Golbahar, M., Srivastava, P., & Stefanova, L. (2013). The impact of climate change on rainfall Intensity-Duration-Frequency (IDF) curves in Alabama. Regional Environmental Change, 13(S1), S25–S33.
Abstract: Changes in the hydrologic cycle due to increase in greenhouse gases are projected to cause variations in intensity, duration, and frequency of precipitation events. Quantifying the potential effects of climate change and adapting to them is one way to reduce vulnerability. Since rainfall characteristics are often used to design water management infrastructures, reviewing and updating rainfall characteristics (i.e., Intensity�Duration�Frequency (IDF) curves) for future climate scenarios is necessary. This study was undertaken to assess expected changes in IDF curves from the current climate to the projected future climate. To provide future IDF curves, 3-hourly precipitation data simulated by six combinations of global and regional climate models were temporally downscaled using a stochastic method. Performance of the downscaling method was evaluated, and IDF curves were developed for the state of Alabama. The results of all six climate models suggest that the future precipitation patterns for Alabama are expected to veer toward less intense rainfalls for short duration events. However, for long duration events (i.e., >4 h), the results are not consistent across the models. Given a large uncertainty existed on projected rainfall intensity of these six climate models, developing an ensemble model as a result of incorporating all six climate models, performing an uncertainty analysis, and creating a probability based IDF curves could be proper solutions to diminish this uncertainty.
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He, J., & Soden, B. J. (2016). The impact of SST biases on projections of anthropogenic climate change: A greater role for atmosphere-only models? Geophys. Res. Lett., 43(14), 7745–7750.
Abstract: There is large uncertainty in the model simulation of regional climate change from anthropogenic forcing. Recent studies have tried to link such uncertainty to intermodel differences in the pattern of sea surface temperature (SST) change. On the other hand, coupled climate models also contain systematic biases in their climatology, largely due to drift in SSTs. To the extent that the projected changes depend on the mean state, biases in the present-day climatology also contribute to the intermodel spread in climate change projections. By comparing atmospheric general circulation model (AGCM) simulations using the climatological SSTs from different coupled models, we show that biases in the climatological SST generally have a larger impact on regional projections over land than do intermodel differences in the pattern of SST change. These results advocate for a greater application of AGCM simulations with observed SSTs or flux-adjusted coupled models to improve regional projections of anthropogenic climate change.
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Hussain, J., Khaliq, T., Asseng, S., Saeed, U., Ahmad, A., & et al. (2020). Climate change impacts and adaptations for wheat employing multiple climate and crop models in Pakistan. Climatic Change, .
Abstract: Comparing outputs of multiple climate and crop models is an option to assess the uncertainty in simulations in a changing climate. The use of multiple wheat models under five plausible future simulated climatic conditions is rarely found in literature. CERES-Wheat, DSSAT-Nwheat, CROPSIM-Wheat, and APSIM-Wheat models were calibrated with observed data form eleven sowing dates (15 October to 15 March) of irrigated wheat trails at Faisalabad, Pakistan, to explore close to real climate changing impacts and adaptations. Twenty-nine GCM of CMIP5 were used to generate future climate scenarios during 2040-2069 under RCP 8.5. These scenarios were categorized among five climatic conditions (Cool/Wet, Cool/Dry, Hot/Wet, Hot/Dry, Middle) on the basis of monthly changes in temperature and rainfall of wheat season using a stretched distribution approach (STA). The five GCM at Faisalabad and Layyah were selected and used in the wheat multimodels set to CO2 571 ppm. In the future, the temperature of both locations will elevate 2-3 °C under the five climatic conditions, although Faisalabad will be drier and Layyah will be wetter as compared with baseline conditions. Climate change impacts were quantified on wheat sown on different dates, including 1 November, 15 November, and 30 November which showed average reduction at semiarid and arid environment by 23.5%, 19.8%, and 31%, respectively. Agronomic and breeding options offset the climate change impacts and also increased simulated yield about 20% in all climatic conditions. The number of GCMs was considerably different in each quadrate of STA, showing the uncertainty in possible future climatic conditions of both locations. Uncertainty among wheat models was higher at Layyah as compared with Faisalabad. Under Hot/Dry and Hot/Wet climatic conditions, wheat models were the most uncertain to simulate impacts and adaptations. DSSAT-Nwheat and APSIM-Wheat were the most and least sensitive to changing temperature among years and climatic conditions, respectively.
