The role of climatic factor timing on grassland net primary productivity in Altay, Xinjiang DOI Creative Commons
Bojian Chen,

Guli Jiapaer,

Yu Tao

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 157, P. 111243 - 111243

Published: Nov. 16, 2023

Grassland, as highly vulnerable ecosystem, requires a comprehensive understanding of its dynamics and response patterns to climate factors in change challenges. While previous research has primarily centered on the influence interannual variability grassland Net Primary Productivity (NPP), knowledge impacts seasonal or monthly variations annual net primary productivity (ANPP) remains limited. This study investigated climatic drivers NPP Xinjiang's Altay region from 2000 2022 using Carnegie-Ames-Stanford approach (CASA) model random forest regression model. The examined significance precipitation, solar radiation, temperature, soil moisture, snowmelt water at three temporal scales. results revealed following key findings: (1) Grassland declined significantly 2009 but showed gradual increase 2022. Spatially, higher values were observed northern lower southern region. (2) Precipitation was influential factor affecting NPP, followed by water. In determining timing ANPP, June played critical role particularly for while August essential radiation. Moreover, importance had bimodal distribution, with peaks April October. (3) exhibited diverse nonlinear spatial heterogeneity various different These findings highlight considering both magnitude local conditions, well when studying dynamic responses predicting future impacts. insights enhance comprehension intricate ecosystems predictions their change.

Language: Английский

Climate Change and Hydrological Extremes DOI
Jinghua Xiong, Yuting Yang

Current Climate Change Reports, Journal Year: 2024, Volume and Issue: 11(1)

Published: Oct. 2, 2024

Language: Английский

Citations

23
Rainfall‐Driven Extreme Snowmelt Will Increase in the Tianshan and Pamir Regions Under Future Climate Projection DOI Creative Commons
Tao Yang, Xi Chen,

Rafiq Hamdi

et al.

Journal of Geophysical Research Atmospheres, Journal Year: 2025, Volume and Issue: 130(1)

Published: Jan. 2, 2025

Abstract Snowmelt and related extreme events can have profound natural societal impacts. However, the studies on projected changes in snow‐related extremes across Tianshan Mountains (TS) Pamir regions been underexplored. Utilizing regional climate model downscaling bias‐corrected CMIP6 data, this study examined snowmelt water available for runoff (SM ROS , rainfall plus snowmelt) during cold seasons these historical (1994–2014) future (2040–2060) periods under shared socioeconomic pathway (SSP) scenarios (SSP245 SSP585). The results demonstrated that accumulated was to rise by 17.98% 20.36%, whereas SM could increase 26.97% 28.95%, respectively, SSP245 SSP585 scenarios. Despite relatively minimal snowmelt, magnitude of daily maximum (10‐year return level) 28.04 mm expected 15.32% 15.31% scenarios, especially western TS exceeding 26%. Meanwhile, areas with a 50 over 13.5%. A notable its area occupation high intensity highlighted an increased risk rainfall‐driven events. absolute snowfall frequent snow‐rain phase transitions season warming (SSP245: 2.19°C SSP585: 2.22°C) benefits high‐intensity rain‐on‐snow events, leading augmentation. findings emphasize significant role rainfall‐trigger exacerbating climate.

Language: Английский

Citations

3
Snowmelt erosion: A review DOI

Zuoli Wu,

Haiyan Fang

Earth-Science Reviews, Journal Year: 2024, Volume and Issue: 250, P. 104704 - 104704

Published: Feb. 1, 2024

Language: Английский

Citations

11
MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022) DOI Creative Commons
Fangbo Pan, Lingmei Jiang, Gongxue Wang

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(5), P. 2501 - 2523

Published: May 29, 2024

Abstract. Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) not sufficient. In this study, multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) multistep spatiotemporal interpolation (MSTI) used to produce MODIS product over AWT region (AWT FSC). The FSC have a spatial resolution of 0.005° span from 2000 2022. 2745 scenes Landsat-8 images areal-scale accuracy assessment. metrics, including coefficient determination (R2), root mean squared error (RMSE) absolute (MAE), 0.80, 0.16 0.10, respectively. binarized identification overall (OA), producer's (PA) user's (UA), 95.17 %, 97.34 % 97.59 Snow depth data observed at 175 meteorological stations evaluate point scale, yielding following metrics: an OA 93.26 PA 84.41 UA 82.14 Cohen kappa (CK) value 0.79. observations also assess resulting different weather conditions, with 95.36 (88.96 %), 87.75 (82.26 86.86 (78.86 %) CK 0.84 (0.72) under clear-sky (spatiotemporal reconstruction MSTI algorithm). can provide quantitative distribution information snowpacks mountain models, land surface models numerical prediction region. This dataset is freely available National Tibetan Plateau Data Center https://doi.org/10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or Zenodo platform https://doi.org/10.5281/zenodo.10005826 2023a).

Language: Английский

Citations

9
Intensified warming suppressed the snowmelt in the Tibetan Plateau DOI Creative Commons
Xiang Li, Peng Cui, Xueqin Zhang

et al.

Advances in Climate Change Research, Journal Year: 2024, Volume and Issue: 15(3), P. 452 - 463

Published: June 1, 2024

Understanding how hydrological factors interrelate is crucial when examining the impact of climate warming on snowmelt. However, these connections are often overlooked, leading to an unclear relationship between temperature and This study investigates complex interplay snowmelt in Tibetan Plateau from 1961 2020, focusing extreme high-temperature events affect frequency Using a structural equation model, we detected three temperature-related that predominantly influenced The annual average was found have significant indirect snowmelt, mediated by changes snowfall, snow depth cover. By contrast, days (daily maximum temperatures exceeding 90th percentile) heat waves (at least consecutive days) negatively affected directly or indirectly. direct effect increasing associated with earlier onset periods, which accelerated shortened duration periods. Additionally, reduction cover owing emerged as main factor suppressing frequencies. We also revealed spatiotemporal variations temperature‒snowmelt highly depended patterns. elucidated why suppresses Plateau, highlighting mediating roles snow-related phenological factors. findings will provide scientific support for simulation water management policymaking alpine regions worldwide.

