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Landslides are destructive processes causing casualties and damage worldwide. The majority of the landslides are triggered by intense and/or prolonged rainfall. Therefore, the prediction of the occurrence of rainfall-induced landslides is an important scientific and social issue. To mitigate the risk posed by rainfall-induced landslides, landslide early warning systems (LEWS) can be built and applied at different scales as effective non-structural mitigation measures. Usually, the core of a LEWS is constituted of a mathematical model that predicts landslide occurrence in the monitored areas. In recent decades, rainfall thresholds have become a widespread and well established technique for the prediction of rainfall-induced landslides, and for the setting up of prototype or operational LEWS. A rainfall threshold expresses, with a mathematic law, the rainfall amount that, when reached or exceeded, is likely to trigger one or more landslides. Rainfall thresholds can be defined with relatively few parameters and are very straightforward to operate, because their application within LEWS is usually based only on the comparison of monitored and/or forecasted rainfall. This Special Issue collects contributions on the recent research advances or well-documented applications of rainfall thresholds, as well as other innovative methods for landslide prediction and early warning. Contributions regarding the description of a LEWS or single components of LEWS (e.g., monitoring approaches, forecasting models, communication strategies, and emergency management) are also welcome.

Research & information: general --- loess landslide --- DAN-W --- numerical simulation --- dynamic analysis --- rainfall thresholds --- Bhutan --- shallow landslides --- landslides --- Idukki --- early warning system --- landslide hazard --- antecedent rainfall threshold --- landslide susceptibility --- satellite-derived rainfall --- TRMM Multisatellite Precipitation Analysis 3B42 (TMPA) --- tropical Africa --- rainfall --- thresholds --- physicallybased model --- hydrological monitoring --- soil water index --- large-scale landslide --- SWI-D threshold --- shallow landslide --- temporal probability --- landslide and debris flow --- China --- quantile regression --- Wayanad --- early warning --- GIS --- rainfall intensity --- optimization --- rainfall thresholds calculation --- mean annual rainfall --- lithology --- Slovenia --- loess landslide --- DAN-W --- numerical simulation --- dynamic analysis --- rainfall thresholds --- Bhutan --- shallow landslides --- landslides --- Idukki --- early warning system --- landslide hazard --- antecedent rainfall threshold --- landslide susceptibility --- satellite-derived rainfall --- TRMM Multisatellite Precipitation Analysis 3B42 (TMPA) --- tropical Africa --- rainfall --- thresholds --- physicallybased model --- hydrological monitoring --- soil water index --- large-scale landslide --- SWI-D threshold --- shallow landslide --- temporal probability --- landslide and debris flow --- China --- quantile regression --- Wayanad --- early warning --- GIS --- rainfall intensity --- optimization --- rainfall thresholds calculation --- mean annual rainfall --- lithology --- Slovenia

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- Water resources management should be assessed under climate change conditions, as historic data cannot replicate future climatic conditions. - Climate change impacts on water resources are bound to affect all water uses, i.e., irrigated agriculture, domestic and industrial water supply, hydropower generation, and environmental flow (of streams and rivers) and water level (of lakes). - Bottom-up approaches, i.e., the forcing of hydrologic simulation models with climate change models’ outputs, are the most common engineering practices and considered as climate-resilient water management approaches. - Hydrologic simulations forced by climate change scenarios derived from regional climate models (RCMs) can provide accurate assessments of the future water regime at basin scales. - Irrigated agriculture requires special attention as it is the principal water consumer and alterations of both precipitation and temperature patterns will directly affect agriculture yields and incomes. - Integrated water resources management (IWRM) requires multidisciplinary and interdisciplinary approaches, with climate change to be an emerging cornerstone in the IWRM concept.

