Luca CiabattaItalian National Research Council | CNR · Research Institute for Geo-Hydrological Protection IRPI
Luca Ciabatta
MsC in Geological Sciences and Technologies
About
104
Publications
56,395
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Introduction
Luca Ciabatta was born in Perugia, Italy. He took his M.S. degree in Geological Sciences and Technologies with excellence, from the University of Perugia. He received a Scholarship to carry research at the Research Institute of Geo-Hydrological Protection (IRPI) of the National Research Council (CNR) of Perugia under the topic: “Applied research, monitoring services and hydrological modeling for the estimation of the water content of the soil for the mitigation of geo-hydrological risk”.
Additional affiliations
July 2013 - present
Vienna University of Technology
Position
- Assessment of satellite soil moisture products reliability through rainfall observations: SM2RAIN
Description
- The main purpose of the project is to apply the SM2RAIN method to estimate rainfall from different satellite soil moisture products and, then, to identify the satellite product(s) that better reproduce the observed rainfall data.
Education
October 2004 - May 2011
Publications
Publications (104)
Satellite-derived high-resolution soil moisture and precipitation data have become widely adopted in natural hazard and climate change research. Landslide susceptibility mapping, which often relies on static predisposing factors, faces challenges in accounting for temporal changes, limiting its efficacy in accurately identifying potential locations...
This study assessed the performance of different remotely sensed soil moisture products with in situ observations; six profile probes for the water content monitoring were selected, operating during 2016–2021 from the Voineşti Experimental Basin in the Romanian Subcarpathian region. The reliability of satellite observations has been analyzed on bot...
High-resolution soil moisture data is crucial in the development of hydrological applications as it provides detailed insights into the spatiotemporal variability of soil moisture. The emergence of advanced remote sensing technologies, alongside the widespread adoption of machine learning, has facilitated the creation of continental and global soil...
Climate change is profoundly affecting the global water cycle, increasing the likelihood and severity of extreme water-related events. Better decision-support systems are vital to accurately predict and monitor water-related environmental disasters and optimally manage water resources. These must integrate advances in remote sensing, in situ, and c...
High-resolution soil moisture data is crucial in the development of hydrological applications as it provides detailed insights into the spatiotemporal variability of soil moisture. The emergence of advanced remote sensing technologies, alongside the widespread adoption of machine learning, has facilitated the creation of continental and global soil...
A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global product that estimates rainfall from satellite soil mois...
Cultural heritage is one of the most exceptional resources characterizing the Italian territory. Archaeological heritage, i.e., the archaeological sites with different types of archaeological artifacts, strongly contributes to enriching the national and international cultural heritage. Nevertheless, it is constantly exposed to external factors, suc...
ERA5-Land service has been released recently as an integral and operational component of Copernicus Climate Change Service. Within its set of climatological and atmospheric parameters, it provides soil moisture estimates at different soil depths, represeting an important tool for retrieving saturation degree for predicting natural hazards as shallo...
In most African countries, the lack of observed rainfall data is a major obstacle for efficient water resources management. The objective of this study is to evaluate satellite rainfall products’ ability to estimate river runoff over 12 basins in Morocco using four hydrological models: IHACRES, MISDc, GR4J, and CREST. Satellite products available w...
The use of satellite sensors to infer rainfall measurements has become a widely used practice in recent years, but their spatial resolution usually exceeds 10 km, due to technological limitations. This poses an important constraint on its use for applications such as water resource management, index insurance evaluation or hydrological models, whic...
Satellite sensors to infer rainfall measurements have become widely available in the last years, but their spatial resolution usually exceed 10 kilometres, due to technological limitation. This poses an important constraint on their use for application such as water resource management, index insurance evaluation or hydrological models, which requi...
The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an increase in global warming can significantly in...
The development of forecasting models for the evaluation of potential slope instability after rainfall event represents an important issue for the scientific community. This topic has received considerable impetus due to climate change effect on the territory [1, 2] as several studies demonstrate that the increase in global warming can significantl...
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 produc...
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 tool to forecast landslide occurrence in space and time, particularly satellite rainfall products that have improved in ter...
Hydrogeological hazards now exacerbated by the ongoing climate change pose serious challenges for the safety of the population worldwide. Among the others, the landslide risk can be mitigated by setting up efficient and reliable early warning systems. To date, rainfall thresholds are one of the most used tools to forecast the possible occurrence of...
A combined method was developed to forecast the spatial and the temporal probability of occurrence of rainfall-induced shallow landslides over large areas. The method also allowed to estimate the dynamic change of this probability during a rainfall event. The model, developed through a data-driven approach basing on Multivariate Adaptive Regression...
Satellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking....
The global availability of satellite rainfall products (SRPs) at an increasingly high temporal and spatial resolution has made their exploitation in hydrological applications possible, especially in data-scarce regions. In this context, understanding how uncertainties transfer from SRPs to river discharge simulations, through the hydrological model...
