Carbon emissions and water use are two major kinds of human activities. To reveal whether these two activities can modify the hydrological cycle and climate system in China, we conducted two sets of numerical experiments using regional climate model RegCM4. In the first experiment used to study the climatic responses to human carbon emissions, the model were configured over entire China because the impacts of carbon emissions can be detected across the whole country. Results from the first experiment revealed that near-surface air temperature may significantly increase from 2007 to 2059 at a rate exceeding 0.1 °C per decade in most areas across the country; southwestern and southeastern China also showed increasing trends in summer precipitation, with rates exceeding 10 mm per decade over the same period. In summer, only northern China showed an increasing trend of evapotranspiration, with increase rates ranging from 1 to 5 mm per decade; in winter, increase rates ranging from 1 to 5 mm per decade were observed in most regions. These effects are believed to be caused by global warming from human carbon emissions. In the second experiment used to study the effects of human water use, the model were configured over a limited region—Haihe River Basin in the northern China, because compared with the human carbon emissions, the effects of human water use are much more local and regional, and the Haihe River Basin is the most typical region in China that suffers from both intensive human groundwater exploitation and surface water diversion. We incorporated a scheme of human water regulation into RegCM4 and conducted the second experiment. Model outputs showed that the groundwater table severely declined by ∼10 m in 1971–2000 through human groundwater over-exploitation in the basin; in fact, current conditions are so extreme that even reducing the pumping rate by half cannot eliminate the groundwater depletion cones observed in the area. Other hydrological and climatic elements, such as soil moisture, runoff generation, air humidity, precipitation, wind field, and soil and air temperature, were also significantly affected by anthropogenic water withdrawal and consumption, although these effects could be mitigated by reducing the amount of water drawn for extraction and application

In this work, a diagnostic scheme for lower atmospheric ducts was established based on the Weather Research and Forecasting (WRF) model. More specifically, a 10-year simulation test was conducted for the China seas to investigate the spatio-temporal characteristics of the lower atmospheric ducts phenomenon. Compared with the sounding data, the long-term simulations showed a high temporal correlation and the root mean square error of the modified atmospheric refractivity remained between 4 M and 7 M. Based on the simulations, significant regional differences in the occurrence probability of lower atmospheric ducts were detected from south to north. Among them, the surface ducts near the sea surface exhibited the highest occurrence probability, with higher probabilities being recorded in autumn and winter, and the probability gradually increased with the decreasing latitude. The spatio-temporal characteristics of duct height, thickness, and strength were generally consistent. In the seas at mid-latitudes, strong ducts mostly occurred in the spring and autumn, with the single-layer ducts being predominant and the first layer duct showing stronger characteristics than the second layer. In the lower latitude regions, the situation was exactly the opposite. The first duct layer, which existed throughout the year, exhibited weaker characteristics with less pronounced seasonal variations. On the other hand, the second duct layer demonstrated stronger features.

Evaporation ducts are abnormal states of the atmosphere in the air–sea boundary layer that directly affect the propagation trajectory of electromagnetic (EM) waves. Therefore, an accurate diagnosis of the evaporation duct height (EDH) is important for studying the propagation trajectory of EM waves in evaporation ducts. Most evaporation duct models (EDMs) based on the Monin–Obukhov similarity theory are empirical methods. Different EDMs have different levels of environmental adaptability. Evaporation duct diagnosis methods based on machine learning methods only consider the mathematical relationship between data and do not explore the physical mechanism of evaporation ducts. To solve the above problems, this study observed the meteorological and hydrological parameters of the five layers of the low-altitude atmosphere in the East China Sea on board the research vessel Xiangyanghong 18 in April 2021 and obtained the atmospheric refractivity profile. An evaporation duct multimodel fusion diagnosis method (MMF) based on a library for support vector machines (LIBSVM) is proposed. First, based on the observed meteorological and hydrological data, the differences between the EDH diagnosis results of different EDMs and MMF were analyzed. When ASTD ≥ 0, the average errors of the diagnostic results of BYC, NPS, NWA, NRL, LKB, and MMF are 2.57 m, 2.92 m, 2.67 m, 3.27 m, 2.57 m, and 0.24 m, respectively. When ASTD < 0, the average errors are 2.95 m, 2.94 m, 2.98 m, 2.99 m, 2.97 m, and 0.41 m, respectively. Then, the EM wave path loss accuracy analysis was performed on the EDH diagnosis results of the NPS model and the MMF. When ASTD ≥ 0, the average path loss errors of the NPS model and MMF are 5.44 dB and 2.74 dB, respectively. When ASTD < 0, the average errors are 5.21 dB and 3.46 dB, respectively. The results show that the MMF is suitable for EDH diagnosis, and the diagnosis accuracy is higher than other models.

In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases.

In this study, an experimental hydrometeorological forecasting system was developed based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model. The system downloads global real-time ocean, atmosphere, and wave forcing data, producing regional forecasts every day. A coastal area in South China, encompassing Hainan Island, Leizhou Peninsula, and surrounding sea areas, was chosen as the study domain. A series of 72-hour forecasting simulations were conducted in the area, lasting from July 27 to August 31, 2019. The forecasts throughout August were chosen for evaluation with station observations, along with two sets of reanalysis data, ERA5 and CLDAS. The evaluation results revealed that the COAWST model had high potential for routine forecasting operations. The 24 h forecasts, with a lead time of 24 hours, had high accuracy, while the 48 h and 72 h forecasts did not differ greatly in terms of performance. The distributions of bias between forecast and reanalysis data showed obvious differences between land and sea, with more forecasted precipitation and lower temperatures in land grids than in sea grids. In most cases, the forecasts were closer to ERA5 in terms of means and other statistical measures. The forecasts enlarged the land-sea differences of temperature when compared with ERA5 and strengthened summer monsoon with more moisture transported to land areas. Resulting from that, a forecasted bias of lower surface pressure, higher air humidity, stronger south wind, and so forth was also detected over the domain but at low values.