分类： 地球科学 >> 水文学 提交时间： 2022-03-15 合作期刊: 《干旱区科学》
摘要： As important freshwater resources in alpine basins, glaciers and snow cover tend to decline due to climate warming, thus affecting the amount of water available downstream and even regional economic development. However, impact assessments of the economic losses caused by reductions in freshwater supply are quite limited. This study aims to project changes in glacier meltwater and snowmelt of the Urumqi River in the Tianshan Mountains under future climate change scenarios (RCP2.6 (RCP, Representative Concentration Pathway), RCP4.5, and RCP8.5) by applying a hydrological model and estimate the economic losses from future meltwater reduction for industrial, agricultural, service, and domestic water uses combined with the present value method for the 2030s, 2050s, 2070s, and 2090s. The results indicate that total annual glacier meltwater and snowmelt will decrease by 65.6% and 74.5% under the RCP4.5 and RCP8.5 scenarios by the 2090s relative to the baseline period (19802010), respectively. Compared to the RCP2.6 scenario, the projected economic loss values of total water use from reduced glacier meltwater and snowmelt under the RCP8.5 scenario will increase by 435.10106 and 537.20106 CNY in the 2050s and 2090s, respectively, and the cumulative economic loss value for 2099 is approximately 2124.00106 CNY. We also find that the industrial and agricultural sectors would likely face the largest and smallest economic losses, respectively. The economic loss value of snowmelt in different sectorial sectors is greater than that of glacier meltwater. These findings highlight the need for climate mitigation actions, industrial transformation, and rational water allocation to be considered in decision-making in the Tianshan Mountains in the future.
分类： 地球科学 >> 地理学 提交时间： 2019-01-17 合作期刊: 《干旱区科学》
摘要： Snowfall is one of the dominant water resources in the mountainous regions and is closely related to the development of the local ecosystem and economy. Snowfall predication plays a critical role in understanding hydrological processes and forecasting natural disasters in the Tianshan Mountains, where meteorological stations are limited. Based on climatic, geographical and topographic variables at 27 meteorological stations during the cold season (October to April) from 1980 to 2015 in the Tianshan Mountains located in Xinjiang of Northwest China, we explored the potential influence of these variables on snowfall and predicted snowfall using two methods: multiple linear regression (MLR) model (a conventional measuring method) and random forest (RF) model (a non-parametric and non-linear machine learning algorithm). We identified the primary influencing factors of snowfall by ranking the importance of eight selected predictor variables based on the relative contribution of each variable in the two models. Model simulations were compared using different performance indices and the results showed that the RF model performed better than the MLR model, with a much higher R2 value (R2=0.74; R2, coefficient of determination) and a lower bias error (RSR=0.51; RSR, the ratio of root mean square error to standard deviation of observed dataset). This indicates that the non-linear trend is more applicable for explaining the relationship between the selected predictor variables and snowfall. Relative humidity, temperature and longitude were identified as three of the most important variables influencing snowfall and snowfall prediction in both models, while elevation, aspect and latitude were of secondary importance, followed by slope and wind speed. These results will be beneficial to understand hydrological modeling and improve management and prediction of water resources in the Tianshan Mountains.
分类： 地球科学 >> 地球科学史 提交时间： 2020-10-20 合作期刊: 《干旱区科学》
摘要： Short-term climate reconstruction, i.e., the reproduction of short-term (several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5 (CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit (CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region (TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature (MAT) and monthly accumulated precipitation (MAP) in the TMR using the delta, physical scaling (SP) and artificial neural network (ANN) methods. Performance and uncertainty during the calibration (1971–1999) and verification (1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis (RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty (positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty (negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850 (or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term (several decades) regional climate reconstruction and longer-term (100 a or more) assessments of regional climate change.
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