NASA’s Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is a major source of precipitation data, having a larger coverage, higher precision, and a higher spatiotemporal resolution than previous products, such as the Tropical Rainfall Measuring Mission (TRMM). However, there rarely has been an application of IMERG products in flash flood warnings. Taking Yunnan Province as the typical study area, this study first evaluated the accuracy of the near-real-time IMERG Early run product (IMERG-E) and the post-real-time IMERG Final run product (IMERG-F) with a 6-hourly temporal resolution. Then the performance of the two products was analyzed with the improved Rainfall Triggering Index (RTI) in the flash flood warning. Results show that (1) IMERG-F presents acceptable accuracy over the study area, with a relatively high hourly correlation coefficient of 0.46 and relative bias of 23.33% on the grid, which performs better than IMERG-E; and (2) when the RTI model is calibrated with the gauge data, the IMERG-F results matched well with the gauge data, indicating that it is viable to use MERG-F in flash flood warnings. However, as the flash flood occurrence increases, both gauge and IMERG-F data capture fewer flash flood events, and IMERG-F overestimates actual precipitation. Nevertheless, IMERG-F can capture more flood events than IMERG-E and can contribute to improving the accuracy of the flash flood warnings in Yunnan Province and other flood-prone areas.

Extreme precipitation events (EPE) often cause catastrophic floods accompanied by serious economic losses and casualties. The latest version (V06) of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) provides global satellite precipitation data from 2000 at a higher spatiotemporal resolution with improved quality. It is scientifically and practically important to assess the accuracy of the IMERG V06 in capturing extreme precipitation. This study evaluates the two widely used products of IMERG during 2000–2018, i.e., IMERG late run (IMERG-L) and IMERG final run (IMERG-F), in the densely populated and flood-prone North China Plain. The accuracy of the IMERG V06 is evaluated with ground measurements from rain gauge stations at multiple scales (hourly, daily, and seasonally). A novel target tracking method is introduced to extract three-dimensional (3D) extreme precipitation events, and the near-real-time uncalibrated PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System) and GSMAP (Global Satellite Mapping of Precipitation) satellite data are added to further evaluate IMERG’s performance during extreme precipitation. Finally, for flash flood events induced by extreme rainfall in the Hebei Province from 15 to 23 July 2016, the accuracy of capturing the event with IMERG-F and IMERG-L was verified. Results reveal that IMERG-F is better than IMERG-L at all investigated scales (hourly, daily, and seasonally), but the difference between the two products is less at higher time resolutions. Both products manifest decreased performance when capturing 3D extreme precipitation events, and comparatively, IMERG-F performs better than IMERG-L. IMERG-F exhibits a distinct discontinuity in extreme precipitation thresholds between land and ocean, which is a limitation of IMERG-F not documented in previous studies. Moreover, IMERG-L and IMERG-F are comparable at an hourly scale for some metrics, which is beyond the expectation that IMERG-F is notably better than IMERG-L. This study provides a scientific basis for the performance of satellite precipitation products and contributes to guiding users when applying global precipitation products.

Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRF_HR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRF_HR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRF_HR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R² amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRF_HR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R², as well as the small RMSE and BIAS amounts, indicate that ET_WRF_HR has achieved a very good performance.

The North China plain (NCP) is an important production base for winter wheat and summer maize in China. Severe droughts seriously restrict agricultural production in this region, threatening food security. Based on the standardized precipitation evapotranspiration index (SPEI), this study explored the spatial and temporal drought characteristics during the winter wheat and summer maize growing seasons in the region. The study found that: 1) From 1980 to 2013, the drought trend of the winter wheat growing season in the NCP has intensified, with Huang-Huai Plain agricultural area (HH_P) showing the most significant drought trend. However, the summer maize growing season has become wetter, with the Shandong hilly agricultural and forestry area (SD_Q) showing the most significant wetting trend. 2) After the year 2003, the results from Mann-Kendall trend analysis revealed that the drought trend of HH_P during the winter wheat growing season became particularly pronounced, but the wetting trend of SD_Q and HH_P during the summer maize growing season became more evident. 3) The dominant spatial patterns observed in the NCP during the growing seasons of winter wheat and summer maize were marked by a consistent distribution of drought and wetness conditions. For winter wheat, the southern regions of the foothill plain area of Yanshan and Taihang Mountains (YT_P) and the low-lying plain area of Hebei, Shandong, and Henan (JLY_P) were more sensitive to the changes of drought and wet conditions. For summer maize, SD_Q was more sensitive to the changes of the drought and wet conditions. The results of this study could provide references for the formulation of drought relief strategies of winter wheat and summer maize in the NCP.

