Many regions worldwide are coping with the climatic global change, which is modifying the water cycle and is increasing the occurrence of extreme hydro-meteorological events. Floods and landslides across a territory could increase significantly respect to actual and past scenarios, causing a modification of the susceptibility of a region and of the frequency of natural hazards.
The use of techniques able to monitor and to improve the prediction of these phenomena at different scales and in scarcely instrumented regions is fundamental. Soil moisture and rainfall estimates measured through remote sensing techniques can furnish reliable and widespread data at different scales. For satellite rainfall measures, state-of-the-art products cover time series of tens of years (e.g., TRMM Multisatellite Precipitation Analysis, Global Precipitation Measurement, EUMETSAT). Regarding soil moisture, different products can bring reliable measurements from a local/landscape to continental scales (e.g., SMMR, AMSR2, SMOS, SMAP, Metop/ASCAT, Sentinel). Innovative products, as soil moisture derived rainfall, allow to retrieve rainfall from different satellite soil moisture products or integrating field measurements of precipitation. Thanks to the improvement of the spatial and temporal resolutions of all of these products, they could become a fundamental tool also for early warning system strategies.
This session aims to collect and present researches concerning the most recent progress on the use of soil moisture and rainfall data from remote sensing for the monitoring and the prediction of landslides and floods. Those phenomena can cause hazards and risks towards population and anthropic elements. We encourage presentations related to:
• inter-comparison and inter-validation between land surface models, remote sensing approaches and in-situ validation networks;
• evaluation and trend analysis of soil moisture or rainfall satellite time series for monitoring landslides or floods and for identifying their possible triggering conditions;
• implementation of satellite measures of rainfall and soil moisture in physically-based or data-driven methods for the prediction of landslides and floods;
• use of remote sensing products of soil moisture and rainfall in early warning system tools;
• use of remote sensing products for investigating the effects of climatic global changes on the susceptibility and hazards towards landslides and floods.

Co-organized by GM3/HS6
Convener: Massimiliano BordoniECSECS | Co-conveners: Luca CiabattaECSECS, Anne FelsbergECSECS, Gabriella Petaccia, Lu ZhuoECSECS
vPICO presentations
| Thu, 29 Apr, 09:00–09:45 (CEST)

Session assets

Session materials

vPICO presentations: Thu, 29 Apr

Jingxuan Zhu, Enze Chen, and Qiang Dai

Raindrop size distributions (DSD) information plays a significant role in many scientific fields, especially in radar meteorology. DSD has spatial and temporal variation across different storm types and climatic regimes. Since the development of polarimetric weather radar, the large-scale DSD estimation has been a long-standing goal in radar meteorology. Traditional polynomial regression algorithms for ground polarimetric radars are widely used to estimate DSD parameters due to their simple methodology and acceptable accuracy. However, a simple polynomial regression may not be able to deeply explore the intrinsic relationship using available observations. This study therefore proposes a DSD retrieval model that uses dual-polarization radar observations based on long short-term memory (LSTM) network techniques. Three schemes of a normalized gamma DSD parameters (LSTM- D0, LSTM- Nw, and LSTM-µ) are designed with different combinations of polarimetric radar measurement inputs. Results show that all LSTM estimators exhibit better performance than the polynomial regression method. The proposed retrieval model using neural network techniques helps to improve quantitative precipitation estimation of weather radar and make sense of a better understanding of precipitation microphysics.

How to cite: Zhu, J., Chen, E., and Dai, Q.: Raindrop size distribution retrieval model from polarization radar observations using neural network techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3799, https://doi.org/10.5194/egusphere-egu21-3799, 2021.

