Blending long-term satellite-based precipitation data with gauge observations for drought monitoring: Considering effects of different gauge densities

Journal Pre-Proof Research papers Blending long-term satellite-based precipitation data with gauge observations for drought monitoring: considering effects of different gauge densities Xiaoyan Bai, Xiaoqing Wu, Peng Wang PII: DOI: Reference:

S0022-1694(19)30727-9 https://doi.org/10.1016/j.jhydrol.2019.124007 HYDROL 124007

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Journal of Hydrology

Received Date: Revised Date: Accepted Date:

26 March 2019 25 July 2019 29 July 2019

Please cite this article as: Bai, X., Wu, X., Wang, P., Blending long-term satellite-based precipitation data with gauge observations for drought monitoring: considering effects of different gauge densities, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol.2019.124007

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Blending long-term satellite-based precipitation data with gauge observations for drought monitoring: considering effects of different gauge densities

Department of Environmental Engineering, School of Environmental Science and Engineering,

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Guangdong University of Technology, Guangzhou 510006, China;

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

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Guangzhou 510535, China; c

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a

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Xiaoyan Bai a, Xiaoqing Wu b, Peng Wang a,c*

Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou 510632, China.

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* Corresponding author: [email protected] (Peng Wang)

Abstract

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Blending satellite-based precipitation estimation (SPE) data and in-situ gauge observation data can generate effective spatially-continuous-precipitation estimates with improved accuracy. This study

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assessed the improvement of the long-term SPE when blending with in-situ gauge observations for drought monitoring, using a simple but effective blending method named the geographical difference

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analysis (GDA) method and with the Precipitation Estimation from Remote Sensed Information by

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using Artificial Neural Networks–Climate Data Records (PERSIANN–CDR) as case study. In-situ precipitation observations from three meteorological station sets with different densities—the sparse (50), medium (200), dense (727) station set—were adopted to evaluate the effect of gauge density on the performance of SPE–gauge data blending. Two widely-used indices—standardized precipitation index (SPI) and self-calibrating Palmer drought severity index (SC_PDSI)—were used as case studies. Except the case of sparse 50-station subset, the SPE-gauge blending shows apparent improvement to the raw PERSIANN-CDR data, for both the accuracy of precipitation input and

JOURNAL PRE-PROOF many aspects of drought monitoring, e.g. reproducing drought magnitude and revealing spatial pattern of drought, in which SC_PDSI shows more significant improvement than SPI. The dense 727-station set shows the largest improvement in the blending data, but the corresponding station-only interpolations also exhibit comparable performance to the blending data, indicating

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lower utilization value of the SPE data for these cases. Only the blending results of the

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medium-density 200-station set shows satisfactory drought monitoring performance as well as

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significant improvements relative to the station-only interpolations. According to the quantitative analyses, the medium density (about 50–75 gauges per 106 km2 in our cases) might be the most

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economic gauge density for SPE-gauge blending, as it has satisfactory improvement in blending results, can make fullest use of the advantages of SPE data and requires relatively fewer gauges. Our

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results can help to understand how the SPE-gauge blending could improve the SPE-based drought monitoring and serves as a reference for applying drought monitoring under the data-limited

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density.

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conditions. Subsequent studies or applications should also carefully consider the effect of gauge

Keywords: Drought monitoring; Satellite Precipitation Estimate (SPE); SPE-gauge Blending;

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Gauge density; PERSIANN–CDR; Geographical Difference Analysis (GDA)

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1. Introduction Precipitation is an important variable for water resource management and drought monitoring (Lin and Wang, 2011; Verdin et al., 2016; Lai et al., 2019; Zhong et al., 2019). Accurate and reliable

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precipitation input usually plays a critical role in the hydrologic modeling and calculation of most

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drought indices to quantify drought conditions. Under the effect of climate change and global

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warming, drought is continuously aggravated globally and regionally (Dai, 2013; IPCC, 2007, 2013;

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Wang et al., 2018) and has led to considerable economic and ecological losses worldwide (Piao et al., 2010; Lai et al., 2018; Zhong et al., 2019). This situation emphasizes the urgency to develop more

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reliable precipitation products for drought monitoring.

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In-situ gauge-based observation is a traditional method to derive precipitation measurements and provide accurate point-scale precipitation records (Lin and Wang, 2011). However, gauge networks are often sparsely and unevenly distributed over complex land surface terrains and in

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various environments, particularly in developing countries and regions (Baez-Villanueva et al.,

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2018). Owing to high levels of uncertainty and the spatial variability of precipitation, sparse and uneven gauge networks often prevent the determination of actual local moisture conditions (Lin and

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Wang, 2011; Jin et al., 2014; Wang et al., 2017a). The inadequacy of low-density gauge networks

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for drought monitoring has been identified in several studies (Sheffield et al., 2012; Dai, 2013; Trenberth et al., 2014), highlighting the necessity to develop the spatial continuous precipitation data for drought monitoring. With advances in spaceborne remote sensing and retrieval algorithms, satellite-based precipitation estimates (SPEs) provide alternative sources of precipitation data. SPEs are normally retrieved from satellite-based infrared (IR), passive microwave (PMW), or spaceborne precipitation radar (PR) (Tang et al., 2017) observation data. SPEs typically feature a wide spatial coverage

JOURNAL PRE-PROOF (mostly the latitude band up to 60° NS) and a high spatial resolution (0.25°–0.04°). Widely used SPEs include Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) (Hsu et al., 1997), Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA) (Huffman et al., 2007), Climate Prediction Center Morphing

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(CMORPH) (Joyce et al., 2004), and Global Precipitation Measurement (GPM) mission (Hou et al.,

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2013). However, these earlier SPEs commonly have short data records (no more than 20 years until

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2017) and thus are not suitable for drought monitoring or other climatologic applications that require data records spanning at least 30 years (Burroughs, 2003; Guo et al., 2016). For this purpose, some

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long-term SPEs spanning more than 30 years have been developed in recent years by combining long-term historical IR observations and in-situ observations, including the PERSIANN–Climate

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Data Record (PERSIANN–CDR) (Ashouri et al., 2014), and Climate Hazards Group (CHG) Infrared Precipitation with Stations (CHIRPS) (Funk et al., 2015). These SPEs provide high-resolution and

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monitoring.

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spatially continuous precipitation estimation fields, as well as potential ideal data sources for drought

Currently, the main challenge to SPE application in drought monitoring is low accuracy, which

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is attributed to several factors, including the indirect relationship between remote sensing

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information and actual precipitation, sampling error, and disturbance of other radiation sources (Jin et al., 2014). Some in-situ ground observations are also used to correct SPEs during production; however, the improvement remains limited because of considerably insufficient in-situ gauges available for SPE producers (Sahoo et al., 2015; Zhong et al., 2018a). Therefore, numerous studies are conducted to evaluate the accuracy and performance of SPEs in drought monitoring (Yong et al., 2010; Sahoo et al., 2015; Duan et al., 2016; Katsanos et al., 2016; Zambrano et al., 2017; Gao et al., 2018; Baez-Villanueva et al., 2018; Lai et al., 2019; Zhong et al., 2019). These studies generally

JOURNAL PRE-PROOF indicate that the performance of SPEs is highly dependent on the local climate condition, topography, and gauge network density used for error correction (for gauge-corrected SPEs). Regions with arid climate and severe environment are more likely to show poor SPE performance than other regions (Sahoo et al., 2015; Guo et al., 2016; Gao et al., 2018). For example, Guo et al. (2016) showed that

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PERSIANN–CDR performs less efficiently over regions in West China than in East China mainly

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because of the complexity of the environment and sparsity of gauge data for correction. Zhong et al.

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(2019) confirmed this view by removing the effect of the interpolation error of the reference data. Even for regions with superior performance in East China, long-term SPEs show apparent deviations

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in quantifying drought conditions (Lai et al., 2019; Zhong et al., 2019). To summarize, errors in SPE cannot be disregarded in drought monitoring and thus should be corrected before they are used in

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drought monitoring, particularly in remote areas with sparse gauges.

