Thanks to the large number of satellites, the multimission approach is becoming a viable method to integrate measurements and intensify the number of samples in space and time for monitoring the earth system. In this paper, we merged data from different satellite missions, optical sensors, and altimetry, for estimating daily river discharge through the application of the artificial neural network (ANN) technique. ANN was selected among other retrieval techniques because it offers an easy but effective way of combining input data from different sources into the same retrieval algorithm. The network is trained in a calibration period and validated in an independent period against in situ observations of river discharge for two gauging sites: Lokoja along the Niger River and Pontelagoscuro along the Po River. For optical sensors, we found that the temporal resolution is more important than the spatial resolution for obtaining accurate discharge estimates. Our results show that Landsat fails in the estimation of extreme events by missing most of the peak values due to its long revisit time (14-16 days). Better performances are obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer. Radar altimetry provides results in between MODIS-TERRA and MODIS-AQUA at Lokoja, whereas it outperforms all single optical sensors at Pontelagoscuro. The multimission approach, involving optical sensors and altimetry, is found the most reliable tool to estimate river discharge with a relative root-mean-square error of 0.12% and 0.27% and Nash-Sutcliffe coefficient of 0.98 and 0.83 for the Niger and Po rivers, respectively.

The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited.

Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km² for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km². The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.

This paper tries to shift the focus of research on the impact of natural disasters on economic growth from global and national levels to sub-national levels. Inadequate sub-national level information is a significant lacuna for planning spatially targeted climate change adaptation investments. A fixed-effect panel regression analyses of 19 states from 2001 to 2015 assess the impacts of exposure to floods and droughts on the growth of gross state domestic product (GSDP) and human development index (HDI) in India. The flood and drought exposure are estimated using satellite data. The 19 states comprise 95% of the population and contribute 93% to the national GDP. The results show that floods indeed expose a large area, but droughts have the most significant impacts at the sub-national level. The most affected GSDPs are in the non-agriculture sectors, positively by the floods and negatively by droughts. No significant influence on human development may be due to substantial investment on mitigation of flood and drought impacts and their influence on better income, health, and education conditions. Because some Indian states still have a large geographical area, profiling disasters impacts at even smaller sub-national units such as districts can lead to effective targeted mitigation and adaptation activities, reduce shocks, and accelerate income growth and human development.

Landslides are among the most dangerous natural hazards, particularly in developing countries, where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important detection and monitoring process to predict landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide prediction in reducing the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1^∘ vs. 0.25^∘) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.