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Physically based distributed hydrological models are widely used for hydrological simulations in various environments. As with conceptual models, they are limited in data-sparse basins by the lack of streamflow data for calibration. Short periods of observational data (less than 1 year) may be obtained from fragmentary historical records of previously existing gauging stations or from temporary gauging during field surveys, which might be of value for model calibration. However, unlike lumped conceptual models, such an approach has not been explored sufficiently for physically based distributed models. This study explored how the use of limited continuous daily streamflow data might support the application of a physically based distributed model in datasparse basins. The influence of the length of the observation period on the calibration of the widely applied soil and water assessment tool model was evaluated in four Chinese basins with differing climatic and geophysical characteristics. The evaluations were conducted by comparing calibrations based on short periods of data with calibrations based on data from a 3-year period, which were treated as benchmark calibrations of the four basins, respectively. To ensure the differences in the model simulations solely come from differences in the calibration data, the generalized likelihood uncertainty analysis scheme was employed for the automatic calibration and uncertainty analysis. In the four basins, contrary to the common understanding of the need for observations over a period of several years, data records with lengths of less than 1 year were shown to calibrate the model effectively, i.e., performances similar to the benchmark calibrations were achieved. The models of the wet Jinjiang and Donghe basins could be effectively calibrated using a shorter data record (1 month), compared with the dry Heihe and upstream Yalongjiang basins (6 months). Even though the four basins are very different, when using 1-year or 6-month (covering a whole dry season or rainy season) data, the results show that data from wet seasons and wet years are generally more reliable than data from dry seasons and dry years, especially for the two dry basins. The results demonstrated that this idea could be a promising approach to the problem of calibration of physically based distributed hydrological models in datasparse basins, and findings from the discussion in this study are valuable for assessing the effectiveness of short-period data for model calibration in real-world applications. [ABSTRACT FROM AUTHOR]

Aimed at reducing simulation uncertainty of hydrological models in data-sparse basins where soil hydraulic data are unavailable, a method of estimating soil water parameters of soil and water assessment tool (SWAT) from readily available soil information using pedotransfer functions was introduced. The method was evaluated through a case study of Jinjiang Basin, China and was performed based on comparison between two model calibrations: (1) soil parameters estimated from pedotransfer functions and other parameters obtained from calibration; and (2) all parameters derived from calibration. The generalized likelihood uncertainty estimation (GLUE) was used as a model calibration and uncertainty analysis tool. The results show that information contained in streamflow data is insufficient to derive physically reasonable soil parameter values via calibration. The proposed method can reduce simulation uncertainty, resulting from greater average performance of behavioral parameter sets identified by GLUE. Exploring the parameter space reveals that the means of estimating soil parameters has little influence on other parameters. These facts indicate the decrease in uncertainty most likely results from a more realistic description of soil water characteristics than calibration. Thus, the proposed method is superior to calibration for estimating soil parameters of SWAT model when basin data are sparse. [ABSTRACT FROM AUTHOR]

The influences of climate change on water resources availability in Jinjiang Basin, China, were assessed using the Block-wise use of the TOPmodel with theMuskingum-Cunge routing method (BTOPMC) distributed hydrological model. The ensemble average of downscaled output from sixteen GCMs (General Circulation Models) for A1B emission scenario (medium CO2 emission) in the 2050s was adopted to build regional climate change scenario.The projected precipitation and temperature data were used to drive BTOPMC for predicting hydrological changes in the 2050s. Results show that evapotranspiration will increase in most time of a year. Runoff in summer to early autumn exhibits an increasing trend, while in the rest period of a year it shows a decreasing trend, especially in spring season. From the viewpoint of water resource availability, it is indicated that it has the possibility that water resources may not be sufficient to fulfill irrigation water demand in the spring season and one possible solution is to store more water in the reservoir in previous summer. [ABSTRACT FROM AUTHOR]

A grid-based erosion model is developed by integrating the distributed hydrological model, BTOPMC, with the modified USLE to estimate soil erosion and sediment outflow during single storms. The possible sheet, rill, channel erosion types, and sediment transport processes are considered within each grid under the model structure. Instead of representing the sheet erosion and rill erosion separately, the classic USLE method is modified to simulate the lumped sheet–rill erosion during storms. In the modification, the runoff ratio and a relevant correction coefficient are brought into the R-factor which improves the model’s applicability in predicting erosion during single storms. Instead of representing a grid with a unique erosion type, a channel component is assumed to exist in each grid, and its width varies with the upstream contributing area of the grid. This assumption avoids the problems that are caused by the difference between the channel widths in the upstream area and the downstream area if the grid is simply recognized as a channel grid. It also enables the model to be applicable in simulating soil erosion and sediment outflow from a large catchment. Through a case study in the Lushi catchment, China, the results show an overall satisfactory accuracy for the selected events. Moreover, by analyzing the spatial distribution of soil erosion or deposition, the erosion-prone areas are identified for the prioritization purpose. [ABSTRACT FROM AUTHOR]

A waste load allocation method was developed for industrial wastewater management based on unfairness factors, an industrial allocation factor and pollution reduction discounts. Three unfairness factors were defined to assess the relative efficiencies of energy consumption, pollution discharge and waste treatment costs for different industries. The overall effect of these factors was described by an industrial allocation factor. Based on the values of these factors, industries were classified into three types, after which waste load allocation proportions among different industries were determined using different pollution reduction discounts. This waste load allocation method was then applied in the Zhangweinan Watershed, which is one of the most seriously polluted watersheds in northern China. The results revealed that extractive, mechanical and food industries comprise the type I industries, which had the lowest pollution reduction discounts of 0, 0.25 and 0.5, respectively. The metallurgical industry and other industries were characterized as type II and discounts of 0.5 and 0.6 were given to their primary reductions. Textile, pharmaceutical, oil and pyrogenic, chemical and paper industries were classified as type III industries and had a waste load reduction of more than 80% of the pollution discharge in 2004. [ABSTRACT FROM AUTHOR]