Application of a stochastic snowmelt model for probabilistic decisionmaking

A stochastic form of the snowmelt runoff model that can be used for probabilistic decision-making was developed. The use of probabilistic streamflow predictions instead of single valued deterministic predictions leads to greater accuracy in decisions. While the accuracy of the output function is important in decisionmaking, it is also important to understand the relative importance of the coefficients. Therefore, a sensitivity analysis was made for each of the coefficients

Relationship of physiography and snow area to stream discharge

The author has identified the following significant results. A comparison of snowmelt runoff models shows that the accuracy of the Tangborn model and regression models is greater if the test data falls within the range of calibration than if the test data lies outside the range of calibration data. The regression models are significantly more accurate for forecasts of 60 days or more than for shorter prediction periods. The Tangborn model is more accurate for forecasts of 90 days or more than for shorter prediction periods. The Martinec model is more accurate for forecasts of one or two days than for periods of 3,5,10, or 15 days. Accuracy of the long-term models seems to be independent of forecast data. The sufficiency of the calibration data base is a function not only of the number of years of record but also of the accuracy with which the calibration years represent the total population of data years. Twelve years appears to be a sufficient length of record for each of the models considered, as long as the twelve years are representative of the population

Improving the effectiveness of multi-objective optimization design of urban drainage systems

This is the final version. Available from Wiley via the DOI in this recordCapacity of urban drainage systems (UDSs) can substantially influence flooding properties of urban catchments. This motivates many studies to optimally design UDSs often using multi-objective evolutionary algorithms (MOEAs) as they can explore trade-offs between conflicting objectives (e.g., cost versus system reliability). However, MOEA-based approaches are typically computationally demanding and their solutions are often practically unacceptable as engineering domain knowledge is often not explicitly considered. To address these two issues, this paper proposes an efficient optimization framework for UDS design, where an engineering23 based design method (EBDM) is developed to generate approximate solutions to initialize the MOEA’s search, thereby greatly enhancing the optimization efficiency. To improve the solution practicality, two ideas have been implemented in the proposed optimization method (PM): (i) the variability of peak depths across pipes is minimized, and (ii) a constraint is introduced to ensure that sizes of pipes in the downstream direction are no smaller than their corresponding upstream diameters. Two real-world UDSs of different size are used to demonstrate the effectiveness of the PM. Results show that: (i) the proposed EBDM is effective in producing initial solutions that are very close to the final solutions identified by the optimization methods, (ii) the minimization of the variability of peak depths in pipes is practically meaningful as it can facilitate to identify solutions with great ability in handling future uncertainties (e.g., rainfall variability), and (iii) the PM significantly improves optimization efficiency and solution practicality compared to the traditional optimization approach, with benefits being more prominent for larger UDSs.National Natural Science Foundation of ChinaExcellent Youth Natural Science Foundation of Zhejiang Province, Chin

Assessment the flood hazard arising from land use change in a forested catchment in northern Iran

The provinces of northern Iran that border the Caspian Sea are forested and may be prone to increased risks of flooding due to deforestation and other land use changes, in addition to climate change effects. This research investigated changes in runoff from a small forested catchment in northern Iran for several land use change scenarios and the effects of higher rainfall and high antecedent soil moisture. Peak discharges and total runoff volumes from the catchment were estimated using the US Soil Conservation Service 'Curve Number' (SCS-CN) method and the SCS dimensionless unit hydrograph. This method was selected for reasons of data availability and operational simplicity for flood managers. A GIS was used to manipulate spatial data for use in the catchment runoff modelling. The results show that runoff is predicted to increase as a result of deforestation, which is dependent on the proportion of the catchment area affected. However, climate change presents a significant flood hazard even in the absence of deforestation. Other land use changes may reduce the peak discharges of all return period floods. Therefore a future ban on timber extraction, combined with agricultural utilisation of rangeland, could prove effective as 'nature-based' flood reduction measures throughout northern Iran

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959-2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free vs. ice-influenced) and bottom depth (shelf vs. deep ocean). The models performed relatively well for the most recent decade and towards the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. . Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling

Internalised Values and Fairness Perception: Ethics in Knowledge Management

This chapter argues for ethical consideration in knowledge management (KM). It explores the effect that internalised values and fairness perception have on individuals’ participation in KM practices. Knowledge is power, and organisations seek to manage knowledge through KM practices. For knowledge to be processed, individual employees—the source of all knowledge—need to be willing to participate in KM practices. As knowledge is power and a key constituent part of knowledge is ethics, individuals’ internalised values and fairness perception affect knowledge-processing. Where an organisation claims ownership over knowledge, an individual may perceive being treated unfairly, which may obstruct knowledge-processing. Through adopting ethical KM practices, individual needs are respected, enabling knowledge-processing. Implications point towards an ethical agenda in KM theory and practice