Introduction to Real Time Rainfall

An integrated urban flood model for Bengaluru city, Karnataka, India has been developed with a modular approach, which includes real-time rainfall and weather monitoring, and a rainfall forecast system feeding into a comprehensive hydrologic flood model.

The raw real-time rainfall data acquired was pertained to three regions of Karnataka North, South and Coastal.

However, the GSMaP Gauge cannot be used to provide real-time rainfall data and is, therefore, inadequate for real-time flood forecasting.

Near real-time rainfall monitoring at local scale is essential for urban flood risk mitigation.

The method presented in this study can be used not only to reasonably and efficiently select target sites for flood prevention facilities, but also to pre-detect areas vulnerable to flooding by using real-time rainfall forecasting.

Accurate and real-time rainfall estimation is a pressing need for forecasting the flood disaster and reducing the loss.

The provision of high resolution near real-time rainfall data has made satellite rainfall products very potential for monitoring hydrological hazards.

The flow analysis is conducted considering the shrink/swell behavior of the Yazoo clay with the real-time rainfall events, as observed in the rain gauges.

Both real-time rainfall data from next-generation radar (NEXRAD) and gaging stations, and forecasted rainfall are needed to make gate control decisions (reservoir releases) in real-time so that at time t, rainfall is known and rainfall over the future time-period (Δt) to time t + Δt can be forecasted.

In the domains dominated by complex topographies, quasi-periodicities, and non-linearities, deep learning (DL)–based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions.

Real-time rainfall data can be used in conjunction with streamflow monitoring to determine when extreme precipitation has occurred within the ungauged watercourses catchment which may result in flooding.

Various forms of phosphorus and runoff process were monitored, and local real-time rainfall data were also collected.

In particular, due to rainfall concentrated in a short period of time during the flood season, it is necessary to plan the exact amount of dam discharge using real-time rainfall forecasting information.

In this paper, real-time rainfall conditions are incorporated into landslide prediction through a unique rainstorm-based database of reported landslides.

In this research, a real-time nowcasting system for regional landslide-hazard assessment under extreme-rainfall conditions was established by integrating a real-time rainfall data retrieving system, a landslide-susceptibility analysis program (TRISHAL), and a real-time display system to show the stability of regional slopes in real time and provide an alert index under rainstorm conditions for disaster prevention and mitigation.

In view of this, the effectiveness of integrating satellite-based near real-time rainfall, i.

The newly designed predictive model utilizes statistically significant lagged <inline-formula> <tex-math notation="LaTeX">$I_{F}$ </tex-math></inline-formula>, improved by antecedent and real-time rainfall data to forecast the next daily <inline-formula> <tex-math notation="LaTeX">$I_{F}$ </tex-math></inline-formula> value.

This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival.

&#160; With this, rainfall-landslide correlation models have been established and updated regularly through statistical means to facilitate the prediction of the severity of landslide based on real-time rainfall recorded in the raingauge network and the rainfall forecast by the Hong Kong Observatory (HKO).

Therefore, a real-world case study was conducted in Wadi al Wala for real-time rainfall forecasting and flood control, using 38 years of daily data from 13 rain gauge stations in the region.