scholarly journals Assessment of an Extreme Rainfall Detection System for Flood Prediction over Queensland (Australia)

Proceedings ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Paola Mazzoglio ◽  
Francesco Laio ◽  
Constantin Sandu ◽  
Piero Boccardo
Keyword(s):  
Real Time ◽  
Detection System ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Remotely Sensed ◽  
Flood Event ◽  
Management Service ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Rainfall Detection

Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS—erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way for researchers as well as non-specialized users. The near real-time rainfall monitoring is performed by taking advantage of NASA GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian rivers) overflowed, expanding to a width of tens of kilometers. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets, it was possible to gain a clear look at the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.

scholarly journals Assessment of an Extreme Rainfall Detection System for Flood Prediction over Queensland (Australia)

Proceedings ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Paola Mazzoglio ◽  
Francesco Laio ◽  
Constantin Sandu ◽  
Piero Boccardo
Keyword(s):  
Real Time ◽  
Detection System ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Remotely Sensed ◽  
Flood Event ◽  
Management Service ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Rainfall Detection

Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS—erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way for researchers as well as non-specialized users. The near real-time rainfall monitoring is performed by taking advantage of NASA GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian rivers) overflowed, expanding to a width of tens of kilometers. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets, it was possible to gain a clear look at the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.

scholarly journals Improving weather forecasts by means of HPC solutions: the LEXIS approach in the 2020 Bitti flood event

2021 ◽  
Author(s):  
Paola Mazzoglio ◽  
Paolo Pasquali ◽  
Andrea Parodi ◽  
Antonio Parodi
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Early Warning ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Detection System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Flood Event ◽  
Heavy Rainfall Event

<p>In the framework of LEXIS (Large-scale EXecution for Industry & Society) H2020 project, CIMA Research Foundation is running a 3 nested domain WRF (Weather Research and Forecasting) model with European coverage and weather radar data assimilation over Italy. Forecasts up to 48 hours characterized by a 7.5 km resolution are then processed by ITHACA ERDS (Extreme Rainfall Detection System), an early warning system for the heavy rainfall monitoring and forecasting. This type of information is currently managed by ERDS together with two global-scale datasets. The first one is provided by NASA/JAXA GPM (Global Precipitation Measurement) Mission through the IMERG (Integrated Multi-satellitE Retrievals for GPM) Early run data, a near real-time rainfall information with hourly updates, 0.1° spatial resolution and a 4 hours latency. The second one is instead provided by GFS (Global Forecast System) at a 0.25° spatial resolution.<br>The entire WRF-ERDS workflow has been tested and validated on the heavy rainfall event that affected the Sardinia region between 27 and 29 November 2020. This convective event significantly impacted the southern and eastern areas of the island, with a daily rainfall depth of 500.6 mm recorded at Oliena and 328.6 mm recorded at Bitti. During the 28th, the town of Bitti (Nuoro province) was hit by a severe flood event.<br>Near real-time information provided by GPM data allowed us to issue alerts starting from the late morning of the 28th. The first alert over Sardinia based on GFS data was provided in the late afternoon of the 27th, about 40 km far from Bitti. In the early morning of the 28th, a new and more precise alert was issued over Bitti. The first alert based on WRF data was instead provided in the morning of the 27th and the system continued to issue alerts until the evening of the 29th, confirming that, for this type of event, precise forecasts are needed to provide timely alerts.<br>Obtained results show how, taking advantage of HPC resources to perform finer weather forecast experiments, it is possible to significantly improve the capabilities of early warning systems. By using WRF data, ERDS was able to provide heavy rainfall alerts one day before than with the other data.<br>The integration within the LEXIS platform will help with the automatization by data-aware orchestration of our workflow together with easy control of data and workflow steps through a user-friendly web interface.</p>

scholarly journals Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ai-Ling Jiang ◽  
Ming-Chieh Lee ◽  
Guofa Zhou ◽  
Daibin Zhong ◽  
Dawit Hawaria ◽  
...  
Keyword(s):  
Malaria Vector ◽  
Malaria Control ◽  
Remotely Sensed ◽  
Larval Habitat ◽  
Validation Dataset ◽  
Significant Shift ◽  
Remotely Sensed Data ◽  
Larval Habitats ◽  
Common Land Model ◽  
The Impact

AbstractLarval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data

2007 ◽  
Vol 109 (3) ◽  
pp. 314-327 ◽  
Author(s):  
Izaya Numata ◽  
Dar A. Roberts ◽  
Oliver A. Chadwick ◽  
Josh Schimel ◽  
Fernando R. Sampaio ◽  
...  
Keyword(s):  
Grazing Intensity ◽  
Remotely Sensed ◽  
Biophysical Properties ◽  
Remotely Sensed Data ◽  
The Impact

scholarly journals Exceptional retreat of Novaya Zemlya's marine-terminating outlet glaciers between 2000 and 2013

