Drought monitoring utility of satellite-based

Accepted Manuscript Research papers Drought monitoring utility of satellite-based precipitation products across mainland China Ruida Zhong, Xiaohong Chen, Chengguang Lai, Zhaoli Wang, Yanqing Lian, Haijun Yu, Xiaoqing Wu PII: DOI: Reference:

S0022-1694(18)30851-5 https://doi.org/10.1016/j.jhydrol.2018.10.072 HYDROL 23238

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

28 June 2018 28 October 2018 30 October 2018

Please cite this article as: Zhong, R., Chen, X., Lai, C., Wang, Z., Lian, Y., Yu, H., Wu, X., Drought monitoring utility of satellite-based precipitation products across mainland China, Journal of Hydrology (2018), doi: https:// doi.org/10.1016/j.jhydrol.2018.10.072

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Drought monitoring utility of satellite-based precipitation products across mainland China Ruida Zhonga,b, Xiaohong Chena, Chengguang Lai b,c,*, Zhaoli Wang b,c,**, Yanqing Liand, Haijun Yue, Xiaoqing Wuf a

Center for Water Resources and Environment Research, Sun Yat-sen University, Guangzhou,

510275, China;

b

School of Civil Engineering and Transportation, South China University of Technology,

Guangzhou 510641, China;

c

Guangdong Engineering Technology Research Center of Safety and Greenization for Water

Conservancy Project, Guangzhou 510641, China;

d

The Prairie Research Institute, University of Illinois at Urbana-Champaign, 2204 Griffith Drive,

Champaign, IL 61820, USA;

e

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research

Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China;

f

South China Institute of Environment Sciences, Ministry of Environment Protection of PRC，

Guangzhou 510535, China.

*Corresponding author. E-mail address: [email protected] (C. Lai); [email protected] (Z. Wang)

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Abstract

This study mainly evaluated and compared satellite-based quantitative precipitation estimate products (QPEs) for the drought monitoring of mainland China. Two long-term (more than 30 a) satellite-based QPEs, i.e. the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and a short-term (18a) QPE, i.e. the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 are considered. Two widely used drought indices, the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI), are chosen to evaluate the drought monitoring utility. The 3B42V7 was only evaluated with PDSI due to the short data records. The results show that all the three QPEs perform satisfactorily in the eastern part of China when using both SPI and PDSI. However, their performances for west China could not be clearly determined due to the sparse gauge networks. 3B42V7 features best performance among the three QPEs in the evaluation using PDSI. To further spatiotemporally evaluate the drought utility of the QPEs, four typical drought-affected regions, i.e. northeast China (NEC), Huang-Huai-Hai plain (3HP), southwest China (SWC), and Loess plateau (LP) were extracted from mainland China for specific case studies. Temporally, all three QPEs are able to detect the typical drought of the four regions with both SPI and PDSI, and 3B42V7 presents the least deviation in PDSI estimate. Spatially, both CHIRPS and 3B42V7 accurately catch the spatial centers and extent of the typical drought events, while PERSIANN-CDR could not match the spatial patterns of drought events well. Generally, the long-term PERSIANN-CDR and CHIRPS perform satisfactorily in drought detection and are

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suitable for drought utility; however, caution should be applied when studying the spatial variation of drought using PERSIANN-CDR. CHIRPS could also be suitable for near-real-time drought monitoring for its shorter time latency of data release. The short-term 3B42V7 also performs well in many cases, and has thus considerable potential for drought monitoring.

Keywords: Satellite-based precipitation product; PERSIANN-CDR; CHIRPS; TMPA 3B42V7; Drought monitoring; Mainland China

1. Introduction

Drought is one of the costliest, most frequent, and most wide-spread natural disaster around the world, deeply impacting agricultural production and the ecological environment. Droughts can occur almost everywhere around the world including in humid regions (Dai, 2011; Mishra and Singh, 2010; Lai et al., 2019). Under the influence of climate change, the global air temperature is continuously increasing, the spatiotemporal pattern of precipitation and land use is expected to significantly change over the next decades (IPCC, 2013; Liu et al., 2014, 2017; Mishra and Lilhare, 2016; Tang et al., 2017; Wu et al., 2018). Hence, drought disasters might be further aggravated around the world, with devastating impact on the respective population (Dai, 2013). Therefore, it is urgent and necessary to develop more accurate and effective drought monitoring tools.

As a complicated meteorological phenomenon, drought has multiple manifestations and is commonly classified into several types, e.g. meteorological drought, agricultural drought, and hydrological drought (American Meteorological Society, 1997). The drought index is a commonly used and effective method to quantitatively characterize and monitor drought events (Xu et al., 3

2015). The Palmer Drought Severity Index (PDSI) (Palmer, 1965; Alley, 1984) and the Standardized Precipitation Index (SPI) (Mckee et al., 1993) are the two most widely used meteorological drought indices that utilize quite different mechanisms. The PDSI is a semi-physical drought index based on the balance of moisture supply and demand, while the SPI is a statistical drought index based on the probability distribution of long term precipitation records.

Precipitation is a key meteorological variable for drought monitoring and for the calculation of many drought indices (including both PDSI and SPI). Conventionally, precipitation data are derived from observations of in-situ rain gauge networks. However, the rain gauge networks are often sparsely and unevenly distributed, and even unavailable in some remote regions. Moreover, unlike other meteorological variables such as air temperature, precipitation always features high spatial variation and uncertainty, thus it is sometimes difficult to obtain an accurate precipitation estimate via spatial interpolation (Tang et al., 2015; Wang et al., 2017c). Since the performance of drought indices is largely influenced by the density of gauge networks (Trenberth et al., 2014), it will be difficult for sparse and uneven distributed in-situ gauge networks to meet the requirements of drought monitoring.

With the evolution of the remote sensing instruments and precipitation retrieval algorithms, a series of semi-global, grid-based quantitative precipitation estimate products (QPEs), based on satellite remote sensing information, were released and available to the public. The most widely used QPEs include the Remotely Sensed Information using Artificial Neural Networks (PERSIANN) (Hsu et al., 2004), the Tropical Rainfall Measurement Mission (TRMM)

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Multi-satellite Precipitation Analysis (TMPA) (Huffman et al., 2010; 2007), the Climate Prediction Center morphing technique (CMORPH) (Joyce et al., 2004), and Integrated Multi-Satellite Retrievals for Global precipitation measurement (IMERG) (Huffman et al., 2012). With wide coverage (above 50°NS latitude band), high spatial resolution (up to 0.25°), and temporal resolution (up to hourly time scale), those QPEs largely compensate for the deficiency of in-situ rain gauge networks. During the past decade, the accuracy and hydrological simulation performance of the QPEs have been fully evaluated around the world (Yong et al., 2013; Qiao et al., 2014; Shah and Mishra, 2014; Tong et al., 2014; Casse et al., 2015; Sun et al., 2016; Wang et al., 2017d), and the QPEs also have been widely applied for several meteorological or hydrological applications such as water resource exploration and the forecasting of extreme precipitations and floods (Awange et al., 2014; Casse et al., 2015; Pombo and Oliveira, 2015; Nastos et al., 2016; Prakash et al., 2016; Tekeli and Fouli, 2016; Fenta et al., 2017; Kim et al., 2017; Hobouchian et al., 2017). However, due to their short observation history (usually shorter than 20 a), applications of the QPEs on the drought monitoring are usually restricted since the data records of at least 30a are required by drought index calculation (Guo et al., 2016). To connect with the long-term historical records is a verified available solution (AghaKouchak and Nakhjiri, 2012; Sheffield et al., 2013; Nijssen et al., 2014; Shah and Mishra, 2015; Shah et al., 2017), but it usually means the extra efforts to maintain the consistency between the historical records and the QPEs.

