Simone Gabellani

Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model

Abstract The reliable estimation of hydrological variables in space and time is of fundamental importance in operational hydrology to improve the flood predictions and hydrological cycle description. Nowadays remotely sensed data can offer a chance to improve hydrological models especially in environments with scarce ground based data. The aim of this work is to update the state variables of a physically based, distributed and continuous hydrological model using four different satellite-derived data (three soil moisture products and a land surface temperature measurement) and one soil moisture analysis to evaluate, even with a non optimal technique, the impact on the hydrological cycle. The experiments were carried out for a small catchment, in the northern part of Italy, for the period July 2012–June 2013. The products were pre-processed according to their own characteristics and then they were assimilated into the model using a simple nudging technique. The benefits on the model predictions of discharge were tested against observations. The analysis showed a general improvement of the model discharge predictions, even with a simple assimilation technique, for all the assimilation experiments; the Nash–Sutcliffe model efficiency coefficient was increased from 0.6 (relative to the model without assimilation) to 0.7, moreover, errors on discharge were reduced up to the 10%. An added value to the model was found in the rainfall season (autumn): all the assimilation experiments reduced the errors up to the 20%. This demonstrated that discharge prediction of a distributed hydrological model, which works at fine scale resolution in a small basin, can be improved with the assimilation of coarse-scale satellite-derived data.

Assimilation of remote sensing observations into a continuous distributed hydrological model: Impacts on the hydrologic cycle

A reliable estimation of soil moisture conditions is fundamental for discharges prediction and, consequently, for flood risk mitigation. Microwave remote sensing can be exploited to estimate soil moisture at large scale. These estimates can be used to enhance the predictions of hydrological models using Data Assimilation techniques and to reduce model uncertainties. This research tested the effects of the assimilation of three different satellite-derived soil moisture products (obtained from ASCAT acquisitions) in a distributed, physically based, hydrological model applied to three small Italian catchments. The products were firstly preprocessed, in order to be to be comparable with the state variables of the model. Subsequently they were assimilated by using different techniques: a simple Nudging applied at both model and satellite scale and the Ensemble Kalman Filter. Finally, observed discharges were compared with the modelled ones. The reanalysis was executed for a multi-year period ranging from July 2012 to June 2014.

Satellite soil moisture assimilation: Preliminary assessment of the sentinel 1 potentialities

First results of the assimilation of high-resolution Sentinel-1A based soil moisture products in a distributed, physically based, hydrological model are presented. A comprehensive evaluation of the assimilations impact on discharge predictions is provided. Results are further compared to those obtained when assimilating the lower-resolution ASCAT-based soil moisture product. The exercise was carried out within the MIDA project framework (funded by the Italian Space Agency) aiming at producing root zone soil moisture maps useful for flood risk management applications. The experimental site is the Orba River Catchment (Italy). The period of investigation is October 2014-February 2015. Using a relatively simple data assimilation technique (Nudging) the results of our case study show that overall the assimilation of currently available Sentinel-1 data only marginally improves discharge simulations. However, the impact becomes more significant when specifically considering predictions of high flow. Further improvements are expected when both Sentinel-1A and B data will be available.

Exploiting Sentinel 1 data for improving (flash) flood modelling via data assimilation techniques

As part of the Copernicus Programme, Sentinel 1 (S1) synthetic aperture radar (SAR) mission represents a unique monitoring tool whose potentialities for hydrological risk mitigation need to be evaluated. To this aim, S1-A derived soil moisture maps with high spatial resolution (100 m) and moderate temporal resolution (12 days) were assimilated within a time-continuous, spatially-distributed, physically-based hydrological model (Continuum) with the specific objective to evaluate the impact on discharge predictions and (flash) flood modelling. A Nudging assimilation scheme was chosen for the DA experiment due to its computational efficiency, particularly useful for operational applications. Results were evaluated in the Orba River catchment (Italy) in the time period October 2014 — November 2016, corresponding to the first two years of activity of the S1-A mission.

Validation of remote sensing soil moisture products with a distributed continuous hydrological model

The reliable estimation of soil moisture in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays several satellite-derived soil moisture products are available and can offer a chance to improve hydrological model performances especially in environments with scarce ground based data. The goal of this work is to test the effects of the assimilation of different satellite soil moisture products in a distributed physically based hydrological model. Among the currently available different satellite platforms, four soil moisture products, from both the ASCAT scatterometer and the SMOS radiometer, have been assimilated using a Nudging scheme. The model has been applied to a test basin (area about 800 km2) located in Northern Italy for the period July 2012-June 2013.

A Particle Filter scheme for multivariate data assimilation into apoint-scale snowpack model in Alpine environment

The accuracy of hydrological predictions in snowdominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling – particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size. The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application.

Monitoring reservoirs' water level from space for flood control applications. A case study in the Italian Alpine region

The objective of this research was to develop a method for water level retrieval in natural and artificial lakes. It was thought to be applied for monitoring purposes and flood control applications, especially in data-scarce environments. The method is based on a combined GIS, remote sensing and statistical modeling approach. It was tested on both optical (Landsat 8) and SAR (Cosmo-SkyMed®) data. The topographic information, required by the method, were obtained from freely available digital elevation models (SRTM and ASTER) to compare their performances. The Place Moulin Lake, an Alpine reservoir, was selected as study area since it represents a very challenging case study for developing the proposed methodology. The results showed that: i) the method provided reasonably accurate results when the degree of filling of the reservoir was high. ii) The accuracy of the results strongly relied on the accuracy of the topographic information. iii) The combination of Cosmo-SkyMed® and SRTM data provided more reliable results. Further analyses are required to evaluate the method in different environmental conditions.