Language: Английский

Citations

6
Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data DOI Creative Commons

Hongling Zhao,

Hongyan Li, Yunqing Xuan

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(22), P. 5823 - 5823

Published: Nov. 17, 2022

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks its ability of representing the effects and permafrost on generation confluence. However, a core method model, temperature index method, assumes both dates for maximum minimum factors threshold, which leads inaccuracies simulating seasonal regions. In this paper, we present development application an improved (SWAT+) daily area Northeast China. improvements include introduction total radiation modification factor variation formula, changing threshold according snow depth derived from passive microwave remote sensing data area. Further, SWAT+ is applied study climate change impact future (2025–2054) under scenarios including SSP2.6, SSP4.5, SSP8.5. Much simulation obtained as result, supported by several metrics, such MAE, RE, RMSE, R2, NSE calibration validation. Compared with baseline period (1980–2019), March–April ensemble average shown decrease SSP8.5 scenario during 2025–2054. This provides valuable insight into efficient utilization spring water resources areas.

Language: Английский

Citations

25
Quantifying the Trends and Variations in the Frost-Free Period and the Number of Frost Days across China under Climate Change Using ERA5-Land Reanalysis Dataset DOI Creative Commons
Hongyuan Li, Guohua Liu, Chuntan Han

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(10), P. 2400 - 2400

Published: May 17, 2022

Understanding the spatio-temporal variations in frost-free period (FFP) and number of frost days (FD) is beneficial to reduce harmful effects climate change on agricultural production enhancing adaptation. However, FFP FD their response remain unclear across China. To investigate impact FD, trends China from 1950 2020 were quantified using ERA5-Land, a reanalysis dataset with high spatial temporal resolution. The results showed that ERA5-Land has good applicability quantifying under change. distribution multi-year average significant latitudinal zonality altitude dependence, i.e., decreased increasing latitude altitude, while increased altitude. As result warming China, an trend increase rate 1.25 d/10a maximum individual region was 6.2 d/10a, decreasing decrease 1.41 −6.7 d/10a. Among five major zones subtropical monsoon zone (SUMZ) greatest 1.73 FFP, temperate (TEMZ) −1.72 FD. In addition, coefficient variation (Cv) greater variability at higher altitudes, Cv lower latitudes southern Without considering adaptation temperature crops, general both terms promoting longer growing reducing damage crops. This study provides comprehensive understanding change, which great scientific significance for adjustment layout adapt

Language: Английский

Citations

24
Mapping soil organic matter content using Sentinel-2 synthetic images at different time intervals in Northeast China DOI Creative Commons
Chong Luo, Wenqi Zhang, Xinle Zhang

et al.

International Journal of Digital Earth, Journal Year: 2023, Volume and Issue: 16(1), P. 1094 - 1107

Published: March 23, 2023

Mapping soil organic matter (SOM) content has become an important application of digital mapping. In this study, we processed all Sentinel-2 images covering the bare-soil period (March to June) in Northeast China from 2019 2022 and integrated observation results into synthetic materials with four defined time intervals (10, 15, 20, 30 d). Then, used corresponding different periods conduct SOM mapping determine optimal interval before finally assessing impacts adding environmental covariates. The showed following: (1) mapping, highest accuracy was obtained using day-of-year (DOY) 120 140 20 d intervals, as well ranked follows: > 15 10 d; (2) when at predict SOM, best for predicting always within May; (3) covariates effectively improved performance, multiyear average temperature most factor. general, our demonstrated valuable potential imagery, thereby allowing detailed large areas cultivated soil.

Language: Английский

Citations

15
Mapping snow depth distribution from 1980 to 2020 on the tibetan plateau using multi-source remote sensing data and downscaling techniques DOI
Ying Ma, Xiaodong Huang,

Xia-Li Yang

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 205, P. 246 - 262

Published: Oct. 18, 2023

Language: Английский

Citations

15
Simulations of Snowmelt Runoff in a High-Altitude Mountainous Area Based on Big Data and Machine Learning Models: Taking the Xiying River Basin as an Example DOI Creative Commons
Guoyu Wang, Xiaohua Hao, Xiaojun Yao

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(4), P. 1118 - 1118

Published: Feb. 18, 2023

As an essential data-driven model, machine learning can simulate runoff based on meteorological data at the watershed level. It has been widely used in simulation of hydrological runoff. Considering impact snow cover high-altitude mountainous areas, remote sensing and atmospheric reanalysis data, this paper we established a model with random forest ANN (artificial neural network) for Xiying River Basin western Qilian region The verification measured showed that NSE (Nash–Sutcliffe efficiency), RMSE (root mean square error), PBIAS (percent bias) values were 0.701 0.748, 6.228 m3/s 4.554 m3/s, 4.903% 8.329%, respectively. influence ice runoff, accuracy both was improved during period significant decreases annual water equivalent from April to May, after introduced into model. Specifically, increased by 0.099, decreased 0.369 1.689%. For 0.207, 0.700 1.103%. In study, effectively processes areas without observational data. particular, simulations snowmelt (especially period) introducing which provide methodological reference prediction alpine mountains.

Language: Английский

Citations

13