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Li, J. - L. F., Richardson, M., Hong, Y., Lee, W. - L., Wang, Y. - H., Yu, J. - Y., et al. (2017). Improved simulation of Antarctic sea ice due to the radiative effects of falling snow. Environ. Res. Lett., 12(8), 084010.
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Nasim, W., Amin, A., Fahad, S., Awais, M., Khan, N., Mubeen, M., et al. (2018). Future risk assessment by estimating historical heat wave trends with projected heat accumulation using SimCLIM climate model in Pakistan. Atmospheric Research, 205, 118–133.
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Obeysekera, J., Graham, W., Sukop, M. C., Asefa, T., Wang, D., Ghebremichael, K., et al. (2017). Implications of climate change on Florida's water resources. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 83–124). Gainesville, FL: Florida Climate Institute.
Abstract: Water resources systems in Florida are unique and exhibit significant diversity in hydrogeologic characteristics and in rainfall and temperature patterns. In many parts of the state, both surface and groundwater systems are complex, highly interconnected, and any change in hydrologic drivers such as rainfall or temperature has the potential to impact the water resources of the urban, agricultural, and ecological systems. Because of this diversity, it is not possible to present a single overall outlook regarding the implications of climate change on the water resources of the state. This chapter presents brief summaries of individual studies that are available for major water resources systems in the state, which include the Everglades, the Tampa Bay region, the St. Johns River watershed, and the Suwannee River and Apalachicola River basins. Available climate models and their downscaled versions have varying degrees of bias and lack of skill that need to be considered in impact analyses. In all regions, projected changes in rainfall, temperature, and sea level may have significant impacts on water supply, water levels in environmentally sensitive areas, flood protection, and water quality.
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Panaou, T., Asefa, T., & Nachabe, M. H. (2018). Keeping Us Honest: Examining Climate States and Transition Probabilities of Precipitation Projections in General Circulation Models. Journal of Water Resources Planning and Management, 144(4).
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Rahman, M. H. ur, Ahmad, A., Wang, X., Wajid, A., Nasim, W., Hussain, M., et al. (2018). Multi-model projections of future climate and climate change impacts uncertainty assessment for cotton production in Pakistan. Agricultural and Forest Meteorology, 253-254, 94–113.
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Tan, Y., Guzman, S. M., Dong, Z., & Tan, L. (2020). Selection of Effective GCM Bias Correction Methods and Evaluation of Hydrological Response under Future Climate Scenarios. Climate, 8(10).
Abstract: Global climate change is presenting a variety of challenges to hydrology and water resources because it strongly affects the hydrologic cycle, runoff, and water supply and demand. In this study, we assessed the effects of climate change scenarios on hydrological variables (i.e., evapotranspiration and runoff) by linking the outputs from the global climate model (GCM) with the Soil and Water Assessment Tool (SWAT) for a case study in the Lijiang River Basin, China. We selected a variety of bias correction methods and their combinations to correct the lower resolution GCM outputs of both precipitation and temperature. Then, the SWAT model was calibrated and validated using the observed flow data and corrected historical GCM with the optimal correction method selected. Hydrological variables were simulated using the SWAT model under emission scenarios RCP2.6, RCP4.5, and RCP8.5. The results demonstrated that correcting methods have a positive effect on both daily precipitation and temperature, and a hybrid method of bias correction contributes to increased performance in most cases and scenarios. Based on the bias corrected scenarios, precipitation annual average, temperature, and evapotranspiration will increase. In the case of precipitation and runoff, projection scenarios show an increase compared with the historical trends, and the monthly distribution of precipitation, evapotranspiration, and runoff shows an uneven distribution compared with baseline. This study provides an insight on how to choose a proper GCM and bias correction method and a helpful guide for local water resources management.
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Wang, B., Liu, D. L., Asseng, S., Macadam, I., & Yu, Q. (2017). Modelling wheat yield change under CO 2 increase, heat and water stress in relation to plant available water capacity in eastern Australia. European Journal of Agronomy, 90, 152–161.
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