Satellite-based meteorological drought indicator to support food security in Java Island

Siswanto, Siswanto; Wardani, Kartika Kusuma; Purbantoro, Babag; Rustanto, Andry; Zulkarnain, Faris; Anggraheni, Evi; Dewanti, Ratih; Nurlambang, Triarko; Dimyati, Muhammad

doi:10.1371/journal.pone.0260982

Cited by 10 publications

(8 citation statements)

References 48 publications

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“…Although some level of consistency between data products reported for GPCC, PERSIANN‐CDR and CHIRPS (Wei et al, 2019) and between GPCC, CRU and the University of Delaware (Wei et al, 2020) precipitation products in representing drought events in most of China, GPCC outperformed the others and data products showed heterogeneity in representing drought events over the plateau area. On the other hand, CHIRPS and TRMM showed better performance over PRESIANN precipitation data in capturing the occurrence of drought in Java Island (Siswanto et al, 2022). In the East African Lake Victoria Basin, CHIRPS was superior to PERSIANN‐CDR in representing the historical drought events (Das et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Many studies have been conducted during the last few years to evaluate the drought monitoring performance of global and quasi‐global precipitation products at different spatial scales and parts of the world (e.g., Chen et al, 2022; Das et al, 2022; Golian et al, 2019; Guo et al, 2016; Katiraie‐Boroujerdy et al, 2016; Pandey et al, 2020; Siswanto et al, 2022; Wei et al, 2019; Yan et al, 2018; Zhao & Ma, 2019; Zhong et al, 2019). For example, Zhao and Ma (2019) have found discrepancies between four precipitation products in representing the spatiotemporal characteristics of drought across the world.…”

Section: Introductionmentioning

confidence: 99%

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Drought monitoring performance of global precipitation products in three wet seasons in Ethiopia: Part I—Quasi‐objective examination

Degefu

Bewket

2023

Meteorological Applications

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The need for representative and accurate climate data such as precipitation useful for drought monitoring has been rapidly increasing among policymakers and practitioners to tackle climate‐change‐induced drought events. Hence, the objective of this article is to evaluate the drought monitoring performance of global precipitation products for the three wet seasons and rainfall regions in Ethiopia. Drought indices were calculated using the Standardized Precipitation Index (SPI) at 3‐month timescale for Belg (March–May) and Autumn (September–November) seasons and at 4‐month timescale for the Kiremt (June–September) seasons. Data products were evaluated for their accuracy in representing drought magnitude, geographical coverage and frequency using quasi‐objective (visual inspection), and frequency and correlation analysis methods. The performance of gridded precipitation products was compared against the SPI value computed for 126 reference stations and the Ethiopian satellite‐gauge merged precipitation data. Precipitation products showed different levels of performance in representing the magnitude, frequency and geographical coverage of drought events for the three wet seasons and rainfall regions. None of the data products outperformed in representing the occurrence of drought for all three wet seasons and the corresponding three rainfall regions. However, the Ethiopian merged precipitation, Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) precipitation products are relatively better than others. The study results generally indicate that no single data outperform the other precipitation products in representing the complex spatiotemporal characteristics of drought events in a mountainous region like Ethiopia.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Drought monitoring performance of global precipitation products in three wet seasons in Ethiopia: Part I—Quasi‐objective examination

Degefu

Bewket

2023

Meteorological Applications

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“…Recent research on Java Island [8] discovered that, in contrast to the SPEI, the SPI predicted drought severity with an abnormally high estimate. Another study also found that satellitebased precipitation monitoring demonstrates the possibility of anticipating meteorological drought conditions in Java and mitigating their impacts several months in advance [6].…”

Section: Introductionmentioning

confidence: 89%

“…Data from 2014 to 2018 in Central Java reveal that the link between yearly rainfall and the drought-affected area is greater than the correlation between rainfall and the flood-affected area [5]. The El Nino and a positive-phase Indian Ocean Dipole (IOD) altered meteorological droughts in the eight main rice-producing regions in Java [6].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the 2015 and 2019 Droughts in the Rice Agriculture Sector in Java

