Investigating the regional ecological environment stability and its feedback effect on interference using a novel vegetation resilience assessment model.

Vegetation resilience is critical for understanding the dynamic feedback effect of regional ecological environment stability against interferences. Thus, based on quantify the interferences of climate dryness and vegetation water deficit affecting vegetation growth function, incorporate mechanical Hooke's law to develop a vegetation resilience assessment model by quantitatively expressing vegetation growth function maintenance ability, to reveal the ecological environment stability and its feedback effect on interferences in the study area. The essential discoveries of the study are as follows: (1) with the increase of precipitation and the improvement of afforestation on soil erosion, the interferences intensity of climate dryness and vegetation water deficit in the ecological environment decreased by 5.88 % and 4.92 % respectively, the regional vegetation growth function loss was improved, especially in the southern region; (2) the decrease of vegetation growth function loss promoted the vegetation resilience level fluctuated from class II to class IV, with the average annual vegetation resilience increased by 7.02 %, reflecting that the regional ecological environment stability increased from difficult to rapid recovery after disturbance, and the benefit was especially noticeable in the eastern and southern forested areas; (3) the contribution rates of climate dryness and vegetation water deficit to the variation of vegetation resilience caused by vegetation restoration were -1.38 % and 4.73 %, respectively, and the prominent positive feedback effect of increasing vegetation resilience with decreasing vegetation water deficit degree in forest restoration area, indicating that the vegetation water deficit greatly impacts ecological environment stability in the study area, and forest restoration constantly improves regional ecological environment stability more than grassland restoration. This research has crucial guiding implications for supporting the sustainable development of regional ecological environments.

Biogenic elements-informed assessment of the impact of human activities on river ecosystems.

River ecosystems, acting as pivotal conduits linking terrestrial, marine, and atmospheric realms, have faced significant disturbances due to human exploitation of their resources. Recent years have witnessed a heightened intensification of human activities, adversely affecting the equilibrium of water ecosystems. To systematically study the various factors that affect river ecosystems under human activities, we introduce a universally applicable approach that considers the diversity of watersheds, biogenic elements, and human activities. Using this method, this application uncovers the sensitive human activity types, biogenic factors, and species significantly influencing river biodiversity within the study area. Incorporating statistical modelling, sensitivity screening, and advanced correlation analyses within a random forest regression framework, Sensitive biogenic elements and biological types affected by human activities were identified in typical watersheds, and the stability of different aquatic ecosystems was evaluated. Suggestions for watershed management measures were proposed When human activities affect the degree of water resource development and utilization, the forms of sensitive biogenic elements include DIC and Tsi; When human activities affect the discharge of pollutants into rivers, the forms of sensitive biogenic elements include TP, PP, and DEP, and the ratio composition includes TC: TN, TC: TP, TP: TSi, and TN: TP, This study pioneers a novel method for assessing human impacts on river ecosystems and successfully applies this approach to inform management decisions for river segments and tributaries in the middle and upper reaches of the Yangtze River basin. thereby enhancing our understanding of the consequences of human-induced impacts on biodiversity.

Identification of pollution source and prediction of water quality based on deep learning techniques.

Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.

Remote sensing retrieval and driving analysis of phytoplankton density in the large storage freshwater lake: A study based on random forest and Landsat-8 OLI.

Remote sensing monitoring of seasonal changes in phytoplankton density and analyses of the driving factors of phytoplankton densities are necessary for assessing the health of aquatic ecosystems, controlling lake eutrophication, and formulating ecological restoration policies. Building upon the satellite-ground synchronization experiment that involves the in situ aquatic ecological monitoring conducted in Nansi Lake, which is the largest storage lake situated along the eastern route of the South-to-North Water Diversion Project, we developed a phytoplankton density retrieval model utilizing the random forest (RF) method and Landsat-8 OLI data. On this basis, we mapped the seasonal fluctuations and spatial disparities in the phytoplankton densities from 2013 to 2023. Subsequently, we conducted a detailed analysis of the driving factors and considered both the natural and anthropogenic aspects. The results indicate that (1) the RF model, when utilizing three band combinations, yielded favorable results with R2, RMSE and MAE values of 0.67, 1.31 × 106 cells/L and 1.18 × 106 cells/L, respectively. (2) The phytoplankton densities exhibited both seasonal and spatial variations, with higher concentrations in summer and autumn than in spring and winter. Significantly, the northwestern region of Zhaoyang Lake and the southeastern region of Weishan Lake had substantially greater phytoplankton densities than did the other areas. Furthermore, overarching upward trends were observed from 2013 to 2023, reflecting an annual rate of increase of 3.32%. (3) An analysis of the causal factors indicated that temperatures and gross agricultural production levels are the primary drivers influencing the seasonal variations and distributions of phytoplankton densities. In the future, we will delve into the potential of deep learning and utilize various satellite sensors to explore the intricacies of phytoplankton monitoring, as well as the complex mechanisms that influence aquatic ecological health.

Study on remote sensing inversion and temporal-spatial variation of Hulun lake water quality based on machine learning.