Satellite precipitation products have been largely improved in the recent years particularly with the launch of the global precipitation measurement (GPM) core satellite. Moreover, the development of techniques for exploiting the information provided by satellite soil moisture to complement/enhance precipitation products have improved the accuracy...
Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agr...
The standard approach for measuring instantaneous rainfall rates from space is based on the inversion of the atmospheric signals reflected or radiated by atmospheric hydrometeors, i.e., a “top-down” approach. Recently, a new “bottom-up” approach has been proposed that exploits satellite soil moisture observations for obtaining accumulated rainfall...
Abstract. The global availability of satellite rainfall products (SRPs) at an increasingly high temporal/spatial resolution has made possible their exploitation in hydrological applications, especially over in-situ data scarce regions. In this context, understand how uncertainties transfer from SRPs into flood simulation, through the hydrological m...
Shallow landslides are very dangerous phenomena, widespread all over the world, which could provoke significant damages to buildings, roads, facilities, cultivations and, sometimes, loss of human lives. It is then necessary assessing the most prone zones in a territory which is particularly susceptible to these phenomena and the frequency of the ev...
Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approac...
Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km². The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agr...
Recenti sviluppi nella stima delle precipitazioni nell’ambito del progetto H SAF
In a changing climate, assessing the effects that the variation of the expected rainfalls can cause to slope stability is of primary importance. Precipitations are expected to increase, and, in particular, there will be more events characterised by extreme rainfalls, which legitimates the possibility of an increase in landslide activity. A probabil...
Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products obtained from rain gauges, remote sensing and meteorological modelling suffer from space and time inconsistency due to non-uniform density of ground networks and the difficulties in...
This work investigates the potential of using the Bayesian-based Model Conditional Processor (MCP) for complementing satellite precipitation products with a rainfall dataset derived from satellite soil moisture observations. MCP – which is a Bayesian Inversion approach – was originally developed for predictive uncertainty estimates of water level a...
Soil moisture is a key environmental variable, important to, e.g., farmers, meteorologists, and disaster management units. Here, we present a method to retrieve surface soil moisture (SSM) from the Sentinel-1 (S-1) satellites, which carry C-band Synthetic Aperture Radar (CSAR) sensors that provide the richest freely available SAR data source so far...
The large heterogeneity in soil surface conditions makes it impracticable to obtain reliable estimates of soil hydraulic parameters for areas larger than few squared kilometers. However, identifying these parameters on a global scale is essential for many hydrological and climatic applications. In this study, a new approach named Drainage from Dryd...
Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data analysis and modelling in combination with geographic information systems...
Soil moisture is a key environmental variable, important to e.g., farmers, meteorologists, and disaster management units. We fuse surface soil moisture (SSM) estimates from spatio-temporally complementary radar sensors through temporal filtering of their joint signal and obtain a kilometre-scale, daily soil water content product named SCATSAR-SWI....
Satellite rainfall products have been available for many years (since '90) with an increasing spatial/temporal resolution and accuracy. Their global scale coverage and near real-time products perfectly fit the need of an early warning landslide system. Notwithstanding these characteristics, the number of studies employing satellite rainfall estimat...
In this study, an integration of microwave data obtained from the SMAP and AMSR2 satellite radiometers has been attempted, to achieve an accurate estimation of the Soil Moisture Content (SMC). This research aimed to overcome the failure of radar sensor in SMAP satellite as well as the failure to generate the radar/radiometer combined SMC product at...
Satellite-based rainfall products (SRPs) are nowadays available at ever increasing accuracy and higher spatial and temporal resolution with respect to the past. Despite this, they are scarcely used in hydrological modeling. The main reasons may be related to: 1) the large bias characterizing satellite precipitation estimates, which is dependent on...
Rainfall is the main driver of the hydrological cycle. Its estimation is fundamental in many applications like climate monitoring, extreme weather prediction, and weather forecasting. Rainfall at the ground level is measured by instruments called rain gauges. However, the number of rain gauges is limited and unevenly distributed over the world. Alt...
An algorithm for retrieving soil moisture content (SMC) from synergic use of both active and passive microwave acquisitions is presented. The algorithm takes advantage of the integration of microwave data from SMAP, Sentinel-1 and AMSR2 for overcoming the SMAP radar failure and obtaining a SMC product at enhanced resolution (0.1° × 0.1°) and improv...
Many satellite soil moisture products are today globally available in near real-time. These observations are of paramount importance for enhancing the understanding of the hydrological cycle and particularly useful for flood forecasting purposes. In recent decades, several studies assimilated satellite soil moisture observations into rainfall-runof...
Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al.,...
First results on the assimilation of the CCI soil moisture products (active , passive and combined) into a hydrological model in about 100 catchments in Europe
Accurate and long-term rainfall estimates are the main inputs for several applications, spanning from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca...
