Time Varying Spatial Downscaling of Satellite-Based Drought Index

Chu, Hsin; Wijayanti, Regita Faridatunisa; Jaelani, Lalu Muhamad; Tsai, Hui Ping

doi:10.3390/rs13183693

Cited by 7 publications

(8 citation statements)

References 43 publications

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“…The monthly GRACE-based GWS changes provide sufficient information on the temporal groundwater variations in the study area. Generally, the transformation from GWS change to groundwater level is a spatial function [24]. Groundwater level maps can be obtained from spatial regression-based data fusion.…”

Section: Fusion Of Grace and Groundwater Level Datamentioning

confidence: 99%

“…Scanlon et al (2018) considered GRACE data and various global hydrological models including GLDAS to assess the impacts of climate and humans on water resources [23]. The previous study proposed a fusion method incorporating spatially and temporally varying relations of data [24]. This study will consider the data fusion based on in situ groundwater observation and GRACE-based GWS.…”

Section: Introductionmentioning

confidence: 99%

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Converting Seasonal Measurements to Monthly Groundwater Levels through GRACE Data Fusion

2023

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Groundwater depletion occurs when the extraction exceeds its recharge and further impacts water resource management around the world, especially in developing countries. In India, most groundwater level observations are only available on a seasonal scale, i.e., January (late post-monsoon), May (pre-monsoon), August (monsoon), and November (early post-monsoon). The Gravity Recovery and Climate Experiment (GRACE) data are available to estimate the monthly variation in groundwater storage (GWS) by subtracting precipitation runoff, canopy water, soil moisture, and solid water (snow and ice) from the GLDAS model. Considering GRACE-based GWS data, the data fusion is further used to estimate monthly spatial maps of groundwater levels using time-varying spatial regression. Seasonal groundwater monitoring data are used in the training stage to identify spatial relations between groundwater level and GWS changes. Estimation of unknown groundwater levels through data fusion is accomplished by utilizing spatial coefficients that remain consistent with the nearest observed months. Monthly groundwater level maps show that the lowest groundwater level is 50 to 55 m below the earth’s surface in the state of Rajasthan. The accuracy of the estimated groundwater level is validated against observations, yielding an average RMSE of 2.37 m. The use of the GWS information enables identification of monthly spatial patterns of groundwater levels. The results will be employed to identify hotspots of groundwater depletion in India, facilitating efforts to mitigate the adverse effects of excessive groundwater extraction.

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Section: Fusion Of Grace and Groundwater Level Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Converting Seasonal Measurements to Monthly Groundwater Levels through GRACE Data Fusion

2023

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“…In addition, satellite-derived rainfall products present uncertainties, as they are produced from multiple sensors or in combination with in-situ observations [135]. A recent study developed method strategies to enhance the spatial resolution of satellite-based precipitation products in the region [143], but more efforts are needed in order to address the issues for more accurate and detailed drought monitoring.…”

Section: Meteorological Droughtsmentioning

confidence: 99%

A Review of Earth Observation-Based Drought Studies in Southeast Asia

Huth

Bachofer

et al. 2022

Remote Sensing

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Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region.

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“…Drought can be categorized into four types: meteorological, hydrological, agricultural, and socioeconomic drought [3]. The drought index is an essential means to monitor drought [4]. In order to reflect different types of droughts, domestic and foreign scholars have constructed hundreds of drought indices [5].…”

Section: Introductionmentioning

confidence: 99%

Copula-Based Joint Drought Index Using Precipitation, NDVI, and Runoff and Its Application in the Yangtze River Basin, China

Wei,

Liu,

Hua

et al. 2023

Remote Sensing

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Drought monitoring ensures the Yangtze River Basin’s social economy and agricultural production. Developing a comprehensive index with high monitoring precision is essential to enhance the accuracy of drought management strategies. This study proposes the standardized comprehensive drought index (SCDI) using a novel approach that utilizes the joint distribution of C-vine copula to effectively combine three critical drought factors: precipitation, NDVI, and runoff. The study analyzes the reliability and effectiveness of the SCDI in detecting drought events through quantitative indicators and assesses its applicability in the Yangtze River Basin. The findings are as follows: (1) The SCDI is a highly reliable and applicable drought index. Compared to traditional indices like the SPI, VCI, and SRI, it has a consistency rate of over 67% and can detect drought events in more sensitive months by over 51%. It has a low false negative rate of only 2% and a false positive rate of 0%, making it highly accurate. The SCDI is also applicable to all the third-level sub-basins of the Yangtze River Basin, making it a valuable tool for regional drought monitoring. (2) The time lag effect of the NDVI can affect the sensitivity of the SCDI. When the NDVI time series data are shifted forward by one month, the sensitivity of the SCDI in detecting agricultural drought improves from 47.8% to 53%. (3) The SDCI can assist in monitoring drought patterns in the Yangtze River Basin. From 2001 to 2018, the basin saw fluctuations in drought intensity, with the worst in December 2008. The western region had less frequent but more intense and prolonged droughts, while the eastern part had more frequent yet less severe droughts.

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Time Varying Spatial Downscaling of Satellite-Based Drought Index

Cited by 7 publications

References 43 publications

Converting Seasonal Measurements to Monthly Groundwater Levels through GRACE Data Fusion

Converting Seasonal Measurements to Monthly Groundwater Levels through GRACE Data Fusion

A Review of Earth Observation-Based Drought Studies in Southeast Asia

Copula-Based Joint Drought Index Using Precipitation, NDVI, and Runoff and Its Application in the Yangtze River Basin, China

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