Snow depth time series Generation: Effective simulation at multiple time scales
Snow depth (SD) is a crucial variable of the water, energy, and nutrient cycles, impacting water quantity and quality, the occurrence of floods and droughts, snow-related hazards, and sub-surface ecological functions. As a result, quantifying SD dynamics is crucial for several scientific and practic...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-05-01
|
| Series: | Journal of Hydrology X |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589915524000075 |
| _version_ | 1797220273715412992 |
|---|---|
| author | Hebatallah Mohamed Abdelmoaty Simon Michael Papalexiou Sofia Nerantzaki Giuseppe Mascaro Abhishek Gaur Henry Lu Martyn P. Clark Yannis Markonis |
| author_facet | Hebatallah Mohamed Abdelmoaty Simon Michael Papalexiou Sofia Nerantzaki Giuseppe Mascaro Abhishek Gaur Henry Lu Martyn P. Clark Yannis Markonis |
| author_sort | Hebatallah Mohamed Abdelmoaty |
| collection | DOAJ |
| description | Snow depth (SD) is a crucial variable of the water, energy, and nutrient cycles, impacting water quantity and quality, the occurrence of floods and droughts, snow-related hazards, and sub-surface ecological functions. As a result, quantifying SD dynamics is crucial for several scientific and practical applications. Ground measurements of SD provide information at sparse locations, and physical global model simulations provide information at relatively coarse spatial resolutions. An approach to complement this information is using stochastic models that generate time series of hydroclimatic variables, preserving their statistical properties in a computationally-effective manner. However, stochastic generation methods to produce SD time series exclusively do not exist in the literature. Here, we apply a stochastic model to produce synthetic daily SD time series trained by 448 stations in Canada. We show that the model captures key statistical properties of the observed records, including the daily distributions of zero and non-zero SD, temporal clustering (i.e., autocorrelation), and seasonal patterns. The model also excelled in capturing the observed higher-order L-moments at multiple temporal scales, with biases between simulated and observed L-skewness and L-kurtosis within (-0.1, +0.1) for 93.0 % and 98.3 % of the stations, respectively. The stochastic modelling approach introduced here advances the generation of SD time series, which are needed to develope Earth-system models and assess the risk of snowmelt flooding that lead to severe damage and fatalities. |
| first_indexed | 2024-04-24T12:46:55Z |
| format | Article |
| id | doaj.art-84376446ed804c779f0f46ecfcbef25c |
| institution | Directory Open Access Journal |
| issn | 2589-9155 |
| language | English |
| last_indexed | 2024-04-24T12:46:55Z |
| publishDate | 2024-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology X |
| spelling | doaj.art-84376446ed804c779f0f46ecfcbef25c2024-04-07T04:36:11ZengElsevierJournal of Hydrology X2589-91552024-05-0123100177Snow depth time series Generation: Effective simulation at multiple time scalesHebatallah Mohamed Abdelmoaty0Simon Michael Papalexiou1Sofia Nerantzaki2Giuseppe Mascaro3Abhishek Gaur4Henry Lu5Martyn P. Clark6Yannis Markonis7Department of Civil Engineering, Schulich School of Engineering, University of Calgary, Canada; Irrigation and Hydraulics Department, Faculty of Engineering, Cairo University, Giza, Egypt; Corresponding author at: Department of Civil Engineering, Schulich School of Engineering, University of Calgary, Canada.Department of Civil Engineering, Schulich School of Engineering, University of Calgary, Canada; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech RepublicDepartment of Civil, Geological and Environmental Engineering, University of Saskatchewan, CanadaSchool of Sustainable Engineering and the Built Environment, Arizona State University, USAConstruction Research Centre, National Research Council Canada; Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, CanadaConstruction Research Centre, National Research Council CanadaDepartment of Civil Engineering, Schulich School of Engineering, University of Calgary, CanadaFaculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech RepublicSnow depth (SD) is a crucial variable of the water, energy, and nutrient cycles, impacting water quantity and quality, the occurrence of floods and droughts, snow-related hazards, and sub-surface ecological functions. As a result, quantifying SD dynamics is crucial for several scientific and practical applications. Ground measurements of SD provide information at sparse locations, and physical global model simulations provide information at relatively coarse spatial resolutions. An approach to complement this information is using stochastic models that generate time series of hydroclimatic variables, preserving their statistical properties in a computationally-effective manner. However, stochastic generation methods to produce SD time series exclusively do not exist in the literature. Here, we apply a stochastic model to produce synthetic daily SD time series trained by 448 stations in Canada. We show that the model captures key statistical properties of the observed records, including the daily distributions of zero and non-zero SD, temporal clustering (i.e., autocorrelation), and seasonal patterns. The model also excelled in capturing the observed higher-order L-moments at multiple temporal scales, with biases between simulated and observed L-skewness and L-kurtosis within (-0.1, +0.1) for 93.0 % and 98.3 % of the stations, respectively. The stochastic modelling approach introduced here advances the generation of SD time series, which are needed to develope Earth-system models and assess the risk of snowmelt flooding that lead to severe damage and fatalities.http://www.sciencedirect.com/science/article/pii/S2589915524000075 |
| spellingShingle | Hebatallah Mohamed Abdelmoaty Simon Michael Papalexiou Sofia Nerantzaki Giuseppe Mascaro Abhishek Gaur Henry Lu Martyn P. Clark Yannis Markonis Snow depth time series Generation: Effective simulation at multiple time scales Journal of Hydrology X |
| title | Snow depth time series Generation: Effective simulation at multiple time scales |
| title_full | Snow depth time series Generation: Effective simulation at multiple time scales |
| title_fullStr | Snow depth time series Generation: Effective simulation at multiple time scales |
| title_full_unstemmed | Snow depth time series Generation: Effective simulation at multiple time scales |
| title_short | Snow depth time series Generation: Effective simulation at multiple time scales |
| title_sort | snow depth time series generation effective simulation at multiple time scales |
| url | http://www.sciencedirect.com/science/article/pii/S2589915524000075 |
| work_keys_str_mv | AT hebatallahmohamedabdelmoaty snowdepthtimeseriesgenerationeffectivesimulationatmultipletimescales AT simonmichaelpapalexiou snowdepthtimeseriesgenerationeffectivesimulationatmultipletimescales AT sofianerantzaki snowdepthtimeseriesgenerationeffectivesimulationatmultipletimescales AT giuseppemascaro snowdepthtimeseriesgenerationeffectivesimulationatmultipletimescales AT abhishekgaur snowdepthtimeseriesgenerationeffectivesimulationatmultipletimescales AT henrylu snowdepthtimeseriesgenerationeffectivesimulationatmultipletimescales AT martynpclark snowdepthtimeseriesgenerationeffectivesimulationatmultipletimescales AT yannismarkonis snowdepthtimeseriesgenerationeffectivesimulationatmultipletimescales |