To satisfy the demand of studies and applications of climate and drought, some long-term QPEs were developed such as the PERSIANN-Climate Data Records (PERSIANN-CDR) (Ashouri et al., 2014) and the Climate Hazards Group Infrared Precipitation with Stations 5

(CHIRPS) (Funk et al., 2014; 2015). Until now, both PERSIANN-CDR and CHIRPS have long-term records of more than 30 a (PERSIANN-CDR provides data records since 1983, while CHIRPS provides data records since 1981), presenting a great potential for drought monitoring. Several relevant evaluations have reported that PERSIANN-CDR has high spatial and temporal consistency and exhibits good agreement of probabilistic distribution with in-situ observations (Ashouri et al., 2014; Miao et al., 2015; Alijanian et al., 2017; Katiraie-Boroujerdy et al., 2017). Some studies proved that CHIRPS performs well in many cases (Katsanos et al., 2015; Zambrano et al., 2016; Zheng et al., 2016; Shrestha et al., 2017). Moreover, As the most famous QPE with the best comprehensive performance among several QPEs (Hu et al., 2013; Tong et al., 2014; Sun et al., 2016; Zhu et al., 2016), the TMPA 3B42 products released by the TRMM mission (Huffman et al., 2010; 2007) might also have the potential for drought monitoring, although they have a relatively short period of data records of only 18 a (up to 2015). According to relevant studies, both the TMPA 3B42 and 3B43 products can temporally and spatially catch the drought events although short-term records (10 a and 12 a) were used (Zeng et al., 2012; Sahoo et al., 2015; Jiang et al., 2017). Therefore, both the long-term products (PERSIANN-CDR and CHIRPS) and the short-term TMPA 3B42 products are valuable for a further exploration of their drought monitoring utility. Some studies have been conducted to preliminarily evaluate the drought monitoring utility of some QPEs on global and regional scales, such as Sahoo et al. (2015) who found that the post-real-time TMPA 3B42 products performed satisfactorily in detecting macroscale droughts even with short-term data records. Vernimmen et al. (2012) found the potential of the near-real-time 3B42 product for drought monitoring after bias correction. Guo et al. (2016) found that PERSIANN-CDR is skillful for drought monitoring in eastern China but performed poorly in 6

northwest China. Zambrano et al. (2016), Agutu et al. (2017), and Aadhar and Mishra (2017) found that CHIRPS performed well in drought utility in Chile, South Asia, and East Africa, respectively. All these studies have verified the enormous potential of QPEs. However, previous studies mostly focused on evaluating the QPEs via SPI (a statistical-based drought index) while the performance of QPEs adopted by the physical-based drought indices (e.g., the PDSI) is relatively less reported. Sheffield et al. (2013) and Nijssen et al. (2014) applied the QPEs for drought monitoring using the land surface models, but their efforts mainly focused on model construction and parameter estimation. The PDSI, with a definite physical meaning and in theory, might also be an ideal drought index for QPE-based drought monitoring, and should be relatively less sensitive to the length of data records hence might be more suitable for some short-term precipitation datasets such as the 3B42V7 product.

Mainland China, with its complicated climatic conditions, large population, fragile ecosystem, and rapidly developing economy, is vulnerable to climate change and drought disaster (Lai et al., 2018). Drought is one of the most frequent and severe natural disasters in China and has caused considerable harvest failure during past decades (Piao et al., 2010). For example, during 1999 to 2000, north China was affected by severe and continuous drought of two year, causing a harvest failure of 20% to 30% (Wei et al., 2004); during 2009 to 2010, a severe drought disaster hit southwest China, affecting a population of nearly eight million and causing an economic loss of more than 3.5 billion U.S. dollars (Barriopedro et al., 2012). Moreover, several regions of mainland China, including some main grain-producing areas, tend to become drier (Wang et al., 2017a). Hence, it is urgent and meaningful to explore the utility of the QPEs for more effective drought monitoring and forecasting in mainland China, especially for areas without rain gauge. 7

However, although some QPEs have been evaluated individually over mainland China, comprehensive evaluation and comparison of several QPEs for their drought monitoring utilities over mainland China, especially the evaluations using both SPI and PDSI, still remain unreported.

Above all, the aim of this study is to evaluate and compare the drought monitoring utility of three widely-used satellite-based QPEs (i.e. PERSIANN-CDR, CHIRPS, and TMPA 3B42V7 (hereafter abbreviated as 3B42V7)) across mainland China using two widely used drought indices (i.e. SPI and PDSI). This study is expected to provide a reference for the usage of the QPEs for drought monitoring.

2. Study area, datasets, and methods

2.1. Study area

Mainland China (Fig.1) is located on the northwestern shore of the Pacific Ocean, comprising vast territory and various climate conditions. According to Xu et al. (2002), mainland China could be divided into nine agricultural regions regarding different climate, topography, water resource, vegetation, and agricultural geographical distribution (Fig.2), including Northeast China (NEC), Inner-Mongolia (IM), Huang-Huai-Hai plain (3HP), Northwest China (NWC), Tibetan plateau (TP), Loess plateau (LP), Southwest China (SWC), Downstream Yangtze River (DYR), and South China (SC). The division helps better understand the results of this study and has also been successfully applied in related studies (Wang et al., 2017b; Li et al., 2018). Among the regions, the TP is dominated by the alpine and plateau climate; SWC, DYR, and SC are governed by the sub-tropical monsoon climate; 3HP and NEC are predominated by temperate monsoon climate while NWC, IM and LP are mainly dominated by temperate continental climate. NEC and 3HP 8

are the major grain-producing regions of China, in which 3HP was ever affected by a continuous severe drought disaster during 1999-2000 (Wei et al., 2004); SWC has also suffered a disastrous drought that occurred over southwest China during 2009-2010 (Barriopedro et al., 2012; Zhang et al., 2013a).

2.2. Satellite-based QPE datasets

PERSIANN-CDR (Ashouri et al., 2014) is developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI) for the Climate Data Record (CDR) program of the National Oceanic and Atmospheric Administration (NOAA). It provides long-term gridded precipitation data from 1983 onwards with a spatial resolution of 0.25° between the latitude band of 60°S to 60°N. PERSIANN-CDR is built to address the need for a consistent, long-term and high resolution global precipitation data for studying the changes and trends in precipitation under the influence of climate change and natural variability. PERSIANN-CDR is generated from the Gridsat-B1 infrared (IR) data by the PERSIANN algorithm, and has been corrected with the monthly Global Precipitation Climatology Project (GPCP) data to reduce bias. Monthly PERSIANN-CDR data from 1983 to 2015 (33a) were derived from the website of CHRS (chrsdata.eng.uci.edu) for the present study.

CHIRPS (Funk et al., 2015; 2014) is developed by the Climate Hazards Group (CHG), providing long-term precipitation data at a spatial resolution of 0.05° between the latitude band of 50°S to 50°N from 1981 to present. CHIRPS is firstly generated by merging the IR-derived precipitation with the Climate Hazards Group Precipitation Climatology (CHPclim) and is then blended with the ground station observations using a modified Inverse Distance Weighing (IDW) 9

method (Funk et al., 2015). The CHIRPS product has been updated to version 2.0 and is available on the CHG website (chg.geog.ucsb.edu/data/). To remain consistent with the PERSIANN-CDR records, only the monthly CHIRPS v2.0 data of 1983-2015 were collected and were spatially aggregated to a 0.25° resolution.

