In order to study the influence of climate change on rainfall extreme value distribution in main flood season, based on the daily rainfall data from 1960 to 2017 in Jiangxi Province, a variety of test methods were used to detect and test the abrupt change points. Genetic algorithm and Fisher's optimal segmentation method were used to stage the flood season before and after the climate abrupt change, and the extreme distribution of staged rainfall and design rainfall before and after the climate abrupt change were discussed. The results show that 1991 was the abrupt change year of precipitation in Jiangxi Province from 1960 to 2017, and it was necessary to pay more attention to the occurrence of extreme heavy rainfall in Guangchang and Poyang Lake regions. Genetic algorithm is suitable for the division and staging of flood season. After the abrupt climate change (1991-2017), the designed rainfall value in the main flood season of Jiangxi Province was greater than that before the abrupt climate change (1960-1990), and also greater than that in the whole period (1960-2017). When calculating the designed flood based on the designed rainfall, the influence of climate change on the designed rainfall value should be considered, and strengthen protection against floods.

The cluster forecasting model is a long-term forecasting physical model based on mathematical statistics. This method has been applied in the long-term forecast of the upper reaches of the Yangtze River. After years of practice, there is space for improvement in accuracy and efficiency of the model. This paper selected the upper reaches of the Yangtze River as the demonstration area for forecast, the monthly areal rainfall from June to August as the forecast object, and used the coincidence rate of anomaly symbol and the percentage of anomaly error as the test methods of the model. And then the construction process and improvement of the model were described. Finally, the improved model was used to forecast the monthly area rainfall in the upper reaches of the Yangtze River from June to August during 2020 to 2022. The prediction results show that the improved model has a great improvement in the anomaly sign agreement rate and the percentage of anomaly error, and it has been implemented easily, which has certain reference value.

Taking four small and medium-sized watersheds in the mountainous city (Chongqing) as the research objects, the application prospects of multi-source rainfall data in short-term hydrological forecasting were explored. The GPM-IMERG remote sensing precipitation data was introduced as the source of the satellite precipitation data, and the Mean Bias Correction (MBC) method, Linear Regression Model (LRM) and Geographically Weighted Regression (GWR) method were used to realize the fusion with the ground station measured data. A leave-one-out validation method was used to evaluate the accuracy of the fused precipitation data, and the SWAT model was applied to simulate the runoff of the selected basins using the precipitation data corrected by MBC, LRM, GWR, the original IMERG-early, and the measured data. The results show that the GWR was the best fused effect. The topography had great influence on the accuracy of the IMERG-early data and the fusion method. The accuracy of the fusion method was relatively high in low elevation regions, while in mountainous areas, the accuracy became low comparatively. The results showed a better agreement between the SWAT model and the measured runoff process using all kinds of precipitation data, in which the model using precipitation data corrected by GWR method was more accurate. Results can be referenced for hydrological forecast in similar small basins in the future.

A similar basin identification method was proposed to improve the reliability of similar basin identification for hydrological forecasting in ungauged basin. The method introduced the basin time-varying feature information and constructed a time-varying feature matrix (TVFM). A dimension reduction method for the TVFM was proposed to obtain the basin feature vector. The classical K-means clustering method was adopted to implement the identification of the similar basins. Seventeen natural catchments in Jiangxi Province were selected and the Xin'anjiang model was applied in this research. A series of studies were carried out which includes feature vector and feature matrix construction, similar basin identification, parameter transfer, and comparison & analysis of hydrological forecasting results. The following conclusions can be drawn. The proposed TVFM can achieve better forecasting results in ungauged basins. Compared with the traditional similar basin identification method based on time-invariant underlying surface information, the prediction accuracy is improved. The similar basin identification based on time-varying comprehensive features generates the best predictions, and the prediction accuracy based on time-varying meteorological features is better than that based only on time-invariant underlying surface features. Comparison with the single factor methods, the directional problem of the parameter transfer can be partially solved by the proposed comprehensive method. Unfortunately, the directional problem cannot be completely overcame until recently.

Vegetation is an important part of terrestrial ecosystem and is vulnerable to extreme climate events. It is of great significance to explore the spatio-temporal evolution and dynamic response relationship between extreme climate events and vegetation for scientific response to climate anomalies and prevention of natural disasters. Therefore, the RClimDex, trend analysis and detrended fluctuation analysis were used to analyze the spatio-temporal evolution of extreme climate and NDVI in the Huaihe River Basin from 1998 to 2019, and discuss their causes and future variation tendency as well as clarify the response of NDVI to extreme climate. The results demonstrate that the NDVI shows an increasing tendency with an increase of 0.002 5 a^-1 from 1998 to 2019. Among the extreme climate indices, warm temperature indices showed an increasing tendency, while the cold temperature indices and precipitation indices (except RX1day) showed a decreasing tendency. The distribution of NDVI in the Huaihe River Basin was closely related to the distribution of urbanization in the region. The NDVI has a lag response of about one month to extreme climate and will continue to rise. The annual variation of the NDVI is closely related to natural and human factors, and the intra-annual variation of the NDVI is mainly affected by the growth rhythm of crops in the cultivated land.

