{ "site": "Hydroinformatics Laboratory", "url": "https://lab.hidro.web.id", "generated": "2026-10-25T11:40:36.102Z", "synced": "2026-10-25", "source": "OpenAlex", "count": 20, "works": [ { "id": "W7167739019", "doi": "10.17632/wswmz8t6zn", "title": "Daily Rainfall Dataset for 68 Rainfall Stations in the Bengawan Solo Water Resources Region, Indonesia (1980–2025)", "year": 2026, "date": "2026-07-08", "type": "dataset", "venue": "Mendeley Data", "authors": [ { "name": "Ginanjar Dwi Prasetyo", "orcid": null }, { "name": "I Istiarto", "orcid": null }, { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" } ], "oa": true, "url": "https://doi.org/10.17632/wswmz8t6zn", "oa_url": "https://doi.org/10.17632/wswmz8t6zn", "publisher": null, "biblio": null, "language": null, "cited_by": 0, "abstract": "This repository provides a daily rainfall dataset compiled from records of 68 rainfall stations located within the Bengawan Solo Water Resources Region (Wilayah Sungai Bengawan Solo), Indonesia. Data availability differs among stations. Some stations contain complete observation records, whereas others contain only partial records or no rainfall observations during the available observation period. The dataset can be used to support hydrological studies, climatological research, rainfall trend analysis, time-series analysis, and other applications requiring long-term daily rainfall observations.", "topics": [ "Precipitation Measurement and Analysis" ], "keywords": [ "daily precipitation", "rainfall stations", "long-term precipitation records", "precipitation trends", "hydrological study", "climatology", "time series" ], "sdgs": [ { "n": 6, "name": "Clean water and sanitation" } ], "slug": "daily-rainfall-dataset-for-68-rainfall-stations-in-the-bengawan-solo-water" }, { "id": "W7215309897", "doi": null, "title": "Pengolahan Data Curah Hujan memakai Metode Dekomposisi BEAST dan STL", "year": 2026, "date": "2026-06-17", "type": "article", "venue": "Prosiding Seminar Nasional Teknik Sipil UMS", "authors": [ { "name": "Ginanjar Dwi Prasetyo", "orcid": null }, { "name": "I Istiarto", "orcid": null }, { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" } ], "oa": true, "url": "https://proceedings.ums.ac.id/sipil/article/view/7223", "oa_url": "https://proceedings.ums.ac.id/sipil/article/download/7223/6565", "publisher": "Muhammadiyah University of Surakarta", "biblio": null, "language": "en", "cited_by": 0, "abstract": "Curah hujan merupakan komponen penting dalam sistem hidrologi. Curah hujan dicatat dalam berbagai interval waktu. Data tersebut umumnya memiliki variasi yang tinggi. Hal tersebut mengakibatkan kecenderungan perubahan jangka panjang sulit diamati secara langsung. Penelitian ini bertujuan untuk mengidentifikasi kecenderungan perubahan curah hujan memakai metode dekomposisi runtut waktu. Data yang digunakan berupa data runtut waktu hujan harian di Stasiun Tawangmangu tahun 1980-2025 yang diubah menjadi data hujan bulanan. Metode yang digunakan adalah Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) dan Seasonal Trend Decomposition using Loess (STL). Metode dekomposisi tersebut dapat memisahkan komponen trend, musiman, dan residu dari data runtut waktu. Hasil penelitian menunjukkan bahwa kedua metode mampu mendeteksi trend curah hujan. Metode BEAST memperlihatkan trend hujan bulanan meningkat dengan titik perubahan hujan bulanan pada 2016. Metode STL menghasilkan trend yang berfluktuasi, sehingga kecenderungan jangka panjang tidak terlihat jelas. Kedua metode menunjukkan pola musiman tahunan yang konsisten dengan residu acak. Berdasarkan hasil tersebut, metode BEAST lebih sesuai digunakan untuk mengidentifikasi trend curah hujan bulanan di Stasiun Tawangmangu tahun 1980-2025.", "topics": [ "Precipitation Measurement and Analysis" ], "keywords": [ "rainfall trends", "monthly rainfall", "time series decomposition", "BEAST", "STL", "change point detection", "seasonal variation", "long-term rainfall variability" ], "sdgs": [ { "n": 13, "name": "Climate action" } ], "slug": "pengolahan-data-curah-hujan-memakai-metode-dekomposisi-beast-dan-stl" }, { "id": "W4417447532", "doi": "10.21203/rs.3.rs-8272546/v1", "title": "Streamflow Prediction Using Long Shot-Term Memory Models for Optimized Irrigation Management", "year": 2025, "date": "2025-12-18", "type": "preprint", "venue": "Research Square", "authors": [ { "name": "Issiami Nursafa", "orcid": null }, { "name": "Hanggar Ganara Mawandha", "orcid": "0009-0001-9093-0775" }, { "name": "Muhamad Khoiru Zaki", "orcid": "0000-0002-9597-9076" }, { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Sigit Supadmo Arif", "orcid": "0000-0002-9263-1293" }, { "name": "Andi Sudirman", "orcid": null } ], "oa": true, "url": "https://doi.org/10.21203/rs.3.rs-8272546/v1", "oa_url": "https://doi.org/10.21203/rs.3.rs-8272546/v1", "publisher": null, "biblio": null, "language": null, "cited_by": 0, "abstract": null, "topics": [ "Hydrological Forecasting Using AI" ], "keywords": [ "streamflow forecasting", "hybrid LSTM-GRU model", "real-time data assimilation", "rainfall input", "forecast horizon", "rotational irrigation", "irrigation scheduling", "water allocation", "tropical watersheds", "water resources planning" ], "sdgs": [ { "n": 