Journal of Global Change Data & Discovery2020.4(2):169-175

[PDF] [DATASET]

Citation:Jiang, Y. H., Li, B. L. , Yuan, Y. C., et al.1 km Grid Precipitation Dataset in the Three-river Headwaters Region (2009–2013)[J]. Journal of Global Change Data & Discovery,2020.4(2):169-175 .DOI: 10.3974/geodp.2020.02.13 .

DOI: 10

1 km Grid Precipitation Dataset in the Three-river Headwaters Region (2009–2013)

Jiang, Y. H.1,2  Li, B. L.1,2*  Yuan, Y. C.1  Gao, X. Z.1 Zhang, T.1,2  Liu, Y.1,2  
Li, Y.1,2  Luo, Z. Y.3  Li, H.3  Ma, Q.3  Wang, X. M.3  CiRen, DuoJi.4

1. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

3. Meteorological Bureau of Mongolian Autonomous County of Henan, Tibetan Autonomous Prefecture of Huangnan 811599, Qinghai, China;

4. Lhasa Meteorological Bureau, Lhasa 850000, Tibet, China

 

Abstract: This dataset includes the downscaled Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation based on rainfall observations from rain gauges and related contrasting or auxiliary data. Downscaled TRMM data was based on a quadratic parabolic profile (QPP) model. This method involves two steps. (1) QPP model parameters estimation. In three-river headwaters region, the elevation of maximum precipitation corresponds to the elevation of maximum NDVI. Thus, the elevation of maximum precipitation could be determined based on the spatial location of the peak NDVI. Subsequently, estimating the precipitation at the elevation of maximum precipitation as well as the quadratic coefficients in parabolic equation of precipitation. (2) Spatial extrapolation of model parameters. The parameters at 0.25° resolution are spatially extrapolated in inverse distance weighted interpolation. The bilinear interpolation method is then employed to resample the parameters from 0.25° to 1-km resolution to obtain the ultimate parameters of the downscaled model for each pixel. The results show that downscaled TRMM 3B43 data in QPP model are more accurate than those obtained in conventional statistical downscaling methods. The average root-mean-square errors (RMSEs) and mean absolute percent errors (MAPEs) calculated with national observation data of May to September and growing season in 20092013 are 14, 18, 19, 13, 16 mm and 14%, 12%, 12%, 12%, and 17%, respectively. The dataset is stored in the WGS84 coordinate system as vector data in a .shp format and raster data in a Grid or tif format. A paper related to this dataset has been published in Remote Sensing of Environment (volume 215, 2018).

Keywords: precipitation; TRMM satellite; downscaling; Three-River Headwaters region and its nearby regions; Remote Sensing of Environment

1 Introduction

Precipitation, an important environmental element, plays a role not to be overlooked in areas such as surface runoff, atmospheric motions, and agricultural resources. However, due to the randomness, precipitation exhibits relatively complex temporal and spatial variation patterns. It is difficult to extrapolate precipitation observation data acquired at the limited number of ground stations, particularly in regions with relatively few stations. Satellite remote sensing products cover large areas and consist of repeated observations. As a consequence, satellite remote sensing has become an important means for acquiring information on temporal and spatial variations of precipitation. However, due to such factors as topography, precipitation in mountainous regions exhibits notable heterogeneity. As a result, satellite precipitation data for mountainous regions are of extremely high uncertainty and unable to meet the actual requirements[13]. In view of this, a finer precipitation dataset was developed in this study by spatial downscaling of precipitation data retrieved from satellite remote sensing data.

1 km Grid Precipitation Dataset in the Three-river Headwaters Region (2009–2013)[4] was supported by Ministry of Science and Technology of the People´s Republic of China and was produced using the widely available TRMM 3B43 V7 data product (0.25°×0.25°)[5, 6]. The TRMM data product in three-river headwaters region is of relatively low accuracy, and to solve the problem, assuming that precipitation is jointly determined by macroscopic geographical factors and local elevations, and there is a strong correlation between normalized difference vegetation index (NDVI) and precipitation in the region. Under these assumptions, a quadratic parabolic profile (QPP) model was employed to downscale the TRMM data. The parameters of the relationship between NDVI and digital elevation model (DEM) were determined based on high-resolution NDVI data, then to estimate the parameters between DEM and precipitation. Finally, downscaled TRMM precipitation data was based on the high-resolution DEM data[3]. The revised product is compared with the results generated from the ground site interpolation method for validation.