Wei Wang, Jia Liu, Chuanzhe Li, Fuliang Yu, and Yuchen Liu

Soil moisture is an important factor affecting atmospheric processes as well as land surface hydrological processes. The description of the infiltration process greatly influences the accuracy of the soil moisture simulation, but there is still a lack of a consistent theoretical framework for predicting the effective fluxes and parameters that control infiltration in the atmospheric-hydrological modeling system. A coupled simulation study of the Weather Research and Forecasting model (WRF) and its terrestrial hydrologic component WRF-Hydro is carried out in two mesoscale watersheds of northern China. An infiltration module that is suitable for convective rainfall with large intensity and mixed runoff generation mechanism is added in WRF-Hydro to replace the original infiltration description. The main principle of the new module is: 1) The grid-based topographic index is used as an indication for the infiltration capacity and the soil water storage capacity across the watersheds; and 2) the infiltration is controlled by the variation of the surface soil moisture during the process of the rain, i.e., the infiltration is in an exponential decline as the increase of the surface soil moisture. Three long-duration rainfall-runoff events during the flood season are selected for this study. WRF runs to provide appropriate meteorological inputs to WRF-Hydro, and the simulated soil moisture results are compared with data from the Global Land Data Assimilation System (GLDAS). The results show that the added infiltration module, compared to the original, produces more consistent simulations with the observations regarding the spatial replication of the soil moisture and thus overall results in a higher simulation accuracy.

Keywords: soil moisture, infiltration, WRF-Hydro, topographic index

How to cite: Wang, W., Liu, J., Li, C., Yu, F., and Liu, Y.: Soil moisture simulation with the WRF-Hydro modeling system by involving a more precise infiltration process module, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4924, https://doi.org/10.5194/egusphere-egu21-4924, 2021.

Binru Zhao, Enze Chen, and Qi Shen

With the development of remote sensing technology, satellite rainfall products have become more and more credible. Although the potential of satellite rainfall products in landslide hazard assessments has been recognized, few studies evaluate the effect of satellite rainfall uncertainty on landslide predictions. This study attempts to explore the effect of satellite rainfall uncertainty on rainfall-triggered landslide predictions. We select the Emilia-Romagna region in northern Italy as the study area, and the NASA GPM-based IMERG data as the representative of satellite rainfall estimates. Satellite rainfall uncertainty is first characterized by generating rainfall ensembles for rainfall conditions responsible for landslides. The generated rainfall ensembles are then applied to the definition of rainfall thresholds using the bootstrap technique. The prediction performance of rainfall thresholds is finally evaluated through calculating the criteria of hit rate and false alarm rate. We anticipate that this study will encourage the research community to account for the satellite rainfall uncertainty when exploring the use of satellite rainfall in landslide hazard assessment.

How to cite: Zhao, B., Chen, E., and Shen, Q.: The effect of satellite rainfall uncertainty on landslide predictions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-67, https://doi.org/10.5194/egusphere-egu21-67, 2021.

Thomas Stanley, Dalia Kirschbaum, and Robert Emberson

The Landslide Hazard Assessment for Situational Awareness system (LHASA) gives a global view of landslide hazard in nearly real time. Currently, it is being upgraded from version 1 to version 2, which entails improvements along several dimensions. These include the incorporation of new predictors, machine learning, and new event-based landslide inventories. As a result, LHASA version 2 substantially improves on the prior performance and introduces a probabilistic element to the global landslide nowcast.

Data from the soil moisture active-passive (SMAP) satellite has been assimilated into a globally consistent data product with a latency less than 3 days, known as SMAP Level 4. In LHASA, these data represent the antecedent conditions prior to landslide-triggering rainfall. In some cases, soil moisture may have accumulated over a period of many months. The model behind SMAP Level 4 also estimates the amount of snow on the ground, which is an important factor in some landslide events. LHASA also incorporates this information as an antecedent condition that modulates the response to rainfall. Slope, lithology, and active faults were also used as predictor variables. These factors can have a strong influence on where landslides initiate. LHASA relies on precipitation estimates from the Global Precipitation Measurement mission to identify the locations where landslides are most probable. The low latency and consistent global coverage of these data make them ideal for real-time applications at continental to global scales. LHASA relies primarily on rainfall from the last 24 hours to spot hazardous sites, which is rescaled by the local 99th percentile rainfall. However, the multi-day latency of SMAP requires the use of a 2-day antecedent rainfall variable to represent the accumulation of rain between the antecedent soil moisture and current rainfall.