13, "name": "Climate action" }, { "n": 6, "name": "Clean water and sanitation" } ], "slug": "streamflow-prediction-using-long-shot-term-memory-models-for-optimized" }, { "id": "W4411154229", "doi": "10.5194/egusphere-2025-584", "title": "Review article: Past and future climate–related hazards in Indonesia", "year": 2025, "date": "2025-06-10", "type": "preprint", "venue": null, "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Adam Pamudji Rahardjo", "orcid": "0000-0002-9023-6089" }, { "name": "Hanggar Ganara Mawandha", "orcid": "0009-0001-9093-0775" }, { "name": "Edvin Aldrian", "orcid": "0000-0002-1851-9148" }, { "name": "Abdul Muhari", "orcid": "0000-0003-2030-6050" }, { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" } ], "oa": true, "url": "https://doi.org/10.5194/egusphere-2025-584", "oa_url": "https://egusphere.copernicus.org/preprints/2025/egusphere-2025-584/egusphere-2025-584.pdf", "publisher": null, "biblio": null, "language": "en", "cited_by": 4, "abstract": "Indonesia, one of the most populous countries, ranked fifth globally for climate-related hazards and disasters in the past five years. This study aims to assess historical trends, future projections, and potential implications of climate-related hazards in Indonesia. We synthesize literature, analyze historical datasets, and examine CMIP projections to assess the trend of extreme climate events and their potential effects on climate hazards. Results show that current records and studies predominantly highlight floods as the most common hazard in the tropical–humid region, followed by landslides, droughts, extreme weather, and wildfires. Historical evidence indicates an increasing threat of flood by intensifying amplitude and frequency of rainfall extremes by around 25 %, especially in most northern parts of Indonesia (Kalimantan, northern Sumatera, parts of Sulawesi, and Papua), while the drought amplified by 60 % over the country across the dry regions from southern Sumatera, Java to Nusa Tenggara. These trends are projected to persist under future climate scenarios (SSPs or RCPs). We found that urban factors like land subsidence and landuse change, particularly in cities like Jakarta, may worsen flood impacts in the future. Future studies should also examine rainfall-induced landslides and flash floods in other vulnerable areas, such as steep areas. Additionally, drought, often overlooked in this country, requires comprehensive research given its unique slow onset and its agricultural and societal effects. Global teleconnection mechanisms (ENSO, IOD, MJO) have intensified wet and dry hazards in recent decades and must be considered. A more integrated approach, combining cascading process models, impact assessments, early warning systems, and adaptive land-use practices, is essential to enhance resilience against climate hazards in Indonesia.", "topics": [ "Climate variability and models" ], "keywords": [ "climate hazards", "climate extremes", "floods", "drought", "landslides", "forest fires", "heavy rainfall", "climate change projections", "land subsidence", "land use change", "Jakarta", "teleconnections", "ENSO", "Indian Ocean Dipole", "Madden-Julian Oscillation", "climate resilience", "early warning systems", "Indonesia" ], "sdgs": [ { "n": 13, "name": "Climate action" }, { "n": 11, "name": "Sustainable cities and communities" } ], "slug": "review-article-past-and-future-climate-related-hazards-in-indonesia" }, { "id": "W4402999365", "doi": "10.1016/j.tfp.2024.100700", "title": "Optimizing afforestation and reforestation strategies to enhance ecosystem services in critically degraded regions", "year": 2024, "date": "2024-09-30", "type": "article", "venue": "Trees Forests and People", "authors": [ { "name": "R Fahrudin", "orcid": "0000-0001-9782-7204" }, { "name": "Anjar Dimara Sakti", "orcid": "0000-0002-7379-1777" }, { "name": "Hazel Yordan Komara", "orcid": null }, { "name": "Elham Sumarga", "orcid": "0000-0002-8581-8535" }, { "name": "Achmad Choiruddin", "orcid": "0000-0003-2568-2274" }, { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Therissia Hati", "orcid": null }, { "name": "Zuzy Anna", "orcid": "0000-0001-7880-6483" }, { "name": "Ketut Wikantika", "orcid": "0000-0001-5003-0601" } ], "oa": true, "url": "https://doi.org/10.1016/j.tfp.2024.100700", "oa_url": "https://doi.org/10.1016/j.tfp.2024.100700", "publisher": "Elsevier BV", "biblio": { "volume": "18", "issue": null, "first_page": "100700", "last_page": "100700" }, "language": "en", "cited_by": 16, "abstract": "Human activity has caused massive forest ecosystem damage, threatening the global environmental balance. Afforestation and reforestation are crucial strategies for the restoration of forest ecosystem functions. This study was conducted on Belitung Island, Indonesia, which has experienced forest degradation due to mining activity and is currently undergoing forest restoration efforts. This study aimed to identify priority areas for afforestation and reforestation using an innovative approach that integrates multi-criteria analysis (MCA) and machine-learning techniques based on ecosystem service (ES) indicators, wildfire susceptibility, and environmental pressure. This study is the first to combine long-term