Understanding the past to interpret the future: comparison of simulated groundwater recharge in the upper Colorado River basin (USA) using observed and general-circulation-model historical climate data

2016 ◽  
Vol 25 (2) ◽  
pp. 347-358 ◽  
Author(s):  
Fred D Tillman ◽  
Subhrendu Gangopadhyay ◽  
Tom Pruitt
Keyword(s):  
Groundwater Recharge ◽  
General Circulation Model ◽  
River Basin ◽  
General Circulation ◽  
Colorado River ◽  
Circulation Model ◽  
Climate Data ◽  
The Past ◽  
Upper Colorado River Basin ◽  
Simulated Groundwater

scholarly journals Climate of Bangladesh: An Analysis of Northwestern and Southwestern Part Using High Resolution Atmosphere-Ocean General Circulation Model (AOGCM)

1970 ◽  
Vol 9 (1-2) ◽  
pp. 143-154 ◽  
Author(s):  
MA Rouf ◽  
MK Uddin ◽  
SK Debsarma ◽  
M Mizanur Rahman
Keyword(s):  
High Resolution ◽  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Southwestern Part ◽  
The Past ◽  
Average Rainfall ◽  
Ocean General Circulation ◽  
Near Future

The past, present and future climatic pattern (temperature and rainfall) of northwestern and southwestern part of Bangladesh was assessed based on the High Resolution Atmospheric-Ocean General Circulation Model (AOGCM) using the present rainfall and temperature data of the Bangladesh Meteorological Department (BMD). Climatology in Bangladesh is derived from 20 km mesh MRI-AGCM (Atmospheric General Circulation Model) calibrated with reference to the observed data for the period of 1979-2006. Then, projections for rainfall and temperature are made for near future (2015-2034) and future (2075-99). Two disaster prone areas (i) northwestern part (Shapahar & Porsha) and (ii) southwestern part (Kalapara & Amtoli) were selected as the study areas. AOGCM model was run for Bangladesh and also for study areas separately. The present mean temperature for Bangladesh was found to rise from the past, rises slightly, but in near future and future the rate of mean temperature rise is projected to be much more than the present rate (increase up to 4.34 °C/100 years), the rate is projected to be 5.39 °C/100 years in case of Shapahar and Porsha a while 4.37 °C/100 years in case of Kalapara and Amtoli. The present, near future and future average rainfall of Bangladesh appeared to fluctuate, but have shown a decreasing trend (decreases up to 1.96 mm/100 years). The mean average rainfall of Shapahar and Porsha presently decreases very slowly (not significant), but in near future and future will decrease slowly (0.66mm/100 years). In case of Kalapara, the average rainfall appears to decrease presently, near future and future will decrease up to 3.62 mm/100 years. The average rainfall of Amtoli appears to decrease @ 1.92mm/100 years but in near future appears to increase slightly and again decrease @ 3.27mm/100years in future. Keywords: Atmosphere-Ocean General Circulation Model (AOGCM); climatology; simulation; temperature; rainfall DOI: http://dx.doi.org/10.3329/agric.v9i1-2.9489 The Agriculturists 2011; 9(1&2): 143-154

scholarly journals A Comparison of Amazonian Climate Data with General Circulation Model Simulations

1998 ◽  
Vol 11 (11) ◽  
pp. 2764-2773 ◽  
Author(s):  
A. D. Culf ◽  
G. Fisch ◽  
J. Lean ◽  
J. Polcher
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
Climate Data ◽  
Model Simulations

scholarly journals Temperature and rainfall estimates for past 18 000 years in Owens Valley, California with a coupled catchment–lake model

2015 ◽  
Vol 12 (7) ◽  
pp. 6505-6539 ◽  
Author(s):  
Z. Yu ◽  
W. Dong ◽  
P. Jiang
Keyword(s):  
General Circulation ◽  
Regional Climate ◽  
Circulation Model ◽  
Climate Data ◽  
Owens Valley ◽  
Climatic Fluctuations ◽  
Closed Basin ◽  
The Past ◽  
Lake Model ◽  
Physically Based