Blending SPE data and in-situ gauge precipitation data can effectively combine the advantages

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of in-situ observations (accurate precipitation measurement) and SPE data (spatially continuous

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surface) for precipitation estimates (Lin and Wang, 2011). The superiority of SPE–gauge data blending has been verified in several studies (Lin and Wang, 2011; Jin et al., 2014; Sun et al., 2014;

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Verdin et al., 2016; Yang et al., 2017), and numerous blending methods with different mechanisms

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have been proposed. Optimal interpolation (OI) is suggested to be an ideal method for merging SPE and gauge data (Xie and Xiong, 2011; Li et al., 2015; Sun et al., 2016; Wu et al., 2018). However, OI involves numerous procedures (e.g., bias correction of SPE and construction of error–distance functions), requires more data inputs and computational resource demand, which implies relatively high implementation costs and subjectivity. Sun et al. (2014) have demonstrated the slight superiority of OI to simple SPE correction methods for midwest regions with sparse gauge data in China. Geographically weighted regression (GWR) has been recently adopted to simultaneously

JOURNAL PRE-PROOF downscale SPE data and blend SPE data with gauge data (Xu et al., 2015; Chao et al., 2018; Chen et al., 2018), which more efficiently improves the accuracy and reliability of SPE data, compared with previous techniques. Nevertheless, GWR-based methods require more geographical information (e.g. high-resolution elevation data in Chen et al. (2018)) and computing resources, which might

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introduce more uncertainties and restrict the application of these methods. Geographical difference

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analysis (GDA) is a simpler and effective method (Cheema and Bastiaanssen, 2012; Duan and

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Bastiaanssen, 2013) based on the direct construction of the spatial distribution of the differences between SPE and in-situ observations. Other blending methods, such as Bayesian methods or

Verdin et al., 2015; Ma et al., 2018).

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merging of multiple SPE data on the basis of in-situ data have also been proposed (Jin et al., 2014;

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The aforementioned studies provide valuable information about improving the quality of SPE data by blending them with gauge data. Although previous studies mostly focused on directly

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assessing the accuracy of the blended precipitation data, the improvement resulting from SPE–gauge

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data blending in drought monitoring is rarely reported. Moreover, most previous studies generally focused on the methodology of SPE–gauge blending using a fixed gauge data set; the effects of the

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adopted gauge network density, which significantly influence the improvement in accuracy of SPE

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data (Lin and Wang, 2011; Wang and Lin, 2015), are usually ignored. This kind of information is also important, considering that denser gauges usually result in greater enhancements in the performance of SPE–gauge data blending; however, denser gauges also usually imply higher costs in deriving gauge data. Thus, the most suitable gauge density for different usages has to be determined. Nevertheless, relevant studies are rare, particularly in drought monitoring. Therefore, the objective of this study is two-fold: (i) to evaluate the performance of SPE–gauge data blending of long-term SPE data for drought monitoring and (ii) to investigate the effect of

JOURNAL PRE-PROOF gauge density on the accuracy and drought utility of SPE–gauge data blending. In contrast to the former studies that focused on SPE data or blending approaches, this study aims to evaluate the improvement in raw SPE data in different aspects of drought monitoring (e.g. drought event detection) by SPE–gauge data blending and to determine the most suitable gauge density range for

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SPE–gauge data blending. PERSIANN–CDR, a commonly used long-term SPE product spanning 35

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years up to 2017, was used as a case study. The other long-term SPEs, such as CHIRPS, were not

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considered mainly because these SPEs were mostly directly corrected by in-situ gauge observation. Such type of observation usually involves gauge data for blending and validation in the present study,

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thereby influencing the validation results; by contrast, PERSIANN-CDR was corrected using a gridded precipitation dataset with a coarse spatial resolution (2.5°) called the Global Precipitation

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Climatology Project (GPCP) (Ashouri et al., 2014), which would exert less effect on the results of SPE–gauge data blending compared with other long-term SPEs. The GDA method is adopted for

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SPE–gauge data blending not only because this method performs reasonably well (Cheema and

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Bastiaanssen, 2012; Jongjin et al., 2016; Verdin et al., 2016; Chen et al., 2017) but also because it has a simpler procedure and fewer extra information dependencies, thus introducing fewer external

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factors and uncertainties. Mainland China, which has abundant terrains and diverse climates, was

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chosen as the study area. The standardized precipitation index (SPI) and the self-calibrating Palmer drought severity index (SC_PDSI), two widely-used indices for drought determination and monitoring, were used as study cases. This study is expected to elucidate how SPE–gauge data blending can improve drought monitoring by using long-term SPEs, present information regarding the effect of gauge density on the accuracy and drought performance of the blended data, and provide reference for conducting drought monitoring given limited in-situ data.

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2. Study area and data 2.1. Study area Mainland China (Fig.1) is located in the northwestern shore of the Pacific Ocean, featuring abundant

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topography types and diverse climate conditions. The vast eastern part of mainland China is

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dominated by subtropical and temperate monsoon climate, the inland northwestern part is dominated

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by temperate continental climate, and the Qinghai–Tibetan plateau in southwestern China is

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dominated by typical alpine climate. Over the past decades, mainland China has suffered disastrous drought events under climate change and global warming (Piao et al., 2010; Zhang et al., 2013;

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Wang et al., 2017c; 2018). For instance, a destructive drought hit the southwestern China in

billion (Zhang et al., 2013).

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2009–2010 because of severe lack of precipitation, which led to economic loss exceeding US$ 3.5

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Similar to other relevant studies (Wang et al., 2017b; Li et al., 2018; Zhong et al., 2019), the current study divided Mainland China into nine regions for SPE evaluation. The division was based

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on local topography, climate, and landscape (see Fig. 2), including the Northeast China (NEC), the Huang–Huai–Hai region (HHH), Inner Mongolia (IM), Loess Plateau (LP), the middle and lower

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regions of the Yangtze River (YR), Southwest China (SWC), South China (SC), Gansu–Xinjiang

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region (GXR), and Qinghai–Tibet Plateau (QTP).

2.2. PERSIANN–CDR PERSIANN–CDR SPE (Ashouri et al., 2014) was developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI). It was designed to provide long-term precipitation data and meet the requirements for consistent, long-term, and high-resolution global precipitation data of climatic studies, with a spatial resolution of 0.25°, spatial

JOURNAL PRE-PROOF coverage among the 60°NS latitude band, and a long-term record spanning 35 years from 1983 to 2017. PERSIANN–CDR is generated using a merged Gridded Satellite-based infrared (IR) dataset named “GridSat–B1” by a neural network trained by the radar-based stage IV precipitation product (Ashouri et al., 2014). Then, the PERSIANN–CDR is error-corrected based on the coarse-scale (2.5°)

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monthly gridded GPCP dataset. Notably, the GPCP dataset also involves several ground-based gauge

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observations which might include some in-situ gauge data in this study; However, the gauge

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observations for the GPCP are spatial-averaged to a coarse resolution (2.5°) much larger than that of PERSIANN–CDR (Adler et al., 2003), which would then only slightly affect SPE–gauge data

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blending in the present study. Data production and error correction of PERSIANN-CDR are carried out by the CHRS, and the details are provided in Ashouri et al. (2014). The PERSIANN–CDR data

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were obtained from the CHRS website (http://chrsdata.eng.uci.edu/).

