scholarly journals Hydrological response to climate change of the Brahmaputra basin using CMIP5 general circulation model ensemble

2017 ◽  
Vol 9 (3) ◽  
pp. 434-448 ◽  
Author(s):  
A. K. M. Saiful Islam ◽  
Supria Paul ◽  
Khaled Mohammed ◽  
Mutasim Billah ◽  
Md. Golam Rabbani Fahad ◽  
...  

Abstract The Ganges–Brahmaputra–Meghna river system carries the world's third-largest fresh water discharge and Brahmaputra alone carries about 67% of the total annual flow of Bangladesh. Climate change will be expected to alter the hydrological cycles and the flow regime of these basins. Assessment of the fresh water availability of the Brahmaputra Basin in the future under climate change condition is crucial for both society and the ecosystem. SWAT, a semi-distributed physically based hydrological model, has been applied to investigate hydrological response of the basin. However, it is a challenging task to calibrate and validate models over this ungauged and poor data basin. A model derived by using gridded rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite and temperature data from reanalysis product ERA-Interim provides acceptable calibration and validation. Using the SWAT-CUP with SUFI-2 algorithm, sensitivity analysis of model parameters was examined. A calibrated model was derived using new climate change projection data from the multi-model ensemble CMIP5 Project over the South Asia CORDEX domain. The uncertainty of predicting monsoon flow is less than that of pre-monsoon flow. Most of the regional climate models (RCMs) show an increasing tendency of the discharge of Brahmaputra River at Bahadurabad station during monsoon, when flood usually occurs in Bangladesh.

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Rui Ito ◽  
Tosiyuki Nakaegawa ◽  
Izuru Takayabu

AbstractEnsembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.


2017 ◽  
Author(s):  
Gonzalo Sapriza-Azuri ◽  
Pablo Gamazo ◽  
Saman Razavi ◽  
Howard S. Wheater

Abstract. Arctic and sub-arctic regions are amongst the most susceptible regions on Earth to global warming and climate change. Understanding and predicting the impact of climate change in these regions require a proper process representation of the interactions between climate, the carbon cycle, and hydrology in Earth system models. This study focuses on Land Surface Models (LSMs) that represent the lower boundary condition of General Circulation Models (GCMs) and Regional Climate Models (RCMs), which simulate climate change evolution at the global and regional scales, respectively. LSMs typically utilize a standard soil configuration with a depth of no more than 4 meters, whereas for cold, permafrost regions, field experiments show that attention to deep soil profiles is needed to understand and close the water and energy balances, which are tightly coupled through the phase change. To address this, we design and run a series of model experiments with a one-dimensional LSM, called CLASS (Canadian Land Surface Scheme), as embedded in the MESH (Modélisation Environmentale Communautaire – Surface and Hydrology) modelling system, to (1) characterize the effect of soil profile depth under different climate conditions and in the presence of parameter uncertainty, and (2) develop a methodology for temperature profile initialization in permafrost regions, where the system has an extended memory, by the use of paleo-records and bootstrapping. Our study area is in Norman Wells, Northwest Territories of Canada, where measurements of soil temperature profiles and historical reconstructed climate data are available. Our results demonstrate that the adequate depth of soil profile in an LSM varies for warmer and colder conditions and is sensitive to model parameters and the uncertainty around them. In general, however, we show that a minimum of 20 meters of soil profile is essential to adequately represent the temperature dynamics. Our results also indicate the significance of model initialization in permafrost regions and our proposed spin-up method requires running the LSM over more than 300 years of reconstructed climate time series.


