Hydrological and climatological parameters in several locations on peatlands in South Sumatra have been measured directly by a system called Sensory data transmission Service Assisted by Midori Engineering laboratory (SESAME). The parameters are rainfall, temperature, soil moisture, and groundwater level. This research has studied the groundwater level fluctuations and looks for the correlation between groundwater level and soil moisture in four locations, namely Saleh River 1 (SR1), Saleh River 2 (SR2), Lumpur River 1 (LR1), and Lumpur River 2 (LR2). The results are expected to be useful for fire disaster mitigation on peatlands, especially in South Sumatra. Based on time series data of groundwater level, the results show that there has been a sharp decrease in groundwater level at locations SR1, SR2, and LR1. The statistic calculation results show that groundwater level has a significant correlation with soil moisture in four study sites. The correlation coefficients obtained for SR1, SR2, LR1, and LR2 are r = 0.88, 0.97, 0.87 and 0.92, respectively.
Climate anomalies can cause natural disasters such as severe fires and floods on peatlands in South Sumatra. Factors that affect the natural disasters on peatlands include rainfall, groundwater level, and soil moisture. This paper aims to study the effect of the climate anomalies in 2019 and 2020 and effects of these influencing factors on peatlands in South Sumatra. The data used in this study was derived from insitu measurement at two SESAME's measurement stations in the study area. The results indicate that in the 2019 dry season, the rainfall was minimal, the lowest groundwater table depth was −1.14 m and the lowest soil moisture was 3.4%. In the 2020 dry season, rainfall was above the monthly average of 100 mm, the lowest groundwater level was −0.44 m, and the lowest soil moisture was 26.64%. There is also a strong correlation between soil moisture and groundwater table depth. The correlation between the two is stronger when there is less rainfall.
The variation of thermodynamic layers over the south coastal Java are important components associated with high potential fishery in this region. In addition, this area influenced by several ocean-atmosphere coupled interactions, such as Ocean Current System and Indonesian Through Flow (ITF) and Monsoon. This study designed to analyze spatial and temporal variations of Mixed Layer Depth (MLD), Isothermal Layer Depth (ILD) and Barrier Layer Thickness (BLT). The data used are salinity, temperature, nitrate and oxygen from CSIRO Atlas of Regional Seas (CARS), surface wind and evaporation from European Centre for Medium-Range Weather Forecasts (ECMWF), and precipitation from Tropical Rainfall Measurement Mission (TRMM). Based on the results, during JJA season (boreal summer) the ILD, MLD and BLT layers over the southern coast of Java are relatively shallower. The thickness of ILD was range from 10 – 30 m, while the thickness of MLD and BLT are about <10 m. Meanwhile, during DJF (boreal winter) and MAM seasons (boreal spring), the thickness of the ILD, MLD and BLT layers are deeper. The nutrient distributions are consistent with thermodynamic layer patterns. The highest concentration of nitrate, phosphate and oxygen tends to occur in the boreal summer season due to the upwelling phenomenon which causes the removal of nutrients in the inner layer toward the surface, whereas the lowest concentration tends to occur in the boreal winter season caused by the downwelling phenomenon.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.