STUDIES: USING GEOSPATIAL APPROACHES Naturally occurring hydro-meteorological disasters are occurred across all the continents and myriad of examples can be archived. In this section, we present some of the case studies of hydro-meteorological disasters which have been assessed using the geospatial approaches (remote sensing and GIS). Five case studies are selected over India where flood, drought and tropical cyclones are commonly noticed. These case studies serve as to illustrate the nature of the damage and vulnerable areas in Southeast Asia. Two cases studies on floods were presented that includes multi-temporal analysis of Daya river floods in 2003, Odisha and the latest cloudburst (flash flood) in June 2013 in Uttarakhand state and the analyses were performed based on satellite imagery and rainfall estimates. Two cases studies on drought were discussed from India wherein one represents western part of India and another represents Eastern part of India.

Flood inundation mapping and Flash Flood Assessment Using Space Technologies In India, floods occur during southwest monsoon (June to September) because 75% of the annual rainfall is received during these four months. As a result, majority of the rivers carry heavy discharge during this period leading to widespread flood. As per Flood Control Programme set up by the Planning Commission in 2004, about 45 million hectares of land area (or 13.6 % of land) is vulnerable to floods. According to National Flood Commission, the average annual loss of land and crop area affected is about 8 million hectares and 3.7 million hectares, respectively. The most flood-prone areas are the Brahmaputra, Ganga and Meghana River basins in the Indo-Gangetic-Brahmaputra plains that account for 60% of the nation's total river flow. Floods are a recurring phenomenon during the rainy season in states viz. Assam, Bihar, Odisha, Uttar Pradesh, West Bengal, Haryana and Punjab. Floods have also occurred in recent years in areas that are normally not flood prone and attributed to climate change (e.g. Rajasthan, Uttarakhand, JK). Floods have been causing loss of lives and public property, especially in the rural areas leading to adverse economic impact.

Here, we presented one case study on mapping the Daya Flooding event, 2003 in Odisha and another one on satellite rainfall estimates of June 2013- Cloudbrust disaster. Case study-1 demonstrates the potential of space borne satellite dataset (i.e. multi-sensors optical and microwave data) and multi-temporal satellite data for capturing and analyzing the inundation extent. Case study-2 provides an overview on the

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utilization of TRMM-3B43 (Satellite Based Rainfall Estimates data) for visualization and investigation of the June 2013- Cloudbrust disaster over the northern part of the Uttarakhand and adjourning Himachal Pradesh in India.

Case study-1. Inundation mapping of Daya Flooding event, 2003 using multi-sensor and multi-temporal satellite imagery, Odisha The study site of Daya River flooding event is located at Puri district in Mahanadi river system (Figure 2). The Mahanadi River delta plain covers 0.9 x 104 km2 and lies between 85°40′- 86°45′ E longitudes and 19°40′-20°35′ N latitudes. Catchments area covers about 1.42 x 105 km2 and the climatic setting is tropical with hot and humid monsoonal climate. The average annual rainfall of the area is 1572 mm and over 70% of rainfall observed during monsoon season (Mohanti, 2001). This region is highly susceptible to natural calamities like flooding, cyclonic storm, which causes adverse effects on economy and society.

INSERT FIGURE 2

Figure 2. A) Mahanadi river network B) embankment breach as observed in IRS-PAN (5.8m spatial resolution) and C) actual field photograph.

To analyse the 2003-flooding phenomenon, which took place during 28 th August 2003 to 20th September 2003, multi-sensor (optical and microwave data) and multi-temporal satellite data before and during the flooding period were acquired. Remotely sensed data that are used are IRS-1C/1D LISS-III (23.5m), IRS-Panchromatic (5.8m) and RADARSAT SAR (50m, 100m) of different dates. IRS -1C/1D LISS III data dated 16th January 2003 (pre-flood) has 4 bands with a spatial resolution of 23.5 meter and temporal resolution of 24 days is used for extraction of permanent water bodies from the study area. Details sensor characteristics and RADARSAT data processing can be obtained from Oinam (2011). The inundation pattern extracted from various multi-temporal datasets by visual interpretation and digital techniques of classification algorithms such as supervised, unsupervised, thresholding/density slicing, Textural analysis and Principal component analysis based classification. Interested readers can refer to Oinam (2011) for details description about various techniques for flood inundation extent mapping.

For studies related to dynamic phenomenon of flood, it is very important to know the lag time between the satellite acquisition and the day when the real phenomenon took place. To determine the maximum inundation extent, it is required to acquire the satellite data corresponding to the exact date of peak flooding observed in the field, which in reality is not possible due to temporal constraint as well as clouds. Examining the time shift between the highest flood levels as measured in the field and the acquisition date of satellite dataset used in the study, it is found that the acquisition of the satellite data i.e. both the microwave and optical data has some delay in regards to flood peak. This shift in time can be observed from gauging sites. Each gauging site has recorded a different local peak flood depending upon their location with respect to the pattern of inundation extent at various locations. Here an illustration is provided to show the time shift between highest flood level and the flood situation registered by the satellite data at Kanas Gauging site (Figure 3).

INSERT FIGURE 3

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Figure 3. Time shift between highest flood level and the flood situation registered by the satellite at Kanas gauging site (20° 06’ 27” N and 85° 38’ 27” E). Multi-temporal RADARSAT imagery of 4 th, 11th, 13th and 20th September 2003 were acquired to monitor the flooding extent and analyze the inundation pattern.

The extent of inundation from RADARSAT images (50 m resolution) was computed by visual interpretation and inundation area during 4th, 11th, 13th and 20th September are estimated as 42.8, 50.3, 38.9 and 36.0%, wherein maximum and minimum flood extent is observed on 11 th and 20th September, respectively. Flood evolution map demonstrates dynamics of flooding situation -the decrease and increase pattern of flooding boundaries (Figure 4) and generated from above mentioned four multi-temporal RADARSAT images depending upon the number of days the flood water get stagnated.

INSERT FIGURE 4

Figure 4. Flood evolution map depicting the propagation of flooding pattern derived from multi-temporal RADARSAT imagery.

Table 2 shows inundated area at different dates and it has seen that in all case, the maximum inundation extent was on 11th September whereas 20th September exhibited lowest flood extent that indicates the decreasing trend in flooding from 11th to 20th September, 2003. Besides visualization method, digital classification methods such as thresholding, unsupervised, supervised, PCA and textural were explored for comparisons of areal extent. It is noted that there is an appreciable changes in areal extent given by each techniques and this is due to the fact that different technique follows a different algorithm to process the extraction of flooded area. The detail comparisons of inundated area (%) and spatial extent are given in Oinam (2011).