remote sensing data with machine learning to develop priority scenarios for afforestation and reforestation. Results show that low-priority afforestation areas cover 24,479.66 ha (20.45 %), medium-priority areas 58,703.30 ha (49.04 %), and high-priority areas 36,521.98 ha (30.51 %). For reforestation, low-priority areas cover 23,123.45 ha (30.45 %), medium-priority areas 38,197.36 ha (50.3 %), and high-priority areas 14,618.27 ha (19.25 %). This study is expected to serve as a reference for sustainable forest ecosystem restoration efforts in various regions by leveraging ES approaches and environmental conditions using remote-sensing technology.", "topics": [ "Land Use and Ecosystem Services" ], "keywords": [ "afforestation", "reforestation", "ecosystem services", "forest restoration", "priority area selection", "wildfire susceptibility", "environmental pressure", "multi-criteria decision making", "machine learning", "remote sensing", "Belitung Island" ], "sdgs": [ { "n": 15, "name": "Life on land" }, { "n": 13, "name": "Climate action" } ], "slug": "optimizing-afforestation-and-reforestation-strategies-to-enhance-ecosystem" }, { "id": "W4413538210", "doi": "10.64628/aan.r9ahwx4rn", "title": "Prediksi iklim Jawa tahun 2100: kekeringan di lumbung pangan, hujan ekstrem di hulu sungai", "year": 2024, "date": "2024-07-29", "type": "other", "venue": null, "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" } ], "oa": false, "url": "https://doi.org/10.64628/aan.r9ahwx4rn", "oa_url": null, "publisher": null, "biblio": null, "language": "id", "cited_by": 0, "abstract": null, "topics": [], "keywords": [ "climate change projections", "food security", "drought", "extreme rainfall", "river headwaters" ], "sdgs": [ { "n": 13, "name": "Climate action" } ], "slug": "prediksi-iklim-jawa-tahun-2100-kekeringan-di-lumbung-pangan-hujan-ekstrem-di" }, { "id": "W4400871117", "doi": "10.1016/j.gecco.2024.e03101", "title": "Modeling Proboscis monkey conservation sites on Borneo using ensemble machine learning", "year": 2024, "date": "2024-07-22", "type": "article", "venue": "Global Ecology and Conservation", "authors": [ { "name": "Anjar Dimara Sakti", "orcid": "0000-0002-7379-1777" }, { "name": "Kurnia Putri Adillah", "orcid": null }, { "name": "Cokro Santoso", "orcid": null }, { "name": "Ismail Al Faruqi", "orcid": null }, { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Parwati Sofan", "orcid": "0000-0001-8115-7664" }, { "name": "Rustam Rustam", "orcid": null }, { "name": "Adam Irwansyah Fauzi", "orcid": "0000-0003-0222-4748" }, { "name": "Yudi Setiawan", "orcid": null }, { "name": "Inggita Utami", "orcid": "0000-0003-1873-3651" }, { "name": "Alinda F.M. Zain", "orcid": null }, { "name": "Muhammad Kamal", "orcid": "0000-0003-4004-086X" } ], "oa": true, "url": "https://doi.org/10.1016/j.gecco.2024.e03101", "oa_url": "https://doi.org/10.1016/j.gecco.2024.e03101", "publisher": "Elsevier BV", "biblio": { "volume": "54", "issue": null, "first_page": "e03101", "last_page": "e03101" }, "language": "en", "cited_by": 8, "abstract": "This study aimed to analyze the habitat suitability of the endangered Proboscis monkey (Nasalis larvatus) on Borneo using a multi-machine-learning approach. This study integrated physical, vegetational, meteorological, and human activity data to develop a comprehensive habitat suitability model. Four machine-learning algorithms, namely, maximum entropy (MaxEnt), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and classification and regression trees (CART), were employed to model the habitat suitability index. A total of 1943 sample points were divided into training (70 %) and validation (30 %) sets for the analysis. This study included three main stages: geospatial database creation, spatial habitat modeling using multi-machine-learning algorithms, and habitat suitability evaluation. In addition, the pressure from human development on the habitat suitability index model was analyzed. This study identified a high level of suitability for Proboscis monkey habitats in nearshore areas. The maximum habitat suitability for Proboscis monkeys was observed to be 11.54 %, as evidenced by the consensus of the MaxEnt value and four machine-learning algorithms. Conversely, the minimum habitat suitability was recorded at 13.27 %, as indicated by disagreement among all algorithms. The AUC values for the machine-learning models ranged from 74 % to 90 %, indicating moderate to high predictive performance. This study provides valuable insights for the formulation of well-planned development programs for Proboscis monkeys. The results of this study will contribute to the accurate identification of potential Proboscis monkey habitats, thereby providing support for conservation efforts aimed at safeguarding this endangered species.", "topics": [ "Species Distribution and Climate Change" ], "keywords": [ "proboscis monkey", "Nasalis larvatus", "habitat suitability", "endangered species conservation", "Borneo", "ensemble learning", "MaxEnt", "random forest", "support vector machine", "gradient boosting", "classification and regression trees", "habitat suitability index", "nearshore habitat" ], "sdgs": [ { "n": 15, "name": "Life on