Real-time de-noising of satellite-derived soil moisture observations presents opportunities to deliver more accurate and timely satellite data for direct satellite users. So far, the most commonly used techniques for reducing the impact of noise in the retrieved satellite soil moisture observations have been based on moving average filters and Four...
A merging procedure is applied to five daily rainfall estimates achieved via SM2RAIN applied to the soil moisture products obtained by the Advanced SCATterometer, the Advanced Microwave Scanning Radiometer 2, the Soil Moisture Active and Passive mission, the Soil Moisture and Ocean Salinity mission and backscattering observations of RapidScat. The...
Remote sensing of soil moisture has reached a level of good maturity and accuracy for which the retrieved products are ready to use in real-world applications. Due to the importance of soil moisture in the partitioning of the water and energy fluxes between the land surface and the atmosphere, a wide range of applications can benefit from the avail...
The objective of this study is to explore the feasibility of using Satellite Rainfall Products (SRPs) in a lumped hydrologic model (MISDc, “Modello Idrologico Semi-Distribuito in continuo”, Masseroni et al., 2017) over 15 basins in the Mediterranean area with different sizes and physiographic characteristics. Specifically, TMPA 3B42-RT, CMORPH, PER...
In this study, the stochastic rainfall error model SREM2D (Hossain et al. 2006) is used for characterizing the retrieval error of both SM2RAIN and a state-of-the-art satellite precipitation product (i.e. 3B42RT). The error characterization serves for an optimal integration between SM2RAIN and 3B42RT for enhancing the capability of the resulting int...
The parameterization of hydrological processes over large areas is extremely difficult. The large heterogeneities in soil surface conditions makes impracticable to obtain reliable estimates of soil hydraulic parameters for areas larger than few squared kilometers. However, the knowledge of these parameters on a global scale is essential for a numbe...
Soil moisture is widely recognized as a key parameter in the mass and energy balance between the land surface and the atmosphere and, hence, the potential societal benefits of an accurate estimation of soil moisture are immense. Recently, scientific community is making a big effort for addressing the estimation of soil moisture over large areas thr...
The accurate estimation of rainfall from remote sensing is of paramount importance for many applications as, for instance, the mitigation of natural hazards like floods, droughts, and landslides. Traditionally, microwave observations in the frequency between 10 and 183 GHz are used for estimating rainfall based on the direct interaction of radiatio...
Remote sensing techniques provide a new way to obtain hydrological variables (i.e. rainfall and soil moisture), mainly in poorly instrumented areas that are fundamental for natural hazard assessment and mitigation. The ever increasing availability of satellite derived products characterized by high temporal and spatial coverage requires the develop...
Presentation of the SM2RAIN validation tool for the assessment of the quality of satellite soil moisture products on a global scale
Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) satellite is used for improving satellite rainfall estimates obtained from the Tr...
High-quality precipitation observations from remote sensing have a significant beneficial impact in many applications such as natural hazards prediction (floods, landslides, drought) and climate modelling. Recent studies proved the value of satellite soil moisture products for precipitation estimation over land. The enhanced value of the soil moist...
Landslides are frequent and widespread geomorphological phenomena causing loss of human life and damage to property. The main tool for assessing landslide risk relies on rainfall thresholds and thus, many countries established early warning systems aimed to landslide hazard assessment. The Umbria Region Civil Protection Centre developed an operatio...
Remote sensing techniques provide a new way to obtain hydrological variables (i.e. rainfall and soil moisture), mainly in poorly instrumented areas that are fundamental for natural hazard assessment and mitigation. The ever increasing availability of satellite derived products characterized by high temporal and spatial coverage requires the develop...
Remote sensing techniques provide a new way to obtain hydrological variables (i.e. rainfall and soil moisture), mainly in poorly instrumented areas that are fundamental for natural hazard assessment and mitigation. The ever increasing availability of satellite derived products characterized by high temporal and spatial coverage requires the develop...
Presentation (with comments) related to the use of multiple soil moisture products for improving the accuracy in rainfall estimation through SM2RAIN. Here a link to the corresponding PPTX file: http://hydrology.irpi.cnr.it/repository/public/presentations/2016/sm-workshop-ny
A simplified model for calculating satellite based surface runoff.
Rainfall estimated by merging multiple satellite soil moisture products (ASCAT, SMAP, SMOS, AMSR2, RapidScat, ..), via SM2RAIN, provides significantly improved performances
A NEW ITALIAN RAINFALL PRODUCT obtained from satellite soil moisture data through the SM2RAIN algorithm (Brocca et al., 2014), at 12.5 km/daily spatial-temporal sampling, has been delivered. The SM2RAIN method was applied to the Advanced SCATterometer (ASCAT, Wagner et al., 2013) soil moisture data for the period from January 2007 to December 2015...