Linking Critical Zone With Watershed Science: The Example of the Heihe River Basin

Li, Xiaoqiong; Cheng, Guodong; Fu, Bojie; Xia, Jun; Zhang, Ling; Yang, Dawen; Chen, Kewei; Liu, Shaomin; Li, Xiubin; Song, Changchun; Kang, Shaozhong; Li, Xiao Yan; Che, Tao; Zheng, Yi; Zhou, Yanzhao; Wang, Haibo; Ran, Youhua

doi:10.1029/2022ef002966

Cited by 14 publications

(5 citation statements)

References 142 publications

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“…A watershed could be an appropriate natural unit. A watershed is the basic unit of the Earth’s terrestrial system, bounded by mountains and rivers, and is a relatively independent material and energy flow unit ( Li et al, 2022 ). Watersheds bound the biogeographic provinces of many plants and animals ( Brasil et al, 2017 ; García-Llamas et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Uncovering the biogeographic pattern of the widespread nematode-trapping fungi Arthrobotrys oligospora: watershed is the key

Deng

Zhang

et al. 2023

Front. Microbiol.

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Studies of biogeographic patterns of fungi have long been behind those of plants and animals. The presence of worldwide species, the lack of systematic sampling design and adequate sampling effort, and the lack of research units are responsible for this status. This study investigates the biogeographical patterns of Arthrobotrys oligospora, the most widespread globally distributed nematode-trapping fungi (NTF), by stratified collecting and analyzing 2,250 samples from 228 sites in Yunnan Province, China. The A. oligospora was isolated, and 149 strains were subjected to ITS, TUB, TEF and RPB2 gene sequencing and multi-gene association phylogeographic analysis. The results show that at population level A. oligospora is randomly distributed throughout Yunnan Province and has no biogeographical distribution pattern. At the genetic level, the phylogenetic tree of A. oligospora diverges into five major evolutionary clades, with a low degree of gene flow between the five clades. However, the correlation between the phylogenetic diversity of A. oligospora and geographical factors was low. There was no clear pattern in the phylogenetic clades distribution of A. oligospora either without dividing the study unit or when the grid was used as the study unit. When watersheds were used as the study unit, 67.4%, 63.3%, 65.9%, 83.3%, and 66.7% of clade 1–5 strains were distributed in the Jinsha river, Red river, Peal river, Lancang river, and Nujiang-Irawaddy river watersheds, respectively. The clades distribution of A. oligospora was highly consistent with the watersheds distribution. Training predictions of the clades distributions using randomly generated polygons were also less accurate than watersheds. These results suggest that watersheds are key to discovering the biogeographic distribution patterns of A. oligospora. The A. oligospora populations are blocked by mountains in the watershed, and gene flow barriers have occurred, which may have resulted in the formation of multiple cryptic species. Watersheds are also ideal for understanding such speciation processes, explaining factors affecting biodiversity distribution and coupling studies of plant and animal and microbial diversity.

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Section: Introductionmentioning

confidence: 99%

Uncovering the biogeographic pattern of the widespread nematode-trapping fungi Arthrobotrys oligospora: watershed is the key

Deng

Zhang

et al. 2023

Front. Microbiol.

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“…There are usually gaps and overlaps at the edges of the separate terrestrial, estuarine, and marine-realm maps, and often no clarity on which specific coastal boundary was used [2]. Watersheds are the basic unit of Earth's terrestrial systems [18]. DEM is one of the important sources in the extraction of watersheds and river networks [26,30].…”

Section: Discussionmentioning

confidence: 99%

“…The land-sea relationship is characterized by the pattern of the pressure on land and the response of the sea [8]. To realize the sustainable development of the coastal zone, the integrated land-sea complex ecosystem of the coastal watershed-nearshore waters should be taken as the management unit to form a land-sea integrated decision-making and governance system [18,19]. Lawrence [20] proposed that a common challenge within coastal management is how to best integrate watershed planning into regional efforts intended to address critical coastal issues, especially those related to water resources and water quality.…”