land" } ], "slug": "modeling-proboscis-monkey-conservation-sites-on-borneo-using-ensemble-machine" }, { "id": "W4399526824", "doi": "10.1002/joc.8537", "title": "Future exposure of rainfall and temperature extremes to the most populous island of Indonesia: A projection based on CORDEX simulation", "year": 2024, "date": "2024-06-11", "type": "article", "venue": "International Journal of Climatology", "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Hanggar Ganara Mawandha", "orcid": "0009-0001-9093-0775" }, { "name": "Anjar Dimara Sakti", "orcid": "0000-0002-7379-1777" }, { "name": "Karlina", "orcid": null }, { "name": "Neil Andika", "orcid": "0000-0002-7715-1830" }, { "name": "Shamsuddin Shahid", "orcid": "0000-0001-9621-6452" }, { "name": "Rachmad Jayadi", "orcid": "0000-0003-1664-8103" } ], "oa": false, "url": "https://doi.org/10.1002/joc.8537", "oa_url": null, "publisher": "Wiley", "biblio": { "volume": "44", "issue": "10", "first_page": "3529", "last_page": "3547" }, "language": "en", "cited_by": 10, "abstract": "The study focuses on understanding the future exposure to rainfall and temperature extremes in one of the world's most populous islands, Java, Indonesia. We use the high‐resolution climate projections from the Coordinated Regional Climate Downscaling Experiment (CORDEX) simulation for Southeast Asia by 2100 under RCP4.5 and RCP8.5 scenarios. Results show that the island will likely experience drying in the lowlands due to annual rainfall decline by approximately 13%–18%, potentially exposing around 27%–73% of the population (varying under different scenarios) during the end of the century (2060–2099). Additionally, the future drying condition may be exacerbated by extreme temperatures with a 1.7–3.1°C increase in maximum daily temperature, linked with more than half of the population (63%) likely to experience at least an unprecedented temperature of 3°C under RCP8.5. Our seasonal analysis also suggests that dry seasons get even drier, and wet seasons get wetter. In terms of landuse areas exposed, we show a higher fraction of crop and forest areas may face both drying and warming, which can potentially lead to crop failure and wildfire. Our study indicates that compound drought and heat may be a common threat in lowland Java in the future, while intensifying rainfall extremes in the uplands may lead to flash flooding downstream and landslides. These findings highlight the urgent need for adaptation and mitigation strategies to reduce the risks associated with climate change in Java as one of Indonesia's most critical regions in the future.", "topics": [ "Climate variability and models" ], "keywords": [ "heavy rainfall", "temperature extremes", "climate change exposure", "Java", "RCP4.5", "RCP8.5", "population exposure", "flash floods", "landslides", "wildfire risk", "crop failure", "climate change adaptation" ], "sdgs": [ { "n": 13, "name": "Climate action" }, { "n": 11, "name": "Sustainable cities and communities" } ], "slug": "future-exposure-of-rainfall-and-temperature-extremes-to-the-most-populous" }, { "id": "W4380853168", "doi": "10.1016/j.ancene.2023.100389", "title": "Crop response pattern to several drought timescales and its possible determinants: A global-scale analysis during the last decades", "year": 2023, "date": "2023-06-15", "type": "article", "venue": "Anthropocene", "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Wonsik Kim", "orcid": "0000-0002-5316-4135" }, { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" } ], "oa": true, "url": "https://doi.org/10.1016/j.ancene.2023.100389", "oa_url": "https://doi.org/10.1016/j.ancene.2023.100389", "publisher": "Elsevier BV", "biblio": { "volume": "43", "issue": null, "first_page": "100389", "last_page": "100389" }, "language": "en", "cited_by": 19, "abstract": "Crop response characteristics to different timescales of precipitation deficit may represent crop system resilience to drought characteristics. In this study, we assess the crop yield response of major crops to meteorological drought estimated by a standardized precipitation index with multiple timescales (1–12 months) during 1981–2016 all over the globe. We estimate that about one- to two-thirds of global harvested areas of maize, rice, soybean, and wheat, were significantly affected by various drought timescales. Soybean and wheat might respond to more prolonged droughts, while rice and maize responded to short-medium drought time scales. Using multiple machine learning models, we reveal that set of determinants could explain most variations of crop response to drought timescale with average accuracies between 45.7% and 56.0% (across models and crop types). Moreover, this study suggests that crops in warmer and higher water availability (precipitation minus potential evapotranspiration) might respond significantly to more short-term drought. The other factors (i.e., socioeconomic, fertilizer, soil, topography, production, irrigation) shows a complex and weaker effect on defining crop vulnerability to the various drought characteristics. This study attempts to fill the gaps in understanding global crop resistance to different drought characteristics. The future challenge in understanding the multifaceted effect of physical and socioeconomic