No abstract

No abstract

No abstract

Based on ten indices of extreme precipitation and one drought index (composite index, CI), the trends in extreme events were investigated using a Mann-Kendall non-parametric method at 39 stations in the Songhua River Basin (SHB) during 1960-2013. The regionally averaged wet-day precipitation (PRCPTOT) increased at a rate of 1.65 mm/year (R 2 = 0.28, P = 0.13), in which 82% of the stations experienced increases, but only 4 stations showed significant positive trends. The annual R95 and R99 exhibited slight upward trends at rates of 1.37 (R 2 = 0.21, P = 0.27) and 1.28 mm/year (R 2 = 0.23, P = 0.23) over the last 54 years; however, there were not significant trends in R95 and R99 at the 0.05 level. PRCPTOT, R95 and R99 showed similar spatial trends, in which positive trends mainly occurred in the northern and southeastern basins. The trends in the maximum 1-day precipitation (RX1day) and maximum 5-day precipitation (RX5day) do not show a prevalent trend (approximately 50% of the stations have a positive trend and the remaining stations have a negative trend). The simple daily intensity index (SDII) significantly decreased at an annual rate of 0.02 mm/d during 1960-2013 (R 2 = 0.66, P b 0.01); spatially, 49% of the stations experienced statistically significant decreases at the 0.05 level based on the Mann-Kendall non-parametric test. The regionally averaged heavy (R10mm) and very heavy precipitation days (R20mm) and consecutive wet days (CWD) showed no significant trends during the past 54 years; however, several individual extreme precipitation events, such as the flood of 1998 in the SHB, were well detected by these indices. The regionally averaged consecutive dry days (CDD) significantly increased (R 2 = 0.79, P b 0.01) at a rate of 0.22 days/year from 1960 to 2013. All of the stations exhibited statistically significant increases in CDD, excluding the Tongyu station in the western basin. The monthly RX5day values were highest in summer, from June to August, in the SHB; a peak occurred in July (67.5 mm) in the SHB during 1960-2013. The CI peaked in July, with the highest value of 0.2 in the SHB during 1960-2013. However, the two lowest CI values occurred in spring and fall, with values of −0.56 and −0.41, respectively. During April and May, when most of the spring drought events occur, a prevalent trend does not exist; moreover, almost no stations have statistically significant CI increases. In August and September, respectively 79% and 97% of the stations exhibited a CI negative trend, but only 2 and 6 stations showed significant decreases at the 0.05 level. The increasing extreme climate events present a challenge for local water resources management.

No abstract

No abstract

Tuberculosis (TB) remains a major public health problem in China, and its incidence shows certain regional disparities. Systematic investigations of the social and environmental factors influencing TB are necessary for the prevention and control of the disease. Data on cases were obtained from the Chinese Center for Disease and Prevention. Social and environmental variables were tabulated to investigate the latent factor structure of the data using exploratory factor analysis (EFA). Partial least square path modeling (PLS-PM) was used to analyze the complex causal relationship and hysteresis effects between the factors and TB prevalence. A geographically weighted regression (GWR) model was used to explore the local association between factors and TB prevalence. EFA and PLS-PM indicated significant associations between TB prevalence and its latent factors. Altitude, longitude, climate, and education burden played an important role; primary industry employment, population density, air quality, and economic level had hysteresis with different lag time; health service and unemployment played a limited role but had limited hysteresis. Additionally, the GWR model showed that each latent factor had different effects on TB prevalence in different areas. It is necessary to formulate regional measures and strategies for TB control and prevention in China according to the local regional effects of specific factors.

The Loess Plateau suffers from severe soil erosion that leads to a series of ecological and economic problems such as reduced land productivity, exacerbated rural poverty, decreased biodiversity and sedimentation of the riverbed in the lower reaches of the Yellow River. Soil erosion models are commonly used on the Loess Plateau to help target sustainable land management strategies to control soil erosion.In this study, we compared eleven soil erosion models that were previously used on the Loess Plateau. We studied their prediction accuracy, process representation, data and calibration requirements, and potential application in scenario studies. The selected models consisted of a broad range of model types, structures and scales.The comparison showed that process-based and empirical models did not necessarily yield more accurate results over one another for the Loess Plateau.Among the process-based models, Si' model, WEPP and MMF had the highest prediction accuracy. However, some of the selected models were tested with total ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T2 sediment load while others were tested with suspended sediment load (i.e. bedload is not included), which is subject to several drawbacks. Research questions that each of the models can address on the Loess Plateau were suggested. Further improvement of soil erosion models for the Loess Plateau should concentrate on enhancing the quality of data for model implementation and testing, incorporating key processes into process-based models according to their aims and scales, comparing models that address the same research questions, and implementing internal and spatial model testing.