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Development of a paddy drought hazard forecasting system to cope with the impact of climate change
Surmaini E.a,c, Ramadhani F.a, Syahputra M.R.b, Dewi E.R.a, Apriyana Y.a
a Indonesia Agency for Agro-climate, Hydrology Research Institute, Bogor, West Java, 16111, Indonesia
b Faculty of Earth Sciences and Technology, Bandung Institute of Technology Jl. TentaraPelajar No. 1, Bogor, West Java, 16111, Indonesia
c Main Contributor Jl. TentaraPelajar No. 1, Bogor, West Java, 16111, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 Published under licence by IOP Publishing Ltd.The relevance of climate extreme events, such as droughts, is well recognized as one of the impacts of climate change. The serious paddy droughts that occur during El Nio climate cycles has increased political awareness of the need to incorporate these in drought-forecasting and warning systems for effective drought management. In this study, we develop a paddy drought hazard forecasting system (PDFS) based on a web-based geographic information system (GIS). The Drought Hazard Index (DHI) was used to derive a deterministic forecast model, which is a simplified version of the probabilistic forecast model used in the previous version of this system. Based on the DHI, rice field areas in Indonesia were classified into four categories: low (1.0< DHI< 3.3), moderate (3.33 < DHI<6.66), high (6.66 <DHI< 10.9), and very high (10.9 < DHI< 16.0). Web-based PDFS is incorporated in the Integrated Cropping Calendar Information System (ICCIS). Predictions are available for the subsequent four months and are regularly updated every two months. The forecasts are made available for the regional areas of Indonesia at the provincial and district level.[/vc_column_text][vc_empty_space][vc_separator css=".vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}"][vc_empty_space][megatron_heading title="Author keywords" size="size-sm" text_align="text-left"][vc_column_text]Climate cycle,Climate extremes,Deterministic forecasts,Drought management,Forecasting system,Hazard indices,Probabilistic forecasts,Regional areas[/vc_column_text][vc_empty_space][vc_separator css=".vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}"][vc_empty_space][megatron_heading title="Indexed keywords" size="size-sm" text_align="text-left"][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=".vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}"][vc_empty_space][megatron_heading title="Funding details" size="size-sm" text_align="text-left"][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=".vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}"][vc_empty_space][megatron_heading title="DOI" size="size-sm" text_align="text-left"][vc_column_text]https://doi.org/10.1088/1755-1315/484/1/012050[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]