Vempi Satriya Adi Hendrawan 교수

The use of flood damage functions, or vulnerability curves, as a relationship between the intensity of the process (hazard) and the degree of potential loss of the exposed elements plays an important role in flood risk assessment. In terms of disaster risk reduction, a vulnerability curve is a helpful tool to quickly evaluate loss and conduct immediate decision making. This study proposes flood vulnerability curves for rice crop using crop yield loss estimated by crop statistics and remote-sensi

Abstract Drought impact on crop production is well known as crop yield is strongly controlled by climate variation. Previous studies assessed the drought impact using a drought index based on a single input data set, while the variability of the drought index to the input data choice is notable. In this study, a drought index based on the standardized precipitation index with multiple timescales using several global precipitation datasets was compared with the detrended anomaly based on the glob

In recent decades, droughts have critically limited crop production, inducing food system shocks regionally and globally. It was estimated that crop yield variability in around one-third to three-fourths of global harvested areas is explained significantly by drought, revealing the notable vulnerability of crop systems to such climate-related stressors. However, understanding the key factors determining the global pattern of crop yield sensitivity to drought is limited. Here, we investigate a wi

Crop response characteristics to different timescales of precipitation deficit may represent crop system resilience to drought characteristics. In this study, we assess the crop yield response of major crops to meteorological drought estimated by a standardized precipitation index with multiple timescales (1–12 months) during 1981–2016 all over the globe. We estimate that about one- to two-thirds of global harvested areas of maize, rice, soybean, and wheat, were significantly affected by various

Abstract The study focuses on understanding the future exposure to rainfall and temperature extremes in one of the world's most populous islands, Java, Indonesia. We use the high‐resolution climate projections from the Coordinated Regional Climate Downscaling Experiment (CORDEX) simulation for Southeast Asia by 2100 under RCP4.5 and RCP8.5 scenarios. Results show that the island will likely experience drying in the lowlands due to annual rainfall decline by approximately 13%–18%, potentially exp

In current years, droughts have critically threatened crop production, inducing food system shocks in a region and globally. This dataset provides input data for a study estimating crop yield variability explained by drought. We investigate factors that may determine the sensitivity of global crop yield to drought based on the Standardized Precipitation Index (SPI) at 0.5° resolution from 1981 to 2016 on a global scale. This study may provide insights for further studies addressing better crop-d

Extended Method K-means ClusteringK-Means clustering is an unsupervised machine learning algorithm used to partition a dataset into distinct clusters based on feature similarity (MacQueen, 1967).In this study, K-Means is applied to climate data, specifically analyzing selected climate variables: Rx5day relative anomaly and anomalies of consecutive dry days (CDD).These variables represent projected changes in extreme rainfall and drought conditions across Indonesia.The analysis is based on CMIP6

Abstract. Indonesia, one of the most populous countries, ranked fifth globally for climate-related hazards and disasters in the past five years. This study aims to assess historical trends, future projections, and potential implications of climate-related hazards in Indonesia. We synthesize literature, analyze historical datasets, and examine CMIP projections to assess the trend of extreme climate events and their potential effects on climate hazards. Results show that current records and studie