湖泊富营养化是目前公众和政府关注的热点问题之一，以往研究多集中在入湖的地表污水处理和农业灌溉排水污染物的拦截与阻滞等方面，严重忽视了地下水对湖泊富营养化的贡献。热红外遥感技术被广泛应用于识别入湖地下水排泄区，但传统的热红外遥感法并未考虑冻结湖泊表面覆盖的积雪和冰层对反演湖水表面温度精度的影响，限制了其精度和适用性。以东北季节性冻土平原区典型湖泊查干湖为研究对象，通过构建基于MODIS反演湖水表面温度的GA-SVR机器学习模型，将冰封期热红外遥感法反演湖水表面温度的R2由0.69提高到0.95，提高了入湖地下水排泄区的识别精度，并以222Rn浓度的空间分布特征来验证GA-SVR模型识别地下水排泄区结果的可靠性，从而为有效识别查干湖营养物质主要来源和支撑查干湖水环境安全管控提供科技支撑。

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 degrees C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.

多年冻土区是对气候变化最敏感的地区之一。随着全球变暖，蒙古高原多年冻土退化加剧，多年冻土区植被生态系统受到严重威胁。面对这一挑战，了解气候变化下蒙古高原不同冻土退化阶段的植被变化至关重要。本文基于多年冻土分布图，依据“空间代替时间”的理念，划分了不同的多年冻土带，探讨了2014-2023年多年冻土退化不同阶段植被对气候变化的响应。研究结果表明：（1）研究区NDVI值呈下降趋势，下降所占面积比例依次为零星多年冻土区>孤岛和稀疏多年冻土区>连续和不连续多年冻土区。（2）不同类型多年冻土区植被生长的主控因子不同，气温是孤岛和稀疏多年冻土区（r=–0.736）以及零星多年冻土区（r=–0.522）植被生长的主控因子，降水是连续和不连续多年冻土区（r=–0.498）植被生长的主控因子。（3）在不同的多年冻土退化阶段，NDVI对气候变化的响应各不相同。在多年冻土退化初期，地表温度和气温升高有利于植被生长，增加植被覆盖，降水量的增加则会阻碍植被生长；随着多年冻土退化，地表温度和气温升高会阻碍植被生长，降水量的增加则会促进植被生长。

The seasonal mountain snowpack of the Western US (WUS) is a key water resource to millions of people and an important component of the regional climate system. Impurities at the snow surface can affect snowmelt timing and rate through snow radiative forcing (RF), resulting in earlier streamflow, snow disappearance, and less water availability in dry months. Predicting the locations, timing, and intensity of impurities is challenging, and little is known concerning whether snow RF has changed over recent decades. Here we analyzed the relative magnitude and spatio-temporal variability of snow RF across the WUS at three spatial scales (pixel, watershed, regional) using remotely sensed RF from spatially and temporally complete (STC) MODIS data sets (STC-MODIS Snow Covered Area and Grain Size/MODIS Dust Radiative Forcing on Snow) from 2001 to 2022. To quantify snow RF impacts, we calculated a pixel-integrated metric over each snowmelt season (1st March-30th June) in all 22 years. We tested for long-term trend significance with the Mann-Kendall test and trend magnitude with Theil-Sen's slope. Mean snow RF was highest in the Upper Colorado region, but notable in less-studied regions, including the Great Basin and Pacific Northwest. Watersheds with high snow RF also tended to have high spatial and temporal variability in RF, and these tended to be near arid regions. Snow RF trends were largely absent; only a small percent of mountain ecoregions (0.03%-8%) had significant trends, and these were typically decreasing trends. All mountain ecoregions exhibited a net decline in snow RF. While the spatial extent of significant RF trends was minimal, we found declining trends most frequently in the Sierra Nevada, North Cascades, and Canadian Rockies, and increasing trends in the Idaho Batholith. This study establishes a two-decade chronology of snow impurities in the WUS, helping inform where and when RF impacts on snowmelt may need to be considered in hydrologic models and regional hydroclimate studies.

