ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data

Arjasakusuma, Sanjiwana; Yamaguchi, Yasushi; Hirano, Yasuhiro; Zhou, Xiang

doi:10.3390/ijgi7030103

Cited by 13 publications

(7 citation statements)

References 53 publications

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“…Several studies [28][29][30] analyzed the covariation of climate indices PDO, NAO, and SOI with precipitation to assess to assess meteorological and hydrological drought in Iran. Also, in other parts of the world, some research were carried out on the relationship between climate indices and hydrological variables [31,32].…”

Section: Introductionmentioning

confidence: 99%

Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices

Dehghani

Salehi

Mosavi

et al. 2020

IJGI

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Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Also, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.

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Section: Introductionmentioning

confidence: 99%

Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices

Dehghani

Salehi

Mosavi

et al. 2020

IJGI

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“…CHIRPS data, which has previously been utilised in Indonesia and other regions of the Maritime Continent for analysing rainfall extremes and trends, has been validated by a number of researchers (Arjasakusuma et al 2018;Nugroho et al 2021). In this study, for validation purposes of CHIRPS, daily rainfall data for two observed stations were obtained: Paseh (Lat:-7.057, Lon:107.764) and Kertasari (Lat:-7.192, Lon:107.677) situated in Majalaya (Fig.…”

Section: Rainfall Datamentioning

confidence: 99%

Spatiotemporal Rainfall Projection in Majalaya basin, West Java, Indonesia under CMIP6 Scenarios

Sa’adi

Rohmat

Stamataki

et al. 2022

Preprint

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In this study, a Coupled Model Intercomparison Project Phase 6 (CMIP6), from the newest set of General Circulation Models (GCMs), was used to assess the spatiotemporal variability in rainfall regimes over the Majalaya basin in West Java, Indonesia. Twenty grid points of the gridded-based daily rainfall from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) datasets spanning from 1981 to 2014 were extracted. For the future projection, a set of 23 GCMs from the Shared Socioeconomic Pathways (SSPs) with SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 common to historical period were selected. Initially, the performance of the GCMs in simulating the local rainfall at each grid point was assess by using seven relative importance metrics. Then, compromise programming index (CPI) and Jenks optimised classification (JOC) were used to aggregate the results from the ranking of GCMs to select the best GCMs in simulating the local rainfall. It was found that EC-Earth3-Veg-LR ranked the best as Class I, followed by EC-Earth3-Veg, INM-CM4-8, EC-Earth3, MPI-ESM1-2-HR and MRI-ESM2-0 as Class II. Subsequently, the linear scaling (LS) method was used in bias correcting the selected GCM, EC-Earth3-Veg-LR using CHIRPS as reference observed data. The spatiotemporal assessment of bias-corrected LS-EC-Earth3-Veg-LR for the different months over the mid-future period 2041–2070 and 2071–2100 was compared with those under SSPs 126, 245, 370 and 585. Our results show a generally wetter tendency over Majalaya under all SSPs toward the end of the 21st century, particularly under SSP370 and SSP585. For the mid-future period, the results suggest a worsening drier period for the month of June to October. Meanwhile, the usually high rainfall month of November is projected to receive lower rainfall than normal. For the far-future period, all SSPs in most of the cases projected more increasing rainfall months as the typically drier period become shorter. The changes in rainfall were observed to move toward a more homogeneous increase in rainfall for all rainfall months across the basin from the mid-future to the far-future period. It is projected that rainfall events will become more frequent, more intense, and happen consecutively for the rainy months of December and January and followed by another consecutive rainy month for March and April.

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“…The preceding warm events (El Niño) in the cold season over the Niño-3 region usually led to following wetter spring and wetter plum rainfall with frequent extreme events before summer in Taiwan, and vice versa [55,58]. Several studies using remotely sensed data showed that spatiotemporal patterns of the vegetative surface were connected with ENSO indicators which were linked to temperature in mid-latitude [43,116], rainfall variability, and flood in tropical Indonesia and the Amazon [49,50,117,118]. Our results showed that the patterns of the first three PCs were primarily controlled by local climatic factors, whereas the weak but substantial climatic signals associated with ENSO only can be captured by the low variance of vegetation using minor PC loadings (Figures 7-10).…”

Section: Spatiotemporal Patterns Of Vegetation Growth Associated With Land Surface Phenology and Climatic Fluctuationsmentioning

confidence: 99%

The Dynamic of Vegetation Growth with Regular Climate and Climatic Fluctuations in a Subtropical Mountainous Island, Taiwan

Wang

Chang

2021

Remote Sensing

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Vegetation phenology is an integrative indicator of environmental change, and remotely–sensed data provide a powerful way to monitor land surface vegetation responses to climatic fluctuations across various spatiotemporal scales. In this study, we synthesize the local climate, mainly temperature and precipitation, and large-scale atmospheric anomalies, El Niño-Southern Oscillation (ENSO)-connected dynamics, on a vegetative surface in a subtropical mountainous island, the northwest Pacific of Taiwan. We used two decadal photosynthetically active vegetation cover (PV) data (2001–2020) from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data to portray vegetation dynamics at monthly, seasonal, and annual scales. Results show that PV is positively related to both temperature and precipitation at a monthly timescale across various land cover types, and the log-linear with one-month lagged of precipitation reveals the accumulation of seasonal rainfall having a significant effect on vegetation growth. Using TIMESAT, three annual phenological metrics, SOS (start of growing season), EOS (end of growing season), and LOS (length of growing season), have been derived from PV time series and been related to seasonal rainfall. The delayed SOS was manifestly influenced by a spring drought, <40 mm during February–March. The later SOS led to a ramification on following late EOS, shorter LOS, and reduction of annual NPP. Nevertheless, the summer rainfall (August–October) and EOS had no significant effects on vegetation growth owing to abundant rainfall. Therefore, the SOS associated with spring rainfall, instead of EOS, played an advantageous role in regulating vegetation development in this subtropical island. The PCA (principal component analysis) was applied for PV time series and explored the spatiotemporal patterns connected to local climate and climatic fluctuations for entire Taiwan, North Taiwan, and South Taiwan. The first two components, PC1 and PC2, explained most of data variance (94–95%) linked to temporal dynamics of land cover (r > 0.90) which was also regulated by local climate. While the subtle signals of PC3 and PC4 explained 0.1–0.4% of the data variance, related to regional drought (r = 0.35–0.40) especially in central and southwest Taiwan and ENSO-associated rainfall variation (r = −0.40–−0.37). Through synthesizing the relationships between vegetation dynamics and climate based on multiple timescales, there will be a comprehensive picture of vegetation growth and its cascading effects on ecosystem productivity.

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ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data

Cited by 13 publications

References 53 publications

Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices

Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices

Spatiotemporal Rainfall Projection in Majalaya basin, West Java, Indonesia under CMIP6 Scenarios

The Dynamic of Vegetation Growth with Regular Climate and Climatic Fluctuations in a Subtropical Mountainous Island, Taiwan

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