Precipitation --- Tropical Rainfall Measurement Mission (TRMM) --- Multi-Satellite Precipitation Analysis (TMPA) --- Upper Indus Basin (UIB) --- Himalaya --- streamflow --- extreme rainfall --- watershed --- dynamics of saline lakes --- extremely changing points --- extreme weather --- temporal trend --- climate change --- salinization --- water resources management --- drinking water --- debris --- water balance --- climatic change --- dam capacity --- simulation of sediment transport --- Athabasca River --- climate projection --- hydrologic modelling --- peak-flow --- return period --- stationary analysis --- non-stationary analysis --- global --- temperature --- precipitation --- Net Irrigation Water Requirement --- maize --- hydrologic modeling --- reanalysis gridded datasets --- ERA-Interim --- Balkan Peninsula

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Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.

satellite radiance --- WRF-Hydro --- meteorological radar --- QPE --- microstructure of rain --- TMPA --- evaluation --- precipitation --- volume matching --- CFSR --- GMI --- terminal velocity --- TRMM-TMPA --- surface rain intensity --- retrieval algorithm --- rain gauges --- tropical cyclone --- CMORPH --- T-Matrix --- Global Precipitation Measurement (GPM) --- statistical evaluation --- vertical air velocity --- heavy rainfall prediction --- GPM IMERG v5 --- Tianshan Mountains --- Red River Basin --- precipitation retrieval --- satellite precipitation --- PERSIANN-CCS --- validation network --- PEMW --- satellite rainfall estimate --- high latitude --- Cyprus --- GPM --- wet deposition --- CloudSat --- thundercloud --- GPS --- satellite remote sensing --- assessment --- numerical weather prediction --- mineral dust --- complex terrain --- mesoscale precipitation patterns --- GNSS meteorology --- lumped models --- satellites --- Southern China --- error analysis --- topography --- cloud scavenging --- radar reflectivity–rain rate relationship --- CHAOS --- RADOLAN --- hydrometeor classification --- TRMM --- thunderstorm --- CHIRPS --- satellite precipitation retrieval --- GPM/IMERG --- GSMaP --- bias correction --- Precise Point Positioning --- Mainland China --- supercooled droplets detection --- SEID --- Saharan dust transportation --- Huaihe River basin --- GPM Microwave Imager --- satellite --- TMPA 3B42RT --- forecast model --- quality indexes --- SEVIRI --- radiometer --- triple collocation --- satellite precipitation product --- Mandra --- synoptic weather types --- drop size distribution (DSD) --- Amazon Basin --- weather radar --- X-band radar --- downscaling --- precipitation rate --- neural networks --- rain rate --- CMIP --- GPM-era IMERG --- GR models --- weather --- typhoon --- satellite rainfall retrievals --- TRMM 3B42 v7 --- validation --- low-cost receivers --- rainfall retrieval techniques --- snowfall detection --- GPM satellite --- Zenith Tropospheric Delay --- 3B42 --- hurricane Harvey --- PERSIANN_CDR --- TRMM 3B42 V7 --- snow water path retrieval --- DPR --- satellite precipitation adjustment --- Peninsular Spain --- RMAPS --- daily rainfall estimations --- streamflow simulation --- regional climate models --- Red–Thai Binh River Basin --- Ensemble Precipitation (EP) algorithm --- cloud radar --- disdrometer --- TRMM-era TMPA --- hydrometeorology --- MSG --- radar data assimilation --- dust washout process --- runoff simulations --- geostationary microwave sensors --- radar --- topographical and seasonal evaluation --- goGPS --- XPOL radar --- TMPA 3B42V7 --- telemetric rain gauge --- harmonie model --- tropical storm rainfall --- linear-scaling approach --- Milešovka observatory --- precipitable water vapor --- heavy precipitation --- hydrological simulation --- reflectivity --- Ka-band --- Tibetan Plateau --- satellite rainfall estimates --- regional rainfall regimes --- Lai Nullah --- microwave scattering --- remote sensing --- pre-processing --- rainfall rate --- MSWEP --- climatology --- VIC model --- CMORPH_CRT --- IMERG --- single frequency GNSS --- PERSIANN --- flood-inducing storm --- climate models --- Pakistan --- precipitating hydrometeor --- data assimilation --- rainfall --- kriging with external drift --- dual-polarization --- quantitative precipitation estimates --- flash flood --- Satellite Precipitation Estimates --- gridded radar precipitation --- regional rainfall sub-regimes --- polar systems --- Environmental engineering. --- Environmental monitoring. --- Biomonitoring (Ecology) --- Ecological monitoring --- Environmental quality --- Monitoring, Environmental --- Applied ecology --- Environmental engineering --- Pollution --- Environmental control --- Environmental effects --- Environmental stresses --- Engineering --- Environmental health --- Environmental protection --- Sustainable engineering --- Measurement --- Monitoring

