Water resource crises are an increasing threat to human survival and development. To reveal the nature of water resource issues under changing situations, the water resources system needs to be studied from a macro and systematic perspective. This report develops a water resources system into a water resources compound system that is constantly evolving under the combined action of the development, resistant, and coordination mechanisms. Additionally, the water quotient is defined as a quantitative representation of the sustainable development state of the water resources compound system. Four cities in China, Beijing, Fuzhou, Urumqi, and Lhasa, were selected as the study areas. The differences in the three types of mechanisms and the water quotient of the water resources compound system of each city in 2013 were compared. The results indicate that the different subsystems that comprise the compound system of a given area have different development mechanisms and resistant mechanisms. There are clear differences in the mechanisms and the water quotients for the water resources compound systems of different regions. Pertinent measures should be taken into account during integrated water resource management to improve the sustainable development status of regional water resources compound systems.

Higher Himalayan catchments are often poorly monitored for hydrological activities involving flood flow prediction for the safety of riverside communities and the successful operation of hydropower projects. This study aimed to estimate the comparative performance of artificial neural network (ANN) based flow prediction models using 10 years of daily river flow data of Kaligandaki catchment at Kotagaun, Nepal, which is a snow-fed catchment in the Himalayan region. The flow prediction models were trained and tested at a hydrological station using the previous 3 days’ river flow data to predict the 1-day ahead flow data. Eight different training functions were employed in an ANN model for comprehensive statistical assessment of accuracy and precision of each training function. The most significant and validated result obtained in this study is the comprehensive comparison of various training functions’ performance, and identification of the most efficient training function for the study case. Among the training functions investigated, the Levenberg-Marquardt backpropagation function exhibits the best performance for the model having Nash-Sutcliffe efficiency, root mean square error and mean absolute error values of 0.866, 209.578 and 75.422, respectively. This study provides a fundamental basis for accurate flow prediction of topographically challenged catchments where hydrological monitoring and data collection may be limited. In particular, this model will help to improve early warning system, hydrological planning, and the safety of riverside communities in the Himalayan region.

In this paper, we adopt stochastic frontier model of translog production function to measure the total factor productivity growth rate of 13 cities in Jiangsu province from the year 2000 to 2020 and study the characteristics of the contribution rates of capital, labor, and total factor productivity in driving economic growth. The study shows that, firstly, the explanatory power of the model could be increased by 35.9% after 6 effect variables, such as urban economic development level, have been taken into consideration when two‐step method has been adopted to explore the model parameters of stochastic frontier; secondly, there is an obviously positive correlation between the 4 variables, urban economic development level, urban size, level of opening up to the outside world and infrastructure construction, and production efficiency, while there is a negative correlation between economic structure and production efficiency, and the research input intensity has an uncertain effect on production efficiency; thirdly, the TFP growth trend of urban economy of the province shows a similar “W” trend, technical progress becomes the first element in affecting total factor productivity, and the negative growth in technical efficiency and scale efficiency result in a dramatic decline in growth rate of TFP; fourthly, among all the elements contributing to urban economic growth, capital has the greatest contribution, TFP takes the second place, and labor element takes the last. The double difference between capital and TFP appears to be the major cause in forming regional difference of urban economy. The regional differences of TFP concerning infrastructure advantage, position advantage, factor advantage, and policy advantage are the key factors in urban economic gap. At the end of this study, we propose some policy suggestions.