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Scientific and accurate prediction of river runoff is important for river flood control and sustainable use of water resources. This study evaluates the ability of a Nonlinear Auto Regressive model (NAR) in predicting runoff volume. Using the Cuntan Hydrological Station in the upper reaches of the Yangtze River as the research object, the model was established based on the runoff characteristics from 1951 to 2020 and tested by NAR. To improve the prediction efficiency, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) preprocessing technique is used to decompose the data. The results show that the coupled CEEMDAN-NAR model has better predictive ability than the single model, with a coupled model deterministic coefficient (DC) of 0.93 and a prediction accuracy of Class A. HIGHLIGHTS Proposed a runoff prediction model using CEEMDAN-NAR hybrid approach.; Decomposition of runoff data using CEEMDAN preprocessing techniques to improve model prediction accuracy.; Using models to predict runoff in the upper Yangtze River from 2005 to 2020 and verifying their accuracy; The CEEMDAN-NAR model provides better predictions than the GRU, NAR and EEMD-NAR prediction models.

Scientific assessment of the hydrodynamic conditions of water system connection is of great significance and value in guiding the ecological construction of urban water system connection and building healthy river–lake relationship. Based on the advantages of SWMM in effectively simulating urban rainfall and surface runoff, a hydrological and hydrodynamic model was constructed before and after the implementation of urban water system connection project, and the model was applied to the analysis of rainfall and flooding processes in two recurrence periods of 5a and 10a after the implementation of urban water system connection in Pingyu County. The results show that the initial flow rate and velocity of river cross-section increased in the same recurrence period after the water system connection, the maximum increase of water exchange capacity was 115.56%, the peak flow rate and velocity were weakened and the peak time was lagged, and the hydrodynamic conditions were improved significantly.

Precipitation forecasting is an important guide to the prevention and control of regional droughts and floods, the rational use of water resources and the ecological protection. The precipitation process is extremely complex and is influenced by the intersection of many variables, with significant randomness, uncertainty and non-linearity. Based on the advantages that complementary ensemble empirical modal decomposition (CEEMD) can effectively overcome modal aliasing, white noise interference, and the ability of long short-term memory (LSTM) networks to handle problems such as gradient disappearance. A CEEMD–LSTM coupled long-term and short-term memory network model was developed and adopted for monthly precipitation prediction of Zhengzhou City. The performance shows that the CEEMD–LSTM model has a mean absolute error of 0.056, a root mean square error of 0.153, a mean relative error of 2.73% and a Nash efficiency coefficient of 0.95, which is better than the CEEMD–Back Propagation (BP) neural network model, the LSTM model and the BP model in terms of prediction accuracies. This demonstrates its powerful nonlinear and complex process learning capability in hydrological factor simulation for regional precipitation prediction. HIGHLIGHTS Complementary ensemble empirical modal decomposition (CEEMD) is a relatively novel data preprocessing method that can effectively reduce the non-smoothness of time series.; Long short-term memory network (LSTM) as a prediction model is more adept at handling long time series.; The CEEMD–LSTM coupled has better nonlinear and complex process learning ability in hydrological factor simulation.

Precipitation is an important component in the climate system and plays a key role in water resources protection, drought and flood prevention. Innovation Trend Analysis (ITA) method, R/S method, maximum entropy spectrum analysis and wavelet analysis were used to study the evolution characteristics of the Yellow River Delta annual precipitation from 1954 to 2014. The results suggest that the precipitation series changed significantly and showed an overall downward trend, and may show an upward trend in the future, which is relatively strong. There are three-time scales of annual precipitation variation: 20-32a, 8-18a, and 3-6a. The quasi-27 year was the main oscillation period of the average annual precipitation in the Yellow River Delta, the secondary major period was 13 years, and the small-scale oscillation period was 5 years. Precipitation changed suddenly in 1980 and 2003. In contrast, there was no significant mutation point in 2002-2014.

