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15 results on '"Clemenzi, Ilaria"'

6. Impact of snow distribution modelling for runoff predictions

Author

Clemenzi, Ilaria, Gustafsson, David, Marchand, Wolf-Dietrich, Norell, Björn, Zhang, Jie, Pettersson, Rickard, Pohjola, Veijo, Clemenzi, Ilaria, Gustafsson, David, Marchand, Wolf-Dietrich, Norell, Björn, Zhang, Jie, Pettersson, Rickard, and Pohjola, Veijo

Abstract

Snow in the mountains is essential for the water cycle in cold regions. The complexity of the snow processes in such an environment makes it challenging for accurate snow and runoff predictions. Various snow modelling approaches have been developed, especially to improve snow predictions. In this study, we compared the ability to improve runoff predictions in the Överuman Catchment, Northern Sweden, using different parametric representations of snow distribution. They included a temperature-based method, a snowfall distribution (SF) function based on wind characteristics and a snow depletion curve (DC). Moreover, we assessed the benefit of using distributed snow observations in addition to runoff in the hydrological model calibration. We found that models with the SF function based on wind characteristics better predicted the snow water equivalent (SWE) close to the peak of accumulation than models without this function. For runoff predictions, models with the SF function and the DC showed good performances (median Nash–Sutcliffe efficiency equal to 0.71). Despite differences among the calibration criteria for the different snow process representations, snow observations in model calibration added values for SWE and runoff predictions.

Published
2023
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7. Tailoring Hydro-Climate Services to Local Conditions with Machine Learning Methods

Author

Du, Yiheng, Clemenzi, Ilaria, and Pechlivanidis, Ilias

Abstract

Intensified natural hazards at a global scale has underscored the urgency of developing advanced tools to enhance the Disaster Risk Reduction cycle, including early warning, early action and long-term strategic plan, where continental and global scale models can contribute by deepening the incorporation of local knowledge and data. This study focuses on five European case studies with different hydro-climatic conditions and user needs, and (1) evaluates the skill of seasonal forecasts from large-scale hydro-climate services and (2) explores machine learning techniques to enhance the seasonal forecasts by better capturing the local information. The E-HYPE hydrological model, driven by bias-adjusted ECMWF SEAS5 seasonal meteorological forecasts, is assessed using pseudo-reality (model simulation) and “real” local observations as reference. This evaluation sets the benchmark for quantifying the improvement obtained from machine-learning-enhanced hydro-climate services. A post-processing approach, based on quantile mapping and deep learning algorithms is then introduced to integrate available local observations and to further enhance the seasonal forecasting skill. The results highlight the way forward for tailoring large-scale hydro-climate services using machine-learning enhanced services, local knowledge and data., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023

12. Improving the snowpack monitoring in the mountainous areas of Sweden from space : a machine learning approach

Author

Zhang, Jie, Pohjola, Veijo, Pettersson, Rickard, Norell, Björn, Wolf-Dietrich, Marchand, Clemenzi, Ilaria, Gustafsson, David, Zhang, Jie, Pohjola, Veijo, Pettersson, Rickard, Norell, Björn, Wolf-Dietrich, Marchand, Clemenzi, Ilaria, and Gustafsson, David

Abstract

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

Published
2021
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14. Understanding and Modelling Snow Accumulation on Glaciers

