Multi-temporal variability forecast of particulate organic carbon in the Indonesian seas.

The current global condition characterized by high levels of CO2 is altering the carbon cycle and elemental biogeochemistry, resulting in subsequent global warming, climate change, ocean acidification, and the indirect response of deoxygenation. The features of Indonesia's coastal ecosystems and continental shelf waters also contribute to spatio-temporal ocean carbon variability. For instance, the level of particulate organic carbon (POC) will change annually, and thus, over a decadal period, ocean dynamics may affect the temporal variability of POC. Motivated by such conditions, future forecasting is needed to envision the productivity of Indonesian seas by predicting vital parameters such as POC. This research aimed to forecast the temporal variability of POC in Indonesian waters. The Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model was used by considering the lowest value of the Akaike information criterion (AIC) and the mean absolute percentage error/MAPE (threshold < 10%). Using the highest correlation coefficient (threshold: 0.75), we obtained the best fit for forecasting POC temporal variability. Hindcast POC data (2002-2020/2021) was used to train the forecasting model. The result shows that forecasting of POC temporal variability can be conducted up to 2030. The validity of prediction is ensured for less than 5 years forward after 2020 with correlation coefficients of 0.65 and 0.83 for seasonal and monthly POC, respectively. The hindcast and forecast estimates of POC in the Indonesian seas show a decreasing trend. The present study emphasizes the different forecasting results obtained using the different approaches of annual versus inter-annual variability. A sustained research effort is still required to assess POC forecasting for its potential benefits in marine system monitoring and assessment.

Carbon-offset potential from tropical seagrass conservation in selected areas of Indonesia.

Seagrass carbon emission is mainly due to the land-use change; therefore, conservation will be an approach required for carbon offset. A method for estimating carbon offset from conservation activities has been developed. This study aims to evaluate the carbon-offset potential of the seagrass ecosystem by applying this method to five provinces in Indonesia. North Maluku has the widest seagrass area, but only 5% of this is the conserved area. Meanwhile, Jakarta has the highest percentage of its conserved seagrass within the area. Emission reduction at the year 2020 ranged 0.03-1.02 tC/year (with leakage) or 0.05-2.04 tC/year (without leakage). The percentage of emission reduction among the five provinces ranged from 0.75% to 11.3%. About 9.03 tC/year emission from seagrass ecosystems in Jakarta will decrease by up to 8.01 tC/year. Further assessment shows a positive correlation between the percentage of the conserved area and the percentage of emission reduction.

Assessing the Potential Earthquake Precursory Information in ULF Magnetic Data Recorded in Kanto, Japan during 2000-2010: Distance and Magnitude Dependences.

In order to clarify ultra-low-frequency (ULF) seismomagnetic phenomena, a sensitive geomagnetic network was installed in Kanto, Japan since 2000. In previous studies, we have verified the correlation between ULF magnetic anomalies and local sizeable earthquakes. In this study, we use Molchan's error diagram to evaluate the potential earthquake precursory information in the magnetic data recorded in Kanto, Japan during 2000-2010. We introduce the probability gain (PG') and the probability difference (D') to quantify the forecasting performance and to explore the optimal prediction parameters for a given ULF magnetic station. The results show that the earthquake predictions based on magnetic anomalies are significantly better than random guesses, indicating the magnetic data contain potential useful precursory information. Further investigations suggest that the prediction performance depends on the choices of the distance (R) and size of the target earthquake events (Es). Optimal R and Es are about (100 km, 108.75) and (180 km, 108.75) for Seikoshi (SKS) station in Izu and Kiyosumi (KYS) station in Boso, respectively.