IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i21p13866-d952539.html
   My bibliography  Save this article

Improved Daily Spatial Precipitation Estimation by Merging Multi-Source Precipitation Data Based on the Geographically Weighted Regression Method: A Case Study of Taihu Lake Basin, China

Author

Listed:
  • Yi Pan

    (School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China)

  • Qiqi Yuan

    (School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China)

  • Jinsong Ma

    (School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China)

  • Lachun Wang

    (School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China)

Abstract

Accurately estimating the spatial and temporal distribution of precipitation is crucial for hydrological modeling. However, precipitation products based on a single source have their advantages and disadvantages. How to effectively combine the advantages of different precipitation datasets has become an important topic in developing high-quality precipitation products internationally in recent years. This paper uses the measured precipitation data of Multi-Source Weighted-Ensemble Precipitation (MSWEP) and in situ rainfall observation in the Taihu Lake Basin, as well as the longitude, latitude, elevation, slope, aspect, surface roughness, distance to the coastline, and land use and land cover data, and adopts a two-step method to achieve precipitation fusion: (1) downscaling the MSWEP source precipitation field using the bilinear interpolation method and (2) using the geographically weighted regression (GWR) method and tri-cube function weighting method to achieve fusion. Considering geographical and human activities factors, the spatial and temporal distribution of precipitation errors in MSWEP is detected. The fusion of MSWEP and gauge observation precipitation is realized. The results show that the method in this paper significantly improves the spatial resolution and accuracy of precipitation data in the Taihu Lake Basin.