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2.3. Observations for SPE–gauge data blending

In-situ precipitation observations from 727 meteorological stations over mainland China derived

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from the China Meteorology Administration (CMA) were used to blend with SPE data. These precipitation data have been processed using rigorous quality control procedures (e.g., spatial and

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temporal consistency check) and thus are of high quality. The distribution of the blending-used

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stations is shown in Fig. 1 and Fig. 2d. To evaluate the impact of the gauge data density on the blending results, three station subsets were extracted from all 727 stations, with 50, 200, and 727 stations over mainland China, as shown in Fig. 2b–2d. To ensure that the subsets were extracted randomly and uniformly, extraction was conducted as described by Chen et al. (2018): the 727 stations were spatially clustered to 25 categories by using the K-means algorithm; subsequently, 2 and 8 stations were randomly selected from each category to generate the 50 and 200 subsets, respectively. SPE–gauge data blending would be performed for the three station subsets

JOURNAL PRE-PROOF independently. The 50 station subsets with a mean density of 5.2 stations per 106 km2 (2.2 to 13.3 stations per 106 km2 over the nine regions), represented the cases with rather sparse gauge data; the 200 station subsets with a mean density of 20.8 stations per 106 km2 (9.5 to 47.7 stations per 106 km2 over the nine regions) represented the cases with medium-density gauge data; the 727

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station set with a mean density of 75.7 stations per 106 km2 (26.3 to 184.2 stations per 106 km2

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over the nine regions) represented the cases with rather dense gauge data. Table 1 lists the number of

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stations and station density in the nine regions, as well as mainland China for the three station subsets.

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Other meteorological data, such as air temperature, exposure to sunshine hours, relative humidity, and wind speed from the same meteorological stations, were also obtained to calculate potential

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evapotranspiration (PET), which is necessary for PDSI calculation. Monthly PETs were first calculated at each meteorological station (shown in Fig. 1) by using the Penman–Monteith method

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(Allen et al., 1998) and then interpolated to the same 0.5° grid cells. Similar to the method used in other studies (Wang et al., 2017b; Wu et al., 2017), TPS interpolation was applied using the R

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package “fields” (Nychka et al., 2015).

2.4. Observations as assessment reference

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The gridded precipitation data named China monthly Precipitation Analysis Product (CPAP) were

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used to assess the results of SPE–gauge data blending. This dataset were not used in SPE–gauge data blending. CPAP monthly gridded precipitation product was developed by CMA, with a spatial resolution of 0.5°. The CPAP is generated by monthly precipitation observations from a dense gauge network consisting of more than 2,400 gauges in the mainland China by using terrain-based thin-plate spline (TPS) interpolation. To minimize the interpolation error in the generation of CPAP, only CPAP gridcells containing gauges are used for assessment. The 727 stations used in the

JOURNAL PRE-PROOF SPE–gauge data blending method mentioned in Section 2.3 are among the 2,400 gauges used to generate CPAP (see Fig. 2a); therefore, to ensure that the results of SPE–gauge data blending are effectively validated, the CPAP gridcells containing the stations for the SPE–gauge data blending mentioned in Section 2.3, which are colored in brown in Fig. 2a are excluded from the assessment.

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Ultimately, only 897 CPAP gridcells (colored in sky blue in Fig. 2a) were adopted for the validation.

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Therefore, the results of SPE–gauge data blending are assessed using the gauge observations that are

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independent of the observation data used for blending.

Although we validated the performance of SPE–gauge data blending only for the gauged grid

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cells, the observations for West China (GX and QTP regions) are still largely unevenly distributed and might have significantly influenced the assessment. Therefore, less attention would be given for

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West China. The source data of PERSIANN–CDR are at a spatial resolution (0.25°) higher than that of CPAP; therefore, the PERSIANN–CDR data are resampled to a 0.5° resolution by performing

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spatial averaging prior to SPE–gauge data blending for direct comparison with CPAP.

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3. Methodology

3.1. Standardized Precipitation Index (SPI)

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The SPI (McKee et al., 1993) is a widely used distribution-based drought index that only requires

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precipitation data as inputs. The SPI is determined by the standardized anomaly of precipitation based on a given distribution. The monthly precipitation records are first fitted by using a given distribution (usually by Pearson-III, lognormal, or Gamma distribution) for each calendar month and the corresponding frequencies of each record are calculated. These frequencies are finally transformed to the corresponding quantiles of the standard normal distribution with a mean value of 0 and a standard deviation of 1 to become the SPI values. The SPI can also be calculated on multiple time scales (usually on 1, 3, 6, 12, and 24 months) to consider short- and long-term drought events

JOURNAL PRE-PROOF using the moving accumulations of precipitation data. The R package “SPEI” (Vicente-Serrano et al., 2010) is used to calculate the SPI in the present study. For brevity, only the SPI of the 12-month timescale (SPI-12) is used. SPI-12 generally has the highest correlation with PDSI and thus is most

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comparable to PDSI.

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3.2. Palmer Drought Severity Index (PDSI)

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In contrast to the SPI, the PDSI (Palmer, 1965) is calculated based on water balance. To calculate the

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PDSI, the precipitation data are first input into a simple two-layer bucket-like model, along with the PET data to calculate the moisture deficits and then further corrected as the moisture anomaly (Z)

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indices. The Z indices are then normalized by the duration factors as the X indices, that is, the

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calculated PDSI. The PDSI has been widely used in drought monitoring and quantifying the effect of climate change on drought (Dai, 2011, 2013; Sheffield et al., 2012; Wang et al., 2017c; Zhong et al.,

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2019). However, owing to fixed coefficients for the normalization of the Z index derived from Central US, the conventional PDSI features poor portability and spatial comparability, largely

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limiting its application worldwide (Vicente-Serrano et al., 2011; Guo et al., 2016; Zhong et al., 2018b). For this reason, the self-calibrating PDSI (SC_PDSI) was developed by Wells et al. (2004).

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SC_PDSI improved the PDSI calculation by re-fitting the coefficients of PDSI calculation adaptively

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for the local climate during the calculation. In the current study, the SC_PDSI was calculated using the R package “scPDSI” (Zhong et al., 2018a). Details of the SC_PDSI calculation are provided in Wells et al. (2004).

3.3. SPE–gauge data blending The GDA method (Cheema and Bastiaanssen, 2012; Duan and Bastiaanssen, 2013) is used to blend the station observation data with SPE data in the study. In the GDA blend method, the differences

JOURNAL PRE-PROOF between in-situ observation data (usually regarded as true values) and the SPE surface are first analyzed at the point scale; subsequently, the SPE data is corrected to become the blended data using the spatial surface of these differences generated using geographic interpolation. The GDA procedure for blending PERSIANN–CDR and in-situ station observation data is as follows:

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I) Calculating the differences 𝐷𝑠 between the in-situ station observation data 𝑂𝑠 and the corresponding PERSIANN–CDR value 𝑃𝑠 at the location of the station s by using 𝐷𝑠 = 𝑂𝑠 − 𝑃𝑠 .

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Similar to Sun et al. (2014), the PERSIANN–CDR value 𝑃𝑠 at the location of the station s is determined by bilinear interpolation of four PERSIANN–CDR grid cells around the station s.

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II) Interpolating the differences 𝐷𝑠 derived in step I into the grid cell i of PERSIANN–CDR, denoted as 𝐷𝑖 . Duan and Bastiaanssen (2013) have evaluated the spline, kriging, and inverse

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distance weighting (IDW) methods and found that IDW outperformed other methods. III) Adding the interpolated differences 𝐷𝑖 onto the original PERSIANN–CDR values to derive the

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simply set to 0.

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blended PERSIANN–CDR values 𝑃𝐵𝑖 : 𝑃𝐵𝑖 = 𝑃𝑖 + 𝐷𝑖 . Negative values can be generated, which are

The PERSIANN–CDR data are blended with the 50, 200, and 727 station subsets referred to in

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Section 2.3 as CDR_B50, CDR_B200, and CDR_B727, respectively, for evaluation and comparison.

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The three station subsets were also independently interpolated to the same 0.5° grid cells by IDW and referred to as IDW50, IDW200, and IDW727 respectively, to investigate the improvement of SPE–gauge data blending relative to the gauge-only interpolation.