2020 ◽  

<p>Two hydrological climate modelling techniques, general circulation model (GCM) and hypothetical climate change scenarios, were used to analyse the hydrological response to the anticipated climate change scenarios in the Subarnarekha river basin in Eastern India. Both models verified individually for the same river basin and a comparative performance of the models was evaluated to relate the two models for the near (2014-2040) period climate. The hydrological response under the anticipated climate change in the Subarnarekha river basin is well assessed by GCM under the RCP 8.5 scenarios compared to the RCPs 4.5. Results indicate GCM best suited over the hypothetical climate change scenarios as GCM has demonstrated their potential in accurately reproducing the past observed climatic changes. The strong performance of the hypothetical climate change scenarios model, particularly for warming climate scenarios, suggests that it may have distinct advantages for the analysis of water balance components in the river basin. The monthly streamflows of Subarnarekha river basin was simulated using a total of 14 years (2000-2013) daily observed streamflow data in the ArcSWAT model integrated with model calibration and uncertainty analysis by means of SUFI-2 algorithm. The results indicate during the calibration the coefficient of determination (R2) and Nash-Sutcliff Efficiency (NSE) were reported as 0.98 and 0.97, respectively, while during the validation the R2 and NSE were obtained as 0.94 and 0.94, respectively, confirms the hydrological model performance was very good both in calibration and validation. The obtained climate change water impact index (ICCWI) values reveal the Subarnarekha river basin is more responsive to climate change. The reduction in precipitation along with the significant warming under the projected future climate is likely to reduce availability of water substantially in the study region. This work would be useful for the effective management of water resources for sustainable agriculture and in mitigating natural hazards such as droughts and floods in the study region.</p>


2021 ◽  
Author(s):  
Laurène J. E. Bouaziz ◽  
Emma E. Aalbers ◽  
Albrecht H. Weerts ◽  
Mark Hegnauer ◽  
Hendrik Buiteveld ◽  
...  

Abstract. To predict future hydrological behavior in a changing world, often use is made of models calibrated on past observations, disregarding that hydrological systems, hence model parameters, will change as well. Yet, ecosystems likely adjust their root-zone storage capacity, which is the key parameter of any hydrological system, in response to climate change. In addition, other species might become dominant, both under natural and anthropogenic influence. In this study, we propose a top-down approach, which directly uses projected climate data to estimate how vegetation adapts its root-zone storage capacity at the catchment scale in response to changes in magnitude and seasonality of hydro-climatic variables. Additionally, the Budyko characteristics of different dominant ecosystems in sub-catchments are used to simulate the hydrological behavior of potential future land-use change, in a space-for-time exchange. We hypothesize that changes in the predicted hydrological response as a result of 2 K global warming are more pronounced when explicitly considering changes in the sub-surface system properties induced by vegetation adaptation to changing environmental conditions. We test our hypothesis in the Meuse basin in four scenarios designed to predict the hydrological response to 2 K global warming in comparison to current-day conditions using a process-based hydrological model with (a) a stationary system, i.e. no changes in the root-zone storage capacity of vegetation and historical land use, (b) an adapted root-zone storage capacity in response to a changing climate but with historical land use, and (c, d) an adapted root-zone storage capacity considering two hypothetical changes in land use from coniferous plantations/agriculture towards broadleaved forest and vice-versa. We found that the larger root-zone storage capacities (+34 %) in response to a more pronounced seasonality with drier summers under 2 K global warming strongly alter seasonal patterns of the hydrological response, with an overall increase in mean annual evaporation (+4 %), a decrease in recharge (−6 %) and a decrease in streamflow (−7 %), compared to predictions with a stationary system. By integrating a time-dynamic representation of changing vegetation properties in hydrological models, we make a potential step towards more reliable hydrological predictions under change.


