Understanding the drivers of extensive plant damage in boreal and Arctic ecosystems: Insights from field surveys in the aftermath of damage.

The exact cause of population dieback in nature is often challenging to identify retrospectively. Plant research in northern regions has in recent decades been largely focussed on the opposite trend, namely increasing populations and higher productivity. However, a recent unexpected decline in remotely-sensed estimates of terrestrial Arctic primary productivity suggests that warmer northern lands do not necessarily result in higher productivity. As large-scale plant dieback may become more frequent at high northern latitudes with increasing frequency of extreme events, understanding the drivers of plant dieback is especially urgent. Here, we report on recent extensive damage to dominant, short, perennial heath and tundra plant populations in boreal and Arctic Norway, and assess the potential drivers of this damage. In the High-Arctic archipelago of Svalbard, we recorded that 8-50% of Cassiope tetragona and Dryas octopetala shoots were dead, and that the ratios of dead shoots increased from 2014 to 2015. In boreal Norway, 38-63% of Calluna vulgaris shoots were dead, while Vaccinium myrtillus had damage to 91% of shoots in forested sites, but was healthy in non-forested sites. Analyses of numerous sources of environmental information clearly point towards a winter climate-related reason for damage to three of these four species. In Svalbard, the winters of 2011/12 and 2014/15 were documented to be unusually severe, i.e. insulation from ambient temperature fluctuation by snow was largely absent, and ground-ice enforced additional stress. In boreal Norway, the 2013/14 winter had a long period with very little snow combined with extremely low precipitation rates, something which resulted in frost drought of uncovered Calluna plants. However, extensive outbreaks of a leaf-defoliating geometrid moth were identified as the driver of Vaccinium mortality. These results suggest that weather and biotic extreme events potentially have strong impacts on the vegetation state of northern lands

Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

In Norway, 30 % of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of 1 km over an area in southern Norway for 2 years (1 September 2014–31 August 2016). Experiments were carried out using two different forcing data sets: (1) hourly forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing) including post-processed temperature (500 m grid spacing) and wind, and (2) gridded hourly observations of temperature and precipitation (1 km grid spacing) combined with meteorological forecasts from AROME MetCoOp for the remaining weather variables required by SURFEX/Crocus. We present an evaluation of the modelled snow depth and snow cover in comparison to 30 point observations of snow depth and MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snowmelt. Both experiments are capable of simulating the snowpack over the two winter seasons, but there is an overestimation of snow depth when using meteorological forecasts from AROME MetCoOp (bias of 20 cm and RMSE of 56 cm), although the snow-covered area in the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season. When using gridded observations, the simulation of snow depth is significantly improved (the bias for this experiment is 7 cm and RMSE 28 cm), but the spatial snow cover distribution is not well captured during the melting season. Underestimation of snow depth at high elevations (due to the low elevation bias in the gridded observation data set) likely causes the snow cover to decrease too soon during the melt season, leading to unrealistically little snow by the end of the season. Our results show that forcing data consisting of post-processed NWP data (observations assimilated into the raw NWP weather predictions) are most promising for snow simulations, when larger regions are evaluated. Post-processed NWP data provide a more representative spatial representation for both high mountains and lowlands, compared to interpolated observations. There is, however, an underestimation of snow ablation in both experiments. This is generally due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snowmelt and biases in the forcing data

Hydrological modelling of the Vistula and Odra river basins using SWAT

<p>This paper presents a large-scale application of the SWAT model for water balance and natural streamflow simulation in the entire basins of the Vistula and the Odra, covering most of the territory of Poland. A tailored calibration approach was designed to achieve satisfactory goodness-of-fit across a range of catchment sizes. Model calibration and evaluation driven by high-resolution climate data showed overall good behaviour for 80 benchmark catchments divided into eight clusters, and spatial evaluation for 30 gauges showed that the designed regionalization scheme performed well (median KGE of 0.76). Basin-averaged estimates of blue and green water flow and green water storage estimated using the calibrated model were 185, 517 and 206 mm, respectively. This study provides a basis for future work, such as assessing climate change impacts on hydrology, assessing flow alterations, and water quality simulation. The model output is publicly available through an online research data archive (doi:10.4121/uuid:b8ab4f5f-f692-4c93-a910-2947aea28f42).</p><p><b>EDITOR</b> A. Castellarin</p><p><b>ASSOCIATE EDITOR</b> G. Thirel</p><p></p> <p><b>EDITOR</b> A. Castellarin</p> <p><b>ASSOCIATE EDITOR</b> G. Thirel</p

Review of snow data assimilation methods for hydrological, land surface, meteorological and climate models: Results from a COST harmosnow survey

The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments

Snow Data Assimilation and Evaluation Methods for Hydrological, Land Surface, Meteorological and Climate Models – A COST Action HarmoSnow Assessment Questionnaire

This chapter is based on outcomes of the working group 3 Questionnaire of the COST Action ES1404 (www.harmosnow.eu) and provides a discussion of snow data assimilation in research and operational applications, which will be presented in detail in a manuscript (Helmert et al., 2018)