Snow hydrology in Mediterranean mountain regions: A review

In 2013, the International Association of Hydrological Sciences (IAHS) launched the hydrological decade 2013–2022 with the theme “Panta Rhei: Change in Hydrology and Society”. The decade recognizes the urgency of hydrological research to understand and predict the interactions of society and water, to support sustainable water resource use under changing climatic and environmental conditions. This paper reports on the first Panta Rhei biennium 2013–2015, providing a comprehensive resource that describes the scope and direction of Panta Rhei. We bring together the knowledge of all the Panta Rhei working groups, to summarize the most pressing research questions and how the hydrological community is progressing towards those goals. We draw out interconnections between different strands of research, and reflect on the need to take a global view on hydrology in the current era of human impacts and environmental change. Finally, we look back to the six driving science questions identified at the outset of Panta Rhei, to quantify progress towards those aims.

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Panta Rhei 2013-2015: Global perspectives on hydrology, society and change

. Subgrid variability introduces non-negligible scale effects on the grid-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow accumulation–depletion curves (ADCs). In this study, terrestrial photography (TP) of a cell-sized area (30  ×  30 m) was used to define local snow ADCs at a Mediterranean site. Snow-cover fraction (SCF) and snow-depth (h) values obtained with this technique constituted the two datasets used to define ADCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting ADCs were associated to certain physical features of the snow, which were used to incorporate them in the point snow model formulated by Herrero et al. (2009) by means of a decision tree. The final performance of this model was tested against field observations recorded over four hydrological years (2009–2013). The calibration and validation of this ADC snow model was found to have a high level of accuracy, with global RMSE values of 105.8 mm for the average snow depth and 0.21 m2 m−2 for the snow-cover fraction in the control area. The use of ADCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

/pdf/subgrid-parameterization-of-snow-distribution-at-a-4mnce9pdet.pdf

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

Mediterranean mountainous regions constitute a climate change hotspot where snow plays a crucial role in water resources The characteristic snow-patched distribution over these areas makes spatial resolution the limiting factor for its correct representation This work assesses the estimation of snow cover area and the contribution of the patchy areas to the seasonal and annual regime of the snow in a semiarid mountainous range, the Sierra Nevada Mountains in southern Spain, by means of Landsat imagery combined with terrestrial photography (TP) Two methodologies were tested: (1) difference indexes to produce binary maps; and (2) spectral mixture analysis (SMA) to obtain fractional maps; their results were validated from “ground-truth” data by means of TP in a small monitored control area Both methods provided satisfactory results when the snow cover was above 85% of the study area; below this threshold, the use of spectral mixture analysis is clearly recommended Mixed pixels can reach up to 40% of the area during wet and cold years, their importance being larger as altitude increases, proving the usefulness of TP for assessing the accuracy of remote data sources Mixed pixels identification allows for determining the more vulnerable areas facing potential changes of the snow regime due to global warming and climate variability

Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery

. Mountain areas in Mediterranean regions constitute key monitoring points for climate variability and its impacts, but long time datasets are not always available due to the difficult access to high areas, relevant for capturing temperature and precipitation regimes, and the predominance of cloudy remote sensing images during the snow season. Sierra Nevada National Park (South Spain), with altitudes higher than 3500 m a.s.l., is part of the Global Change in Mountain Regions network. Snow occurrence just 40 km from the seaside determines a wide range of biodiversity, a snowmelt fluvial regime, and the associated ecosystem services. This work presents the local trend analysis of weather variables at this area together with additional snow-related variables. For this, long term point and distributed observations from weather stations and remote sensing sources were studied and used as input and calibration datasets of a physically based snow model to derive long term series of mean and maximum daily fraction of snow covered area, annual number of days with snow, annual number of days with precipitation, mean and maximum mean daily snow water equivalent, and snowmelt and evaporation volumes. The joint analysis of weather and snow variables showed a decrease trend in the persistence and extent of the snow cover area. The precipitation regime, rather than the temperature trend, seems to be the most relevant driver on the snow regime forcing in Mediterranean areas. This poses a constraint for rigorous scenario analysis in these regions, since the precipitation pattern is poorly approximated by climatic models in these regions.

/pdf/extreme-values-of-snow-related-variables-in-mediterranean-47xvpp6g7l.pdf

Extreme values of snow-related variables in Mediterranean regions: trends and long-term forecasting in Sierra Nevada (Spain)

Snow cover simulation is a complex task in mountain regions because of its highly irregular distribution. GIS-based calculations of snowmelt–accumulation models must deal with nonnegligible scale effects below cell size, which may result in unsatisfactory predictions depending on the study scale. Terrestrial photography, whose scales can be adapted to the study problem, is a cost-effective technique, capable of reproducing snow dynamics at subgrid scale. A series of high-frequency images were combined with a mass and energy model to reproduce snow evolution at cell scale (30 m × 30 m) by means of the assimilation of the snow cover fraction observation dataset obtained from terrestrial photography in the Sierra Nevada, southern Spain. The ensemble transform Kalman filter technique is employed. The results show the convenience of adopting a selective depletion curve parameterization depending on the succession of accumulation–melting cycles in the snow season in these highly variable environments. A...

https://journals.ametsoc.org/doi/pdf/10.1175/JHM-D-14-0046.1

Study of Snow Dynamics at Subgrid Scale in Semiarid Environments Combining Terrestrial Photography and Data Assimilation Techniques

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Study of Snow Dynamics at Subgrid Scale in Semiarid Environments Combining Terrestrial Photography and Data Assimilation Techniques