scholarly journals Impact of possible climate changes on river runoff under different natural conditions

2018 ◽  
Vol 379 ◽  
pp. 293-300 ◽  
Author(s):  
Yeugeniy M. Gusev ◽  
Olga N. Nasonova ◽  
Evgeny E. Kovalev ◽  
Georgy V. Ayzel
Keyword(s):  
Air Temperature ◽  
River Runoff ◽  
Land Surface ◽  
General Circulation ◽  
General Circulation Models ◽  
River Basins ◽  
Annual Runoff ◽  
Surface Model ◽  
Natural Conditions ◽  
Rcp Scenarios

Abstract. The present study was carried out within the framework of the International Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) for 11 large river basins located in different continents of the globe under a wide variety of natural conditions. The aim of the study was to investigate possible changes in various characteristics of annual river runoff (mean values, standard deviations, frequency of extreme annual runoff) up to 2100 on the basis of application of the land surface model SWAP and meteorological projections simulated by five General Circulation Models (GCMs) according to four RCP scenarios. Analysis of the obtained results has shown that changes in climatic runoff are different (both in magnitude and sign) for the river basins located in different regions of the planet due to differences in natural (primarily climatic) conditions. The climatic elasticities of river runoff to changes in air temperature and precipitation were estimated that makes it possible, as the first approximation, to project changes in climatic values of annual runoff, using the projected changes in mean annual air temperature and annual precipitation for the river basins. It was found that for most rivers under study, the frequency of occurrence of extreme runoff values increases. This is true both for extremely high runoff (when the projected climatic runoff increases) and for extremely low values (when the projected climatic runoff decreases).

scholarly journals Climate change impact on streamflow in large-scale river basins: projections and their uncertainties sourced from GCMs and RCP scenarios

2018 ◽  
Vol 379 ◽  
pp. 139-144 ◽  
Author(s):  
Olga N. Nasonova ◽  
Yeugeniy M. Gusev ◽  
Evgeny E. Kovalev ◽  
Georgy V. Ayzel
Keyword(s):  
Climate Change ◽  
River Runoff ◽  
Climate Change Impact ◽  
Large Scale ◽  
River Basins ◽  
Surface Model ◽  
Second Phase ◽  
Rcp Scenarios ◽  
Data Set ◽  
Change Impact

Abstract. Climate change impact on river runoff was investigated within the framework of the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP2) using a physically-based land surface model Soil Water – Atmosphere – Plants (SWAP) (developed in the Institute of Water Problems of the Russian Academy of Sciences) and meteorological projections (for 2006–2099) simulated by five General Circulation Models (GCMs) (including GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) for each of four Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Eleven large-scale river basins were used in this study. First of all, SWAP was calibrated and validated against monthly values of measured river runoff with making use of forcing data from the WATCH data set and all GCMs' projections were bias-corrected to the WATCH. Then, for each basin, 20 projections of possible changes in river runoff during the 21st century were simulated by SWAP. Analysis of the obtained hydrological projections allowed us to estimate their uncertainties resulted from application of different GCMs and RCP scenarios. On the average, the contribution of different GCMs to the uncertainty of the projected river runoff is nearly twice larger than the contribution of RCP scenarios. At the same time the contribution of GCMs slightly decreases with time.

scholarly journals Modelling river runoff and estimating its weather-related uncertainty for 11 large-scale rivers located in different regions of the globe

2017 ◽  
Vol 49 (4) ◽  
pp. 1072-1087 ◽  
Author(s):  
Yeugeniy M. Gusev ◽  
Olga N. Nasonova ◽  
Evgeny E. Kovalev ◽  
Georgii V. Aizel
Keyword(s):  
River Runoff ◽  
Land Surface ◽  
Input Data ◽  
Large Scale ◽  
Land Surface Model ◽  
Grid Cell ◽  
Surface Model ◽  
Model Parameters ◽  
Data Sets ◽  
Natural Conditions