Suggested Citation

  • Yi Pan & Qiqi Yuan & Jinsong Ma & Lachun Wang, 2022. "Improved Daily Spatial Precipitation Estimation by Merging Multi-Source Precipitation Data Based on the Geographically Weighted Regression Method: A Case Study of Taihu Lake Basin, China," IJERPH, MDPI, vol. 19(21), pages 1-18, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:13866-:d:952539
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/21/13866/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/21/13866/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Corey Lesk & Pedram Rowhani & Navin Ramankutty, 2016. "Influence of extreme weather disasters on global crop production," Nature, Nature, vol. 529(7584), pages 84-87, January.
    2. Xu, Bin & Lin, Boqiang, 2021. "Investigating spatial variability of CO2 emissions in heavy industry: Evidence from a geographically weighted regression model," Energy Policy, Elsevier, vol. 149(C).
    3. Seong-Hoon Cho & Dayton Lambert & Zhuo Chen, 2010. "Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data," Applied Economics Letters, Taylor & Francis Journals, vol. 17(8), pages 767-772.
    4. B. Tellman & J. A. Sullivan & C. Kuhn & A. J. Kettner & C. S. Doyle & G. R. Brakenridge & T. A. Erickson & D. A. Slayback, 2021. "Satellite imaging reveals increased proportion of population exposed to floods," Nature, Nature, vol. 596(7870), pages 80-86, August.
    5. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    6. Bengtsson, Thomas & Cavanaugh, Joseph E., 2006. "An improved Akaike information criterion for state-space model selection," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2635-2654, June.
    7. Yang, Linchuan & Chu, Xiaoling & Gou, Zhonghua & Yang, Hongtai & Lu, Yi & Huang, Wencheng, 2020. "Accessibility and proximity effects of bus rapid transit on housing prices: Heterogeneity across price quantiles and space," Journal of Transport Geography, Elsevier, vol. 88(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zice Ma & Rui Yao & Peng Sun & Zhen Zhuang & Chenhao Ge & Yifan Zou & Yinfeng Lv, 2023. "Quantitative Evaluation of Runoff Simulation and Its Driving Forces Based on Hydrological Model and Multisource Precipitation Fusion," Land, MDPI, vol. 12(3), pages 1-23, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Berlemann, Michael & Bumann, Silke & Methorst, Joel, 2024. "Do climate-related disasters cause dissatisfaction with environmental policies?," HWWI Working Paper Series 1/2024, Hamburg Institute of International Economics (HWWI).
    2. Go Shimada, 2022. "The Impact of Climate-Change-Related Disasters on Africa’s Economic Growth, Agriculture, and Conflicts: Can Humanitarian Aid and Food Assistance Offset the Damage?," IJERPH, MDPI, vol. 19(1), pages 1-16, January.
    3. He, Liuyue & Xu, Zhenci & Wang, Sufen & Bao, Jianxia & Fan, Yunfei & Daccache, Andre, 2022. "Optimal crop planting pattern can be harmful to reach carbon neutrality: Evidence from food-energy-water-carbon nexus perspective," Applied Energy, Elsevier, vol. 308(C).
    4. Jin, Tanhua & Cheng, Long & Wang, Kailai & Cao, Jun & Huang, Haosheng & Witlox, Frank, 2022. "Examining equity in accessibility to multi-tier healthcare services across different income households using estimated travel time," Transport Policy, Elsevier, vol. 121(C), pages 1-13.
    5. Zhang, Pengyan & Yang, Dan & Qin, Mingzhou & Jing, Wenlong, 2020. "Spatial heterogeneity analysis and driving forces exploring of built-up land development intensity in Chinese prefecture-level cities and implications for future Urban Land intensive use," Land Use Policy, Elsevier, vol. 99(C).
    6. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    7. El-Saied E. Metwaly & Hatim M. Al-Yasi & Esmat F. Ali & Hamada A. Farouk & Saad Farouk, 2022. "Deteriorating Harmful Effects of Drought in Cucumber by Spraying Glycinebetaine," Agriculture, MDPI, vol. 12(12), pages 1-16, December.
    8. Pede, Valerien O. & Florax, Raymond J.G.M. & Holt, Matthew T., 2009. "A Spatial Econometric Star Model With An Application To U.S. County Economic Growth, 1969–2003," Working papers 48117, Purdue University, Department of Agricultural Economics.
    9. Ahmad Adeel & Bruno Notteboom & Ansar Yasar & Kris Scheerlinck & Jeroen Stevens, 2021. "Insights into the Impacts of Mega Transport Infrastructures on the Transformation of Urban Fabric: Case of BRT Lahore," Sustainability, MDPI, vol. 13(13), pages 1-32, July.
    10. Chrisendo, Daniel, 2023. "Gender-based discrimination and global crop yield," 2023 Annual Meeting, July 23-25, Washington D.C. 335489, Agricultural and Applied Economics Association.
    11. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    12. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    13. N. Zhang & H. Huang, 2018. "Assessment of world disaster severity processed by Gaussian blur based on large historical data: casualties as an evaluating indicator," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 173-187, May.
    14. Liu, Zhipeng & Jiao, Xiyun & Zhu, Chengli & Katul, Gabriel G. & Ma, Junyong & Guo, Weihua, 2021. "Micro-climatic and crop responses to micro-sprinkler irrigation," Agricultural Water Management, Elsevier, vol. 243(C).
    15. Teresa Armada Brás & Jonas Jägermeyr & Júlia Seixas, 2019. "Exposure of the EU-28 food imports to extreme weather disasters in exporting countries," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(6), pages 1373-1393, December.
    16. Hengyu Gu & Hanchen Yu & Mehak Sachdeva & Ye Liu, 2021. "Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression," Growth and Change, Wiley Blackwell, vol. 52(1), pages 443-459, March.
    17. Liu, Jixiang & Xiao, Longzhu, 2023. "Non-linear relationships between built environment and commuting duration of migrants and locals," Journal of Transport Geography, Elsevier, vol. 106(C).
    18. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).
    19. Alfredo García-Hiernaux & José Casals & Miguel Jerez, 2012. "Estimating the system order by subspace methods," Computational Statistics, Springer, vol. 27(3), pages 411-425, September.
    20. Singh, Kuntal & McClean, Colin J. & Büker, Patrick & Hartley, Sue E. & Hill, Jane K., 2017. "Mapping regional risks from climate change for rainfed rice cultivation in India," Agricultural Systems, Elsevier, vol. 156(C), pages 76-84.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:13866-:d:952539. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.