2017 ◽  
Author(s):  
J. Rachel Carr ◽  
Heather Bell ◽  
Rebecca Killick ◽  
Tom Holt
Keyword(s):  
Sea Ice ◽  
Sea Level Rise ◽  
Sea Level ◽  
Remotely Sensed ◽  
Glacier Retreat ◽  
Remotely Sensed Data ◽  
Novaya Zemlya ◽  
The Past ◽  
Air Temperatures ◽  
The Impact

Abstract. Novaya Zemlya (NVZ) has experienced rapid ice loss and accelerated marine-terminating glacier retreat during the past two decades. However, it is unknown whether this retreat is exceptional longer-term and/or whether it has persisted since 2010. Investigating this is vital, as dynamic thinning may contribute substantially to ice loss from NVZ, but is not currently included in sea level rise predictions. Here, we use remotely sensed data to assess controls on NVZ glacier retreat between the 1973/6 and 2015. Glaciers that terminate into lakes or the ocean receded 3.5 times faster than those that terminate on land. Between 2000 and 2013, retreat rates were significantly higher on marine-terminating outlet glaciers than during the previous 27 years, and we observe widespread slow-down in retreat, and even advance, between 2013 and 2015. There were some common patterns in the timing of glacier retreat, but the magnitude varied between individual glaciers. Rapid retreat between 2000–2013 corresponds to a period of significantly warmer air temperatures and reduced sea ice concentrations, and to changes in the NAO and AMO. We need to assess the impact of this accelerated retreat on dynamic ice losses from NVZ, to accurately quantify its future sea level rise contribution.

scholarly journals Improving an Extreme Rainfall Detection System with GPM IMERG data

2019 ◽  
Vol 11 (6) ◽  
pp. 677 ◽  
Author(s):  
Paola Mazzoglio ◽  
Francesco Laio ◽  
Simone Balbo ◽  
Piero Boccardo ◽  
Franca Disabato
Keyword(s):  
Detection System ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Mean Annual Precipitation ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Time Aggregation ◽  
Spatial Coverage ◽  
Rainfall Detection

Many studies have shown a growing trend in terms of frequency and severity of extreme events. As never before, having tools capable to monitor the amount of rain that reaches the Earth’s surface has become a key point for the identification of areas potentially affected by floods. In order to guarantee an almost global spatial coverage, NASA Global Precipitation Measurement (GPM) IMERG products proved to be the most appropriate source of information for precipitation retrievement by satellite. This study is aimed at defining the IMERG accuracy in representing extreme rainfall events for varying time aggregation intervals. This is performed by comparing the IMERG data with the rain gauge ones. The outcomes demonstrate that precipitation satellite data guarantee good results when the rainfall aggregation interval is equal to or greater than 12 h. More specifically, a 24-h aggregation interval ensures a probability of detection (defined as the number of hits divided by the total number of observed events) greater than 80%. The outcomes of this analysis supported the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS: erds.ithacaweb.org). This system is now able to provide near real-time alerts about extreme rainfall events using a threshold methodology based on the mean annual precipitation.

scholarly journals Remote Sensing of Ecosystem Structure: Fusing Passive and Active Remotely Sensed Data to Characterize a Deltaic Wetland Landscape

2020 ◽  
Vol 12 (22) ◽  
pp. 3819 ◽  
Author(s):  
Daniel Peters ◽  
K. Olaf Niemann ◽  
Robert Skelly
Keyword(s):  
Remotely Sensed ◽  
Ecosystem Structure ◽  
Remotely Sensed Data ◽  
Horizontal Structure ◽  
Multiple Sensors ◽  
Data Mining Approach ◽  
Input Variables ◽  
Future Work ◽  
The Impact ◽  
Northern Alberta

A project was constructed to integrate remotely sensed data from multiple sensors and platforms to characterize range of ecosystem characteristics in the Peace–Athabasca Delta in Northern Alberta, Canada. The objective of this project was to provide a framework for the processing of multisensor data to extract ecosystem information describing complex deltaic wetland environments. The data used in this study was based on a passive satellite-based earth observation multispectral sensor (Sentinel-2) and airborne discrete light detection and ranging (LiDAR). The data processing strategy adopted here allowed us to employ a data mining approach to grouping of the input variables into ecologically meaningful clusters. Using this approach, we described not only the reflective characteristics of the cover, but also ascribe vertical and horizontal structure, thereby differentiating spectrally similar, but ecologically distinct, ground features. This methodology provides a framework for assessing the impact of ecosystems on radiance, as measured by Earth observing systems, where it forms the basis for sampling and analysis. This final point will be the focus of future work.

Sign in / Sign up

Export Citation Format

Share Document