2017 ◽  
pp. 67-81
Author(s):  
Hugo G. Hidalgo ◽  
Eric J. Alfaro

Two methods for selecting a subset of simulations and/or general circulation models (GCMs) from a set of 30 available simulations are compared: 1) Selecting the models based on their performance on reproducing 20th century climate, and 2) random sampling. In the first case, it was found that the performance methodology is very sensitive to the type and number of metrics used to rank the models and therefore the results are not robust to these conditions. In general, including more models in a multi-model ensemble according to their rank (of skill in reproducing 20th century climate) results in an increase in the multi-model skill up to a certain point and then the inclusion of more models degrades the skill of the multi-model ensemble. In a similar fashion when the models are introduced in the ensemble at random, there is a point where the inclusion of more models does not change significantly the skill of the multi-model ensemble. For precipitation the subset of models that produces the maximum skill in reproducing 20th century climate also showed some skill in reproducing the climate change projections of the multi-model ensemble of all simulations. For temperature, more models/simulations are needed to be included in the ensemble (at the expense of a decrease in the skill of reproducing the climate of the 20th century for the selection based on their ranks). For precipitation and temperature the use of 7 simulations out of 30 resulted in the maximum skill for both approaches to introduce the models. Citation: Hidalgo, H. & E.J. Alfaro. 2012. Global Model selection for evaluation of climate change projections in the Eastern Tropical Pacific Seascape. Rev. Biol. Trop. 60 (Suppl. 3): 67-81. Epub 2012 Dec 01.


2018 ◽  
Vol 22 (2) ◽  
pp. 1017-1032 ◽  
Author(s):  
Andreas Marx ◽  
Rohini Kumar ◽  
Stephan Thober ◽  
Oldrich Rakovec ◽  
Niko Wanders ◽  
...  

Abstract. There is growing evidence that climate change will alter water availability in Europe. Here, we investigate how hydrological low flows are affected under different levels of future global warming (i.e. 1.5, 2, and 3 K with respect to the pre-industrial period) in rivers with a contributing area of more than 1000 km2. The analysis is based on a multi-model ensemble of 45 hydrological simulations based on three representative concentration pathways (RCP2.6, RCP6.0, RCP8.5), five Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M) and three state-of-the-art hydrological models (HMs: mHM, Noah-MP, and PCR-GLOBWB). High-resolution model results are available at a spatial resolution of 5 km across the pan-European domain at a daily temporal resolution. Low river flow is described as the percentile of daily streamflow that is exceeded 90 % of the time. It is determined separately for each GCM/HM combination and warming scenario. The results show that the low-flow change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean region, while they increase in the Alpine and Northern regions. In the Mediterranean, the level of warming amplifies the signal from −12 % under 1.5 K, compared to the baseline period 1971–2000, to −35 % under global warming of 3 K, largely due to the projected decreases in annual precipitation. In contrast, the signal is amplified from +22 (1.5 K) to +45 % (3 K) in the Alpine region due to changes in snow accumulation. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. However, it is not possible to distinguish climate-induced differences in low flows between 1.5 and 2 K warming because of (1) the large inter-annual variability which prevents distinguishing statistical estimates of period-averaged changes for a given GCM/HM combination, and (2) the uncertainty in the multi-model ensemble expressed by the signal-to-noise ratio. The contribution by the GCMs to the uncertainty in the model results is generally higher than the one by the HMs. However, the uncertainty due to HMs cannot be neglected. In the Alpine, Northern, and Mediterranean regions, the uncertainty contribution by the HMs is partly higher than those by the GCMs due to different representations of processes such as snow, soil moisture and evapotranspiration. Based on the analysis results, it is recommended (1) to use multiple HMs in climate impact studies and (2) to embrace uncertainty information on the multi-model ensemble as well as its single members in the adaptation process.


2002 ◽  
Vol 35 ◽  
pp. 409-415 ◽  
Author(s):  
Philippe Huybrechts ◽  
Ives Janssens ◽  
Chantal Poncin ◽  
Thierry Fichefet

AbstractWe present results from a greenhouse warming experiment obtained from an atmosphere–ocean–sea-ice general circulation model that is interactively coupled with a three-dimensional model of the Greenland ice sheet. the experiment covers the period 1970–2099 and is driven by the mid-range Intergovernmental Panel on Climate Change SRESB2 scenario. the Greenland model is a thermomechanical high-resolution (20 km) model coupled with a viscoelastic bedrock model. the melt-and-runoff model is based on the positive degree-day method and includes meltwater retention in the snowpack and the formation of superimposed ice. the atmospheric–oceanic general circulation model (AOGCM) is a coarse-resolution model without flux correction based on the Laboratoire de Météorologie Dynamique (Paris) LMD 5.3 atmospheric model coupled with a primitive-equation, free-surface oceanic component incorporating sea ice (coupled large-scale ice–ocean (CLIO)). By 2100, average Greenland annual temperature is found to rise by about 4.5˚C and mean precipitation by about 35%. the total fresh-water flux approximately doubles over this period due to increased runoff from the ice sheet and the ice-free land, but the calving rate is found to decrease by 25%. the ice sheet shrinks equivalent to 4 cm of sea-level rise. the contribution from the background evolution is not more than 5% of the total predicted sea-level rise. We did not find significant changes in the patterns of climate change over the North Atlantic region compared with a climate-change run without Greenland fresh-water feedback.


