**1. Introduction**

With successive and rapid warming during the past decades, increasing evidence suggests that climate extremes (e.g., extreme precipitation, heatwaves, and droughts) have changed across the world [1]. Of climate extremes, extreme precipitation is believed to be one major cause of the water-related disasters, e.g., floods and landslides [2–5]. These water-related disasters often result in enormous loss of life and destruction and have become a major obstacle to the sustainable development of society and the economy [6–8]. The Global Emergency Disaster Database stated that from 1970 to 2013 across the world, more than ten thousand water-related disasters happened, impacting more than 6.6 billion people, and leading to more than USD 2600 billion in damage, with the death of 3.5 million people [9]. In one word, the adverse impact induced by extreme precipitation on life and socio-economy are enormous, and therefore it is very necessary and critical to understand extreme precipitation (e.g., spatial patterns, changes, and underlying mechanisms) to reduce the related disasters and to develop reasonable prevention strategies.

Despite that, studying extreme precipitation still presents immense challenges because of difficulties in obtaining accurate, uninterrupted, and uniform precipitation data

**Citation:** Sun, S.; Wang, J.; Shi, W.; Chai, R.; Wang, G. Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China. *Remote Sens.* **2021**, *13*, 1747. https://doi.org/ 10.3390/rs13091747

Academic Editor: Joo-Heon Lee

Received: 23 March 2021 Accepted: 28 April 2021 Published: 30 April 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

at the regional and global scale. So far, three pathways are employed to measure precipitation, i.e., direct observation with various gauges and retrievals from radar and satellite techniques. Gauge precipitation is believed to be the most accurate measurement [10]. However, due to inaccessibility and higher costs for installations and maintenance, there are issues to using gauge precipitation, i.e., limited spatial representativeness and coverage, time discontinuity, and short time span [11]. In view of this, gauge precipitation cannot fully satisfy the specific requirements of academic studies and practical applications, e.g., long-term (>30 years), continuous (space and time) precipitation observations for climate studies. Radar-based precipitation retrievals have filled gaps in gauge precipitation to some extent, e.g., more extensive coverage. However, radar-based precipitation still has potential issues, e.g., the backwardness of radar technology in some countries and radar blockage due to topography [12–14]. In the past decades, very great advances have been made in technologies of satellites and sensors. Subsequently, various satellite-based precipitation products have been proposed based on radiance information received by satellite-carried sensors and different statistical and/or physics-based retrieval algorithms, such as Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (GSFC; [15]); the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH; [16]); the Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP; [17]), the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN; [18]); the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG; [19]); and the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS; [20]). These satellite-based products provide an opportunity for academic studies and practical applications to fulfill various requirements of precipitation data.

Considering specific needs and goals, it is necessary to conduct quantitative evaluations of a given satellite-based precipitation product using dependable reference data, which can improve the confidence level of the related academic studies and ensure high efficiencies in practical applications. Studies have extensively assessed various satellitebased precipitation data over the world with a variety of statistical metrics [11,21–23]. For example, Shen et al. [21] suggested that CMORPH performed better than TRMMM and PERSIANN in capturing spatial and temporal variations in most of China, especially for reproducing summer precipitation characteristics. Tan et al. [22] compared multiple satellitebased precipitation estimates over Malaysia and found that TRMM showed higher coincidence with the observational precipitation. Results from Alijanian et al. [11] showed that Multi-Source Weighted-Ensemble Precipitation (MSWEP), PERSIANN-Climate Data Record (CDR), and TRMM could better identify rainfall and non-rainfall events in Iran, and PERSIANN-CDR had higher capacity than the other datasets in representing heavy rainfall. These studies provided references for theoretical understanding, and development of satellite-retrieved methodologies and their practical applications.

In China, more and more evidence indicates that extreme precipitation and related disasters have varied [24]. Taking the Huai River Basin (HRB) as an example, it frequently suffers from floods, with more than 350 floods during the past 50 decades and local and regional floods occurring nearly every two years [25]. Since the beginning of the 21st century, several severe extreme precipitation events (e.g., 2003, 2005, and 2007) and related floods happened in the HRB [25,26]. Incomplete statistics reported that more than 58 million people in the HRB were affected by the 2003 floods, with the flood-affected arable area exceeding 52,000 km<sup>2</sup> , 390 thousand houses collapsed, and direct economic losses of more than CNY 35 billion [27]. Yin et al. [28] projected that extreme precipitation with return periods of 20- and 50-years during 2001–2050 would considerably increase with exacerbating global warming, particularly in some places with increases of more than 30%; this implies that the HRB has the potential to face a larger flood risk in future. Therefore, reasonably managing extreme precipitation-induced floods and taking efficient prevention measures are very important for regional and national food security and food production capacity. To this end, selecting a reliable, long-term, continuous precipita-