factors on global crop vulnerability to drought may remain and should be addressed in further studies.", "topics": [ "Climate change impacts on agriculture" ], "keywords": [ "meteorological drought", "Standardized Precipitation Index", "crop resilience", "crop vulnerability", "maize", "rice", "soybean", "wheat" ], "sdgs": [ { "n": 2, "name": "Zero hunger" }, { "n": 13, "name": "Climate action" } ], "slug": "crop-response-pattern-to-several-drought-timescales-and-its-possible" }, { "id": "W4367316450", "doi": "10.1007/s00704-023-04461-w", "title": "Correlation analysis of agricultural drought risk on wet farming crop and meteorological drought index in the tropical-humid region", "year": 2023, "date": "2023-04-28", "type": "article", "venue": "Theoretical and Applied Climatology", "authors": [ { "name": "Amalia Nafisah Rahmani Irawan", "orcid": "0000-0001-8190-245X" }, { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" }, { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" } ], "oa": true, "url": "https://doi.org/10.1007/s00704-023-04461-w", "oa_url": "https://link.springer.com/content/pdf/10.1007/s00704-023-04461-w.pdf", "publisher": "Springer Science+Business Media", "biblio": { "volume": "153", "issue": "1-2", "first_page": "227", "last_page": "240" }, "language": "en", "cited_by": 23, "abstract": "In the tropical-humid region, wet farming crops (e.g., paddy) are a common agricultural commodity with a high-water requirement. Usually planted in the Asia monsoon region with a high precipitation rate, these crops are divided into the wet cropping season and the dry cropping season. During the dry cropping season, they are particularly vulnerable to agricultural drought caused by the decrease in precipitation. This study used Indonesia as a case study and is aimed at assessing the agricultural drought risk on a wet farming crop during the dry cropping season by examining the correlation between the drought hazard and its risk. For hazard assessment, Standardized Precipitation Index (SPI) was used to assess the agricultural drought, by using the Global Satellite Mapping of Precipitation (GSMaP) which has 0.1° × 0.1° spatial resolution. The result of correlation analysis between the SPI and drought-affected areas on a city scale showed that SPI-3 in August is the most suitable timescale to assess the agricultural drought in Indonesia. The agricultural drought risk assessment was conducted on the grid scale, where the crop yield estimation model was developed with the help of Normalized Difference Vegetation Index (NDVI). Based on the correlation analysis between SPI-3 and the detrended crop yield as drought risk indicators, the higher yield loss was found in the area above the threshold value ( r -value ≤ 0.6) indicating that those areas were more vulnerable to drought, while the area below the threshold value has lower crop yield loss even in the area that was hit by the most severe drought, because the existing irrigation system was able to resist the drought’s impact on crop yield loss.", "topics": [ "Hydrology and Drought Analysis" ], "keywords": [ "agricultural drought risk", "paddy rice", "Standardized Precipitation Index", "crop yield loss", "NDVI", "humid tropics", "Indonesia" ], "sdgs": [ { "n": 2, "name": "Zero hunger" }, { "n": 13, "name": "Climate action" }, { "n": 6, "name": "Clean water and sanitation" } ], "slug": "correlation-analysis-of-agricultural-drought-risk-on-wet-farming-crop-and" }, { "id": "W4318921106", "doi": "10.1371/journal.pone.0281287", "title": "Possible factors determining global-scale patterns of crop yield sensitivity to drought", "year": 2023, "date": "2023-02-02", "type": "article", "venue": "PLoS ONE", "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" }, { "name": "Wonsik Kim", "orcid": "0000-0002-5316-4135" } ], "oa": true, "url": "https://doi.org/10.1371/journal.pone.0281287", "oa_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0281287&type=printable", "publisher": "Public Library of Science", "biblio": { "volume": "18", "issue": "2", "first_page": "e0281287", "last_page": "e0281287" }, "language": "en", "cited_by": 30, "abstract": "In recent decades, droughts have critically limited crop production, inducing food system shocks regionally and globally. It was estimated that crop yield variability in around one-third to three-fourths of global harvested areas is explained significantly by drought, revealing the notable vulnerability of crop systems to such climate-related stressors. However, understanding the key factors determining the global pattern of crop yield sensitivity to drought is limited. Here, we investigate a wide range of physical and socioeconomic factors that may determine crop-drought vulnerability in terms of yield sensitivity to drought based on the Standardized Precipitation Index at 0.5° resolution from 1981 to 2016 using machine learning approaches. The results indicate that the spatial variations of the crop-drought sensitivity were mainly explained by environmental factors (i.e., annual precipitation, soil water-holding capacity, soil acidity, annual potential evapotranspiration) and crop management factors (i.e., fertilizer rate, growing season). Several factors might have a positive