Remote Sensing of Water-Related Hazards. Группа авторов
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Название: Remote Sensing of Water-Related Hazards
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: География
isbn: 9781119159148
isbn:
Drought poses signicant risks to ecosystems and human society. Effective drought monitoring tools are important for scientists to understand the underlying drives and mechanisms and for decision makers to develop operational strategies for drought management and mitigation.
In chapter 9, Li et al. review the current and emerging drought monitoring approaches based on remote sensing techniques. They present a case study on using remotely sensed precipitation and vegetation data for meteorological and agricultural drought monitoring in Jiangsu Province, China. This chapter also discusses the major gaps, challenges, and potential future research directions for remote sensing of drought.
In chapter 10, Zhang et al. review the definition and quantification of drought and mechanisms that control vegetation responses to it. They then introduce two methods to detect vegetation stress under drought disturbances based on remote sensing of vegetation and hydroclimatology. Yunnan Province, a key ecozone in China, is presented as a case study area to analyze drought dynamics and their impacts on ecosystem water use efficiency. This provides a valuable reference for studying and monitoring vegetation responses to drought using spaceborne optical and near‐infrared sensors.
Physical water scarcity indicates the lack of sufficient water resources to meet human and environmental demands. In chapter 11, Hasan and Tarhule present a study on quantifying physical water scarcity based on the water scarcity indicator derived from the Gravity Recovery and Climate Experiment (GRACE) data. They introduce an approach for quantifying potentially available water through the integration of the GRACE data, reanalysis precipitation, and total evapotranspiration estimates using the Noah land surface model.
In chapter 12, Lv and Ma examine water cycle variation in the Yellow River Basin using both satellite remote sensing and numerical modeling. Considering that current evapotranspiration (ET) products have large uncertainties and irrigation effects on ET are not well represented, this chapter describes a method to reconstruct ET from the Global Land Data Assimilation System (GLDAS) land surface models using observation‐based precipitation, streamflow, and irrigation water. The chapter then describes the attribution analyses of changes in streamflow and GRACE‐derived terrestrial water storage in response to climate, watere cycle, and land use/cover changes. This line of research can provide guidance for the river basin water resource management and risk assessment.
Climate variability and change have dramatically influenced some agricultural areas, including the historic 5‐year drought in California’s San Joaquin Valley and 20‐year drier‐than‐normal conditions in the Red River Valley of the midwestern USA. One impact of protracted droughts is increased soil salinity in the root zone of agricultural areas. Inventorying and monitoring soil salinity is crucial to evaluate the extent of the problem, recognize the trends, and formulate irrigation and crop management strategies for maintaining a sustainable agricultural productivity. In chapter 13, Corwin and Scudiero provide an overview of salinity assessment technologies, including proximal sensor and satellite remote sensing methodologies at multiple scales.
This collection of chapters provides an up‐to‐date overview of the available data, sensors, models, and indicators developed for monitoring and predicting various kinds of water hazards, with case studies drawn from different parts of the world. By presenting contributions from a diverse group of scientists, we hope to bridge some of the gaps between the various disciplines engaged in the remote sensing of hazards.
REFERENCES
1 AghaKouchak, A., Cheng, L. Y., Mazdiyasni, O., & Farahmand, A. (2014). Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophys. Res. Lett., 41(24), 8847–8852.
2 AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., & Hain, C. R. (2015). Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics, 53(2), 452–480.
3 AghaKouchak, A., et al. (2021). Anthropogenic drought: definition, challenges, and opportunities. Reviews of Geophysics, 59. doi:10.1029/2019RG000683
4 Andaru, R., Rau, J. Y., Syahbana, D. K., Prayoga, A. S., & Purnamasari, H. D. (2021). The use of UAV remote sensing for observing lava dome emplacement and areas of potential lahar hazards: An example from the 2017–2019 eruption crisis at Mount Agung in Bali. J Volcanol Geoth Res, 415. doi:10.1016/j.jvolgeores.2021.107255
5 Andreas, H., Abidin, H. Z., Sarsito, D. A., & Pradipta, D. (2020). Remotes sensing capabilities on land subsidence and coastal water hazard and disaster studies. Fifth International Conferences of Indonesian Society for Remote Sensing: The Revolution of Earth Observation for a Better Human Life, 500. doi:10.1088/1755‐1315/500/1/012036
6 Argaz, A., Ouahman, B., Darkaoui, A., Bikhtar, H., Ayouch, E., & Lazaar, R. (2019). Flood hazard mapping using remote sensing and GIS Tools: A case study of Souss Watershed. Journal of Materials and Environmental Sciences, 10(2), 170–181.
7 Bech, J., & Chau, J. L. (2012). Doppler radar observations: Weather radar, wind profiler, ionospheric radar, and other advanced applications. Rijeka, Croatia: InTech.
8 Boni, G., De Angeli, S., Taramasso, A. C., & Roth, G. (2020). Remote sensing‐based methodology for the quick update of the assessment of the population exposed to natural hazards. Remote Sens., 12(23). doi:10.3390/rs12233943
9 Casas, A., Riano, D., Ustin, S. L., Dennison, P., & Salas, J. (2014). Estimation of water‐related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response. Remote Sens. Environ., 148, 28–41.
10 Du, J., Kimball, J. S., & Jones, L. A. (2016). Passive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR‐E. IEEE Transactions on Geoscience and Remote Sensing, 54(1), 597–608.
11 Du, L., Mikle, N., Zou, Z. H., Huang, Y. Y., Shi, Z., Jiang, L. F., et al. (2018). Global patterns of extreme drought‐induced loss in land primary production: Identifying ecological extremes from rain‐use efficiency. Sci. Total Environ., 628–629, 611–620. doi:10.1016/j.scitotenv.2018.02.114
12 Dubovyk, O., Ghazaryan, G., Gonzalez, J., Graw, V., Low, F., & Schreier J. (2019). Drought hazard in Kazakhstan in 2000–2016: A remote sensing perspective. Environmental Monitoring and Assessment, 191(8). doi:10.1007/s10661‐019‐7620‐z
13 Ehrler, C., Seidel, K., & Martinec, J. (1997). Advanced analysis of snow cover based on satellite remote sensing for the assessment of water resources. Remote Sensing and Geographic Information Systems for Design and Operation of Water Resources Systems, 242, 93–101.
14 Elsadek, W. M., Ibrahim, M. G., & Mahmod, W. E. (2019). Runoff hazard analysis of Wadi Qena Watershed, Egypt based on GIS and remote sensing approach. Alex Eng J, 58(1), 377–385. doi:10.1016/j.aej.2019.02.001
15 Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., et al. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704–716.
16 Franci, F., Mandanici, E., & Bitelli, G. (2015). Remote sensing analysis for flood risk management in urban sprawl contexts. Geomatics, Natural Hazards and Risk, 6, 583–599. СКАЧАТЬ