3.4. Assessment metrics Several statistical metrics are used to quantify the performance of the SPE–gauge data blending. The Pearson correlation coefficient (R) is used to quantify the consistency between the validated and referenced series; the root–mean–square–error (RMSE) is used to measure the absolute error of the

JOURNAL PRE-PROOF validated series; the Nash–Sutcliffe coefficient of efficiency (NSE) is used to evaluate the closeness between the validated and referenced series; the log-NSE is similar to NSE but is more sensitive to low values; the relative bias is used to quantify the systematic bias of the validated series, which is typically used for evaluating the precipitation data. The probability of detection (POD) and false

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alarm ratio (FAR) are used to evaluate the ability of SPE and blended SPE in detecting drought

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events. POD quantifies the probability of SPE data to catch drought months, and FAR quantifies the

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probability of mistaking non-drought months for drought months. Similar to Zhong et al. (2019), the present study uses -1 and -2 as thresholds for the determination of drought months for the SPI and

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SC_PDSI, respectively. The equations for calculation of these metrics are listed in Table 2.

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4. Results

4.1. Validation of SPE–gauge data blending results

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Here the monthly PERSIANN-CDR data are blended with the precipitation observations from the three station subsets by using the GDA method. Before the data are applied in drought monitoring,

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the blended data need to be validated to understand their improvements when blended stations with different densities are used. Spatial patterns of the annual mean precipitation of CPAP, original and

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blended PERSIANN–CDR data, and interpolated station data are presented in Fig. 3. Both the

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original and blended PERSIANN–CDR reveal the spatial trend of precipitation distribution in China. However, the original PERSIANN–CDR loses many details of precipitation patterns, which may be attributed to the coarse resolution of the GPCP data for correcting PERSIANN–CDR (Zhong et al., 2019). This defect tends to be solved when the in-situ observations are blended. More details of the precipitation pattern are reproduced when more station data are blended with PERSIANN–CDR, as shown in Fig. 3f-3h. The interpolation results of the stations also reveal the superiority of SPE–gauge data blending, because the precipitation patterns of the interpolated results are generally

JOURNAL PRE-PROOF not closer to the CPAP than those of SPE–gauge data blending results when the stations are less dense. However, when using the densest station data, the SPE–gauge data blending result (CDR_B727) is considerably close to the corresponding interpolation result (IDW727) that reproduces almost all CPAP precipitation pattern. This finding suggests that the SPE data show

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fewer advantages when the in-situ data are adequately dense.

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Fig. 4 presents the spatial patterns of the five assessment metrics for the original and blended

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PERSIANN–CDR, as well as the interpolated station data, with reference to CPAP. Consistent with the findings of Lin and Wang (2011), the blended PERSIANN–CDR data and station data generally

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outperform the original PERSIANN–CDR data as well as the interpolation data, with a wider area featuring higher R, NSE, and log-NSE values and lower RMSE and bias; the station density

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significantly affects the blending results. These results indicate that when sparse stations are used, the results of SPE–gauge data blending are superior to the results of station-only interpolation; when

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dense stations are used, the results of SPE–gauge data blending are superior to the raw SPE data.

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Meanwhile, the blending results of the medium-density cases (CDR_B200) evidently improve relative to both the original PERSIANN–CDR and interpolation results. However, for the

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727-station case (on average 76 stations /106 𝑘𝑚2 over mainland China) with densely distributed

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stations, the interpolated station data (IDW727) show comparable performance to the SPE–gauge data blending results (CDR_B727), with highly similar magnitudes and patterns of assessment metrics. This finding may reveal less improvement of applying SPE data in cases with abundant gauges, such as the 727-station case presented in this study. The assessment metrics for the blending and interpolation results of the validation grid cells in the nine regions of Mainland China are listed in Table 3. The boxplots of these metrics are shown in Fig. 5. These results quantitatively illustrate the apparent differences in blending performance among

JOURNAL PRE-PROOF different station densities and regions. Comparison with the original PERSIANN–CDR showed that the improvement in blending results was negligible for the 50-station cases, except for the YR region, which suggested relatively apparent increases in R, RMSE, and log-NSE; for the GX region, decreases in R and NSEs were observed. These results might be related to the station density for the

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regions, considering that the YR region occupied 13.3 stations/106 km2, whereas the GX region

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only occupied 2.6 stations/106 km2 on the average for the 50-station cases. For the 200-station case,

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the blending results were more evidently improved relative to the raw PERSIANN–CDR data, with NSE increments reaching 0.14 even for the GX and QTP regions with sparse stations. Except for

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some cases, the narrow boxes in Fig. 5 also indicate apparent improvements in the reliability of the blending results for the 200-station case. The interpolation results also improved with the denser

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200-station set; however, the blending results still outperformed the interpolation data for the 200 station subsets. The blending results for the 727-station cases exhibited the largest improvement,

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with NSEs increased by about 0.1 to 0.2; however, the interpolated station data without SPEs are

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also comparable with the blending results. Therefore, the density of the 200-station subsets, with the blending results outperforming the raw PERSIANN–CDR data, as well as the interpolated station

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data, could be the most suitable density for SPE–gauge data blending among the three subsets.

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Fig. 6 shows the intra-annual variation in assessment metrics for the blending and interpolation

results. For brevity, only six typical regions were selected as examples in this study. For most cases, the SPE–gauge data blending results improved more relative to the original PERSIANN–CDR data during the dry seasons (winter) and relative to the interpolation results during the wet seasons (summer). The reason might be the difference in error structure between the interpolated precipitation and SPE (e.g., SPE usually shows different error patterns between the wet and dry seasons (AghaKouchak et al., 2012)). Fig. 6 also shows that the blending results for the 200-station

JOURNAL PRE-PROOF case, among the cases evaluated, are the most significantly improved relative to the original PERSIANN–CDR data and interpolation results.

4.2. Drought monitoring utility of SPE–gauge blended data

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Fig. 7 presents the spatial patterns of the R value of SPI-12 and SC_PDSI calculated from the

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original and blended PERSIANN–CDR data and interpolation results, with reference to the drought

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indices of CPAP. The spatial patterns in R of the drought index are consistent with the R of the

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precipitation input; however, the spatial pattern of the R value of the drought index shows greater spatial heterogeneity and variability than the precipitation inputs, particularly for SC_PDSI. The R

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value of SPI-12 was apparently lower than the R value of the precipitation input; for areas with

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lower accuracy, differences in R between SPI-12 and the precipitation input are considerably larger. These differences indicate that the error of the SPE data could be aggravated in the SPI calculation,

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given that SPIs are usually calculated using the moving aggregations of the precipitation data. SC_PDSI performed more poorly than SPI-12 as spatial heterogeneity increased markedly. In the

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study by Zhong et al. (2019), which used a corrected conventional PDSI without a self-calibrating procedure, the PDSI showed accuracy patterns that were more similar to those of SPI-12 over

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mainland China than in the present study; thus, the large discrepancies between SC_PDSI and

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SPI-12 may be attributed to the instability of the self-calibrating procedure developed by Wells et al. (2004). The high instability and high sensitivity to data sources of the self-calibrating procedure has been demonstrated by Liu et al. (2016). Regardless, blending with in-situ gauge observation data still improved the performance of the calculated drought indices. This improvement became more significant as the size of the station sets increased; meanwhile, the performance of the station-only interpolation results also showed a corresponding increase as the station density rose. Fig. 8 shows the spatial patterns of the POD and FAR of SPI-12 and SC_PDSI for the blending

JOURNAL PRE-PROOF and interpolation results. The results associated with denser station subsets also exhibited better detection of drought events regardless of the method used to obtain data—that is, blending or interpolation; however, unlike the results for R, the differences in POD and FAR between interpolation results and blending results are apparently smaller, as they showed satisfactory PODs

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F

and FARs for both SPI-12 and SC_PDSI, with PODs generally exceeding 0.8 (more than 80%

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drought events could be actually detected) and FARs below 0.2 (less than 20% of the detected

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drought events are false alarms), except for some ungauged areas. These results indicate that SPE data contribute less to improving the capacity for drought event recognition than to reproducing the

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drought magnitude.

