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Weather and Climate Extremes Monitoring Based on Remote Sensing Methods

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 11170

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Dr. Yuriy Kuleshov

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Australian Bureau of Meteorology, 700 Collins Street, Docklands, Melbourne, VIC 3008, Australia
Interests: climatology of severe weather phenomena (tropical cyclones, thunderstorms and lightning); climate monitoring and prediction; satellite remote sensing for climate monitoring
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Special Issue Information

Dear Colleagues,

The increased frequency and severity of extreme weather and climate events, fueled by climate change, has resulted in an increased number of disasters impacting vulnerable countries in Asia–Oceania, one of the world's most disaster-prone regions. Weather and climate extremes are of particular concern, such as tropical cyclones, hydrological extremes (droughts and floods) and their impacts.

Scientific results obtained through the World Meteorological Organization’s (WMO) Space-based Weather and Climate Extremes Monitoring (SWCEM) in the Asia-Pacific region and the Climate Risk and Early Warning Systems (CREWS) activities will be central to this Special Issue. Proactive disaster responses maximize disaster risk reduction and preparation efforts in non-disaster periods. In this Special Issue, reports on proactive adaptation strategies for addressing the impacts of natural hazards, such as droughts, floods and tropical cyclones, will be presented, with a particular focus on two key climate adaptation strategies—climate risk assessments and early warning systems.

This Special Issue aims to include papers which discuss, but are not limited to, the following topics:

Remote sensing applications for monitoring extreme weather and climate events, e.g., drought, floods, tropical cyclones;
Remote sensing applications for climate risk assessments.

Prof. Dr. Yuriy Kuleshov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing
weather and climate extremes
Asia–Oceania
climate risk assessments
natural hazards
tropical cyclones
floods
droughts

Published Papers (7 papers)

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20 pages, 14650 KiB

Open AccessArticle

Towards Improved Satellite Data Utilization in China: Insights from an Integrated Evaluation of GSMaP-GNRT6 in Rainfall Patterns

by Zunya Wang and Qingquan Li

Remote Sens. 2024, 16(5), 755; https://doi.org/10.3390/rs16050755 - 21 Feb 2024

Viewed by 467

Abstract

To improve the utilization of satellite-based data and promote their development, this analysis comprehensively evaluates the performance of GSMaP Near-real-time Gauge-adjusted Rainfall Product version 6 (GSMaP_GNRT6) data in depicting precipitation over China from 2001 to 2020 by comparing four precipitation indices—accumulated precipitation, number of rainy days and rainstorm days, and precipitation maxima—with daily precipitation data from 2419 stations across China on monthly and annual time scales. The results show that the GSMaP-GNRT6 data effectively capture the overall spatial pattern of the four precipitation indices, but biases in the spatial distribution of the number of rainy days from July to September and the precipitation maxima during the wintertime are evident. A general underestimation of GSMaP-GNRT6 data is observed in the average for China. The annual precipitation amount, the number of rainy days and rainstorm days, and the precipitation maxima based on the GSMaP-GNRT6 data are 17.6%, 35.5%, 31.6% and 11.8% below the station observations, respectively. The GSMaP-GNRT6 data better depict the precipitation in eastern China, with the errors almost halved. And obvious overestimation of the number of rainstorm days and precipitation maxima occurs in western China, reaching up to 60%. Regarding the accumulated precipitation, the number of rainstorm days and the precipitation maxima, the GSMaP-GNRT6 data show an almost consistent interannual variation and increasing trends that are consistent with the station observations. However, the magnitude of the increasing trend based on the GSMaP-GNRT6 data is larger, especially at the beginning of the 21st century. Conversely, a considerable discrepancy in the annual variation and an almost opposite trend can be observed in the number of rainy days between the GSMaP-GNRT6 data and the station observations. Full article

(This article belongs to the Special Issue Weather and Climate Extremes Monitoring Based on Remote Sensing Methods)

