Rural Domestic Sewage Treatment Model: Case Study Based on Dongying Section of the Yellow River Basin

The 20th National Congress of the Communist Party of China proposed to promote the improvement of urban and rural living environment and build livable and workable villages and beautiful villages. The development and challenges of rural domestic sewage treatment coexist. Based on the field investigation of 15 administrative villages in 3 districts and counties of Dongying City, there is a big gap between the development status of rural domestic sewage treatment and expectations. Investigate rural domestic sewage treatment cases in-depth, and condense four modes of primitive, developmental, mature and advanced in a variety of different rural domestic sewage treatment models for discussion, among which, the village sewage treatment work under the mature mode has achieved remarkable results, and is at the forefront of the current rural domestic sewage treatment. Through the multi-case analysis method, the practical dilemma of sewage treatment in different models of villages is summarized, and the feasible improvement path is explored, which contributes to the ecological protection and high-quality development of Dongying and the Yellow River Basin.

Comparisons of Simulations of Soil Moisture Variations in the Yellow River Basin Driven by Various Atmospheric Forcing Data Sets

Based on station observations, The European Centre for Medium-Range Weather Forecasts reanalysis （ERA40）, the National Centers for Environmental Prediction/National Center for Atmospheric Research （NCEP/NCAR） reanalysis and Princeton University＇s global meteorological forcing data set （Princeton）, four atmospheric forcing fields were constructed for use in driving the Community Land Model version 3.5 （CLM3.5）. Simulated soil moisture content throughout the period 1951-2000 in the Yellow River basin was validated via comparison with corresponding observations in the upper, middle, and lower reaches. The results show that CLM3.5 is capable of reproducing not only the characteristics of intra-annual and annual variations of soil moisture, but also long-term variation trends, with different statistical significance in the correlations between the observations and simulations from different forcing fields in various reaches. The simulations modeled with station-based atmospheric forcing fields are the most consistent with observed soil moisture, and the simulations based on the Princeton data set are the second best, on average. The simulations from ERA40 and NCEP/NCAR are close to each other in quality, but comparatively worse to the other sources of forcing information that were evaluated. Regionally, simulations are most consistent with observations in the lower reaches and less so in the upper reaches, with the middle reaches in between. In addition, the soil moisture simulated by CLM3.5 is systematically greater than the observations in the Yellow River basin. Comparisons between the simulations by CLM3.5 and CLM3.0 indicate that simulation errors are primarily caused by deficiencies within CLM3.5 and are also associated with the quality of atmospheric forcing field applied.

Methodology to determine regional water demand for instream flow and its application in the Yellow River Basin

In order to realistically reflect the difference between regional water demand for instream flow and river ecological water demand as well as to resolve the problem that water demand may be counted repeatedly, a concept of regional water demand for minimum instream flow have been developed. The concept was used in the process of determining river functions and calculating ecological water demand for a river. The Yellow River watershed was used to validate the calculation methodology for regional water demand. CaIculation results indicate that there are significant differences in water demands among the different regions. The regional water demand at the downstream of the Yellow River is the largest about 14.893 × 10^9 m^3/a. The regional water demand of upstream, Lanzhou-Hekou section is the smallest about -5.012 × 10^9 m^3/a. The total ecological water demand of the Yellow River Basin is 23.06 × 10^9 m^3/a, about the 39% of surface water resources of the water resources should not exceed 61% in the Yellow River Basin. Yellow River Basin. That means the maximum available surface The regional river ecological water demands at the Lower Section of the Yellow River and Longyangxia-Lanzhou Section exceed the surface water resources produced in its region and need to be supplemented from other regions through the water rational planning of watershed water resources. These results provides technical basis for rational plan of water resources of the Yellow River Basin.

Farm Production Growth in the Upper and Middle Parts of the Yellow River Basin,China,During 1980-1999

