Assessment of Precipitation Simulations in Central Asia by CMIP5 Climate Models

Ta, Zhijie; Yu, Yang; Sun, Lingxiao; Chen, Xi; Mu, Guijin; Yu, Rucong

doi:10.3390/w10111516

Cited by 36 publications

(32 citation statements)

References 51 publications

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“…As climate simulations remain uncertain, there is a need to examine the possible changes in maize biomass under different climate scenario models to obtain a reliable fluctuation range for future projections of maize biomass yields. Based on previous studies in the Xinjiang area, the CGCM3 and NORESM models can accurately simulate changes in temperature [36], while the MIROC5 model can predict changes in precipitation very well [42,43]. Based on the accuracy of these three climate models for our study area, we used the CGC3M, NORESM, and MIROC5 to drive the DNDC model and found that the maize biomass yield fluctuates between −6.5% and 10.3% compared to the baseline scenarios.…”

Section: Discussionmentioning

confidence: 99%

Assessment of the Effect of Mulch Film on Crops in the Arid Agricultural Region of China under Future Climate Scenarios

Deng

Meng

et al. 2019

Water

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Plastic mulch film is widely used in agricultural production. However, there are very few studies on degradable mulch film. In order to investigate the effects of using degradable mulch film in arid regions on crop yield and water use efficiency, we used fully biodegradable mulch films on both maize and bare land cultivation experimental areas. The DeNitrification-DeComposition (DNDC) model was used to analyze changes in maize biomass in the future under different climate scenario models. We found that using fully biodegradable mulch film in an arid region had a positive effect on biomass yields. In 2015-2017, the annual maize biomass yield increased by 24.5%, 28.9%, and 32.9%, respectively. Hence, this method has expansion and promotion value. A comparison of the DNDC model simulated biomass yields and actual measured values found that the ranges of R2, root mean square error (RMSE), and model efficiency (ME) were 0.98-0.99, 0.38-0.86 mg C ha −1 , and 0.80-0.98. This result shows that the DNDC model can accurately simulate changes in maize biomass in this region. Under the premise of a good model fit, future climate scenario model data were used to drive the DNDC model. The results showed that the possible range of maize biomass yields in the future is −6.5% to 10.3%, with the most probable range being 0.2-1.5%. Using future climatic conditions, our work suggests that degradable mulch films can increase water use efficiency by an average of 9.5%. The results of this study can be used to promote the use of degradable mulch films in arid regions, significantly improving sustainable agricultural development.

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Section: Discussionmentioning

confidence: 99%

Assessment of the Effect of Mulch Film on Crops in the Arid Agricultural Region of China under Future Climate Scenarios

Deng

Meng

et al. 2019

Water

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“…But highest improvement not necessarily mean the highest performance of model, rather it is the change in the skill of a given model before and after the correction. Actually, model performance depends on various factors such as, variables affecting earth-climate interaction, geographical locations (Choi et al, 2016;Purwaningsih and Hidayat, 2016;Homsi et al, 2020), spatial resolution of model structure (Lovino et al, 2018;Jain et al, 2019), temporal resolution of simulating variables and spatial and temporal scale of the considered variables (Sheffield et al, 2013;Ta et al, 2018). Comparison of model performances of different categories (as identified in this study) revealed that the models in category-I are more skilful than models in category-II for decadal hindcast precipitation.…”

Section: Discussionmentioning

confidence: 71%

Intercomparison of drift correction alternatives for CMIP5 decadal precipitation

Hossain

Garg

Anwar

et al. 2021

Intl Journal of Climatology

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Decadal experiments' output of the coupled model inter-comparison project phase-5 (CMIP5) contains significant model drift. For practical use of CMIP5 decadal climate variables, it is necessary to correct this model drift. In previous studies, drift correction of CMIP5 decadal data of temperature and temperature-based climate indices have been investigated, but there is no study that investigated the drift correction of decadal precipitation at a catchment scale.This study investigates the practical use of CMIP5 decadal precipitation data on seasonal scale using different drift correction alternatives for Brisbane catchment in Australia. For this, decadal monthly precipitation data from eight GCMs (EC-EARTH, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MIROC4h, MIROC5 and CanCM4) were cut for the Australian region. By using the bi-linear interpolation, the GCM data were re-gridded to 0.05 × 0.05 spatial resolution matching with the observed gridded precipitation data collected from the Australian Bureau of Meteorology. Both model and observed data were subset for the Brisbane catchment and aggregated into seasonal means for Australian seasons. Four drift correction alternatives including one new modified method were employed for the selected GCM models and the models were categorized based on their performances using different skill scores. The results revealed that the performance of drift correction alternatives vary among different models and initialization years. Although some drift correction methods showed better performances than others for a given model but it is still difficult to suggest the most suitable method for seasonal precipitation. Drift correction approaches for other time scales such as monthly precipitation, and their application for individual ensemble members may be investigated further to better understand the implications of alternative drift corrections for decadal forecasting.