effect in mitigating crop-drought vulnerability, such as annual precipitation, soil water holding capacity, and fertilizer rate. This study quantitatively assesses the possible effect of various determinants which might control crop vulnerability to drought. This understanding may provide insights for further studies addressing better crop vulnerability measures under future drought stress.", "topics": [ "Climate change impacts on agriculture" ], "keywords": [ "Standardized Precipitation Index", "annual precipitation", "soil water holding capacity", "soil acidity", "potential evapotranspiration", "fertilizer dose", "growing season" ], "sdgs": [ { "n": 2, "name": "Zero hunger" }, { "n": 13, "name": "Climate action" } ], "slug": "possible-factors-determining-global-scale-patterns-of-crop-yield-sensitivity-to" }, { "id": "W7208387577", "doi": null, "title": "Global crop vulnerability to drought and various climate extremes and the possible factors", "year": 2022, "date": "2022-12-09", "type": "dissertation", "venue": "Doctoral dissertation, Tohoku University", "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" } ], "oa": true, "url": "https://tohoku.repo.nii.ac.jp/records/139189", "oa_url": "https://tohoku.repo.nii.ac.jp/records/139189", "publisher": "National Institute of Informatics", "biblio": null, "language": "en", "cited_by": 0, "abstract": null, "topics": [ "Climate change impacts on agriculture" ], "keywords": [ "crop vulnerability", "drought", "climate extremes", "climate change impacts on agriculture" ], "sdgs": [ { "n": 2, "name": "Zero hunger" }, { "n": 13, "name": "Climate action" } ], "slug": "global-crop-vulnerability-to-drought-and-various-climate-extremes-and-the" }, { "id": "W4224949220", "doi": "10.1007/s00704-022-04063-y", "title": "Climate projections over different climatic regions of Afghanistan under shared socioeconomic scenarios", "year": 2022, "date": "2022-04-27", "type": "article", "venue": "Theoretical and Applied Climatology", "authors": [ { "name": "Mohammad Naser Sediqi", "orcid": "0000-0002-3569-161X" }, { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" } ], "oa": false, "url": "https://doi.org/10.1007/s00704-022-04063-y", "oa_url": null, "publisher": "Springer Science+Business Media", "biblio": { "volume": "149", "issue": "1-2", "first_page": "511", "last_page": "524" }, "language": "en", "cited_by": 16, "abstract": null, "topics": [ "Climate variability and models" ], "keywords": [ "climate change projections", "Afghanistan", "Shared Socioeconomic Pathways", "precipitation change", "temperature change", "CMIP6", "compromise programming", "ACCESS-CM2", "MPI-ESM1-2-LR", "regional climate variability" ], "sdgs": [ { "n": 13, "name": "Climate action" } ], "slug": "climate-projections-over-different-climatic-regions-of-afghanistan-under-shared" }, { "id": "W4224238774", "doi": "10.1142/9789811260100_0052", "title": "Crop yield response to meteorological drought over Asian monsoon region during the last decades", "year": 2022, "date": "2022-04-21", "type": "conference-paper", "venue": null, "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" } ], "oa": true, "url": "https://doi.org/10.1142/9789811260100_0052", "oa_url": "https://doi.org/10.1142/9789811260100_0052", "publisher": null, "biblio": { "volume": null, "issue": null, "first_page": "153", "last_page": "155" }, "language": "en", "cited_by": 3, "abstract": null, "topics": [ "Climate change impacts on agriculture" ], "keywords": [ "crop yield", "meteorological drought", "Asian monsoon region", "drought impacts on agriculture" ], "sdgs": [ { "n": 2, "name": "Zero hunger" }, { "n": 13, "name": "Climate action" } ], "slug": "crop-yield-response-to-meteorological-drought-over-asian-monsoon-region-during" }, { "id": "W4394120945", "doi": "10.6084/m9.figshare.21298785", "title": "Major crop yield sensitivity to drought on a global scale", "year": 2022, "date": "2022-01-01", "type": "dataset", "venue": "Figshare", "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" } ], "oa": true, "url": "https://doi.org/10.6084/m9.figshare.21298785", "oa_url": "https://figshare.com/articles/dataset/Major_crop_yield_sensitivity_to_drought_on_a_global_scale/21298785", "publisher": "Figshare (United Kingdom)", "biblio": null, "language": "en", "cited_by": 0, "abstract": "In current years, droughts have critically threatened crop production, inducing food system shocks in a region and globally. This dataset provides input data for a study estimating crop yield variability explained by drought. We investigate factors that may determine the sensitivity of global crop yield to drought based on the Standardized Precipitation Index (SPI) at 0.5° resolution from 1981 to 2016 on a global scale. This study may provide insights for further studies addressing better crop-drought vulnerability measures under future stress.", "topics": [ "Climate change impacts on agriculture" ], "keywords": [ "crop yield", "drought", "crop yield sensitivity", "Standardized Precipitation Index", "food security", "global crop production" ], "sdgs": [ { "n": 2, "name": "Zero hunger" }, { "n": 13, "name": "Climate action" } ], "slug": "major-crop-yield-sensitivity-to-drought-on-a-global-scale" }, { "id": "W4226391026", "doi": "10.2208/journalofjsce.10.1_195", "title": "Mechanism