The impact of inputs on farm production growth was evaluated by analyzing the economic data of the upper and middle parts of the Yellow River basin, China for the period of 1980-1999. Descriptive statistics were employed to characterize the temporal trends and spatial patterns in farm production and five pertinent inputs of cultivated cropland, irrigation ratio, agricultural labor, machinery power and chemical fertilizer. Stochastic frontier production function was applied to quantify the dependence of the farm production on these inputs. The growth of farm production was decomposed to reflect the contributions by input growths and change in total factor productivity.. The change in total factor productivity was further decomposed into the changes in technology and in technical efficiency. The gross value of farm production in the region of study increased by 1.6 fold during 1980-1999. Among the five selected farm inputs, machinery power and chemical fertilizer increased by 1.8 and 2.8 fold, respectively. The increases in cultivated cropland, irrigated cropland, and agricultural labor were all less than 0.16 fold. The growth in the farm production was primarily contributed by the increase in the total factor productivity during 1980-1985, and by input growths after 1985. More than 80% of the contributions by input growths were attributed to the increased application of fertilizer and machinery. In the change of total factor productivity, the technology change dominated over the technical efficiency change in the study period except in the period of 1985-1990, implying that institution and investment played important roles in farm production growth. There was a decreasing trend in the technical efficiency in the region of study, indicating a potential to increase farm production by improving the technical efficiency in farm activities. Given the limited natural resources in the basin, the results of this study suggested that, for a sustainable growth of farm production in the area, efforts should be directed to technology progress and improvement in technical efficiency in the use of available resources.

Chaotic characters of the Yellow River Basin based on the sediment time series： An attempt to integrated research in geography

The sediment content of the Yellow River is resulted from the interactions of natural, economic, and social factors, so it includes some evolutive information of the Yellow River Basin system. Sediment contents from 1952 to 2007 on Toudaoguai, Tongguan, Huayuankou and Lijin sections along the river are chosen as the study time series, and correlation dimensions （D2）, Kolmogorov entropies （K2）, and Hurst indexes （H） of the time series were calculated. Correlation dimensions on Toudaoguai, Tongguan, Huayuankou, and Lijin sections are 3.24, 5.69, 6.57 and 7.34 respectively, and the Kolmogorov entropies are 0.13, 0.37, 0.40 and 0.38 respectively, which indicates that the systems controlled by different sections along the Yellow River are chaotic systems and the chaotic degrees increase gradually from the upper to lower section. The average predictable period of the sediment contents is 8 years on Toudaoguai section and 3 years on the other sections with the reciprocals of the Kolmogorov entropies. The more obvious the chaotic degree is, the shorter the average predictable period is. Hurst indexes on the sections are above 0.5, with the maximum of 0.86 on Tongguan section and the minimum of 0.68 on Toudaoguai section, which indicates that the time series have persistent trends in the average predictable period. Eight state variables and two control parameters are necessary to construct the dynamic model of the Yellow River Basin system.

Mathematical Statistics and Spatial Aggregation Characteristics Analysis of Culture, Sports, and Tourism Resources in the Yellow River Basin Based on POI Data

As a priority of China’s regional development strategy in recent years, promoting the ecological protection and high-quality development of the Yellow River Basin is one of the important tasks to boost the country’s economy and also the key to the development strategy in the central region at this stage. High-quality development of the Yellow River Basin based on the perspective of culture, sports, and tourism integration is significant for the regional ecological protection and economic revitalization. In order to measure the development of culture, sports, and tourism resources in the basin, the study applied the CRITIC model in Mathematical Statistics, the fuzzy comprehensive evaluation model, and the entropy weight TOPSIS analysis model. The results indicated that the development of the three resources in the basin varies from region to region, among which Shaanxi, Shandong, and Henan provinces have developed outstandingly while western provinces in China haven’t. In addition, this study identified the spatial geographic extent and analyzed the POI (points of interest) of culture, sports, tourism, and mixed resource based on the data analysis to explore the spatial distribution characteristics and trend of changes in the Yellow River Basin. The study also made suggestions for the development of culture, sports, and tourism resources in the basin from different perspectives based on the developed theoretical model, aiming to promote high-quality development of the area.

黄河流域径流变化

The driving factors of runoff changes can be divided into precipitationfactor and non-precipitation factor, and they can also be divided into natural factor and humanactivity factor. In this paper, the ways and methods of these driving factors impacting on runoffchanges are analyzed at first, and then according to the relationship between precipitation andrunoff, the analytical method about impacts of precipitation and non-precipitation factors onbasin's natural runoff is derived. The amount and contribution rates of the two factors impacting onnatural runoff between every two adjacent decades during 1956-1998 are calculated in the YellowRiver Basin (YRB). The results show that the amount and contribution rate of the two factorsimpacting on natural runoff are different in different periods and regions. For the YRB, thenon-precipitation impact is preponderant for natural runoff reduction after the 1970s. Finally, bychoosing main factors impacting on the natural runoff, one error back-propagation (BP) artificialneural network (ANN) model has been set up, and the impact of human activities on natural runoffreduction in the YRB is simulated. The result shows that the human activities could cause a 77 x10^8 m^3·a^(-1) reduction of runoff during 1980-1998 according to the climate background of1956-1979.