Table 4 lists the R, POD, and FAR values calculated for the drought indices of the validation

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grid cells in the nine regions. Similar to Table 3, Table 4 shows that the 200-station case generally shows a more evident improvement in blending results, compared with both the raw

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PERSIANN–CDR data and the gauge-only interpolation results in the assessment metrics for both

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SPI-12 and SC_PDSI. In the 200-station case, SPI-12 shows satisfactory performance as the R values are generally improved by up to 0.1 for the nine regions. The PODs and FARs of SPI-12 are

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also improved when blended with more station data; however, the original PERSIANN–CDR

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features satisfactory PODs (over 0.9) and FARs (below 0.1) for SPI-12. The overall lower performance of SC_PDSI than that of SPI-12 is also quantitatively shown; however, the improvement in SPE–gauge data blending is more significant, with R increased by up to 0.16 for the nine regions. The drought detection capacity of the raw PERSIANN–CDR for SC_PDSI was much lower than that for SPI-12, with an average POD and FAR of about 0.8 and 0.2, respectively. However, they could be improved to nearly 0.9 and 0.1 respectively, after blending with the station data. Overall, SPE–gauge data blending could apparently improve the temporal performance of SPE

JOURNAL PRE-PROOF in the calculation of drought indices when using a medium-size or denser station set, and the improvement is greater for SC_PDSI than SPI-12; however, the station-only interpolation results could be comparable to the SPE–gauge data blending results when using a dense station set. The ability to reveal the spatial pattern of drought is also important for SPE to monitor

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F

macroscale drought. To further evaluate the improvement in the spatial performance of SPE–gauge

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data blending, spatial RMSEs for both the blending and interpolation results were calculated over

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each month from 1983 to 2017 to validate the grid cells for the nine regions. The time series is shown in Fig. 9. Owing to the weak spatial representability of sparse observations, larger differences

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were found in spatial performance than in temporal performance between the interpolation results and blending results, particularly for the 50-station case. The blending results for the 50-station case

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are still slightly superior to those of the original PERSIANN–CDR, particularly for the YR region, which exhibited the highest density for the 50-station case. With regard to the 727-station case, the

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spatial RMSEs were the lowest and closest between the blending results and interpolation results for

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all regions. The 200-station case remained as the most ideal case for SPE–gauge data blending to obtain an apparent improvement relative to the raw PERSIANN–CDR data and interpolation results.

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To further assess the ability of SPE–gauge data blending in revealing drought spatial patterns, two

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typical drought events with the largest affected area, highest severity, and largest losses in mainland China over the past two decades were selected as cases—the continuous drought from 1999 to 2000 (most severe in May 2000) in North China and the severe drought from 2009 to 2010 (most severe in March 2010) in Southwest China (Wang et al., 2017d; Zhong et al., 2019). The spatial patterns of the two drought events revealed by SPI-12 and SC_PDSI are presented in Fig. 10. With CPAP as the benchmark, the raw PERSIANN–CDR overestimated the extent and severity of drought, and the illustrated centroids of the drought varied from those of CPAP to a certain degree. When integrated

JOURNAL PRE-PROOF with in-situ observations, the spatial patterns of drought revealed by blending analysis are closer to CPAP; for the 200-station case, the patterns of both drought events shown by blending analysis are sufficiently close to CPAP when either SPI-12 or SC_PDSI are used. For interpolation results, large discrepancies in the spatial patterns of drought revealed by CPAP were found for the low- and

O

F

mid-density station cases because of inadequate spatial information, particularly for the 50-station

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cases that could barely match the center and shape of the drought pattern; only the interpolation

and

blending

results.

Notably,

blending

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results of the 727-station case with the highest density showed high consistency with both the CPAP results

generally

outperformed

the

original

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PERSIANN–CDR and interpolation results with respect to spatial performance; the most superior SPE–gauge data blending results were observed in the 200-station case. Station-only interpolation

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5. Discussion

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could hardly reveal the spatial patterns of drought.

Numerous studies have been conducted on blending SPE with in-situ gauge observation; however,

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the effects of the density of gauge observation data remain largely ignored, except in a few studies such as those by Lin and Wang (2011) and Wang and Lin (2015). Therefore, the current study

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mainly aimed to explore the relationships between the improvement in SPE–gauge data blending and

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gauge density. According to the number and distribution of available gauge stations over mainland China and with reference to Lin and Wang (2011), three station subsets were established with the density set from low to high. Although different interpolation (ordinary Kriging for Lin and Wang (2011) and IDW for this study) and blending methods as well as different time scales were applied, Lin and Wang (2011) and Wang and Lin (2015) obtained similar results: for the sparse-density gauge network (nearly 6 gauges per 106 km2, close to the 50-station case of this study), blending results was superior to gauge-only interpolation, but the improvement relative to the original SPE

JOURNAL PRE-PROOF data was also limited; for the high-density gauge network (nearly 30 gauges per 106 km2, denser than the 200-station case of this study), the blending results significantly outperformed the original SPE data; nevertheless, their superiority to the gauge-only interpolation results also largely decreased; the medium-density gauge network seemed the most ideal prospect for using SPE data in the blending

O

F

method because the blending results evidently improved relative to both the original SPE data and

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the interpolation results. Consistent findings could also be inferred from several related studies,

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although the effects of gauge densities were not discussed. Chen et al. (2018) used considerably denser gauge networks in eastern China to blend the TMPA product, and found that the

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kriging-based GDA method did not outperform gauge-only interpolation by using the same interpolation method. However, in the studies by Chen et al. (2017) and He et al. (2017) in which the

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same TMPA data were used but sparser gauges were employed, the blending results consistently performed better, compared with both the raw SPE data and the gauge-only interpolation results.

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Therefore, although the simplest IDW method was adopted for blending and interpolation in the

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present study, we suggest the results are reasonable. In Lin and Wang (2011) and Wang and Lin (2015), although the effect of gauge density on the

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performance of SPE–gauge data blending was evaluated, the relationship between gauge density and

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improvement in blending results was not quantitatively analyzed. Regardless, this is important to determine the economic range of gauge density for SPE–gauge data blending. For this reason, the differences in some assessment metrics (the R and NSE values of the precipitation input as well as the R values of the calculated SPI-12 and SC_PDSI) between the blending results and the original PERSIANN–CDR results, as well as the corresponding interpolation results for different station densities, are calculated. The results are shown in Fig. 11. The data samples were the metrics of the seven regions (except for GX and QTP for their largely unevenly distributed stations) with three

JOURNAL PRE-PROOF station subsets (a total of 21 samples for each assessment metric). Fig. 11 shows the strong logarithmic relationships between the station density and the improvement in blending results relative to the raw PERSIANN-CDR data, and the strong inversely proportional relationship between the station density and the difference in blending results to the interpolation results in the

O

F

same station set. Generally, for the precipitation input, the differences of R and NSE between the

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interpolation results and the blending results converge to 0 when the station density exceeds about 75

PR

per 106 km2; for the SPI-12 and SC_PDSI, the differences of R value converge to 0 when the station density exceeds about 50 per 106 km2. The improvement in SPE–gauge data blending results relative

E-

to the original PERSIANN-CDR data also significantly increases with an increase in gauge density before the similar range of 50–75 gauges per 106 km2, in accordance with the results in the previous

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sections. The blending results also show satisfactory drought performance within the aforementioned range of station density. When the station density exceeds this range, the improvement in blending

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results relative to raw SPE data also apparently decreases. Consequently, the medium-gauge density

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(about 50–75 gauges per 106 km2) might be the “balance point” when performing SPE–gauge data blending—that is, the most cost-efficient gauge density that fully uses the advantages of the SPE

