2024
Fahrudin,; Sakti, A. D.; Komara, H. Y.; Sumarga, E.; Choiruddin, A.; Hendrawan, V. S. A.; Hati, T.; Anna, Z.; Wikantika, K.
Optimizing afforestation and reforestation strategies to enhance ecosystem services in critically degraded regions Journal Article
In: Trees, Forests and People, vol. 18, 2024, ISSN: 26667193.
Abstract | Links | BibTeX | Tags: Aforestation reforestation, Belitung, Ecosystem services, Remote-sensing, Spatial modeling
@article{Fahrudin2024,
title = {Optimizing afforestation and reforestation strategies to enhance ecosystem services in critically degraded regions},
author = {Fahrudin and A. D. Sakti and H. Y. Komara and E. Sumarga and A. Choiruddin and V. S. A. Hendrawan and T. Hati and Z. Anna and K. Wikantika},
doi = {10.1016/j.tfp.2024.100700},
issn = {26667193},
year = {2024},
date = {2024-01-01},
journal = {Trees, Forests and People},
volume = {18},
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.},
keywords = {Aforestation reforestation, Belitung, Ecosystem services, Remote-sensing, Spatial modeling},
pubstate = {published},
tppubtype = {article}
}
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.
Sakti, A. D.; Adillah, K. P.; Santoso, C.; Faruqi, I. A.; Hendrawan, V. S. A.; Sofan, P.; Rustam, R.; Fauzi, A. I.; Setiawan, Y.; Utami, I.; Zain, A. F.; Kamal, M.
Modeling Proboscis monkey conservation sites on Borneo using ensemble machine learning Journal Article
In: Global Ecology and Conservation, vol. 54, 2024, ISSN: 23519894.
Abstract | Links | BibTeX | Tags: Borneo, Habitat suitability, Machine learning, Proboscis monkeys, Remote sensing
@article{Sakti2024,
title = {Modeling Proboscis monkey conservation sites on Borneo using ensemble machine learning},
author = {A. D. Sakti and K. P. Adillah and C. Santoso and I. A. Faruqi and V. S. A. Hendrawan and P. Sofan and R. Rustam and A. I. Fauzi and Y. Setiawan and I. Utami and A. F. Zain and M. Kamal},
doi = {10.1016/j.gecco.2024.e03101},
issn = {23519894},
year = {2024},
date = {2024-01-01},
journal = {Global Ecology and Conservation},
volume = {54},
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.},
keywords = {Borneo, Habitat suitability, Machine learning, Proboscis monkeys, Remote sensing},
pubstate = {published},
tppubtype = {article}
}
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.
Hendrawan, V. S. A.; Mawandha, H. G.; Sakti, A. D.; Karlina,; Andika, N.; Shahid, S.; Jayadi, R.
Future exposure of rainfall and temperature extremes to the most populous island of Indonesia: A projection based on CORDEX simulation Journal Article
In: International Journal of Climatology, vol. 44, iss. 10, 2024, ISSN: 10970088.
Abstract | Links | BibTeX | Tags: climate extreme, exposure, Java Island, landuse, population, RCM
@article{Hendrawan2024,
title = {Future exposure of rainfall and temperature extremes to the most populous island of Indonesia: A projection based on CORDEX simulation},
author = {V. S. A. Hendrawan and H. G. Mawandha and A. D. Sakti and Karlina and N. Andika and S. Shahid and R. Jayadi},
doi = {10.1002/joc.8537},
issn = {10970088},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Climatology},
volume = {44},
issue = {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.},
keywords = {climate extreme, exposure, Java Island, landuse, population, RCM},
pubstate = {published},
tppubtype = {article}
}
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.
2023
Hendrawan, Vempi Satriya Adi; Komori, Daisuke; Kim, Wonsik
Possible factors determining global-scale patterns of crop yield sensitivity to drought Journal Article
In: PLoS ONE, vol. 18, iss. 2 February, no. 2 February, pp. 1-20, 2023, ISSN: 19326203.