LHASA merges these predictors with XGBoost, a commonly used machine-learning tool, relying on historical landslide inventories to develop the relationship between landslide occurrence and various risk factors. The resulting model relies heavily on current daily rainfall, but other factors also play an important role. LHASA outputs the probability of landslide occurrence on a grid of roughly one kilometer over all continents from 60 North to 60 South latitude. Evaluation over the period 2019-2020 shows that LHASA version 2 doubles the accuracy of the global landslide nowcast without increasing the global false alarm rate.

LHASA also identifies the areas where the human exposure to landslide hazard is most intense. Landslide hazard is divided into 4 levels: minimal, low, moderate, and high. Next, the number of persons and the length of major roads (primary and secondary roads) within each of these areas is calculated for every second-level administrative district (county). These results can be viewed through a web portal hosted at the Goddard Space Flight Center. In addition, users can download daily hazard and exposure data.

LHASA version 2 uses machine learning and satellite data to identify areas of probable landslide hazard within hours of heavy rainfall. Its global maps are significantly more accurate, and it now includes rapid estimates of exposed populations and infrastructure. In addition, a forecast mode will be implemented soon.

How to cite: Stanley, T., Kirschbaum, D., and Emberson, R.: Using satellite soil moisture and rainfall in the Landslide Hazard Assessment for Situational Awareness system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5620, https://doi.org/10.5194/egusphere-egu21-5620, 2021.

Luísa Vieira Lucchese, Guilherme Garcia de Oliveira, and Olavo Correa Pedrollo

Rainfall-induced landslides have caused destruction and deaths in South America. Accessing its triggers can help researchers and policymakers to understand the nature of the events and to develop more effective warning systems. In this research, triggering rainfall for rainfall-induced landslides is evaluated. The soil moisture effect is indirectly represented by the antecedent rainfall, which is an input of the ANN model. The area of the Rolante river basin, in Rio Grande do Sul state, Brazil, is chosen for our analysis. On January 5th, 2017, an extreme rainfall event caused a series of landslides and debris flows in this basin. The landslide scars were mapped using satellite imagery. To calculate the rainfall that triggered the landslides, it was necessary to compute the antecedent rainfall that occurred within the given area. The use of satellite rainfall data is a useful tool, even more so if no gauges are available for the location and time of the rainfall event, which is the case. Remote sensing products that merge the data from in situ stations with satellite rainfall data are increasingly popular. For this research, we employ the data from MERGE (Rozante et al., 2010), that is one of these products, and is focused specifically on Brazilian gauges and territory. For each 12.5x12.5m raster pixel, the rainfall is interpolated to the points and the rainfall volume from the last 24h before the event is accumulated. This is added as training data in our Artificial Neural Network (ANN), along with 11 terrain attributes based on ALOS PALSAR (ASF DAAC, 2015) elevation data and generated by using SAGA GIS. These attributes were presented and analyzed in Lucchese et al. (2020). Sampling follows the procedure suggested in Lucchese et al. (2021, in press). The ANN model is a feedforward neural network with one hidden layer consisting of 20 neurons. The ANN is trained by backpropagation method and cross-validation is used to ensure the correct adjustment of the weights. Metrics are calculated on a separate sample, called verification sample, to avoid bias. After training, and provided with relevant information, the ANN model can estimate the 24h-rainfall thresholds in the region, based on the 2017 event only. The result is a discretized map of rainfall thresholds defined by the execution of the trained ANN. Each pixel of the resulting map should represent the volume of rainfall in 24h necessary to trigger a landslide in that point. As expected, lower thresholds (30 - 60 mm) are located in scarped slopes and the regions where the landslides occurred. However, lowlands and the plateau, which are areas known not to be prone to landslides, show higher rainfall thresholds, although not as high as expected (75 - 95 mm). Mean absolute error for this model is 16.18 mm. The inclusion of more variables and events to the ANN training should favor achieving more reliable outcomes, although, our results are able to show that this methodology has potential to be used for landslide monitoring and prediction.

