This study aims at assessing drought for Amman-Zarqabasin, north Jordan. This basin is one of the important basins in Jordan where most of the agricultural and hydrological activates are located. During the last decades, Amman-Zarqabasin had faced a high variability of the rainy season which starts every year in October and ends in April. The main objective of this research is to find out if this basin is currently facing drought conditions. Two different drought indices were used in this study; these are the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) to evaluate droughtusing rainfall data and satellite images . Geographical information systems (GIS) software were used in this study to; 1) Create spatial digital database to hold meteorological information for the study area, 2) Generate thematic layers representing spatial distribution of drought for both SPI and NDVI and 3) Delineate areas with high drought risk using SPI and NDVI and compare the results of both models .
However, the presented results are in good agreement with previous data [1,5,6,13–17,33]. In average all these studies stated an annual groundwater level drawdown in the order of 0.65 m to 2.0 m. 3.2. RemoteSensing
Figure 5a shows the linear trend from GRACE adjusted terrestrial water storage (including soil moisture and vegetation changes) during January 2003 to July 2014. The results indicate a decrease in soil moisture (approximately −15 mm per year in water column) over the country, which is dominated mainly over the northeastern and western regions. A linear trend of groundwater is shown in Figure 5b), which indicates a decline of groundwater at the rate of up to approximately −10 mm per year in the water column concentrated over the study area. This value is equivalent with approximately 160 mm per year in groundwater change concentrated over the model area.
Conventional geomorphometric studies were carried out to explore the relationship between morphometric properties of drainage networks and climate, relief, lithology, structure and tectonics in order to interpret the morphometric parameters  -. The role of tectonic control on geomorphologcial processes in shaping drainage networks was reported for selected river basins from Kerala, Southern India . In the recent past, morphometric analysis of stream networks was employed for a wide range of applications. Assessment of natu- ral resources and geo-environmental hazard, especially flash floods for arid watersheds, was addressed particu- larly in developing countries, such as Egypt - and Turkey . Groundwater recharge potentials from flash floods in arid land alluvial basins, southern Red Sea coast in Egypt, were also investigated . Morpho- metric analysis techniques were adopted to evaluate groundwater potential and hydrological behavior of water- sheds  . Watershed prioritization for soil and water conservation measures  was implemented in several parts of India. Such applications confirm the role of geographic information system (GIS), remote sens- ing (RS) and morphometric analysis as an efficient tool for locating water harvesting structures by prioritizing mini-watersheds in Gujarat and western Ghats regions, India   , and Bago River, Myanmar . Stu- dies regarding the identification of artificial recharge sites in Manchi basin, eastern Rajasthan, India were carried out using morphometric analysis and GIS techniques . Analysis of drainage basin morphometry based on multivariate statistical methods was achieved to delimit morphological regions in south-west Uganda  . Evaluation of geomorphometric characteristics was carried out on a catchment level in India  and on a re- gional level in the western Arabian Peninsula . Assessment of surface runoff in arid and data-scarce
Zarqa District is the third largest governorate in Jordan by population. It is located 25 km to northeast of the Jordanian capital Amman (Figure 1). Zarqa city is the capital of Zarqa Governorate. It is the industrial capital of Jordan, with many factories and Jordan’s major oil refinery. Also, it functions as a large military center, with several military camps. It extends between latitudes 31˚58'N and 32˚06'N and longitudes 36˚01'E and 36˚20'E, covering an area of 253.31 km 2 with altitudes ranging between 508 to 803 meters above sea level.
Results of cross-tabulating the 1983 and 2013 land use/cover maps (Table 2) showed important trends of land use/cover changes during the 30 year period. Part of the mixed agricultural areas was changing into urban or open rangelands. In terms of area, 2640 ha of the mixed agricultural areas were urbanized. Another important change that was noticed in the study area was the con- version of 13.3% of the forest into urban and 41% into mixed agricultural areas. According to Reference , this trend was seen as the major cause of land degrada- tion in the high rainfall zone in Jordan. The change from mixed agricultural areas to open rangelands, and vice versa, was also noticed when the maps of land use/cover classes were cross-tabulated. This change could be at- tributed to the crop rotations and rainfall distribution that affected the spatial distribution of rainfed barley and other field crops in the study area. Changing the irrigated farms into open rangelands, on the other hand, would imply the abandonment of irrigated farms due to soil salinization as indicated by Reference .
There are five types of land use in Zarqabasin. The land use classes were divided into bare soil, build-up area, forest, orchard and cultivated or cultivable land. The calculated monthly runoff depth at the outlet of the basin was close to the observed monthly runoff during the period of winter 2012 to 2014. The point of control of measurement was at the bridge of Jarash. The monthly average rainfall over the basin was calculated with Thiessen coefficients derived from Thiessen Polygon coverage.