tion measurement strategy with a relatively high spatio-temporal resolution is of much significance when attempting to mitigate the extreme precipitation-induced flood risk in the HRB; consequently, we chose the PERSIANN-CDR precipitation product here for evaluation with a high density of gauge records. Despite the fact that this product has been assessed over different regions of the world and even in China [23,29–32], issues still exist. For example, how good is the overall performance (e.g., Kling–Gupta Efficiency (*KGE*), which integrates impacts of bias, variability, and correlation coefficient on the overall performance [33]) of the PERSIANN-CDR in detecting extreme precipitation, and does this product reproduce linear trends of extreme precipitation? Particularly, the latter issue has been paid more and more attention in recent years (e.g., [34,35]) because the assessments regarding precipitation trends are the necessary foundation on which to accurately explore precipitation long term changes, especially for the regions with limited and even no observations. Therefore, this study aimed to: (1) comprehensively validate the PERSIANN-CDR performance in detecting different extreme precipitation indices (e.g., the precipitation amount-, duration-, and intensity-based indices) over the HRB, based on four validation metrics (i.e., three continuous validation metrics and one overall performance metric), and (2) detect the PERSIANN-CDR capacity to reproduce linear trends of various extreme precipitation indices. Results of this study will serve as a valuable reference for potential users in the HRB and for the PERSIANN-CDR developers to use to improve the algorithm for obtaining a more accurate extreme precipitation product. measurement strategy with a relatively high spatio-temporal resolution is of much significance when attempting to mitigate the extreme precipitation-induced flood risk in the HRB; consequently, we chose the PERSIANN-CDR precipitation product here for evaluation with a high density of gauge records. Despite the fact that this product has been assessed over different regions of the world and even in China [23,29–32], issues still exist. For example, how good is the overall performance (e.g., Kling–Gupta Efficiency (*KGE*), which integrates impacts of bias, variability, and correlation coefficient on the overall performance [33]) of the PERSIANN-CDR in detecting extreme precipitation, and does this product reproduce linear trends of extreme precipitation? Particularly, the latter issue has been paid more and more attention in recent years (e.g., [34,35]) because the assessments regarding precipitation trends are the necessary foundation on which to accurately explore precipitation long term changes, especially for the regions with limited and even no observations. Therefore, this study aimed to: (1) comprehensively validate the PER-SIANN-CDR performance in detecting different extreme precipitation indices (e.g., the precipitation amount-, duration-, and intensity-based indices) over the HRB, based on four validation metrics (i.e., three continuous validation metrics and one overall performance metric), and (2) detect the PERSIANN-CDR capacity to reproduce linear trends of various extreme precipitation indices. Results of this study will serve as a valuable reference for potential users in the HRB and for the PERSIANN-CDR developers to use to improve the algorithm for obtaining a more accurate extreme precipitation product.

production capacity. To this end, selecting a reliable, long-term, continuous precipitation

*Remote Sens.* **2021**, *13*, x FOR PEER REVIEW 3 of 20

#### **2. Data and Methodology 2. Data and Methodology**

#### *2.1. Study Region and Data 2.1. Study Region and Data*

The HRB is located in eastern China between 30–39◦N and 111–123◦E (Figure 1). It has a drainage area of approximately 33,000 km<sup>2</sup> , covering the northern parts of Jiangsu and Anhui, a small part of Hubei, and most of Shandong and Henan. The HRB has a vast plain, with many lakes and depressions, and is moderately mountainous (elevation generally from 1000 to 2000 m above sea level) near the western boundary, mid-eastern part, and Shandong peninsula. A typical semi-humid monsoon climate prevails in this basin, with regional average annual temperature of 14 ◦C and precipitation of 806 mm. The HRB is located in eastern China between 30–39°N and 111–123°E (Figure 1). It has a drainage area of approximately 33,000 km2, covering the northern parts of Jiangsu and Anhui, a small part of Hubei, and most of Shandong and Henan. The HRB has a vast plain, with many lakes and depressions, and is moderately mountainous (elevation generally from 1000 to 2000 m above sea level) near the western boundary, mid-eastern part, and Shandong peninsula. A typical semi-humid monsoon climate prevails in this basin, with regional average annual temperature of 14 °C and precipitation of 806 mm.

**Figure 1.** Location of the HRB with gauges and PERSIANN-CDR grids. The digital elevation model (DEM) with a spatial resolution of 90 m is from http://srtm.csi.cgiar.org/ (accessed on 1 January 2021). **Figure 1.** Location of the HRB with gauges and PERSIANN-CDR grids. The digital elevation model (DEM) with a spatial resolution of 90 m is from http://srtm.csi.cgiar.org/ (accessed on 1 January 2021).