The TMPA 3B42 series products (Huffman et al., 2007; 2010) are level-3 products released by the TRMM, a satellite-based precipitation measurement project jointly launched by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA). Until now TMPA 3B42 products have been updated to V7 version (3B42V7) and are available on the NASA website (pmm.nasa.gov/data-access/downloads/trmm). The 3B42V7 products provide gridded precipitation data from January 1998 with a spatial resolution of 0.25°, covering the latitude band between 50°S and 50°N (covering most of mainland China). The 3B42V7 products consist of two products: the near-real-time 3B42V7RT product and the post-real-time 3B42V7 product, in which the former is generated only from the satellite remote sensing information, while the latter is further corrected by the ground-based rain gauge observations. Here we only focus on the post-real-time 3B42V7 product. Daily 3B42V7 data from 1998 to 2015 (18a) were collected and were aggregated into monthly time scale for the calculation of drought indices.

2.3. In-situ observation datasets

The China Gauge-based Precipitation Daily Analysis (CGDPA) product (Shen and Xiong, 2016) was selected as a reference to evaluate the drought monitoring utility of the QPEs. CGDPA is a grid-based precipitation data product developed by the China Meteorological Administration 10

(CMA), covering mainland China with a spatial resolution of 0.25°. CGDPA is generated from the daily precipitation observations of more than 2,400 gauge stations (Fig.2) distributed throughout mainland China by using an improved climatology-based optimal interpolation (OI) method with topographic correction (Shen and Xiong, 2016). The precipitation observations used to generate the CGDPA are subject to rigorous quality control (Shen and Xiong, 2016); therefore, they feature high accuracy and reliability. Due to the high density of the gauge network and the high quality of the precipitation observations, the CGDPA is reliable and is suitable as an in-situ precipitation observation reference to evaluate the QPEs. In this study, daily CGDPA data from 1983 to 2015 (33 a) were downloaded from the CMA website (data.cma.cn) and were accumulated to a monthly time scale for the evaluation of QPEs.

To calculate the potential evapotranspiration (PET) data for the calculation of PDSI, several relevant meteorological data (e.g. daily maximum and minimum air temperature, wind speed, and sunshine duration) of 818 meteorological stations among mainland China (see Fig.1) were collected. Since the PET estimation methods might significantly influence the performance of the PDSI (Frank et al., 2017; Sheffield et al., 2012), the Penman-Monteith (PM) equation (Penman, 1948; Allen et al., 1998), which is a physically-based PET estimation method with higher accuracy and reliability, was adopted to calculate the PET. The shortwave radiation data required by the PM equation were converted from the sunshine duration with the Angstrom formula (Martinez-Lozano et al., 1985). Additionally, shortwave radiation data from 129 radiation stations were used to estimate linear regression parameters (𝑎 and 𝑏) in the Angstrom formula. The PET data were firstly calculated at each meteorological station and then interpolated into each 0.25° grid cell via the thin plate spline (TPS) method (Green and Silverman, 1994). 11

2.4. The Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI)

The Standardized Precipitation Index (SPI) (Mckee et al., 1993) is a widely used statistical drought index based on the standardization of the precipitation anomaly. In the standardized procedure of the SPI, the precipitation records would firstly be used to fit a typical distribution (e.g. Pearson III or Gamma distribution). Then, the frequency of each of the precipitation records would be calculated according to the fitted distribution. Finally, these frequencies would be transformed into their corresponding quantiles of the standardized normal distribution to become the SPI value. The SPI could also be calculated at several time scales (normally 1-24 months) by accumulating precipitation records before the standardized procedure, to represent short and long-term drought events. For example, the SPI with a time scale of 3 months (the so called SPI3) represents seasonal drought, while the SPI at 12-month time scale (SPI12) represents annual drought events. Considering the characteristic of climate and agriculture of mainland China, the SPI3 representing the seasonal drought is selected for the present study.

2.5. The Palmer Drought Severity Index (PDSI)

The Palmer Drought Severity Index (PDSI) (Palmer, 1965) is a semi-physical drought index based on the land surface water balance process. The PDSI considers several factors including precipitation, evapotranspiration, and soil moisture recharge, hence could more comprehensively assess drought conditions under climate change (Liu et al., 2012; Sheffield et al., 2012; Dai, 2013). The PDSI is calculated by standardizing moisture departure, and the moisture departure is calculated by a two-layer bucket-type hydrological model using precipitation and PET data. With the close linkage to soil moisture, the PDSI could also be used for agricultural drought (Dai et al., 12

2004; Dai, 2011; Huang et al., 2015). The parameters of the standardized procedure of the conventional PDSI, including the climatic characteristic and duration factors, are empirically derived using the meteorological data of the central USA with its semi-arid climate. Therefore, the portability and spatial comparability of the conventional PDSI are relatively poor. Wells et al. (2014) developed a self-calibrating procedure to automatically obtain the climatic parameters that fit the local climate. However, this self-calibrating procedure might be unsuitable for the satellite-based QPEs with the short-term records, especially for 3B42V7. A China national standard of classification of meteorological drought with standard number of GB/T 20481-2006 (hereafter abbreviated as GB/T) (Zhang et al., 2006) provides a corrected calculation procedure of the PDSI specific for China:

𝑍𝑖 = 𝐾𝑚 ⋅ 𝑑𝑖 16.84 )⋅ ̅ ′ 𝑗=1 𝐷𝑗 𝐾𝑚

(1)

𝐾𝑚 = (∑12

′ 𝐾𝑚

′ 𝐾𝑚 = 1.6 ⋅ log 10 [(

̅̅̅̅𝑚+𝑅̅𝑚 +𝑅𝑂 ̅̅̅̅𝑚 𝑃𝐸 𝑃̅𝑚+𝐿̅𝑚

(2) ̅𝑚 ] + 0.4 + 2.8) ⁄𝐷

𝑋𝑖 = 0.755 ⋅ 𝑋𝑖−1 + 𝑍𝑖 ⁄1.63

(3)

(4)

Where 𝑑𝑖 represents the moisture departure of time step 𝑖, 𝑍𝑖 represents the moisture ̅𝑗 anomaly index, 𝐾𝑚 represents the climate characteristic at month 𝑚 (𝑚 = 1,2, … ,12 ), 𝐷 represents the annual mean absolute moisture departure of month 𝑚, 𝑃̅𝑚 , 𝐿̅𝑚 , ̅̅̅̅ 𝑃𝐸𝑚 , 𝑅̅𝑚 , and ̅̅̅̅ 𝑅𝑂𝑚 represent the annual mean precipitation, soil moisture loss, PET, soil moisture recharge, and runoff of month 𝑚, and 𝑋𝑖 represents the PDSI value of time step 𝑖.

The PDSI calculation procedure of GB/T is built based on long-term (about 40 a)

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meteorological data of several in-situ stations distributed around China, hence could represent the climate characteristic of mainland China. Therefore, this PDSI calculation procedure, without the complicated self-calibrating procedure of Wells et al. (2014), is expected to be suitable for the evaluation of the QPEs over mainland China, and was adopted in the present study.

2.6. Statistical metrics

To quantify the performance of the QPEs for drought monitoring, several statistical metrics were adopted for the evaluation. The Pearson Correlation Coefficient (CC) was used to quantify the consistency between the monthly precipitation or drought indices calculated via in-situ and QPE precipitation data, and the Root Mean Square Error (RMSE) was adopted to quantify the deviation of the QPE-based drought indices from the references. The relative bias (BIAS) was used to quantify the systematic bias of the QPEs. Their calculation formulas are as follows: 𝐶𝐶 =

̅ ̅ ∑𝑛 𝑖=1(𝑄𝑖 −𝑄 )(𝑂𝑖 −𝑂 )

(5)

̅ 2 𝑛 ̅ 2 √∑𝑛 𝑖=1(𝑄𝑖 −𝑄 ) ⋅∑𝑖=1(𝑂𝑖 −𝑂 ) 1

𝑅𝑀𝑆𝐸 = √ ∑𝑛𝑖=1(𝑄𝑖 − 𝑂𝑖 )2

(6)

𝑛

𝑄̅

BIAS = (𝑂̅ − 1) × 100%

(7)

Where 𝑄𝑖 and 𝑂𝑖 represent the precipitation records of the QPEs and in-situ observation, respectively, while 𝑄̅ and 𝑂̅ represent their averages. Higher CC and lower RMSE indicate better performance.