2 Metadata of Dataset

The metadata for the “1 km Grid Precipitation Dataset in the Three-river Headwaters Region (2009–2013)[4] is summarized in Table 1. It includes the full name, short name, authors, year, temporal resolution, spatial resolution, data format, data size, data files, data publisher, data sharing policy, etc.

3 Method

3.1 Algorithm principle

According to the theory of precipitation in mountainous established by Fu[8], precipitation in a mountainous region is jointly determined by precipitation affected by macroscopic geographic factors and precipitation variation resulting from the difference in local elevation, and can thus be represented and calculated by a parabolic equation. The elevation difference of region is large, and precipitation in this area is greatly affected by the terrain, which satisfies the application condition of parabolic equation. In the studying region, the annual average precipitation is less than 1,000 mm, and precipitation has a positive correlation wtih NDVI in such region when excluding the effects of local topography. Therefore, the same function should fit the relationships between elevation and precipitation or NDVI. When not taking into consideration the effects of local topography, there is a positive correlation between precipitation and vegetation growth in sub-humid and semiarid regions, which can be represented by the linear response relationship between precipitation and NDVI[9, 10]. Hence, algorithm used in this study is to assume that both precipitation and NDVI are in a quadratic parabolic relationship with elevation. The parameters of the NDVI–DEM function

Table 1  Metadata summary of “1 km grid precipitation dataset in the Three-river headwaters region (2009–2013)”

Item

Description

Dataset full name

1 km grid precipitation dataset in the Three-river headwaters region (2009–2013)

Dataset short name

PrecipThreeRiverHeadwaters_2009-2013

Authors

Jiang, Y. H. N-8765-2019, Institute of Geographic Sciences and Natural Resources Research, ChineseAcademy of Sciences, jiangyh@lreis.ac.cn

Yuan, Y.C. N-9047-2019, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, yuanyc@lreis.ac.cn

Gao, X, Z. N-1655-2019, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, gaoxz@lreis.ac.cn

Zhang, T. N-8690-2019, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, zhangtao@lreis.ac.cn

Liu, Y. N-8844-2019, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, liuy.18b@igsnrr.an.c.cn

Li, Y. Y-4384-2019, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, liying9391@126.com

Luo, Z. Y., Meteorological Bureau of Mongolian Autonomous County of Henan, Tibetan Autonomous Prefecture of Huangnan, 393352158@qq.com

Li, H., Meteorological Bureau of Mongolian Autonomous County of Henan, Tibetan Autonomous Prefecture of Huangnan, lh691208@163.com

Ma, Q., Meteorological Bureau of Mongolian Autonomous County of Henan, Tibetan Autonomous Prefecture of Huangnan, 107082968@qq.com

Wang, X. M., Meteorological Bureau of Mongolian Autonomous County of Henan, Tibetan Autonomous Prefecture of Huangnan, 2444869807@qq.com

Ciren, D. J., Lhasa Meteorological Bureau, LSNSE111@126.com

Geographical region

31.6536.27°N, 89.40102.38°E, including 16 counties and one town in central and southern Qinghai: Xinghai county, Zekog county, Henan county, Gade county, Maqin county, Banma county, Yushu Tibetan autonomous prefecture, Chindu county, Zadoi county, Zhidoi county, Madoi county, Qumarleb county, Nangqen county, Dari county, Jiuzhi county, Tongde county, Tanggula town

Year

2009–2013       Temporal resolution   1 month      Spatial resolution   1 km

Data format

.shp, .tif, .grid               Data size   344 MB (compressed)

Dataset files

The dataset consists of four folders.

(1) The “QPR_Precip” folder contains 5.Grid files:

QPR_2009QPR_2010QPR_2011QPR_2012 and QPR_2013 are downscaled TRMM 3B43 cumulative precipitation (spatial resolution: 1 km; unit: mm) in QPP model for May, June, July, August, September, and the growing season of 2009, 2010, 2011, 2012 and 2013, respectively.

(2) Control_Precip folder contains 15.Drid files:

ER_2009ER_2010ER_2011ER_2012 and ER_2013 are downscaled TRMM 3B43 cumulative precipitation (spatial resolution: 1 km; unit: mm) in for exponential regression (ER) model May, June, July, August, September, and the growing season of 2009, 2010, 2011, 2012 and 2013, respectively.

MLR_2009MLR _2010MLR _2011MLR _2012 and MLR _2013 are downscaled TRMM 3B43 cumulative precipitation (spatial resolution: 1 km; unit: mm) in multiple linear regression (MLR) model for May, June, July, August, September, and the growing season of 2009, 2010, 2011, 2012 and 2013, respectively.

