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Land use/cover changes (LUCCs) are an important cause of regional climate changes, but the contribution of LUCCs to regional climate changes is not clear. In this study, the Weather Research and Forecasting (WRF) model and statistical methods were used to investigate changes in meteorologic variables in January, April, July, and October 2013 due to local LUCCs from 1990 to 2010 in southern Shandong province, China. The results indicate that the WRF model simulates temperatures in the region well, with high correlation coefficients (0.86-0.97, p < 0.001) between the modeled and measured values. The model simulates precipitation less well, with correlation coefficients of 0.41-0.91, but they are all at statistically significant levels, with p < 0.05. During the 20-year period, the LUCCs in the study area consisted mainly of conversions from dry land to urbanized land (747.3 km 2 ) and bare/sparse vegetation (132.4 km 2 ). The LUCCs caused a 0.16 °C temperature increase in January and October and 0.01 and 0.18 °C temperature decreases in April and July, respectively. The range of temperature changes over mixed forest and water bodies due to the LUCCs was wide (0.39-1.31 °C) and was narrower over deciduous broadleaf forest and wetland (0.01 to 0.06 °C). The LUCCs did not change the precipitation greatly in January, April, and October but did affect the precipitation in July substantially, causing a decrease of 23.71 mm. The LUCCs did not affect wind speed and direction substantially during these four months: average wind speeds increased by 0.02 and 0.01 m/s in January and October, respectively, and decreased by 0.02 and 0.05 m/s in April and July, respectively. Overall, The LUCCs affected spring temperatures the least and summer precipitation the most.

Much attention has recently been focused on the effects that precipitation and potential evapotranspiration (PET) have had on runoff change; however, the influence of temperature on runoff needs to be further studied. We attempted to employ the improved elasticity method to evaluate the effects of climate factors (CF, especially temperature) on runoff change for Aksu River in the arid region of northwest China. Data from Aksu River in the arid region of northwest China were analyzed to investigate changes in annual runoff and CF during the period of 1960-2010. The key findings of this study indicated that the annual runoff had a significant ( P < 0.01) increasing trend with a rate of 3.78 × 10 m/decade, and the temperature and precipitation also exhibited significant rising trends, at a rate of 0.28 °C/decade ( P < 0.01) and 15.11 mm/decade ( P < 0.05), respectively, while PET showed a decreasing trend (22.66 mm/decade, P < 0.01). Step change point in runoff occurred in the year 1993. Thus, we employed the mean runoff and climate factors during the period 1960-1993 as the benchmark value to measure the change. In 1994-2010, mean runoff increased by 22 %. Results also revealed that temperature rising was the most important factor that increased runoff with contribution of 45 %, while precipitation and PET were responsible for 22 and 27 % of the runoff change, respectively, indicating that the runoff of increasing percentage only accounted for 6 % owing to human activities and other factors, and showed that climate variability was the main reason for the runoff change in Aksu River. [ABSTRACT FROM AUTHOR]

Abstract: This study quantitatively evaluated the effect of carbon dioxide emissions (CDE) on temperature change in five regions and globally through an elasticity coefficient method. The results revealed that for the period 1960–2008, the sensitivity of temperature to CO2 emission in North America was the highest with an elasticity coefficient of 0.317; South America was the lowest with elasticity of 0.005. The contribution of CO2 to temperature rise in North America and Oceania are the largest during 1990–2008, reaching 57.8% and 51.5% respectively. The contribution rates in Asia, South America, and Africa were 43.7%, 38.9%, and 34.2%, respectively. For the period 1960–2010, the temperature elasticity values of the entire world in relation to CDE are 0.0213, indicating that a 100% change in CDE will result in a 2.13% change in global temperature. It is estimated that CDE is responsible for 50.2% of the global temperature increase during 1990–2010, which in turn leads to the implication that CDE is the important reason for global warming. [Copyright &y& Elsevier]