Abstract In order to study the possibility of reproducing river runoff with making use of the land surface model Soil Water–Atmosphere–Plants (SWAP) and information based on global data sets 11 river basins suggested within the framework of the Inter-Sectoral Impact Model Intercomparison Project and located in various regions of the globe under a wide variety of natural conditions were used. Schematization of each basin as a set of 0.5° × 0.5° computational grid cells connected by a river network was carried out. Input data including atmospheric forcing data and land surface parameters based, respectively, on the global WATCH and ECOCLIMAP data sets were prepared for each grid cell. Simulations of river runoff performed by SWAP with a priori input data showed poor agreement with observations. Optimization of a number of model parameters substantially improved the results. The obtained results confirm the universal character of SWAP. Natural uncertainty of river runoff caused by weather noise was estimated and analysed. It can be treated as the lowest limit of predictability of river runoff. It was shown that differences in runoff uncertainties obtained for different rivers depend greatly on natural conditions of a river basin, in particular, on the ratio of deterministic and random components of the river runoff.

scholarly journals Realistic Initialization of Land Surface States: Impacts on Subseasonal Forecast Skill

2004 ◽  
Vol 5 (6) ◽  
pp. 1049-1063 ◽  
Author(s):  
Randal D. Koster ◽  
Max J. Suarez ◽  
Ping Liu ◽  
Urszula Jambor ◽  
Aaron Berg ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
General Circulation ◽  
Surface States ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Surface Model ◽  
Monthly Precipitation ◽  
Near Surface

Abstract Forcing a land surface model (LSM) offline with realistic global fields of precipitation, radiation, and near-surface meteorology produces realistic fields (within the context of the LSM) of soil moisture, temperature, and other land surface states. These fields can be used as initial conditions for precipitation and temperature forecasts with an atmospheric general circulation model (AGCM). Their usefulness is tested in this regard by performing retrospective 1-month forecasts (for May through September, 1979–93) with the NASA Global Modeling and Assimilation Office (GMAO) seasonal prediction system. The 75 separate forecasts provide an adequate statistical basis for quantifying improvements in forecast skill associated with land initialization. Evaluation of skill is focused on the Great Plains of North America, a region with both a reliable land initialization and an ability of soil moisture conditions to overwhelm atmospheric chaos in the evolution of the meteorological fields. The land initialization does cause a small but statistically significant improvement in precipitation and air temperature forecasts in this region. For precipitation, the increases in forecast skill appear strongest in May through July, whereas for air temperature, they are largest in August and September. The joint initialization of land and atmospheric variables is considered in a supplemental series of ensemble monthly forecasts. Potential predictability from atmospheric initialization dominates over that from land initialization during the first 2 weeks of the forecast, whereas during the final 2 weeks, the relative contributions from the two sources are of the same order. Both land and atmospheric initialization contribute independently to the actual skill of the monthly temperature forecast, with the greatest skill derived from the initialization of both. Land initialization appears to contribute the most to monthly precipitation forecast skill.

scholarly journals Application of a technique for scenario prediction of climate change impact on the water balance components of northern river basins

2014 ◽  
Vol 62 (3) ◽  
pp. 197-208 ◽  
Author(s):  
Yeugeniy M. Gusev ◽  
Olga N. Nasonova
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Water Balance ◽  
River Basin ◽  
General Circulation ◽  
General Circulation Models ◽  
River Basins ◽  
Surface Model ◽  
Water Balance Components ◽  
Northern River

Abstract The scenario forecasting technique for assessing changes of water balance components of the northern river basins due to possible climate change was developed. Three IPCC global emission scenarios corresponding to different possible scenarios for economic, technological, political and demographic development of the human civilization in the 21st century were chosen for generating climate change projections by an ensemble of 16 General Circulation Models with a high spatial resolution. The projections representing increments of monthly values of meteorological characteristics were used for creating 3-hour meteorological time series up to 2063 for the Northern Dvina River basin, which belongs to the pan-Arctic basin and locates at the north of the European part of Russia. The obtained time series were applied as forcing data to drive the land surface model SWAP to simulate possible changes in the water balance components due to different scenarios of climate change for the Northern Dvina River basin

scholarly journals Monthly streamflow forecasting at varying spatial scales in the Rhine basin

2017 ◽  
Author(s):  
Simon Schick ◽  
Ole Rössler ◽  
Rolf Weingartner
Keyword(s):  
Land Surface ◽  
General Circulation ◽  
Initial Conditions ◽  
Spatial Scales ◽  
General Circulation Models ◽  
Surface Model ◽  
Rhine River ◽  
Streamflow Forecasting ◽  
Rhine Basin ◽  
Monthly Streamflow

