A model for soil moisture dynamics estimation based on artificial neural network

Research on soil moisture estimation models can effectively improve the growth environment of crops. In this paper, the author studied the artificial neural network and variation pattern of soil moisture. Then, application of the model for water diversion estimation was explored based on artificial neural network. On this basis, an optimization algorithm was presented to simulate water diversion. Furthermore, a model for remote sensing of soil moisture dynamics was applied to artificial neural network. It has been proven that the research can optimize the application of the proposed model, laying a solid foundation for future study

The Law Analysis of the Weihe River Basin (Shaanxi Section) Precipitation

Abstract: Based on 1956-2010 of precipitation of Weihe river basin in shanxi section, this paper statistical analysis the precipitation characteristics, the cycle, the abrupt, the trend and sustainable. The results show that the average of many years is 606.63 mm, the maximum is 931.86 in 2003, the minimum is 368.56 in 1995; the precipitation is more in 20 century 50, 60 and 80s, less in 70, 90s and the 2000s and the average of precipitation is close to average of many years in 70s and 2000s; The rainfall process has the remarkable periodic, the first main cycle for 18 years, the second cycle for 7 years, the third for three years; The average rainfall not happened obviously mutations; and the precipitation is not significant decrease trend; Hurst index greater than 0.5, which suggests that past trends of precipitation will be continue in the future, namely in the future precipitation is not significant digressive tendency. The conclusions have the important reference value to weigh river water resources development

A mutual information theory-based approach for assessing uncertainties in deterministic multi-category precipitation forecasts

The very nature of weather forecasts and verifications and the way they are used make it impossible for one single or absolute standard of evaluation. However, little research has been conducted on verifying deterministic multi-category forecasts, which is based on the attribute of uncertainty. The authors propose a new approach using two mutual information theory-based scores for assessing the comprehensive uncertainty of all categories and the uncertainty for a certain category in deterministic multi-category precipitation forecasts, respectively. Specifically, the comprehensive uncertainty is defined as the average reduction in uncertainty about the observations resulting from the use of a predictive model to provide all categories forecasts; the uncertainty of a certain category is defined as the reduction in uncertainty about the observations resulting from the use of a predictive model to provide a certain category forecast. By applying the proposed approach and traditional verification methods, the four precipitation forecasting products from the China Meteorological Administration (CMA), European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) and United Kingdom Meteorological Office (UKMO) were verified in the Dahuofang Reservoir Drainage Basin, China. The results indicate that: (1) the proposed approach can better capture the changing patterns of uncertainties with lead times and distinguish the forecasting performance among different forecast products; (2) the proposed approach is resistant to the extreme bias; (3) the proposed approach needs a careful choice of bin width; and (4) the bias analysis is necessary before verifying the uncertainties in precipitation forecasts

Variations in the Runoff-Sediment Relationship of the Weihe River Basin Based on the Copula Function

This study investigates the runoff-sediment relationship (RSR) of the Weihe River, a sandy waterway originating from the Loess Plateau, and considers the potential variations in RSR under an evolving environment. The double mass curve method was used to investigate RSR inflection points at six hydrologic stations located in the Weihe River basin (WRB) spanning the period from 1956 to 2010. Because of its ability to accurately define nonlinear and asymmetric correlations between variables, the Copula function provided the joint probability distributions and revealed the joint probabilities of annual runoff and sediment yield through different periods. The results indicated: (1) The sediment yield and runoff exhibit decreasing trends, which was principally related to human activity such as soil and water conservation measures, water projects and industrial and domestic water use, (2) the RSR inflection points principally occurred around 1983 at the Weijiabu, Xianyang, Huaxian and Zhuangtou stations, whereas they were non-significant at the Linjiacun and Zhangjiashan stations. Changes in RSR are attributed to the irregular effect of human activity reducing the runoff and sediment output; and (3) the joint probability distributions of annual runoff and sediment yield varied under an evolving environment and were characterized by spatial variability, which is more evident in the mainstream areas of the Weihe River than in the tributary regions