Abstract. Closed-basin lakes are intricately linked to the hydrological systems and are very sensitive recorders of local hydro-climatic fluctuations. Lake records in closed-basins are usually used to investigate the paleoclimate condition which is critical for understanding the past and predicting the future. In this study, a physically based catchment–lake model was developed to extract quantitative paleoclimate information including temperature and rainfall over the past 18 000 years (ka) from lake records in a hydrologically closed basin in the Owens River Valley, California, US. The initial model inputs were prepared based on current regional climate data, boundary conditions from the General Circulation Model, and fossil proxy data. The inputs subsequently were systematically varied in order to produce the observed lake levels. In this way, a large number of possible paleoclimatic combinations can quickly narrow the possible range of paleoclimatic combinations that could have produced the paleolake level and extension. Finally, a quantitative time-series of paleoclimate information for those key times was obtained.

Changes in Projected Spatial and Seasonal Groundwater Recharge in the Upper Colorado River Basin

Ground Water ◽  
2017 ◽  
Vol 55 (4) ◽  
pp. 506-518 ◽  
Author(s):  
Fred D Tillman ◽  
Subhrendu Gangopadhyay ◽  
Tom Pruitt
Keyword(s):  
Groundwater Recharge ◽  
River Basin ◽  
Colorado River ◽  
Colorado River Basin ◽  
Upper Colorado River Basin

scholarly journals Changes in groundwater recharge under projected climate in the upper Colorado River basin

2016 ◽  
Vol 43 (13) ◽  
pp. 6968-6974 ◽  
Author(s):  
Fred D Tillman ◽  
Subhrendu Gangopadhyay ◽  
Tom Pruitt
Keyword(s):  
Groundwater Recharge ◽  
River Basin ◽  
Colorado River ◽  
Colorado River Basin ◽  
Upper Colorado River Basin

scholarly journals Time evolutions of various radiative forcings for the past 150 years estimated by a general circulation model

2006 ◽  
Vol 33 (19) ◽  
Author(s):  
Toshihiko Takemura ◽  
Yoko Tsushima ◽  
Tokuta Yokohata ◽  
Toru Nozawa ◽  
Tatsuya Nagashima ◽  
...  
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
Radiative Forcings ◽  
The Past

scholarly journals Estimating monthly rainfall in rural river basins under climate change: an improved bias-correcting statistical downscaling approach

2013 ◽  
Vol 10 (6) ◽  
pp. 6847-6896
Author(s):  
D. L. Jayasekera ◽  
J. J. Kaluarachchi
Keyword(s):  
Climate Change ◽  
General Circulation Model ◽  
River Basin ◽  
Bias Correction ◽  
General Circulation ◽  
Circulation Model ◽  
Correction Method ◽  
Annual Rainfall ◽  
Bias Correction Method ◽  
Time Space

Abstract. This study extended the work of Kim et al. (2008) to generate future rainfall under climate change using a discrete-time/space Markov chain based on historical conditional probabilities. A bias-correction method is proposed by fitting suitable statistical distributions to transform rainfall from the general circulation model (GCM) scale to watershed scale. The demonstration example used the Nam Ngum River Basin (NNRB) in Laos which is a rural river basin with high potential for hydropower generation and significant rain-fed agriculture supporting rural livelihoods. This work generated weekly rainfall for a 100 yr period using historical rainfall data from 1961 to 2000 for ten selected weather stations. The bias-correction method showed the ability to reduce bias of the mean values of GCMs when compared to the observed mean amount at each station. The simulated rainfall series is perturbed using the delta change estimated at each station to project future rainfall for the Special Report on Emission Scenarios (SRES) A2. GCMs consisting of third generation coupled general circulation model (CGCM3.1 T63) and European center Hamburg model (ECHAM5) projected an increasing trend of mean annual rainfall in the NNRB. Seasonal rainfall percent changes showed an increase in the wet and dry seasons with the highest increase in the dry season mean rainfall of about 31% from 2051 to 2090. While the GCM projections showed good results with appropriate bias corrections, the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model significantly underestimated historical behavior and produced higher mean absolute errors compared to the corresponding GCM predictions.

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