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data, exhibits satisfactory accuracy and drought utility in SPE–gauge data blending, and uses the

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smallest number of gauges. When the density of available gauges is below this range, SPE–gauge blending is also effective; however, when the density is extremely sparse (below 10 gauges per 106 km2 in this study), the accuracy of blending results might even be reduced, in which case directly using raw SPE data would be preferable. When the gauge density is above this range, the SPE data tend to be less valuable, rendering gauge-only interpolation more suitable. Notably, our results were derived from PERSIANN–CDR data by using the simple GDA method. The integration of other auxiliary data (e.g., elevation and vegetation indices) also

JOURNAL PRE-PROOF significantly changed the performance of SPE data (Duan and Bastiaanssen, 2013; Chen et al., 2018). Further studies on other complicated blending approaches and other areas are not performed in the current study owing to limited data availability and space. The proposed gauge density range in this study might be similar for drought monitoring application in other areas outside China with

O

F

PERSIANN–CDR or similar error-corrected SPE data (e.g. the post-processed TMPA or CHIRPS);

O

however, the suitability might need further local validation. We suggest that subsequent studies on

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SPE–gauge data blending should emphasize the effects of gauge density. Moreover, we also suggest that the mechanism underlying the effect of gauge density and distribution on the performance of

E-

SPE–gauge data blending needs to be more systematically analyzed.

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6. Conclusions

This study mainly assesses the performance of the SPE–gauge data blending procedure with

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long-term SPE for drought monitoring, as well as the impact of gauge density on the performance of SPE–gauge data blending with three station subsets at different densities. PERSIANN–CDR, a

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widely used long-term SPE with records spanning 35 years until 2017, is used as a case study. The drought monitoring capability of the blending results is determined by the performance of the

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calculated SPI-12 and SC_PDSI.

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SPE–gauge data blending, except that using the sparse 50-station subset, could apparently

improve the accuracy of PERSIANN–CDR in revealing spatial patterns of precipitation; in addition, the improvements are more evident when the adopted station networks are denser. However, the accuracy and performance of station-only interpolation results also increase as the station network density increased, indicating the reduced need for combining SPE data with gauge data. Generally, only the blending results of the medium-density (200-station) subset exhibit satisfactory improvement relative to both the raw PERSIANN-CDR data and the station-only interpolation

JOURNAL PRE-PROOF results. Apparent improvements in the results of SPE–gauge data blending is also found for the two drought indices, particularly for the magnitude and spatial patterns of drought; compared with SPI, SC_PDSI exhibited higher sensitivity to data input and greater improvement in SPE-gauge blending.

O

F

SPI-12 can satisfactorily detect drought events without the need of blending with gauge data,

O

whereas SC_PDSI still required blending with gauge data. The 200-station case seemed to be the

lowest requirement for number of used gauges.

PR

most suitable case for SPE–gauge data blending owing to its enhancement of drought indices and the

E-

In the blending procedure, station density and performance showed strong logarithmic relationship and inversely proportional relationship between the station density and improvement of

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blending results relative to the raw SPE and interpolation results respectively. The improvement in blending results relative to raw SPE increased with increasing gauge density; however, the

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improvement relative to interpolation results also decreased correspondingly, revealing the reduced need to use additional SPE data. The range of 50–75 gauges per 106 km2 is determined to be the

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“balance point” for blending SPE with in-situ gauge observations in drought monitoring—that is, the

U

cost-efficient gauge density that shows satisfactory blending performance and required few gauges.

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For different SPE data, blending methods, or application areas, the ideal range of gauge density might vary; thus, the impact of gauge density should be further considered in subsequent relevant studies.

Acknowledgements This research was financially supported by the National Natural Science Foundation of China (Grant Nos. 51509040, 51709127, 91547202), the Natural Science Foundation of Guangdong Province, China (Grant No. 2017A030310172). Our cordial gratitude also should be owed to the Editor, Prof.

JOURNAL PRE-PROOF Geoff Syme, and the four anonymous reviewers for their professional and pertinent suggestions and comments, which are greatly helpful for further improvements of the quality of this manuscript.

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blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems. IEEE T. Geosci. Remote 54(5), 2552-2562. https://doi.org/10.1109/TGRS.2015.2502956. Verdin, A., Rajagopalan, B., Kleiber, W. and Funk, C., 2015. A Bayesian kriging approach for blending and

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Wang, X.L. and Lin, A., 2015. An algorithm for integrating satellite precipitation estimates with in situ precipitation data on a pentad time scale. J. Geophys. Res.-Atmos. 120(9), 3728-3744. https://doi.org/10.1002/2014JD022788. Wang, Z., Li, J., Lai, C., Zeng, Z., Zhong, R., Chen, X., Zhou, X. and Wang, M., 2017c. Does drought in China show a significant decreasing trend from 1961 to 2009? Sci. Total Environ. 579, 314-324. https://doi.org/10.1016/j.scitotenv.2016.11.098. Wang, Z., Xie, P., Lai, C., Chen, X., Wu, X., Zeng, Z. and Li, J., 2017b. Spatiotemporal variability of

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Zhong, R., Chen, X., Lai, C., Wang, Z., Lian, Y., Yu, H. and Wu, X., 2019. Drought monitoring utility of satellite-based precipitation products across mainland China. J. Hydrol. 568, 343-359.

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https://doi.org/10.1016/j.jhydrol.2018.10.072.

Zhong, R., Chen, X., Wang, Z., Lai, C., Goddard, S., Wells, N., Hayes, M., 2018a. scPDSI: Calculation the

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meteorological factors under climate change of the Tibetan plateau. Atmos. Res. 214, 296-310.

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U

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https://doi.org/10.1016/j.atmosres.2018.08.008.

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O

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JOURNAL PRE-PROOF

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U

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AL

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Fig.1. Topography and distribution of meteorological stations of mainland China.

Fig.2. (a) Division of the nine regions and distribution of gauged gridcells, and (b-d) spatial distribution of stations for the 50, 200, and 727 stations experiments. “Blend grid” denotes the gridcells including the meteorological stations used for blending with SPE; “Valid grid” denotes the gridcells including gauges for generating CPAP but without the stations for blending with SPE.

R N

AL

PR

E-

PR

O

O

F

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Fig.3. Spatial patterns of annual mean precipitation of CPAP, original and blended PERSIANN-CDR, and

JO

U

interpolated station data.

AL

PR

E-

PR

O

O

F

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R N

Fig.4. Spatial patterns of assessment metrics for original and blended PERSIANN-CDR, and interpolated

JO

U

station data, w.r.t. CPAP.

AL

PR

E-

PR

O

O

F

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Fig.5. Boxplots for the assessment metrics of the original and blended PERSIANN-CDR, and interpolated station data w.r.t. CPAP for the validation gridcells for the six selected regions. The metrics are calculated

JO

U

R N

only for the validation grid cells shown in Fig. 2.

E-

PR

O

O

F

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Fig. 6. Assessment metrics for the original and blended PERSIANN-CDR, and interpolated station data w.r.t. CPAP for each calendar months of six selected regions. The metrics are calculated only for the

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U

R N

AL

validation grid cells shown in Fig. 2.

JO

U

R N

AL

PR

E-

PR

O

O

F

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Fig.7. CC spatial patterns of the SPI-12 and SC_PDSI calculated by the blended PERSIANN-CDR, and interpolated station data, w.r.t. CPAP.

R N

AL

PR

E-

PR

O

O

F

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Fig.8. Spatial patterns of POD (left two columns) and FAR (right two columns) of the SPI-12 and

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SC_PDSI calculated by the original and blended PERSIANN-CDR, and interpolated station data, w.r.t.

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CPAP.