Detection of Abrupt Changes in Runoff in the Weihe River Basin

Climate change and human activities are two major driving factors for variations in hydrological patterns globally, and it is of significant importance to distinguish their effects on the change of hydrological regime in order to formulate robust water management strategies. Hilbert-Huang transform-based time-frequency analysis is employed in this study to detect abrupt changes and periods of the runoff at five hydrological stations in the Weihe River Basin, China, from 1951 to 2010. The key part of the method is the empirical decomposition mode with which any complicated data set can be decomposed into small number of intrinsic mode functions that admit well adaptive Hilbert transforms. Moreover, an attempt has been made to find out the specific reason for the abrupt point at the five hydrological stations in the Weihe River Basin. The results are presented as follows: (1) annual runoff significantly declined in the basin in intervals of 8~15 years; (2) abrupt changes occurred in 1971, 1982, and 1994 at Huaxian, 1972 and 1982 at Xianyang, 1992 at Zhangjiashan, 1990 at Zhuangtou, and 1984 at Beidao; (3) changes were more frequent and complex in the mainstream and downstream reaches than in tributaries and upstream reaches, respectively

Do Greener Urban Streets Provide Better Emotional Experiences? An Experimental Study on Chinese Tourists

Compared to the usual environment, the potential momentary emotional benefits of exposure to street-level urban green spaces (UGS) in the unusual environment have not received much academic attention. This study applies an online randomized control trial (RCT) with 299 potential tourists who have never visited Xi’an and proposes a regression model with mixed effects to scrutinize the momentary emotional effects of three scales (i.e., small, medium and large) and street types (i.e., traffic lanes, commercial pedestrian streets and culture and leisure walking streets). The results identify the possibility of causality between street-level UGS and tourists’ momentary emotional experiences and indicate that tourists have better momentary emotional experiences when urban streets are intervened with large-scale green vegetation. The positive magnitude of the effect varies in all three types of streets and scales of intervention, while the walking streets with typical cultural attractions, have a larger impact relative to those with daily commute elements. These research results can provide guidance for UGS planning and the green design of walking streets in tourism

Spatiotemporal Impacts of Climate, Land Cover Change and Direct Human Activities on Runoff Variations in the Wei River Basin, China

Previous studies that quantified variations in runoff have mainly focused on the combined impacts of climate and human activities or climate and land cover change. Few have separated land cover change from human activities, which is critical for effective management of water resources. This study aims to investigate the impact of changing environmental conditions on runoff using the Soil and Water Assessment Tool (SWAT) model; we examined three categories: climate, land cover change and direct human activities. The study area was the Wei River Basin, a typical arid to semi-arid basin that was divided into five sub-zones (UZ, MZ, DZ, JZ and BZ). Our results showed the following: (1) the calibrated SWAT model produced satisfactory monthly flow processes over the baseline period from 1978 to 1986; (2) compared to the baseline period, the impact of climatic variations decreased and the impact of direct human activities increased from the 1990s to the 2000s, while the impact of land cover change was generally stable; and (3) climatic variations were the main cause of runoff declines over the entire basin during the 1990s and in the UZ, MZ and JZ areas during the 2000s, while direct human activities were most important in the DZ and BZ areas during the 2000s

Study of Discharge Model in South-to-North Water Diversion Middle Route Project Based on Radial Basis Function Neural Network

The technology for water dispatch is very complex in South-to-North Water Diversion Middle Route Project, and it is necessary to take advantage of automation system for water delivery. The model for calculating flow rate is important to water dispatch, but traditional method often needs to rectify parameters manually. A model based on radial basis function neural network is established to describe the relationship between water level, gate opening and flux. The model uses the network to simulate the optimal function between water level, gate opening and flux coefficient, and calculates the flow rate by the coefficient. By taking the new method into South-to-North Water Diversion Middle Route Project and comparing the neural network model with traditional methods, the results show that the radial basis function neural network model has higher accuracy and efficienc