Spatial and temporal precipitation patterns characterized by TRMM TMPA over the Qinghai-Tibetan plateau and surroundings

Ma, Zhanshan; Xu, Yaping; Peng, Jie; Chen, Qiuxiao; Wan, Dan; He, Kang; Shi, Zhou; Li, Hongyi

doi:10.1080/01431161.2018.1441565

Cited by 41 publications

(22 citation statements)

References 37 publications

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“…However, due to the harsh environment and complex terrain, the rain gauges were sparsely and unevenly distributed over the TP. A majority of the gauges were located in north and east and at middle or low altitudes, while few gauges in the western part of the TP and at high elevations [51]. Thus, the assessment may not be representative and reasonable in the ungauged areas in western part of the TP and regions with high elevations.…”

Section: Future Researchmentioning

confidence: 95%

Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Tan

et al. 2018

Remote Sensing

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Accurate precipitation data is crucial in many applications such as hydrology, meteorology, and ecology. Compared with ground observations, satellite-based precipitation estimates can provide much more spatial information to characterize precipitation. In this study, the satellite-based precipitation products of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were firstly evaluated over the Tibetan Plateau (TP) in 2015 against ground observations at both annual and monthly scales. Secondly, random forest algorithm was used to obtain the annual downscaled results (~1 km) based on IMERG and TMPA data and the downscaled results were examined against rain gauge data. Thirdly, a disaggregation algorithm was used to obtain the monthly downscaled results based on those at annual scale. The results indicated that (1) IMERG performed better than TMPA at both annual and monthly scales; (2) IMERG had few anomalies while TMPA displayed significant numbers of outliers in central and western parts of the TP; (3) random forest was a promising algorithm in acquiring high resolution precipitation data with improved accuracy; (4) the downscaled results based on IMERG had better performances than those based on TMPA.

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Section: Future Researchmentioning

confidence: 95%

Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Tan

et al. 2018

Remote Sensing

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“…Due to the different topographical features and atmospheric circulation conditions, the climate of this region demonstrates significant spatial variabilities (Ren et al, ). Under the influences of the East Asian Monsoon, the water vapor resources of the YKP are characterized by dry winters and humid summers (Ma, Xu, et al, ). The annual precipitation in the YKP is generally between 600 and 2,000 mm/year, but it is unevenly distributed in time and space (Gemmer et al, ).…”

Section: Study Area and Materialsmentioning

confidence: 99%

“…With recent huge advancements in remote sensing techniques and satellite‐based retrieval algorithms (Joyce et al, ), a series of satellite precipitation products with different spatial and temporal resolutions have been developed under several scientific missions and centers, for example, Tropical Rainfall Measurement Mission (TRMM) (Huffman et al, ; Huffman et al, ), Climate Precipitation Center morphing method (Joyce et al, ), and the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) (Huffman et al, ). Generally, TRMM 3B42 and 3B43 data sets demonstrated high accuracies and qualities in the TRMM era from 1998 to 2014 (Ma et al, ; Wang et al, ), and based on the latest correction algorithm, the TRMM Multisatellite Precipitation Analysis (TMPA) data has been updated to those of the V7 with lesser random error than the V6 version (Ming‐zhe et al, ). However, the TRMM satellite reentered Earth's atmosphere on 17 June 2015 after more than 17 years in service.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Assessments on the Satellite‐Based Precipitation Products From Fengyun and GPM Over the Yunnan‐Kweichow Plateau, China