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23 pages, 6656 KiB

Open AccessArticle

Remote Sensing-Based Outdoor Thermal Comfort Assessment in Local Climate Zones in the Rural–Urban Continuum of eThekwini Municipality, South Africa

by Terence Darlington Mushore, John Odindi, Rob Slotow and Onisimo Mutanga

Remote Sens. 2023, 15(23), 5461; https://doi.org/10.3390/rs15235461 - 22 Nov 2023

Viewed by 879

Abstract

Due to the need to continuously monitor and understand the thermal environment and its socioeconomic implications, this study used remotely sensed data to analyze thermal comfort variation in LCZs, including along the rural to urban gradient of the eThekwini Municipality in KwaZulu-Natal province of South Africa. LCZs were mapped using multi-temporal and multi-spectral Landsat 8 and Landsat 9 data using the approach by World Urban Database and Access Portal Tools (WUDAPT), while thermal data were used to retrieve land surface temperatures (LSTs). Data for training classification of LCZs and accuracy assessment were digitized from GoogleEarth guided by knowledge gained and data collected during a field survey in March 2022 as well as pre-existing maps. LCZs were mapped using the random forest classifier in SAGA GIS software while a single channel algorithm based on band 10 was used to compute LST for different scenes. The LSTs were adjusted and further used to derive thermal comfort based on the Universal Thermal Comfort Index (UTCI) categories as an indicator for outdoor thermal comfort on the extremely low- and extremely high-temperature periods in the cool and hot seasons, respectively. LCZs were mapped with high accuracy (overall accuracy of 90.1% and kappa of 0.88) while inter-class separability was high (>1.5) for all LCZ pairs. Built-up LCZs dominate the eastern parts of the municipality, signifying the influence of the sea on development within the area. Average LST was coolest in the dense forest, open low-rise and water LCZs in the cool and hot seasons, respectively. The compact high-rise LCZ was the warmest in both the hot (36 °C) and the cool (23 °C) seasons. The sea sands were among coolest regions in both seasons due to their high water content, attributed to their high water table and close proximity to the ocean. There was no thermal stress during the cool season, while most areas recorded moderate to strong heat stress in the hot season. Some areas in the densely built-up LCZs recorded very strong heat stress in the hot season. The findings suggest that policies and strategies should enhance heat mitigation capacities in strong-heat-stress areas during the hot season. Municipal authorities and citizens must work together to build strategies to minimize temperature extremes and associated socioeconomic pressures. Urban development policies, plans and strategies should consider implications on the thermal environment as well as the value of conservation of LCZs with high-heat mitigation value such as dense forests and expansion of built-up LCZs with low-heat absorption levels such as open low-rise. The study was based mainly on remotely sensed temperatures with some ground data used to validate results, which may limit the assessment. Overall, the study provides insights towards achievement of global sustainable and climate-smart development targets. Full article

(This article belongs to the Special Issue Weather and Climate Extremes Monitoring Based on Remote Sensing Methods)

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20 pages, 10974 KiB

Open AccessArticle

Comprehensive Evaluation of High-Resolution Satellite Precipitation Products over the Qinghai–Tibetan Plateau Using the New Ground Observation Network

by Zhaofei Liu

Remote Sens. 2023, 15(13), 3381; https://doi.org/10.3390/rs15133381 - 02 Jul 2023

