Dynamic-stochastic modeling of snow cover formation on the European territory of Russia

A dynamic-stochastic model, which combines a deterministic model of snow cover formation with a stochastic weather generator, has been developed. The deterministic snow model describes temporal change of the snow depth, content of ice and liquid water, snow density, snowmelt, sublimation, re-freezing of melt water, and snow metamorphism. The model has been calibrated and validated against the long-term data of snow measurements over the territory of the European Russia. The model showed good performance in simulating time series of the snow water equivalent and snow depth. The developed weather generator (NEsted Weather Generator, NewGen) includes nested generators of annual, monthly and daily time series of weather variables (namely, precipitation, air temperature, and air humidity). The parameters of the NewGen have been adjusted through calibration against the long-term meteorological data in the European Russia. A disaggregation procedure has been proposed for transforming parameters of the annual weather generator into the parameters of the monthly one and, subsequently, into the parameters of the daily generator. Multi-year time series of the simulated daily weather variables have been used as an input to the snow model. Probability properties of the snow cover, such as snow water equivalent and snow depth for return periods of 25 and 100 years, have been estimated against the observed data, showing good correlation coefficients. The described model has been applied to different landscapes of European Russia, from steppe to taiga regions, to show the robustness of the proposed technique

Описание макромасштабной структуры поля снежного покрова равнинной территории с помощью динамико-стохастической модели его формирования

Possibilities to investigate the spatial structure of snow cover by means of dynamic-stochastic model are discussed in this article. Basin of the Cheboksary reservoir (area of 376 500 sq.km) was used as an example. Results of numerical experiments show that our dynamic-stochastic model of the snow cover formation reproduces a snow field structure with adequate accuracy. The fractal dimensions of the modeled fields are in good correspondence with respective dimensions of fields obtained from data of the in situ observations.Показаны возможности динамико-стохастической модели формирования снежного покрова для исследования особенностей его пространственной структуры на примере территории бассейна Чебоксарского водохранилища (площадь 376 500 км2). Представлены результаты численных экспериментов, показывающие, что разработанная модель с удовлетворительной точностью воспроизводит структуру поля снежного покрова. Фрактальные размерности рассчитанных полей указанных характеристик близки к соответствующим размерностям полей, оценённым по данным снегомерных наблюдений

Advancing catchment hydrology to deal with predictions under change

Throughout its historical development, hydrology as an earth science, but especially as a problem-centred engineering discipline has largely relied (quite successfully) on the assumption of stationarity. This includes assuming time invariance of boundary conditions such as climate, system configurations such as land use, topography and morphology, and dynamics such as flow regimes and flood recurrence at different spatio-temporal aggregation scales. The justification for this assumption was often that when compared with the temporal, spatial, or topical extent of the questions posed to hydrology, such conditions could indeed be considered stationary, and therefore the neglect of certain long-term non-stationarities or feedback effects (even if they were known) would not introduce a large error. However, over time two closely related phenomena emerged that have increasingly reduced the general applicability of the stationarity concept: the first is the rapid and extensive global changes in many parts of the hydrological cycle, changing formerly stationary systems to transient ones. The second is that the questions posed to hydrology have become increasingly more complex, requiring the joint consideration of increasingly more (sub-) systems and their interactions across more and longer timescales, which limits the applicability of stationarity assumptions. Therefore, the applicability of hydrological concepts based on stationarity has diminished at the same rate as the complexity of the hydrological problems we are confronted with and the transient nature of the hydrological systems we are dealing with has increased. The aim of this paper is to present and discuss potentially helpful paradigms and theories that should be considered as we seek to better understand complex hydrological systems under change. For the sake of brevity we focus on catchment hydrology. We begin with a discussion of the general nature of explanation in hydrology and briefly review the history of catchment hydrology. We then propose and discuss several perspectives on catchments: as complex dynamical systems, self-organizing systems, co-evolving systems and open dissipative thermodynamic systems. We discuss the benefits of comparative hydrology and of taking an information-theoretic view of catchments, including the flow of information from data to models to predictions. In summary, we suggest that these perspectives deserve closer attention and that their synergistic combination can advance catchment hydrology to address questions of change

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the 21st century

During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include: warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land-use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia's role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large scale water withdrawals, land use and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that Integrated Assessment Models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts

Modelling hydrological consequences of climate change in the permafrost region and assessment of their uncertainty

