Projected 21st century climate change on snow conditions over Shasta Dam watershed by means of dynamical downscaling

Snow is an important component of the Earth's climate system and is particularly vulnerable to global warming. It has been suggested that warmer temperatures may cause significant declines in snow water content and snow cover duration. In this study, snowfall and snowmelt were projected by means of a regional climate model that was coupled to a physically based snow model over Shasta Dam watershed to assess changes in snow water content and snow cover duration during the 21st century. This physically based snow model requires both physical data and future climate projections. These physical data include topography, soils, vegetation, and land use/land cover, which were collected from associated organizations. The future climate projections were dynamically downscaled by means of the regional climate model under 4 emission scenarios simulated by 2 general circulation models (fifth‐generation of the ECHAM general circulation model and the third‐generation atmospheric general circulation model). The downscaled future projections were bias corrected before projecting snowfall and snowmelt processes over Shasta Dam watershed during 2010–2099. This study's results agree with those of previous studies that projected snow water equivalent is decreasing by 50–80% whereas the fraction of precipitation falling as snowfall is decreasing by 15% to 20%. The obtained projection results show that future snow water content will change in both time and space. Furthermore, the results confirm that physical data such as topography, land cover, and atmospheric–hydrologic data are instrumental in the studies on the impact of climate change on the water resources of a region.     

Climate change impacts on extreme precipitation of water supply area in Istanbul: use of ensemble climate modelling and geo-statistical downscaling

ABSTRACT

Numerous statistical downscaling models have been applied to impact studies, but none clearly recommended the most appropriate one for a particular application. This study uses the geographically weighted regression (GWR) method, based on local implications from physical geographical variables, to downscale climate change impacts to a small-scale catchment. The ensembles of daily precipitation time series from 15 different regional climate models (RCMs) driven by five different general circulation models (GCMs), obtained through the European Union (EU)-ENSEMBLES project for reference (1960–1990) and future (2071–2100) scenarios are generated for the Omerli catchment, in the east of Istanbul city, Turkey, under scenario A1B climate change projections. Special focus is given to changes in extreme precipitation, since such information is needed to assess the changes in the frequency and intensity of flooding for future climate. The mean daily precipitation from all RCMs is under-represented in the summer, autumn and early winter, but it is overestimated in late winter and spring. The results point to an increase in extreme precipitation in winter, spring and summer, and a decrease in autumn in the future, compared to the current period. The GWR method provides significant modifications (up to 35%) to these changes and agrees on the direction of change from RCMs. The GWR method improves the representation of mean and extreme precipitation compared to RCM outputs and this is more significant, particularly for extreme cases of each season. The return period of extreme events decreases in the future, resulting in higher precipitation depths for a given return period from most of the RCMs. This feature is more significant with downscaling. According to the analysis presented, a new adaption for regulating excessive water under climate change in the Omerli basin may be recommended.     

Precipitation and temperature trends for the Southwest China: 1960–2007

Daily temperature and precipitation data from 136 stations of Southwest China (SWC) during the last five decades, from 1960 to 2007, were analysed to determine the spatial and temporal trends by using the Mann–Kendall trend test. Results show that SWC has become warmer over the last five decades, especially in the recent 20–25 years. The increasing trends in winter months are more significant than those in the months of other seasons, and spatially Tibet, Hengduan mountains area and west Sichuan Plateau have larger temperature trend in magnitude than the other regions have. A downward trend was detected in Sichuan Basin also, but the region with cooler temperature was shrinking due to the statistically significant increasing trend of temperature after 1990s. Both annual and seasonal means of daily maximum and minimum temperatures show an increasing trend, but trend magnitude of minimum temperature was larger than that of maximum temperature, resulting in the decrease of diurnal temperature range for SWC in the last 50 years. Annual precipitation showed slightly and statistically insignificant increasing trend, but statistically significant increasing trend has been detected in winter season while autumn witnessed a statistically significant decreasing trend. The results could be a reference for the planning and management of water resources under climate change. Copyright © 2010 John Wiley & Sons, Ltd.     

Trend and extreme occurrence of precipitation in a mid‐latitude Eurasian steppe watershed at various time scales

The confounding effects of step change invalidate the stationarity assumption of commonly used trend analysis methods such as the Mann–Kendall test technique, so previous studies have failed to explain inconsistencies between detected trends and observed large precipitation anomalies. The objectives of this study were to (1) formulate a trend analysis approach that considers nonstationarity due to step changes, (2) use this approach to detect trends and extreme occurrences of precipitation in a mid‐latitude Eurasian steppe watershed in North China, and (3) examine how runoff responds to precipitation trends in the study watershed. Our results indicate that annual precipitation underwent a marginal step jump around 1995. The significant annual downward trend after 1994 was primarily due to a decrease in summer rainfall; other seasons exhibited no significant precipitation trends. At a monthly scale, July rainfall after 1994 exhibited a significant downward trend, whereas precipitation in other months had no trend. The percentage of wet days also underwent a step jump around 1994 following a significant decreasing trend, although the precipitation intensity exhibited neither a step change nor any significant trend. However, both low‐frequency and high‐frequency precipitation events in the study watershed occurred more often after than before 1994; probably as either a result or an indicator of climate change. In response to these precipitation changes, the study watershed had distinctly different precipitation‐runoff relationships for observed annual precipitations of less than 300 mm, between 300 and 400 mm, and greater than 400 mm. Copyright © 2013 John Wiley & Sons, Ltd.     

