Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides Occurrence

Rainfall-triggered shallow landslide events have caused losses of human lives and millions of euros in damage to property in all parts of the world. The need to prevent such hazards combined with the difficulty of describing the geomorphological processes over regional scales led to the adoption of empirical rainfall thresholds derived from records of rainfall events triggering landslides. These rainfall intensity thresholds are generally computed, assuming that all events are not influenced by antecedent soil moisture conditions. Nevertheless, it is expected that antecedent soil moisture conditions may provide critical support for the correct definition of the triggering conditions. Therefore, we explored the role of antecedent soil moisture on critical rainfall intensity-duration thresholds to evaluate the possibility of modifying or improving traditional approaches. The study was carried out using 326 landslide events that occurred in the last 18 years in the Basilicata region (southern Italy). Besides the ordinary data (i.e., rainstorm intensity and duration), we also derived the antecedent soil moisture conditions using a parsimonious hydrological model. These data have been used to derive the rainfall intensity thresholds conditional on the antecedent saturation of soil quantifying the impact of such parameters on rainfall thresholds.

Geographical distribution of the weather stations and landslide events for the study area. The graph in the inset shows the monthly distribution of landslides in Basilicata from 2001 to 2018.

How to cite: Lazzari, M., M. Piccarreta, R. L. Ray and S. Manfreda, Modelling antecedent soil moisture to constrain rainfall thresholds for shallow landslides occurrence, Landslides edited by Dr. Ram Ray, IntechOpen, pp. 1-331, (10.5772/intechopen.92730) 2020. [Link]

Giornate dell’Idrologia 2020

La Società Idrologica Italiana (SII) insieme alle Università della Campania stanno organizzando le Giornate dell’Idrologia 2020 che si terranno a Napoli il 16-18 luglio 2020. Save the date!

The GEOframe-NewAge Modelling System Applied in a Data Scarce Environment

In this work, the semi-distributed hydrological modeling system GEOframe-NewAge was integrated with a web-based decision support system implemented for the Civil Protection Agency of the Basilicata region, Italy. The aim of this research was to forecast in near real-time the most important hydrological variables at 160 control points distributed over the entire region. The major challenge was to make the system operational in a data-scarce region characterized by a high hydraulic complexity, with several dams and infrastructures. In fact, only six streamflow gauges were available for the calibration of the model parameters. Reliable parameter sets were obtained by simulating the hydrological budget and then calibrating the rainfall-runoff parameters. After the extraction of the flow-rating curves, six sets of parameters were obtained considering the different streamflow components (i.e., the baseflow and surface runoff) and using a multi-site calibration approach. The results show a good agreement between the measured and modeled discharges, with a better agreement in the sections located upstream of the dams. Moreover, the results were validated using the inflows measured at the most important dams (Pertusillo, San Giuliano and Monte Cotugno). For rivers without monitoring points, parameters were assigned using a principle of hydrological similarity in terms of their geology, lithology, and climate.

Figure: Representation of the simplified embedded reservoir model.

How to Cite: Bancheri, M., R. Rigon and S. Manfreda, The GEOframe-NewAge modelling system applied in a data scarce environment, Water, 12, 86, 2019. [pdf]

Exploiting the use of physical information for the calibration of a lumped hydrological model

In hydrological modelling, the challenge is to identify an optimal strategy to exploit tools and available observations in order to enhance model reliability. The increasing availability of data promotes the use of new calibration techniques able to make use of additional information on river basins. In the present study, a lumped hydrological model—designed with the aim of utilizing remotely sensed data—is introduced and calibrated, adopting four different schemes that adopt, to varying extents, available physical information. The physically consistent conceptualization of the hydrological model used allowed development of a step by step calibration based on a combination of information, such as remotely sensed data describing snow cover, recession curves obtained from streamflow measurements, and time series of surface run‐off obtained with a baseflow mathematical filter applied to the streamflow time‐series. Results suggest that the use of physical information in the calibration procedure tends to increase model reliability with respect to approaches where the parameters are calibrated using an overall statistic based, considerably or exclusively, on streamflow data.

How to cite: Manfreda, S., Mita, L., S. F. Dal Sasso, C. Samela, L. Mancusi, Exploiting the Use of Physical Information for the Calibration of a Lumped Hydrological ModelHydrological Processes, 32(10), 1420-1433, (doi: 10.1002/hyp.11501) 2018.  [pdf]

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks.
The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only
NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

How to cite: Guiomar Ruiz-Pérez, Julian Koch, Salvatore Manfreda, Kelly Caylor and Félix Francés, Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVIHydrology and Earth System Sciences (doi: 10.5194/hess-21-6235-2017) 2017. [pdf]

MY SIRR v.3.0

Minimalist agro-hYdrological model for Sustainable IRRigation management – soil moisture and crop dynamicsMY SIRR v.3.0 is a software written in python programming language with a simple Graphical User Interface (GUI) for quantitatively assess and compare agricultural enterprises across climates, soil types, crops, and irrigation strategies, accounting for the unpredictability of the hydro-climatic forcing.

