Rainfall estimation over the Wadi Dhuliel arid catchment, Jordan from GSMaP_MVK+

Abstract

The GSMaP_MVK+ (Global Satellite Mapping of Precipitation) dataset was used to evaluate the precipitation rates over the Wadi Dhuliel arid catchment in Northeast Jordan for the period of January 2003 to March 2008. The scarcity of the ground rain gauge network alone did not adequately show the detailed structure of the rainfall distribution, independent form interpolation techniques used. This study combines GSMaP_MVK+ and ground rain gauges to produce accurate, high-resolution datasets. Three meteorological stations and six rain gauges were used to adjust and compare GSMaP_MVK+ estimates. Comparisons between GSMaP_MVK+ measurements and ground rain gauges records showed distinct regions where they correlate, as well as areas where GSMaP_MVK+ systematically over- and underestimated ground rain gauge records. A multiple linear regression (MLR) model was used to derive the relationship between rainfall and GSMaP_MVK+ in conjunction with temperature, relative humidity, and wind speed. The MLR equations were defined for the three meteorological stations. The "best" fit of MLR model for each station was chosen and used to interpolate a multiscale temporal and spatial distribution. Results show that the rainfall distribution over the Wadi Dhuliel is characterized by clear west-east and north-south gradients. Estimates from the monthly MLR model were more reasonable than estimates obtained using daily data. The adjusted GSMaP_MVK+ performed well in capturing the spatial patterns of the rainfall at monthly and annual time scales while daily estimation showed some weakness in light and moderate storms

Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach

“…Based on the precipitation observed in the period 1961-2000, independent precipitation events were separated for which statistical distributions were determined. To obtain the best possible fit of the theoretical data to the empirical precipitation data (including rainfall depth -P tot , rainfall duration -t r , and average rainfall intensity -i, for precipitation of appropriate genesis), the following statistical distributions were considered (Adams and Papa, 2000;Bacchi et al, 2008;Andrés-Doménech et al, 2010): Weibull, chi-squared (Chi), exponential, generalized extreme value (GEV), Gumbel, gamma, Johnson, log-normal, Pareto, and beta. Kolmogorov-Smirnov (KS) and chi-squared tests were used to assess the conformity of the empirical and theoretical distributions.…”

“…It seems puzzling that the time course and the dynamics of the rainfall as the result of air masses advection (Vicente-Serrano et al, 2009;Alhammoud et al, 2014;Dayan et al, 2015) were not taken into account when rainfall generators were used to simulate storm overflows. The problem of the modeling of complex atmospheric phenomena is the subject of many works (Madsen et al, 1995;Paquet et al, 2006;Vicente-Serrano et al, 2009;Garavaglia et al, 2010;Abushandi and Merkel, 2011). The models created consider simulations of meteorological conditions changing in time and determining the distribution of temperature, pressure, and humidity, which affects the dynamics of air advection and, consequently, the patterns of precipitation phenomena.…”

Performance of High Resolution Satellite Rainfall Products over Data Scarce Parts of Eastern Ethiopia

“…The data are archived by the temporal interpolation of passive microwave retrievals using a PMW-IR blended algorithm [1] and a Kalman filter [49] using IR information. GSMaP retrieves rainfall data from polar orbiting satellites with cloud motion vectors using infrared images [36]. PMW imagers compute the rate of rainfall by the algorithm of the GSMaP project [31] using various attributes from TRMM data.…”

Dam Site Selection Using Remote Sensing Techniques and Geographical Information System to Control Flood Events in Tabuk City

“…MSR-E, TRMM, GSMAP, ASTER, SAR and several others). Many researchers over the past 20 years have focused on satellite imagery applications in hydrology [5][6][7][8][9]. GIS and remote sensing can provide a huge amount of valuable data in spatial and temporal resolutions for areas where ground data are not easily available.…”