Ding

et al. 2020

Earth and Space Science

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High-quality precipitation data are vital for hydrological applications and climate change research. In this study, we evaluated two satellite-based precipitation products from Chinese Fengyun (FY) 2G (G is one of the third batch of operational geostationary satellites in the Chinese FY-2 series) and the Global Precipitation Measurement (GPM) against the rain gauge-based precipitation data, respectively, over the Yunnan-Kweichow Plateau, China, at different temporal scales (e.g., monthly, daily, and hourly). And the main findings of this study were as follows: (1) FY2G Quantitative Precipitation Estimation (QPE) and Integrated Multisatellite Retrievals for GPM (IMERG) shared similar spatial precipitation patterns with those of rain gauge data, while the FY2G QPE general underestimated the precipitation (bias, −30%-0%), and IMERG obviously overestimated the precipitation (bias, 0%-60%) at monthly scale over the Yunnan-Kweichow Plateau; (2) the FY2G QPE correlated significantly better (Correlation coefficient, CC, 0.80) than IMERG (CC~0.20) against rain gauge data at daily scale, meanwhile, the average daily bias value of IMERG (~30%) was around 6 times larger than that of FY2G QPE (~5%); (3) The FY2G QPE (CC 0.7, bias~− 8%, root-mean-square error~2.0 mm/hr, mean absolute error~0.6 mm/hr) also generally outperformed the IMERG (CC~0.1, bias~13%, root-mean-square error~3.0 mm/hr, mean absolute error1.0 mm/hr), at hourly scale; (4) The FY2G QPE significantly underestimated the precipitation in the period between 16:00 and 20:00 with the probability of detection (POD) decreased from 0.8 to 0.5, while the IMERG significantly overestimated the precipitation in the period between 4:00 and 12:00, at diurnal scale; and (5) one of the reasons resulting the significant overestimations of IMERG might due to its weak abilities in detecting the precipitation events with POD~0.2 and false alarm ratio (FAR)~0.7 at diurnal scale, which performed worse than those of the FY2G QPE (POD~0.8 and FAR~0.4). These findings would provide valuable recommendations for using these satellite-based precipitation products in various application fields, such as hydrology, agriculture, and meteorology over the YKP, and also for improving the algorithms of GPM. . (2020). Spatiotemporal assessments on the satellite-based precipitation products from Fengyun and GPM over the Yunnan-Kweichow Plateau, China. Earth and Space Science, 7, e2019EA000857. https://doi.

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“…Salinization is a worldwide problem and is particularly acute in semi-arid areas. The area of salinized soil in the world is about 9.55 × 10 8 hm 2 , among which the salinized soil area accounts for about 3.78% of the total area in China [1,2], most of which is distributed in arid and semi-arid areas of China [3]. The stability of an oasis ecological environment is related to the survival of humans, social stability, and sustainable development of the economy in the whole arid area.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China

Zhang

2019

Sustainability

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Soil salinization is a global problem, which threatens agricultural productivity and sustainability, especially in arid and semi-arid regions. Soil salinity and moisture are important factors affecting agricultural production in arid regions. However, few studies have considered the influence of topographic factors on the spatial distribution patterns of soil salinity and moisture. This research aims to explore the spatial distribution characteristics and its influencing factors of soil salinity and moisture in the oasis farmland of arid areas. In this paper, GIS and geostatistics methods were applied to analyze the spatial distribution characteristics and variability of soil salinity and moisture, and then the corresponding proxy variables were used to quantitatively study the influence factors by using the geographical detector model. The results showed the coefficients of the variation of soil salinity and moisture to be 71.25% and 31.89%, respectively. There was moderate spatial autocorrelation of soil salinity and moisture. Soil salinity in the southwest was higher than in the northeast, and soil moisture in the northwest and southeast were lower than in the center and the northeast edge. The main influencing factors were available phosphorus, roughness of terrain, alkaline nitrogen, available potassium, and elevation. Combined action of topographic factors and soil nutrients has a major influence on the spatial distribution of soil salinity and moisture. Therefore, developing a suitable fertilizer regime under different topographic conditions could be an effective way to promote the sustainability of oasis agriculture in arid areas.

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Spatial and temporal precipitation patterns characterized by TRMM TMPA over the Qinghai-Tibetan plateau and surroundings

Cited by 41 publications

References 37 publications

Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Spatiotemporal Assessments on the Satellite‐Based Precipitation Products From Fengyun and GPM Over the Yunnan‐Kweichow Plateau, China

Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China

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