Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

Callegari, Mattia; Mazzoli, Paolo; Gregorio, Ludovica De; Notarnicola, Claudia; Pasolli, Luca; Petitta, Marcello; Pistocchi, Alberto

doi:10.3390/w7052494

Cited by 28 publications

(11 citation statements)

References 58 publications

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“…Daily gridded values of average temperature and precipitation were derived, in the context of early developments of a regional soil water balance model [67] from temperature and precipitation measurements at ground stations operated by the province of Bolzano's Hydrographic Office (see [16] for details) using regression kriging with external drift (given by elevation) for temperature, and ordinary kriging for precipitation. The choice of ordinary kriging was due to the unclear patterns of correlation between precipitation and elevation emerging from the analysis of the available data.…”

Section: Testing the Approach Using Regional Informationmentioning

confidence: 99%

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

Pistocchi

Bagli²,

Callegari

et al. 2017

Water

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Abstract:We present a novel method for the direct determination of the snowmelt coefficient of widely used degree-day models, using only cumulated temperature and precipitation over the days of snow cover. We develop a proof of concept using (1) local measurements of precipitation, temperature and snow water equivalent (SWE) at a set of well-monitored sites in the US, and (2) available time series of snow cover from satellite and gridded daily precipitation and daily average temperature for the study region of South Tyrol, in the Italian Alps. We demonstrate how the method can reproduce the snow water balance to an acceptable extent, critically depending on the accuracy of input precipitation and temperature, highlighting the importance of a reliable representation of weather forcing if the estimate has to be robust and representative. Although not always accurate at a point, our approach yields a SWE reasonably consistent with observations, and snowmelt flows compatible with measured streamflow. At the same time, the model allows an interpretation of discrepancies between observations and simulations to detect inconsistencies between snow cover and weather forcing. This method is in principle applicable for large-scale hydrological assessments thanks to the increasing global coverage of snow cover, precipitation and temperature data. As the only other type of observation available to calibrate models is often streamflow, the direct calibration of the snow component of a model using snow cover and weather forcing reduces the number of model processes and parameters to be calibrated with streamflow, and is expected to increase model robustness.

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Section: Testing the Approach Using Regional Informationmentioning

confidence: 99%

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

Pistocchi

Bagli²,

Callegari

et al. 2017

Water

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“…Based on this logic, many performance assessment approaches are presented, such as the NSE [1], RMSE and the R or R 2 . Most of them are based on the residuals of the model for the simulated hydrological processes [47,48]. The residuals could have acceptable threshold, which would be resistance from the perspective of reliability theory.…”

Section: The Acceptable Threshold Value Of the Performance Functionmentioning

confidence: 99%

Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold

Dong

Lü

2015

Water

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Abstract:It is essential to consider the acceptable threshold in the assessment of a hydrological model because of the scarcity of research in the hydrology community and errors do not necessarily cause risk. Two forecast errors, including rainfall forecast error and peak flood forecast error, have been studied based on the reliability theory. The first order second moment (FOSM) and bound methods are used to identify the reliability. Through the case study of the Dahuofang (DHF) Reservoir, it is shown that the correlation between these two errors has great influence on the reliability index of hydrological model. In particular, the reliability index of the DHF hydrological model decreases with the increasing correlation. Based on the reliability theory, the proposed performance evaluation framework incorporating the acceptable forecast error threshold and correlation among the multiple errors can be used to evaluate the performance of a hydrological model and to quantify the uncertainties of a hydrological model output.

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“…Time-series of SCA provide a measurement of the snow cover depletion rate, which can be either exploited for snow water equivalent (SWE) reconstruction, e.g., [14] or assimilated in hydrological models, e.g., [15], [16]. Furthermore, SCA is a proxy for many variables, e.g., related to assess the impact of climate changes [11] or to predict the availability of water discharge [17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on a Hierarchical Multiresolution Analysis of Optical Time Series to Reconstruct the Daily High-Resolution Snow Cover Area

Premier

Marín

Steger

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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High-resolution (HR) snow cover maps derived by remotely sensed images are an asset for data assimilation in hydrological models. However, the current satellite missions do not provide daily HR multispectral observations suitable for an accurate snow monitoring in alpine environments. On the contrary, low-resolution (LR) sensors acquire daily information of snow cover fraction (SCF) but at an inappropriate spatial scale. This paper proposes a novel approach that combines multi-source and multi-scale acquisitions to infer the daily HR snow cover area (SCA) for mountainous basins. The approach builds on the assumption that inter-annual snow patterns are both affected by the local geomorphometry and meteorology. We derive these patterns through a hierarchical multi-step approach based on historical statistical analyses on a long and sparse HR time-series. At each step, we obtain HR snow cover maps with higher number of reconstructed pixels but decreasing level of confidence. Historical data are used to estimate the probability that a HR pixel is covered by snow according to two possible multi-scale strategies: i) HR gap-filling, or ii) LR downscaling. These analyses lead to the identification of the patterns that regularly appear given certain conditions. When no systematic patterns are observed, we reinforce the inference of the pixel class by a generalized additive model (GAM) that exploits not only the historical data but also explicit geomorphometric, global snow and multi-temporal properties. The proposed approach has been validated on a catchment in the Sierra Nevada, USA, for three hydrological years (2017)(2018)(2019) showing an average overall accuracy of 92%.

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Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

Cited by 28 publications

References 58 publications

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold

A Novel Approach Based on a Hierarchical Multiresolution Analysis of Optical Time Series to Reconstruct the Daily High-Resolution Snow Cover Area

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