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- Water resources management should be assessed under climate change conditions, as historic data cannot replicate future climatic conditions. - Climate change impacts on water resources are bound to affect all water uses, i.e., irrigated agriculture, domestic and industrial water supply, hydropower generation, and environmental flow (of streams and rivers) and water level (of lakes). - Bottom-up approaches, i.e., the forcing of hydrologic simulation models with climate change models’ outputs, are the most common engineering practices and considered as climate-resilient water management approaches. - Hydrologic simulations forced by climate change scenarios derived from regional climate models (RCMs) can provide accurate assessments of the future water regime at basin scales. - Irrigated agriculture requires special attention as it is the principal water consumer and alterations of both precipitation and temperature patterns will directly affect agriculture yields and incomes. - Integrated water resources management (IWRM) requires multidisciplinary and interdisciplinary approaches, with climate change to be an emerging cornerstone in the IWRM concept.

Research & information: general --- Precipitation --- Tropical Rainfall Measurement Mission (TRMM) --- Multi-Satellite Precipitation Analysis (TMPA) --- Upper Indus Basin (UIB) --- Himalaya --- streamflow --- extreme rainfall --- watershed --- dynamics of saline lakes --- extremely changing points --- extreme weather --- temporal trend --- climate change --- salinization --- water resources management --- drinking water --- debris --- water balance --- climatic change --- dam capacity --- simulation of sediment transport --- Athabasca River --- climate projection --- hydrologic modelling --- peak-flow --- return period --- stationary analysis --- non-stationary analysis --- global --- temperature --- precipitation --- Net Irrigation Water Requirement --- maize --- hydrologic modeling --- reanalysis gridded datasets --- ERA-Interim --- Balkan Peninsula --- Precipitation --- Tropical Rainfall Measurement Mission (TRMM) --- Multi-Satellite Precipitation Analysis (TMPA) --- Upper Indus Basin (UIB) --- Himalaya --- streamflow --- extreme rainfall --- watershed --- dynamics of saline lakes --- extremely changing points --- extreme weather --- temporal trend --- climate change --- salinization --- water resources management --- drinking water --- debris --- water balance --- climatic change --- dam capacity --- simulation of sediment transport --- Athabasca River --- climate projection --- hydrologic modelling --- peak-flow --- return period --- stationary analysis --- non-stationary analysis --- global --- temperature --- precipitation --- Net Irrigation Water Requirement --- maize --- hydrologic modeling --- reanalysis gridded datasets --- ERA-Interim --- Balkan Peninsula

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Water resources are the most valuable resources of sustainable socio-economic development, which is significantly affected by climate change and human activities. Water resources assessment is an urgent need for implementation of the perfect water resources management, but it is difficult to accurately evaluate the quantity and quality of water resources, especially in arid regions and high-altitude regions with sparse gauged data. This book hosts 24 papers devoted to remote sensing in hydrology and water resources management, which summarizes the recent advancement in remote sensing technology for hydrology analysis such as satellite remote sensing for water resources management, water quality monitoring and evaluation using remote sensing data, remote sensing for detecting the global impact of climate extremes, the use of remote sensing data for improved calibration of hydrological models, and so on. In general, the book will contribute to promote the application of remote sensing technology in water resources.