How to cite: Lucchese, L. V., de Oliveira, G. G., and Pedrollo, O. C.: Towards the effective use of Artificial Neural Networks for accessing rainfall thresholds for rainfall-induced landslides, a study based on in-situ and satellite merged rainfall data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1623, https://doi.org/10.5194/egusphere-egu21-1623, 2021.

Valerio Vivaldi, Massimiliano Bordoni, Luca Brocca, Luca Ciabatta, and Claudia Meisina

Rainfall-induced shallow landslides affect buildings, roads, facilities, cultivations, causing several damages and, sometimes, loss of human lives. It is necessary assessing the most prone zones in a territory where these phenomena could occur and the triggering conditions of these events, which generally correspond to intense and concentrated rainfalls. The most adopted methodologies for the determination of the spatial and temporal probability of occurrence are physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall scenarios. Whereas, they are limited to be applied in a reliable way in little catchments, where geotechnical and hydrological characteristics of the materials are homogeneous. Data-driven models could constraints these, when the predisposing factors of shallow instabilities, allowing to estimate only the susceptibility of a territory, are combined with triggering factors of shallow landslides to allow these methods to estimate also the probability of occurrence and, then, the hazard. This work presents the implementation of a data-driven model able to assses the spatio-temporal probability of occurrence of shallow landslides in large areas by means of a data-driven techniques. The models are based on Multivariate Adaptive Regression Technique (MARS), that links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to soil saturation degree and rainfall amounts, which are available thanks to satellite measures (ASCAT and GPM). The methodological approach is testing in different catchments of Oltrepò Pavese hilly area (northern Italy), that is representative of Italian Apeninnes environment. This work was made in the frame of the project ANDROMEDA, funded by Fondazione Cariplo.

How to cite: Vivaldi, V., Bordoni, M., Brocca, L., Ciabatta, L., and Meisina, C.: Using satellite soil moisture and rainfall data for the monitoring and the prediction of natural hazards, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14812, https://doi.org/10.5194/egusphere-egu21-14812, 2021.

Rholan Houngue, Kingsley Ogbu, Adrian Almoradie, and Mariele Evers

The variability and changes noted in the climate over the past decades emphasizes the importance of climate information such as precipitation datasets in the management of flood risks in Benin and Togo. The lack of extensive and functional ground observation networks, introduces satellite-based rainfall datasets as a better alternative which needs however to be evaluated beforehand. This study investigated the performance of four satellite and gauge-based rainfall products –Climate Hazards Group Infrared Precipitation with Station data version v2.0 (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT) and the Global Precipitation Climatology Centre full daily data (GPCC) – at gauge point level over the Mono River basin which is stretched over Benin and Togo territories. Three synoptic stations located in Tabligbo, Atakpamé and Sokodé were considered because of the completeness of their time series during the study period 1983-2012. The assessments were conducted at daily, dekadal (10-day period), seasonal and annual scale using both continuous and categorical statistics. Results show poor performances at daily and annual temporal scales while the seasonal cycles were well reproduced with Nash-Sutcliffe efficiency equal or higher than 0.94, and correlation coefficient above 0.9. At Tabligbo, CHIRPS and GPCC showed the best statistical results whereas the performance of PERSIANN and TAMSAT varies with the temporal scale and the station. The probability of rainfall detection (POD) and the capability of reproducing extreme daily maxima indicate GPCC as the best product for flood monitoring purposes at daily scale. However, all assessed products exhibited high POD and low false alarm ratio (FAR) at dekadal scale.

How to cite: Houngue, R., Ogbu, K., Almoradie, A., and Evers, M.: Evaluation of the Performance of Remotely Sensed Rainfall Datasets for Flood Monitoring in the Transboundary Mono River Catchment, Togo and Benin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11176, https://doi.org/10.5194/egusphere-egu21-11176, 2021.