2013 ◽  
Vol 10 (2) ◽  
pp. 263-272

The spatial resolution of General Circulation Models (GCMs) is too coarse to represent regional climate changes at the scales required for environmental impact assessment. Therefore, downscaling of precipitation and temperature has to be carried out from the GCM grids to smaller scales of a few square kilometres. Daily precipitation and temperature are modelled as stochastic processes coupled to atmospheric circulation. Precipitation is linked to circulation patterns (CPs) using conditional model parameters. Temperature is modelled using a simple autoregressive model conditioned on atmospheric circulation and local daily precipitation. The models use an automated objective classification of daily atmospheric circulation patterns based on optimized fuzzy rules. Both temperature and precipitation are downscaled to several locations taking into account the CP dependent spatial correlation. The models were applied to the Mesochora medium-sized mountainous catchment in Central Greece for validation using observed precipitation and temperature and observed classified geo-potential heights (at 700 hPa). GCM scenarios of the ECHAM4 model for 1xCO2 and 2xCO2 cases were used to make climate change predictions (by using classified GCM geopotential heights). Simulated values agree fairly well with historical data. Most of the GCM results (incl. mean daily values, renewal process probabilities, spell lengths) under the 2xCO2 case reflect a somewhat wetter and a more variable precipitation regime over the Mesochora catchment with significantly increased daily mean temperatures.


10.29007/d2g9 ◽  
2018 ◽  
Author(s):  
Enrico Creaco ◽  
Sara Todeschini ◽  
Marco Franchini

This paper presents results about the hydrological modelling of the Cascina Scala catchment. Following a preliminary analysis that proved the Nash cascade of reservoirs superior to the Clark model in the event-based rainfall-runoff simulation for this catchment, a new analysis was carried out to compare two different parameterization approaches applicable in gauged rain events. The former is based on estimating the optimal set of model parameters in each gauged event and then obtaining the ultimate set by averaging the values obtained over the whole group of events; the latter is based on estimating the optimal set of parameters directly on the whole group of gauged events. The results of the analysis proved the better performance of the latter, which enables better representation of the hydrological response of the catchment, evaluated in terms of water discharge pattern at the outlet.


1999 ◽  
Vol 30 (3) ◽  
pp. 209-230 ◽  
Author(s):  
Stefan Hagemann ◽  
Lydia Dümenil

In this study, a hydrological discharge model is presented which may be applied as a tool to validate the simulation of the hydrologic cycle of atmospheric models that are used in climate change studies. It can also be applied in studies of global climate change to investigate how changes in climate may affect the discharge of large rivers. The model was developed for the application with the climate models used at the Max-Planck-Institute for Meteorology. It describes the translation and retention of the lateral waterflows on the global scale as a function of the spatially distributed land surface characteristics which are globally available. Here, global scale refers to the resolution of 0.5° and lower, corresponding to a typical average gridbox area of about 2,500 km2. The hydrological discharge model separates between the flow processes of overland flow, baseflow and riverflow. The model parameters are mainly functions of the gridbox characteristics of topography and gridbox length. The hydrological discharge model is applied to the BALTEX (Baltic Sea Experiment) region using input from an atmospheric general circulation model (ECHAM4) as well as from a regional climate model (REMO). The simulated inflows into the Baltic Sea and its sub-catchments are compared to observed and naturalized discharges. The results of this comparison are discussed and the simulated values of precipitation, surface air temperature and accumulated snowpack are compared to both observed data and surrogate data.


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