1,721,019 research outputs found
ASSIMILATION OF MODIS SNOW COVER AND REAL TIME SNOW DEPTH POINT DATA IN A SNOW DYNAMIC MODEL
No full textTaratura e applicazione di un metodo per il calcolo dell’infiltrazione derivato dall’equazione di Horton
No full text" An operational flash-flood forecasting chain applied to the test cases of the EU project HYDROPTIMET"
No full textThe application of a flash-flood prediction chain, developed by CIAM, to some testcases for the Tanaro
river basin in the framework of the EU project HYDROPTIME is presented here. The components of
the CIMA chain are: forecast rainfall depths, a stochastic downscaling procedure and a hydrological
model.
Different meteorological Limited Area Models (LAMs) provide the rainfall input to the hydrological
component. The flash-flood prediction chain is run both in a deterministic and in a probabilistic
configuration. The sensitivity of forecasting chain performance to different LAMs providing rainfall
forecasts is discussed. The result of the application shown how the probabilistic forecasting system can
give, especially in the case of convective events, a valuable contribution in addressing the uncertainty
at different spatial-temporal scales involved in the flash-flood forecasting problem in small and
medium basin with complex orography
General calibration methodology for a combined Horton-SCS infiltration scheme in flash flood modeling
No full textComparisons of Satellite Soil Moisture, an Energy Balance Model Driven by LST Data and Point Measurements
No full textand atmosphere, thereby influencing climate and weather, and it is important in determining the rainfall-runoff
response of catchments; moreover, in hydrological modelling and flood forecasting, a correct definition of
moisture conditions is a key factor for accurate predictions.
Different sources of information for the estimation of the soil moisture state are currently available: satellite data,
point measurements and model predictions. All are affected by intrinsic uncertainty. Among different satellite
sensors that can be used for soil moisture estimation three major groups can be distinguished: passive microwave
sensors (e.g., SSMI), active sensors (e.g. SAR, Scatterometers), and optical sensors (e.g. Spectroradiometers). The
last two families, mainly because of their temporal and spatial resolution seem the most suitable for hydrological
applications
In this work soil moisture point measurements from 10 sensors in the Italian territory are compared of with the
satellite products both from the HSAF project SM-OBS-2, derived from the ASCAT scatterometer, and from
ACHAB, an operative energy balance model that assimilate LST data derived from MSG and furnishes daily an
evaporative fraction index related to soil moisture content for all the Italian region. Distributed comparison of the
ACHAB and SM-OBS-2 on the whole Italian territory are performed too
Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model
Full text linkFull process description and distributed hydrological models are very useful tools in hydrology as they can be applied in different contexts and for a wide range of aims such as flood and drought forecasting, water management, and prediction of impact on the hydrologic cycle due to natural and human-induced changes. Since they must mimic a variety of physical processes, they can be very complex and with a high degree of parameterization. This complexity can be increased by necessity of augmenting the number of observable state variables in order to improve model validation or to allow data assimilation. <br><br> In this work a model, aiming at balancing the need to reproduce the physical processes with the practical goal of avoiding over-parameterization, is presented. The model is designed to be implemented in different contexts with a special focus on data-scarce environments, e.g. with no streamflow data. <br><br> All the main hydrological phenomena are modelled in a distributed way. Mass and energy balance are solved explicitly. Land surface temperature (LST), which is particularly suited to being extensively observed and assimilated, is an explicit state variable. <br><br> A performance evaluation, based on both traditional and satellite derived data, is presented with a specific reference to the application in an Italian catchment. The model has been firstly calibrated and validated following a standard approach based on streamflow data. The capability of the model in reproducing both the streamflow measurements and the land surface temperature from satellites has been investigated. <br><br> The model has been then calibrated using satellite data and geomorphologic characteristics of the basin in order to test its application on a basin where standard hydrologic observations (e.g. streamflow data) are not available. The results have been compared with those obtained by the standard calibration strategy based on streamflow data
Water-balance response to climate variability, a small-to-large scale Italian dataset
No full textUnderstanding how deficit of precipitation impacts the hydrological cycle is of growing interest and is essential for water resource management. It has been recently observed that the relationship between precipitation and runoff during droughts is subjected to a shift in the sense that the predicted runoff is much less than the one expected due to the deficit in precipitation. Unraveling why this occurs requires an accurate knowledge of all the components of the water balance equation. However, large-scale and consistent samples of precipitation, runoff, evapotranspiration, ET and change in storage have always been challenging to collect. Here, we hypothesized that blending ground-based and remote-sensing data products could fill this gap. We present a countrywide dataset of catchment-scale water balance, covering the last 10 water years in Italy. Italy shows a broad variety of climatic and topographic features and faced several droughts over recent years. We use ground-based daily runoff data, interpolated precipitation maps, and a remote-sensed daily evapotranspiration dataset from the LSASAF ET product. The ET dataset is additionally compared with flux towers data across the country, obtaining root mean square errors on the order of 30 mm/month. Lastly, changes in storage are estimated to close the water balance. More than 100 catchments - including the major Italian basins - are selected, according to data availability and reliability. These catchments cover a wide range of size, morphologic and climatic characteristics.
This dataset is a strategic source of information to analyze catchment-scale runoff, ET and storage response to climatic variability across climates and landscapes
La sensibilità della risposta idrologica alla struttura spazio-temporale della precipitazione
No full textReducing parameters uncertainty of a distributed hydrological model by using ground stations and remote sensing data
No full textComplete and distribute models, based on physical equations must mimic a variety of hydro-meteorological
processes. This often leads to design very complex models with a high degree of parameterization.
The necessity to assimilate data of different nature observed by ground stations and remote sensors can be
sometimes incompatible with the degree of complexity and parameterization of such models.
This work presents an attempt to reduce the uncertainty of the parameters of a continuous distributed model by
augmenting the parameters constraints. This latter objective is pursued using both ground stations and remote
sensed data and exploiting the characteristic of the model of simulating various state variables, specifically the
land surface temperature and the soil humidity of the root zone.
The model has been then calibrated introducing satellite and ground stations data in a simple multi-objective
function. The results have been compared with those obtained by a standard calibration strategy based on
streamflow data
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