Reasonable prediction of suspended sediment concentration is of great significance for river regulation and water conservancy project scheduling in upper and middle reaches. In order to solve the influence of nonlinear and non-stationary characteristics of sediment sequences on the prediction results and improve the prediction accuracy, a prediction model of sediment based on CEEMDAN-GRU was constructed. The monthly suspended sediment concentration data of Huayuankou hydrological station from 1960 to 2014 were ‘decomposed-predicted-reconstructed’ and compared with single gated recurrent unit (GRU), support vector machine (SVM) and long short-term memory (LSTM) model. The results reveal that the CEEMDAN-GRU coupling model has provided a superior alternative to the single model and its determination coefficients (DC) of the training set and testing set are greater than 0.71, qualified rate (QR) reaches up to 81%, average absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) are 1.2841, 0.9675 and 4.3560 respectively. It is proved that the CEEMDAN-GRU model has a better performance and can be used in the mid- and long-term prediction of non-linear and non-stationary suspended sediment concentration series.

Precipitation is greatly affected by natural conditions and human activities, and its sequence has the characteristics of periodic variation and non-stationarity. In order to study the periodic characteristics of regional precipitation, reduce the non-stationarity of the water sequence and improve the forecasting accuracy, the Morlet wavelet model and CEEMD − Elman + ARIMA model are introduced, and applied to the precipitation sequence of Zhengzhou. The results show that the annual precipitation in Zhengzhou is mainly affected by periodic fluctuations of 55a, 40a and 15a, and the mean absolute error of the CEEMD − Elman + ARIMA model in 2013–2017 is 14.1%. Based on the analysis of precipitation period and forecast of future precipitation, the characteristics and evolution of precipitation in Zhengzhou are revealed. This provides a theoretical basis for rational utilization of water resources, prevention, industrial and agricultural production in Zhengzhou.

The study of the temporal and spatial evolution of precipitation is of great importance for the efficient use of water resources. This paper examines the long series of precipitation in Henan province from 1959-2018. Innovative Trend Analysis (ITA) method and Mann-Kendall (MK) test were used to analyze the characteristics of precipitation trend changes. Mann-Kendall (MK) mutation test and the sliding T method were used to study the jump features of precipitation. Wavelet analysis of the cyclical characteristics of precipitation. The results show that the spatial distribution of precipitation in Henan Province is uneven, with a gradual increase from north to south. The precipitation of northern, central and southern regions showed a downward trend, while that of western regions showed an upward trend. Both the northern and southern regions experienced jump features in precipitation around 1975 and 2008, while the western region experienced jump features around 1962 and 1980, and the central region experienced jump features around 1980; There are 3.5 different scales of “abundance and depletion” in the northern, western and southern regions, with a first principal cycle of 28a, and 5.5 different scales of “abundance and depletion” in the central region, with a first principal cycle of 17a.

There are many factors influencing the evolution of sediment concentration, and it is difficult to determine and extract, which brings great difficulties to the high-precision prediction of sediment concentration. Accurate prediction of annual sediment concentration in the lower Yellow River can provide a theoretical basis for flood control and disaster reduction and rational utilization of water and soil resources in the lower Yellow River. For the defects of pseudo-components in data decomposition of Complementary EEMD, the Modified EEMD (MEEMD) method proposed in this paper has the advantage of eliminating pseudo components of IMF and reducing non-stationarity of sediment bearing sequences. Then, combined with the Autoregressive Integrated Moving Average (ARIMA) model with strong approximation ability to the stationary sequence, the MEEMD-ARIMA model for predicting the annual sediment concentration in the lower Yellow River was constructed. Through fitting and predicting the annual sediment concentration in Gaocun Station, it is shown that the model not only considers the evolution of sediment concentration in various frequency domains, but also solves the problem that the ARIMA model requires sequence to be stable, the relative error of prediction is within ±6%, and the prediction accuracy is high, thus providing a new method for the prediction of sediment concentration.

