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The discrepancies among the variations in global ice volume, cave stalagmite δ18O and rainfall reconstructed by cosmogenic 10Be tremendously restrain our understanding of the evolution of the East Asian summer monsoon (EASM). Here, we present a 430-ka EASM mean annual precipitation record on the Chinese Loess Plateau obtained using branched glycerol dialkyl glycerol tetraethers based on a deep learning neural network; this rainfall record corresponds well with cave-derived δ18O data from southern China but differs from precipitation reconstructed by 10Be. Both branched tetraether membrane lipids and cave δ18O may be affected by soil moisture and atmospheric temperature when glacial and interglacial conditions alternated and were thus decoupled from atmospheric precipitation; instead, they represent variations in the intensity of the EASM. Furthermore, we demonstrate that the brGDGT-DLNN method can significantly extend the temporal scale record of the EASM and is not restricted by geographic location compared with stalagmite records. In considering Asian monsoon intensity and precipitation during glacial-interglacial transitions in Chinese Loess Plateau, a new study finds that brGDGT-DLNN method can significantly extend the temporal scale record of the EASM and is not restricted by geographic location compared with stalagmite records. [ABSTRACT FROM AUTHOR]

Precipitation estimation with high-accuracy and spatiotemporal resolution is the key for distributed hydrological modelling. Gauge-satellite precipitation merging provides an effect way to improve the spatial precipitation estimation. In spite of the encouraging outcomes of gauge-satellite merging, it is quite a challenging job to derive precipitation appropriate for distributed watershed hydrological modeling when facing the following problems: (1) the spatial resolution of satellite-based precipitation product is much coarser than the modeled one; (2) the spatial distribution information of daily/sub-daily precipitation input to distributed hydrological models cannot be well captured by a single satellite-based precipitation product. In allusion to these problems, a two-step strategy for multi-source information merging was proposed in this paper to improve the spatial resolution and accuracy of daily precipitation estimates. The strategy is based on geographically weighted ridge regression kriging (GWRRK) with emphasis on dealing with non-stationarity and local collinearity in the regression context. Firstly, several individual satellite-based precipitation products, e.g., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Aanlysis (TMPA) 3B42RT, Climate Prediction Center MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP), were downscaled to 1km by employing the relationships between precipitation and other environmental factors such as topography and vegetation. And then, all these downscaled products were merged with gauge observations. Considering the strongly skewed distribution of daily precipitation, Box-Cox transformation was carried out prior to the GWRRK approach to give a more normal distribution. To our knowledge, this is the first research to downscale satellite-based precipitation product at daily scale using geographically weighted regression method and to merge gauge observations and multiple satellite-based precipitation products. The two-step merging strategy was applied to an experiment conducted in Xijiang Basin of China from 2010 to 2017. Results showed that: (1) the regression relationships in spatial downscaling and gauge-multisatellites merging were strongly spatially non-stationary; (2) local collinearity existed in both steps: this problem was not serious in spatial downscaling but serious enough to be the main barrier for the application of geographically weighted (GWR) in gauge-multisatellites merging; (3) the spatial trend of the downscaled data was consistent with that of the original data for all the four satellite-based daily precipitation products; (4) the merging of gauge observations and multiple satellite-based precipitation products provided more accurate estimates than the merging of gauge observations and a single satellite-based precipitation product. The GWRRK-based two-step merging strategy presented in this study has provided an efficient way to derive daily precipitation with high-accuracy and spatiotemporal resolution. [ABSTRACT FROM AUTHOR]

Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42RT and TRMM-3B43 improved to 0.89, 37.51 mm, –0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data. [ABSTRACT FROM AUTHOR]

Hourly or 3-hourly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite products and rain gauge records are used to characterize East Asian summer monsoon rainfall, including spatial patterns in June–August (JJA) mean precipitation amount, frequency, and intensity, as well as the diurnal and semidiurnal cycles. The results show that the satellite products are comparable to rain gauge data in revealing the spatial patterns of JJA precipitation amount, frequency, and intensity, with pattern correlation coefficients for five subregions ranging from 0.66 to 0.94. The pattern correlation of rainfall amount is higher than that of frequency and intensity. Relative to PERSIANN, the TRMM product has a better resemblance with rain gauge observations in terms of both the pattern correlation and root-mean-square error. The satellite products overestimate rainfall frequency but underestimate its intensity. The diurnal (24 h) harmonic dominates subdaily variations of precipitation over most of eastern China. A late-afternoon maximum over southeastern and northeastern China and a near-midnight maximum over the eastern periphery of the Tibetan Plateau are seen in the rain gauge data. The diurnal phases of precipitation frequency and intensity are similar to those of rainfall amount in most regions, except for the middle Yangtze River valley. Both frequency and intensity contribute to the diurnal variation of rainfall amount over most of eastern China. The contribution of frequency to the diurnal cycle of rainfall amount is generally overestimated in both satellite products. Both satellite products capture well the nocturnal peak over the eastern periphery of the Tibetan Plateau and the late-afternoon peak in southern and northeastern China. Rain gauge data over the region between the Yangtze and Yellow Rivers show two peaks, with one in the early morning and the other later in the afternoon. The satellite products only capture the major late-afternoon peak. [ABSTRACT FROM AUTHOR]

Satellite-based and reanalysis precipitation products provide a practical way to overcome the shortage of gauge precipitation data because of their high spatial and temporal resolution. This study compared two reanalysis precipitation datasets (the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), the National Centers for Environment Prediction Climate Forecast System Reanalysis (NCEP-CFSR)) and two satellite-based datasets (the Tropical Rainfall Measuring Mission 3B42 Version 7 (3B42V7) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR)) with observed precipitation in the Xiang River basin in China at two spatial (grids and the whole basin) and two temporal (daily and monthly) scales. These datasets were then used as inputs to a SWAT model to evaluate their usefulness in hydrological prediction. Bayesian model averaging was used to discriminate dataset performance. The results show that: (1) for daily timesteps, correlations between reanalysis datasets and gauge observations are >0.55, better than satellite-based datasets; The bias values of satellite-based datasets are <10% at most evaluated grid locations and for the whole baseline. PERSIANN-CDR cannot detect the spatial distribution of rainfall events; the probability of detection (POD) of PERSIANN-CDR at most evaluated grids is <0.50; (2) CMADS and 3B42V7 are better than PERSIANN-CDR and NCEP-CFSR in most situations in terms of correlation with gauge observations; satellite-based datasets are better than reanalysis datasets in terms of bias; and (3) CMADS and 3B42V7 simulate streamflow well for both daily (The Nash-Sutcliffe coefficient (NS) > 0.70) and monthly (NS > 0.80) timesteps; NCEP-CFSR is worst because it substantially overestimates streamflow; PERSIANN-CDR is not good because of its low NS (0.40) during the validation period. [ABSTRACT FROM AUTHOR]