Arid central Asia (ACA) is dominated by mid-latitude westerlies and characterized by a climate optimum (a relatively humid climate that has supported the development of human culture) in clear contrast with the climate of monsoonal Asia during the Holocene. Significantly, whether the onset of the Holocene Climate Optimum (HCO) had an impact on cultural exchanges along the ancient Silk Road remains unknown. In this study, we compared the onset of the HCO in different parts of the vast ACA region by referring to a variety of previously established paleo-moisture/precipitation records. Intriguingly, we found significant differences in the onset of the HCO between the western and eastern parts of ACA. The onset of the HCO in the western part of ACA (i.e., to the west of the Tianshan Mountains) mainly occurred at ~8 ka BP (1 ka = 1000 cal yr BP). In contrast, the onset of the HCO occurred at ~6 ka in northern Xinjiang and even as late as ~5 ka in southern Xinjiang; this is a delay of 2–3 thousand years compared with the western part of ACA. These results likely indicate that the onset of the HCO occurred in a time-transgressive manner in ACA, namely, ‘early in the west but late in the east’. On the other hand, we found that the onset of the HCO in the western part of ACA may have resulted in the inception of wheat planting and the development of agricultural civilization and that the onset of the HCO in northern Xinjiang may have prompted the southward migration of Afanasievo culture after ~5 ka. Additionally, the initiation of the HCO in southern Xinjiang could provide an environmental basis for the spread and planting of wheat and millet in this area after ~4.5 ka. We speculate that the spatial differences in the onset of the HCO in ACA are mainly related to temporal changes in the intensity and position of the mid-latitude westerly jet. Although the increase in insolation and reduction in the global ice volume would have led to an increase in the water vapor feeding the western part of ACA around 8 ka, the climate in the eastern part of ACA (namely, the Xinjiang region) could have only become humid after 6 ka when the westerlies were intensified and became positioned in the south. Moreover, the delayed HCO in southern Xinjiang probably benefited from the stronger westerly winds that appeared around 5 ka, which could have overcome the influence of the tall topography of the Tianshan Mountains. Therefore, in addition to external forcing (i.e., insolation), the ocean–atmospheric teleconnection, the regional topography, and their connection to the climate system are important in determining the spatial differences in the time-transgressive onset of the HCO in ACA. Our findings contribute to understanding the spatio-temporal characteristics of the hydroclimate in regions with complex eco-environmental systems and a diverse history of human activity.

Low-molecular-weight organic acids (LMWOAs), a type of small organic molecules with one or more carboxyl groups present in soil, play an important role in the soil ecosystem. Greenhouse soil could be as a key example to understand LMWOA distribution and its ecological effects on heavy metals. In this study, 54 soil samples and 24 vegetable samples were collected from Wuqing, Tianjin, China, to investigate the distribution of LMWOAs in greenhouse soil and its effect on the transfer of heavy metals from soil to vegetables. The results showed that the mean concentrations of lactic acid, acetic acid, formic acid, propionic acid, butyric acid, malic acid, and oxalic acid detected in the greenhouse soil were 5.00, 20.57, 0.62, 23.38, 0.64, 4.67, and 29.03 mg/kg. Except for formic acid, the concentrations of LMWOAs in greenhouse soils were higher than LMWOAs in field soils. The content of oxalic acid, acetic acid, lactic acid, malic acid, propionic acid, and butyric acid in greenhouse soils is 2.3 times, 2.25 times, 1.82 times, 2.38 times, 1.38 times, 1.93 times that of LMWOAs in field soil. LMWOAs in the soils had obvious surface cohesion characteristics. The correlation between LMWOAs indicates that propionic acid has the same origin and/or similar variation characteristics as malic acid, oxalic acid, acetic acid, butyric acid, and lactic acid. Oxalic acid, formic acid, acetic acid, and lactic acid have a significant contribution to soil acidification. Accumulation of available phosphorus (AP) and available potassium (AK) in the soils may result in increased acetic acid, butyric acid, oxalic acid, and lactic acid. The above results indicate that LMTOA has different effects on the transfer of heavy metals from soil to vegetable. In addition, the accumulation of LMWOAs could stimulate the transfer of Cd, Zn, and Cu from soil to vegetable, but inhibit the absorption of Pb by vegetables.