Permafrost in High Mountain Asia (HMA) is becoming increasingly vulnerable to thaw due to climate change. However, the lack of either in situ ground surface or borehole temperature data beyond the Tibetan Plateau prevents comprehensive assessments of its impact on the regional hydrologic cycle and local cascading hazards. Although past studies have generated estimates of permafrost extent in Central Asia, many are limited to the Tibetan Plateau, excluding the more remote reaches of the Tien Shan, Pamirs, and Himalayas. By leveraging surface temperatures from both the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infra-Red Sounder (AIRS), this study advances further understanding of remotely sensed permafrost occurrence at high altitudes, which are prone to error due to frequent cloud cover. We demonstrate that the fusion of MODIS and AIRS products can accurately estimate long-term thermal regimes of the subsurface, with reported correlation coefficients of 0.773 and 0.560, RMSEs of 0.890 degree celsius and 0.680 degree celsius, and biases of 0.003 degree celsius and 0.462 degree celsius, respectively, for the ground surface and the depth of zero annual amplitude, during a reference period of 2003-2016. Furthermore, we provide a range of possible permafrost extents based on established equations for calculating the temperature at the top of the permafrost to demonstrate temperature sensitivity to soil moisture and snow cover. The MODIS-AIRS product is recommended to be a robust source of ground temperature estimates, which may be sufficient for inferring mountain permafrost presence in HMA. Incorporating the influence of soil moisture and snow depth, although limited by biased estimates, also produces estimates of permafrost regional areas comparable to previously reported permafrost indices. A total permafrost area of 1.69 (+/- 0.32) million km(2) is estimated for the entire HMA, across 15 mountain subregions.

Polar amplification appears in response to greenhouse gas forcing, which has become a focus of climate change research. However, polar amplification has not been systematically investigated over the Earth's three poles (the Arctic, Antarctica, and the Third Pole). An index of polar amplification is employed, and the annual and seasonal variations of land surface temperature over the Earth's three poles are examined using MODIS (Moderate Resolution Imaging Spectroradiometer) observations for the period 2001-2018. As expected, the warming of the Arctic is most conspicuous, followed by the Third Pole, and is weakest in Antarctica. Compared to the temperature changes for the global land region, positive polar amplification appears in the Arctic and the Third Pole on an annual scale, whereas Antarctic amplification disappears, with a negative amplification index of -0.72. The polar amplification for the Earth's three poles shows seasonal differences. Strong Arctic amplification appears in boreal spring and winter, with a surface warming rate of more than 3.40 times the global mean for land regions. In contrast, the amplification of the Third Pole is most conspicuous in boreal summer. The two poles located in the Northern Hemisphere have the weakest amplification in boreal autumn. Differently from the positive amplification for the Arctic and the Third Pole in all seasons, the faster variations in Antarctic temperature compared to the globe only appear in austral autumn and winter, and the amplification signal is negative in these seasons, with an amplification index of -1.68 and -2.73, respectively. In the austral winter, the strong negative amplification concentrates on West Antarctica and the coast of East Antarctica, with an absolute value of amplification index higher than 5 in general. Generally, the polar amplification is strongest in the Arctic except from June to August, and Antarctic amplification is the weakest among the Earth's three poles. The Earth's three poles are experiencing drastic changes, and the potential influence of climate change should receive attention.

