water Article Spatio-Temporal Analysis of Precipitation Frequency in Texas Using High-Resolution Radar Products Dawit Ghebreyesus * and Hatim O. Sharif Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA; [email protected] * Correspondence: [email protected] Received: 13 April 2020; Accepted: 9 May 2020; Published: 13 May 2020 Abstract: Understanding the frequency and intensity of precipitation is needed for many vital applications including water supply for agricultural, municipal, industrial, and power generation uses, design of hydraulic structures, and analysis and forecasting of hazards such as flood, drought, and landslide. This study examines, in detail, the spatial and temporal variability of precipitation frequency over the State of Texas and its trends from 2002 to 2019. The results indicate that Texas receives around 325 wet hours on average annually (3.7% of the time). The northern part of the Gulf Coast region witnesses the highest average precipitation frequency reaching 876 wet hours annually. The year 2015 was found to have the highest precipitation frequency across the state with an average frequency of 6% (525 wet hours) and 2011 was the driest, with an average frequency of 1.9% (170 wet hours). In terms of seasonality, the highest precipitation frequency was observed in the summer with a frequency of 4.1%. The areal average time-series of the precipitation frequency indicates that the 2011–2012 drought to be a change point. The Mann–Kendall trend analysis shows that 16.2% of the state experienced a significant positive trend in precipitation frequency including the dry western region and major cities. The results can provide useful information about storm characteristics and recent change and variability of precipitation at high spatial resolutions and can be used in a multitude of practical applications. Keywords: NEXt-generation Weather RADar (NEXRAD); precipitation; Texas; radar; STAGE IV 1. Introduction Precipitation is an essential input for water resource management studies and modeling of hydrological processes. In addition to being primary variables in climate classification, the frequency and intensity of precipitation are necessary for many vital applications including water supply for agricultural, municipal, industrial, and power-generation uses, design of hydraulic structures, and flood, drought, and landslide analysis and forecasting. However, an accurate representation of the spatial and temporal distribution of precipitation remains a major challenge facing the scientific community. Rain gauges provide accurate point measurements of precipitation, but their measurements are typically not enough to capture its spatial variability. Yatagai [1] showed that the use of spatially interpolated data obtained from a dense network of rain gauges significantly improved the spatial representation of the precipitation, but such resolutions are rarely available from existing rain gauge networks. Accordingly, numerous studies suggested that it is more efficient to use remotely sensed precipitation estimation techniques (radar-based and satellite-based) to capture the spatio-temporal variability of the precipitation [2–8]. The NEXt-generation Weather RADar (NEXRAD) systems have revolutionized the National Weather Service (NWS) forecast and warning programs by enhancing the detection mechanism of the severe weather such as wind, heavy rainfall, hail, hurricanes and tornadoes [9,10]. The network Water 2020, 12, 1378; doi:10.3390/w12051378 www.mdpi.com/journal/water Water 2020, 12, 1378 2 of 16 consists of 160 WSR-88D (Weather Surveillance Radar-1988 Doppler) radars distributed across the entire conterminous United States. The National Centers for Environmental Prediction (NCEP) stage IV quantitative precipitation estimates (QPEs) are the most widely used NEXRAD precipitation products by the research community [6,11]. Examples of the studies conducted using the products include hydrologic model evaluation [12], flood forecasting [13], radar QPE evaluation and comparison with ground measurements [2], and assessment of different satellite precipitation products [14]. Gourley [15] used both NCEP Stage IV and Satellite QPEs to force a hydrological model and compare their performance against observed discharge, citing encouraging results. Yilmaz [12] compared gauge-based, radar-based, and satellite-based precipitation estimates accounting for hydrologic modeling and highlighted the benefits of spatial coverage by the radar product. Furl [6] assessed several satellite-based precipitation products versus the NCEP stage IV after deciding that the radar product was superior even to the sparse rain gauge estimates. Several studies were performed to compare the stage IV product with rain gauges. Wang’s [2] findings suggested that NEXRAD Multi-sensor Precipitation Estimator (MPE) has a much higher correlation than the previous products and underestimates by only 7% on average in a study conducted over the Upper Guadalupe River in Texas. The study concluded that the spatial variability of the precipitation was captured at a reasonable accuracy by the NEXRAD MPE. Westcott [16] compared monthly gauges amounts to that of the stage IV product over the Midwest and found that stage IV overestimated precipitation at the low end and underestimated it for high values. Even though the comparison was carried on the averaged values at a county level, MPE accuracy increased as the value of precipitation increases, except for rare heavy events. A similar conclusion was reached by Habib [17] in a validation study using a dense network of gauges carried over the south of Louisiana. Habib [17] further emphasized that the MPE Stage IV algorithm showed a significant improvement in its performance compared to its predecessors. All of the aforementioned studies did not claim that the Stage IV product was as accurate as rain gauge but suggested that it was capable of providing a very reasonable spatial representation of the precipitation with acceptable accuracy. Radar products were highly recommended for modeling practices that require high accuracy in the spatial and temporal distribution of the precipitation. In recent decades, numerous studies reported significant changes in mean precipitation over many regions not only in the US but throughout the world [18]. Several studies reported changes in the frequency and intensity of extreme events, i.e., floods and drought. Heavy precipitation events have become more frequent since the middle of the last century as reported by Osborn [19] in the UK, Sen Roy [20] in India, and by Karl [21] in the US. Precipitation has increased substantially in the state of Texas for the months December to March with an increase as large as 47% per century in Panhandle and Plains [22]. The steadiest increase, with a 15% per century rise, has been seen in East Texas, which receives a greater amount of precipitation during December to March than any other Texas climatic regions, in both absolute and relative terms. Overall, long-term precipitation trends range from about 5% per century in the Panhandle and Plains and Far West Texas to about 20% per century in South Texas and Southeast Texas. Drought has persisted in the TexMex Region, including Eastern Texas in 2011 and 2012 [23] but was followed by very wet years starting from 2015. The cost of the 2011 drought in Texas and surrounding regions in terms of U.S. agricultural losses was estimated to be $12 billion by The National Climatic Data Center [24]. Even though the most significant attribute of precipitation is the amount of precipitation that most of the rain gauges capture daily, precipitation frequency is another crucial component that needs in-depth analysis. The frequency of precipitation reveals information that is crucial in the water resource management and agricultural sectors, e.g., for better estimation of the amount and timing of irrigation and fertilizer application [25]. Only a few studies examined the precipitation frequency on regional and global scales [25,26]. The main limitation of these studies was that the majority were conducted at coarse spatial and temporal resolutions and, therefore, mask a significant part of the inherent variability of precipitation. For example, a daily analysis will provide very limited information Water 2020, 12, 1378 3 of 16 Waterbecause 2020 it, 12 seldom, x FOR PEER rains REVIEW all day. Moreover, the spatial resolution used in these studies (0.5 0.53 ofand 17 ◦ × ◦ 2.5 2.5 ) is only suitable for synoptic analysis at a global or continental scale. 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