Abstract A physically-based, distributed model of runoff generation in the permafrost regions is presented. The model describes processes of snow cover formation, taking into account blowing snow sublimation, snowmelt, freezing and thawing of the ground, water detention by a basin storage, infiltration, evaporation, overland, subsurface and channel flow. An important feature of the model is the detailed description of water and heat transfer within the active layer of soil during its seasonal thawing and freezing. A case study has been carried out for the Pravaya Hetta River basin (the catchment area is 1200 km 2 ) of Western Siberia within the Lower Ob River basin. The basin is located in tundra and forest-tundra vegetation zones. It has been shown that after precipitation, melt of ground ice is the second largest input to the basin water balance and accounts for about 70% of annual precipitation. Seasonal snow losses due to sublimation during blowing snow transport can reach almost 30% of the maximum snow accumulation. The model has been applied to assess the impact of climate change on hydrological processes in the permafrost basin. Uncertainty of the simulated hydrological consequences of climate change has been assessed by the multi-scenario approach. Simulated runoff response to the projected climate change varies significantly as a result of the uncertainty of the climate change scenario

Ensemble seasonal forecast of extreme water inflow into a large reservoir

An approach to seasonal ensemble forecast of unregulated water inflow into a large reservoir was developed. The approach is founded on a physically-based semi-distributed hydrological model ECOMAG driven by Monte-Carlo generated ensembles of weather scenarios for a specified lead-time of the forecast (3 months ahead in this study). Case study was carried out for the Cheboksary reservoir (catchment area is 374 000 km2) located on the middle Volga River. Initial watershed conditions on the forecast date (1 March for spring freshet and 1 June for summer low-water period) were simulated by the hydrological model forced by daily meteorological observations several months prior to the forecast date. A spatially distributed stochastic weather generator was used to produce time-series of daily weather scenarios for the forecast lead-time. Ensemble of daily water inflow into the reservoir was obtained by driving the ECOMAG model with the generated weather time-series. The proposed ensemble forecast technique was verified on the basis of the hindcast simulations for 29 spring and summer seasons beginning from 1982 (the year of the reservoir filling to capacity) to 2010. The verification criteria were used in order to evaluate an ability of the proposed technique to forecast freshet/low-water events of the pre-assigned severity categories

Describing macro-scale structure of the snow cover by a dynamic-stochastic model

Possibilities to investigate the spatial structure of snow cover by means of dynamic-stochastic model are discussed in this article. Basin of the Cheboksary reservoir (area of 376 500 sq.km) was used as an example. Results of numerical experiments show that our dynamic-stochastic model of the snow cover formation reproduces a snow field structure with adequate accuracy. The fractal dimensions of the modeled fields are in good correspondence with respective dimensions of fields obtained from data of the in situ observations

Climate noise effect on uncertainty of hydrological extremes: numerical experiments with hydrological and climate models

An approach has been proposed to analyze the simulated hydrological extreme uncertainty related to the internal variability of the atmosphere ("climate noise"), which is inherent to the climate system and considered as the lowest level of uncertainty achievable in climate impact studies. To assess the climate noise effect, numerical experiments were made with climate model ECHAM5 and hydrological model ECOMAG. The case study was carried out to Northern Dvina River basin (catchment area is 360 000 km2), whose hydrological regime is characterised by extreme freshets during spring-summer snowmelt period. The climate noise was represented by ensemble ECHAM5 simulations (45 ensemble members) with identical historical boundary forcing and varying initial conditions. An ensemble of the ECHAM5-outputs for the period of 1979–2012 was used (after bias correction post-processing) as the hydrological model inputs, and the corresponding ensemble of 45 multi-year hydrographs was simulated. From this ensemble, we derived flood statistic uncertainty caused by the internal variability of the atmosphere

Use of satellite-derived data for characterization of snow cover and simulation of snowmelt runoff through a distributed physically based model of runoff generation

A technique of using satellite-derived data for constructing continuous snow characteristics fields for distributed snowmelt runoff simulation is presented. The satellite-derived data and the available ground-based meteorological measurements are incorporated in a physically based snowpack model. The snowpack model describes temporal changes of the snow depth, density and water equivalent (SWE), accounting for snow melt, sublimation, refreezing melt water and snow metamorphism processes with a special focus on forest cover effects. The remote sensing data used in the model consist of products include the daily maps of snow covered area (SCA) and SWE derived from observations of MODIS and AMSR-E instruments onboard Terra and Aqua satellites as well as available maps of land surface temperature, surface albedo, land cover classes and tree cover fraction. The model was first calibrated against available ground-based snow measurements and then applied to calculate the spatial distribution of snow characteristics using satellite data and interpolated ground-based meteorological data. The satellite-derived SWE data were used for assigning initial conditions and the SCA data were used for control of snow cover simulation. The simulated spatial distributions of snow characteristics were incorporated in a distributed physically based model of runoff generation to calculate snowmelt runoff hydrographs. The presented technique was applied to a study area of approximately 200 000 km<sup>2</sup> including the Vyatka River basin with catchment area of 124 000 km<sup>2</sup>. The correspondence of simulated and observed hydrographs in the Vyatka River are considered as an indicator of the accuracy of constructed fields of snow characteristics and as a measure of effectiveness of utilizing satellite-derived SWE data for runoff simulation