Section: Introductionmentioning

confidence: 99%

Integrated Zoning and Spatial Heterogeneity of Coastal Watershed-Nearshore Waters

2023

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Integrated land-sea development and protection are important for the sustainable development of coastal areas. To promote the transition from separate land-sea management to integrated land-sea governance, the scientific delineation of the integrated spatial zoning units of coastal watershed-nearshore waters is an important tool for integrated land-sea management. From the perspective of physical geography, this study uses digital hydrological analysis based on DEM data to determine the coastal basin range and generate multilevel watersheds and river networks using different thresholds, and establishes spatial correspondence among physical geospatial spaces, such as watershed zones, coastlines, and nearshore waters, after considering the boundaries of major estuaries and bays. On this basis, the coastal zone is divided into several integrated spatial zoning units of coastal watershed-nearshore waters, and a physical geography-based integrated spatial zoning method is developed to form a physical geography-based land-sea integrated spatial zoning scheme. This study conducted spatial heterogeneity research of the integrated spatial zoning units, from the perspectives of topography analysis, land use analysis, socioeconomic analysis of the watershed, sea use analysis, seawater quality analysis of the nearshore waters, and load pressure analysis of the watershed-nearshore waters. The elemental differences of zoning units are studied in detail, which can provide a data reference for establishing the relationship between watersheds and nearshore waters. Applying this research method to the Guangxi Zhuang Autonomous Region in China, where the land-sea linkage pattern is forming, can provide a spatial reference and scientific basis for land-sea integrated management for ecological protection and sustainable resource utilization in the coastal zone.

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“…Moreover, it is important to note that the dynamics of glacier mass balance (GMB) modelling are intricately linked to climate changes, influencing both the timing and magnitude of transitions in hydrological regimes [6]. Therefore, accurate GMB modeling is imperative in order to assess the impact of climate change on glaciers and provides a vital foundation for elucidating glacier dynamic processes, predicting changes, and effectively managing basin water resources [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia

Ren,

Zhu,

Wang

et al. 2024

Remote Sensing

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Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used for the simulation of annual glacier-wide GMB data, with a specific focus on comparing maritime glaciers in the Niyang River basin and continental glaciers in the Manas River basin. For this purpose, meteorological predictive factors derived from monthly ERA5-Land datasets, and topographical predictive factors obtained from the Randolph Glacier Inventory, along with target GMB data rooted in geodetic mass balance observations, were employed to drive four selective ML models: the random forest model, the gradient boosting decision tree (GBDT) model, the deep neural network model, and the ordinary least-square linear regression model. The results highlighted that ML models generally exhibit superior performance in the simulation of GMB data for continental glaciers compared to maritime ones. Moreover, among the four ML models, the GBDT model was found to consistently exhibit superior performance with coefficient of determination (R2) values of 0.72 and 0.67 and root mean squared error (RMSE) values of 0.21 m w.e. and 0.30 m w.e. for glaciers within Manas and Niyang river basins, respectively. Furthermore, this study reveals that topographical and climatic factors differentially influence GMB simulations in maritime and continental glaciers, providing key insights into glacier dynamics in response to climate change. In summary, ML, particularly the GBDT model, demonstrates significant potential in GMB simulation. Moreover, the application of ML can enhance the accuracy of GMB modeling, providing a promising approach to assess the impacts of climate change on glacier dynamics.

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Linking Critical Zone With Watershed Science: The Example of the Heihe River Basin

Cited by 14 publications

References 142 publications

Uncovering the biogeographic pattern of the widespread nematode-trapping fungi Arthrobotrys oligospora: watershed is the key

Uncovering the biogeographic pattern of the widespread nematode-trapping fungi Arthrobotrys oligospora: watershed is the key

Integrated Zoning and Spatial Heterogeneity of Coastal Watershed-Nearshore Waters

Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia

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