Renato Morbidelli

University of Perugia

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Carla Saltalippi

University of Perugia

Alessia Flammini

University of Perugia

Corrado Corradini

Università di Camerino

Jacopo Dari

University of Perugia

Rao S. Govindaraju

Purdue University West Lafayette

Luca Brocca

Research Institute for Geo-Hydrological Protection

F. Melone

KLE Society Hospital

Tommaso Moramarco

National Research Council

Tommaso Picciafuoco

University of Perugia

Marco Cifrodelli

Regione Umbria

Cooperative Institutions

National Research Council

University of Perugia

Research Institute for Geo-Hydrological Protection

National Academies of Sciences, Engineering, and Medicine

The University of Texas at Austin

Universidade Federal de Santa Maria

TU Wien

Centre National de la Recherche Scientifique

Consejo Superior de Investigaciones Científicas

University of Bonn

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Initial Soil Water Content as Input to Field-Scale Infiltration and Surface Runoff Models

Water Resources Management (2012)

Renato Morbidelli Corrado Corradini Carla Saltalippi Luca Brocca

Infiltration (HVAC)

Kinematic wave

10.1007/s11269-012-9986-3

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Citations (37)

Combining signal decomposition and deep learning model to predict noisy runoff coefficient

Journal of Hydrology (2024)

Arash Rahi Mehdi Rahmati Jacopo Dari Heye Bogena Harry Vereecken

SIGNAL (programming language)

10.1016/j.jhydrol.2024.131815

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Citations (0)

A plot-scale uncertainty analysis of saturated hydraulic conductivity of a clay soil

Journal of Hydrology (2020)

Vincenzo Bagarello Emanuele Barca Mirko Castellini Massimo Iovino Renato Morbidelli

Plot (graphics)

10.1016/j.jhydrol.2020.125694

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Citations (7)

Development and Analysis of Soil Water Infiltration Global Database

Mehdi Rahmati Lutz Weihermüller Jan Vanderborght Yakov Pachepsky Lili Mao

Abstract. In this paper, we present and analyze a global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and USA. In addition to its global spatial coverage, the collected infiltration curves cover a time span of research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use were gathered along with the infiltration data, which makes the database valuable for the development of pedo-transfer functions for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (~76 %) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on the land use is available for 76 % of experimental sites with agricultural land use as the dominant type (~40 %). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for use by public domain only and can be copied freely by referencing it. Supplementary data are available at doi:10.1594/PANGAEA.885492. Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend/update the SWIG by uploading new data to it.

Infiltration (HVAC)

Silt

Soil texture

Digital Soil Mapping

10.5194/essd-2018-11

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Citations (8)

Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water (2020)

Hamed Adab Renato Morbidelli Carla Saltalippi Mahmoud Moradian Gholam Abbas Fallah Ghalhari

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

10.3390/w12113223

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Citations (131)

[Distribution of anti-Toxoplasma antibodies in relation to age in the Turin area].

PubMed (1978)

G Giuliani Paggi Gc G Errico G Varetti O Novara

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Effective Saturated Hydraulic Conductivity for Representing Field-Scale Infiltration and Surface Soil Moisture in Heterogeneous Unsaturated Soils Subjected to Rainfall Events

Water (2017)

Richa Ojha Corrado Corradini Renato Morbidelli Rao S. Govindaraju

Spatial heterogeneity in soil properties has been a challenge for providing field-scale estimates of infiltration rates and surface soil moisture content over natural fields. In this study, we develop analytical expressions for effective saturated hydraulic conductivity for use with the Green-Ampt model to describe field-scale infiltration rates and evolution of surface soil moisture over unsaturated fields subjected to a rainfall event. The heterogeneity in soil properties is described by a log-normal distribution for surface saturated hydraulic conductivity. Comparisons between field-scale numerical and analytical simulation results for water movement in heterogeneous unsaturated soils show that the proposed expressions reproduce the evolution of surface soil moisture and infiltration rate with time. The analytical expressions hold promise for describing mean field infiltration rates and surface soil moisture evolution at field-scale over sandy loam and loamy sand soils.

Infiltration (HVAC)

Pedotransfer function

10.3390/w9020134

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Citations (15)

Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Hydrology and earth system sciences (2021)

Sara Modanesi Christian Massari A. Gruber Hans Lievens Angelica Tarpanelli

Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

Backscatter (email)

10.5194/hess-25-6283-2021

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Citations (31)

EFFETTO DELL’AGGREGAZIONE TEMPORALE DEI DATI PLUVIOMETRICI SULLE ANALISI DEI TREND DEGLI EVENTI INTENSI

Renato Morbidelli Corrado Corradini Carla Saltalippi Alessia Flammini Tommaso Picciafuoco

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Flow simulation in ungaged basins for water resources planning

international conference on Modelling and simulation (1998)

Corrado Corradini F. Melone Renato Morbidelli

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