This study assesses the physical and optical properties and estimated the radiative forcing of aerosol at Agra over the Indo-Gangetic Basin (IGB) during July 2016-December 2019 using black carbon (BC) mass concentration (AE-33 aethalometer), data sets from satellite and model simulations. The optical properties of aerosol and radiative forcing have been measured by the Optical and Physical Properties of Aerosols and Clouds (OPAC) and Santa Barbara Discrete Ordinate Radiative Transfer Atmospheric Radiative Transfer (SBDART) model. The high BC mass concentration has been observed in November and lowest in August. An adverse meteorological condition due to a combination of temperature and low wind speed results in poor dispersion in the wintertime is a common factor for high concentration level pollutants over Agra. The diurnal and temporal cycle of BC mass concentration exhibits a high concentration at nighttime due to the lower atmospheric boundary layer. The seasonal variation of absorption coefficient (& beta;abs) and Absorption Angstrom Exponent (AAE) is found to be higher during post-monsoon and lowest in monsoon season. This suggests that black carbon concentration over Agra is mainly generated from crop burning, waste burning, automobile exhaust and long-range transport from Punjab and Haryana as the present site is downwind. OPAC-derived aerosol optical depth (AOD), single-scattering albedo (SSA), Angstrom Exponent (AE) and asymmetry parameter (AsyP) were estimated to be 0.57 & PLUSMN; 0.07, 0.78 & PLUSMN; 0.16, 0.99 & PLUSMN; 0.21 and 0.81 & PLUSMN; 0.15, respectively. AOD and AE from the OPAC and the moderate resolution imaging spectroradiometer (MODIS) have shown the consistent relationship. The mean radiative forcing is 18.3 & PLUSMN; 2.1 W m-2 at the top of the atmosphere while, at the surface, net radiative forcing is -42.4 & PLUSMN; 7.2 and 59.1 & PLUSMN; 6.5 W m-2 at the atmosphere during the study period. Vertical profiles were estimated using the observations from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the change in heating rate from the SBDART model over Agra. First-time short-lived climate forcer black carbon mass concentration along with optical properties of aerosols has been reported, and quantification of radiative forcing has been done at the Agra region.The radiative forcing due to black carbon has been found to be high highlighting the heat risk over this region.image

植被物候是指示植被对自然环境变化响应的重要指标。大兴安岭多年冻土区是我国唯一的高纬度多年冻土区，该区植被物候的研究有助于认识寒区生态系统对全球气候变化的响应。本文首先比较了归一化植被指数（NDVI）、增强型植被指数（EVI）和日光诱导叶绿素荧光（SIF）在多年冻土区物候研究中的差异和适应性，结果表明EVI的应用效果最佳。其次，结合2000—2019年MODIS EVI时间序列数据和气象数据，采用Savitzky-Golay（S-G）滤波和动态阈值等方法提取植被生长季开始（SOS）、结束（EOS）和长度（LOS）等关键物候参数，分析大兴安岭多年冻土区植被物候的时空变化及其对气候变化的响应。结果表明：（1）NDVI、EVI和SIF的时间序列均能反映研究区植被生长的季节变化，与NDVI相比，EVI与SIF的变化曲线更加一致。（2）研究区2000—2019年SOS变化范围为年序日96～144 d，平均值为129.46 d。EOS变化范围为272～320 d，平均值为295 d。LOS集中分布在128～224 d，平均值为165.65 d。由于植被类型差异和逆温现象的存在，大片连续多年冻土区的L...

The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index eta with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index eta. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003-2007, 2008-2012, 2013-2017, and 2018-2021, and the permafrost area is, respectively, 28.35 x 10(4) km(2), 35.14 x 10(4) km(2), 28.96 x 10(4) km(2), and 25.21 x 10(4) km(2). The proportion of extremely stable permafrost (< -5.0 degrees C), stable permafrost (-3.0 similar to -5.0 degrees C), sub-stable permafrost (-1.5 similar to -3.0 degrees C), transitional permafrost (-0.5 similar to -1.5 degrees C), and unstable permafrost (0 similar to -0.5 degrees C) is 0.50-1.27%, 6.77-12.45%, 29.08-33.94%, 34.52-39.50%, and 19.87-26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of +/- 1 degrees C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 mu m, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 mu m in northeast China, 2001-2019. The mean value was 441.78 mu m, with an annual increase of 0.26 mu m/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 mu m, followed by January and February with 450.77 mu m and 417.78 mu m, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 mu m to 497.141 mu m. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow-ice albedo and their radiative forcing effect.