Temporal trends in δ18O composition of precipitation in Germany: insights from time series modelling and trend analysis

Temporal and spatial variations of stable oxygen (¹⁸O) and hydrogen (²H) isotope measurements in precipitation act as important proxies for changing hydro‐meteorological and regional and global climate patterns. Temporal trends in time series of the stable isotope composition in precipitation were rarely observed, and they are poorly understood. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here, we investigate temporal trends of δ¹⁸O in precipitation at 17 observation stations in Germany between 1978 and 2009. We test if significant trends in the isotope time series from different models can be observed. Mann–Kendall trend tests are applied on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models, which account for first and higher order serial correlations. Effects of temperature, precipitation, and geographic parameters on isotope trends are also investigated in the proposed models. To benchmark our proposed approach, the ARIMA results are compared with a trend‐free pre‐whitening procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we further explore whether higher order serial correlations in isotope series affects our trend results. Overall, three out of the 17 stations show significant changes when higher order autocorrelation are adjusted, and four show a significant trend when temperature and precipitation effects are considered. The significant trends in the isotope time series generally occur only at low elevation stations. Higher order autoregressive processes are shown to be important in the isotope time series analysis. Results suggest that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative. Copyright © 2014 John Wiley & Sons, Ltd.     

Trends in timing of low stream flows in Canada: impact of autocorrelation and long‐term persistence

The annual timing of river flows might indicate changes that are climate related. In this study, trends in timing of low flows for the Reference Hydrometric Basin Network were investigated under three different hypotheses namely: independence, short‐term persistence (STP) and long‐term persistence (LTP). Both summer and winter time series were characterized with scaling behaviour providing strong evidence of LTP. The Mann–Kendall trend test was modified to account for STP and LTP, and used to detect trends in timing of low flows. It was found that considering STP and LTP resulted in a significant decrease in the number of detected trends. Numerical analysis showed that the timing of summer 7‐day low flows exhibited significant trends in 16, 9 and 7% of stations under independence, STP and LTP assumptions, respectively. Timing of summer low flow shifted toward later dates in western Canada, whereas the majority of stations in the east half of the country (except Atlantic Provinces) experienced a shift toward earlier dates. Timing of winter low flow experienced significant trends in 20, 12, and 6% of stations under independence, STP and LTP assumptions, respectively. Shift in timing of winter low flow toward earlier dates was dominant all over the country where it shifted toward earlier dates in up to 3/4 of time series with significant trends. There are local patterns of upward significant/insignificant trends in southeast, southwest and northern Canada. This study shows that timing of low flows in Canada is time dependent; however, addressing the full complexity of memory properties (i.e. short term vs long term) of a natural process is beyond the scope of this study. Copyright © 2009 John Wiley & Sons, Ltd.     

Variation analysis of precipitation during past 286 years in Beijing area,China, using non‐parametric test and wavelet analysis

Non‐parametric methods including Mann–Kendall (M–K) test, continuous wavelet transform (CWT) and discrete wavelet transform analysis are applied in this paper to detect the trend and periodic trait of precipitation data series in Beijing area where the data set spans nearly 300 years from 1724 to 2009. First, the trend of precipitation variables is elaborated by the M–K test (Sequential M–K test). The results show that there is an increasing trend (the value of this trend is 1.98) at the 5%‐significance level and there are not turning points in the whole data series. Then, CWT and wavelet variance are used to check for significant periodic characteristics of data series. In the plots of wavelet transform coefficients and figure of wavelet variance, some periodic events affect the trend of the annual total precipitation series in Beijing area. 85‐year, 35‐year and 21‐year periodic events are found to be the main periodic series of long‐term precipitation data, and they are all statistically significant. Moreover, the results of non‐parametric M–K test are exhibited on seven different combinations of discrete wavelet components. D5 (32‐year periodicity) periodic component is the effective and significant component on data. It is coincident with the result (35‐year periodic event as one part of main periodicity) by using CWT analysis. Moreover, approximation mode shows potential trend of the whole data set because it is the residuals as all periodicities are removed from data series. Thus, the mode A + D5 is responsible for producing a real basic structure of the trend founded on the data. Copyright © 2012 John Wiley & Sons, Ltd.     

Impacts of climate change and land use change on runoff of forest catchment in northeast China

Hydrological processes change from the impacts of climate variability and human activities. Runoff in the upper reaches of the Hun‐Taizi River basin, which is mainly covered by forests in northeast China, decreased from 1960 to 2006. The data used in this study were based on runoff records from six hydrological stations in the upper reaches of the Hun‐Taizi River basin. Nonparametric Mann–Kendall statistic was used to identify change trends and abrupt change points and consequently analyze the change characteristics in hydrological processes. The abrupt change in the annual runoff in most subcatchments appeared after 1975. Finally, the effects of climate change and land cover change on water resources were identified using regression analysis and a hydrology model. Results of the regression analysis suggest that the correlation coefficients between precipitation and runoff prior to the abrupt change were higher compared with those after the abrupt change. Moreover, using hydrology model analysis, the water yield was found to increase because of the decrease in forest land. Copyright © 2012 John Wiley & Sons, Ltd.