Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin

被引：15

作者：

Noor, Rabeea ^{[1
]}

Arshad, Arfan ^{[2
,3
]}

Shafeeque, Muhammad ^{[4
]}

Liu, Jinping ^{[5
,6
,7
]}

Baig, Azhar ^{[1
,8
]}

Ali, Shoaib ^{[9
]}

Maqsood, Aarish ^{[1
]}

Pham, Quoc Bao ^{[10
]}

Dilawar, Adil ^{[11
,12
]}

Khan, Shahbaz Nasir ^{[13
]}

Anh, Duong Tran ^{[14
,15
]}

Elbeltagi, Ahmed ^{[16
]}

机构：

[1] Bahauddin Zakariya Univ, Dept Agr Engn, Multan 60800, Pakistan

[2] Oklahoma State Univ, Dept Biosyst & Agr Engn, Stillwater, OK 74078 USA

[3] Univ Agr Faisalabad, Fac Agr Engn & Technol, Dept Irrigat & Drainage, Faisalabad 38000, Pakistan

[4] Univ Bremen, Inst Geog, Climate Lab, D-28359 Bremen, Germany

[5] North China Univ Water Resources & Elect Power, Coll Surveying & Geoinformat, Zhengzhou 450046, Peoples R China

[6] Hohai Univ, Key Lab Hydrol Water Resources & Hydraul Engn, Nanjing 210098, Peoples R China

[7] Katholieke Univ Leuven, Hydraul & Geotech Sect, Kasteelpk Arenberg 40, BE-3001 Leuven, Belgium

[8] McGill Univ, Dept Bioresource Engn, 21111 Lakeshore, Ste Anne De Bellevue, PQ H9X 3V9, Canada

[9] Northeast Agr Univ, Sch Water Conservancy & Civil Engn, Harbin 150030, Peoples R China

[10] Univ Silesia Katowice, Inst Earth Sci, Fac Nat Sci, Bedzinska St 60, PL-41200 Sosnowiec, Poland

[11] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China

[12] Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China

[13] Univ Agr Faisalabad, Dept Struct & Environm Engn, Faisalabad 38000, Pakistan

[14] Van Lang Univ, Inst Computat Sci & Artificial Intelligence, Lab Environm Sci & Climate Change, Ho Chi Minh City 700000, Vietnam

[15] Van Lang Univ, Fac Environm, Ho Chi Minh City 700000, Vietnam

[16] Mansoura Univ, Fac Agr, Agr Engn Dept, Mansoura 35516, Egypt

来源：

REMOTE SENSING | 2023年 / 15卷 / 02期

关键词：

RF; MGWR; TRMM; spatial downscaling; calibration; rain gauges; GEOGRAPHICALLY WEIGHTED REGRESSION; RIVER-BASIN; ANALYSIS TMPA; SATELLITE; CLIMATE; 3B43; NONSTATIONARY; NDVI;

D O I：

10.3390/rs15020318

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (similar to 10 km-75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km x 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (similar to 25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km x 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of similar to 88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of similar to 30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g., zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions.

引用

页数：27