It was planned to carry out excavation and treatment of the deep trough in Maozhou Estuary for the construction of the new town, which occupied the -3 m deep trough. The Pearl River estuary physical model was used to carry out tide hydrodynamics modeling test and suspended load model test to simulate the siltation distribution of the deep trough after one year of implementation. The test results show that the hydrodynamic characteristics and flow pattern of the study area have changed and the average siltation near the Maozhou Estuary reaching 0.75 m is more serious than that in downstream with 0.53 m, the -3 m deep trough remains connected with the whole widening rate is 1.57% and the thalweg is distributed along the Shenzhen shoreline with the minimum elevation is -3.40 m. The section morphology shows that the siltation on the right side is more serious than the left side and the upstream is more serious than the downstream. The treatment scheme changed the riverbed, the river regime will be significantly adjusted in the short term, but the -3 m deep trough can be maintained. The scheme is generally feasible.

After the operation of the Three Gorges Project, the process of water and sediment in the lower reaches of the reservoir changed greatly and led to the adjustment of the evolution law of the curved channel, which may have adverse effects on the stability of the river and the safety of flood control. Based on the measured data, the evolution law of the typical bends in the lower reaches of the reservoir and its main causes were analyzed. The results show that after the operation of the Three Gorges Project, the typical bends in the lower reaches of the reservoir all appear "cutting beaches", and the main reason is that the sediments enter each river reaches. The amount of sand, especially fine-grained sand, has been greatly reduced. The main reasons for the "skipping bends" in most of the bends in the Lower Jingjiang are that the variation coefficient of the monthly average runoff process is large and the bending radius of the bends itself is small. With the successive operation of cascade reservoirs in the main and tributaries of the upper reaches of the Yangtze River, it is expected that the variation coefficient of the monthly average runoff process in the lower reaches of the reservoir will continue to increase, which may lead to the occurrence of bends with relatively small bending radius that do not currently have "skipping bends".

Since total organic carbon (TOC) is not a routine hydrological monitoring data, the monitoring frequency is much lower than that of flow data and it often has missing values, which makes it difficult to evaluate TOC flux. This study proposes an improved computational model based on functional data analysis (FDA) for the evaluation of riverine TOC fluxes. First, the discrete data set is functionalized, and all the indicators are transformed into s function curves in the same evaluation time domain. And then the TOC flux of the evaluation section during the study period is calculated by Riemann integral. The results of the TOC evaluation at the Zhutuo station on the Yangtze River show that, The average value of the annual TOC flux at Zhutuo Station from 2018 to 2020 is 702 000 t; The intra-annual distribution of TOC fluxes is extremely uneven, with a high concentration in the flood season, especially in summer; The annual average value of TOC flux during the flood season (June to September) is 438 200 t, equivalent to 62.43% of the total; The annual average TOC flux in summer is 349 000 t, equivalent to 49.72% of the total; While the annual average value of TOC flux during the dry season (October to May) is 263 700 t, equivalent to 37.57% of the total; The annual average TOC flux in winter is 71 300 t, equivalent to 10.15% of the total; Compared with the traditional method, the improved TOC flux model is also able to deal with missing values and inconsistencies in multi-indicator monitoring sequences more effectively, and to accurately assess the course of TOC fluxes over different seasons and periods of abundance and depletion.

The paper aims to comprehensively grasp the carrying capacity of water and soil resources in Ningxia, and promotes the coordinated development of water and soil resources in arid and semi-arid areas. An evaluation index system was constructed based on DPSIR model. The CRITIC-entropy weight-TOPSIS was applied to evaluate the carrying capacity of water and soil resources in Ningxia from 2012 to 2019. The obstacle degree model and R/S method were used to explore the influencing factors and predict the future trend. The results showed that from 2012 to 2019, the carrying capacity of water and soil resources in the whole Ningxia region shows an upward trend, but the overall situation is basically overloaded. The carrying capacity in Guyuan City is greatest in the Ningxia region. Based on the obstacle analysis, the driving force subsystem has the strongest restriction on the carrying capacity of water and soil resources in Ningxia. Economic density, natural population growth rate, per capita GDP, grain output per unit of arable land and per capita water resources are the main factors affecting the water and soil resources carrying capacity in Ningxia. According to the R/S method, in the future, the water and soil resources carrying capacity in Ningxia will show an upward trend. Guyuan and Zhongwei have a significant upward trend, while other cities have a weak upward trend.

Water footprint evaluation is of guiding significance for the rational development of regional water resources. The input-output method was used to account for the water footprint of Hubei Province from 2007 to 2017, and the driving factors of water footprint changes were analyzed using the structural decomposition model. The results show that the virtual water content in Hubei Province decreased significantly, and water use efficiency improved from 2007 to 2017. The total water footprint showed a trend of rising and then falling, with a net increase of 19.9%, and the main growth sectors were construction and services. Technology level and economic scale were the main factors inhibiting and promoting the increase of water footprint, respectively. However, their effects on water footprint changes gradually weakened, and both showed a reduction effect. From 2007 to 2017, the sectoral linkages changed from positive to negative driving force, and the inhibitory effect on water footprint was revealed, which shows that optimizing the industrial production process helps carry out water conservation. The impact of driving factor on different sectors showed heterogeneity. The adjustment of industrial structure inhibited the increase of water footprint in high water-consuming sectors such as agriculture while promoting the increase in construction and services. In the future, the industry scale and residential demand in sectors producing high-value-added products will grow, such as services, and water-saving technology development should be shifted to these sectors promptly.