Probability of detection (POD) and false alarm ratio (FAR) were adopted to evaluate the capability to detect drought events of the QPEs, and their calculation formulas are: 𝑃𝑂𝐷 =

𝑛11

(8)

𝑛11 +𝑛10

14

𝐹𝐴𝑅 =

𝑛01

(9)

𝑛11 +𝑛01

Where n11 represents the number of months under drought condition detected by both in-situ observation and QPE, n10 represents the number of drought months that were only detected by in-situ observation, n01 represents the number of drought months that were only detected by QPE. Higher POD and lower FAR indicate better capability of drought event detection.

3. Results and analysis

3.1. Evaluation and comparison of the QPEs

Although PERSIANN-CDR and 3B42V7 have been evaluated individually among mainland China (Guo et al., 2016; Tang et al., 2016), the CHIRPS is still less concerned, suggesting a direct evaluation of the QPE precipitation is necessary. Fig.3 shows the spatial distribution of the CC and BIAS for the three QPEs versus in-situ observations during 1998-2015. We can see that the three QPEs show similar spatial patterns in correlation and bias with in-situ observations, with high CC (over 0.9) and little bias (BIAS between -25% to 25%) among most areas of east China and east Tibetan plateau, indicating their high accuracies for these areas. The 3B42V7 presents the highest accuracy and CHIRPS ranks the second. For the west China, especially the NWC, low correlations and large discrepancies between QPEs and observations are found. All the three QPEs mainly show large overestimation in the northwest China, while large underestimation is mainly found in the west of Tibetan plateau. Comparing with Fig.2, we can find that the CC and BIAS have high spatial correlation with the density of in-situ gauges. Table 2 presents the quantiles of the statistical indices of the gridcells for the nine agricultural regions. We can find that the regions in 15

east China with denser gauges generally show high correlations and little bias between the QPEs and in-situ data; NWC and TP, with sparse gauges, generally show lower value but with high variation in CC and much larger range of bias. These results indicate that the high discrepancies of the QPEs with in-situ observations might be induced by lack of observation sites in the west China, causing the high uncertainties for evaluations of QPEs for these areas, which was also mentioned by Guo et al. (2016) and Tang et al. (2016). Additionally, the dryer climate, complicate terrain, and less in-situ gauges for error-correction of QPEs might also influence the accuracy of the QPEs (Sahoo et al., 2015; Guo et al., 2016). In all, we suggest that the high accuracy of the QPEs in the east China with dense gauge network is credible, but the accuracy in most areas of west China is still uncertain due to the sparse-distributed gauges.

The Quantile-Quantile (Q-Q) plot (Gao et al., 2017) is an effective tool to visually assess the fitness and bias between the simulation and observation for different quantiles. The Q-Q plots of monthly precipitation of the three QPEs for the nine regions can be expressed as log-log coordinate for convenient assessment of low values and is shown in Fig.4. For the high precipitation, the QPEs generally show a little underestimate in the east China while present large underestimate for NWC and TP in west China, which is most likely due to the uncertainty of the sparse gauges. For middle and low precipitations, the three QPEs for the nine regions exhibit different biases (positive or negative). For instances, for 3HP, LP, NWC and TP, all the three QPEs shows overestimation for low precipitation; for NEC, both PERSIANN-CDR and CHIRPS show apparent underestimation for low values while 3B42V7 shows large overestimation for mid and low values; for SWC, by contrast, both PERSIANN-CDR and CHIRPS show overestimation while 3B42V7 shows underestimation for mid and low precipitation. For the arid and semi-arid 16

areas e.g. TP, NWC, and LP, common overestimations might be caused by the local complicated landsurface, less precipitation, and lack of in-situ data for bias correction, which have also found by relevant studies (Tong et al., 2014). For the relatively humid areas with abundant rainfall e.g. SWC, DYR, and SC, large discrepancies of biases of low intensity precipitation might also be caused by uncertainties and little representativeness of few samples of small rainfall months.

3.2. Comparison of SPI and PDSI from QPEs and in-situ observations

The SPI was calculated based on PERSIANN-CDR, CHIRPS, and in-situ observation data at each 0.25° grid cell in mainland China from 1983 to 2015. The SPI of four timescales, i.e. the 1-, 3-, 6-, and 12-month SPI are calculated. CC, RMSE, POD, and FAR were calculated from the QPE-based SPIs against the in-situ SPIs. For the calculation of POD and FAR, the criterion of SPI < −1.0 (moderate or severer drought) was selected to determine the drought condition. Spatial patterns of the statistical metrics of QPE-based SPI are shown in Fig.5 for PERSIANN-CDR, and in Fig.6 for CHIRPS, distribution metrics (5% quantile, median and 95% quantile) of CCs of the QPEs for the nine agricultural regions are listed in Table 3.

PERSIANN-CDR-based SPI presents similar spatial pattern with those of CHIRPS, with high CC above 0.7 in the east China and low CC below 0.3 in the western part. As expected, those spatial patterns are also similar with those of monthly precipitation, since the precipitation data are the only input to calculate SPI. RMSE of the both QPE-based SPI3 shows similar spatial pattern with CC, with low values below 0.8 in most of the east mainland China. Some regional discrepancies exist between the both QPEs. As shown in Table 3, PERSIANN-CDR shows apparent higher CC in NEC and IM, while CHIRPS tends to show higher CC in southern of China 17

including SWC and SC. For the four timescales of SPI of both PERSIANN-CDR and CHIRPS, the spatial patterns of statistic indices do not present apparent difference while the trends of the indices in many areas increase or decrease with the increasing of timescale. For instance, the mid-north of TP, some areas of NEC and DYR tend to show higher CC values while the west TP and part of NWC tend to present lower CC with larger timescales (6- to 12-month). This trend could be also found in Table 3, such as the low CC values (5% quantile) for NEC, 3HP, IM, and LP for PERSIANN-CDR and high CC values (95% quantile) for NWC and TP. This might be because the error of the QPEs might be differently dominated by systematic or random error for different areas, since the systematic error could be accumulated while the random error could be offset at the timescale increases.

With regard to the capability to detect drought, apparently, both QPEs are more likely to miss drought events rather than mistake a normal state for a drought event, as both POD and FAR are relatively low (Fig.5 and Fig.6i-p). In the east China, POD for the both QPE-based SPI3 is mainly between 0.5 to 0.8, indicating that 50% to 80% of drought months were caught by the QPEs, which is not very high but is still acceptable. Spatial discrepancies of POD and FAR between PERSIANN-CDR and CHIRPS are similar to CC and RMSE, and the apparent regional increase/decrease trends of CC along with the increased SPI timescales can also be found for both POD and FAR. Since the drought indices would be more sensitive to the mid and low precipitation, those trends to miss drought months might due to some overestimation of low precipitation, which can be found in Fig.4 and in some humid zones (Alijanian et al., 2017; Katiraie-Boroujerdy et al., 2017).

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Above all, both PERSIANN-CDR and CHIRPS are satisfactorily for SPI calculation in the eastern part of mainland China for the four timescales, with acceptable accuracy and capability to detect drought. For the west mainland China, the performance of the both products need further determine.

Next, the PDSI was calculated via PERSIANN-CDR, CHIRPS, 3B42V7, and in-situ precipitation data with the in-situ PET data. PDSI of all QPEs were calculated during 1998-2015 to maintain the comparability, and the corresponding CC, RMSE, POD, and FAR are also calculated. To maintain the comparability with SPI, here, the criterion of PDSI < −2.0 (moderate drought or severer) was selected to determine drought condition of PDSI.