Abstract | Links | BibTeX | Tags:
@article{Hendrawan2023,
title = {Possible factors determining global-scale patterns of crop yield sensitivity to drought},
author = {Vempi Satriya Adi Hendrawan and Daisuke Komori and Wonsik Kim},
url = {http://dx.doi.org/10.1371/journal.pone.0281287},
doi = {10.1371/journal.pone.0281287},
issn = {19326203},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {PLoS ONE},
volume = {18},
number = {2 February},
issue = {2 February},
pages = {1-20},
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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
Irawan, Amalia Nafisah Rahmani; Komori, Daisuke; Hendrawan, Vempi Satriya Adi
Correlation analysis of agricultural drought risk on wet farming crop and meteorological drought index in the tropical-humid region Journal Article
In: Theoretical and Applied Climatology, 2023, ISSN: 14344483.
Abstract | Links | BibTeX | Tags:
@article{Irawan2023,
title = {Correlation analysis of agricultural drought risk on wet farming crop and meteorological drought index in the tropical-humid region},
author = {Amalia Nafisah Rahmani Irawan and Daisuke Komori and Vempi Satriya Adi Hendrawan},
doi = {10.1007/s00704-023-04461-w},
issn = {14344483},
year = {2023},
date = {2023-01-01},
journal = {Theoretical and Applied Climatology},
publisher = {Springer},
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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
Hendrawan, V. S. A.; Kim, W.; Komori, D.
Crop response pattern to several drought timescales and its possible determinants: A global-scale analysis during the last decades Journal Article
In: Anthropocene, vol. 43, 2023, ISSN: 22133054.
Abstract | Links | BibTeX | Tags: Crop yield, Crop-drought determinants, Drought timescales, Meteorological drought
@article{Hendrawan2023b,
title = {Crop response pattern to several drought timescales and its possible determinants: A global-scale analysis during the last decades},
author = {V. S. A. Hendrawan and W. Kim and D. Komori},
doi = {10.1016/j.ancene.2023.100389},
issn = {22133054},
year = {2023},
date = {2023-01-01},
journal = {Anthropocene},
volume = {43},
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.},
keywords = {Crop yield, Crop-drought determinants, Drought timescales, Meteorological drought},
pubstate = {published},
tppubtype = {article}
}
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.
2022
Hendrawan, V. S. A.; Komori, D.
Crop Yield Response To Meteorological Drought Over Asian Monsoon Region During the Last Decades Journal Article
In: no. Aogs 2021, pp. 153–155, 2022, ISBN: 9789811260100.
Links | BibTeX | Tags: agriculture, crop, disaster, drought, monsoon, yield
@article{Hendrawan2022,
title = {Crop Yield Response To Meteorological Drought Over Asian Monsoon Region During the Last Decades},
author = {V. S. A. Hendrawan and D. Komori},
doi = {10.1142/9789811260100_0052},
isbn = {9789811260100},
year = {2022},
date = {2022-01-01},
number = {Aogs 2021},
pages = {153--155},
keywords = {agriculture, crop, disaster, drought, monsoon, yield},
pubstate = {published},
tppubtype = {article}
}
Hendrawan, Vempi Satriya Adi; Kim, Wonsik; Touge, Yoshiya; Ke, Shi; Komori, Daisuke
A global-scale relationship between crop yield anomaly and multiscalar drought index based on multiple precipitation data Journal Article
In: Environmental Research Letters, vol. 17, no. 1, pp. 014037, 2022, ISSN: 17489326.
Abstract | Links | BibTeX | Tags: crop, drought, multisource, standardized precipitation index, yield loss
@article{Hendrawan2022a,
title = {A global-scale relationship between crop yield anomaly and multiscalar drought index based on multiple precipitation data},
author = {Vempi Satriya Adi Hendrawan and Wonsik Kim and Yoshiya Touge and Shi Ke and Daisuke Komori},
doi = {10.1088/1748-9326/ac45b4},
issn = {17489326},
year = {2022},
date = {2022-01-01},
journal = {Environmental Research Letters},
volume = {17},
number = {1},
pages = {014037},
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.},
keywords = {crop, drought, multisource, standardized precipitation index, yield loss},
pubstate = {published},
tppubtype = {article}
}
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.
Sediqi, Mohammad Naser; Hendrawan, Vempi Satriya Adi; Komori, Daisuke
Climate projections over different climatic regions of Afghanistan under shared socioeconomic scenarios Journal Article
In: Theoretical and Applied Climatology, vol. 149, no. 1-2, pp. 511–524, 2022, ISSN: 14344483.