GWR_2009GWR_2010GWR_2011GWR_2012 and GWR_2013 are downscaled TRMM 3B43 cumulative precipitation (spatial resolution: 1 km; unit: mm) in geographically weighted regression (GWR) model for May, June, July, August, September, and the growing season of 2009, 2010, 2011, 2012 and 2013, respectively.

(3) DEM file contains a .Grid file named dem1km, which is the information on elevation variation within the study area (spatial resolution: 1 km).

 

(4) NDVI folder contains five .grid files named ndv1km_2009, ndv1km_2010, ndv1km_2011, ndv1km_2012, and ndv1km_2013, which are vegetation growth data (spatial resolution: 1 km) for the growing season of 2009, 2010, 2011, 2012, and 2013, respectively.

Foundations

Ministry of Science and Technology of P. R. China (2016YFC0500205, 2015CB954103)

Computing Enviroment

Python 2.7; ArcGIS campus license of Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.

Data publisher

Global Change Research Data Publishing & Repository, http://www.geodoi.ac.cn

(To be continued on the next page)

(Continued)

Item

Description

Address

No. 11A, Datun Road, Chaoyang District, Beijing 100101, China

Data sharing policy

Data from the Global Change Research Data Publishing & Repository includes metadata, datasets (data products), and publications (in this case, in the Journal ofGlobal Change Data & Discovery). Datasharing policy includes: (1) Data are openly available and can be free downloaded via the Internet; (2) End users are encouraged to use Data subject to citation; (3) Users,who are by definition also value-added service providers, are welcome to redistribute Data subject to written permission from the GCdataPR Editorial Office and the issuance of a Data redistribution license; and (4) If Data are used to compile new datasets, the ‘ten percent principal’ should be followed such that Data records utilized should not surpass 10% of the new dataset contents, while sources should be clearly noted in suitable places in the new dataset[6]

Communication and searchable system

DOI, DCI, CSCD, WDS/ISC, GEOSS, China GEOSS

Figure 1  Technical route for the dataset development

are first determined based on high-resolution NDVI and DEM data, then to estimate the parameters between DEM and precipitation. Finally, downscaled TRMM precipitation data was based on the high-resolution DEM data[3].

3.2 Technical route

The dataset was produced in TRMM data downscaling method that accounts for macroscopic geographic factors and local elevations[3]. This method involves the following two main

steps (Figure 1): (1) Estimation of the QPP model parameters. Based on the assumption in the algorithm principle that there are similar parabolic relationships between precipitation and elevation or NDVI, the maximum precipitation should be at the location where the peak NDVI occurs[9–10]. (2) Spatial extrapolation of model parameters. The parameters at 0.25° resolution are spatially extrapolated in inverse distance weighted interpolation. The bilinear interpolation method is then employed to resample the parameters from 0.25° to 1-km resolution to obtain the ultimate parameters of the downscaled model for each pixel.

4 Data Results and Validation

4.1 Data Products

The dataset is composed of 4 folders (QPR_Precip, Control_Precip, DEM and NDVI) as shown in Table 1.

4.2 Data results

DEM and NDVI were shown in Figure 2 and Figure 3. Precipitation data of July and growing season in the region between 2009–2013 were shown in Figures 4 and 5.

As shown in Figure 2, the elevation in three-river headwaters region gradually increases from southeast to northwest. A decrease trend of growing season NDVI can be observed in Figure 3 from southeast to northwest.

Figure 2  DEM for and spatial distribution of national meteorological stations in the Three-river headwaters region

Figure 3  Spatial distribution of NDVI in the three-river headwaters region in the growing season of 2012

In Figures 4 and 5, precipitation of July and the whole growing season in this region mainly gradually decreases from southeast to northwest, which corresponds to the topography and the direction of water vapor (southeast monsoon) in three-river headwaters region. Locally, the maximum precipitation often occurs on mountain slopes instead of valleys, and this corresponds to the theory of maximum precipitation elevations.

4.3 Validation of data results

Mean Absolute Error (MAE) and Mean Absolute Percent Error (MAPE) are the accuracy evaluation indicator to test the accuracy of the downscaled TRMM monthly and cumulative precipitation data for the growing season in the period 20092013. The downscaled data are of relatively high accuracy and can relatively satisfactorily meet the accuracy requirements of relevant research for the spatial and temporal distribution patterns of precipitation (Table 2).

