ASSESSING PREDICTABILITY OF HYDROLOGICAL PROCESSES (ON THE EXAMPLE OF FROZEN SOIL WATER CONTENT DYNAMICS)

Abstract. A method has been developed for assessing the limits of predictability of the frozen soil water content (according to observations at the Nizhnedevitskaya water balance station). The method is based on the analysis of the convergence of a given probabilistic measure (the variance of the calculated soil water content at a given date) to its stable value. The soil water content was simulated by the physically based model of heat and water transfer in a frozen soil column during a autumn-winter seasons. To assess variability of the modelled soil water content at a given date, the boundary meteorological conditions for the autumn-winter period were simulated by the Monte Carlo procedure using a stochastic  weather generator. The initial conditions were assigned as the constant soil temperature and soil moisture values over the 1-meter soil column. The predictability of the soil water content in the one-meter layer of the studied soils has occurred to be about 1.5 months; it means that for the forest-steppe conditions, the soil water content before the beginning of soil freezing cannot serve as an indicator of soil water content before spring. Numerical experiments have shown that the soil water content predictability: (1) grows with an increase in the thickness of the considered soil layer and its depth; (2) decreases for coarser soils as compared to finely dispersed soils; (3) is more sensitive to changes in the soil texture than to changes in the climatic norms of precipitation and air temperatureAbstract. A method has been developed for assessing the limits of predictability of the frozen soil water content (according to observations at the Nizhnedevitskaya water balance station). The method is based on the analysis of the convergence of a given probabilistic measure (the variance of the calculated soil water content at a given date) to its stable value. The soil water content was simulated by the physically based model of heat and water transfer in a frozen soil column during a autumn-winter seasons. To assess variability of the modelled soil water content at a given date, the boundary meteorological conditions for the autumn-winter period were simulated by the Monte Carlo procedure using a stochastic  weather generator. The initial conditions were assigned as the constant soil temperature and soil moisture values over the 1-meter soil column. The predictability of the soil water content in the one-meter layer of the studied soils has occurred to be about 1.5 months; it means that for the forest-steppe conditions, the soil water content before the beginning of soil freezing cannot serve as an indicator of soil water content before spring. Numerical experiments have shown that the soil water content predictability: (1) grows with an increase in the thickness of the considered soil layer and its depth; (2) decreases for coarser soils as compared to finely dispersed soils; (3) is more sensitive to changes in the soil texture than to changes in the climatic norms of precipitation and air temperatur

Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide - A synthesis

An intercomparison of climate change impacts projected by nine regional-scale hydrological models for 12 large river basins on all continents was performed, and sources of uncertainty were quantified in the framework of the ISIMIP project. The models ECOMAG, HBV, HYMOD, HYPE, mHM, SWAT, SWIM, VIC and WaterGAP3 were applied in the following basins: Rhine and Tagus in Europe, Niger and Blue Nile in Africa, Ganges, Lena, Upper Yellow and Upper Yangtze in Asia, Upper Mississippi, MacKenzie and Upper Amazon in America, and Darling in Australia. The model calibration and validation was done using WATCH climate data for the period 1971–2000. The results, evaluated with 14 criteria, are mostly satisfactory, except for the low flow. Climate change impacts were analyzed using projections from five global climate models under four representative concentration pathways. Trends in the period 2070–2099 in relation to the reference period 1975–2004 were evaluated for three variables: the long-term mean annual flow and high and low flow percentiles Q 10 and Q 90, as well as for flows in three months high- and low-flow periods denoted as HF and LF. For three river basins: the Lena, MacKenzie and Tagus strong trends in all five variables were found (except for Q 10 in the MacKenzie); trends with moderate certainty for three to five variables were confirmed for the Rhine, Ganges and Upper Mississippi; and increases in HF and LF were found for the Upper Amazon, Upper Yangtze and Upper Yellow. The analysis of projected streamflow seasonality demonstrated increasing streamflow volumes during the high-flow period in four basins influenced by monsoonal precipitation (Ganges, Upper Amazon, Upper Yangtze and Upper Yellow), an amplification of the snowmelt flood peaks in the Lena and MacKenzie, and a substantial decrease of discharge in the Tagus (all months). The overall average fractions of uncertainty for the annual mean flow projections in the multi-model ensemble applied for all basins were 57% for GCMs, 27% for RCPs, and 16% for hydrological models

Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability

An approach is proposed to assess hydrological simulation uncertainty originating from internal atmospheric variability. The latter is one of three major factors contributing to uncertainty of simulated climate change projections (along with so-called "forcing" and "climate model" uncertainties). Importantly, the role of internal atmospheric variability is most visible over spatio-temporal scales of water management in large river basins. Internal atmospheric variability is represented by large ensemble simulations (45 members) with the ECHAM5 atmospheric general circulation model. Ensemble simulations are performed using identical prescribed lower boundary conditions (observed sea surface temperature, SST, and sea ice concentration, SIC, for 1979–2012) and constant external forcing parameters but different initial conditions of the atmosphere. The ensemble of bias-corrected ECHAM5 outputs and ensemble averaged ECHAM5 output are used as a distributed input for the ECOMAG and SWAP hydrological models. The corresponding ensembles of runoff hydrographs are calculated for two large rivers of the Arctic basin: the Lena and Northern Dvina rivers. A number of runoff statistics including the mean and the standard deviation of annual, monthly and daily runoff, as well as annual runoff trend, are assessed. Uncertainties of runoff statistics caused by internal atmospheric variability are estimated. It is found that uncertainty of the mean and the standard deviation of runoff has a significant seasonal dependence on the maximum during the periods of spring–summer snowmelt and summer–autumn rainfall floods. Noticeable nonlinearity of the hydrological models' results in the ensemble ECHAM5 output is found most strongly expressed for the Northern Dvina River basin. It is shown that the averaging over ensemble members effectively filters the stochastic term related to internal atmospheric variability. Simulated discharge trends are close to normally distributed around the ensemble mean value, which fits well to empirical estimates and, for the Lena River, indicates that a considerable portion of the observed trend can be externally driven

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

Hydroclimatic processes as the primary drivers of the Early Khvalynian transgression of the Caspian Sea: new developments

It has been well established that during the late Quaternary, the Khvalynian transgression of the Caspian Sea occurred, when the sea level rose tens of meters above the present level. Here, we evaluate the physical feasibility of the hypothesis that the maximum phase of this extraordinary event (known as the “Early Khvalynian transgression”) could be initiated and maintained for several thousand years solely by hydroclimatic factors. The hypothesis is based on recent studies dating the highest sea level stage (well above +10 m a.s.l.) to the final period of deglaciation, 17–13 kyr BP, and studies estimating the contribution of the glacial waters in the sea level rise for this period as negligible. To evaluate the hypothesis put forward, we first applied the coupled ocean and sea-ice general circulation model driven by the climate model and estimated the equilibrium water inflow (irrespective of its origin) sufficient to maintain the sea level at the well-dated marks of the Early Khvalynian transgression as 400–470 km3 yr−1. Secondly, we conducted an extensive radiocarbon dating of the large paleochannels (signs of high flow of atmospheric origin) located in the Volga basin and found that the period of their origin (17.5–14 ka BP) is almost identical to the recent dating of the main phase of the Early Khvalynian transgression. Water flow that could form these paleochannels was earlier estimated for the ancient Volga River as 420 km3 yr−1, i.e., close to the equilibrium runoff we determined. Thirdly, we applied a hydrological model forced by paleoclimate data to reveal physically consistent mechanisms of an extraordinarily high water inflow into the Caspian Sea in the absence of a visible glacial meltwater effect. We found that the inflow could be caused by the spread of post-glacial permafrost in the Volga paleocatchment. The numerical experiments demonstrated that the permafrost resulted in a sharp drop in infiltration into the frozen ground and reduced evaporation, which all together generated the Volga runoff during the Oldest Dryas, 17–14.8 kyr BP, up to 360 km3 yr−1 (i.e., the total inflow into the Caspian Sea could reach 450 km3 yr−1). The closeness of the estimates of river inflow into the sea, obtained by three independent methods, in combination with the previously obtained results, gave us reason to conclude that the hypothesis put forward is physically consistent.</p

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