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Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface-subsurface hydrological model that employs a finite volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma Watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://shuddata.com), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis. [ABSTRACT FROM AUTHOR]

The data assimilation technique is one of the important ways to reduce the uncertainty of atmospheric chemistry model input and improve the model forecast accuracy. In this paper, an ensemble optimal interpolation assimilation (EnOI) system for a regional online chemical weather numerical forecasting system (GRAPES_Meso5.1/CUACE) is developed for operational use and efficient updating of the initial fields of chemical components. A heavy haze episode in eastern China was selected, and the key factors affecting the EnOI, such as localization length-scale, ensemble size, and assimilation moment, were calibrated by sensitivity experiments. The impacts of assimilating ground-based PM2.5 observations on the model chemical initial field and PM2.5, visibility forecasts were investigated. The results show that assimilation of PM2.5 significantly reduces the uncertainty of the initial PM2.5 field. The mean error and root mean square error (RMSE) of initial PM2.5 for mainland China have all decreased by more than 75%, and the correlation coefficient could be improved to more than 0.95. Even greater improvements appear in North China. For the forecast fields, assimilation of PM2.5 improves PM2.5 and visibility forecasts throughout the lead time window of 24 h. The PM2.5 RMSE can be reduced by 10%-21% within 24 h, but the assimilation effect is most obvious in the first 12 h. The assimilation moment chosen at 1200 UTC is more effective than that at 0000 UTC for improving the forecast, because the discrepancy between simulation and observation at 1200 UTC is larger than that at 0000UTC, indicating the assimilation efficiency will be higher when the bias of the model is higher. Assimilation of PM2.5 also improves visibility forecast accuracy significantly. When the PM2.5 increment is negative, it corresponds to an increase in visibility, and when the PM2.5 analysis increment is positive, visibility decreases. It is worth noting that the improvement of visibility forecasting by assimilating PM2.5 is more obvious in the light pollution period than in the heavy pollution period, since visibility is much more affected by humidity during the heavy pollution period accompanied by low or extreme low visibility. To get further visibility improvement, especially for extreme low visibility during severe haze pollution, not only PM2.5 but also relative humidity should be simultaneously assimilated as well. [ABSTRACT FROM AUTHOR]

As Earth system modeling develops ever finer grid resolutions, the inputting and outputting (I/O) of the increasingly large data fields becomes a processing bottleneck. Many models developed in China, as well as the Community Coupler (C-Coupler), do not fully benefit from existing parallel I/O supports. This paper reports the design and implementation of a Common parallel Input/Output Framework based on C-Coupler2.0 (CIOFC1.0). Parallelization by CIOFC1.0 can accelerate the I/O of large data fields. The framework also allows convenient specification by users of the I/O settings; e.g., the data fields for I/O, the time series of the data files for I/O, and the data grids in the files. The framework can also adaptively input data fields from a time-series dataset during model integration, automatically interpolate data when necessary, and output fields either periodically or irregularly. CIOFC1.0 demonstrates the cooperative development of an I/O framework and coupler, and thus enables convenient and simultaneous use of a coupler and an I/O framework. [ABSTRACT FROM AUTHOR]