Table 2. Comparison of inundation extent by visual interpretation of optical and RADARSAT images. Total areal extent is 116008 hectares (1160.08 km2). Sl.No. Data Used Date of

acquisitionVisual

InterpretationInundation extent

in km2Inundation

(%)OPTICAL DATASET

(A)1 Panchromatic (5.8 m) 8-09-2003 575.04 49.572 Pan sharpened LISS-III (5.8 m) 8-09-2003 564.80 48.683 IRS-1C (LISS-III,23.5m) 8-09-2003 575.03 49.75RAD

ARSAT (B)(Scan

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SAR Narro

w) 50m dB

value (backscatter coefficient)

4 RADARSAT 4-09-2003 496.28

5 11-09-2003 583.61

6 13-09-2003 450.86

7 20-09-2003 418.00

RADARSAT (C)(Scan SAR

Narrow)

100m DN

value8 RADARSAT 4-09-2003 614.60

9 11-09-2003 650.00

10 13-09-2003 520.95

11 20-09-2003 501.44

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This case study highlights the potential and capability of extracting the maximum relative flood extent using remotely sensed multi-sensor and multi-date dataset. For flood inundation mapping, digital methods of flood mapping are found to be quite useful for site specific studies comparing with time consuming visual interpretation. For dynamic flood events, more multi-temporal and time series dataset should be incorporated to understand the real dynamic and the pattern of inundation. Overall, it can be infer that spatial resolution of the data and the inundation mapping techniques has a profound effect on the inundation extent extraction. The choice of techniques to be adopted is largely governed by the type (optical/microwave), quality and availability of remotely sensed datasets. Nevertheless, ground information and the experience of the analyst have a role to play for accurate mapping the inundation extent. Regardless of the type of data and methods used, a practical oriented approach towards quick monitoring and mapping the inundation extent is very much essential for flood disaster management and preparedness activities by different local authorities and government.

Case study-2. Assessment of the June 2013- Cloudburst disaster, Uttarakhand, IndiaUttarakhand state located in Northern part of India and it lies on the south slope of the Himalaya range, and the climate and vegetation vary greatly with elevation, from glaciers at the highest elevations to subtropical forests at the lower elevations. It is rich in natural resources especially water and forests with many glaciers, rivers, dense forests and snow-clad mountain peaks (Directorate of Economics and Statistics, 2013). In the month of June 2013, northern part of Uttarakhand received intense rainfall which was about 375% more than the normal rainfall during monsoon season. It was a multiday cloud burst which started from 14th till 17th of June 2013. Due to this, sudden and massive flash flood along with landslides and mudflow occurred in various part of Uttarakhand and greatly affected districts were Uttarkashi, Rudraprayag, Chamoli and Pithoragarh (Das, 2013). This disaster is quoted as “The Himalayan Tsunami” due to its widespread and massive destruction with about 5,700 fatalities and destruction of buildings and properties. This case study demonstrates the capability of Earth Observation satellite based information i.e. the usage of pre and post disaster Satellite Based Rainfall Estimates (SBRE) data for analyzing the situation in affected districts. Calibration of satellite based rainfall estimation methods are reported by various studies (Artan et al., 2007) that suggests SBRE are more reliable in tropical regions because precipitation is usually associated with deep convection (McCollum et al., 2000).

Analyses and visualizations of TRMM data were produced with the Giovanni online system, developed and maintained by the NASA. Using the pre and post flooded TRMM data (3B42_v7, daily products) over northern part of India, the spatial distribution and temporal trend of rainfall were analyzed. TRMM data exhibits an increase in precipitation over a very short period of time (daily time step). Figure 5 shows heavy rainfall which starts from 14th June 2013; peak on 17th June 2013 and then fall from 18th of June 2013.

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INSERT FIGURE 5

Figure 5. Area averaged times series of TRMM_3B42 daily precipitation rate in mm during 14 th to 22nd

June, 2013 across 73.0371° to 83.4082° E and 25.4619° to 35.6572° N, Uttarakhand.

Maps depicting the spatial and temporal pattern of precipitation distribution over Uttarakhand on 14 th, 15th, 16th and 20th June 2013 (Figure 6. A to D). These maps clearly indicate the trend of increase in precipitation from 14th June and decrease from 20th June 2013. A time averaged precipitation map for the northern part of Uttarakhand (from 15th till 17th June) is provided for visualization (Figure 7). It was found that most part of Uttarakhand, especially the northern part and its neighboring states like Himachal Pradesh and Uttar Pradesh received a heavy rainfall from 15th till 17th of June 2013.

INSERT FIGURE 6

Figure 6. Spatial pattern of daily precipitation rate (0.25 degree spatial resolution) over the northern region of Uttarakhand.

INSERT FIGURE 7

Figure 7. Spatial pattern of time averaged daily precipitation rate (0.25 degree) over the northern region of Uttarakhand from 15th to 17th June 2013.

Due to cloudburst associated with heavy rainfall between 14 th and 15th June 2013, it is reported that the lake situated in Chaurabari glacier above Kedernath village in Rudra Prayag district of Uttarkhand bursts causing flash floods and landslides. This led to heavy destruction and inundation of all the villages downstream (Figure 8). From the flood damage extent, it was observed that 4 districts namely, Chamoli, Rudraprayag, Uttarkashi and Pithoragarh were worst affected. It is reported by various NGOs and relief organization (United Nation Disaster Management Team) working in this worst hit areas that total number of villages affected in these districts were: Chamoli (1166 districts), Rudraprayag (658 districts), Uttarkashi (682 districts) and Pithoragarh (1,579 districts).

INSERT FIGURE 8

Figure. 8 Depicts the worst affected districts by June 2013 -Cloudburst and showing the location of lake at Chaurabari glacier above Kedernath village which triggered cloudburst.

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From the topographic profile of the region, it can be clearly observed that the lake (located in Chaurabari glacier) which eventually bursts is located at higher elevation causing the excess flooded water to inundate the lower plain areas in the downstream region. It is to be noted here that Kedernath village (well-known tourist and religious pilgrimage site) just below the Chaurabari glacier, completely get washed away due to flash floods.

From the scale of the worst hit areas, property damage and loss of human life, this event can be categories as a multi disasters (triggering multiple events like flooding, landslides, mudflow etc.), and it need multiple quick responses for evacuation and mitigation purposes. Various NGOs and International relief organizations is still working and helping the affected people of the region to rebuild their communities that get destroyed and displaced by this uneventful disasters. Nevertheless, it become a lesson for all to equip and trained the communities and local people to have knowledge on disaster management and mitigations. From this assessment, it is seen that in areas of limited ground rainfall stations and inaccessible regions, information from satellite based rainfall estimates provides firsthand information on flood related disasters and its mitigations (indirectly facilitates rescue and relief operations).

Particularly, flood disaster management provides timely information on the spatial extent of the affected flood area for taking decisions and actions in the form of a map. This spatial information needs to be updated in real-time for the successful execution of the operations. In this regard, satellite remote sensing data provides crucial information on spatial flood extent and flood damage. For effective flood management, disaster manager requires information at different phases of the flood disaster cycle as preparation phase, during floods and during mitigation phase (Bhanumurthy, 2010). During preparation phase, satellite imagery are used to provide the chronically flood prone areas in the form of a map showing severely affected, occasionally affected, etc. Hydrological model can be used to provide probable flood affected area. In GIS environment, optimum evacuation plans can be generated for carrying out rescue operations. During flood phase, flood damage assessment are performed that provides statistics like district-wise flood affected area, submerged crop, marooned villages and length of submerged road/rail. Further, it helps decision makers for providing relief and rehabilitation. During mitigation phase, satellite data are used for mapping flood control works, changes in the river configuration, and studies on bank erosion/deposition. It is also possible to demarcate the drainage congestion areas in the chronic flood prone areas. Floodplain zoning and risk maps can be created using multiple satellite data as a planning of future flood control works. National Remote Sensing Centre (NRSC) has been extensively using IRS satellites and microwave data from Canadian satellite RADARSAT for flood mapping and monitoring in near real-time basis and estimate the flood damages across India.