The prediction of annual runoff in the Lower Yellow River can provide an important theoretical basis for effective reservoir management, flood control and disaster reduction, river and beach management, rational utilization of regional water and sediment resources. To solve this problem and improve the prediction accuracy, permutation entropy (PE) was used to extract the pseudo-components of modified ensemble empirical mode decomposition (MEEMD) to decompose time series to reduce the non-stationarity of time series. However, the pseudo-component was disordered and difficult to predict, therefore, the pseudo-component was decomposed by ensemble empirical mode decomposition (EEMD). Then, intrinsic mode functions (IMFs) and trend were predicted by autoregressive integrated moving average (ARIMA) which has strong ability of approximation to stationary series. A new coupling model based on MEEMD-ARIMA was constructed and applied to runoff prediction in the Lower Yellow River. The results showed that the model had higher accuracy and was superior to the CEEMD-ARIMA model or EEMD-ARIMA model. Therefore, it can provide a new idea and method for annual runoff prediction.

Analyzing and evaluating the ecosystem service value of wetlands can provide technical support and theoretical basis for regional socio-economic development, ecological environmental protection, and other planning measures. Based on the factors of hydrology, meteorology, society, economy, resources, environment, and culture, the wetland ecosystem services value of Yellow River Delta (YRD) was evaluated by the method of functional value and equivalent factor, which has certain reference significance for future research. Two methods were used to calculate the ecosystem services value of YRD in 2016, and the results were $3.28 × 109 per year and $4.53 × 109 per year, respectively, and the regulation services accounted for 76.3% and 16.7%, respectively. Through comparative analysis of ecosystem services value from 2001 to 2016, the average proportion of regulation services was 51%. Regulation service is the leading function of wetland ecosystem of YRD. Equivalent factor method is more suitable for the assessment of wetland ecosystem service value of YRD in 2016. In terms of time, ecosystem services value tended to increase, with an average rate of $2.02 × 109 per year. In terms of space, it gradually increased from inland to shallow sea water. On the premise of rational development and utilization, it is predicted that the value of ecosystem services will continue to grow in the future.

Research on the periodic characteristics of the runoff evolution in the Lower Yellow River is of great importance for flood control, beach regulation and water resources utilization in the Lower Yellow River. By using wavelets to conduct scale analysis of runoff series, the periodic change rule of runoff series on different scales can be obtained. By using the maximum entropy spectrum to analyze the spectrum of runoff, the main period of runoff sequence can be obtained. In this paper, these two methods are applied to the annual runoff of the Lower Yellow River. The results show that: the annual runoff in the Lower Yellow River has multi-scale change law; the four stations have the same main period; there are differences in periodicity between stations, as the catchment area increases, the quasi-periodic value decreases, and the periodic fluctuation becomes more obvious; after 2018, the annual runoff of the Lower Yellow River will be in the dry season. Furthermore, the study can reveal the change law of runoff sequence in the Lower Yellow River to a certain extent, and provide a theoretical basis for river management.

Ecological vulnerability assessment of wetlands is a very important early-stage task for wetland protection, pollution treatment, ecological restoration and water resources utilization. On the basis of the study of vulnerability evolution mechanism of wetland ecosystem in the Yellow River Delta, this paper proposes a Pressure-Support-State-Response Model (PSSR) for ecosystem vulnerability assessment and employs a single-factor evaluation method. The proposed model is applied to assessing ecosystem vulnerability of wetland in Yellow River Delta (a case study of Dongying City). Research results show that: from 2005 to 2009, the ecosystem vulnerability of wetlands in Yellow River Delta was 0.62, and 0.68 from 2010 to 2014; the ecosystem vulnerability grade is moderately vulnerable during the two periods. Generally speaking, regional ecosystem vulnerability slightly decreases. More efforts should be made to protect and restore the ecosystem and prevent it from deterioration and wetlands degradation. This study may provide a theoretical support for wetland protection, management, exploitation and policy making in the Yellow River Delta.

