Cell Statistics Under GIS environments [37], cell statistics calculates a per-cell statistic from numerous rasters (59 rasters), ter the current case the &#x0201c,Mean&#x0201d, instruction which calculates the average of all input raster values spil illustrated ter Figure Trio .

Mohamed Elhag

Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment &, Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Jarbou A. Bahrawi

Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment &, Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia


The amount of water on earth is the same and only the distribution and the reallocation of water forms are altered ter both time and space. To improve the rainwater harvesting a better understanding of the hydrological cycle is mandatory. Clouds are major component of the hydrological cycle, therefore, clouds distribution is the keystone of better rainwater harvesting. Remote sensing technology has shown sturdy capabilities te resolving challenges of water resource management ter arid environments. Soil moisture content and cloud average distribution are essential remote sensing applications te extracting information of geophysical, geomorphological, and meteorological rente from satellite pics. Current research investigate aimed to opbergmap the soil moisture content using latest Landsat 8 pictures and to schrijfmap cloud average distribution of the corresponding area using 59 MERIS satellite imageries collected from January 2006 to October 2011. Cloud average distribution opbergmap shows specific location ter the examine area where it is always cloudy all the year and the webpagina corresponding soil moisture content opbergmap came te agreement with cloud distribution. The overlay of the two previously mentioned maps overheen the geological schrijfmap of the examine area shows potential locations for better rainwater harvesting.

1. Introduction

Water cycle or the hydrological cycle assures that the quantity of water ter the earth’s environment under no circumstances switches, regardless of the state of the water spil a liquid, gas, or solid state. Water repetitively circulates inbetween the land, the oceans, and the atmosphere.

Adequate water management is founded on understanding the interconnections ter the hydrological cycle. Informative skill of the designated catchment water balance is needed [1]. Catchment area by definition is the total area of terrestrial which catches rainfall and contributes the placid water to a certain surface water or potential groundwater recharge [Two].

Te semiarid regions climates, there is no accurate estimation of groundwater recharge. Existing estimation is based on the difference inbetween the total amounts of rainfall and actual evapotranspiration due to indeterminate statistics of similar extents. Therefore no reliable information concerning absolute values of recharge can be obtained by the surface water balance [Three, Four]. Recharge quantification problems from different sources are addressed by Gee and Hillel [Five], Lerner et ofschoon. [6], Allison et alreeds. [7], Stephens [8], Lerner [9], and Simmers [Ten], among others.

The influence of lithology and geomorphology te semiarid regions is exemplified by variances inbetween designated areas and the corresponding geological feature [11–,14]. Sinkholes te Saudi Arabia receive about 47% of the average rainfall (100 ,mm/year) and withdraw surface runoff into its sinkholes interconnections [15].

The formulation of cloud water is based on the interception of befalls droplets on different earth surface features including mainly the vegetation voorkant [16–,20]. Lack of vegetation voorkant leads to insufficient cloud water formation and decrease te water precipitation into the soil ter remarkable quantities [21]. Several elements stimulate the formation of cloud water interception. According to Elhag and Bahrawi [22], average cloud spatial distribution, droplet size, vegetation voorkant, and wind velocity are basically encountered. Presence of mountainous chain and precipitous slopes ter a designated area are the origination of what is so called the cloud stortplaats, cloud interception ter the investigate area is expected to be a snaak phenomenon along the area [23].

Clouds exert a superior influence on solar energy absorbed by the earth and on infrared radiation emitted to space. It is known that clouds present a problem, they act to cool the planet by reflecting solar radiation to space and warm the planet by reducing radiation emitted to space [24–,26]. Accurate detection of clouds from remote sensing pictures are with a major concern for a broad range of remote sensing applications, especially by sensors detecting ultraviolet (UV) and visible and near-infrared (VNIR) range of the electromagnetic spectrum [27, 28].

To optimize the use of limited water resources ter arid environments unconventional methods of programma are required [23]. Soil moisture monitoring is a crucial feature of managing water requirements of agricultural fields founded on advanced irrigation technics [29].

The aim of the current explore is to examine the interconnection inbetween spatiotemporal distribution of the conducted cloud likelihood maps and clouds underneath terrain features to improve potential rainwater harvesting ter the probe area.

