Improving the Performance of an Operational Flood Early Warning System with the Assimilation of Satellite-Soil-Moisture Data

Abstract. We communicate the results of a preliminary investigation aimed at improving a state-of-the-art RSLEWS (regional-scale landslide early warning system) based on rainfall thresholds by integrating mean soil moisture values averaged over the territorial units of the system. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are not expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods are substituted with soil moisture thresholds. A back analysis demonstrated that both approaches consistently reduced false alarms, while the second approach reduced missed alarms as well.

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Brief communication: Using punctual soil moisture estimates to improve the performances of a regional scale landslide early warning system

10.5194/nhess-2017-361 ◽

2017 ◽

Cited By ~ 1

Author(s):

Samuele Segoni ◽

Ascanio Rosi ◽

Daniela Lagomarsino ◽

Riccardo Fanti ◽

Nicola Casagli

Keyword(s):

Soil Moisture ◽

Early Warning ◽

Early Warning System ◽

Regional Scale ◽

Back Analysis ◽

Warning System ◽

Threshold Value ◽

False Alarms ◽

Rainfall Thresholds ◽

Landslide Early Warning

Abstract. We improved a state-of-art RSLEWS (regional scale landslide early warning system) based on rainfall thresholds by integrating punctual soil moisture estimates. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are never expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods were substituted by soil moisture thresholds. A back analysis demonstrated that both approaches reduced consistently false alarms, while the second approach reduced missed alarms as well.

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Flood and landslide warning based on rainfall thresholds and soil moisture indexes: the HEWS (Hydrohazards Early Warning System) for Sicily

Advances in Geosciences ◽

10.5194/adgeo-44-79-2017 ◽

2017 ◽

Vol 44 ◽

pp. 79-88 ◽

Cited By ~ 6

Author(s):

Giuseppina Brigandì ◽

Giuseppe Tito Aronica ◽

Brunella Bonaccorso ◽

Roberto Gueli ◽

Giuseppe Basile

Keyword(s):

Soil Moisture ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Rainfall Runoff ◽

Rainfall Thresholds ◽

Rainfall Runoff Model ◽

Moisture Conditions ◽

Runoff Model ◽

Scs Cn

Abstract. The main focus of the paper is to present a flood and landslide early warning system, named HEWS (Hydrohazards Early Warning System), specifically developed for the Civil Protection Department of Sicily, based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF). The warning system is referred to 9 different Alert Zones in which Sicily has been divided into and based on a threshold system of three different increasing critical levels: ordinary, moderate and high. In this system, for early flood warning, a Soil Moisture Accounting (SMA) model provides daily soil moisture conditions, which allow to select a specific set of three rainfall thresholds, one for each critical level considered, to be used for issue the alert bulletin. Wetness indexes, representative of the soil moisture conditions of a catchment, are calculated using a simple, spatially-lumped rainfall–streamflow model, based on the SCS-CN method, and on the unit hydrograph approach, that require daily observed and/or predicted rainfall, and temperature data as input. For the calibration of this model daily continuous time series of rainfall, streamflow and air temperature data are used. An event based lumped rainfall–runoff model has been, instead, used for the derivation of the rainfall thresholds for each catchment in Sicily characterised by an area larger than 50 km2. In particular, a Kinematic Instantaneous Unit Hydrograph based lumped rainfall–runoff model with the SCS-CN routine for net rainfall was developed for this purpose. For rainfall-induced shallow landslide warning, empirical rainfall thresholds provided by Gariano et al. (2015) have been included in the system. They were derived on an empirical basis starting from a catalogue of 265 shallow landslides in Sicily in the period 2002–2012. Finally, Delft-FEWS operational forecasting platform has been applied to link input data, SMA model and rainfall threshold models to produce warning on a daily basis for the entire region.

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Study of soil moisture sensor for landslide early warning system: Experiment in laboratory scale

Journal of Physics Conference Series ◽

10.1088/1742-6596/739/1/012034 ◽

2016 ◽

Vol 739 ◽

pp. 012034 ◽

Cited By ~ 2

Author(s):

E Yuliza ◽

H Habil ◽

M M Munir ◽

M Irsyam ◽

M Abdullah ◽

...