Viewed by 907

Abstract

Satellite precipitation products (SPPs) have been widely evaluated at regional scales. However, there have been few quantitative comprehensive evaluations of SPPs using multiple indices. Ten high-resolution SPPs were quantitatively and comprehensively evaluated from precipitation occurrence and series indices using an improved rank score (RS) method in the data-scarce Qinghai–Tibetan Plateau (QTP). The new observation network, along with a number of national basic stations, was applied for SPP evaluation to obtain more reliable results. The results showed that the GPM and MSWEP showed the strongest overall performance, with an RS value of 0.75. CHIRPS and GPM had the strongest performance at measuring precipitation occurrence (RS = 0.92) and series (RS = 0.75), respectively. The optimal SPPs varied in evaluation indices, but also concentrated in the MSWEP, GPM, and CHIRPS. The bias of SPPs was markedly in the QTP, with relative error generally between −80% and 80%. In general, most SPPs showed the ability to detect precipitation occurrence. However, the SPPs showed relatively weak performance at measuring precipitation series. The mean Kling–Gupta efficiency of all stations was <0.50 for each SPP. The SPPs showed better performance in monsoon-affected regions, which mainly include the Yangtze, Yellow, Nu–Salween, Lancang–Mekong, Yarlung Zangbo–Bramaputra, and Ganges river basins. Performance was relatively poor in the westerly circulation areas, which mainly include the Tarim, Indus, and QTP inland river basins. The performance of SPPs showed a seasonal pattern during the year for most occurrence indices. The performance of SPPs in different periods was opposite in different indices. Therefore, multiple indices representing different characteristics are recommended for the evaluation of SPPs to obtain a comprehensive evaluation result. Overall, SPP measurement over the QTP needs further improvement, especially with regard to measuring precipitation series. The proposed improved RS method can also potentially be applied for comprehensive evaluation of other products and models. Full article

(This article belongs to the Special Issue Weather and Climate Extremes Monitoring Based on Remote Sensing Methods)

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19 pages, 4745 KiB

Open AccessArticle

Multi-Hazard Tropical Cyclone Risk Assessment for Australia

by Cameron Do and Yuriy Kuleshov

Remote Sens. 2023, 15(3), 795; https://doi.org/10.3390/rs15030795 - 31 Jan 2023

Cited by 5 | Viewed by 4188

Abstract

Tropical cyclones (TCs) have long posed a significant threat to Australia’s population, infrastructure, and environment. This threat may grow under climate change as projections indicate continuing rises in sea level and increases in rainfall during TC events. Previous Australian TC risk assessment efforts have focused on the risk from wind, whereas a holistic approach requires multi-hazard risk assessments that also consider impacts of other TC-related hazards. This study assessed and mapped TC risk nationwide, focusing on the impacts on population and infrastructure from the TC-related hazards of wind, storm surges, flooding, and landslides. Risk maps were created at the Local Government Area (LGA) level for all of Australia, using collated data on multiple hazards, exposure, and vulnerability. The results demonstrated that the risk posed by all hazards was highest for coastal LGAs of eastern Queensland and New South Wales, followed by medium risk across Northern Territory and north-western Western Australia. Further enhancement and validation of risk maps developed in this study will provide decision makers with the information needed to reduce TC risk, save lives, and prevent damage to infrastructure. Full article

(This article belongs to the Special Issue Weather and Climate Extremes Monitoring Based on Remote Sensing Methods)

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18 pages, 4489 KiB

Open AccessArticle

Assessment of Tropical Cyclone Risk to Coral Reefs: Case Study for Australia

by Cameron Do, Georgia Elizabeth Saunders and Yuriy Kuleshov

Remote Sens. 2022, 14(23), 6150; https://doi.org/10.3390/rs14236150 - 04 Dec 2022

Cited by 4 | Viewed by 1895

Abstract

In this study, we attempt to expand tropical cyclone (TC) risk assessment methodology and build an understanding of TC risk to Australia’s natural environment by focusing on coral reefs. TCs are natural hazards known to have the potential to bring destruction due to associated gale-force winds, torrential rain, and storm surge. The focus of TC risk assessment studies has commonly centred around impacts on human livelihoods and infrastructure exposed to TC events. In our earlier study, we created a framework for assessing multi-hazard TC risk to the Australian population and infrastructure at the Local Government Area level. This methodology is used in this study with coral reefs as the focus. TC hazard, exposure, and vulnerability indices were created from selected coral-related datasets to calculate an overall TC risk index for the Ningaloo Reef (NR) and the Great Barrier Reef (GBR) regions. The obtained results demonstrate that the northern NR and the southern GBR had the highest risk values within the study area; however, limitations in data quality have meant that results are estimates at best. The study has shown the potential benefits of such a TC risk assessment framework that can be improved upon, as coral data collection becomes more readily available. Full article

