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As an effective technique to improve the rainfall forecast, data assimilation plays an important role in meteorology and hydrology. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the Numerical Weather Prediction (NWP) systems. The Weather Research and Forecasting (WRF) model is applied to simulate three typhoon storm events in southeast coast of China. Radar data from Changle Doppler radar station are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data (radar reflectivity, radial velocity, radar reflectivity and radial velocity) and three assimilation time intervals (1 h, 3 h and 6 h). The rainfall simulations in a medium-scale catchment, Meixi, are evaluated by three indices including relative error (RE), critical success index (CSI) and root mean square error (RMSE). Assimilating radial velocity with time interval of 1 h can significantly improve the rainfall simulations and outperforms the other modes for all the three storm events. Shortening the assimilation time interval can improve the rainfall simulations in most cases, while assimilating radar reflectivity always leads to worse simulation as the time interval shortens. The rainfall simulation can be improved by data assimilation as a whole, especially for the heavy rainfall with strong convection. The findings provide references for improving the typhoon rainfall forecasts in catchment scale and have great significance on typhoon rainstorm warning.

The coupled atmospheric-hydrologic modeling system is an effective way in improving the accuracy of rainfall-runoff modeling and extending the lead time in real-time flood forecasting. The aim of this study is to explore the appropriate coupling scale of the coupled atmospheric-hydrologic modeling system, which is established by the Weather Research and Forecasting (WRF) model and the gridded Hebei model with three different sizes (1 × 1 km, 3 × 3 km and 9 × 9 km). The soil moisture storage capacity and infiltration capacity of different grids in the gridded Hebei model are obtained and dispersed using the topographic index. The lumped Hebei model is also used to establish the lumped atmospheric-hydrologic coupled system as a reference system. Four 24 h storm events occurring at two small and medium-scale sub-watersheds in northern China are selected as cases study. Contrastive analyses of the flood process simulations from the gridded and lumped systems are carried out. The results show that the flood simulation results may not always be improved with higher dimension precision and more complicated system, and the grid size selection has a great relationship with the rainfall evenness. For the storm events with uniform spatial distribution, the coupling scale has less impact on flood simulation results, and the lumped system also performs well. For the storm events with uneven spatiotemporal distribution, the corrected rainfall can improve the simulation results significantly, and higher resolution lead to better flood process simulation. The results can help to establish the appropriate coupled atmospheric-hydrologic modeling system to improve the flood forecasting accuracy.

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin.

Due to easily causing severe disasters in mountainous areas, typhoon rainfall forecast with numerical weather prediction (NWP) system plays an important role for meteorological and hydrological use. In order to investigate the applicability of physical parameterisations for typhoon rainstorms, thirty-six physical parameterisation combinations are designed by three microphysics (Lin, WSM6 and WDM6), three pairs of longwave/shortwave radiations (RRTM/Dudhia, RRTMG/RRTMG and CAM/CAM) and four cumulus parameterisations (KF, BMJ, GD and Grell 3D). The Weather Research and Forecasting (WRF) model is used to simulate the three representative typhoon storm events occurred in southeast coast of China and the rainfall simulations in Meixi catchment are evaluated. Not only for the individual parameterisation but also for the parameterisation combination, WSM6, RRTMG/RRTMG and KF outperform the other physical parameterisation as a whole. The WRF model has poor capability in simulating the rainfall caused by strong convection, and the storm events with uneven distributed rainfall tend to have worse rainfall simulation in space and time dimensions. The findings provide references for choosing the physical parameterisation, which can help to obtain relatively reliable typhoon rainfall forecast and flood warning in catchment scale. The shortage of the NWP system and the solution for improve the rainfall simulation are also put forward.

Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff.

As floods could be effectively forecasted by distributed hydrological model, their study and application became the key points of flood forecasting and early warning. Based on high performance computing clusters, a parallel flood forecasting and warning platform with the characteristics of partition, classification, and complicated process coupled was established to forecast and warn flood across China, especially for flash flood in China. In addition, the platform was based on China Flash Flood Hydrological Model (CNFF-HM). It used files (not MPI), which based on a shared hierarchical storage system, to pass message to control the start and stop of simulation processes, and the rapid communication among simulation processes was realized; pre-allocation and dynamic allocation methods was together applied to manage the resource of the high performance computing clusters; the automatic switch among different time scale models was realized by simulation driven strategy based on rainfall events; the reboot framework was designed to deal with the process crash and delayed rainfall data. The effectiveness and stability of the platform has been tested by the flood events of 2017. Finally, a case of Weishui catchment in Hunan Province was shown.

For inconsistency phenomenon of tide level data series of tidal reach of downstream for Qinhuai River, we take the annual highest tide level from 1947 to 2010 of Nanjing Xiaguan Station for example, analyze its wavelet to determine energy cycle of the series, and make consistency correction on the series with the use of the extracted trend term of each year for tide level data. Analyze the correlativity between the tide level datum of Qixiang estuary and Jiuxiang estuary into the Yangtze River from Nanjing Station and the Qinhuai East River, establish prediction model of tide level into the Yangtze River estuary from the Qinhuai East River through regression analysis, and make use of the corrected tide level series to predict the high and low tidal levels into the Yangtze River estuary from the Qinhuai East River. The results show that this method corrects the rising trend of tide level data of many years for the measured data, the established regression model can achieve better prediction accuracy.1