Abstract. Model output statistics (MOS) methods empirically relate an environmental variable of interest to predictions from general circulation models (GCMs). This variable often belongs to a spatial scale not resolved by the GCM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the GCM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In so doing, the MOS method is tested for catchments areas covering four orders of magnitude. Using data from the period 1981–2011, the results show that skill, with respect to climatology, is restricted to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduces the mean absolute error of the former in the range of 5 to 11 percent, which is consistently reproduced at the subcatchment scale. The results further indicate that bias corrected runoff from the H-TESSEL land surface model is an interesting option when it comes to seasonal streamflow forecasting in large river basins.

scholarly journals On the role of Eurasian autumn snow cover in dynamical seasonal predictions 

2021 ◽  
Author(s):  
Paolo Ruggieri ◽  
Marianna Benassi ◽  
Stefano Materia ◽  
Daniele Peano ◽  
Constantin Ardilouze ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
General Circulation ◽  
Dominant Role ◽  
General Circulation Models ◽  
The Arctic ◽  
Seasonal Forecasts ◽  
Eurasian Snow

<p>Seasonal climate predictions leverage on many predictable or persistent components of the Earth system that can modify the state of the atmosphere and of relant weather related variable such as temprature and precipitation. With a dominant role of the ocean, the land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between land surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere both locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been investigated and documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of Autumn Eurasian snow in recent dynamical seasonal forecasts is therefore unclear. In this study we assess the role of Eurasian snow cover in a set of 5 operational seasonal forecast system characterized by a large ensemble size and a high atmospheric and oceanic resolution. Results are compemented with a set of targeted idealised simulations with atmospheric general circulation models forced by different snow cover conditions. Forecast systems reproduce realistically regional changes of the surface energy balance associated with snow cover variability. Retrospective forecasts and idealised sensitivity experiments converge in identifying a coherent change of the circulation in the Northern Hemisphere. This is compatible with a lagged but fast feedback from the snow to the Arctic Oscillation trough a tropospheric pathway.</p>

scholarly journals An ensemble of AMIP simulations with prescribed land surface temperatures

2018 ◽  
Author(s):  
Duncan Ackerley ◽  
Robin Chadwick ◽  
Dietmar Dommenget ◽  
Paola Petrelli
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
General Circulation ◽  
Global Climate ◽  
General Circulation Models ◽  
Solar Constant ◽  
Surface Temperatures ◽  
Co2 Concentrations ◽  
Land Surface Temperatures ◽  
Intercomparison Project

Abstract. General circulation models (GCMs) are routinely run under Atmospheric Modelling Intercomparison Project (AMIP) conditions with prescribed sea surface temperatures (SSTs) and sea ice concentrations (SICs) from observations. These AMIP simulations are often used to evaluate the role of the land and/or atmosphere in causing the development of systematic errors in such GCMs. Extensions to the original AMIP experiment have also been developed to evaluate the response of the global climate to increased SSTs (prescribed) and carbon-dioxide (CO2) as part of the Cloud Feedback Model Intercomparison Project (CFMIP). None of these international modelling initiatives has undertaken a set of experiments where the land conditions are also prescribed, which is the focus of the work presented in this paper. Experiments are performed initially with freely varying land conditions (surface temperature and, soil temperature and mositure) under five different configurations (AMIP, AMIP with uniform 4 K added to SSTs, AMIP SST with quadrupled CO2, AMIP SST and quadrupled CO2 without the plant stomata response, and increasing the solar constant by 3.3 %). Then, the land surface temperatures from the free-land experiments are used to perform a set of “AMIP-prescribed land” (PL) simulations, which are evaluated against their free-land counterparts. The PL simulations agree well with the free-land experiments, which indicates that the land surface is prescribed in a way that is consistent with the original free-land configuration. Further experiments are also performed with different combinations of SSTs, CO2 concentrations, solar constant and land conditions. For example, SST and land conditions are used from the AMIP simulation with quadrupled CO2 in order to simulate the atmospheric response to increased CO2 concentrations without the surface temperature changing. The results of all these experiments have been made publicly available for further analysis. The main aims of this paper are to provide a description of the method used and an initial validation of these AMIP-prescribed land experiments.