In the developing countries, the acute problem of slum formation is found not only in the big cities but also in medium and small cities and towns "A slum is a contiguous settlement where the inhabitants are characterized as having inadequate housing and basic services” - . Because of the high, proportion of slum dwellers among the urban population, the problems of urban poverty areas are of particular concern. In most of the municipal areas proper up-to-date maps of slums along with proper database and genesis of its growth are not available which create problem in developmental process. Thus, it is important to analyze the slum formation, slum morphology and impact on surroundings to improve quality of life of slum dwellers. Hence, the main aim of the project is to map the slums and identify the physical characteristics of the slum areas and to present a comprehensive picture of slums in two aspects, i.e., spatial distribution and growth, and physical infrastructural services related to slums. The slum selected for the present study is Pochammakunta (Burial ground); is a under non notified slum in Warangal municipal boundary. Cartosat_II image is used for mapping of pochammakunta. Survey slum map is used for mapping of pochammakunta. Non spatial data for this slums have been collected from the field survey with the Questionnaire. Slum spatial layers have been digitized for all the houses in GIS environment. The attribute data obtained from the field survey has been added to the spatial layers. Mapping has been done based on the facilities in a particular house and severity maps showing the condition of the slum have been generated. In pochammakunta slum all houses have severity grade poor and very poor. Pochammakunta contains 111 houses. It contain 84 'houses under kachacha, 22 under pakka, and 5 under poor condition. Hence, these slum maps based on different parameters helps the user to analyze and understand the condition easily.
The study area is located 8 km south of Aqaba on the eastern coast of the Gulf of Aqaba. The climate of the area is hyper-arid, with low mean annual rainfall and moderately high temperature. The mean annual rainfall in Aqaba is 37mm, while the mean annual rainfall in the middle and upper catchment of wadi Yutum ranges from 100 to 150 mm. Seventy percent of rainfall events occur between December and February, and originate in southerly trajectories of Mediterranean depressions.The spring and autumn rainfall originates in local convective cells associated with an incursion of the Red Sea trough (Grodek et al. 2000; Khana et al. 2002). The area is characterized by intense rainfall of short duration within limited areas (localized type), where 60 % of the total rainfall comes from spotty rain (Sharon 1972). Spottiness is clearly pronounced in the fall and late spring. The storms of March 1991,December 1993 and January 1994, which caused heavy damage to the Aqaba Back Road, are an example (Farhan 1999). Although arid watersheds are dormant for 98 % of the time, when it becomes active, it transports large quantities of sediments both as bed load and suspension load, far outstripping the performance of comparable perennial rivers (Reid et al.1998).Similarly, the high relief of the bare rugged granite facilitates the occurrence of extremely destructive floods in the downstream terrain zone chosen for urban development along the coast. Adequate hydraulic structures do not exist to convey the expected high- magnitude,low-frequency flash floods. During the 20 th century, the lower Wadi Araba witnessed a number of large rainstorm floods which affected the inhabitants of the region. In 1940, one half of the modern town of Aqaba (built on the alluvial fan of Wadi Shallalah) was flooded and destroyed, as reported by Glueck (1941).Again in 1953, Aqaba town suffered a severe flood.The1960s witnessed a disproportionate number of floods. Three examples are (1) the event of April 1963, in which Aqaba town was flooded and 25 French tourists lost their lives in Petra, southern Jordan. (2) In March 1966, Aqaba was flooded again, and 70 persons lost their lives in Ma’an, with over 250 injured, while hundreds became homeless (Central Water Authority 1966; Schick 1971).(3) In 2006,Aqaba area flooded again, and 5 persons lost their lives, and extensive damage to infrastructure occurred.
The reader might have noticed that we did not mention the word "desertification" throughout our results and discussion and, we preferred the term "LD prone areas" rather than "degraded areas." To confirm that LD in fact occurred, we would need post-drought data, which is to date a limitation of our study, rather, we indicated LD prone areas based on loss of vegetation trends and changes in ET, which might potentially feedback the LD cycle. Lessons learned from the Sahel case make us cautious on drawing conclusions about LD in the NEB. The Sahel has been experiencing some cases of regreening, due to land management and changes in the local climate, although, there is an intense debate about the region's dynamics. In our study, we found some signs of vegetation recovering in BA1+BA2 after a severe drought; however, for this region, the drought was not as prolonged as for the northern regions of the studied area. Finally, as Marengo et al. (2016) warned, considering climate change projections, the NEB is likely to undergo increases in temperature and precipitation reduction, leading to higher frequency and intensity of dry spells. These findings and cited literature draw a scenario of imminent LD, which is partially confirmed by our study.