of rainfall inundation caused by the 2019 Typhoon Hagibis in Iwate Prefecture coastal zone, Japan", "year": 2022, "date": "2022-01-01", "type": "article", "venue": "Journal of JSCE", "authors": [ { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" }, { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Akihiro ICHIBA", "orcid": null }, { "name": "Keitaro Yamada", "orcid": "0000-0003-0611-8133" }, { "name": "Akihiro Goda", "orcid": null } ], "oa": true, "url": "https://doi.org/10.2208/journalofjsce.10.1_195", "oa_url": "https://www.jstage.jst.go.jp/article/journalofjsce/10/1/10_195/_pdf", "publisher": null, "biblio": { "volume": "10", "issue": "1", "first_page": "195", "last_page": "205" }, "language": "en", "cited_by": 3, "abstract": "This study aims to clarify the mechanism of rainfall inundation caused by the 2019 Typhoon Hagibis in Iwate prefecture coastal zone, Japan, using probable one-hour rainfalls estimated for the 2 periods of 1976-2005 and 1989-2018. The differences between rainfalls by the 2019 Typhoon Hagibis and the ten-year return level of one-hour rainfall (P10) of 1976-2005, which is generally assumed as the discharge standard of drainage system in Japan, at Fudai village, Iwaizumi town, Miyako city, and Yamada town were in the ranged of 40.7 mm–106.6 mm for maximum three-hour rainfall during the 2019 Typhoon Hagibis, and the estimated massive rainfall inundations. On the other hand, massive rainfall inundations were also observed at Kamaishi city, although the amount of water overflow from the existing drainage system was only 3.5 mm for a maximum of three-hour rainfall during the 2019 Typhoon Hagibis. These were caused by water overflow at narrow sections in the streams and drainage systems by huge sediment and large wood and debris from landslides dammed up, and by damming up rainfall inundation at coastal levees for tsunami. In addition, P10 at all study areas were increased by 8–24% from 1976-2005 to 1989-2018, which corresponded with increasing frequency of P10 of 1976-2005 by 1.4-2.1 times. Thus, increasing frequency of rainfall inundations in the Iwate prefecture coastal zone is expected in the future.", "topics": [ "Flood Risk Assessment and Management" ], "keywords": [ "Typhoon Hagibis" ], "sdgs": [ { "n": 13, "name": "Climate action" }, { "n": 11, "name": "Sustainable cities and communities" } ], "slug": "mechanism-of-rainfall-inundation-caused-by-the-2019-typhoon-hagibis-in-iwate-2022" }, { "id": "W4200297623", "doi": "10.1088/1748-9326/ac45b4", "title": "A global-scale relationship between crop yield anomaly and multiscalar drought index based on multiple precipitation data", "year": 2021, "date": "2021-12-22", "type": "article", "venue": "Environmental Research Letters", "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Wonsik Kim", "orcid": "0000-0002-5316-4135" }, { "name": "Yoshiya Touge", "orcid": "0000-0002-3002-4789" }, { "name": "Ke Shi", "orcid": "0000-0003-0441-8554" }, { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" } ], "oa": true, "url": "https://doi.org/10.1088/1748-9326/ac45b4", "oa_url": "https://doi.org/10.1088/1748-9326/ac45b4", "publisher": "IOP Publishing", "biblio": { "volume": "17", "issue": "1", "first_page": "014037", "last_page": "014037" }, "language": "en", "cited_by": 42, "abstract": "Drought impact on crop production is well known as crop yield is strongly controlled by climate variation. Previous studies assessed the drought impact using a drought index based on a single input data set, while the variability of the drought index to the input data choice is notable. In this study, a drought index based on the standardized precipitation index with multiple timescales using several global precipitation datasets was compared with the detrended anomaly based on the global dataset of historical yield for major crops over 1981–2016. Results show that the drought index based on the ensemble precipitation dataset correlates better with the crop yield anomaly than a single dataset. Based on the drought index using ensemble datasets, global crop areas significantly affected by drought during the study period were around 23%, 8%, 30%, and 29% for maize, rice, soybean, and wheat, respectively, induced mainly by medium to longer drought timescale (5–12 months). This study indicates that most crops cultivated in dry regions were affected by droughts worldwide, while rice shows less correlation to drought as it is generally irrigated and cultivated in humid regions with less drought exposure. This study provides a valuable framework for data choices in drought index development and a better knowledge of the drought impact on agriculture using different timescales on a global scale towards understanding crop vulnerability to climate disruptions.", "topics": [ "Hydrology and Drought Analysis" ], "keywords": [ "drought", "crop yield anomalies", "drought indices", "Standardized Precipitation Index", "crop vulnerability", "maize", "rice", "soybean", "wheat", "aridity", "climate change impacts on agriculture" ], "sdgs": [ { "n": 2, "name": "Zero hunger" }, { "n": 13, "name": "Climate