Drought Monitoring and Assessment Using Space Technologies On the basis of nature and severity of the impacts droughts are classified as meteorological, hydrological, agricultural, socio-economic and ecological drought. Here, agricultural drought is given a prime focus which refers to a situation in which the moisture in the soil is no longer sufficient to meet the needs of the crops growing in the area. Based on time of occurrence of drought and general climatic conditions, the agricultural drought is characterized into five distinct categories in Southeast Asia. This is classified as early season, mid season, late season drought, apparent and permanent drought.

Drought commonly leads to substantial impacts, which spans many sectors of the economy, especially the agriculture sector. Drought leads to decline in food grain production depending upon the intensity, duration and spatial coverage of drought stress. It was reported that about 68% of the area in India is prone to drought and most of the areas are vulnerable under recurring drought. Furthermore, these drought-prone areas are mainly confined to western and peninsular India –primarily arid, semi-arid and sub-humid regions. Recently, India has faced worst drought episode in terms of magnitude, spacing,

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dispersion and duration and has impacts on human and livestock to economic losses. Thus, monitoring drought is of utmost concern to planner or decision makers from view point of food security and trade. In India, the point based meteorological drought indices have been extensively used for drought monitoring. But sparse meteorological network and lack of timely availability of weather data always hinders the accurate and timely monitoring of regional drought.

Innovations in remote sensing technology have provided newer dimensions of spatial solutions to many environmental problems, including natural hazard monitoring. Satellite remote sensing has become crucial particularly for timely detection and monitoring of drought due to more prompt availability of spatio-temporal data over entire globe. The most commonly used Normalized Difference Vegetation Index (NDVI) from remote sensing often fall short in real time drought monitoring (Park et al., 2004). This index cannot detect drought events instantaneously because of a lagged vegetation response to drought (Park et al., 2004). On the other hand, surface temperature (Ts) is sensitive to water stress and has been identified as good indicator of water stress (Jackson et al., 1981). Thus, accurate and real time drought monitoring needs combination of the thermal and visible/near infrared wavelength to provide information on vegetation and moisture condition simultaneously. The scatter plot of Ts-NDVI space based on remotely sensed temperature and spectral vegetation index often exhibits a triangular or trapezoidal (Moran et al., 1994) shape if a full range of fractional vegetation cover and soil moisture content is represented. Ts-NDVI space has been widely exploited to derive various types of hydrological information such as air temperature, evapotranspiration and soil moisture. Drought stress effects on agriculture are closely linked to actual evapotranspiration by crop canopies throughout the growth period. Therefore, drought index which is closely related to crop water status holds a key place in drought monitoring. Recently, many drought indices like Temperature Vegetation Dryness Index (TVDI), Vegetation Temperature Condition Index (VTCI), Water Deficit Index (WDI), and the Crop Water Stress Index (CWSI) have been defined for quantification and real-time monitoring of the spatial extent and magnitude of drought. Hence, looking into enormous potential of these indices, the present study was undertaken to investigate the potential of VTCI and TVDI from Terra/MODIS satellites for assessment of agricultural drought in semi-arid regions of India as well as Northeast region of India.

Case study-3. Agricultural drought monitoring using MODIS derived drought index VTCI over Gujarat state, Western India The study was carried out in Gujarat state, situated between 20o 01’ to 24o 07’ N latitudes and 68o 04’ to 74o 04’ E longitudes. Agriculture is the dominant land use in the state and it accounts for ~50%. In particular, food crops (i.e. cereals and pulses) account for about 50% of the total cropped area and the remaining area is oil seed, fibre, and fodder crops. A drought index “VTCI” is computed using the algorithm developed by Wan et al. (2004) based on the interpretation of a simplified Ts-NDVI space that represents “warm edge” (water stress restriction) and “cold edge” (no water limitation). This index is derived using the 8-day composite MODIS satellite images, specifically land surface temperature products (MOD11A2) and band 1 (red) and band 2 (near infrared) reflectance data (MOD09A1). NDVI was computed using red and infrared bands. The computed index VTCI generally ranges from 0 (stress condition) to 1 (favorable condition).

INSERT FIGURE 9

Figure 9. VTCI spatial pattern map for the year 2002 (drought year).

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Drought in 2002 showed lower VTCI values (less than 0.5) across the state as compared to normal years (Figure 9). During normal years in 2003 and 2004, VTCI values were more than 0.5 due to favorable weather conditions. Due to drought in 2002, Northern Gujarat, Central Gujarat, and Saurashtra regions are showing stress conditions with lower VTCI values as compared to other area. A few districts like Vadodara, Bharuch, and Normada in South Gujarat also exhibited lower VTCI values. Other southern districts such as Valsad, Navsari, Surat, Bharuch, Narmada, Vadodara had higher VTCI values (>0.75) and implied that these districts were less affected by drought stress. The higher VTCI values occurred in some areas even during drought years was mainly ascribed to the availability of irrigation facilities which tends to reduce moisture stress to the agricultural crops. Besides, few districts such as Valsad and Dang in extreme south are covered by forest. However, in 2003 and 2004, the entire state represents a balanced VTCI having value of more than 0.45. To ascertain the robustness of these results, a meteorological based drought index called “Crop Moisture Index” (CMI) was computed which was developed by Palmer (1968). It measures the short-term changes in moisture conditions of crops and gives current status of agricultural drought or moisture surplus, which can change rapidly from week to week. It is normally calculated with a weekly time step by using the field-based meteorological data like mean temperature, total precipitation for each week, and the CMI value from the previous week.

INSERT FIGURE 10

Figure 10. CMI plotted for the meteorological standard week between 27 to 43 weeks (i.e. 2nd July to 28th

October) representing kharif season (rainy season). The week no. 35 to 43 (i.e. early September to end of October) implies as late stage of the crop. AMD and RAJ represents two meteorological station located at Ahmedabad and Rajkot, respectively and CMI plotted for the year 2002 (red line) and 2004 (blue line).

The CMI curves suggest that in both the stations AMD and RAJ, CMI values are negative (drought stress) for the year 2002 starting from the beginning of the kharif season (Figure 10). By contrast, CMI values are positive (no drought stress) in the year 2004 throughout the kharif season. These results suggest that the CMI could detect the drought stress and can be used to confirm the results obtained from satellite based VTCI indicator showing the spatial coverage of drought stress.

INSERT FIGURE 11

Figure 11. Cotton production anomaly for Ahmedabad (primary axis) and Rajkot (secondary axis) districts for the period 1998-2011.

To assess the effect of 2002 drought on cotton production, we plotted production anomaly from the time series agriculture statistics obtained from Ministry of Agriculture, India. Figure 11 reveals that district level productions were adversely affected in the year 2002 in both the districts and production anomalies were 3.11x10-4 and 1.78 x10-5 tonnes in AMD and RAJ, respectively. In the state level, the cotton yield was 175 kg/ha during 2002 whereas it was 421 kg/ha during 2004.