Apparently, PDSI based on the QPEs shows a higher correlation with in-situ observations than when using SPI, as the CCs are generally higher than those of SPI for east China (Table 4). Participation of the same PET data and relatively longer time scale might be the reason. Spatial patterns of CC and RMSE for the QPE-based PDSI are also similar to those of SPI in Fig.7, with better values in the east China and poorer values in west China. 3B42V7 presents the highest accuracy among the three QPEs for PDSI in east mainland China, as its presents generally higher CC than PERSIANN-CDR and CHIRPS, especially for 3HP, LP, SWC and DYR (Table 4), with the widest area with high CC over 0.8 and low RMSE below 1.5 (Fig.7a-f).

According to Fig.7g-l, QPE-based PDSIs apparently have better capability for drought detection than those of SPI3 shown in Fig.1e-h, with higher POD and lower FAR. In the east China, three QPE-based PDSIs present high POD mainly above 0.7, indicating that more than 70% of drought events were caught by the QPEs. Referring to Fig.1e-h, similar regional differences 19

between the QPEs can also be found in Fig.7g-l as PDSI of 3B42V7 and CHIRPS presents higher POD than PERSIANN-CDR in southwest China. In summary, three QPEs perform well in the eastern part of China when using PDSI. 3B42V7 presents best performance with higher accuracy in many cases.

3.3. Case studies of typical drought-affected regions

To further temporally and spatially evaluate the drought indices estimated from all three QPEs, four typical drought-affected regions are selected from the nine agricultural regions for regional-specific case studies, including Northeast China (NEC), Huang-Huai-Hai plain (3HP), Southwest China (SWC), and Loess plateau (LP). The 3HP and SWC are selected to analyze two typical drought events, i.e. the continuous drought of 1999-2000 in north China (Wei et al., 2004) and the severe drought of 2009-2010 in southwest China (Barriopedro et al., 2012; Zhang et al., 2013). The NEC is selected due to a major grain-producing region of China. The LP is selected for its fragile environment sensitive to drought (Zhang et al., 2013b, 2016) and it could also represent the performance of the QPEs in the semi-arid areas. The drought events from Jan. 2001 to Jan. 2002 in NEC, and from Sep. 1999 to Sep. 2000 in LP are selected as typical drought events for NEC and LP, respectively. The agricultural regions of west China are not considered because of the sparse- and uneven-distributed gauge network. Additionally, we did not consider the meteorological drought for the arid areas like the Northwest China (Xu et al., 2015). For a brief, only the 3-month SPI (SPI3) that is more capable to reveal seasonal drought (Xu et al., 2015) was selected as a represent case for the following regional evaluations.

Time series of regional-averaged SPI3 and fractional drought area based on 20

PERSIANN-CDR and CHIRPS for the four regions are shown in Fig.8. CCs and RMSEs of the regional-averaged SPI3 are listed in Table 5. Both PERSIANN-CDR and CHIRPS are able to accurately detect typical drought events of all four regions, as their SPI3 could catch the start and end of the drought events, although some biases were found in drought intensity estimates (Fig.8). For the four regions (NEC, 3HP, SWC, and LP), SPI3 of both QPEs fit well with in-situ observations (Fig.8). For LP, agreement between the QPEs and in-situ observations is poorest, but the QPEs can still identify the drought events (Fig.8d,h) and the CCs are still high. PERSIANN-CDR outperforms CHIRPS for the four regions for temporal correlation, but their differences are small (Table 5).

Spatial patterns of the SPI3 in the specific month of the typical drought events for the four regions are shown in Fig.9. The drought intensity was roughly reflected by both PERSIANN-CDR and CHIRPS. PERSIANN-CDR could not accurately match the spatial pattern of the typical drought events for the four regions, especially for 3HP and SWC, although it performs satisfactorily for the temporal evaluation above. In comparison, CHIRPS picks up the spatial centers and extent of the typical drought events relatively accurately, although some underestimates of drought intensity are found. Spatial CCs of the QPEs against in-situ observations for the four typical drought events are listed in Table 5. Among the four regions, both QPEs present the highest spatial correlation in 3HP (CCs of nearly 0.8), while the poorest correlation is found in LP (CCs of 0.344 to 0.537).

We then evaluated and compared the performance of the QPEs in PDSI estimating, including 3B42V7. Fig.10 presents the time series of regional-averaged PDSI and the fractional drought area

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based on PDSI for the three QPEs. QPEs fit well with in-situ observations when using PDSI, and their correlations with in-situ observations are even better than when using SPI3. Drought events for the four regions were all accurately caught by all three QPEs. Among the QPEs, 3B42V7 performed the best, with the least deviation in the estimates of drought severity. In comparison, some apparent underestimates and overestimates can be found in both PERSIANN-CDR and CHIRPS estimates (Fig.10). Table 5 also lists the temporal CCs of the QPE-based PDSI for the four regions. 3B42V7 presents the best agreement with in-situ observations in PDSI estimation as it features the highest CCs and lowest RMSEs for all four regions. Discrepancies between PERSIANN-CDR and CHIRPS of PDSI are similar with those of SPI3, as PERSIANN-CDR performs slightly better than CHIRPS for the four regions.

Similar to Fig.9, spatial maps of the PDSI in the specific month of the typical drought events are also shown in Fig.11. Apparently, spatial consistencies of PERSIANN-CDR and CHIRPS are also mostly improved when SPI3 is replaced by PDSI especially for NEC and SWC, but except for 3HP. All the three QPEs caught the spatial centers and extent of the drought events for the four regions. PERSIANN-CDR still generally presents the poorest spatial correlation with in-situ data (Table 5) and shows the poorest skill to match the spatial pattern of drought when using PDSI (Fig.11), but the gap with other QPEs is apparently reduced especially in SWC. Among the four regions, the spatial correlation of the QPE-drived PDSI is poorest in LP, but it is still acceptable.

4. Discussion

4.1. Validation of the CNSCMD-based PDSI

The GB/T 20481-2006 national standard (Zhang et al., 2006) provides a modified PDSI 22

calculation procedure that considers the climate characteristic of China to rationalize and simplify PDSI calculation in China. However, few studies focused on the reliability of the GB/T procedure when using QPE data. Therefore, it is necessary to verify the capability of the GB/T procedure for the calculation with satellite QPE data in the present study. According to Fig.12, distributions of the different PDSI-based drought classifications of both in-situ data and QPEs are generally reasonable for both the whole mainland China and the four regions (Fig.12). Specifically, the frequencies of normal spell (−1 < 𝑃𝐷𝑆𝐼 < 1) mainly ranged from 0.35 to 0.40, while frequencies of extreme drought spell (PDSI < −4) are nearly 0.025, which is similar to normal distribution and roughly meets the aim of the self-calibrating procedure proposed by Wells et al. (2014). Please note that frequencies of extreme wet spell (PDSI > 4) are a little overestimated for some regions (Fig.12a,b,d); however, their impact on drought monitoring could be ignored. Distributions of the drought classifications of the regions except NEC are similar with those of the whole mainland China, while NEC relatively underestimates the frequency of normal spell (nearly 0.30) but is still acceptable (Fig.12a). Therefore, we suggest that the GB/T-based PDSI has satisfactory adaptability and spatial-comparability for mainland China, and is reasonable for the QPE-based PDSI calculation in the present study.

