Changes in climate and land use land cover (LULC) are important factors that affect water yield (WY). This study explores which factors have more significant impact on changes in WY, spatially and temporally, within the Citarum River Basin Unit (RBU), West Java Province, Indonesia with an area of ±11.317 km2. The climate in the area of Citarum RBU belongs to the Am climate type, which is characterized by the presence of one or more dry months. The objectives of the study were: (1) To estimate a water yield model using integrated valuation of ecosystem services and tradeoffs (InVEST), and (2) to test the sensitivity of water yield (WY) to changes in climate variables (rainfall and evapotranspiration) and in LULC. The integration of remote sensing (RS), geographic information system (GIS), and the integrated valuation of ecosystem services and tradeoffs (InVEST) approach were used in this study. InVEST is a suite of models used to map and value the goods and services from nature that sustain and fulfill human life. The parameters used for determining the WY are LULC, precipitation, average annual potential evapotranspiration, soil depth, and plant available water content (PAWC). The results showed that the WY within the territory of Citarum RBU was 12.17 billion m3/year, with mean WY (MWY) of 935.26 mm/year. The results also show that the magnitude of MWY in Citarum RBU is lower than the results obtained in Lake Rawa Pening Catchment Areas, Semarang Regency and Salatiga City, Central Java (1.137 mm/year) and in the Patuha Mountain region, Bandung Regency, West Java (2.163 mm/year), which have the same climatic conditions. The WY volume decreased from 2006, to 2012, and 2018. Based on the results of the simulation, climatic parameters played a major role affecting WY compared to changes in LULC in the Citarum RBU. This model also shows that the effect of changes in rainfall (14.06–27.53%) is more dominant followed by the effect of evapotranspiration (10.97–23.86%) and LULC (10.29–12.96%). The InVEST model is very effective and robust for estimating WY in Citarum RBU, which was indicated by high coefficient of determination (R2) 0.9942 and the RSME value of 0.70.
With 25% confirmed cases of the country’s total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran’s I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.
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