The time series of food grain and oilseeds productivity (1981-2003) were linearly de-trended to remove the effect of improvements in crop science, technology. The de-trended yields were derived by

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subtracting the per year yield variation from historical record of food grain and oilseeds yields. The de-trended yield anomaly (DYa) is computed as:

DY ai=Y ai

Y ti−1(1)

Where, DYai is de-trended yield anomaly for the ith year, Yai and Yti are actual and 22 years time trend based yield (Yt = a + b × year) of ith year, respectively. The slope ‘b’ of this regression line for each district was used as an indicator of the overall trend in productivity. Table 3. Districts level de-trended food grains and oilseeds yield anomaly during 2000-03.

Food grains yield

anomaly

Oil seeds yield

anomaly

2000 2001 2002 2003 2000 2001 2002 2003Ahmedabad -46.31 48.36 -81.79 82.29 5.09 -35.94 -58.96 -48.46

Rajkot -16.89 84.01 -27.60 116.89 -11.46 -28.41 -30.59 1082.85State level

average-28.17 31.45 -11.20 43.85 -20.50 -36.22 -35.49 85.15

The de-trended yield anomaly of food grains and oilseeds were negative for the majority of districts (other districts not shown) in the state during drought years 2000 and 2002. Table 3 shows that the yield anomaly of AMD and RAJ districts for food grains was negative in drought years whereas positive in non-drought years 2003 and 2004. The average yield anomalies of the state for food grains were also negative in drought years -28.17 (2000), -11.20 (2002), respectively. Similarly, yield anomalies for oilseeds were negative in drought years. These negative anomalies provide an evidence of adverse effect of drought stress on crop productivity. The most affected districts in the state are Ahmedabad, Rajkot, Surendranagar, Banaskantha, Gandhinagar, Jamnagar, Kutchh, Kheda, Sabarkantha, Surat, Dangs, and Valsad.

Case study-4. Agricultural drought monitoring using MODIS derived drought index TVDI over Assam, Northeast India Assam state is located in the north-eastern part of India between 24°50'N to 28°00'N latitudes and 89°42'E to 96°00'E longitudes. It is surrounded on all other sides by predominantly hilly or mountainous tracts. The Brahmaputra River flows through the entire length of the State from east to west. Assam is mainly an agrarian state and ~89% of the people live in rural areas. A drought in the northeast of India is a contradiction in terms because this area is hit by a flood in nearly every year due to heavy rainfall. The scenario was just opposite in 2006 due to scanty rainfall in most of the districts in Assam. Compared to the mean average monsoon rains, Assam has received about 30% less rainfall in 2006. The cumulative rainfall during the monsoon in northeastern India has decreased significantly. District-wise distribution in the region also shows that a number of districts were seriously affected but some districts were hit badly, such as Jorhat, Morigaon, Nowgong, and Sonitpur in Assam. A severe drought-like situation affected 14 districts of Assam with the rainfed paddy fields going dry due to a deficit in monsoon rains and most of the irrigation systems lying defunct in the affected districts.

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To assess the drought in the year 2006, an empirical method called Temperature Vegetation Dryness Index (TVDI) has been used and derived from MODIS satellite data (Parida and Oinam, 2008). TVDI is a simple and effective method for regional drought monitoring. Spatial distribution of TVDI map shows that the upper Assam and central Assam districts were badly affected by the drought during 2006 (Figure 6 in Parida and Oinam, 2008). As a consequence it has a direct impact on agriculture crop yields in the state. The results based on the TVDI approach suggest that there are accurate geographical positions of drought areas where vegetation was mainly affected. Further, we analysed rice yield data (Ministry of Agriculture) from 1998 to 2011 for validation of drought stress in Assam state.

INSERT FIGURE 12

Figure 12. Districts level rice productions anomaly (in Tonnes) during 1998-2011. The mean production was 1.52x105, 1.78x105 tonnes for Nalburi and Dhubri, respectively.

Rice production anomaly plot (Figure 12) indicates that the worst affected districts such as Nalbari and Dhubri show a sharp decline in rice production during 2006 as compared to the normal years showing positive anomalies. Rice production anomalies were 4.71x10-4 and 3.29 x10-4 tonnes in Nalburi and Dhubri, respectively. These negative anomalies provide a confirmation of impact of drought on agriculture located in Northeast region of India. Thereby occurrence of intense drought in this region may have an adverse effect on food security under global climate change.

Monitoring and Warning of Tropical Cyclones Using Space Technologies In the tropical region, cyclones are identified as the most dangerous meteorological phenomena. Tropical cyclones are common in Indian sub-continent region having a coast line of 7516 kms covering 13 coastal states/UTs encompassing 84 coastal districts. Out of the 13 vulnerable states to cyclone disasters, East coast states (Andhra Pradesh, Odisha, Tamil Nadu and West Bengal) and one UT (Pondicherry) and one West coast state (Gujarat) are more vulnerable. The statistics of the cyclonic disturbances during 1997-2013 were shown in Table 4 Based on the 17 years data from RSMC (2013), it can be inferred that the total number of tropical cyclones varies from 2-6 events per year wherein the category CS occurred every year whereas SuCS indicates very rare events. Albeit cyclonic events are rare under SuCS, the damages were higher and in particular coastal states of India are more vulnerable to risk. In this context, case study-5 provides an illustration on nature of impact and differences between two severe cyclone in Odisha: Super cyclone 1999 and Phalin 2013.

Table 4. Tropical Cyclones formed over the north Indian Ocean during 1997-2013. Year CS SCS VSCS SuCS Total

1997 1 1 1 0 3

1998 1 2 3 0 6

1999 1 0 2 1 4

2000 2 0 2 0 4

2001 3 0 1 0 4

2002 2 1 0 0 3

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2003 0 2 1 0 3

2004 0 3 1 0 4

2005 4 0 0 0 4

2006 1 1 1 0 3

2007 2 0 1 1 4

2008 3 0 1 0 3

2009 3 1 0 0 4

2010 1 2 1 0 4

2011 1 0 1 0 2

2012 2 0 0 0 2

2013 1 1 3 0 5

Case study-5. Odisha Super cyclone 1999 and Phalin 2013 -Damages and Economic Impact The Odisha coast line is one of the longest coast lines with a length of 550 km spreading from Andhra Pradesh to West Bengal. Costal districts such as Ganjam, Khordha, Puri, Jagatsinghpur, and Kendrapada to Bay of Bengal facing the direction of flow of the storm are vulnerable to cyclones. Odisha coast has witnessed major cyclones in 1971, 1973, 1977, 1981, 1983, 1984, 1985, 1987, 1989, 1999 and 2013 (Patra et al., 2013). But the super-cyclone on October, 1999 was the strongest one with wind speed exceeding 250 km per hour (kmph) and a storm surge of 8 metre. This storm brought torrential downpours with average rainfall of ~550 mm (ranges from 400 to 950 mm) within 4 days has resulted in high flood particularly in Baitarani, Salandi, Budhabalanga, Kharasua, and Brahmani rivers. The super-cyclone (equivalent to a category 5 hurricane) in Odisha, recorded 10,000 fatalities (UNEP, 2002). Cyclone Phailin battered Odisha on October 12, 2013 at a speed of 220 kmph and it was the second-strongest tropical cyclone (equivalent to VSCS) to hit Odisha in 14 years (GoO, 2013). But there is a big difference with respect to number of fatalities between 1999 and 2013 cyclone and the later recorded only 44 fatalities (Table 5). The lower fatalities are mainly due to improved warning system and mass evacuations in 2013 as compared to 1999. According to GoO report (2013), ~1.2 million people were evacuated and ~30,000 animals were relocated, resulting in the largest evacuation operation in India in 23 years.