Moreover, although the PDSI of GB/T has considered the climate characteristic of China, several local climatic components are also included. Hence, reliability of PDSI might also be impacted by the length of the data records; therefore, the reasonability of using short-term QPE data to calculate the PDSI in the present study also requires verification. We additionally calculated the GB/T-based PDSI with longer in-situ observation records of 1983-2015 (33 a). With 3HP and SWC as example, we compared the PDSI temporally and spatially based on the 33 a and 23

18 a (1998-2015, same as 3B42V7) of in-situ data and 3B42V7 data of 1998-2015 (18 a) (Fig.13). Apparently, high temporal consistency between the PDSI derived from 33 a and 18 a in-situ data can be found as their time series nearly coincided, especially in 3HP (Fig.13a,b). Spatially, the PDSI of 18 a in-situ data accurately match the spatial pattern of the typical drought in the both regions shown by the PDSI of 33 a data (Fig.13c-d, e-f). Fig.13 also shows that 3B42V7-based PDSI presents a high temporal and spatial correlation with in-situ data of both 33 a and 18 a. This shows that the short-term QPEs, e.g. 3B42V7, also have great potential for drought monitoring when using the GB/T PDSI in mainland China. Sahoo et al. (2015) also performed a similar validation on the SPI calculated via long-term (31 a) and short-term (10 a) data records, respectively. They also found that the length of data records presents little impact on the SPI calculation, and supposed that it might be because both normal and extreme events are included in the period of data record. As both several severe drought and flood disasters have taken placed during the past two decades in China (Piao et al., 2010), might also explain our findings. As a result, we suggest that the evaluation in the present study using the precipitation data with 18 a records for PDSI is reasonable.

4.2. Comparison of the usability of the satellite QPEs in drought monitoring

According to the results above, temporally, all three QPEs generally performed well for drought detection in the eastern part of China, even including the southwest and mid-west area of China. Although PERSIANN-CDR presents superior performance in several regions, CHIRPS also performs satisfactorily in where PERSIANN-CDR performs well. Comparability of CHIRPS to PERSIANN-CDR also can be found in the humid and semi-arid area of other countries (Dembélé

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and Zwart, 2016; Zambrano et al., 2016). Hence, both PERSIANN-CDR and CHIRPS are capable to be used for regional drought monitoring and even have great potential to substitute the in-situ gauge observations in these areas. However, PERSIANN-CDR is relatively weak in revealing spatial patterns of drought events. Since PERSIANN-CDR is corrected by the gridded GPCP data (Ashouri et al., 2014) while CHIRPS and 3B42V7 are corrected by the station data, the coarse spatial resolution (2.5°) of GPCP might be a reason of the poorer spatial performance. As a result, caution should be taken when studying the spatial characteristics of drought with PERSIANN-CDR. 3B42V7 performs superior than the other two QPEs over the east of China, and its drought utility has also been validated in other studies (Sahoo et al., 2015; Zeng et al., 2012). However, due to its short data records, stability, and reasonability of 3B42V7 for drought monitoring or other climatic applications, it might need further evaluation, as there are still only few case studies. Specifically, we evaluated the 3B42V7 via only PDSI in mainland China. Nevertheless, 3B42V7 also shows considerable potential for drought monitoring.

For west China, large differences between the QPEs and in-situ data are found not only in the present study but also in the studies of Tang et al. (2016), Guo et al. (2016), and Shen and Xiong (2016), which pointed out that the sparse gauge networks in west China largely limit the evaluation of QPEs. We believe the large differences might be induced by the low quality of the QPEs caused by the dry climate, special land surface, and lack of in-situ error-correction (Gao and Liu, 201; Sahoo et al., 20153; Gao et al., 2016). Similar to Shen and Xiong (2016), here we further explored the accuracy of the QPEs for the two gauge-sparse regions of west China, i.e. NWC and TP, by only evaluating the gauged gridcells for the two regions. Distributions of the CC and BIAS of the QPEs for the two regions at all or only gauged gridcells are shown by the quantiles in Table 25

6. Low CC values (5% and 25% quantile) of the three QPEs are apparently higher for the gauged gridcells than the all gridcells for both NWC and TP; and in some cases, ranges of BIAS are smaller for the gauged gridcells (e.g. TP). Less change of the high CC values (75% and 95% quantile) indicates that the gridcells with higher accuracy of QPEs are mainly located with gauges. However, compared with the regions in east China, CCs are generally still low and the ranges of BIAS are still large for NWC and TP even only the gauged gridcells are evaluated, as the medians (50% quantile) of CCs for NWC are even below 0.8 and BIASes are ranging from down to -50% to up to 140% for both NWC and TP for the three QPEs. This might also reveal the inherent poor accuracy for the QPEs, indicating the unsuitability of the QPEs for drought monitoring over these areas. Denser gauge networks might also be an important reason for the higher accuracy of the QPEs as they could obtain better bias correction in these areas, which was also mentioned by Sahoo et al. (2015), especially for CHIRPS and 3B42V7. In all, we suggest that the large differences between the QPEs and in-situ data in the west China are induced by both the sparse gauge networks and the low accuracy of the QPEs. Therefore, the performance of the three QPEs in west China may be beyond acceptable limits and then the current QPEs are not recommended for drought monitoring. Further evaluation and correction of the QPEs using local gauge observations for these areas would be necessary for further works.

Although the present study evaluates three QPEs only in mainland China, we hope that our results can be used as reference for other regions around the world. In a global evaluation, Sahoo et al. (2015) reported that post-process 3B42 products present high agreement with non-TMPA products than the near-real-time one (without in-situ correction) in the regions with dense gauge networks, e.g. the eastern USA, eastern South America, southern Africa, India, south-eastern Asia, 26

and Australia. Those findings are similar with our findings and discussion above and reveal the importance of the in-situ bias correction for the QPEs. Significant improvements of the accuracy of near-real-time QPEs after in-situ correction have also been validated by AghaKouchak and Nakhjiri (2012) and Vernimmen et al. (2012), for global and regional scales respectively. In all, we expect that the QPEs might also perform similarly in the areas discussed by former studies above.

At present we have confirmed the utility of the three QPEs for the retrospective drought assessment. However, usability of the QPEs for the real-time or near-real-time drought applications is also important and thus needs discussion. It takes quite a long time to process and obtain the in-situ observations for error correction and thus some days are required before the release of the global-scale data of CHIRPS and 3B42V7, therefore, these two QPEs may be unsuitable for the strict-real-time applications. However, since drought is a long-term phenomenon with much larger timescale than flood and rain storm, the latency of data release of the two QPEs might be acceptable for the “near-real-time” drought monitoring. The lag time of PERSIANN-CDR is longer (usually several months later) as this QPE mainly aims at the researches of historical climate (Ashori et al., 2015) and therefore the PERSIANN-CDR might be only suitable for the retrospective historical drought assessment. Some efforts have been made to develop the real-time or near-real-time long-term QPEs with higher timeliness by combining the short-term near-real-time QPEs and long-term QPEs (e.g. GPCP). AghaKouchak and Nakhjiri (2012) developed a near-real-time QPE by combining the near-real-time 3B42RT and PERSIANN products and the long-term GPCP product, but the spatial resolution of up to 0.5° might not meet the requirement of local drought monitoring. Aadhar and Mishra (2017) also developed a near-real-time QPE with high quality and spatial resolution (up to 0.05°) while it is only specific 27

for south Asia. Accordingly, the global or semi-global scale, high spatial resolution long-term real-time QPEs still require further development.

Above all, for most of the eastern part of China, long-term (PERSIANN-CDR and CHIRPS) perform satisfactorily and are suitable for drought utilities, in which CHIRPS could also be applied for near-real-time drought monitoring. The short-term QPE (3B42V7) also shows great potential for drought monitoring. For west China, the QPEs are not suitable for drought monitoring in these areas.