R N

AL

PR

E-

PR

O

O

F

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Fig.9. Spatial RMSE of the SPI-12 and SC_PDSI based on the original and blended PERSIANN-CDR, and interpolated station data w.r.t. CPAP, for the validation gridcells of the nine regions. The RMSEs are

JO

U

calculated only for the validation grid cells shown in Fig. 2.

R N

AL

PR

E-

PR

O

O

F

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Fig.10. Spatial pattern of the two typical drought events of May 2000 in north China (left two columns) and March 2010 in southwest China (right two columns), represented by SPI-12 and SC_PDSI of the

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CPAP, original and blended PERSIANN-CDR, and interpolated station data respectively.

JOURNAL PRE-PROOF Fig. 11. Relationships of the station density and difference in validation metrics of the blending results to the original PERSIANN-CDR data (red points) and corresponding interpolation data (blue points) respectively. The metrics include (a) CC and (b) NSCE of monthly precipitation, and CC of (c) SPI-12 and (d) SC_PDSI data. Note: the “x” in the formulas means the density of the blended stations, the “y” means the differences of the validation metrics; calculation of the coefficient of determination (R2 ) please

JO

U

R N

AL

PR

E-

PR

O

O

F

see Table 2.

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Tables Table 1. Area, number of stations, and station density for the three station subsets for the nine regions and the mainland China. Note: N denotes the number of stations for SRE-gauge blending; Nvalid denotes the number of validation gridcells.

103 km2

The 200 station

subset

The 727 station

subset 6

2

Nvalid

subset 6

2

6

2

N

N / 10 km

N

N / 10 km

N

N / 10 km 71.5

99

149.8

107

F

Region

The 50 station

Area

1035

6

5.8

26

25.1

74

HHH

414

2

4.8

16

38.7

62

IM

831

5

6.0

11

13.2

42

50.6

85

LP

368

3

8.1

15

40.7

56

152.1

77

YR

901

12

13.3

43

47.7

166

184.2

166

SWC

949

7

7.4

27

28.4

129

135.9

156

SC

542

4

7.4

15

27.7

67

123.6

77

GX

2322

6

2.6

22

9.5

61

26.3

80

QTP

2236

5

2.2

25

11.2

70

31.3

50

9598

50

5.2

727

75.7

897

d

AL R N U JO

O

PR

200

PR

China

E-

Mainlan

O

NEC

20.8

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Table 2. Statistical metrics for validation of SRE and SRE-gauge blending results. Statistical metric

Perfect

Formulation

value ̅𝑖 ) ∑𝑛𝑖=1(𝑆𝑖 − 𝑆̅𝑖 ) ⋅ (𝑂𝑖 − 𝑂

R=

R

Comments

̅𝑖 )2 √∑𝑛𝑖=1(𝑆𝑖 − 𝑆̅𝑖 )2 ⋅ ∑𝑛𝑖=1(𝑂𝑖 − 𝑂

1 𝑆𝑖 : the validated series;

2 ∑𝑛𝑖=1(log(𝑂𝑖 ) − ̅̅̅̅̅̅̅̅̅ log(𝑂𝑖 ))

n11 n10 + n11

FAR

n01 FAR = n11 + n01

PR

̅𝑖 ))2 (∑𝑛𝑖=1(𝑆𝑖 − 𝑆̅𝑖 ) ⋅ (𝑂𝑖 − 𝑂 𝑛 𝑛 ̅𝑖 )2 ∑𝑖=1(𝑆𝑖 − 𝑆̅𝑖 )2 ⋅ ∑𝑖=1(𝑂𝑖 − 𝑂

JO

U

R N

AL

R2 =

n: the length of validated series;

1

n11 : number of months that both SRE and CPAP show

0%

drought; n10 : number of months that

1

E-

POD

POD =

R2

1

∑𝑛𝑖=1(log(𝑆𝑖 ) − log(𝑂𝑖 ))2

∑𝑛𝑖=1 𝑆𝑖 RB = ( 𝑛 − 1) × 100% ∑𝑖=1 𝑂𝑖

RB

𝑆;

PR

log-NSE

log-NSE = 1 −

𝑂𝑖 : the referenced series; 𝑆̅: the mean value of series

O

NSE

∑𝑛𝑖=1(𝑆𝑖 − 𝑂𝑖 )2 NSE = 1 − 𝑛 ̅𝑖 )2 ∑𝑖=1(𝑂𝑖 − 𝑂

0

O

RMSE = √

F

∑𝑛𝑖=1(𝑆𝑖 − 𝑂𝑖 )2 𝑛

RMSE

0 1

only CPAP shows drought; n01 : number of months that only SRE shows drought.

JOURNAL PRE-PROOF

Table 3. Assessment metrics of the original and blended PERSIANN-CDR w.r.t. CPAP for the validation gridcells of the nine regions. Station Region

PERSIANN-CDR

Interpolated station data

subset R

RMSE (m)

NSE

log-NSE

RB (%)

R

RMSE (m)

NSE

log-NSE

RB (%)

CDR_ori

0.950

19.2

0.893

0.856

11.1

50

0.946

20.0

0.884

0.885

7.1

0.903

27.9

0.774

0.827

13.8

200

0.967

15.0

0.935

0.937

-0.1

0.960

16.6

0.921

0.941

1.6

727

0.981

11.5

0.962

0.965

0.0

0.981

11.6

0.961

CDR_ori

0.933

24.9

0.867

0.876

8.0

50

0.934

24.5

0.872

0.896

0.9

0.840

37.8

0.694

0.786

10.4

200

0.961

18.8

0.924

0.941

0.9

0.939

23.7

0.880

0.925

4.5

727

0.986

11.4

0.972

0.983

0.4

0.985

11.9

0.970

0.984

0.9

CDR_ori

0.938

15.1

0.876

0.890

3.6

50

0.932

16.2

0.858

0.902

2.1

0.878

23.8

0.692

0.828

16.2

200

0.950

14.1

0.892

0.929

3.7

0.928

18.0

0.824

0.909

12.5

727

0.976

9.4

0.952

0.969

1.0

0.975

9.6

0.950

0.969

3.2

CDR_ori

0.923

19.0

0.851

0.883

0.0

50

0.923

19.1

0.850

0.863

-0.8

0.877

24.1

0.760

0.859

6.9

200

0.962

13.6

0.924

0.954

0.2

0.955

14.7

0.911

0.953

2.9

727

0.985

8.4

0.971

0.983

-0.6

0.986

8.3

0.972

0.985

0.0

CDR_ori

0.915

40.8

0.836

0.772

-0.2

50

0.939

35.1

0.879

0.873

3.4

0.890

46.5

0.788

0.828

4.2

200

0.964

26.7

0.930

0.935

0.3

0.954

30.3

0.910

0.943

-0.4

727

0.986

17.1

0.971

0.973

0.0

0.986

17.0

0.972

0.981

0.1

CDR_ori

0.912

35.3

0.831

0.827

-3.4

50

0.921

34.7

0.837

0.842

3.5

0.881

41.3

0.769

0.831

5.4

used /

R N

0.5

O

O

PR

E-

/

/

/

/

200

0.941

29.4

0.883

0.906

1.3

0.930

31.7

0.863

0.904

2.4

727

0.978

17.8

0.957

0.963

0.5

0.979

17.5

0.958

0.971

0.8

CDR_ori

0.913

50.9

0.827

0.702

-1.9

50

0.920

49.1

0.840

0.801

3.1

0.840

67.3

0.699

0.783

2.2

200

0.931

44.9

0.866

0.854

-2.3

0.908

51.5

0.823

0.899

-1.4

727

0.975

27.2

0.951

0.948

-0.2

0.974

27.6

0.949

0.970

-0.1

CDR_ori

0.758

15.4

0.563

0.646

-13.9

50

0.735

16.1

0.524

0.590

-17.1

0.525

20.8

0.201

0.328

2.2

200

0.774

14.9

0.590

0.725

0.6

0.699

17.5

0.434

0.662

8.4

727

0.844

12.6

0.709

0.815

-6.3

0.814

13.6

0.662

0.787

-3.2

CDR_ori

0.896

31.7

0.706

0.741

15.8

50

0.892

29.2

0.750

0.705

6.4

0.832

33.0

0.681

0.666

5.2

200

0.924

22.9

0.846

0.818

1.1

0.902

25.5

0.810

0.831

0.3

727

0.966

15.3

0.931

0.918

2.3

0.966

15.1

0.933

0.924

1.4

U

SWC

AL

YR

PR

LP

0.972

/

HHH

IM

F

NEC

/

JO

SC

/

GX

/

QTP

JOURNAL PRE-PROOF Table 4. Correlation coefficients (Rs) between SPI-12 and SC_PDSI of the original and blended PERSIANN-CDR, and the interpolated station precipitation observations w.r.t. CPAP for validation gridcells of the nine regions. Number