Research & information: general --- precipitation datasets --- evaluation --- spatial scale --- temporal scale --- climate --- Yellow River Basin --- data assimilation --- WRF --- WRFDA --- 3DVar --- water levels --- surface areas --- volume variations --- hypsometry --- bathymetry --- lakes --- reservoirs --- remote sensing --- DAHITI --- modified strahler approach --- airborne LiDAR --- DEM --- flood inundation --- flood map --- flood model --- LiDAR --- terrestrial LiDAR --- evapotranspiration --- variability --- uncertainty --- unmanned aerial system --- sUAS --- multispectral --- viticulture --- water resources management --- California --- lake --- Tibetan Plateau --- hydrological changes --- water balance --- Chindwin basin --- hydrological modelling --- multi-variable calibration --- satellite-based rainfall product --- TRMM --- temporal resolution --- rainfall erosivity --- combined approach --- multi-objective optimization --- modeling uncertainty --- model constraint --- SWAT --- semiarid area --- hydrological variations --- normalized difference vegetation index --- total water storage change --- groundwater change --- extreme precipitation --- estimation --- TMPA 3B42-V7 --- regional frequency analysis --- China --- satellite datasets --- accuracy evaluation --- hydrological applicability --- Bosten Lake Basin --- actual evapotranspiration --- available water resources --- climate change --- vegetation greening --- VIP-RS model --- Lancang-Mekong river basin --- MSWEP --- AgMERRA --- APHRODITE --- CHIRPS --- PERSIANN --- error correction --- agricultural water management --- crop water consumption --- remote sensing model --- evapotranspiration allocation --- inland water --- IWCT --- Tianjin --- Landsat data --- Tarim River Basin --- desert-oasis ecotone --- land-use change --- CA-Markov model --- remote sensing in hydrology --- precipitation --- performance evaluation --- GPM --- Poyang Lake --- Yangtze River --- assimilation --- nonparametric modeling --- multi-source --- landscape pattern --- spatiotemporal changes --- influencing factors --- watershed --- China SE --- satellite data --- LUE-GPP --- SPEI --- copula function --- conditional probability --- soil moisture --- neural network --- downscaling --- microwave data --- MODIS data --- precipitation datasets --- evaluation --- spatial scale --- temporal scale --- climate --- Yellow River Basin --- data assimilation --- WRF --- WRFDA --- 3DVar --- water levels --- surface areas --- volume variations --- hypsometry --- bathymetry --- lakes --- reservoirs --- remote sensing --- DAHITI --- modified strahler approach --- airborne LiDAR --- DEM --- flood inundation --- flood map --- flood model --- LiDAR --- terrestrial LiDAR --- evapotranspiration --- variability --- uncertainty --- unmanned aerial system --- sUAS --- multispectral --- viticulture --- water resources management --- California --- lake --- Tibetan Plateau --- hydrological changes --- water balance --- Chindwin basin --- hydrological modelling --- multi-variable calibration --- satellite-based rainfall product --- TRMM --- temporal resolution --- rainfall erosivity --- combined approach --- multi-objective optimization --- modeling uncertainty --- model constraint --- SWAT --- semiarid area --- hydrological variations --- normalized difference vegetation index --- total water storage change --- groundwater change --- extreme precipitation --- estimation --- TMPA 3B42-V7 --- regional frequency analysis --- China --- satellite datasets --- accuracy evaluation --- hydrological applicability --- Bosten Lake Basin --- actual evapotranspiration --- available water resources --- climate change --- vegetation greening --- VIP-RS model --- Lancang-Mekong river basin --- MSWEP --- AgMERRA --- APHRODITE --- CHIRPS --- PERSIANN --- error correction --- agricultural water management --- crop water consumption --- remote sensing model --- evapotranspiration allocation --- inland water --- IWCT --- Tianjin --- Landsat data --- Tarim River Basin --- desert-oasis ecotone --- land-use change --- CA-Markov model --- remote sensing in hydrology --- precipitation --- performance evaluation --- GPM --- Poyang Lake --- Yangtze River --- assimilation --- nonparametric modeling --- multi-source --- landscape pattern --- spatiotemporal changes --- influencing factors --- watershed --- China SE --- satellite data --- LUE-GPP --- SPEI --- copula function --- conditional probability --- soil moisture --- neural network --- downscaling --- microwave data --- MODIS data