5. Conclusions

This study mainly evaluated and compared the drought monitoring utilities of the two long-term satellite QPEs, i.e. PERSIANN-CDR (since 1983) and CHIRPS (since 1981) and a short-term QPE, i.e. TMPA 3B42V7 (since 1998) over mainland China. CGDPA, a gridded precipitation dataset generated from the dense rain gauge networks in mainland China, was used as in-situ observations to evaluate the QPEs. Both the SPI and the PDSI, the two most widely used meteorological drought indices, were selected as examples to verify the drought utility of the QPEs.

a) Three QPEs generally show high monthly accuracy in east China, with high correlation with in-situ observations and little bias, but the accuracy for west China could not be clearly ensured due to the sparse in-situ gauge networks. Underestimations of high precipitation normally exist in the QPEs, while different types of bias (over- or underestimations) of mid and low precipitation are found for the QPEs.

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b) For the evaluation with SPI, with reference to in-situ observations, SPI of both PERSIANN-CDR and CHIRPS present similar spatial patterns, with high correlation in east part of mainland China while low correlation in west China e.g. NWC and TP. As the timescale of SPI increases, accuracy of QPE-based SPIs consistently increase or decreases for different regions. With regard to the evaluation with PDSI, spatial patterns and discrepancies of the QPEs are similar with those of SPI3. 3B42V7 presents the best performance among the three QPEs, with higher CC and POD in many cases.

c) Temporally, when using SPI (with SPI3 as example), both PERSIANN-CDR and CHIRPS are able to catch the typical drought events for the four agricultural regions (i.e. northeast China (NEC), Huang-Huai-Hai plain (3HP), southwest China (SWC) and Loess plateau (LP)). However, the PERSIANN-CDR is relatively weak in matching the spatial pattern of the typical drought events, while CHIRPS could accurately catch the spatial extent and centers of drought events.

d) When turned to PDSI, PERSIANN-CDR still has the poorest spatial performance for detecting typical drought events. 3B42V7-drived PDSI features the best temporal performance among all three QPEs, with least deviation from the in-situ data, and performs comparably with CHIRPS in detecting the spatial pattern of typical drought events.

In summary, the long-term PERSIANN-CDR and CHIRPS are suitable for drought monitoring in the eastern part of mainland China, regardless of whether SPI or PDSI are used. However, caution should be applied when using PERSIANN-CDR to study the spatial variation of drought. The short-term 3B42V7 product also performed satisfactorily in drought detection when using PDSI, presenting considerable potential for drought monitoring. However, for west China, 29

all three QPEs perform unacceptably, indicating the unsuitability of the QPEs for drought monitoring in these areas.

Acknowledgements

The research is financially supported by the National Key R&D Program of China (2017YFC0405900), the National Natural Science Foundation of China (Grant No.51579105, 51709117, 91547202, 51479216); the Science and Technology Program of Guangzhou City (201707010072); the Science and Technology Planning Project of Guangdong Province, China (2017A040405020); the special fund of water resources conservation and protection of Guangdong Province (2017).

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42

Fig. 1. Spatial pattern of annual mean precipitation, distribution of meteorological and radiation stations of mainland China

Fig. 2. Density of gauges used by CGDPA and division of 9 agricultural regions of mainland China. Note: NWC: Northwest China; IM: Inner-Mongolia; NEC: Northeast China; LP: Loess plateau; 3HP: Huang-Huai-Hai plain; TP: Tibetan plateau; SWC: Southwest China; DYR: Downstream Yangtze River; SC: South China.

43

Fig. 3. CC and BIAS of the three QPEs w.r.t in-situ observations in 1998-2015.

Fig. 4. Q-Q plot of the QPEs and in-situ observations for the nine agricultural regions.

44

Fig. 5. CC, RMSE, POD and FAR of the 1-, 3-, 6-, and 12-month SPI based on PERSIANN-CDR w.r.t in-situ observations in 1983-2015 (Columns for different timescales while rows for different statistical indices).

Fig. 6. Same as Fig. 5 but for CHIRPS.

45

Fig. 7. CC, RMSE, POD and FAR of the PDSI based on PERSIANN-CDR, CHIRPS and 3B42V7 w.r.t in-situ observations in 1998-2015. Figures a, d, g, j are PERSIANN-CDR, figures b, e, g, k are CHIRPS, figures c, f, i, l are 3B42V7.

Fig. 8. Time series of regional-averaged SPI3 and ratio of drought area of the four typical drought-affected areas during 1983-2015. Grey backgrounds indicate the period of the typical drought events.

46

Fig. 9. Spatial pattern of the typical drought events for the four typical drought-affected agricultural regions based on in-situ observations (a, d, g, j), PERSIANN-CDR (b, e, h, k) and CHIRPS (c, f, i, l).

47

Fig. 10. Same as Fig. 8, but for PDSI during 1998-2015.

Fig. 11. Same as Fig. 9, but for PDSI with 3B42V7 added.

48

Fig. 12. Frequencies of the different drought classification based on PDSI for in-situ observations and QPEs, in northeast China (a), Huang-Huai-Hai plain (b), southwest China (c), Loess plateau (d) and the whole mainland China (e) during 1998-2015. Note: ED, extreme drought; SD, severe drought; MD, moderate drought; LD, slight drought; N, normal; LW, slight wet; MW, moderate wet; SW, severe wet; EW, extreme wet.

49

Fig. 13. Temporal and spatial comparison of the PDSI based on the long-term (1983-2015, 33a) and short-term (1998-2015, 18a) in-situ observations and 3B42V7 data.

50

Table 1. Drought classifications of SPI and PDSI.

Drought index

SPI

PDSI

Index value

Drought class

Index value

Drought class

≥ 2.0

Extreme wet

−1.5~ − 1.0

Moderate drought

1.5~2.0

Very wet

−2.0~ − 1.5

Severe drought

1.0~1.5

Moderate wet

≤ −2.0

Extreme drought

−1.0~1.0

Near normal

≥ 4.0

Extreme wet

−2.0~ − 1.0

Mild drought

3.0~4.0

Very wet

−3.0~ − 2.0

Moderate drought

2.0~3.0

Moderate wet

−4.0~ − 3.0

Severe drought

1.0~2.0

Mild wet

≤ −4.0

Extreme drought

−1.0~1.0

Normal

51

Table 2. 5%, 50% and 95% quantiles of monthly CC and BIAS of the three QPEs w.r.t. in-situ observations for the nine agricultural regions for 1998-2015.

Agricultural region

NEC

3HP

IM

LP

DYR

SWC

SC

NWC

TP

Quantile

PERSIANN-CDR

CHIRPS

3B42V7

CC

BIAS (%)

CC

BIAS (%)

CC

BIAS (%)

5%

0.905

-3.6

0.886

-7.8

0.901

-4.6

50%

0.941

11.3

0.933

3.7

0.943

7.4

95%

0.963

28.9

0.960

14.6

0.969

20.3

5%

0.879

-6.8

0.880

-7.6

0.909

-7.6

50%

0.920

8.4

0.920

2.2

0.946

5.2

95%

0.952

19.1

0.953

14.7

0.967

13.8

5%

0.870

-7.1

0.837

-13.3

0.838

-13.7

50%

0.921

7.8

0.909

1.2

0.932

2.8

95%

0.946

27.4

0.941

17.1

0.965

19.3

5%

0.866

-17.8

0.867

-12.8

0.879

-17.7

50%

0.909

-1.2

0.918

1.6

0.943

1.6

95%

0.941

23.1

0.947

19.2

0.964

16.4

5%

0.857

-16.8

0.842

-4.7

0.908

-9.2

50%

0.905

-0.5

0.904

4.6

0.940

1.1

95%

0.928

25.3

0.935

16.8

0.966

13.1

5%

0.869

-19.3

0.877

-17.4

0.906

-20.5

50%

0.913

-3.2

0.921

2.4

0.945

-3.9

95%

0.943

20.1

0.953

18.8

0.968

11.3

5%

0.864

-22.0

0.869

-14.0

0.904

-18.6

50%

0.915

1.4

0.920

3.9

0.948

-3.1

95%

0.946

24.6

0.957

18.0

0.974

18.0

5%

0.402

-57.7

0.212

-69.2

0.320

-54.3

50%

0.693

0.6

0.571

-20.4

0.648

-18.9

95%

0.893

69.8

0.854

47.0

0.868

52.0

5%

0.469

-54.5

0.410

-54.8

0.393

-45.8

50%

0.900

18.8

0.890

-9.0

0.877

-5.3

95%

0.958

157.9

0.960

131.3

0.968

121.0

52

Table 3. CC of the 1-, 3-, 6-, and 12-month SPI calculated by PERSIANN-CDR and CHIRPS for the nine agricultural regions.