Cyclonic storms in India commonly accompanied with exceptionally heavy rains which led to devastating floods. The aftermath of cyclone Phailin and extensive flood have taken a heavy toll on tourism in the

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Odisha state (GoO, 2013). Flood-affected areas gave faced acute drinking water shortage. Around 1.6 million people from villages were affected by the floods triggered by incessant rain that followed the cyclone. The rivers Budhabalanga, Bansadhara, Baitarani, Bramahani, Kani and Rushikulya are in spate and the districts affected are Kendrapada, Balesore, Bhadrak, Ganjam, Keonjhar, and Mayurbhanj. Interestingly, these areas are far from where Phailin made landfall. About 0.66 million hectares (ha) of agricultural land, of which 0.5 million hectares had ripe paddy were severely affected. Economic losses across Odisha reported as 42.4 billion rupees ($688 million) and other major differences were shown in Table 5.

Table 5. Shows the difference between two severe cyclone in Odisha Damaging Indicators Super-cyclone 1999 Phailin 2013

Cost $2.5 billion $0.68 billion

Death toll 10,000 44

People affected 15.6 mill. 12.4 mill.

Houses damage 13 lakhs 2.3 lakhs

Agriculture area affected 1.84 mill. ha 0.66 mill. ha

Storm surge 8 m 2-2.5 m

District affected 14 17

Rainfall during storm 550 mm (400-995)* 232-381 mm

People evacuated 0.85 mill 1.2 mill

Early warning 2 days 4 days

* average value of 22 stations and ranges from 400-995 mm (Kalsi, 2006)

Over three decades, satellite imagery in the range of visible, infrared and microwave regions has been used comprehensively for monitoring and forecasting of development of tropical cyclones. Studies have shown the capability of satellite data to track cyclonic “eye” formation to its increasing diameter with time (Kalsi, 2006). In India, IMD provides advance warning of cyclones for timely actions and to minimize loss of life and property. It has the capabilities to forecasts cyclonic storms accurately. Nevertheless, due to lack of preparedness damages are higher in 1999 but with better communication and adequate preparation, impacts of disasters have been alleviated in 2013. Further, lessons learned from Cyclone 05B and government cooperation and community level preparedness contributed to the successful evacuation operation, effective preparation activities and impact mitigation. To deal with Cyclone 05B, government established “Odisha State Disaster Management Authority – OSDMA” in 1999, the first state agency to address disaster management. GoO has also established Orissa Disaster Rapid Action Force (ODRAF) to assist the civil Administration at the time of calamities and help in management of disasters. Under OSDMA initiative, more than 203 multipurpose cyclone shelters were constructed and operating in places such as schools and community centers. Further, 65 cyclone shelters were constructed by Indian Red Cross Society (IRCS) in coastal districts.

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IMD has a surface observatories network and Automated Weather Stations (AWS) which provide hourly basis data for monitoring and forecasting cyclones. IMD also analyses Indian satellite images from Kalpana-1, INSAT-3A, and Oceansat-II for monitoring cyclonic storm. IMD is the nodal agency for issuing early warnings for cyclones. Modernization has been undertaken after super cyclone 1999 for correct and timely early warning. The early warning starts with formation of depression until its movement, speed, direction and likely areas of land-fall. National Disaster Management Authority (NDMA) has a provision to place personnel of the National Disaster Response Force (NDRF) at the vulnerable places as a proactive measure. The NDRF personnel are highly trained exclusively in disaster management and have the state of the art equipment to help the district administration in evacuation of population. Due to the prevention, mitigation and preparedness measures undertaken by the various stakeholders such as State Governments/UTs, IMD, NDMA, NGOs, the loss of life was restricted to 44 during 2013 event as compared to 10,000 casualties in 1999. The efforts of communities to protect mangroves in the coastal belt also have played a major role in bringing down the damage due to cyclonic storms. Furthermore, the severity of cyclone Phailin has triggered speculations about whether it is linked to climate change. But the impact of climate change on intensity and frequency of cyclones is not well understood. Nonetheless, it has been observed that there is an increase in numbers and proportion of tropical cyclones and hurricanes of the category 4 and 5 globally.

HYDRO-METEOROLOGICAL HAZARDS UNDER CLIMATE CHANGE AND IMPACTS Since industrial revolution, atmospheric concentration of greenhouse gases, particularly carbon dioxide has increased significantly. The annual mean temperatures are expected to further increase by 1.3–3.5°C in Southern Asia (Christensen et al., 2013). There is a high confidence that projected rising temperatures and the resultant warming lead to more intense patterns of rainfall, droughts, desertification, extreme weather conditions and more persistent inundations (IPCC, 2013). There is medium confidence that Indian summer monsoon precipitation is projected to increase and its extremes will be the largest among all monsoons (Christensen et al., 2013). Enhanced warming and increased summer season rainfall variability due to ENSO also will increase. The communities living in this region would be impacted by increase in tropical cyclone intensity and magnitude and that would cause heavy damage to crops, uprooting of trees, loss of lives and houses. Projected increase in extreme precipitation near the centers of tropical cyclones will make landfall along coasts of Bay of Bengal and Arabian Sea which suggest vulnerability and risk is highest in coastal districts. Moreover, this region would be impacted by increase in floods, flash floods, cloudbursts, mud flows, and avalanches in the short-term time scale (IPCC, 2007). Coastal areas, especially heavily populated regions in Southern Asia will be at greatest risk due to increased flooding from the sea and in some regions flooding from the rivers. Due to projected changes in the hydrological cycle, endemic diseases primarily associated with floods and prolonged droughts are expected to rise in Southern Asia. It is likely that area affected by drought and land degradation increases which will lead to lower yields by crop damage/failure, increased livestock deaths, increased risk of wildfire, etc. It is very likely increase in frequency of hot extremes, heat waves and heavy precipitation. Global climate change and associated hydro-meteorological hazards viz. droughts, land degradation, floods, storms, and extreme weather conditions have enormous impacts on socio-economic systems that could derail the process of social and economic development. Climate variability and climate change may intensify the severity of risks, as it can alter the frequency, magnitude, and complexity of climate hazards.

EARLY WARNING SYSTEMS FOR HYDRO-METEOROLOGICAL HAZARDS It has been recognized that events of hydro-meteorological hazards such as tropical cyclones, flood, drought, land degradation are associated with climate change and variability, so Early Warning Systems

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(EWS) are an important means to decrease disaster losses. EWS helps to alleviate damage effectively caused by natural disasters due to better monitoring and forecasting of hydro-meteorological hazards and more effective emergency preparedness. But especially in South East Asia, damages are significantly larger since people and infrastructure in these regions are more vulnerable and exposed to higher risks. EWS include understanding the mechanisms and procedures for the prediction, forecasting, monitoring in response to global climate change. Further, designing EWS for hydro-meteorological hazards include timely planning for disaster preparedness, emergency management and social response with respect to early warning. Developing tools to integrate emerging new generation of climate prediction systems are also vital for anticipated disaster risks associated with climate change and variability. Implementation of EWS at community level can effectively contribute to the risk management process and risk reduction. According to WMO, effective EWS have four components and coordinated across many agencies at national to local levels:

a) detection, monitoring and forecasting the hazards;b) analyses of risks involved;c) dissemination of timely warnings - which should carry the authority of government;d) activation of emergency plans to prepare and respond.

