Evaluating the performance of regional climate models to simulate the US drought and its connection with El Nino Southern Oscillation

Aryal, Yog; Zhu, Jianting

doi:10.1007/s00704-021-03704-y

Cited by 6 publications

(3 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of the model was analysed in terms of basic (mean, standard deviation and skew coefficient) and drought statistics (frequency, duration, magnitude and intensity). The meteorological and hydrological drought analysis was developed by applying the Standard Precipitation index (SPI) (Bonaccorso et al, 2003;Livada and Assimakopoulos, 2007) and Standard Streamflow index (SSI) (Salimi et al, 2021), respectively. They were estimated for periods of aggregation equal to 12 months.…”

Section: Classification Of the Rcmsmentioning

confidence: 99%

See 1 more Smart Citation

An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts

Collados‐Lara¹,

Gómez-Gómez

Pulido-Velázquez³

et al. 2022

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. This paper describes the benefits of using more reliable local climate scenarios to analyse hydrological responses. It assumes that Regional Climate Model (RCM) simulations are more reliable when they provide better approximations to the historical basic and drought statistics after applying a bias correction to them. We have investigated whether the best solutions in terms of their approximation to the local meteorology may also provide the best hydrological assessments. We have carried out a classification of the corrected RCM simulations used for both approximations. This has been applied in the Cenajo basin (south-eastern Spain), where we show that the best approximations of the historical meteorological statistics also provide the best approximations for the hydrological statistics. The selected RCMs were used to generate future (2071–2100) local scenarios under the RCP8.5 emission scenario. The two selected RCMs predict significant changes in mean precipitation (−31.6 % and −44.0 %) and mean temperature (+26.0 % and +32.2 %). They also predict higher frequency (from 5 events in the historical period to 20 and 22 in the future), length (4.8 to 7.4 and 10.5 months), magnitude (2.53 to 6.56 and 9.62 SPI) and intensity (0.48 to 1.00 and 0.94 SPI) of extreme meteorological droughts. These two RCMs also predict higher changes in mean streamflow (−43.5 % and −57.2 %) and hydrological droughts. The two RCMs also predict worrying changes in streamflow (−43.5 % and −57.2 %) and hydrologically extreme droughts: frequency (from 3 to 11 for the first model and 8 events for the second model), length (8.3 to 15.4 and 29.6 months), magnitude (from 3.98 to 11.84 and 31.72 SSI), and intensity (0.63 to 0.90 and 1.52 SSI).

show abstract

Section: Classification Of the Rcmsmentioning

confidence: 99%

“…frequency, duration, magnitude and intensity) should be studied . In the literature there are few studies that analyse the reliability of RCMs for considering meteorological droughts (Peres et al, 2020;Aryal and Zhu, 2021). In this study we also analyse the propagation of meteorological droughts to hydrological droughts.…”

Section: Introductionmentioning

confidence: 99%

An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts

Collados‐Lara¹,

Gómez-Gómez

Pulido-Velázquez³

et al. 2022

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Weather has always affected people's production and lives, and some harsh weather events even pose a serious threat to people's life and normal order of society (Aryal and Zhu, 2021;Villén-Peréz et al, 2020). Weather forecasts offer crucial support for people to know future weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal forecasting model based on hybrid convolution for local weather prediction post-processing

Xiang²,

Guan³

et al. 2022

Front. Earth Sci.

View full text Add to dashboard Cite

Future weather conditions can be obtained based on numerical weather prediction (NWP); however, NWP is unsatisfied with precise local weather prediction. In this study, we propose a spatiotemporal convolutional network (STCNet) based on spatiotemporal modeling for local weather prediction post-processing. To model the spatiotemporal information, we use a convolutional neural network and an interactive convolutional module, which use two-dimensional convolution for spatial feature extraction and one-dimensional convolution for time-series processing, respectively. We performed experiments at several stations, and the results show that our model considerably outperforms the traditional recurrent neural network–based Seq2Seq model while demonstrating the effectiveness of the fusion of observation and forecast data. By investigating the influences of seasonal changes and station differences, we conclude that the STCNet model has high prediction accuracy and stability. Finally, we completed the hour-by-hour local weather prediction using the 3-h forecast data and attained similar results to the 3-h local weather prediction that efficiently compensated for the temporal resolution of the forecast data. Thus, our model can enhance the spatial and temporal resolutions of forecast data and achieve remarkable local weather prediction.

show abstract

Identifying inter-seasonal drought characteristics using binary outcome panel data models

Niaz

Hussain

Almazah

et al. 2023

Geocarto International

View full text Add to dashboard Cite

Evaluating the performance of regional climate models to simulate the US drought and its connection with El Nino Southern Oscillation

Cited by 6 publications

References 69 publications

An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts

An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts

Spatiotemporal forecasting model based on hybrid convolution for local weather prediction post-processing

Identifying inter-seasonal drought characteristics using binary outcome panel data models

Contact Info

Product

Resources

About