Assessment of GPM IMERG and GSMaP daily precipitation products and their utility in droughts and floods monitoring across Xijiang River Basin

Due to the frequent occurrence of extreme precipitation events on a global scale, accurate estimation of regional precipitation has emerged as a critical concern. Specifically, region-scale hydrological modeling demands precipitation data with high spatiotemporal resolution and precision. Existing research has primarily concentrated on the correction and spatial downscaling of precipitation products. However, a considerable challenge persists in concurrently generating precipitation data with three key characteristics: high precision, high spatiotemporal resolution, and high spatial coverage (termed '3H'). This entails the provision of daily precipitation data at no more than a 1 km resolution, encompassing a full spatial extent. To address this challenge and obtain 3H precipitation data for regional hydrology research, this study introduces a multi-source precipitation data fusion and downscaling approach known as the 'Generate high Resolution, Accurate, Seamless data using Point-Surface (GRASPS) fusion method. This method combines the strengths of several satellite and model data sources to produce a more precise precipitation dataset at a daily scale and 1 km resolution, covering the Wuhan Urban Agglomeration. These sources include the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) from the Global Precipitation Measurement (GPM) mission, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR). Validation against precipitation data from 36 ground gauges yielded a Pearson Correlation Coefficient of 0.77, with Root Mean Squared Error, Mean Absolute Error, and Bias reduced to 6.08 mm, 2.20 mm, and −0.13 mm, respectively. Compared to prior studies, this research not only improved the spatial resolution of the precipitation dataset to 1 km but also enhanced the accuracy of extreme precipitation, resulting in an accuracy increase from 76.92 % to 91.67 %. Additionally, the generated precipitation dataset exhibited excellent performance at both daily and monthly scales. In terms of different land-cover types, the proposed method displayed improved performance in urban areas. Furthermore, the data obtained was subjected to testing across different input variables, precipitation levels, and downscaling algorithms. In conclusion, this study successfully obtained 3H precipitation data to bridge the gap in high-quality and fine-scale precipitation data. The proposed method and the generated dataset hold substantial implications for regional hydrology research and its practical applications. 

Data fusion of satellite imagery and downscaling for generating highly fine-scale precipitation

Intraseasonal variation of rainfall extremes within boreal summer in the Indo-Pacific region is driven by the Boreal Summer Intraseasonal Oscillation (BSISO), a quasi-periodic north-eastward movement of convective precipitation from the Indian Ocean to the Western Pacific. Predicting the spatiotemporal location of the BSISO is essential for subseasonal prediction of rainfall extremes but still remains a major challenge due to insufficient understanding of its propagation pathway. Here, using unsupervised machine learning, we characterize how rainfall extremes travel within the region and reveal three distinct propagation modes: north-eastward, eastward-blocked, and quasi-stationary. We show that Pacific sea surface temperatures modulate BSISO propagation - with El Niño-like (La Niña-like) conditions favoring quasi-stationary (eastward-blocked) modes-by changing the background moist static energy via local overturning circulations. Finally, we demonstrate the potential for early warning of rainfall extremes in the region up to four weeks in advance. 

Propagation pathways of Indo-Pacific rainfall extremes are modulated by Pacific sea surface temperatures

Abstract The effect of contact load and relative displacement on tribo-corrosion interaction of parallel steel wires of main cable in the suspension bridge was investigated in this study. A self-made tribo-corrosion test bench was employed to conduct tribo-corrosion tests of parallel steel wires in 3.5% (wt%) NaCl solution and deionized water under different contact loads and different relative displacements. The friction coefficient and wear coefficient of wires were presented. Electrochemical corrosion behavior (Tafel polarization curves, Nyquist diagram, and equivalent circuit diagram) was characterized by electrochemical analyzer. Wear morphology was observed by scanning electron microscope. Wear volume loss and corrosion-wear interaction were quantitatively demonstrated by high-precision weighing balance. The results show that the electrochemical corrosion ability of the steel wires increases with the increase of the contact load or relative displacement. The increased contact load or relative displacement increases the volume loss of corrosion-wear and pure wear, but decreases the wear coefficient. The wear mechanisms in 3.5% NaCl solution are adhesive wear, abrasive wear, and corrosive wear as compared to adhesive wear and abrasive wear in deionized water under different contact loads. The wear mechanisms of parallel steel wires are slightly different under different relative displacements. But the main wear mechanisms are similar to that under different contact loads. The interaction effects of corrosion and wear produced by the contact load and relative displacement are all the synergistic effects. 