Pratiwi¹,

Murti²,

Widiastuti³

2022

Advances in Biological Sciences Research

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Drought is a natural phenomenon that occurs due to low rainfall. Drought has a slow onset character, but its effects are far-reaching. With climate change, drought events are increasing in frequency and intensity. Drought in Indonesia is related to the natural phenomena of El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Agriculture is the sector most affected by the drought. Drought can damage food crops and ultimately threaten food security. In the last decade, there have been two severe droughts, namely in 2015 and 2019. This study aimed to assess the impact of the droughts in 2015 and 2019 on rice agriculture in Java, Indonesia's main food barn. The Standardized Precipitation Evaporation Index expresses the dryness level with a time-scale of 3 months (SPEI-3) and 6 months (SPEI-6). The level of the drought was mapped to see its spatial distribution using the gaussian krigging interpolation method. The impact of drought is expressed in the area of agricultural rice land affected by drought by province. The evaluation results show that the 2015 drought had the lowest SPEI-3 value of -2.06 in September 2015, and the lowest SPEI-6 value of -1.52 occurred in January 2016. The rice agriculture land affected by drought throughout 2015 was around 246 thousand hectares. The 2019 drought lasted longer because it started in the middle of 2018. The lowest SPEI-3 value of -2.06 occurred in August 2019, and the lowest SPEI-6 value of -1.96 occurred in October 2019. The drought impacted around 120 thousand and 223 thousand hectares of rice land in 2018 and 2019, respectively. The results of the SPEI value mapping indicate that the eastern part of Java Island is experiencing a more severe drought than the western part. However, the impact of the drought on rice agriculture is more significant in the west. It indicated that the western part of Java Island is an area that is prone to drought, while the eastern part is more resilient to drought disasters.

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“…Java Island is chosen as domain for this study since Java Island is the main rice-producing island in Indonesia, as the 8.1 million hectares of rice farming land in Indonesia, about 40% of which are settled in the Java Island area [5,6]. This is due to the fertile soil on the island of Java because it is an area surrounded by active volcanoes, which is one of the factors supporting soil fertility [7].…”

Section: Introductionmentioning

confidence: 99%

The comparison of relationship between climate variables and rice productivity in the clustering area on Java Island, Indonesia

Aprilina

Sopaheluwakan

Susandi

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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This study aims to compare the relationship between climate variables and rice productivity under different irrigation systems (irrigated and rainfed) in the clustering area on Java Island, Indonesia. This study used the clustering areas resulting from the previous study. The climate variables used are bias-corrected MERRA2 data from the period 1987–2017, cropped for Java Island. The rice productivity and reference evapotranspiration data used in this study are the results of the simulation of Aquacrop modeling. The result from the cluster method used tends to divide Java Island into 2 clusters with different altitudes (lowland and highland) areas. The results show that the correlation values between the precipitation variable and rice productivity from Aquacrop simulation (both irrigated or rainfed) in cluster 1 (dominated lowlands) are higher than in cluster 2 (dominated highlands), contrary to that the correlation values between the reference evapotranspiration variable with rice productivity from Aquacrop simulation (both irrigated or rainfed) are higher in cluster 2 (dominated highlands) areas, compared to cluster 1 areas (dominated lowlands). R-square values from response surface methodology (RSM) on the rainfed system in both clusters are higher than those on the irrigated system. This indicates that rainfed agriculture is highly dependent on climate variables, especially precipitation and reference evapotranspiration variables compared with the regular irrigated agricultural system. The RSM result also shows that climate variables significantly contribute to the variation of rice productivity generated by Aquacrop modeling in irrigated and rainfed systems and in all clusters.

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Satellite-based meteorological drought indicator to support food security in Java Island

Cited by 10 publications

References 48 publications

Drought monitoring performance of global precipitation products in three wet seasons in Ethiopia: Part I—Quasi‐objective examination

Drought monitoring performance of global precipitation products in three wet seasons in Ethiopia: Part I—Quasi‐objective examination

Assessment of the 2015 and 2019 Droughts in the Rice Agriculture Sector in Java

The comparison of relationship between climate variables and rice productivity in the clustering area on Java Island, Indonesia

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