A typical informal landfill in a rainy area of southern China was taken as an example in this study. The comprehensive control ideas and processes of the informal landfill site were systematically reviewed. The basic situation for the early stage of the government survey and investigation was provided, including a waste stock survey, water volume measurement, and a waste source survey. The main contents and key factors of a comprehensive investigation of the environmental quality status were briefly summarized. The water quality in the landfill, groundwater quality inside and outside of the site, and heavy metals in the bottom sediment were all determined. A low-cost practical landfill technology was explored to reduce the Chemical Oxygen Demand CODCr concentration of polyaluminum ferric chloride (PAFC), and NH4+-N was removed by calcium hypochlorite. Soil backfill was replaced, such that the informal landfill site was immobilized, which was perfectly suitable for this southern rainy area. This study proposes rules for a comprehensive improvement scheme for a landfill, and provides a reliable theoretical basis and practical experience for the treatment of similar informal landfills.

Studying on evolution law of sediment concentration in the lower Yellow River, revealing the evolution cycle and the relationship with the grain size of sediment have an important guiding significance for the governance of sediment in the Lower Yellow River. Based on the sediment data concentration of four important hydrological stations, Huayuankou, Gaocun, Aishan and Lijin, in the lower Yellow River. The evolution of the sediment sequence cycle were studied using Morlet wavelet analysis the complexity of the multi time structure of sediment transport in the Yellow River are revealed. The results show that sediment concentration have periods of 55 years, 26-34 years, 17-18 years and 6-11 in the lower Yellow River. Period of 55 years is global, not affected by human activities and a finer sand cycle in the Yellow River. Now it is in less time. Period of 26-34 years is global, small affected by human activities and a fine sand cycle in the Yellow River. Now it is in more time. Period of 17-18 years is along path property, affected by human activities and riverbed. It s a medium sand cycle in the Yellow River. Period of 6-11 years is locality, much affected by human activities, is a coarse sand cycle. The mutation appeared in 1950-1960, 1985-1986 and 2000.Sediment control should be based on different of particle sizes, so source area of coarse sand is governed combine its cycle.

By means of analyzing formation mechanism and impact factors of ecological vulner-ability of estuarine wetland of the Yellow River Delta, an ecological vulnerability assessment index system based on relevant criteria is constructed. Criteria include resource and environment, human factors and ecological balance. There are 14 indexes which are Annual mean temperature difference, population density, saline-alkali soil area, average annual frost-free period and so on. An assessment model based on entropy weight is further constructed by using entropy method and is applied in the ecological vulnerability assessment. Research results show that mildly vulnerable area occupies 25.87% of total estuarine wetland of the Yellow River Delta. Mildly vulnerable area is where there are abundant natural resources, ecosystem functions well and human activities have weak influence. General vulnerable area takes 15.93% where fresh water supplement is adequate and ecosystem functions averagely. Moderately and severel...

This paper studied water source area of the Central Line Project of South-to-North Water Diversion. It built an assessment indicators system on eco-environmental vulnerability of water source area which includes four levels target layer, effect layer, state layer and response layer. Aiming at the uncertainties of experts score on effect layer, state layer and response layer in the analysis and evaluation of eco-environmental vulnerability in water source areas, it built a cloud model-based scale criterion for eco-environmental vulnerability analysis of water source areas based on the index scale. The discreteness, ambiguity and randomness were considered in the calculation process. The cloud model weights, a calculation results of the ecological environment vulnerability analysis in the water source area, were calculated by constructing matrix that is a two-by-two judgment matrix through using the cloud scale criterion. The results were got by calculating three numerical characteristics which includes the Expected Value(Ex), the Entropy(En) and the Hyper Entropy(He) of the cloud model. The results showed that the six factors of the Ex is 0.058–0.282, the En is 0.081–0.297 and the He is 0.081–0.297 where they stayed in the state layer in the evaluation index system of ecological environment vulnerability in the water source. Then, the results showed that the sixteen factors of the Ex is 0.008–0.177, the En is 0.013–0.171 and the He is 0.013–0.171 where they stayed in the response layer. The dense and relative small data showed that all these factors are micro-sensitive factors which indicated that the impact of a single factor on eco-environmental vulnerability of water source area is not obvious. And the consequence was caused by the long-term interaction of multiple factors. The algorithm provided a new idea for the assessment of eco-environmental vulnerability in water source areas.