Abstract: Groundwater is one of the greatest hidden natural resources of the earth’s crust. It has ubiquitous occurrence. The deformative forces and different earth processes depending upon the nature and composition of the rocks produces characteristic structural, morphological features, which themselves are responsible for the groundwater accumulation, storage and movement, a detailed understanding of all these pathfinders and appreciation of their relative significance in different hydro geological environment is essential in proper planning and management of the groundwater resources. Assessing the groundwater potential zone is particularly important for the safety of water quality and the management of groundwater systems. A case study was conducted in Nandhiyar sub basin to find out the groundwater potential zone. The thematic maps such as geomorphology, geology, soil, drainage, lineament map were prepared in Arc GIS. All the thematic layers were integrated in to Arc GIS 9.3 software to find a groundwater potential zone map of the study area. Thus, three different groundwater potential zones were identified, namely 'Poor', 'Moderate' and 'Good'.
Abstract: The present project work is aimed toward assessing the water quality index (WQI) for the floor water of Palar Sub bowl. This technique has been picked by strategy for social affair groundwater tests and presenting the guide to an absolute physico engineered examination. The physico-engineered examination have been stood out from the extraordinary needed characteristics as empowered by technique for method for the field health relationship for ingesting and general prosperity that lets in you to possess unique a summary of this groundwater satisfactory assessment. For finding out Groundwater best in class Index following eleven parameters were thought about: pH, Totalhardness, chlorides, Dissolvedsolids, calcium, Magnesium, sulfate, Nitrate, Flouride, Alkalinity, and sodium. The Water wonderful record for the ones sampl characteristics ranges from fifty 5.85 to 191.26. The most rate of Water first rate record has been explicitly from the better estimations of normal hardness, chlorides, Dissolved solids, Magnesium and alkalinity inside the ground water. usingGIS forming strategies with ArcGis 10.1 Spatial movement maps of pH, in vogue hardness, chlorides, Dissolved solids, calcium, Magnesium, sulfate, Nitrate, Flouride, Alkalinity, sodium and WQI were made. Water dumbfounding rundown changed into used to assess the suitability of groundwater from the look at an area for human confirmation. From the WQI appraisal over 90% of the water tests fall in loathsome water bearings. The examinations most likely comprehended that the groundwater of the district dreams some acknowledgment of treatment before confirmation.
Drought is explained as the unusual low level of precipitation over a certain period as meteorological drought. The shortage of water propagates through the hydrological cycle and combines with high evaporation, the insufficient moisture in the topsoil may cause an agricultural drought and subsequently a hydrological drought may develop when groundwater and streamflow are reduced. The different types of drought are significantly influenced by each other through the natural hydrological cycle (Tallaksen and Van Lanen, 2003). The year's rainfall is concentrated in the monsoon season (June–September) contributes about 75–90% of the total annual rainfall, small changes in the location or timing of precipitation can have a big regional impact on agriculture.
The Adwa River is the tributary of the Belan river and the Belan river is the tributary of the Tons and then the Ganges river system. The Adwa River meets the Belan River in the Mirzapur district of Uttar Pradesh though the pear shaped basin of the Adwa River stretches between two states i.e. Uttar and Madhya Pradesh. The boundary of the two states almost bisects the Adwa river basin. The latitudinal and longitudinal extent of this basin is from 24 o 34’ N to 24 o 54’ N and from 82 o
The study area and the stream network was delineated using ArcGIS from the Digital Elevation Model (DEM) obtained from the websitehttp://earthexplorer.usgs.gov/. The spatial variation of the average annual rainfall was plotted to check the drought sensitive areas. The assured rainfall zone and scarcity zone was identified to be among the drought sensitive areas due the consistent availability of low rainfall. The rainfall records for a period of 1970 to 2012 of 57 rain gage stations in the study area are used for the calculation of SPI values. The available data was first analysed and the missing data were filled using regression method. The fortnightly rainfall for each station is calculated, with month having two fortnightly rainfall values. The SPI (1, 2, 3, 6 & 9) values were calculated for both fortnights separately for the entire period from 1970 to 2012. From this calculated SPI values, values from 2000 to 2012 was considered for the present study. The growing season of Kharif crops in Maharashtra starts in June and extends up to November. So from the calculated SPI values, the values extending from 2000 to 2012 for the fortnights from June to December was considered. This was done to analyse the variation of meteorological drought in the study area. Then the fortnightly average rainfall for each agro- climatic zones were calculated and corresponding SPI (1, 2, 3, 6 & 9) values were derived to establish zone wise relationship between NDVI and SPI of each zone.