SPI-12 of

SPI-12 of

SC_PDSI of

SC_PDSI of

of

PERSIANN-CDR

Interpolated data

PERSIANN-CDR

Interpolated data

Regio n stations

R

POD

FAR

CDR_or

0.85

0.93

0.06

R

POD

FAR

R

POD

FAR

0.75

0.87

0.14

9

6

7

R

/

/

1

5

5

0.84

0.93

0.06

0.70

0.91

0.08

0.76

0.85

0.13

1

0

3

4

2

1

6

0

2

0.89

0.94

0.05

0.87

0.94

0.05

0.82

0.89

0.10

3

7

4

8

7

4

9

0

0.93

0.95

0.03

0.93

0.96

0.03

0.87

7

8

8

9

1

9

8

0.82

0.93

0.06

50

0.82

0.89

0.10

1

4

9

7

0.91

0.07

0.87

0.92

0.07

4

6

9

3

9

0.88

0.15

2

6

2

0.77

0.87

0.15

7

6

0.83

0.93

0.06

0.61

0.91

0.08

50 4

5

6

0.95

0.04

0.84

0

3

5

4

0.96

0.96

0.02

0.96

5

9

9

6

0.79

0.91

727 CDR_or

6

2

O 0.62

5

0

5

9

7

0.90

0.11

0.79

0.86

0.12

3

0

8

5

4

0

9

7

0.97

0.02

0.91

0.94

0.06

0.91

0.94

0.06

0

6

9

3

3

4

5

7

0.68

0.82

0.17

4

7

4

0.07

IM

0.79

0.91

9

0.08

0.71

/

0.89

0.09

0.68

0.80

0.16

0.61

0.77

0.20

6

6

2

8

0

3

5

5

7

4

9

1

0.83

0.92

0.07

0.79

0.91

0.07

0.73

0.84

0.15

0.69

0.83

0.17

6

4

2

5

1

6

3

4

1

6

8

9

0.92

0.95

0.04

0.92

0.95

0.03

0.81

0.89

0.12

0.83

0.90

0.11

3

3

8

8

6

9

9

9

0

1

8

7

0.80

0.93

0.06

0.70

0.78

0.17

2

6

0

0.73

0.81

0.15

R N

200

5

AL

50

2

0.18

0.82

/

i

0.83

0.06

PR

200

4

/

0.94

E-

7 0.90

HHH

O

0.74

PR

9

0.17 6

727

i

0.82 9

200

/

0.68 4

NEC

CDR_or

FAR

F

i

POD

727

CDR_or

U

i

/

0

2

7

0.78

0.92

0.06

0.66

/

0.91

0.08

0.65

0.74

0.17

50

4

9

6

0

9

5

0

1

8

9

8

9

0.90

0.95

0.04

0.89

0.95

0.04

0.81

0.86

0.11

0.83

0.86

0.11

1

2

2

8

3

1

3

2

8

3

3

8

0.96

0.97

0.03

0.96

0.97

0.02

0.90

0.91

0.08

0.90

0.91

0.07

0

1

1

5

5

5

3

4

4

7

0

6

0.86

0.93

0.06

0.67

0.81

0.19

3

0

5

JO

LP

200

727 CDR_or

/ i

/

0

9

6

0.89

0.94

0.05

0.80

0.92

0.06

0.76

0.83

0.14

0.73

0.82

0.16

2

3

4

4

7

8

8

1

9

2

1

6

0.93

0.96

0.03

0.93

0.95

0.04

0.82

0.87

0.12

0.81

0.86

0.12

8

0

9

1

5

0

9

6

0

5

9

2

0.97

0.97

0.02

0.97

0.97

0.02

0.86

0.89

0.08

0.88

0.91

0.07

50 YR 200 727

45

JOURNAL PRE-PROOF

CDR_or

6

2

6

0.75

0.92

0.07

9

6

3

9

6

1

0.59

0.81

0.22

2

9

3

7

/ i

0

8

/

6

9

4

0.77

0.93

0.06

0.69

0.91

0.07

0.62

0.83

0.20

0.59

0.79

0.19

8

4

8

1

8

8

3

2

0

3

1

5

0.84

0.94

0.05

0.82

0.93

0.05

0.66

0.84

0.18

0.67

0.84

0.18

2

4

7

8

9

9

6

5

4

3

8

0

0.93

0.96

0.03

0.94

0.96

0.03

0.78

0.89

0.13

0.80

0.89

0.12

6

2

4

4

3

1

2

2

8

0

9

5

0.77

0.92

0.07

0.69

0.82

0.18

2

0

5

50 SWC 200

CDR_or i

/

5

4

7

0.79

0.92

0.07

0.70

0.91

0.08

0.72

0.82

0.17

0.69

0.83

0.22

0

6

5

9

8

4

2

9

O

/

F

727

2

8

9

8

0.85

0.93

0.06

0.84

0.94

0.06

0.78

0.87

0.14

0.79

0.87

0.15

1

8

4

0

0

3

0

1

9

9

9

8

0.92

0.95

0.04

0.94

0.96

0.03

0.86

0.91

0.11

0.87

0.92

0.11

9

9

1

2

3

9

4

6

3

8

6

1

CDR_or

0.69

0.91

0.08

0.49

0.70

0.27

i

6

2

1

0.68

0.91

0.08

0.60

2

2

1

5

0.77

0.93

0.06

8

0

0.84 1

SC

PR

200

727

8

/

0.45

0.74

0.28

0.32

0.67

0.32

2

9

2

5

0

4

7

7

0.74

0.92

0.06

0.59

0.79

0.24

0.52

0.79

0.25

4

7

8

4

5

9

6

2

0

2

0.93

0.05

0.84

0.94

0.05

0.52

0.80

0.22

0.57

0.81

0.21

8

5

5

2

2

6

4

6

2

4

1

0.50

0.76

0.25

9

1

5

AL

727

PR

200

0.91

0.08

/

/

6

1

6

0.60

0.91

0.09

0.46

0.89

0.11

0.47

0.77

0.26

0.46

0.73

0.29

8

2

1

2

1

1

8

1

9

3

3

2

0.76

0.93

0.06

0.73

0.92

0.06

0.63

0.79

0.21

0.63

0.77

0.20

3

1

6

7

2

7

2

8

5

4

0

9

0.86

0.94

0.05

0.89

0.95

0.04

0.67

0.85

0.18

0.75

0.83

0.16

4

6

1

2

2

6

5

5

6

1

5

7

R N

50

7

0.08

GX

i

2

0.90

50

0.66

E-

/

CDR_or

O

50

QTP

200

JO

U

727

46

JOURNAL PRE-PROOF

Highlights SPE–gauge blending can significantly improve the drought monitoring utility of SPEs



Impacts of different gauge densities on SPE-gauge blending are investigated



Improvements of blending are different for different aspects in drought monitoring



Medium gauge density is most ideal for SPE–gauge blending in drought monitoring

O

O

F



PR

Conflict of interest statement

E-

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all

PR

authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for

JO

U

R N

enclosed.

AL

publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is

47