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“…The SWAT model has been applied in several hydrological modeling studies in various catchments around the world [14,25,97]. There may be a few areas of uncertainty in modeling snow and glacier melt, such as orographic impacts and hydrological model parameterization, as well as heterogeneity in forest cover, slope, and features, which are evident issues in snow and glacier hydrology.…”

Section: Uncertainties and Limitationsmentioning

confidence: 99%

Evaluation of the CRU TS3.1, APHRODITE_V1101, and CFSR Datasets in Assessing Water Balance Components in the Upper Vakhsh River Basin in Central Asia

et al. 2021

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In this study, the applicability of three gridded datasets was evaluated (Climatic Research Unit (CRU) Time Series (TS) 3.1, “Asian Precipitation—Highly Resolved Observational Data Integration Toward the Evaluation of Water Resources” (APHRODITE)_V1101, and the climate forecast system reanalysis dataset (CFSR)) in different combinations against observational data for predicting the hydrology of the Upper Vakhsh River Basin (UVRB) in Central Asia. Water balance components were computed, the results calibrated with the SUFI-2 approach using the calibration of soil and water assessment tool models (SWAT–CUP) program, and the performance of the model was evaluated. Streamflow simulation using the SWAT model in the UVRB was more sensitive to five parameters (ALPHA_BF, SOL_BD, CN2, CH_K2, and RCHRG_DP). The simulation for calibration, validation, and overall scales showed an acceptable correlation between the observed and simulated monthly streamflow for all combination datasets. The coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) showed “excellent” and “good” values for all datasets. Based on the R2 and NSE from the “excellent” down to “good” datasets, the values were 0.91 and 0.92 using the observational datasets, CRU TS3.1 (0.90 and 0.90), APHRODITE_V1101+CRU TS3.1 (0.74 and 0.76), APHRODITE_V1101+CFSR (0.72 and 0.78), and CFSR (0.67 and 0.74) for the overall scale (1982–2006). The mean annual evapotranspiration values from the UVRB were about 9.93% (APHRODITE_V1101+CFSR), 25.52% (APHRODITE_V1101+CRU TS3.1), 2.9% (CFSR), 21.08% (CRU TS3.1), and 27.28% (observational datasets) of annual precipitation (186.3 mm, 315.7 mm, 72.1 mm, 256.4 mm, and 299.7 mm, out of 1875.9 mm, 1236.9 mm, 2479 mm, 1215.9 mm, and 1098.5 mm). The contributions of the snowmelt to annual runoff were about 81.06% (APHRODITE_V1101+CFSR), 63.12% (APHRODITE_V1101+CRU TS3.1), 82.79% (CFSR), 81.66% (CRU TS3.1), and 67.67% (observational datasets), and the contributions of rain to the annual flow were about 18.94%, 36.88%, 17.21%, 18.34%, and 32.33%, respectively, for the overall scale. We found that gridded climate datasets can be used as an alternative source for hydrological modeling in the Upper Vakhsh River Basin in Central Asia, especially in scarce-observation regions. Water balance components, simulated by the SWAT model, provided a baseline understanding of the hydrological processes through which water management issues can be dealt with in the basin.

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Assessment of Precipitation Simulations in Central Asia by CMIP5 Climate Models

Cited by 36 publications

References 51 publications

Assessment of the Effect of Mulch Film on Crops in the Arid Agricultural Region of China under Future Climate Scenarios

Assessment of the Effect of Mulch Film on Crops in the Arid Agricultural Region of China under Future Climate Scenarios

Intercomparison of drift correction alternatives for CMIP5 decadal precipitation

Evaluation of the CRU TS3.1, APHRODITE_V1101, and CFSR Datasets in Assessing Water Balance Components in the Upper Vakhsh River Basin in Central Asia

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