The daily PERSIANN-CDR product has near-global (60°S–60°N) coverage with a time span from 1983 to the present and a spatial resolution of 0.25° × 0.25°. It is a new retrospective satellite-based dataset developed by the U.S National Climatic Data Center The daily PERSIANN-CDR product has near-global (60◦S–60◦N) coverage with a time span from 1983 to the present and a spatial resolution of 0.25◦ × 0.25◦ . It is a new retrospective satellite-based dataset developed by the U.S National Climatic Data Center (NCDC) Climate Data Record program in NOAA [36] and can be downloaded from the U.S NOAA National Centers for Environment Information (NCEI; https://www.ncdc.noaa.

gov/cdr/atmospheric/precipitation-persiann-cdr, accessed on 1 January 2021) and the Centre for Hydrometeorology and Remote Sensing (CHRS) data portal (http://chrsdata. eng.uci.edu, accessed on 1 January 2021). For this evaluation, daily precipitation observed at more than 200 gauges during 1983–2012 were collected from the China Meteorological Administration (CMA). The basic quality issues within the observation precipitation data, e.g., sensors and measurement errors and inherent errors in measurement procedures and methods [37–39], were solved by the CMA. However, it should be noted that data quality issues of missing values and inhomogeneity (e.g., inhomogeneity due to changes in measurement procedures, methods, and locations [36–38]) within observations still remained, and thus we preprocessed the observation data following the procedures below. Firstly, we determined days with missing values for each year and each site. Sites with data available for more than 330 days per year were retained, and missing values of these sites were filled with data from nearby sites by bilinear regression. Subsequently, time series homogeneity was examined with the Pettitt test [40], and the sites with time series not passing the significance test (*p* < 0.05) were removed. Finally, 182 sites remained (Figure 1). To match the PERSIANN-CDR data, we followed Katiraie-Boroujerdy et al. [41] and gridded the sites into grids with a resolution 0.25◦ × 0.25◦ (Figure 1). The final observational value for a certain grid was calculated by averaging daily records of the gauge(s) within this grid. Here, the study period is 1983–2012, considering the data availability of both the PERSIANN-CDR and observations.

#### *2.2. Methodology*

#### 2.2.1. Extreme Precipitation Index

Due to a lack of a unified definition of extreme event indicators in different regions, further research of global extreme weather and climate events has been hindered to some extent. For addressing this issue, the World Meteorological Organization (WMO) and the World Climate Research Program (WCRP) jointly established the Expert Team on Climate Change Detection and Indices (ETCCDI) in the early 21st century and defined a series of climate indices to study extreme climate change globally and regionally. Since then, the ETCCDI extreme climate indices have been extensively used across the globe [41–46]. In this study, we selected 12 indices to comprehensively evaluate the performance of the PERSIANN-CDR across the HRB. Considering characteristics of extreme precipitation, we categorized the 12 indices into four classes (Table 1), i.e., (1) precipitation amount-based indices, (2) precipitation duration-based indices, (3) precipitation frequency-based indices, and (4) precipitation intensity-based indices.

#### 2.2.2. Validation Metrics

To quantitatively evaluate the performance of PERSIANN-CDR data, we selected a relatively new, widely-used validation metric, the Kling–Gupta Efficiency (*KGE*; [33]), which can be used to measure overall performance. The equations can be expressed as

$$KGE = 1 - \sqrt{(R - 1)^2 + (\beta - 1)^2 + (\gamma - 1)^2},\tag{1}$$

$$R = \frac{\sum\_{i=1}^{N} (\mathcal{S}\_i - \mu\_s)(\mathcal{O}\_i - \mu\_o)}{\sqrt{\sum\_{i=1}^{N} (\mathcal{S}\_i - \mu\_s)^2} \sqrt{\sum\_{i=1}^{N} (\mathcal{O}\_i - \mu\_o)^2}},\tag{2}$$

$$
\beta = \frac{\mu\_{\rm s}}{\mu\_0} \,\,\,\,\,\tag{3}
$$

$$\gamma = \frac{\sigma\_{s}/\mu\_{s}}{\sigma\_{0}/\mu\_{0}},\tag{4}$$

where *S<sup>i</sup>* is the PERSIANN-CDR precipitation value of the *i*th data pair, and *O<sup>i</sup>* is the observational value. *µ<sup>s</sup>* and *µ<sup>o</sup>* (*σ<sup>s</sup>* and σ*o*) are means (standard deviations) of PERSIANN-CDR and observational precipitation, respectively. *KGE* ranges between—∞ and 1, of which

1 implies a perfect overall performance. *R* is the correlation coefficient. *β* measures the average tendency of PERSIANN-CDR precipitation to be larger (i.e., *β* > 1) or smaller (i.e., *β* < 1) than the observation, with an optimal value of 1. Regarding *γ*, its optimal value of 1 represents that the PERSIANN-CDR can perfectly reproduce the observational precipitation variability, while values below and above 1, respectively, indicate the underestimated and overestimated variability. After calculating these metrics at each grid with the above equations, their spatial maps were drawn using the ArcGIS 10.2 software package for conveniently comparing the PERSIANN-CDR performance at space.


**Table 1.** Definitions of the selected 12 extreme precipitation indices.