Drought indices have been developed to detect, assess and monitor drought. Widely used drought indices include Crop Moisture Index (CMI), Palmer Drought Severity Index (PDSI), Surface Water Supply Index (SWSI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardised Vegetation Index (SVI). (Hayes, Svoboda et al. 1999). Some indices are more suitable for certain applications than others. For instance, the U.S. Department of Agriculture has widely used the PSDI to decide when to grant emergency drought assistance. However, the PSDI works better for large areas of uniform topography. Water resources planning authorities in Western states of the USA, with mountainous terrain and resulting complex regional microclimates, find it useful to supplement PSDI values with other indices such as the SWSI that is based on snow pack. Meanwhile, the National Drought Mitigation Centre is using SPI to assess soil moisture supply conditions. Features that distinguish this index are that it identifies emerging droughts months sooner than the PSDI and that it can be computed on various time scales. Most water supply planners find it useful to consult one or more indices before making a decision related to water management.
The thematic maps derived through the interpretation of satellite data i.e., geology, geomorphology, lineament, drainage patterns and soil were digitized, edited and saved as shape files in GIS software. The lineament and drainage maps were digitized as line coverage whereas geological and geomorphological maps were digitized and saved as polygon coverage, assigning unique polygon to each geological /geomorphological unit. The maps were then projected to a common UTM projection system so as to subsequently superimpose in ArcGIS using to weighted overlay sub-module to demarcate groundwater prospect zones based on above themes. Integration of thematic maps led to the demarcation of groundwater potential map which qualitatively defines the prospect zones for future groundwater development in and around Bengaluru urban district. Thus, the groundwater prospective zones are obtained for the study area was represented in the figure 10.
This procedure of rainfall modeling was done through the use of SPI timescales trend curves. From these various time scales curves, water scarce and dry years were identified. These were the season months which had the negative SPI val- ues. Since SPI used normal distribution curve, the values are normalized . Computation of the SPI involves fitting a gamma probability density function to a given frequency distribution of precipitation totals for a station. Figure 7 shows SPI curves for various timescales for Nakuru meteorological rainfall sta- tion and Figure 8 shows SPI curves for various timescales for Olkaria meteoro- logical station. These SPI timescales curves graphically shows the SPI values; highest being the positive value and lowest is the negative value according to McKee et al ., 1993 in Table 2. On the other hand, Kenya has three seasons ac- cording to Kenya meteorological department report . These seasons are MAM, JJA, and OND, hence dry periods for these seasons were identified from the curves. Figures 9-11 show the SPI timescales curves for these three climatic sea- sons. Modeling the distribution of dry areas was done using IDW and categories
Using the information stored in the database, we could create a display symbolizing the roads according to the type of information that needs to be shown. A GIS also uses the stored feature attributes to compute new information about map features, for example, to calculate the length of a particular road segment or to determine the total area of a particular soil type. In this study, there are two data sources for GIS: Hard Copy Maps and Existing GIS Layers.
Abstract—Flood is one of the most devastating natural hazards which lead to the loss of lives, properties and resources. Floods resulting from excessive rainfall within a short duration of time and consequent high river discharge damage crops and infrastructures. They also result in siltation of the reservoirs and hence limit the capacity of existing dams to control floods. The purpose of flood risk assessment is to identify the areas within a plan that are at risk of flooding based on factors that are relevant to flood risks. It has therefore become important to create easily read, rapidly accessible flood map. Maps give a more direct and stronger impression of the spatial distribution of the flood risk than other forms of presentation like verbal description and diagrams. Remotesensing (RS) is a reliable way of providing required data over a wide area in a very cost-effective manner. It also overcomes the limitation of the ground stations to register data in an extreme condition. This paper is aimed at assessing flood risk in the Anigunta region, Sangareddy district, Telangana state, India. Remotesensing technology along with geographic information system (GIS) is the key tool for flood monitoring. The map will be made using Geographic Information System (GIS). A GIS database of indicators for the evaluation of hazard will be created. The indicators are road network, settlements, drainage, contours, Triangulated Irregular Network (TIN), Digital Elevation Model (DEM), slope, aspect, flow accumulation, flow direction, Landuse-Landcover (LULC), soil map, Geomorphology, and Ground water maps. Each indicator will be analyzed and weighted, after which, the weights of the indicators will be combined to obtain the final map. The results obtained can provide useful information to suggest artificial recharge structures for decision making.