Table 2  Average RMSEs and MAPEs between the downscaled data and observation data acquired at national stations for the growing season in the period 20092013[3]

Index

May

June

July

August

September

Cumulative

RMSE (mm)

14

18

19

13

16

62

MAPE (%)

14

12

12

12

17

11

5 Discussion and Conclusion

The dataset generation algorithm in this study primarily takes into account the mechanism of formation of precipitation in mountainous regions[8]. Based on the theory of maximum precipitation elevations and the linear response relationship between NDVI and precipitation, the TRMM precipitation data for three-river headwaters region were downscaled to improve their accuracy and provide a more accurate data product for analyzing the temporal and

Figure 4  Spatial distribution of precipitation in the three-river headwaters region in July in the period 2009–2013

Figure 5  Spatial distribution of precipitation in the three-river headwaters region in the growing season in the period 2009–2013

spatial distribution patterns of precipitation. A notable decrease is found in the simulated regional precipitation from southeast to northwest, which corresponds to the topography and

the direction of water vapor (southeast monsoon) in three-river headwaters region. Locally, the maximum precipitation often occurs on mountain slopes instead of valleys, and this corresponds to the theory of maximum precipitation elevations. The errors of the downscaled data are also within a reasonable range, and these data can meet the requirements of regional hydrological research.

The shortcomings of the algorithm in this study were not take into consideration the topographical and geomorphic factors of three-river headwaters region, and also neglected solid precipitation. Potential impact includes: (1) Ignoring the different effects of terrain uplift on precipitation on the windward and leeward slopes; (2) This algorithm is only applicable in downscaling of growing-season precipitation, and was not for solid precipitation in winter, thus was unable to generate precipitation data for the whole year. Additionally, the accuracy of downscaled data is significantly affected by the accuracy of the original product. If the pre-downscaling precipitation data contain large errors, the accuracy of the downscaled data product will remain inadequate. Therefore, this algorithm will give rise to significant uncertainties when used in application research that requires relatively high accuracy in absolute precipitation.

Author Contributions

Li, B. L. and Jiang, Y. H. created the overall design for the data collection development; Liu, Y. and Li, Y. processed the TRMM precipitation data; Zhang, T. and Yuan, Y. C. designed the model and algorithm; Gao, X. Z. performed data verification; Luo, Z. Y., Li, H., Ma, Q., Wang, X. M. and Ci, ren, Duoji. provided the weather station data. Jiang, Y. H. and Li, B. L. wrote the data paper.

 

References

[1]       Hao, Z. C., Tong, K., Zhang, L. L., et al. Applicability Analysis of TRMM Precipitation Estimates in Tibetan Plateau [J]. Water, 2011, 31(5): 1823.

[2]       Liu, Y. B., Fu, Q. N., Song, P., et al. Satellite Retrieval of Precipitation: A Review[J]. Advance in Earth Sciences, 2011, 26(11): 1162–1172.

[3]       Zhang, T., Li, B., Yuan, Y., et al. Spatial downscaling of TRMM precipitation data considering the impacts of macro-geographical factors and local elevation in the Three-River Headwaters Region [J]. Remote Sensing of Environment, 2018, 215: 109127.

[4]       Jiang, Y. H., Li, B. L., Yuan, Y. C., et al. A 1-km grid precipitation dataset for the Three-River Headwaters region and its nearby regions for the period 2009–2013 [DB/OL], Global Change Research Data Publishing & Repository, 2019. DOI: 10.3974/geodb.2019.05.17.V1.

[5]       Kummerow, C., Barnes, W. The tropical rainfall measuring mission (TRMM) sensor package [J]. Journal of Atmospheric and Oceanic Technology, 1998, 15: 809817.

[6]       Huffman, G. J., Bolvin, D. T., Nelkin, E. J., et al. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales [J]. Journal of Hydrometeorology, 2007, 8(1): 3855.

[7]       GCdataPR Editorial Office. GCdataPR data sharing policy [OL]. DOI: 10.3974/dp.policy.2014.05 (Updated 2017).

[8]       Fu, B. P. Mountain climate [M]. Beijing: Science Press, 1983.

[9]       Groeneveld DP, Baugh WM. Correcting satellite data to detect vegetation signal for eco-hydrologic analyses[J]. Journal of Hydrology (Amsterdam), 2007, 344(1-2): 135-145.

[10]    Chamaille-Jammes S, Fritz H, Murindagomo F. Spatial patterns of the NDVI–rainfall relationship at the seasonal and interannual time scales in an African savanna. International Journal of Remote Sensing[J], 2006, 27(23): 5185-5200.

Co-Sponsors
Superintend