Abstract | Links | BibTeX | Tags:
@article{Sediqi2022,
title = {Climate projections over different climatic regions of Afghanistan under shared socioeconomic scenarios},
author = {Mohammad Naser Sediqi and Vempi Satriya Adi Hendrawan and Daisuke Komori},
url = {https://doi.org/10.1007/s00704-022-04063-y},
doi = {10.1007/s00704-022-04063-y},
issn = {14344483},
year = {2022},
date = {2022-01-01},
journal = {Theoretical and Applied Climatology},
volume = {149},
number = {1-2},
pages = {511--524},
publisher = {Springer Vienna},
abstract = {The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used for spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2–4.5, and 5–8.5) and two future time horizons, early (2020–2059) and late (2060–2099). The compromise programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975–2014). Three models, namely, ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2–0, showed the highest skill in simulating all three variables (precipitation, maximum temperature, and minimum temperature), and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5 − 2.5 °C, 2.7 − 4.3 °C, and 4.5 − 5.3 °C and minimum temperature by 1.3 − 1.8 °C, 2.2 − 3.5 °C, and 4.6 − 5.2 °C for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, in the later period. Meanwhile, the changes in precipitation in the range of − 15 − 18%, − 36 − 47%, and − 40 − 68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used for spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2–4.5, and 5–8.5) and two future time horizons, early (2020–2059) and late (2060–2099). The compromise programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975–2014). Three models, namely, ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2–0, showed the highest skill in simulating all three variables (precipitation, maximum temperature, and minimum temperature), and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5 − 2.5 °C, 2.7 − 4.3 °C, and 4.5 − 5.3 °C and minimum temperature by 1.3 − 1.8 °C, 2.2 − 3.5 °C, and 4.6 − 5.2 °C for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, in the later period. Meanwhile, the changes in precipitation in the range of − 15 − 18%, − 36 − 47%, and − 40 − 68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.
Komori, Daisuke; Hendrawan, Vempi Satriya Adi; Ichiba, Akihiro; Yamada, Keitaro; Goda, Akihiro
Mechanism of Rainfall Inundation Caused By the 2019 Typhoon Hagibis in Iwate Prefecture Coastal Zone, Japan Journal Article
In: Journal of Japan Society of Civil Engineers, vol. 10, no. 1, pp. 195–205, 2022, ISSN: 21875103.
Abstract | Links | BibTeX | Tags: Iwate Prefecture coastal zone, probable one-hour rainfall, rainfall inundation, Typhoon Hagibis
@article{Komori2022,
title = {Mechanism of Rainfall Inundation Caused By the 2019 Typhoon Hagibis in Iwate Prefecture Coastal Zone, Japan},
author = {Daisuke Komori and Vempi Satriya Adi Hendrawan and Akihiro Ichiba and Keitaro Yamada and Akihiro Goda},
doi = {10.2208/journalofjsce.10.1_195},
issn = {21875103},
year = {2022},
date = {2022-01-01},
journal = {Journal of Japan Society of Civil Engineers},
volume = {10},
number = {1},
pages = {195--205},
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.},
keywords = {Iwate Prefecture coastal zone, probable one-hour rainfall, rainfall inundation, Typhoon Hagibis},
pubstate = {published},
tppubtype = {article}
}
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.
2021
Hendrawan, Vempi Satriya Adi; Komori, Daisuke
Developing flood vulnerability curve for rice crop using remote sensing and hydrodynamic modeling Journal Article
In: International Journal of Disaster Risk Reduction, vol. 54, no. August 2020, pp. 102058, 2021, ISSN: 22124209.
Abstract | Links | BibTeX | Tags: Crop yield loss, Flood, Remote sensing, Submergence, Vulnerability curve
@article{Hendrawan2021,
title = {Developing flood vulnerability curve for rice crop using remote sensing and hydrodynamic modeling},
author = {Vempi Satriya Adi Hendrawan and Daisuke Komori},
url = {https://doi.org/10.1016/j.ijdrr.2021.102058},
doi = {10.1016/j.ijdrr.2021.102058},
issn = {22124209},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Disaster Risk Reduction},
volume = {54},
number = {August 2020},
pages = {102058},
publisher = {Elsevier Ltd},
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.},
keywords = {Crop yield loss, Flood, Remote sensing, Submergence, Vulnerability curve},
pubstate = {published},
tppubtype = {article}
}
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.