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  • Author or Editor: Kaicun Wang x
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Kaicun Wang
and
Shunlin Liang

Abstract

A simple and accurate method to estimate regional or global latent heat of evapotranspiration (ET) from remote sensing data is essential. The authors proposed a method in an earlier study that utilized satellite-determined surface net radiation (Rn ), a vegetation index, and daytime-averaged/daily maximum air temperature (Ta ) or land surface temperature (Ts ) data. However, the influence of soil moisture (SM) on ET was not considered and is addressed in this paper by incorporating the diurnal Ts range (DTsR). ET, measured by the energy balance Bowen ratio method at eight enhanced facility sites on the southern Great Plains in the United States and by the eddy covariance method at four AmeriFlux sites during 2001–06, is used to validate the improved method. Site land cover varies from grassland, native prairie, and cropland to deciduous forest and evergreen forest. The correlation coefficient between the measured and predicted 16-day daytime-averaged ET using a combination of Rn , enhanced vegetation index (EVI), daily maximum Ts , and DTsR is about 0.92 for all the sites, the bias is −1.9 W m−2, and the root-mean-square error (RMSE) is 28.6 W m−2. The sensitivity of the revised method to input data error is small. Implemented here is the revised method to estimate global ET using diurnal Ta range (DTaR) instead of DTsR because DTsR data are not available yet, although DTaR-estimated ET is less accurate than DTsR-estimated ET. Global monthly ET is calculated from 1986 to 1995 at a spatial resolution of 1° × 1° from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly dataset and is compared with the 15 land surface model simulations of the Global Soil Wetness Project-2. The results of the comparison of 118 months of global ET show that the bias is 4.5 W m−2, the RMSE is 19.8 W m−2, and the correlation coefficient is 0.82. Incorporating DTaR distinctively improves the accuracy of the estimate of global ET.

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Yun Li
,
Kaicun Wang
,
Guocan Wu
, and
Yuna Mao

Abstract

Since the 1950s, precipitation has been measured at national weather stations in China using national standard precipitation gauges. Gauges without a wind fence can significantly underestimate precipitation amounts, while this undercatch bias is closely related to surface wind speed and precipitation type. The observed surface wind speed across China has substantially declined during the past decades. Therefore, this study investigated the wind-induced error of the observed precipitation and its impact on regional and national mean trends in precipitation over China due to the reduction in surface wind speed. It was found that the wind-induced error for the mean annual precipitation nationwide was 29.28 mm yr−1, accounting for 3.92% of total precipitation amount. The variation of precipitation at the regional scale was large but the trends were both positive and negative, approximately cancelling at the national level and resulting in a small national mean trend. The raw observation data showed that the national mean precipitation increased at a rate of 1.85 mm yr−1 (10 a)−1 from 1960 to 2018, which was reduced to 0.33 mm yr−1 (10 a)−1 after correction, demonstrating that the correction of wind-induced error had an important impact on the trend of annual precipitation. Meanwhile, the reduction of surface wind speed was consistent at both the regional and national levels. On average, the wind-induced errors decreased at rates of −1.52, −1.34, and −0.14 mm yr−1 (10 a)−1 for total precipitation, rainfall, and snowfall, respectively. It illustrates that the decreases of the wind-induced error result in the increasing precipitation of raw observation.

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Jiayi Lu
,
Kaicun Wang
,
Guocan Wu
, and
Yuna Mao

Abstract

The spatiotemporal characteristics of extreme precipitation intensity are crucial for hydroclimatic studies. This study delineates the spatiotemporal distribution features of extreme precipitation intensity across China from 2001 to 2019 using the gridded daily precipitation dataset CN05.1, constructed from an observation network of over 2400 stations. Furthermore, we evaluate the reliability of 12 widely used precipitation datasets (including gauge-based, satellite retrieval, reanalysis, and fusion products) in monitoring extreme precipitation events. Our findings indicate the following: 1) CN05.1 reveals a consistent spatial distribution characterized by a decline in extreme precipitation intensity from the southeastern coastal regions toward the northwestern inland areas of China. From 2001 to 2019, more pronounced declining intensity trends are discernible in the northern and southwestern regions of China, whereas marked increasing trends manifest in the northeastern and the Yangtze River plain regions. National mean extreme precipitation indices consistently exhibit significant increasing trends throughout China. 2) Datasets based on station observations generally exhibit superior applicability concerning spatiotemporal distribution. 3) Multisource weighted precipitation fusion products effectively capture the temporal variability of extreme precipitation indices. 4) Satellite retrieval datasets exhibit notable performance disparities in representing various intensity indices. Most products tend to overestimate the increasing trends of national mean intensity indices. 5) Reanalysis datasets tend to overestimate extreme precipitation indices, and inadequately capture the trends. ERA5 and JRA-55 underestimate trends, while CFSR and MERRA-2 significantly overestimate the trends. These findings serve as a basis for selecting reliable precipitation datasets for extreme precipitation and hydrological simulation research in China.

Significance Statement

Extreme precipitation events have increasingly become more widespread, posing significant threats to human lives and property. Accurately understanding the spatiotemporal patterns of these events is imperative for effective mitigation. Despite the proliferation of precipitation products, their capacity to faithfully represent extreme events remains inadequately validated. In this study, we utilize a gauge-based dataset derived from over 2400 gauge stations across China to investigate the spatiotemporal changes in extreme precipitation events from 2001 to 2019. Subsequently, we conduct a rigorous evaluation of 12 widely used precipitation datasets to assess their efficacy in depicting extreme events. The results of this research offer valuable insights into the strengths and weaknesses of various precipitation products in depicting extreme events.

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Yun Li
,
Kaicun Wang
,
Guocan Wu
, and
Yuna Mao

Abstract

Rainfall and snowfall have different effects on energy balance calculations and land–air interactions in terrestrial models. The identification of precipitation types is crucial to understand climate change dynamics and the utilization of water resources. However, information regarding precipitation types is not generally available. The precipitation obtained from meteorological stations across China recorded types only before 1979. This study parameterized precipitation types with air temperature, relative humidity, and atmospheric pressure from 1960 to 1979, and then identified precipitation types after 1980. Results show that the main type of precipitation in China was rainfall, and the average annual rainfall days (amounts) across China accounted for 83.08% (92.55%) of the total annual precipitation days (amounts). The average annual snowfall days (amounts) in the northwestern region accounted for 32.27% (19.31%) of the total annual precipitation days (amounts), which is considerably higher than the national average. The average annual number of rainfall and snowfall days both displayed a downward trend while the average annual amounts of these two precipitation types showed an upward trend, but without significance at 0.1 levels. The annual number of rainfall and snowfall days in the southwestern region decreased significantly (−2.27 and −0.31 day decade−1, p < 0.01). The annual rainfall amounts in the Jianghuai region increased significantly (40.70 mm decade−1, p < 0.01), and the areas with the most significant increase in snowfall amounts were the northwestern (3.64 mm decade−1, p < 0.01). These results can inform our understanding of the distribution and variation of precipitation with different types in China.

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