Agricultural region

NEC

3HP

IM

LP

DYR

SWC

SC

NWC

TP

Quantile

PERSIANN-CDR

CHIRPS

SPI1

SPI3

SPI6

SPI12

SPI1

SPI3

SPI6

SPI12

5%

0.767

0.760

0.739

0.718

0.677

0.697

0.707

0.675

50%

0.839

0.832

0.834

0.837

0.778

0.778

0.795

0.802

95%

0.889

0.885

0.897

0.909

0.837

0.841

0.863

0.884

5%

0.793

0.799

0.772

0.704

0.739

0.765

0.756

0.711

50%

0.843

0.848

0.833

0.804

0.838

0.837

0.827

0.815

95%

0.895

0.894

0.884

0.879

0.884

0.885

0.880

0.884

5%

0.716

0.704

0.726

0.679

0.579

0.608

0.683

0.656

50%

0.800

0.795

0.807

0.802

0.702

0.719

0.784

0.791

95%

0.856

0.854

0.873

0.894

0.783

0.801

0.841

0.881

5%

0.680

0.675

0.671

0.579

0.643

0.640

0.647

0.531

50%

0.818

0.821

0.810

0.794

0.795

0.804

0.818

0.797

95%

0.860

0.869

0.861

0.859

0.843

0.860

0.874

0.884

5%

0.752

0.780

0.774

0.729

0.805

0.801

0.769

0.683

50%

0.831

0.842

0.844

0.831

0.852

0.853

0.846

0.823

95%

0.873

0.881

0.893

0.893

0.886

0.891

0.899

0.899

5%

0.577

0.602

0.601

0.579

0.620

0.640

0.637

0.599

50%

0.729

0.733

0.748

0.752

0.774

0.782

0.777

0.773

95%

0.821

0.838

0.846

0.858

0.852

0.860

0.856

0.865

5%

0.659

0.641

0.583

0.519

0.731

0.698

0.641

0.566

50%

0.761

0.778

0.772

0.773

0.817

0.813

0.783

0.775

95%

0.827

0.847

0.851

0.871

0.880

0.881

0.873

0.872

5%

0.379

0.331

0.336

0.298

0.235

0.250

0.247

0.236

50%

0.626

0.612

0.606

0.611

0.491

0.494

0.519

0.540

95%

0.818

0.817

0.821

0.825

0.736

0.741

0.745

0.746

5%

0.302

0.249

0.219

0.202

0.226

0.195

0.200

0.202

50%

0.574

0.556

0.584

0.587

0.574

0.575

0.634

0.668

95%

0.707

0.706

0.749

0.803

0.717

0.727

0.793

0.855

53

Table 4. CC of the PDSI calculated by PERSIANN-CDR, CHIRPS, and 3B42V7 for the nine agricultural regions.

QPE

Quantile

NEC

3HP

IM

LP

DYR

SWC

SC

NWC

TP

5%

0.764

0.737

0.708

0.617

0.778

0.573

0.598

0.283

0.121

50%

0.886

0.856

0.855

0.799

0.863

0.758

0.776

0.628

0.569

95%

0.943

0.916

0.937

0.885

0.919

0.871

0.858

0.855

0.792

5%

0.756

0.699

0.697

0.655

0.791

0.613

0.652

0.264

0.145

50%

0.875

0.839

0.838

0.817

0.872

0.808

0.820

0.579

0.618

95%

0.934

0.922

0.929

0.901

0.922

0.903

0.907

0.787

0.841

5%

0.751

0.788

0.706

0.664

0.837

0.643

0.721

0.175

0.019

50%

0.890

0.896

0.870

0.869

0.911

0.846

0.866

0.580

0.545

95%

0.942

0.949

0.942

0.935

0.952

0.931

0.925

0.814

0.866

PERSIANN-CDR

CHIRPS

3B42V7

Table 5. Temporal CC and RMSE of the regional averaged drought indices, and the spatial CC of the typical drought events for the four drought-affected regions. SPI3 Regions

PDSI

PERSIANN-

PERSIANNCHIRPS

CDR

CHIRPS

3B42V7

CDR

NEC

0.975

0.917

0.982

0.967

0.981

CC of regional

3HP

0.977

0.937

0.976

0.921

0.987

averaged drought index

SWC

0.947

0.913

0.931

0.912

0.966

LP

0.944

0.899

0.952

0.925

0.979

NEC

0.18

0.28

0.40

0.49

0.35

RMSE of regional

3HP

0.19

0.26

0.44

0.69

0.27

averaged drought index

SWC

0.17

0.21

0.36

0.41

0.24

LP

0.28

0.33

0.55

0.62

0.32

NEC

0.590

0.658

0.824

0.879

0.834

3HP

0.770

0.837

0.554

0.574

0.779

SWC

0.630

0.815

0.914

0.941

0.924

LP

0.344

0.537

0.497

0.486

0.755

Spatial CC of drought index in selected month (Fig. 9)

54

Table 6. Correlation and biases of QPEs w.r.t. in-situ observations for all and only gauged gridcells of the gauge-sparse regions. CC

BIAS (%)

Agricultural Quantile

PERSIANN-

region

PERSIANNCHIRPS

3B42V7

CDR

CHIRPS

3B42V7

CDR

5%

0.449

0.231

0.320

-58.0

-67.7

-54.3

25%

0.620

0.477

0.525

-26.9

-39.5

-34.4

50%

0.723

0.599

0.648

-0.5

-18.8

-18.9

75%

0.815

0.729

0.754

22.4

-0.7

1.5

95%

0.901

0.864

0.868

63.3

45.6

52.0

5%

0.524

0.353

0.488

-47.3

-45.7

-44.9

25%

0.663

0.579

0.610

-9.3

-24.5

-21.8

50%

0.766

0.696

0.732

11.6

-11.0

-9.4

75%

0.864

0.809

0.817

39.4

0.1

16.6

95%

0.901

0.883

0.890

91.2

36.1

64.9

5%

0.500

0.422

0.393

-53.1

-55.1

-45.8

25%

0.743

0.692

0.678

-12.7

-26.4

-20.0

50%

0.900

0.888

0.877

19.5

-9.9

-5.3

75%

0.935

0.939

0.940

67.7

14.9

21.3

95%

0.954

0.958

0.968

178.6

133.0

121.0

5%

0.688

0.652

0.691

-41.3

-41.2

-30.2

25%

0.892

0.897

0.880

-1.1

-15.7

-14.3

50%

0.924

0.935

0.937

16.2

-8.3

-3.3

75%

0.940

0.950

0.957

43.4

2.8

12.9

95%

0.954

0.962

0.973

143.5

33.3

87.0

SWC (all gridcells)

SWC (gauged gridcells)

TP (all gridcells)

TP (gauged gridcells)

55

SPI and PDSI were used to evaluate the drought monitoring utility of different QPEs. Long-term CHIRPS and PERSIANN-CDR are suitable for drought monitoring in China. PERSIANN-CDR is relatively week to reveal spatial pattern of drought. The short-term TMPA 3B42V7 shows great potential for drought monitoring.

56