Failure in one component or improper coordination among agencies could lead to the failure of the EWS. The warnings of any hazards via EWS are a national responsibility and thus responsibilities lies with the various stakeholders for implementation of EWS. Tropical cyclones warning are provided by IMD and it has developed the necessary infrastructure. A satellite based communication system called "Cyclone Warning Dissemination System (CWDS)" is already operational in India. The CWDS network covers 352 stations spread over Indian coast. RMSC, New Delhi is also operating under the World Weather Watch Program of WMO. The first stage warning called "Cyclone Alert" issued 2 days in advance and in the second stage "Cyclones warning" is issued in 1 day advance (Charabi, 2010). Warnings are issued to public in general, and the administration responsible for disaster mitigation and relief. Flood forecasting and warning system plays major role in reducing the loss of life and property during floods. The “National Flood Forecasting and Warning Network” of Central Water Commission (CWC) has 175 flood forecasting stations on 71 interstate river sub-basins. Depending upon the severity and magnitudes of floods, the CWC has categorized four categories of flood situations such as Low, moderate, high and unprecedented floods (CWC, 2010). In high and unprecedented flood Situations, a special “Orange Bulletin” and “Red Bulletin” is issued, respectively by the CWC which contains the “special flood message”. The data of the river gauges and the rainfall are transmitted to the flood forecasting centers from all the stations. Based on these data and the historical correlation curves, the forecasts are issued to the concerned authorities for taking appropriate measures.

International Water Management Institute (IWMI) has developed a drought monitoring system in Southeast Asia that covers western India, Afghanistan and Pakistan. It uses vegetation indices from remote sensing data to monitor the health of vegetation. Further, IWMI supported by Global Water Partnership (GWP) and the WMO is developing a prototype model for a South Asian Drought Monitoring System (SADMS) to meet the requirement for a high spatial resolution drought affected areas and to support the diverse needs of decision makers for preventive actions (GWP, 2014). This monitor system has the potential to have information on early warning against drought which integrates the land surface characteristics (e.g. water and thermal), vegetation growth conditions, and biophysical information. The system uses meteorological data, vegetation canopy indicators from satellite imagery and ground moisture and crop-yield data. In India, Ministry of Agriculture (MoA) established the Crop Weather Watch Group (CWWG) for National Early Warning System that has two components such as drought forecasting and drought monitoring. CWWG monitors the impact of the monsoon on agricultural activities and suggests remedial measures to minimize crop losses against drought. IMD also has a drought early warning system which provides drought outlook maps. National Agricultural Drought Assessment and Monitoring System

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(NADAMS) operated by the NRSC uses satellite based indices for drought impact assessment and are in process for developing drought early warning system (GWP, 2014). NADAMS uses broad range of satellite data including the latest sensor Advanced Wide Field Sensor (AWiFS) of RESOURCESAT-1 and has demonstrated the operational validity of drought EWS in drought affects states like Andhra Pradesh, Haryana, Karnataka and Maharashtra.

HAZARD RISK ASSESSMENT AND POST-DISASTER RESPONSESHazard risk assessment comprises quantification of risk by understanding hazard, vulnerabilities and exposure patterns. This knowledge is critical for development of strategies and measures for reducing the risks associated with the disaster. An important requirement for risk assessment is the availability of historical and real-time data that includes satellite data, hydro-meteorological data, socio-economic data, and forecast products etc. This must be complemented with vulnerability and exposure information, tools and methodologies for hazard analysis, risk mapping, assessment and modeling. Post disaster losses and damage data provides information on number of fatalities, affected people, and damage to physical assets and these serve as input for estimating loss and damage associated with the hazards. Risk assessments or risk mapping comprise characteristics of hazards such as location, intensity, frequency and probability. The activities include analysis of exposure and vulnerability arising from various physical, social, economic and environmental dimensions and the estimation of the effectiveness of prevailing and alternative coping capacities under risk scenarios. Hazard risk assessment evaluates the level of risk imposed by the hazard sources, for instance flood risk assessment. Therefore, the risk assessment is being benefitted the decision-maker with an appropriate understanding of the relationship between the actions proposed to be taken and the resultant reductions in risk.

The conventional approach to disasters in Southeast regions has been conducted on responding to events and reconstructing damaged assets after the disasters. The response of the major stakeholders of these regions are not proactive that resulted in more casualties and higher economic losses. However, if the vulnerability regions and assets had been understood and addressed through various preventative measures, the losses underwent over time would have been lower. Increase in awareness and understanding of disaster risk are leading to take a proactive approach to managing disaster risk. Governments at state and national levels have identified disaster risk management as a mainstream agenda for planning and policy making. Uncertainty is a key component of risk assessment analysis cycle arises from uncertainty in the data and models used to assess risk. Further, uncertainty about the future climate and demographic conditions also prevents decisions for effectiveness preparedness toward disaster and strategic planning.

Southeast Asia countries are at different stages of development with respect to disaster reduction and preparedness. India is evolving for effective post-disaster management operations and has also formulated and implemented pre-disaster mitigation and development programmes to reduce the impact of disasters and to alleviate the socio-economic vulnerabilities. The reconstruction programmes in the aftermath of disasters aimed at building disaster resistant structures to withstand the impact of natural hazards in the future. Structural methods of flood mitigation include construction of multi-purpose dams and reservoirs to mitigate the impact of disasters across the country in the long-run. To deal with recurring tropical cyclones measures such as building of cyclone shelters, afforestation in coastal areas, etc. have been also undertaken across the vulnerable states in the country. To mitigate the impact of drought a number of programmes have been launched such as Drought Prone Area Programme (DPAP), Desert Development Programme (DDP), National Watershed Development Project for Rainfed Areas (NWDPRA), Watershed Development Programme for Shifting Cultivation (WDPSC), Integrated Water Development Project (IWDP), Integrated Afforestation and Eco-development Project Scheme (IAEPS).

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As per Disaster Management Act, 2005, the stakeholder or agencies designated to mitigate hydro-metrological hazards related losses in India are National Disaster Management Authority (NDMA), State Disaster Management Authorities (SDMAs), District Disaster Management Authorities (DDMAs), National Institute of Disaster Management (NIDM), National Disaster Response Force (NDRF), etc. Besides, externally aided schemes include GoI-UNDP Disaster Risk Reduction Programme, GoI-USAID Disaster Management Support Project, National Cyclone Risk Mitigation Project (GoI, 2011). Forecasting about climate change is vital for taking preparedness measure and it is the most important element of disaster management. GoI has identified the nodal agencies for early warning of different hydro-metrological hazards and these agencies are IMD, CWC and responsible for forecasting rainfall, forecasting and warning of cyclones, and flood forecasting. The Disaster Management Support (DMS) Programme of Indian Space Research Organization (ISRO) also provides timely support and services towards efficient management of disasters in the country by providing imaging and communications platform. The DMS programme addresses disasters such as cyclone, flood, drought, extreme weather events waves, forest fire, landslide and Earthquake. These comprise creation of digital georeferenced data and a GIS based repository of data for enabling hazard zonation, damage assessment, etc. It enables monitoring of drought using satellite imagery, development of appropriate techniques and tools for decision support system (DSS), early warning of disasters and establishing satellite based communication network for monitoring disasters.

