We elucidated ocean physical and biogeochemical responses to slow‐moving Typhoon Meari using a new method combining Argo float and satellite observations. Meari‐driven upwelling brought colder, nutrient‐rich deep water to the surface layer, causing sea surface cooling (3–6°C) and threefold enhancement of primary production (PP). Maximum surface cooling (and hence nutrient injection) and peak PP enhancement lagged Meari's passage by 1 and 3 days, respectively, implying that remarkable PP enhancement was attributed to new production (NP). This NP accounted for approximately 3.8% of annual carbon export in the East China Sea (ECS) outer shelf, suggesting that typhoon‐driven upwelling is important for biogeochemical processes in the ECS. Given the wide coverage of Argo float and satellite data, our new approach may prompt comparative studies in other basins and advance the understanding of the role of tropical cyclones in the global ocean biogeochemical cycle.

ABSTRAKBencana tanah longsor di Indonesia semakin sering terjadi dari tahun ke tahun. Bencana tanah longsor telah terjadi di Dusun Tangkil, Desa Banaran, Kecamatan Pulung, Kabupaten Ponorogo, Provinsi Jawa Timur pada tanggal 1 April 2017. Lokasi tanah longsor di Desa Banaran, Kecamatan Pulung, Kabupaten Ponorogo, Jawa Timur, terletak pada zona kerentanan tinggi. Tipologi tanah longsor berupa longsoran bahan rombakan, yang kemudian ke arah bawah (Kali Tangkil) berkembang menjadi tipe aliran bahan rombakan. Faktor-Faktor yang berpengaruh terhadap terjadinya tanah longsor lokasi penelitian adalah: kelerengan, batuan dan tanah, rekahan/retakan batuan, konversi lahan, drainase dan keairan, curah hujan tinggi, dan aktivitas manusia. Dari kesemuanya faktor-faktor tersebut,  yang paling dominan dan berpengaruh terhadap tanah longsor adalah: lereng yang curam, soil hasil pelapukan sangat gembur dan tebal, alih fungsi lahan dan curah hujan yang tinggi. Material longsoran tidak terkonsolidasi dengan baik sehingga masih mudah bergerak, dan kemungkinan pembendungan pada Kali Tangkil oleh material longsoran tersebut bisa berpotensi terjadinya banjir bandang. Beberapa permukiman yang berada di saekitar lokasi longsor mempunyai risiko tinggi dan sedang terhadap longsor, sehingga perlu dibangun kesiapsiagaan masyarakat, pembangunan sistem peringatan dini longsor serta untuk jangka panjang adalah relokasi jika memang kondisi semakin parah. Pertanian lahan kering pada lereng-lereng sebaiknya menggunakan pola agroforestry. Kawasan sub DAS berisiko longsor, sebaiknya dikembalikan fungsi lahan sebagai hutan konservasi atau hutan lindung seperti sebelumnya.Kata kunci: longsor, Ponorogo, curam, soil tebal, degradasi lahan, curah hujan tinggi, risikoABSTRACTLandslides in Indonesia are becoming increasingly frequent from year to year. A landslide disaster has occurred in Tangkil, Banaran Village, Pulung Sub-District, Ponorogo District, East Java Province on April 1, 2017. The location of landslides in Banaran Village, Pulung Sub-District, Ponorogo District, East Java, lies in the high vulnerability zone. The landslide typology is a debris slide, which then in the downstream direction (Tangkil River) develop into a type of debris flow. Factors that influence the occurrence of landslides in the study area are: slope, rock and soil, fracture, land conversion, drainage and irrigation, high rainfall, and human activities. Of all the influential factors, the most dominant factors for landslides are: steep slopes, weathered soil is very loose and thick,   land conversion, and high rainfall. Landslide material is not well consolidated so that it is still easy to move, and the possibility of damming the Tangkil River by landslide material can potentially cause flash floods. Some settlements located near landslide locations have high and moderate risks of landslides, so community preparedness needs to be built,     the establishment of landslide early warning systems and long-term relocation if the condition is getting worse. Dryland farming on slopes should use agroforestry patterns. Sub-watershed areas are at risk of landslides, the land should be restored as conservation forest or protected forest as before.Keywords: landslide, Ponorogo, steep slopes, thick soil, land degradation, high rainfall, risk

