A comparative analysis of the precipitation extremes obtained from tropical rainfall‐measuring mission satellite and rain gauges datasets over a semiarid region

Abstract

The objectives of this research were to compare precipitation extremes obtained from Tropical Rainfall-Measuring Mission (TRMM) satellite and those of rain gauges over a semiarid area in Iran. Extreme precipitation indices (EPIs) (i.e., the number of days with a precipitation value over 10 mm, the maximum duration of wet and dry days, the number of days with precipitation over the 95th percentile, total precipitation higher than the 95th percentile, and maximum daily precipitation) were calculated across Fars province, Iran, 2000-2014 on seasonal time scales. The gauges data were interpolated at a spatial resolution of 0.25 × 0.25 to match the 3B42 data using Inverse Distance Weighting (IDW). Then, EPIs from the two datasets were compared with each other. The findings showed that mean values computed from gauges and satellite data did not present any significant differences among all of the extreme indices. Furthermore, their variances presented a good level of congruence. Finally, the majority of indices presented a satisfactory correlation between the two dataset. To evaluate the prediction of extreme events in different temporal and tolerated distances, a fuzzy method was used. The results showed that the percentage of grid cells with useful predictions tripled with spatial tolerance extending by just one pixel. To evaluate methods of eliminating the uncertainty of probable missing rainfall data and the seasonal changes in rainfall averages, probabilistic methods based on Weibull distribution and truncated geometric distribution (TGD) were employed to eliminate uncertainties in estimation of extreme precipitation amounts and extreme wet periods (WPs). The results showed that as to extreme precipitation amounts, a satisfactory method could not be drawn for arid southern regions of Fars, Iran. Similarly, as to extreme WPs, the consistency between gauges and satellite data could not be improved significantly.

Detailed Trend Analysis of Extreme Climate Indices in the Upper Geum River Basin

“…A set of standard measurements of the extreme climate indices based daily precipitation, and daily (minimum and maximum) temperatures were provided by the Expert Team on Climate Change Detection and Indices (ETCCDI) [9,10]. For the past two decades, studies on trend analysis of ETCCDI indices [11][12][13][14][15][16][17][18][19][20][21][22][23], has been widely performed in different regions around the globe, through the use of Mann-Kendall (MK) trend test [24][25][26] and Theil-Sen (TS) slope estimator [27,28], both tests are rank-based non-parametric tests, that are insensitive to outliers and missing data. These recent studies were analyzed based on various temporal scales, ranging from annual [11][12][13][14][15][16][17][18][19][20][21][22][23], seasonal [13,[17][18][19], and monthly [19] time scales.…”

“…For the past two decades, studies on trend analysis of ETCCDI indices [11][12][13][14][15][16][17][18][19][20][21][22][23], has been widely performed in different regions around the globe, through the use of Mann-Kendall (MK) trend test [24][25][26] and Theil-Sen (TS) slope estimator [27,28], both tests are rank-based non-parametric tests, that are insensitive to outliers and missing data. These recent studies were analyzed based on various temporal scales, ranging from annual [11][12][13][14][15][16][17][18][19][20][21][22][23], seasonal [13,[17][18][19], and monthly [19] time scales. While, majority of the studies focused on annual scales, and lesser on monthly scales, the former is insufficient in providing detailed information in a watershed, such as detecting shifting of precipitation patterns.…”

Appraising the Potential of Using Satellite‐Based Rainfall Estimates for Evaluating Extreme Precipitation: A Case Study of August 2014 Event Across the West Rapti River Basin, Nepal

“…After then, systematic biases can be reduced. There are many studies (Aghakouchak et al., 2011; Almazroui, 2011; Hong et al., 2007; Mahbod et al., 2019; Pakoksung & Takagi, 2016; Pombo & de Oliveira, 2015; Talchabhadel, Aryal, Kawaike, Yamanoi, Nakagawa, Bhatta, et al., 2021; Tarek et al., 2017; Yoshimoto & Amarnath, 2017) dealing with error evaluation of SREs and their applications but most of these studies are carried out on a daily, monthly, seasonal or annual scale. Here, we tried to assess the potential of using SREs during an extreme precipitation event.…”

On the Performance of Satellite-Based Precipitation Products in Simulating Streamflow and Water Quality During Hydrometeorological Extremes

“…Understanding the spatiotemporal behavior of hydrometeorological events is of critical importance for water resource management including flood mitigation and response, ecosystem restoration, river and water supply reservoir recharge, and water quality impacts. Evaluating how hydrometeorological extremes have historically behaved, including variations in intensity, duration, and frequency is of upmost importance not only for current water resource management, but also to understand long-term climate impacts and provide accurate predictions of future behavior (Alexander et al, 2019;Maggioni and Massari, 2019;Mahbod et al, 2019;Tongal, 2019).…”