• Energy Research

In this study, the hydrological system of the Weather Research and Forecasting model (WRF-Hydro) is applied to simulate the streamflow at the Kaidu River Basin, which is vital to the ecological system in the lower reaches of the Tarim River in Northwest China. The offline WRF-Hydro model is coupled with the Noah multi-parameterization land surface model (Noah-MP) and is forced by the China Meteorological Forcing Dataset (CMFD), with the grid spacing of the hydrological routing modules being 250 m. A 3-year period (1983–1985) is used for calibration and a 17-year period (1986–2002) for the evaluation. Several key parameters of WRF-Hydro and four Noah-MP parameterization options are calibrated, and the performance of WRF-Hydro with the optimized model setting is evaluated using the daily streamflow observations. The results indicate that WRF-Hydro can reproduce the observed streamflow reasonably, with underestimation of the streamflow peaks. The simulated streamflow is sensitive to the parameters of bexp, dksat, smcmax, REFKDT, slope, OVROUGHRTAC and mann in the Kaidu River Basin. At the same time, the parameterization options of Noah-MP also have a large influence on the streamflow simulation. The WRF-Hydro model with optimized model settings can achieve correlation coefficient (CC) and Nash efficiency coefficient (NSE) statistical scores of 0.78 and 0.61, respectively, for the calibration period. Meanwhile, for the evaluation period, the scores are 0.7 and 0.50, respectively. This study indicates the importance of applying the physical-based WRF-Hydro model over Northwest China and provides a reference for the nearby regions.

China’s forest ecosystem plays a crucial role in carbon sequestration, serving as a cornerstone in China’s journey toward achieving carbon neutrality by 2060. Yet, previous research primarily emphasized climate change’s influence on forest carbon sequestration, neglecting tree species’ suitable area changes. This study combinates the Lund–Potsdam–Jena model (LPJ) and the maximum entropy model (MaxENT) to reveal the coupling impacts of climate and tree species’ suitable area changes on forest aboveground biomass carbon (ABC) in China. Key findings include the following: (1) China’s forests are distributed unevenly, with the northeastern (temperate coniferous broad-leaved mixed forest, TCBMF), southwestern, and southeastern regions (subtropical evergreen broad-leaved forest, SEBF) as primary hubs. Notably, forest ABC rates in TCBMF exhibited a worrisome decline, whereas those in SEBF showed an increasing trend from 1993 to 2012 based on satellite observation and LPJ simulation. (2) Under different future scenarios, the forest ABC in TCBMF is projected to decline steadily from 2015 to 2060, with the SSP5-8.5 scenario recording the greatest decline (−4.6 Mg/ha/10a). Conversely, the forest ABC in SEBF is expected to increase under all scenarios (2015–2060), peaking at 1.3 Mg/ha/10a in SSP5-8.5. (3) Changes in forest ABC are highly attributed to climate and changes in tree species’ highly suitable area. By 2060, the suitable area for Larix gmelinii in TCBMF will significantly reduce to a peak of 65.71 × 104 km2 under SSP5-8.5, while Schima superba Gardner & Champ and Camphora officinarum in SEBF will expand to peaks of 94.07 × 104 km2 and 104.22 × 104 km2, respectively. The geographic detector’s results indicated that the climate and tree species’ suitable area changes showed bi-variate and nonlinear enhanced effects on forest ABC change. These findings would offer effective strategies for achieving carbon neutrality.

China faces a difficult choice of maintaining socioeconomic development and carbon emissions mitigation. Analyzing the decoupling relationship between economic development and carbon emissions and its driving factors from a regional perspective is the key for the Chinese government to achieve the 2030 emission reduction target. This study adopted the logarithmic mean Divisia index (LMDI) method and Tapio index, decomposed the driving forces of the decoupling, and measured the sector’s decoupling states from carbon emissions in Xinjiang province, China. The results found that: (1) Xinjiang’s carbon emissions increased from 93.34 Mt in 2000 to 468.12 Mt in 2017. Energy-intensive industries were the key body of carbon emissions in Xinjiang. (2) The economic activity effect played the decisive factor to carbon emissions increase, which account for 93.58%, 81.51%, and 58.62% in Xinjiang during 2000–2005, 2005–2010, and 2010–2017, respectively. The energy intensity effect proved the dominant influence for carbon emissions mitigation, which accounted for −22.39% of carbon emissions increase during 2000–2010. (3) Weak decoupling (WD), expansive coupling (EC), expansive negative decoupling (END) and strong negative decoupling (SND) were identified in Xinjiang during 2001 to 2017. Gross domestic product (GDP) per capita elasticity has a major inhibitory effect on the carbon emissions decoupling. Energy intensity elasticity played a major driver to the decoupling in Xinjiang. Most industries have not reached the decoupling state in Xinjiang. Fuel processing, power generation, chemicals, non-ferrous, iron and steel industries mainly shown states of END and EC. On this basis, it is suggested that local governments should adjust the industrial structure, optimize energy consumption structure, and promote energy conservation and emission reduction to tap the potential of carbon emissions mitigation in key sectors.