Keyword(s):

Soil Moisture ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Laboratory Scale ◽

Moisture Sensor ◽

Soil Moisture Sensor ◽

System Experiment ◽

Landslide Early Warning

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Distribution of Desert Locust Schistocerca gregaria in Rajasthan, India and Establishing an Early Warning System for Locust Control in India

10.21203/rs.3.rs-558472/v1 ◽

2021 ◽

Author(s):

Hameer Jhiknaria

Keyword(s):

Soil Moisture ◽

Early Warning ◽

Early Warning System ◽

Vegetation Index ◽

Schistocerca Gregaria ◽

Warning System ◽

Desert Locust ◽

Open Government Data ◽

Locust Control ◽

The Impact

Abstract Desert Locust is considered to be the most serious pests that cause a devastated damage to the crops and the other agricultural products during their invasions. The Desert Locust is a major threat for food security, livelihoods, environment and economic development in a region. The recent Locust Outbreak caused major damage to standing crops and vegetables in the Central and Western states of India, including Rajasthan, Punjab, Haryana, and Madhya Pradesh, with Rajasthan being the most affected. India had experienced such massive locust invasion after two decades. Establishing an Early Warning System for Locust Control in India is essential to reduce the impact by providing timely and relevant information in a systematic way contributing to increasing in resilience of the country. The distribution of Desert Locusts in Rajasthan, India has been presented from June 2019 to August 2020, along with the key Environmental Factors of Temperature, Rainfall, Soil Moisture and Prevalence of Vegetation significantly affecting Locust Activity. All the datasets used were obtained from Secondary sources. These datasets were obtained from Open Government Data (OGD) Platform. The Maps created in the study show the Distribution of Desert Locusts in Rajasthan, India; along with this the Choropleth map show Average- Temperature, Rainfall, Soil Moisture and Normalized Difference Vegetation Index (NDVI), all at District level. The Early Warning System for Desert Locust Control in India is a key integration of four key elements of: Risk Knowledge, Monitoring and Warning Service, Dissemination and Communication and Response Capability, and four-four sub elements of each key element. Establishing an Early Warning System for Locust Control in India is of paramount importance and a major achievement for the nation itself.

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Artificial Neural Network Intelligent System on the Early Warning System of Landslide

Pertanika Journal of Science and Technology ◽

10.47836/pjst.29.2.13 ◽

2021 ◽

Vol 29 (2) ◽

Author(s):

Aghus Sofwan ◽

Sumardi ◽

Najib ◽

Indrah Wendah Atma Bhirawa

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Soil Moisture ◽

Early Warning ◽

Early Warning System ◽

Intelligent System ◽

Warning System ◽

Ground Movement ◽

Sloping Ground ◽

Artificial Neural

Landslide is a natural sloping ground movement disaster that can occur due to several factors such as high rainfall, soil moisture in the depth of the soil of an area, vibrations experienced in the region, and the slope of the ground structure. A system that can deliver these factor values into the levels of vulnerability of landslide disasters is needed. The system uses Arduino Mega 2560 to process the level of vulnerability. It can predict the moment and the probability of the disaster occurring as an early warning system. The artificial neural network (ANN) intelligent system can expect an event of a disaster. The designed ANN used five parameters causing landslide as input data: rainfall, slope, soil moisture on the surface, soil moisture in the ground’s depth, and soil vibration. The ANN system output delivered three-level conditions: the safe, the standby, and the hazardous. The feed-forward backpropagation (FFBP) and the cascade forward backpropagation (CFBP) methods were analyzed. The performance of both methods was compared in terms of minimum square error (MSE). The MSE results of FFBP and CFBP in the safe, the standby, and the hazardous conditions were 0.017076 and 0.034952; 0.049597 and 0.046764; 0.062105 and 0.060355; respectively. The results point to the supremacy of CFBP to FFBP in standby and hazardous conditions. Therefore, the CFBP is implemented into the hardware of the early warning system.

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