**Zain Nawaz 1,2, Xin Li 3,4, Yingying Chen 3,4,\*, Naima Nawaz 5, Rabia Gull <sup>6</sup> and Abdelrazek Elnashar <sup>7</sup>**


Received: 30 September 2020; Accepted: 22 October 2020; Published: 6 November 2020

**Abstract:** Spatial and temporal precipitation data acquisition is highly important for hydro-meteorological applications. Gridded precipitation products (GPPs) offer an opportunity to estimate precipitation at different time and resolution. Though, the products have numerous discrepancies that need to be evaluated against in-situ records. The present study is the first of its kind to highlight the performance evaluation of gauge based (GB) and satellite based (SB) GPPs at annual, winter, and summer monsoon scale by using multiple statistical approach during the period of 1979–2017 and 2003–2017, respectively. The result revealed that the temporal magnitude of all the GPPs was different and deviate up to 100–200 mm with overall spatial pattern of underestimation (GB product) and overestimation (SB product) from north to south gradient. The degree of accuracy of GB products with observed precipitation decreases with the increase in the magnitude of precipitation and vice versa for SB precipitation products. Furthermore, the observed precipitation revealed the positive trend with multiple turning points during the period 1979–2005. However, the gentle increase with no obvious break point has been detected during the period of 2005–2017. The large inter-annual variability and trends slope of the reference data series were well captured by Global Precipitation Climatology Centre (GPCC) and Tropical Rainfall Measuring Mission (TRMM) products and outperformed the relative GPPs in terms of higher R2 values of <sup>≥</sup> 0.90 and lower values of estimated RME ≤ 25% at annual and summer monsoon season. However, Climate Research Unit (CRU) performed better during winter estimates as compared with in-situ records. In view of significant error and discrepancies, regional correction factors for each GPPs were introduced that can be useful for future concerned projects over the study region. The study highlights the importance of evaluation by the careful selection of potential GPPs for the future hydro-climate studies over the similar regions like Punjab Province.

**Keywords:** gridded precipitation products; abrupt changes; trends; statistical indicators; agriculture; Pakistan

#### **1. Introduction**

Accurate and reliable estimates of global climate patterns are directly associated with the regional variation in precipitation [1]. The changes in amount and pattern of precipitation could directly influence the water resources and agriculture of the concerned regions [2]. Therefore, understanding the spatiotemporal variation in precipitation on the regional scales is of great importance in climate monitoring and in hydro-climate studies [3]. Several researchers have reported the spatiotemporal variations of precipitation for different regions of the world [4–7]. There is a growing agreement that long term changes in precipitation could alter the ecological and hydrological processes [8] and underpin our knowledge of global and regional climate change [9]. These accurate and reliable precipitation records underpin our knowledge of regional and global climate change, as well as their possible impacts on water resources [10,11].

In general, gauge measurements are the basic and reliable way of precipitation data acquisition [12]. Unfortunately, scarce gauge records, irregular distribution, limited data access, and poor spatial coverage hinder their use in conducting hydro-meteorological studies and climate change assessments [13,14]. In recent decades, with the advancement in remote sensing and geo-information technology, the gridded precipitation products (hereafter GPPs) has proven to be a reliable and cost-effective way of retrieving gridded precipitation data at various spatial and temporal scales across the globe [15]. These precipitation data either derived from satellite products or from the nationwide meteorological stations by using different interpolated algorithms and computational techniques by considering the physical characteristics (slope and elevation) of different regions. These multi-source data products are often applied as climatological input for hydro-climate simulation studies in data scarce extents to bridge the gap at regional scale [16] and there has been a considerable increase in the use of these products, owing to their easy accessibility, spatiotemporal coverage, and fine resolutions [17].

The evaluation of GPPs has also proved useful for different hydro-climate applications. The precipitation variability in different Gridded Data Products (GDPs) has been quantified for different regions across the globe [18–20]. Various studies were carried out in recent years to assess the performance of GDPs, revealing considerable differences between the products at the regional scale [21]. Furthermore, there are uncertainties that are associated with GPPs, because of the variability in spatial and temporal coverage, lack of in-situ observations, relocation of gauges, and data processing practices [22]. Thus, the reliability and accuracy of GPPs varies with time and regional climate [23]. Therefore, it is highly important to assess and evaluate the performance and capability of the GPPs at regional scales, especially the arid and semi-arid regions that are more sensitive to insignificant changes in climatic characteristics due to its fragile ecosystems [24]. Such regions are characterized by very complex hydrological systems that often exhibit extreme behaviors, such as extended droughts due to prolonged dry spell and floods due to high-intensity precipitation [25]. The predominantly arid and semi-arid climate and geographical location in the fast temperature rising region have made Pakistan one of the most vulnerable countries in the world to climate change [1]. Moreover, the natives of the country are mostly engaged in agriculture, a highly susceptible sector to climate change, with limited resources to adapt to changing circumstances [26].

Few studies were carried out in order to evaluate the performance of different GPPs against the reference data, which mainly focused on the basin level or higher altitude sites of the country and ignored the important segment of spatiotemporal precipitation variations in agricultural region of the country. For instance, [27] assessed the precipitation distribution in the high altitude region of Hindu-Kush-Himalaya basins by using different precipitation products. The study reported the better performance of ECMWF Re-Analysis (ERA-Interim) product at high catchments as compared with WATCH Forcing Data Methodology (WFDEI) and APHRODITE products. Using different gridded precipitation products, [28,29] reported the significant errors in different gridded product; however, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product performed better in the high catchments of the Indus basin. On the contrary, [30] reported

the overestimation of TMPA products against reference data using the simple statistical metric approach over the complex topography of Pakistan. The present study is first of its kind, which aims to bridge the gap of knowledge with a detailed multiple-scale assessment of the spatiotemporal uncertainties of selected global precipitation products that are generated by different sources by using different statistical metrics, trends evaluation and comparison approach over the Punjab province, Pakistan. It is worth mentioning that Punjab province is highly important in the perspective of agriculture and irrigated farming as it produces major agriculture commodities of the country and it is highly vulnerable to changes in most of meteorological parameters with high frequency events and, hence. is highly prone to climate change [31]. In this study, we aim to assess the quality and differences of the different GPPs generated from multiple sources, i.e., Gauge Based (GB) products, Global Precipitation Climatology Centre (GPCC), Center for Climatic Research, University of Delaware (UDel), Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Climate Prediction Centre (CPC), Climatic Research Unit, University of East Angelia (CRU) and Satellite Based (SB) products, Tropical Rainfall Measuring Mission (TRMM, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS), Global Satellite Mapping of Precipitation (GSMap) and Climate Hazard Group Infrared Precipitation with Station Data(CHIRPS) during the period of 1979–2017 and 2003–2017, respectively, over the Punjab province in Pakistan. Secondly, to evaluate and compare the changes in temporal trends and abrupt turning points in selected GPPs against the reference data for the study region. We perceive the usefulness of this study as multi-directional, because the findings of the study could be used as baseline for the selection of potential GB and SB GPPs over multiple time scales for different hydro-meteorological studies.
