Stochastic Temporal Data Upscaling Using the Generalized k-Nearest Neighbor Algorithm

Three methods of temporal data upscaling, which may collectively be called the generalized k-nearest neighbor (GkNN) method, are considered. The accuracy of the GkNN simulation of month by month yield is considered (where the term yield denotes the dependent variable). The notion of an eventually well-distributed time series is introduced and on the basis of this assumption some properties of the average annual yield and its variance for a GkNN simulation are computed. The total yield over a planning period is determined and a general framework for considering the GkNN algorithm based on the notion of stochastically dependent time series is described and it is shown that for a sufficiently large training set the GkNN simulation has the same statistical properties as the training data. An example of the application of the methodology is given in the problem of simulating yield of a rainwater tank given monthly climatic data.

Drought severity and change in Xinjiang, China, over 1961–2013

Monthly climatic data from 53 sites across Xinjiang, China, were used to compare drought severity from the widely accepted Standardized Precipitation Index (SPI) with the recently proposed Standardized Precipitation Evapotranspiration Index (SPEI), as well as trends in the data from 1961 to 2013. Monthly Thornthwaite based (ETo.TW) and Penman-Monteith based reference evapotranspiration (ETo.PM) were computed and subsequently used to estimate SPEITW and SPEIPM, respectively. The indices' sensitivity, spatiotemporal distributions and trends were analyzed. The results showed that the TW equation underestimated ETo, which affected the accuracy of the SPEI estimation. Greater consistency was found between SPI and SPEIPM than between SPI and SPEITW at different timescales. SPI and SPEIPM were sensitive to precipitation, but SPEITW and SPEIPM were insensitive to ETo. The scope of spatial SPEIPM was wider than that of SPI at the same timescale. Obvious differences in SPI, SPEITW and SPEIPM existed between northern and southern Xinjiang. SPEIPM was a better indicator of global warming than SPI. Both SPI and SPEIPM had increasing trends, which contradict previously reported trends in global drought. In conclusion, the decrease in drought severity observed over the last 53 years may indicate some relief in the water utilization crisis in Xinjiang, China.

The Impact of the Choice of Computational Methods on the Results of Simulations of Specific Heat Consumption for Heating a Family House

There are almost 100 passive houses registered in the Czech Republic, whose design was influenced by simulations coming from various design software from early stages with maximization of energy savings in mind. The accuracy of the simulations however, depends on the level of simplification and accuracy of inputs, such as climatic data, etc. Especially the energy simulations require databases with hourly or monthly climatic data. The questions are, whether these are corresponding with reality and to what extend the difference between the database data and reality will affect the simulations and subsequently the use of the houses.This paper deals with modelling of and energy-efficient house under several sets of climatic data. The analysis was done in monthly steps using Energie 2013 (methodology of ČSN EN ISO 13790:2009) and PHPP 2007 software tools and in hourly steps on the basis of reference data of a climatic year described in ČSN EN ISO 15927-4:2011 (TRNSYS) and from the database of Meteonorm (TRNSYS). The aim of this paper is to point out the differences in the determination of heat consumption using different calculation methods..

Climate Variability and Residential Water Use in the City of Phoenix, Arizona

In this investigation, how annual water use in the city of Phoenix, Arizona, was influenced by climatic variables between 1980 and 2004 is examined. Simple correlation coefficients between water use and annual mean temperature, total annual precipitation, and annual mean Palmer hydrological drought index values are +0.55, −0.69, −0.52, respectively, over the study period (annual water use increases with higher temperature, lower precipitation, and drought). Multivariate analyses using monthly climatic data indicate that annual water use is controlled most by the overall state of drought, autumn temperatures, and summer-monsoon precipitation. Model coefficients indicate that temperature, precipitation, and/or drought conditions certainly impact water use, although the magnitude of the annual water-use response to changes in climate was relatively low for an urban environment in which a sizable majority of residential water use is for outdoor purposes. People’s perception of the landscape’s water needs and their willingness and ability to respond to their perceptions by changing landscaping practices are probably more important than the landscape’s need for water in assessing residential water demand and the variation therein.