This paper highlights detailed projected changes in rainfall over Thailand for the early (2011–2040), middle (2041–2070) and late (2071–2099) periods of the 21st century under the representative concentration pathways (RCP) 4.5 and RCP 8.5 using the high‐resolution multi‐model simulations of the Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia. The ensemble mean is calculated based on seven members consisting of six general circulation models (GCMs) and three regional climate models (RCMs). Generally, the ensemble mean precipitation agrees reasonably well with observations, best represented by the Global Precipitation Climatology Center (GPCC) data, over Thailand during the historical period (1976–2005). However, inter‐model variations can be large among ensemble members especially during dry months (December to March) for northern‐central‐eastern parts, and throughout the year for the southern parts of Thailand. Similarly for future projection periods, inter‐model variations in the sign and magnitude of changes exist. The ensemble means of projected changes in rainfall for both RCPs during dry months show distinct contrast between the northern‐central‐eastern parts and the southern parts of Thailand with generally wetter and drier conditions, respectively. The magnitude of change can be as high as 15% of the historical period, which varies depending on the sub‐region, season, projection period, and RCP scenario. In contrast, generally drier conditions are projected during the wet season (June to September) throughout the country for both RCPs where the rainfall reduction can be as high as 10% in some areas. However, the magnitude of projected rainfall changes of some individual models can be much larger than the ensemble means, exceeding 40% in some cases. These projected changes are related to the changes in regional circulations associated with the winter and summer monsoons, which are projected to weaken. The drier (wetter) condition is associated with the enhanced subsidence (rising motion).
Informasi spasial curah hujan dibutuhkan oleh berbagai sektor namun karena keterbatasan pengamatan, proses interpolasi harus dilakukan. Metode interpolasi spasial terbaik untuk suatu tempat perlu ditentukan secara khusus. Penggunaan metode interpolasi Inverse Distance Weight (IDW) P=5 di Stasiun Klimatologi Malang perlu dikaji ulang. Tujuan penelitian ini adalah mencari justifikasi parameter interpolasi, membandingkan hasil interpolasi, dan pada akhirnya menentukan metode interpolasi terbaik untuk curah hujan bulanan Jawa Timur. Tiga metode yang diperbandingkan adalah IDW, Ordinary Kriging (OK), dan Regression Kriging (RK). Data curah hujan bulanan yang digunakan adalah 197 titik selama 204 bulan. Prediktor RK menggunakan ketinggian, kelerengan, dan estimasi curah hujan satelit. Parameter interpolasi seperti ukuran piksel, jumlah pencarian (NN), model variogram, dan power IDW dijustifikasi terlebih dahulu. Korelasi spasial digunakan untuk membandingkan hasil interpolasi. Validasi silang lipat sepuluh digunakan untuk menghasilkan galat. Galat interpolasi yang digunakan berupa nilai dan selisih kategori warna peta standar. RMSE dan MAE digunakan sebagai parameter validasi. Analisis waktu komputasi juga dilakukan. Piranti lunak R Statistics dan QGIS digunakan untuk membentuk bahan maupun mencari parameter interpolasi sedangkan interpolasi dilakukan menggunakan SAGA. Parameter interpolasi ditentukan sebagai berikut: ukuran piksel=0,01; NN=9; model variogram sperikal dengan Nugget=0, Sill=1, dan range bervariasi; power IDW=1,5. Hasil interpolasi RK jauh berbeda dari IDW maupun OK. Secara umum, IDW memiliki galat paling kecil (MAE kategori=0,871) dibandingkan OK (0,890) maupun RK (1,188).
This study aims to evaluate the impact of the National Economic Recovery Program—Pemulihan Ekonomi Nasional (PEN) and digitalization on micro, small, and medium enterprises’ (MSMEs) resilience during the COVID-19 pandemic. This research is based on primary data from a survey of 6009 Bank Rakyat Indonesia customers conducted from March–June 2021. Using the generalized ordered logistic regression technique, this study found that a combination of new loans, credit restructuring, and/or interest subsidies was the most successful PEN for enhancing MSME resilience. Meanwhile, providing new loans merely improved liquidity, not sales or profitability. However, just providing a restructuring program weakened resiliency. This research also discovered that MSMEs that have been digitalizing for more than a year are more resilient than those that have not. This study highlights the necessity of offering several interventions for MSMEs and assisting MSMEs in going digital to improve MSME resilience during the COVID-19 pandemic.
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