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This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future. Significance Statement: The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty. [ABSTRACT FROM AUTHOR]

Taxis pose a higher threat to global climate change and human health through air emissions. However, the evidence on this topic is scarce, especially, in developing countries. Therefore, this study conducted estimation of fuel consumption (FC) and emission inventories on Tabriz taxi fleet (TTF), Iran. A structured questionnaire to obtain operational data of TTF, municipality organizations, and literature review were used as data sources. Then modeling was used to estimate fuel consumption ratio (FCR), emission factors (EFs), annual FC, and emissions of TTF using uncertainty analysis. Also, the impact of COVID-19 pandemic period was considered on the studied parameters. The results showed that TTF have high FCRs of 18.68 L/100 km (95% CI=17.67–19.69 L/100 km), which are not affected by age or mileage of taxis, significantly. The estimated EFs for TTF are higher than Euro standards, but the differences are not significant. However, it is critical as can be an indication of inefficiency of periodic regulatory technical inspection tests for TTF. COVID-19 pandemic caused significant decrease in annual total FC and emissions (9.03–15.6%), but significant increase in EFs of per-passenger-kilometer traveled (47.9–57.3%). Annual vehicle-kilometer-traveled by TTF and the estimated EFs for gasoline-compressed natural gas bi-fueled TTF are the main influential parameters in the variability of annual FC and emission levels. More studies on sustainable FC and emissions mitigation strategies are needed for TTF. [ABSTRACT FROM AUTHOR]