Search

Lake Toba's water level has significantly declined during the last six decades. Unfortunately, the studies that have been conducted are only sectoral, both in terms of temporal aspects and influencing parameters. Hence, a comprehensive study has been conducted to explain the significant factor causing the Lake Toba's water level decline. Climate change and hydrological issues have become significant concerns. Change point analysis, trend tests, and Structural Equation Model (SEM) were used in this study. Since the results of the analysis show a significant decrease in the water level of Lake Toba in 1984, the discussion of the water level of Lake Toba is divided into two periods, namely Period I (1957–1983) and Period II (1984–2020). This division is also in line with the periods before and after the operation of Siruar Dam. This study revealed that climate change has occurred in the Lake Toba watershed with a significant increasing trend in temperature, evaporation, and rainfall of 0.003 °C, 1.4 mm, and 8.9 mm per year, respectively. Climate factors primarily contributed to the decline in Lake Toba's water level in Period I, whereas the use of water for hydroelectric power plants had a greater impact in Period II. However, with effective regulation of the Siruar Dam during Period II, the water level of Lake Toba has been maintained at a stable level within the predetermined threshold. The study's findings are intended to be useful to the government, stakeholders, and water users in devising mitigation and adaptation measures for the long-term management of Lake Toba's water resources.

Peatlands in OKI Regency are highly flammable during extreme dry seasons and experience flooding during extreme rainy seasons. Research related to peatlands is important to be carried out in disaster mitigation efforts on peatlands. In this study, we aimed to examine the impact of climate anomalies occurring between 2019 and 2022 on the Rainfall (RF), Groundwater Level (GWL), and Soil Moisture (SM) in this region. We analyzed data collected at the CJ2 station to understand the dynamics of these parameters throughout the occurrence of the ENSO and IOD phenomena in 2019–2022. The findings of this study indicate a positive correlation between RF and GWL. Specifically, a higher amount of RF resulted in a stronger correlation. Moreover, the rate of the GWL decline was 0.24 mm/d, while the rate of the SM decline was 0.06%/day. Furthermore, the soil moisture in CJ2 OKI must be maintained at a minimum of 20% to prevent fires. In addition, an empirical equation was derived to express the relationship between GWL (m) and SM (%) as SM = 49,044 GWL + 59,142. The findings of this study provide valuable in-sights for all stakeholders involved in efforts to mitigate the impact of natural disasters on peatlands.

Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.

Abstract In general, it is known that extreme climatic conditions such as El Niño and positive Indian Ocean Dipole (IOD+) cause prolonged drought in Indonesia's tropical peatlands so that groundwater levels (GWL) drop and peat is prone to fire. However, 27 years of GWL measurements in Central Kalimantan peat forests show the opposite condition, where the lowest GWL occurs several weeks before El Niño and after IOD+ reaches its peaks. We show that the dropped sea surface temperature anomaly induced by anomalously easterly winds along the southern Java-Sumatra occurs several weeks before the GWL drop to the lowest value. Local rainfall decreased, and GWL dropped sharply by 1.0 to 1.5 m, during the super El Niño events in 1997/98 and 2015, as well as remarkable events of IOD+ in 2019. It is suggested that the tropical peatland ecohydrological system (represented by the GWL), El Niño Southern Oscillation (ENSO), and IOD+ are teleconnected. Hence, monitoring GWL variability of peatland over the IMC is a possibility an alert for extreme climate events associated with El Niño and/or moderate IOD+.

This study investigates the dynamics of water resources in the Citarum watershed during periods of weak La Niña, normal, and weak El Niño conditions occurring sequentially. The Citarum watershed serves various purposes, being utilized not only by seven (7) districts and two (2) cities in West Java, Indonesia but also as a source of raw water for drinking in the City of Jakarta. Using a time-series analysis of surface water data, data-driven (machine learning) methods, and statistical analysis methods, spatiotemporal predictions of surface water have been made. The surface water time series data (2017–2021), obtained from in situ instruments, are used to assess water resources, predict groundwater recharge, and analyze seasonal patterns. The results indicate that surface water follows a seasonal pattern, particularly during the monsoon season, corresponding to the groundwater recharge pattern. In upstream areas, water resources exhibit an increasing trend during both weak La Nina and weak El Niño, except for Jatiluhur Dam, where a decline is observed in both seasons. Machine learning predictions suggest that water levels and groundwater recharge tend to decrease in both upstream and downstream areas.