Purpose We conducted this project to develop a feasible method for mapping tropical peat lands of Bengkalis Island-as a test site-in Indonesia. Materials and methods The method based on limited availability of field measurements and a wide range of remotely sensed spatial datasets like radar elevation product, MODIS, and Landsat imageries. We applied land use category based sampling to extend existing field data of peat thickness. New peat thickness data was collected by boring and simultaneous electrical resistivity tomography (ERT). Based on remotely sensed and field data sets, peat maps were compiled by simulated spatial annealing. Peat map statistics were derived after 500 runs including mean, median, minimum, maximum, and percentile values. Results and discussion The resulted maps represent the limiting values of expected peat thickness using 90% confidence level. Results showed that ERT is suitable for determining peat layer thickness. Using independent samples, we found that peat thickness predictions tend to overestimate peat thickness by ca. 2 m in general. Conclusions According to predictions, the peat volume of Bengkalis Island is estimated to be in the range of 3.28-3.58 km 3 .

Landslides often occur in Indonesia, including in Puncak which is a tourist area. A landslide disaster occurred at Puncak Pass, Cipanas Sub-district, Cianjur District, West Java on Wednesday, March 28, 2018 at around 08.00 PM. Typology of landslides that occur is a debris slide consisting of debris materials such as soil, rocks and large trees, and form a basin such as the shape of a horseshoe on the former landslide. Landslide occurred on the slope of the road and destroyed the hotel building, the park behind the hotel and pine forest. Many factors that influence the occurrence of landslide in Puncak Pass, from the analysis there are three main factors causing the landslide: the topography of the landslide is very steep, the occurrence of heavy rain for several consecutive days before the occurrence of landslides, and the slope which always disrupted the transport load of vehicles on it. Arrangement of landslide areas is very important to re-arrange the sustainable condition of the area against similar landslide disaster in the future. These arrangements are: handling of landslides during emergency response, determining the location of new road development, water and drainage management, cliff strengthening, land management, potentially affected settlements, and landslide disaster management.

Peat forest is a natural swamp ecosystem containing buried biomass from biomass deposits originating from past tropical swamp vegetation that have not been decomposed. In the dry season, this accumulated biomass called peat, which was originally submerged in water, is exposed to the surface and prone to fire. Once it burns, smouldering peat fires consume biomass 15 times larger than open flame. Peat smouldering fires are very difficult to extinguish. These will continuously occur for weeks to months. The most recommended effort by experts and practitioners of peat smouldering fires is to prevent them before they occur with the strategy: 'detect early, locate the fire, deliver the most appropriate technology'. Monitoring methods and early detection of forest and land fires or 'wildfire' have been highly developed and applied in Indonesia, for example monitoring with hotspot data, FWI (Fire Weather Index), and FDRS (Fire Danger Rating System). These 'physical simulator' based methods have some weaknesses (1) the accuracy is very low; (2) having predictive biases in areas with different ecosystem characteristics; (3) it is often difficult to implement because it requires a large and complex number of 'expert rules'; and (4) involving many variables, complex, and heterogeneous data formats. Slowly but surely this method has been replaced by the Machine Learning (ML) method as it is developing in Europe, China, India, Japan, North America and Australia. What about the potential application of ML in the forest and land fires, especially smouldering peat fires in Indonesia? This paper tries to answer this question through the following points: State of the art of machine learning in science and management of forest and land fire Outlook on technology of Disaster Risk Reduction (DRR) in BPPT in the field of forest and land fire and the opportunities of ML implementation Impact based forecasting and machine learning for DRR of forest and land fire This paper recommends a conceptual design: Impact-Based Learning for DRR of Forest, Land Fire and Peat Smouldering