To clarify runoff and climate change trends and its relation in typical rivers in the arid region of northwest China (ANC), this study takes the runoff and meteorological data of 11 rivers in 5 typical river areas from mountain-pass as the research objects. The Mann–Kendall test, Extrapolation of Variance Analysis of Time-series Period and Correlation Analysis Method are applied to analyze the temporal and spatial variations of climate and runoff. The results show that in the past 50 years, the temperature, precipitation and runoff in the each river area exhibited an upward tendency. However, the runoff from the south slope of Altai Mountains and the north slope of Kunlun Mountains and the precipitation on the north slope of Qilian Mountains, the north slope of Kunlun Mountains and the south slope of Tianshan Mountains show inconspicuous changes. The increasing rates of temperature and precipitation in the river area of Northern Xinjiang is the largest, an average of 0.44 °C/10a and 15.39 mm/10a; followed by that in the river area of Hexi Corridor, 0.29 °C/10a and 7.64 mm/10a. The lowest one is in the river area of Southern Xinjiang, only 0.24 °C/10a and 5.50 mm/10a. However, the increasing rate of runoff is the slowest in Northern Xinjiang while that in Southern Xinjiang is the fastest, mainly related to runoff recharge difference. The runoff recharge proportions from glaciers and precipitation have great effects on the relation between runoff and temperature and precipitation. [ABSTRACT FROM AUTHOR]

It is of profound significance to improve the forecasting accuracy of runoff using reasonable methods for scientific formulation of water resources management strategies. Currently, many scholars have paid much attention to the hybrid forecasting simulation of runoff variation, while it is rare to construct an ensemble prediction model based on the nonlinear change process of runoff. In this study, the freezing level height and summer runoff in the Hotan River are employed to analyze the nonlinear relationships of climatic and hydrological factors at different time scales using three nonlinear decomposition methods, which are wavelet analysis, ensemble empirical mode decomposition and extreme-point symmetric mode decomposition (ESMD). Combined with linear regression and back-propagation artificial neural network (BPANN), six hybrid forecasting models are established. The decomposition results of three nonlinear methods are compared, which indicate that ESMD shows better performance than the other two methods. Among the six forecasting models, ESMD-BPANN shows the highest accuracy according to the indicators of Cronbach's α, π^(rel) value, Akaike information criterion, etc. The ESMD-BPANN model is thus selected for forecasting.Our forecasts indicate that the runoff will have fluctuations, and a trend for a slight increase from 2014 to 2030, at a rate of 0.124×10^8 m³/year. This model predicts low runoff in 2014, 2017, 2024, and 2028, and high runoff in 2018, 2023, 2029, and 2030. This demonstrates that the inter-annual variations of runoff will be large and unstable. Therefore, hydrologists must devote further attention to variations of runoff so they can formulate strategies for improved management of water resources. [ABSTRACT FROM AUTHOR]

[Display omitted] Snowfall is an important component of the High Mountain Asia (HMA) cryosphere; its changes affect the regional energy and water balance. Snowfall data from numerous gridded products provide the basis for conducting climate-change studies; however, the applicability of these datasets in HMA has not been assessed comprehensively in terms of precipitation. This study comprehensively evaluates the applicability of the following widely used gridded datasets in HMA for the period 1991–2014: fifth generation of European ReAnalysis (ERA5), Global Land Data Assimilation System (GLDAS), High Asia Refined analysis (HAR), Multi-Source Weather (MSWX), National Centers for Environmental Prediction (NCEP), and China Meteorological Forcing Dataset (CMFD). The accuracy features of each dataset were evaluated based on observations from 148 gauges at various temporal scales (i.e., annual, monthly, and daily), elevations, sub-regions, and snowfall intensities. The results show that CMFD performs better at multiple temporal scales (i.e., annual, cool season, monthly, and daily) than other snowfall data. In addition, the CMFD accuracy has good stability, even at different elevations. CMFD is advantageous regarding interpreting the frequency and amount of different levels of (even extreme) snowfall events. The slightly inferior MSWX also performs well at different timescales, spatial scales, and elevations, and constitutes an attractive alternative to CMFD. HAR captures better light and moderate snowfall, and is the most accurate of the three sets of gridded products that contain direct snowfall data. This study constitutes an important reference for selecting suitable snowfall datasets for future scientific research. [ABSTRACT FROM AUTHOR]