The short-lived tropical squall lines could trigger weather-related hazards to the northern part of the Indonesia Maritime Continent (IMC), such as Sumatra and Kalimantan. Herein, we investigated the rare propagation event of the long-lived Sumatra squall line associated with a severe storm surge that induced coastal inundation in Java-Bali with devastating impacts from 22 May–2 June 2020. With a comprehensive approach combining observational, numerical, and analytical studies, for the first time, we proposed the possible mechanism related to the long-lived squall line over the IMC, which represents the largest equatorial tropical region with the most complicated air-sea interaction area in the world. Our findings suggest that the long-lived squall line related to the supercell-like thunderstorm initiated from multicell over central Sumatra on May 20, 2020, continuously propagated southeastward until several days later reached Bali. The near-quasi steady convective line has 6 hours of time travel from central Sumatra to west Java. The supercell-like rapidly develops from multicell with a deep convective updraft under the strong and fast cold pool (∼13.8 m s−1). The further southeastward propagation of squall line with broken line type seems reinforced by low-level moist transport from the Java Sea. This study also suggested that this unusual event of a long-lived squall line might occur more frequently in the warming upper ocean in the IMC.

During the dry season, extensive peatland fires in South Sumatra and another peatland in Indonesia result in environmental damage and pose health risks to humans. The Indonesian Government has implemented several measures to prevent the recurrence of these fires. One such measure involves the establishment of observation stations to monitor hydrometeorological parameters in different peatlands across Indonesia, including those in South Sumatra. To effectively control fires in South Sumatra’s peatland and minimize hotspot occurrences, it is essential to determine hydrometeorological parameters that can serve as fire control indicators. Therefore, this study aimed to investigate the relationship between groundwater levels and hotspot occurrences by analyzing groundwater level data collected from six Peat Restoration Agency stations in South Sumatra’s peatland, along with hotspot data obtained from Moderate Resolution Imaging Spectroradiometer satellite measurements. The findings reveal a significant correlation between groundwater levels and hotspots at the six stations. As the GWL increased, the number of hotspots tended to decrease, and vice versa. This means that GWL can be used as a controlling variable for hotspot emergence. To effectively minimize hotspot occurrences, it is recommended to maintain a minimum groundwater level of −0.45 ± 0.09 m in the peatland of South Sumatra.

Tropical peatland in Southeast Asia has undergone rapid degradation and shows large subsidence due to oxidation and peat shrinkage. The measurement of those deformations is thus valuable for evaluating the peat condition and assessing peat restoration. The time series interferometric synthetic aperture radar (TInSAR), especially with the small baseline subsets (SBAS) method, is capable of measuring long-term deformation. However, the dynamic surface scatterers often change in tropical peatland, which degrades the coherent scatterer (CS) distribution density. This article presents a simple and efficient TInSAR approach that enhances the CS density under such dynamic surface scatter variation based on the SBAS method. In the presented approach, a long-time series of single-look complex images is separated into subsets, and deformation estimation is performed for each subset. The effectiveness of this simple solution was investigated by InSAR simulation and validated using SAR observation data. We applied the subset SBAS approach to the three-year Sentinel-1A C-band SAR dataset acquired over tropical peatland in Indonesia. The analyses showed an improved number of CSs for the introduced subset approach. We further introduce the color representation of CS temporal behavior per subset for visual interpretation of scatterer change.

Deforestation in peatland areas such as Kalimantan, Indonesia has been going on for decades. The deforestation has indirectly increased peatlands to become degraded and flammable. The Synthetic Aperture Radar (SAR) interferometry approach for identification of degraded peatlands can be performed using ALOS-2 PALSAR-2 data by converting land deformation data generated from SAR interferometry analysis into water table (WT) depth data using Wosten models. Peatlands with WT depth conditions of more than 40 cm are classified as degraded peatlands which are flammable. By using fire data from previous studies, this research confirms that identification of degraded peatlands using SAR interferometry approach by ALOS-2 PALSAR-2 is more reliable with high precision related to forest fires, with a precision level of 88% compared to 5% precision level using the WT depth monitoring system that has been installed in Central Kalimantan. The highest wavelength of ALOS-2 PALSAR-2 (L-Band) data can resolve the limitation due to temporal and volumetric decorrelation, compared to C-Band and X-Band satellite data. The combination methods of SAR interferometry approach and the real-time WT depth monitoring system to identify degraded peatlands can be more efficient, faster, and accurate. The advantage of this research result shows that SAR interferometry analysis can reach blank spot areas that are not covered by the observation station of WT depth monitoring system. It also gives a benefit as a guide to select precise locations of observation stations related to degraded peatland and forest fire.