Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

Ghanjkhanlo, Hojat; Vafakhah, Mehdi; Zeinivand, Hossein; Fathzadeh, Ali

doi:10.1007/s11629-018-4875-8

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…Regarding the former, the field of multivariate data assimilation is ripe (Piazzi et al, 2018), although scaling up these approaches across the landscape may come with significant computational requirements. Regarding new assimilation data, statistical learning is making promising steps towards mining new information from traditional, sometimes even sparse and fuzzy data across geosciences (Avanzi et al, 2019;Ghanjkhanlo et al, 2020;Dramsch, 2020;Grossi et al, 2021;Maurer et al, 2021;Shen et al, 2021;Mosaffa et al, 2022). Meanwhile, Cluzet et al (2020) have shown some degree of success in assimilating satellite reflectance into snowpack simulations as a way to better capture snow microstructure and so the energy balance.…”

Section: Applicability and Future Developmentsmentioning

confidence: 99%

Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt

Avanzi

Gabellani²,

Delogu³

et al. 2022

Geosci. Model Dev.

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Abstract. By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models. Such models represent seasonal snow and glaciers with various complexities and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present Snow Multidata Mapping and Modeling (S3M) v5.1, a spatially explicit and hydrology-oriented cryospheric model that simulates seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier mass balances, snow rheology and hydraulics, a hybrid temperature-index and radiation-driven melt parametrization, and a data-assimilation protocol. Comparatively novel aspects of S3M are an explicit representation of the spatial patterns of snow liquid-water content, the implementation of the Δh parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the northwestern Italian Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises a user manual as well as resources to prepare input data and set up computational environments and libraries.

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Section: Applicability and Future Developmentsmentioning

confidence: 99%

Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt

Avanzi

Gabellani²,

Delogu³

et al. 2022

Geosci. Model Dev.

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“…This research includes novel image threshold methods and clustering [18], parabolic fitting [19], apex detection by fitting an analytical hyperbola function to the profile edges detected with a Canny filter [20], template matching algorithms [21], and a neural network approach [22]. SWE can also be estimated on a large scale using neural networks to perform a nonlinear mapping between datasets of manual measurements to predict SWE [23], and by combining snow model simulation, manual measurements, and auxiliary products derived from remote sensing in a k-nearest neighbors (k-NN) algorithm [24].…”

Section: Introductionmentioning

confidence: 99%

Measurement of Snow Water Equivalent Using Drone-Mounted Ultra-Wide-Band Radar

Jenssen

Jacobsen

2021

Remote Sensing

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The use of uav-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate swe using uav-mounted radar and to perform initial field experiments. In this paper, we present an improved scheme for measuring swe using uwb (0.7GHz–4.5GHz) pseudo-noise radar on a moving uav, which is based on airborne snow depth and density measurements from the same platform. The scheme involves autofocusing procedures with the f-k migration algorithm combined with the Dix equation for layered media in addition to altitude correction of the flying platform. Initial results from field experiments show high repeatability (R>0.92) for depth measurements up to 5.5 m, and good agreement with Monte Carlo simulations for the statistical spread of snow density estimates with standard deviation of 0.108 g/cm3. This paper also outlines needed system improvements to increase the accuracy of a snow density estimator based on an f-k migration technique.

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Evaluating different machine learning algorithms for snow water equivalent prediction

et al. 2022

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Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

Cited by 8 publications

References 30 publications

Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt

Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt

Measurement of Snow Water Equivalent Using Drone-Mounted Ultra-Wide-Band Radar

Evaluating different machine learning algorithms for snow water equivalent prediction

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