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The self-doping of oxygen vacancy and Ti3+ by electrochemical reduction (ER) method has been proved to be an effective means to improve the PEC performance of TiO2. However, the effect of the surface structure on ER treatment remains ambiguous. In this work, three kinds of nanostructured rutile TiO2 (nanowire arrays (TNWs), etched nanowire arrays (E-TNWs) and nanorod arrays (TNRs)) were reduced electrochemically to explore the factors influencing the ER process of rutile TiO2. The experimental results show that alkaline environment (1 M NaOH) is more conducive to the occurrence of ER reaction. And the reduced three kinds of nanostructured TiO2 photoanodes show a significantly higher photocurrent density of about 1.46, 1.65 and 1.45 mA cm−2 at 1.23 V vs. relative hydrogen electrode (RHE), respectively, which are 15, 16 and 1.1 times that of pristine TiO2. The different degrees of photocurrent density enhancement can be ascribed to the different degrees of electrochemical reduction of TiO2 with different crystallinity and exposed crystal facets as well as specific surface area. This study provides new insights into the mechanism of electrochemical reduction method. [ABSTRACT FROM AUTHOR]

The self-doping of oxygen vacancy and Ti3+ by electrochemical reduction (ER) method has been proved to be an effective means to improve the PEC performance of TiO2. However, the effect of the surface structure on ER treatment remains ambiguous. In this work, three kinds of nanostructured rutile TiO2 (nanowire arrays (TNWs), etched nanowire arrays (E-TNWs) and nanorod arrays (TNRs)) were reduced electrochemically to explore the factors influencing the ER process of rutile TiO2. The experimental results show that alkaline environment (1 M NaOH) is more conducive to the occurrence of ER reaction. And the reduced three kinds of nanostructured TiO2 photoanodes show a significantly higher photocurrent density of about 1.46, 1.65 and 1.45 mA cm−2 at 1.23 V vs. relative hydrogen electrode (RHE), respectively, which are 15, 16 and 1.1 times that of pristine TiO2. The different degrees of photocurrent density enhancement can be ascribed to the different degrees of electrochemical reduction of TiO2 with different crystallinity and exposed crystal facets as well as specific surface area. This study provides new insights into the mechanism of electrochemical reduction method. [ABSTRACT FROM AUTHOR]

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models' representation of PCI. Moreover, the models' poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau, often referred to as Asia's water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0–4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0–2.1) than rainfall (main range of RMSE: 2.0–3.8) and sleet (main range of RMSE: 1.5–3.8). This study's findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

With global warming, extreme weather frequently and severely appears globally. Extreme precipitation is one of the extreme weather events that can cause many natural disasters, such as floods and waterlogging. In this study, Global Precipitation Climatology Project (GPCP) daily precipitation data were used to investigate extreme precipitation and its contribution to annual precipitation in different global climate regions and typical river basins. The climate types included equatorial climates (EC), arid climates (AC), warm temperate climates (WTC), snowy climates (SC) and polar climates (PC). R99p, Rx5day, CWD and R20 was selected as extreme precipitation indices in this study; extreme precipitation days were defined by CWD and R20. The results showed that EC and WTC had higher extreme precipitation level; SC and PC had lower extreme precipitation amounts and days than AC. R99p, Rx5day and CWD monitored higher extreme precipitation contribution degrees in AC; however, R20 monitored higher contribution degrees in EC and WTC. R99p, Rx5day and CWD showed higher extreme precipitation contribution degrees in North Africa, the Middle East, Australia and northwestern China; R20 showed higher contribution degrees in South America, the southeastern United States and South Asia. Based on historical observational data, Heilongjiang Basin (HB), Yellow River Basin (YERB), Yangtze River Basin (YARB), Ganges River Basin (GRB), Danube River Basin (DRB) and Mekong River Basin (MERB) had high-frequency extreme precipitation in summer. The research results are helpful for understanding the characteristics of extreme precipitation and provide a reference for flood control and disaster reduction in different climatic regions and main river basins. [ABSTRACT FROM AUTHOR]

Precipitation is a main part of the regional hydrological cycle, and global warming affects the hydrological cycle because regional precipitation is impacted by mechanistic changes in the hydrological cycle under global warming. This study presents an exploration of the composition variation characteristics of precipitation time series under global climate change. Twelve CMIP6 models were used to forecast precipitation changes in the Yellow River Basin (YRB). Climatic Research Unit (CRU) data were applied in the analysis of historical precipitation. Trend analysis, precipitation bias correction, and Fourier functions were used to analyze the future precipitation change characteristics. Our results showed that the IPSL-CM6A-LR and EC-Earth3-CC models had excellent performances in simulating precipitation in the YRB. Most CMIP6 models showed that precipitation under the SSP5-8.5 scenario had a higher increasing trend and was higher than that under the SSP2-4.5 scenario. The multimodel ensemble means (MEM) of CMIP6 precipitation showed that the future trend and stochastic component of precipitation had a higher degree of contribution than the historical trend and stochastic component of precipitation. However, the future periodic component of precipitation had a lower degree of contribution than the historical component. Most models showed that the degree of contribution of the periodic component of precipitation in Period IV (2015–2057) was higher than that in Period V (2058–2100). Most models showed similar degrees of contribution in Period I (1901–1938), Period II (1939–1976), and Period III (1977–2014). However, the degree of contribution of the stochastic component in 2058–2100 was lower than that in 2015–2057. This study improves the understanding of future precipitation change characteristics and provides a reference for disaster prevention and mitigation in the YRB. [ABSTRACT FROM AUTHOR]