Two. Materials and Methods

Two.1. Probe Area

Asir region is located at the southwest of Saudi Arabia ( Figure 1 ). Asir consists of about 100,000 ,km Two of Crimson Sea coastal plains and high mountains, and the upper valleys of the wadis (seasonal watercourses) are Bī,shah and Tathlī,th. Asir is a prosperous agricultural region. It has an area of 77,088 ,km², and an estimated population of 1,563,000. It shares a brief border with Yemen. Its capital is Abha. The average annual rainfall te the highlands very likely ranges from 300 to 500 ,mm falling ter two rainy seasons, the chief one being ter March and April with some rain te the summer. Temperatures are extreme, with diurnal temperature ranges te the highlands being the greatest ter the world. It is common for afternoon temperatures to be overheen 30°,C, yet mornings can be frosty and fog can cut visibility to near zero procent. Spil a result, there is much more natural vegetation te Asir than te any other part of Saudi Arabia.

General structure of the cloud detection algorithm is illustrated ter Figure Two . During development of the algorithm by Fischer and Grassl [30] and Fell and Fischer [31], using the radiative transfer monster MOMO (matrix technicus method), simulated cloud and noncloud top of atmosphere radiance have bot produced and an artificial neural netwerk has bot trained. Thus, artificial neural network is now used ter the cloud probability processor, where it is fed with the reflectances and the pressure spil shown te Figure Two . Postprocessing is applied after the televisiekanaal (nn2prop) which scales the output of the artificial neural network into a probability value.

Two.Two. Methodological Framework

Two.Two.1. Algorithm Basics

According to Lindstrot et ofschoon. [32], clouds are effortless to detect when a manual classification of satellite pics is done, their automatic detection is difficult. Clouds have four special radiative properties that enable their detection: (1) clouds are white, (Two) clouds are bright, (Trio) clouds are higher than the surface, and (Four) clouds are cold. However clouds, spil the most variable atmospheric constituent, seldom voorstelling all four properties at the same time.

Skinny clouds demonstrate a portion of the underlying surface spectral properties, and low clouds are sometimes warm. Also, some surface types like snow and ice have spectral properties that are similar to some of the cloud properties. Therefore plain thresholding algorithms often fail, and existing cloud detection schemes use several different cascaded threshold based tests to account for the complexity [33–,35].

Two.Two.Two. Algorithm Specification

The cloud probability algorithm has bot developed and implemented by Free University Berlin and Brockmann Consultatie. It is also used ter the Global MERIS Land Albedo Ordner project [36]. The cloud probability algorithm is using nine spectral bands of MERIS. Specifically, the ratio of plakband Ten (cloud optical thickness and cloud-top pressure reference), plakband 11 (Cloud-top/Surface pressure) and betrekking 12 (aerosol, vegetation), which is an oxygen absorption indicator. According to the European Centre for Medium-Range Weather Forecasts (ECMWF), surface pressure and the precies wavelength of tape 11 used spil algorithm input parameters. Spil an output, it yields a probability value (0 to 1) indicating if a pixel can be regarded spil a cloud or not. Such a probability permits a more lithe way to work with identified clouds compared to a binary cloud mask. The algorithm uses two different artificial neural networks.

MERIS measures radiances ter 15 channels inbetween 400 ,nm and 1000 ,nm. Thus the very valuable thermal information and information about the liquid and ice water absorption at 1.6 ,μ,m and Three ,μ,m are not available. The cloud detection for MERIS therefore relies on bands Ten, 11, and 12 according to Lindstrot et alhoewel. [32]. Ter addition a slight absorption of snow at 900 ,nm could be used to discriminate snow from low clouds [36].

Watershed delineations and its companions of DEM analyses are processed under GIS environment using conventional methods.

Two.Two.Trio. Cell Statistics

Under GIS environments [37], cell statistics calculates a per-cell statistic from numerous rasters (59 rasters), te the current case the “,Mean”, directive which calculates the average of all input raster values spil illustrated te Figure Three . Resulting cloud average distribution is then converted into percentages raster based on 0 and 1 cloud probability. Classifying the final spatiotemporal cloud average distribution schrijfmap wasgoed based on Jenks rule of classification, where the output classes were based on natural groupings natural te the gegevens [37]. Jenks rule identifies pauze points by picking the class cracks that best group similar values and heighten the differences inbetween classes. The features were divided into classes whose boundaries were set where there were fairly big leaps ter the gegevens values. The final output ordner wasgoed divided into three classes:

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