(This article belongs to the Special Issue Weather and Climate Extremes Monitoring Based on Remote Sensing Methods)

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19 pages, 748 KiB

Open AccessArticle

Improving Methodology for Tropical Cyclone Seasonal Forecasting in the Australian and the South Pacific Ocean Regions by Selecting and Averaging Models via Metropolis–Gibbs Sampling

by Guoqi Qian, Lizhong Chen and Yuriy Kuleshov

Remote Sens. 2022, 14(22), 5872; https://doi.org/10.3390/rs14225872 - 19 Nov 2022

Cited by 1 | Viewed by 998

Abstract

A novel model selection and averaging approach is proposed—through integrating the corrected Akaike information criterion (AICc), the Gibbs sampler, and the Poisson regression models, to improve tropical cyclone seasonal forecasting in the Australian and the South Pacific Ocean regions and sub-regions. It has been found by the new approach that indices which describe tropical cyclone inter-annual variability such as the Dipole Mode Index (DMI) and the El Niño Modoki Index (EMI) are among the most important predictors used by the selected models. The core computational method underlying the proposed approach is a new stochastic search algorithm that we have developed, and is named Metropolis–Gibbs random scan (MGRS). By applying MGRS to minimize AICc over all candidate models, a set of the most important predictors are identified which can form a small number of optimal Poisson regression models. These optimal models are then averaged to improve their overall predictability. Results from our case study of tropical cyclone seasonal forecasting show that the MGRS-AICc method performs significantly better than the commonly used step-wise AICc method. Full article

(This article belongs to the Special Issue Weather and Climate Extremes Monitoring Based on Remote Sensing Methods)

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15 pages, 10590 KiB

Open AccessTechnical Note

by Wei Wei, Ting Liu, Liang Zhou, Jiping Wang, Peng Yan, Binbin Xie and Junju Zhou

Remote Sens. 2023, 15(18), 4362; https://doi.org/10.3390/rs15184362 - 05 Sep 2023

Cited by 3 | Viewed by 1080

Abstract

Vegetation is one of the most important indicators of climate change, as it can show regional change in the environment. Vegetation health is affected by various factors, including drought, which has cumulative and time-lag effects on vegetation response. However, the cumulative and time-lag effects of drought on different terrestrial vegetation in China are still unclear. To address this issue, this study examined the cumulative and time-lag effects of drought on vegetation from 2001 to 2020 using the Standardized Precipitation Evapotranspiration Index (SPEI) in the Global SPEI database and the Normalized Difference Vegetation Index (NDVI) in MOD13A3. Based on Sen-Median trend analysis and the Mann–Kendall test, the change trend and significance of the NDVI from 2001 to 2020 were explored. The Pearson correlation coefficient was used to analyze the correlation between the SPEI and NDVI at each cumulative scale and time-lag scale and to further analyze the cumulative and time-lag effects of drought on vegetation. The results show the following: (1) The NDVI value increased at a rate of 0.019/10 years, and the increased area of the NDVI accounted for 80.53% of mainland China, with a spatial trend of low values in the west and high values in the east. (2) The average SPEI cumulative time scale most relevant to the NDVI was 7.3 months, and the cumulative effect demonstrated a high correlation at the scale of 9–12 months and revealed different distributions in different areas. The cumulative effect was widely distributed at the 9-month scale, followed by the 12-month scale. The correlation coefficients of cumulative effects between the SPEI and NDVI for cropland, woodland and grassland peaked at 9 months. (3) The average SPEI time-lag scale for the NDVI was 6.9 months, and the time-lag effect had the highest correlation coefficient at the 7-month scale. The strongest time-lag effect for cropland and grassland was seen at 7 months, while the strongest time-lag effect for woodland was seen at 6 months. Woodland had a lower time-lag effect than grassland at different scales. The research results are significant for their use in aiding the scientific response to drought disasters and making decisions for climate change precautions. Full article

(This article belongs to the Special Issue Weather and Climate Extremes Monitoring Based on Remote Sensing Methods)

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