Download from: https://github.com/ElsevierSoftwareX/SOFTX-D-15-00079

How to cite: Albano, R., S. Manfreda, G. Celano, MYSIRR: Minimalist agro-hYdrological model for Sustainable IRRigation management – soil moisture and crop dynamicsSoftwareX, 6, 107–117, (doi: 10.1016/j.softx.2017.04.00), 2017. [pdf]

Hydrological Prediction for Hydropower Production

The present paper describes a hydrological model aimed at improving prediction systems for a hydropower production plant. A significant merit of the work lies in the model structure that incorporates physically based processes allowing a new calibration strategy. In fact, calibration procedure is three phases according to the simulated processes: i) parameters of snowmelt module are calibrated using the snow coverage obtained by satellite imaes, ii) baseflow is identified using a mathematical filter, iii) this allow the calibration of the parameters controlling surface runoff with the time series of surface runoff. This allows the definition of a reliable model structure able to provide good estimates of the streamflow. The runoff forecast is generally useful in water resources and flood risk management, but is also very important for the electricity market. In fact, its prediction provides information at great interest to the participants of the electricity market, selling and buying electricity for each at the 24 hours of the following day. Model is used on the Aniene river basin generating deterministic forecast obtained from COSMO—LAMI. Analyses have been used to make prediction with 1, 2 and 3 days in advance. Results show a good level of the performances of the forecast with 1 day in advance while errors increase more markedly at 2 and 3 days. Model may represent a useful tool for power production optimization in hydropower plants.

How to cite: Salvatore Manfreda e Leonardo Mancusi, Hydrological Prediction for Hydropower ProductionL’ACQUA 4/2013 Luglio – Agosto, 2013. [pdf]

A physically based approach for the estimation of root-zone soil moisture from surface measurements

In the present work, we developed a new formulation for the estimation of the soil moisture in the root zone based on the measured value of soil moisture at the surface. The method sheds lights on the relationship between surface and root zone soil moisture and has applications in the use of satellite remote sensing retrievals of soil moisture. It derives from a simplified form of the soil water balance equation and provides a closed form of the relationship between the root zone and the surface soil moisture with a limited number of physically consistent parameters. The approach was first used to interpret soil moisture dynamics at the point scale using soil moisture measurements taken from the African Monsoon Multidisciplinary Analysis (AMMA) database. There after it was also tested over an extended domain using modeled soil moisture data obtained from the North American Land Data Assimilation System (NLDAS). The NLDAS database provides modeled soil moisture data averaged over different depths for the conterminous US covering different climatic and physical conditions. In general, the method performed better than a traditional low pass filter and its results are found to be influenced by rainfall dynamics and also by the observed variance of soil moisture in the lower layer. The limited number of the parameters and their physical interpretation allows a direct application of the procedure to other regions.

How to cite: S. Manfreda, L. Brocca, T. Moramarco, F. Melone and J. Sheffield, A physically based approach for the estimation of root-zone soil moisture from surface measurements, Hydrology and Earth System Sciences Discussions, 9, Pages 14129–14162 (doi: 10.5194/hessd-9-14129-2012), 2012. [pdf

Effects of morphology on global solar radiation and potential evapotranspiration

In this work we present an application of two different models for the calculation of extraterrestrial solar radiation and main components of surface radiation balance under clear sky conditions. These models account for the effects of the morphology on solar radiation and potential evapotranspiration exploiting the slope and aspect of the considered surfaces. The solar radiation was evaluated with two algorithms (Allen et al., 2006 and Kumar et al., 1997) and is used in the Penman-Monteith equation to estimate the potential evapotranspiration. By comparing the maps and the profiles obtained with these two models we highlighted the main differences due to the structure of the two different algorithms considered. Results show that the two methods produces almost same results when applied at the yearly scale, while the algorithm by Allen et al. (2006) outperform the one proposed by Kumar et al. (1997) at the daily time scale. Results highlighted the role of morphology (slope and aspect) on the global solar radiation and evapotranspiration at the local scale.

How to cite: Pizzolla, T., A. Acampora, S. Manfreda, Effetti legati alla morfologia nella stima della Radiazione Solare globale e dell’Evapotraspirazione potenzialeL’Acqua, n.2, 45-53, 2012. [pdf]

Influences of Leaf Area Index estimations on the soil water balance predictions in Mediterranean regions

In the present work, the role played by vegetation parameters, necessary to the hydrological distributed modeling, is investigated focusing on the correct use of remote sensing products for the evaluation of hydrological losses in the soil water balance. The research was carried out over a medium-sized river basin in Southern Italy, where the vegetation status is characterised through a data-set of multi- temporal NDVI images. The model adopted uses one layer of vegetation whose status is defined by the Leaf Area Index (LAI), which is often obtained from NDVI images. The inherent problem is that the vegetation heterogeneity – including soil disturbances – has a large influence on the spectral bands and so the relation between LAI and NDVI is not unambiguous. We present a rationale for the basin scale calibration of a non-linear NDVI-LAI regression, based on the comparison between NDVI values and literature LAI estimations of the vegetation cover in recognized landscape elements of the study catchment. Adopting a process-based model (DREAM) with a distributed parameterisation, the influence of different NDVI-LAI regression models on main features of water balance predictions is investigated. The results show a significant sensitivity of the hydrological losses and soil water regime to the alternative LAI estimations. These crucially affects the model performances especially in low flows simulation and in the identification of the intermittent regime.

How to cite: Gigante V., P. Milella, V. Iacobellis, S. Manfreda, and I. Portoghese, Influences of Leaf Area Index estimations on the soil water balance predictions in Mediterranean regionsNatural Hazard and Earth System Sciences, 9, 979-991, (doi:10.5194/nhess-9-979-2009), 2009. [pdf]