action" } ], "slug": "a-global-scale-relationship-between-crop-yield-anomaly-and-multiscalar-drought" }, { "id": "W3125359429", "doi": "10.1016/j.ijdrr.2021.102058", "title": "Developing flood vulnerability curve for rice crop using remote sensing and hydrodynamic modeling", "year": 2021, "date": "2021-01-19", "type": "article", "venue": "International Journal of Disaster Risk Reduction", "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" } ], "oa": true, "url": "https://doi.org/10.1016/j.ijdrr.2021.102058", "oa_url": "https://doi.org/10.1016/j.ijdrr.2021.102058", "publisher": "Elsevier BV", "biblio": { "volume": "54", "issue": null, "first_page": "102058", "last_page": "102058" }, "language": "en", "cited_by": 52, "abstract": "The use of flood damage functions, or vulnerability curves, as a relationship between the intensity of the process (hazard) and the degree of potential loss of the exposed elements plays an important role in flood risk assessment. In terms of disaster risk reduction, a vulnerability curve is a helpful tool to quickly evaluate loss and conduct immediate decision making. This study proposes flood vulnerability curves for rice crop using crop yield loss estimated by crop statistics and remote-sensing modeling as a loss indicator. Flood parameters (depth, velocity, and duration) were simulated using a hydrodynamic model. Thus, the degree of crop yield loss and flood characteristics could be compared to derive vulnerability curves. In this study, we used a case study of the 2007 flood in the Solo river basin of Indonesia. Our results show that the relationship between the intensity of flood parameters and the degree of rice crop yield loss fits logarithmic regression functions, where water depth is considered the most significant parameter in loss estimation. Moreover, the minimum values of water depth, flow velocity, and duration relationship, that induce loss are 0.2 m, 0.03 m/s, and 8 days, respectively, while the maximum values, that induce complete yield loss, are 5.2 m, 0.08 m/s, and 22 days. This study's framework can be potentially used to obtain flood vulnerability curve or flood damage function, particularly for data-scarce regions.", "topics": [ "Flood Risk Assessment and Management" ], "keywords": [ "flood damage functions", "flood risk assessment", "hydrodynamic modeling", "water depth", "flow velocity", "flood duration", "data-scarce regions", "disaster risk reduction" ], "sdgs": [ { "n": 2, "name": "Zero hunger" }, { "n": 6, "name": "Clean water and sanitation" }, { "n": 13, "name": "Climate action" } ], "slug": "developing-flood-vulnerability-curve-for-rice-crop-using-remote-sensing-and" }, { "id": "W4394518871", "doi": "10.6084/m9.figshare.14880396", "title": "SPI (Standardized Precipitation Index) based on multiple input dataset", "year": 2021, "date": "2021-01-01", "type": "dataset", "venue": "Figshare", "authors": [ { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" }, { "name": "Wonsik Kim", "orcid": "0000-0002-5316-4135" } ], "oa": true, "url": "https://doi.org/10.6084/m9.figshare.14880396", "oa_url": "https://figshare.com/articles/dataset/SPI_Standardized_Precipitation_Index_based_on_multiple_input_dataset/14880396", "publisher": "Figshare (United Kingdom)", "biblio": null, "language": "en", "cited_by": 0, "abstract": "Multiple timescale (1-12 month) Standardized Precipitation Index (SPI) based on ensemble mean of multiple precipitation dataset spanning 1981-2016", "topics": [ "Hydrology and Drought Analysis" ], "keywords": [ "Standardized Precipitation Index", "drought", "multiple time scales", "ensemble mean", "precipitation datasets" ], "sdgs": [ { "n": 13, "name": "Climate action" } ], "slug": "spi-standardized-precipitation-index-based-on-multiple-input-dataset" }, { "id": "W3115303473", "doi": "10.2208/jscejhe.76.1_264", "title": "Mechanism of rainfall inundation caused by the 2019 Typhoon Hagibis in Iwate Prefecture coastal zone, Japan", "year": 2020, "date": "2020-01-01", "type": "article", "venue": "Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering)", "authors": [ { "name": "Daisuke Komori", "orcid": "0000-0001-5156-9182" }, { "name": "Vempi Satriya Adi Hendrawan", "orcid": "0000-0003-3414-6422" }, { "name": "Akihiro ICHIBA", "orcid": null }, { "name": "Keitaro Yamada", "orcid": "0000-0003-0611-8133" }, { "name": "Akihiro Goda", "orcid": null } ], "oa": true, "url": "https://doi.org/10.2208/jscejhe.76.1_264", "oa_url": "https://www.jstage.jst.go.jp/article/jscejhe/76/1/76_264/_pdf", "publisher": null, "biblio": { "volume": "76", "issue": "1", "first_page": "264", "last_page": "273" }, "language": "en", "cited_by": 1, "abstract": "令和元年台風19号により甚大な被害が生じた岩手県沿岸域における発災メカニズムを考察した．岩手県沿岸域における今次災害は主に短時間豪雨による土石流や内水氾濫であった．1976-2005年および1989-2018年の各30ヶ年を統計期間として，それぞれ推定された年最大1時間降水量の10年確率降水量は，対象5地点にて1989-2018年の方が8-24%大きくなった．1976-2005年の10年確率降水量と今次災害の最大3時間降水量の差は，普代村・宮古市・岩泉町小本地区にて50mmを上回り，短時間豪雨により大規模な内水氾濫が発生する状況であったことが確認された．一方，釜石市では大きな溢水量は推定されず，土石流による下水道の堰止まりや津波堤防が被害拡大の一因であることが確認された．", "topics": [ "Flood Risk Assessment and Management" ], "keywords": [ "Typhoon Hagibis", "coastal flooding", "debris flows" ], "sdgs": [ { "n": 13, "name": "Climate action" } ], "slug": "mechanism-of-rainfall-inundation-caused-by-the-2019-typhoon-hagibis-in-iwate-2020" } ] }