CONCLUSIONS Disasters caused by natural hazards have no boundaries between countries and they are a burden for socio-economic growth since it damages enormous economy and environment. Unlike manmade disasters, natural hydro-meteorological hazards like floods, droughts, and cyclones cannot be avoided which makes difficult to respond quickly. However, with mitigation measures and proper planning of developmental work in the vulnerable area, these hazards can be prevented from turning into disasters. Therefore, it requires transnational solutions and an effective framework for cooperation by strengthening the resources and developing early warning systems. If governments want to respond rapidly to manage disasters effectively then they need timely and reliable data. Without reliable data, effective disaster monitoring and mapping is difficult and further the governments and agencies cannot locate and assist the poor and most vulnerable people in the society. The GIS repository database is an effective tool to access information in terms of crucial parameters for the disaster affected areas. It includes location of the public facilities, communication links and transportation network at national, state and district levels. The GIS database already in place with different nodal agencies of the government and is being under upgradation. The database provides multilayered maps and GIS maps in conjunction with the satellite images for a particular area which can enable the district administration as well as State governments to accomplish hazard zonation mapping and vulnerability assessment, as well as to coordinate post-disaster response.

Despite the efforts so far made there are still shortcoming which needs to establish by governments and policy makers. To address the critical gaps, NDMA identified following shortcoming (NDMA, 2010):

● inadequate accountability because of ad-hoc nature of arrangements and no prior training for effective performance;

● deficiencies and limited use of proper communication plan, inefficient use of available resources;

● lack of predetermined approach to effectively integrate inter-agency requirements into the disaster management structures and planning process;

● inadequate coordination between the first responders and individuals, professionals and NGOs with specialized skills during the response phase; and

● lack of insurance policies for poor people and inadequate support systems for affected communities and

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● lack of use of common terminology for different resources resulting in inappropriate resource mobilization etc.

Different measures and activities have been adopted in India to reduce the flood losses and protect the flood plains. The structural measures for flood control bring relief to the flood prone areas by reducing flood flows. Besides, non-structural mitigation actions using natural mechanisms also play a crucial role to alleviate the damage. For instance, wetlands reduce floods and at the same time it maintains the ground level water storage which can alleviate the sever scarcity of water during drought events. As per National Water Policy (2002 and 2012), it emphasized non-structural measures such as flood forecasting and early warning, flood proofing, and enforcing flood plain zoning regulation by discouraging creation of valuable assets or settlement of the people in the areas of frequent flooding. These measures were taken for the minimization of losses and to reduce the recurring expenditure on flood relief. The Brahmaputra and the Koshi basins are prone to the impacts of climate change. Glacier melt as well as snowmelt due to climate change in the eastern Himalayas could lead to increased dry season runoff in the short term (Cruz et al., 2007) and may lead to increased flood frequency and magnitude. Further, extreme rainfall events in the upstream hilly areas may lead to overtopping and breach of embankments resulting in flash floods and devastation –a recent example is the “JK September 2014 unprecedented floods” triggered by heavy downpour from 2nd to 5th September. Unprecedented rains, mismanagement, unplanned urbanisation and a lack of preparedness are credited for the JK September 2014 floods but importantly one cannot winnowed out link to climate change that may be attributed to interaction between western disturbances and the monsoon system. Thereby, the observed and anticipated effects of climate change on flooding must be accounted in the flood management plans. GoI has launched "Flood Management Programme" during XII five year plan for undertaking critical river management and flood protection works including anti erosion, bank protection, restoration of existing works, drainage development, anti-sea erosion works (GoI and Planning Commission, 2011). In particular, despite these initiative and efforts are being made, what has been missing in reality is the control center showing vulnerable flood areas in the form of geospatial maps, evacuation routes, location of shelters, drinking water and food supply. Moreover, the existing communication networks, roads, and bridges are being dislocated due to flood and landslides. This demands a robust telecommunication system, evacuation routes and reachable shelters which can withstand natural disasters more effectively and to ensure they are not inundated in future.

The frequency, intensity, and duration of droughts are expected to rise under climate change, with an increasing human and economic toll and it may also lead to devastating food security impacts. However, effective drought management policies and frameworks are yet to formulate in most parts of South Asian countries. In this context, the WMO, the FAO and the UN Convention to Combat Desertification (UNCCD) and other partners have formulated a National Drought Policy in 2013 to focus on drought preparedness and management policies. Quantification of drought impacts and monitoring drought development becomes a critical issue. But the ability of governments and international relief agencies to deal with droughts is constrained by reliable data and lack of technical and institutional capacities. Desertification or land degradation is one of the region’s most serious problems and often related to poor land use practices and further drought can deepen the effect and extends of cultivable area. Declining vegetation covers due to drought stress may enhance soil erosion and can lead to an irreversible loss of nutrients and subsequently desertification. Hence, modification of agricultural and water policies in the drought-affected areas may require additional national level actions and measures to mitigate drought affected areas.

Over the last few decades, more extreme events and frequent droughts have led to devastating impacts on agriculture as well as food security. As climate change due to anthropogenic forcing continues, extreme weather events such as heat waves, cold waves are likely to become more common (IPCC, 2013), which demands further increasing the need for preparedness and early warning systems. Given the enormous burden of disasters, considerable effort has been made during the past decade to disaster preparedness,

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assessment and mitigation. Recently, National Institute of Disaster Management (NIDM), GoI also mandated under the Disaster Management Act, 2005 and initiated for conducting research, training, documentation and assisting the government in policy planning on all the aspects of disaster management. The preparedness and responses in the disaster management cycle are critical in reducing the impact of disasters. The involvement of multiple stakeholders and institutional nodal agencies should ensure the mitigation measures along with proper planning of developmental work in the risk prone area and a tangible development in handling the hydro-meteorological hazards. While significant achievements have been made in post-disaster response and reconstruction, there are still challenges to reducing the risk of future disasters as the frequency and intensity of flash floods, droughts, cyclones, and extreme weather events are expected to increase in upcoming decades. Thus, disaster management is becoming difficult due to mushrooming population and climate change and improving management and better preparedness become critical for disaster reduction.

REFERENCES Artan G., Gadain H., Smith J. L., Asante K., Bandaragoda C. J. and Verdin J. P. (2007). Adequacy of

satellite derived rainfall data for stream flow modelling. Natural Hazards, 43(2), 167-185. Bhanumurthy, V., Manjusree, P., & Rao, S. (2010). Remote Sensing Applications. In P. S. Roy & R. S.

Dwivedi (Eds.), National Remote Sensing Center (NRSC), Chapter 12. Charabi, Y. (2010). Indian Ocean Tropical Cyclones and Climate Change, Springer.Chaudhury, S. K., Gore, J. M., Sinha Ray, K. C. (2000). Impact of heat waves over India, Current

Science, 79 (2), 153-155.CWC (2010). Flood Forecasting And Warning Network Performance Appraisal 2008, Flood Forecast

Monitoring Directorate, Government Of India. Christensen, J. H. et al., (2013), Climate Phenomena and their Relevance for Future Regional Climate

Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Stocker, T.F., et al., chap. 14, pp. 1217–1308, Cambridge Univ. Press, New York.