/pdf/tribo-corrosion-interaction-of-the-parallel-steel-wires-in-3ov77rd7.pdf

Tribo-corrosion interaction of the parallel steel wires in the suspension bridges

The main cable is the primary load-bearing component of a long-span multi-tower suspension bridge. The interaction between a dead load, vehicle load, wind load, and the corrosion environment leads the main cable wire to exhibit tribo-corrosion-fatigue behaviors. This behavior causes wire wear and deterioration, as well as a reduction in the effective cross-sectional area. This leads to the gradual deterioration of the wire’s load-bearing strength and seriously affects the load-bearing safety of the main cable. In order to ensure the safety of suspension bridges, it is critical to investigate the gradual deterioration behavior of the main cable wire’s load-bearing strength. A wire tribo-corrosion-fatigue test rig was established to test the wire under different friction pairs (saddle groove or parallel wires). The cross-sectional failure area of the wire with different pairs was obtained by super-depth electron microscopy and calculation. The damage degree evolution model and the deterioration model of the wire load-bearing strength were established by combining the theory of damage mechanics and the finite element method. The results show that, as contact and fatigue loads increase, so does the cross-sectional failure area of the fatigue steel wire. The fatigue wire’s damage degree has a good quadratic function relationship with fatigue cycles. The damage degree of the wire increases and the load-bearing strength decreases with increasing contact load and fatigue load. The load-bearing strength of the wire changes little at the beginning and decreases with increasing fatigue cycles. The results have fundamental significance for the life prediction of the main cable wires of suspension bridges.

/pdf/gradual-deterioration-behavior-of-the-load-bearing-strength-3owhefex.pdf

Gradual Deterioration Behavior of the Load-Bearing Strength of Main Cable Wires in a Suspension Bridge

Intercomparison of multiple high-resolution precipitation products over China: Climatology and extremes

Dynamic contact behaviors of saddle materials for suspension bridge

Abstract. High quality gridded soil moisture products are essential for many Earth system science applications, and they are usually available from remote sensing or model simulations with coarse resolution. Here we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained with in-situ measurements of 1,789 stations, named as SMCI1.0. Random Forest is used to predict soil moisture using ERA5-land time series, leaf area index, land cover type, topography and soil properties as covariates. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2010–2020. Using in-situ soil moisture as the benchmark, two independent experiments are conducted to investigate the estimation accuracy of the SMCI1.0: year-to-year experiment (ubRMSE ranges from 0.041–0.052 and R ranges from 0.883–0.919) and station-to-station experiment (ubRMSE ranges from 0.045–0.051 and R ranges from 0.866–0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4 and SoMo.ml. However, the high errors of soil moisture often located in North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to studies on the spatial-temporal patterns. As SMCI1.0 is based on in-situ data, it can be useful complements of existing model-based and satellite-based datasets for various hydrological, meteorological, and ecological analyses and modeling. SMCI1.0 can be accessed at http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022). 

/pdf/a-1-km-daily-soil-moisture-dataset-of-china-based-on-in-situ-1in2x57a.pdf

A 1-km daily soil moisture dataset of China based on in-situ measurement using machine learning

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Intercomparison of multiple high-resolution precipitation products over China: Climatology and extremes

Dynamic contact behaviors of saddle materials for suspension bridge

A 1-km daily soil moisture dataset of China based on in-situ measurement using machine learning