Author

Clemenzi, Ilaria, Burlando, Paolo, and Pellicciotti, Francesca

Subjects
GLACIER ACCUMULATION (GLACIOLOGY), SNOW COVER MEASUREMENTS (GLACIOLOGY), GLETSCHERABLATION, ABSCHMELZEN, ZURÜCKWEICHEN DER GLETSCHER (GLAZIOLOGIE), SCHNEEDECKENEIGENSCHAFTEN (GLAZIOLOGIE), WIND EFFECTS (METEOROLOGY), SNOW DRIFTING (GLACIOLOGY), GLETSCHERZUWACHS (GLAZIOLOGIE), AROLLA GLACIER (VALAIS ALPS), GLAZIOLOGISCHE MODELLEXPERIMENTE (GLAZIOLOGIE), AROLLA-GLETSCHER (WALLISER ALPEN), METEOROLOGISCHE BEOBACHTUNGEN, Earth sciences, WINDWIRKUNGEN (METEOROLOGIE), MASSEN- UND ENERGIEBILANZ VON GLETSCHERN (GLAZIOLOGIE), SCHNEEVERFRACHTUNG (GLAZIOLOGIE), GLACIOLOGICAL MODEL EXPERIMENTS (GLACIOLOGY), METEOROLOGICAL OBSERVATIONS, GLACIER ABLATION (GLACIOLOGY), MASS AND ENERGY BALANCE OF GLACIERS (GLACIOLOGY), ddc:550
Published
2016

15. Exploring the spatial variability of baseflow δ18O and δ2H in a sub-Arctic glacierized catchment in northern Sweden.

Author

Clemenzi, Ilaria, Fischer, Benjamin, and Rosqvist, Gunhild

Subjects
*COMPOSITION of water, *SNOWMELT, *WATER quality, *MELTWATER, *BIOLOGICAL transport, *WATERSHEDS, *SNOW accumulation
Abstract

Sub-Arctic glacierized catchments play a key role for the downstream regions sustaining withtheir runoff the baseflow in dry periods, influencing the timing and magnitude of floods andaffecting the water quality and nutrient transport for the biological production in the river upto the sea.Tracers such as the stable isotopes of the water (18O and 2H) are useful tools to reveal thewater flow paths and understand the runoff generation mechanisms. By collecting daily orsub daily water samples from baseflow, rainfall and stormflow at the catchmentoutlet it is possible to separate the hydrograph and infer which component eventor pre-event water contribute to streamflow. The underlying assumption of thisapproach is that the stream water, collected at the catchment outlet, represents thewater composition of the entire catchment. We question whether this assumptionis valid for runoff process or monitoring of sub-arctic glacierized catchments orwhether it is necessary to consider the spatial variability of isotopic composition. Toassess this, we collected water samples of streamflow, snow or firn, ice, glaciermeltwater, lake water, exfiltrating soil water and groundwater in three baseflowsnapshot campaigns during August-September 2018 in the Tarfala catchment (part ofthe SITES Water - Swedish Infrastructure for Ecosystem Science). The differentwater samples were analysed for their stable isotope composition to explore 1)the spatial variability of the baseflow isotopic composition in northern glacierizedcatchments and 2) changes of the isotopic signal of the different baseflow elements (e.g.glacier, lake, shallow and deep groundwater storages) within the catchment anddownstream.Our results show that the isotopic composition of streamflow within the catchment wasspatially variable and the sources snow, glacier ice, glacier meltwater, lake water,streamflow could be identified due to their isotopic composition. Differences inthe isotopic signal were found among the three campaigns reflecting differencesin the catchment states and indicating temporal changing contributing sources.The isotopic composition of the lake water remained largely stable and did notshow fractionation due to evaporation. Tracing the water through the catchmentrevealed, that some springs were fed by nearby melting snow patches, due to a similarisotopic composition, and did not change their isotopic signature until the mainstream was reached. Other springs instead were dissimilar to the snow isotopiccomposition indicating the contribution of deeper groundwater, which ceased duringlonger dry periods. Overall, the isotopic signal at the catchment outlet was similar toglacier melt water due to the high glacier melt at that time. This result highlights theimportance of glaciers providing baseflow to downstream regions. Our snapshotdata complement the long-term ones available for the catchment (SITES Water,www.fieldsites.se) highlighting the large variability of spatial and temporal pools of water,that needs to be investigated to accurately predict future changes in runoff contributingsources. [ABSTRACT FROM AUTHOR]

Published
2019

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