To learn about the changing trend of water resources quantity in the Yangtze River Delta, make rational allocation of water resources, and prevent regional drought & flood risks, the spatial and temporal distribution characteristics of precipitation and drought & flood in the Yangtze River Delta were investigated. Sliding average method, standardized precipitation index method (SPI), and inverse distance weighted interpolation method (IDW) etc. were used to analyze the hydrologic characteristics of YRD based on the daily precipitation data from 15 national meteorological stations in the Yangtze River Delta during the period from 1960 to 2010. According to the result: (1) The Yangtze Delta exhibited a gradually increasing trend of annual precipitation, while the flood frequency has been higher than drought frequency over the past 51 years; (2) At the Yangtze Delta, the maximum mean precipitation was normally observed in June, the minimum mean precipitation in December, the highest flood frequency in April and July, and the highest drought frequency in March; (3) The precipitation gradually increased from north to south at the Yangtze Delta, spatially, the precipitation was more evenly distributed in the north than in the south; the flood prone area was larger than drought prone area at the Yangtze Delta with West Hangzhou, East Shaoxing and West Ningbo susceptible to both drought and flood.1

The performance evaluation of a city’s flood control system is essentially based on accurate storm designs, where a particular challenge is the development of the joint distributions of dependent rainfall variables. When it comes to the research design for consecutive rainfall, the analytical investigation is only focused on the maximum of consecutive rainfalls, and it does not consider the probabilistic relations between the first day of rainfall and the overall rainfall included in consecutive rainfall events. In this study, the copula method is used to separate the dependence structure of multi-day rainfall from its marginal distribution and analyse the different impacts of the dependence structure and marginal distribution on system performance. Three one-parameter Archimedean copulas, including the Clayton, Gumbel, and Frank families, are fitted and compared for different combinations of marginal distributions that cannot be rejected by statistical tests. The fitted copulas are used to generate rainfall events for a system performance analysis, including the conditional probability and design values for different return periods. The results obtained in this study highlight the importance of taking into account the dependence structure of one-day and multi-day rainfall in the context of storm design evaluations and reveal the different impacts of the dependence structure and the marginal distributions on the probability.

Prediction of the groundwater dynamics via models can help better manage the groundwater resources and guarantee their sustainable use. Three types of data-driven models are built for groundwater depth prediction in the plain of Shijiazhuang, the capital of Hebei Province in North China. The data-driven models include the Power Function Model (PFM), Back-Propagation Artificial Neural Network (BPANN) and Support Vector Machines (SVM) with two kernel functions of linear kernel function (LKF) and radial basis function (RBF). Five classes of factors (including 12 indices) are considered as potential model input variables. The Gamma Test (GT) is adopted in this study to help identify the relative importance of the input indices and tackle the tricky issue of the optimal input combinations for the data-driven models. The established models are evaluated in both fitting and testing procedures based on the root mean squared error (RMSE) and Nash-Sutcliffe efficiency (E) for different input combination schemes. The results show that SVM (RBF) performs the best. It is interesting to find that the natural factors (i.e., precipitation and evaporation) are less relevant to the groundwater depth variations. The methods used in this study have much significance for groundwater depth prediction in areas lacking hydrogeological data.

Flash floods occur in mountainous catchments with short response times, which are among the most devastating natural hazards in China. This paper intends to forecast and provide warnings of flash floods timely and precisely using the flash flood warning system, which is established by a new distributed hydrological model (the China flash flood hydrological model, CNFF-HM). Two ungauged mountainous regions, Shunchang and Zherong, are chosen as the study areas. The CNFF-HM is calibrated in five well-monitored catchments. The parameters for the ungauged regions are estimated by regionalization. River water stage data and reservoir water stage data from Shunchang, and reservoir water stage data from Zherong are used to validate the model. The model performs well and the average Nash–Sutcliffe efficiency (NSE) is above 0.8 for the five catchments. The validation shows the difference in the timing of flood peaks using the two types of water stage data is less than 1 h. The rising and declining trends of the floods correspond to the observed trends over the entire validation process. Furthermore, the flash flood warning system was effectively applied in flash flood event on 28 September 2016 in Zherong. Thus, the CNFF-HM with regionalization is effective in forecasting flash floods for ungauged mountainous regions.

The rainfall-runoff process (RRP) is an important part of hydrologic process. There is an effective measure to study RRP through artificial rainfall simulation. This paper describes a study on three growing stages (jointing stage, tasseling stage, and mature stage) of spring maize in which simulated rainfall events were used to study the effects of various factors (rainfall intensity and slope gradient) on the RRP. The RRP was tested with three different rainfall intensities (0.67, 1.00, and 1.67 mm/min) and subjected to three different slopes (5°, 15°, and 20°) so as to study RRP characteristics in semiarid regions. Regression analysis was used to study the results of this test. The following key results were obtained: (1) With the increase in rainfall intensity and slope, the increasing relationship with rainfall duration, overland flow, and cumulative runoff, respectively, complied with logarithmic and quadratic functions before reaching stable runoff in each growing stage of spring maize, (2) The runoff coefficient increased with the increase in rainfall intensity and slope in each growing stages of spring maize. The relationship between runoff coefficient, slope, rainfall intensity, rainfall duration, antecedent soil moisture, and vegetation coverage was multivariate and nonlinear, (3) The runoff lag time decreased with the increase in rainfall intensity and slope within the same growing stage. In addition, the relationship between runoff lag time, slope, rainfall intensity, antecedent soil moisture, and vegetation coverage could also be expressed by a multivariate nonlinear equation, (4) The descent rate of soil infiltration rate curve increased with the increased rainfall intensity and slope in the same growing stage. Furthermore, by comparing the Kostiakov, Horton, and Philip models, it was found that the Horton infiltration model was the best for estimating soil infiltration rate and cumulative infiltration under the condition of test.