Cruz, R. V. et al., (2007). Asia, Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 469-506.

Das, P. K. (2013). The Himalayan Tsunami’-Cloudburst, Flash Flood & Death Toll: A Geographical Postmortem. IOSR Journal Of Environmental Science, Toxicology And Food Technology (IOSR-JESTFT), 7, 33-45.

Davis, M. (2001), Late Victorian Holocausts: El Niño Famines and the Making of the Third World, London: Verso, p9.

Directorate of Economics and Statistics, (2013). Uttarakhand at a Glance (2012-13). Govt. of Uttarakhand. www.uk.gov.in/files/pdf/Uttarakhand_at_a_glance_in_english_2012-13.pdf

De, U. S., and Sinha Ray, K. C. (2000). Weather and climate related impacts on health in Mega cities. WMO Bulletin, 44(4), 340-348.

De, U. S., Dube, R. K., Prakasa Rao, G. S. (2005). Extreme weather events over India in the last 100 years. J. Indian Geophys. Union, 9, 173–187.

Dhar, O. N. and Nandargi S. (2003). Hydrometerological Aspects of Floods in India. Natural Hazards, 28, 1-33.

EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université catholique de Louvain – Brussels – Belgium.

ESCAP (2013). Statistical Yearbook for Asia and the Pacific 2013, United Nations publication.

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GoI (2011), State level Programmes for Strengthening Disaster Management in India Initiatives by Ministry of Home Affairs, GoI.

GoI & Planning Commission (2011), Report of Working Group on Flood Management and Region Specific Issues for XII Plan, Government of India, Planning Commission.

GoO (2013). Memorandum on the Very Severe Cyclone Phailin and the Subsequent Flood 12-15 October 2013. The Revenue and Disaster Management Department Government of Odisha. http://www.osdma.org/userfiles/file/MEMORANDUMPhailin.pdf

GWP (2014). Drought Monitoring System for South Asia, Summary Report of the Need Assessment Survey on the Development of South Asian Drought Monitoring System (SADMS). Global Water Partnership, South Asia with support from the WMO/GWP Integrated Drought Management Programme (IDMP).

IFRC (2013). International Federation of Red Cross and Red Crescent Societies (IFRC). DREF MDRIN010.

IPCC, (2007). Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri, R.K and Reisinger, A. (eds.)]. IPCC, Geneva, Switzerland, 104 pp.

IPCC, (2013). Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Jackson, R. D., Idso, S. B., Beginato, R. J., & Pinter, P. J., Jr. (1981). Canopy temperature as a crop water stress indicator. Water Resources Research, 17, 1133–1138.

Kalsi, S. R. (2006), Orissa super cyclone, A Synopsis. Mausam, 57 (1), 1-20. http://metnet.imd.gov.in/mausamdocs/15711_F.pdf

McCollum, J., Gruber, A. and Ba M., (2000). Discrepancy between gauges and satellite estimates of rainfall in Equatorial Africa. Journal of Applied Meteorology, 39, (5), 666-679.

Mohanti, M. (2001). Mahanadi River Delta,East Coast of India: An Overview on Evolution and Dynamic Processes, Bhubaneshwor.

Moran, M. S., Clarke, T. R., Inoue, U., & Vidal, A. (1994). Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index. Remote Sensing of Environment, 49, 246–263.

NDMA (2010). National Disaster Management Guidelines—Incident Response System A publication of the National Disaster Management Authority, Government of India, ISBN 9789380440033.

Oldeman, L. R. (ed.). (1988). Guidelines for general assessment of the status of human induced soil degradation. ISRIC, Wageningen, The Netherlands.

Oinam, B. C. (2011). Reconstruction of 2003 Daya River Flood, using Multi-resolution and Multi-temporal Satellite Imagery. Msc Thesis, IIRS, Dehradun & ITC, The Netherlands.

Palmer, W. C. (1968). Keeping track of crop moisture conditions, nationwide: The new Crop Moisture Index. Weatherwise, 21, 156–161.

Patra, M., Tripathy, S., & Jena, I. (2013). Health Hazards by Sea Cyclones in Odisha, the Supercyclone and the Phailin. Odisha Review, Nov 2013.

Pai, D. S., Thapliyal, V., & Kokate, P. D. (2004). Decadal variation in the heat and cold waves over India during 1971-2000. Mausam, 55, 2 (April 2004), 281-292.

Parida B. R. and B Oinam, (2008). Drought monitoring in India and the Philippines with satellite remote sensing measurements. EARSeL eProceedings, 7(1), 81-91.

Park, S., Feddema, J. J., & Egberts, S. L. (2004). MODIS land surface temperature composite data and their relationships with climatic water budget factors in the central Great Plains. International Journal of Remote Sensing, 26, 1127–1144.

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RSMC (2013). Report on cyclonic disturbances over the north Indian Ocean during 2012, RSMC- Tropical Cyclone Report No. 02/2012.

Sepehr, A., Hassanli, A. M., Ekhtesasi, M. R., Jamali, J. B. (2007). Quantitative assessment of desertification in south of Iran using MEDALUS method. Environ Monit Assess. 134, 243-254.

Singh, O. P., Alikhan, T. M., & Rahman, M. S. (2000). Changes in the frequency of tropical cyclones over the north Indian ocean. Meteorol. Atmos. Phys. 75, 11-20.

Thenkabail, P.S., Gamage, N., Smakhin, V. (2004), The use of remote sensing data for drought assessment and monitoring in south west Asia. IWMI Reseach report #85, IWMI, Colombo, Sri Lanka (2004) 25 pp.

UNEP (2002). GEO: Global Environmental Outlook 3, Past, Present and Future Perspectives, pp 446.UNISDR (2009). 2009 UNISDR terminology on disaster risk reduction published by the United Nations

International Strategy for Disaster Reductions (UNISDR), Geneva. UNCOD (1977). World Map of desertification. Document of the UN conference on desertification in

Nairobi, Kenya, A/CONF. 74/2. Wan, Z., Wang, P., and Li, X. (2004). Using MODIS Land Surface Temperature and Normalized

Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing, 25, 61–72.

Wisner, B., P. Blaikie, T. Cannon, and I. Davis, (2004). At Risk, Natural Hazards, People’s Vulnerability and Disasters. Routledge, London, UK.

WMO (2001). WMO statement on the states of the global climate in 2000, WMO No. 920.

KEY TERMS AND DEFINITIONS Climate change: It refers to any change in climate over time due to natural variability or human activity.

Disaster Risk Management: The systematic process of using administrative directives, organizations, and operational skills and capacities to implement strategies, policies and improved coping capacities in order to lessen the adverse impacts of hazards and the possibility of disaster.

Early warning system: The set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss.

Non-structural measures: Any measure not involving physical construction that uses knowledge, practice or agreement to reduce risks and impacts, in particular through policies and laws, public awareness raising, training and education.

Risk assessment: A methodology to determine the nature and extent of risk by analysing potential hazards and evaluating existing conditions of vulnerability that together could potentially harm exposed people, property, services, livelihoods and the environment on which they depend.

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Structural measures: Any physical construction to reduce impacts of hazards, or application of engineering techniques to achieve hazard-resistance and resilience in structures or systems.

Vulnerability: The characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard.

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