The «rainfall-runoff» system and its long-term fluctuations in the Siverskyi Donets River Basin (Ukraine)

2021 ◽  
Author(s):  
Hanna Bolbot ◽  
Vasyl Grebin
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
River Basin ◽  
Air Temperature ◽  
River Runoff ◽  
Annual Runoff ◽  
Annual Precipitation ◽  
Spring Flood ◽  
Current Period

<p>The current patterns estimation of the water regime under climate change is one of the most urgent tasks in Ukraine and the world. Such changes are determined by fluctuations in the main climatic characteristics - precipitation and air temperature, which are defined the value of evaporation. These parameters influence on the annual runoff distribution and long-term runoff fluctuations. In particular, the annual precipitation redistribution is reflected in the corresponding changes in the river runoff.<br>The assessment of the current state and nature of changes in precipitation and river runoff of the Siverskyi Donets River Basin was made by comparing the current period (1991-2018) with the period of the climatological normal (1961-1990).<br>In general, for this area, it was defined the close relationship between the amount of precipitation and the annual runoff. Against the background of insignificant (about 1%) increase of annual precipitation in recent decades, it was revealed their redistribution by seasons and separate months. There is a decrease in precipitation in the cold period (November-February). This causes (along with other factors) a decrease in the amount of snow and, accordingly, the spring flood runoff. There are frequent cases of unexpressed spring floods of the Siverskyi Donets River Basin. The runoff during March-April (the period of spring flood within the Ukrainian part of the basin) decreased by almost a third.<br>The increase of precipitation during May-June causes a corresponding (insignificant) increase in runoff in these months. The shift of the maximum monthly amount of precipitation from May (for the period 1961-1990) to June (in the current period) is observed.<br>There is a certain threat to water supply in the region due to the shift in the minimum monthly amount of precipitation in the warm period from October to August. Compared with October, there is a higher air temperature and, accordingly, higher evaporation in August, which reduces the runoff. Such a situation is solved by rational water resources management of the basin. The possibility of replenishing water resources in the basin through the transfer runoff from the Dnieper (Dnieper-Siverskyi Donets channel) and the annual runoff redistribution in the reservoir system causes some increase in the river runoff of summer months in recent decades. This is also contributed by the activities of the river basin management structures, which control the maintenance water users' of minimum ecological flow downstream the water intakes and hydraulic structures in the rivers of the basin.<br>Therefore, in the period of current climate change, the annual runoff distribution of the Siverskyi Donets River Basin has undergone significant changes, which is related to the annual precipitation redistribution and anthropogenic load on the basin.</p>

Estimates of the Earth surface influence on the accuracy of numerical prediction of air temperature in Belarus using the WRF model

2021 ◽  
Vol 4 ◽  
pp. 50-68
Author(s):  
S.А. Lysenko ◽  
◽  
P.О. Zaiko ◽  
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Lead Time ◽  
Weather Prediction ◽  
Wrf Model ◽  
Surface Model ◽  
Numerical Weather ◽  
Global Land

The spatial structure of land use and biophysical characteristics of land surface (albedo, leaf index, and vegetation cover) are updated using the GLASS (Global Land Surface Satellite) and GLC2019 (Global Land Cover, 2019) modern satellite databases for mesoscale numerical weather prediction with the WRF model for the territory of Belarus. The series of WRF-based numerical experiments was performed to verify the influence of the updated characteristics on the forecast quality for some difficult to predict winter cases. The model was initialized by the GFS (Global Forecast System, NCEP) global numerical weather prediction model. It is shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the root-mean-square error (RMSE) of short-range (to 48 hours) forecasts of surface air temperature by 16–33% as compared to the GFS. The RMSE of the temperature forecast for the weather stations in Belarus for a forecast lead time of 12, 24, 36, and 48 hours decreased on average by 0.40°С (19%), 0.35°С (10%), 0.68°С (23%), and 0.56°С (15%), respectively. The most significant decrease in RMSE of the numerical forecast of temperature (up to 2.1 °С) was obtained for the daytime (for a lead time of 12 and 36 hours), when positive feedbacks between albedo and temperature of the land surface are manifested most. Keywords: numerical weather prediction, WRF, digital land surface model, albedo, leaf area index, forecast model validation

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