Hulun Lake is facing significant water quality degradation, necessitating effective monitoring for safety. Traditional methods lack the necessary spatial and temporal coverage, underscoring the need for a remote sensing model. In this study, we utilized the Landsat 8 OLI dataset, incorporating cross-section monitoring and field sampling data comprehensively. Employing the random forest algorithm, we constructed a remote sensing inversion model for six water quality parameters in Hulun Lake: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and dissolved oxygen (DO). The model was applied to the non-freezing period of Hulun Lake from 2016 to 2021, exhibiting commendable performance and generating high-resolution maps. Time series analysis revealed that during the study period, the pollution levels of TN, TP, and COD in Hulun Lake were extremely serious, exceeding the Class V water standard of China's surface water environmental quality standard. Regional analysis indicated lower pollutant concentrations in the central lake area compared to the lake inlet. The inflowing rivers with high pollution adversely impacted Hulun Lake's water quality. To ensure the continued health of Hulun Lake's water quality, it is imperative to monitor lake water quality attentively and implement necessary measures to prevent further deterioration. This study holds crucial importance for shaping and executing ecological protection and restoration strategies for Hulun Lake.

Study on spatio-temporal simulation and prediction of regional deep soil moisture using machine learning.

Deep soil moisture (SM) plays a crucial role in vegetation restoration, particularly in semi-arid areas. However, current SM products have limited access and do not meet the spatio-temporal scale and soil depth requirements in eco-hydrological research. Thus, this study constructs a random forest prediction model for SM at different depths by identifying driving factors and quantifying the correlation effect of vertical SM based on the international SM network dataset. Subsequently, the SMAP product is integrated into the model to expand SM from point scale to regional scale, yielding an SM data product with a suitable scale and continuous time and space. The results indicate that the correlation between precipitation and SM changes into the interaction between adjacent SM layers as the depth increases. The lag time of SM in the shallow surface layer (0-3 cm) to precipitation was 1 day, and there was no delay on the daily scale in the 3-20 cm layers of the three underlying surface types. The response time of 50 cm SM to 20 cm SM was 1-2 days in cropland and grassland and 2 days in forest. Slope, land use type, clay proportion, leaf area index, potential evapotranspiration, and land surface temperature were the key driving factors of SM in the Shandian River region. The random forest model established in this study demonstrated good prediction performance for SM at both site and regional scales. The obtained daily products had higher spatial fineness than CLDAS products and could describe the SM characteristics of different underlying surfaces. This study offers new ideas and technical support for acquiring deep SM data in arid and semi-arid areas of northern China.

A flexible framework for regionalization of base flow for river habit maintenance and its thresholds.

Information on base flow for river habit maintenance (BFRH) and its thresholds is necessary for water resource utilization and protection. BFRH and its thresholds have significant spatial differences; however, it is still unclear how to identify and assess these characteristics. In this study, a technical framework was proposed to clarify the specific procedures and methods for regionalization of BFRH and its thresholds in large-scale areas. The framework includes four parts: construction of controlling factor system, sub-region division, identification of dominant factors, and determination of the thresholds in sub-regions. The framework was then applied to China to analyzed the regionalized characteristics of BFRH and its thresholds from a national perspective. The results illustrate the following: (1) the country is divided into nine sub-regions, and the controlling factors and their action paths to BFRH vary greatly. The elements of climate, vegetation, soil, topography and morphology are satisfactory in explaining the variance of BFRH and its thresholds, as R2 of the partial least squares structural equation modeling is between 0.503 and 0.848. (2) The value of BFRH/MAF (i.e. the proportion of BFRH to mean annual natural flow) differs greatly among sub-regions. The mean value is the largest in the Northwest Region, reaching 20 %, while it is only 1.7 % in the Northeast Cold Region. (3) The dynamic and static thresholds are obtained by using the precipitation and other indices as the explanatory variables in the sub-regions. In general, the more abundant the water resources, the higher may be the threshold. Moreover, attention should be paid to the positive and negative effects of vegetation restoration on this threshold. The case study proves that the framework can guide the determination of BFRH, especially for ungagged rivers. Importantly, the framework is flexible and highly adaptable in different regions.

Changes in vegetation cover and its influencing factors in the inner Mongolia reach of the yellow river basin from 2001 to 2018.

Vegetation cover is one of the primary indicators of changes in ecosystems. China has implemented a few large-scale afforestation programs in the arid and semi-arid areas, including the Inner Mongolia Reach of the Yellow River Basin to prevent and control soil erosion. Although these programs have alleviated the environment problems in the region to a certain extent, the effects of the increasing vegetation greenness on the environments under climate change remain controversial for the argued large water consumption. In this study, the spatio-temporal characteristics of Normalized Difference Vegetation Index (NDVI) in the vegetation coverage area of the study area based on remote sensing data from 2001 to 2018. Meanwhile, using the Extreme Gradient Boosting (XGBoost) method - an excellent algorithm for ensemble learning methods - to forecast vegetation growth in the following ten years. The results indicated that, despite of the spatial heterogeneity, the vegetation NDVI exhibited a significant increase across the study area. Based on the NDVI trend, the area of improved vegetation in this region was much larger than the degraded area from 2001 to 2018, accounting for 85.9% and 8.6% of the total vegetation coverage area, respectively. However, the forecasting result by the Hurst index shows the future growth and carbon sequestration capacity in most areas showed a declining trend. Further, based on the Coupled Model Inter comparison Project - Phase 6 (CMIP6) data, the XGBoost method is used to predict the growth status and carbon sequestration capacity of vegetation in this area under different climate scenarios. The results showed that different climate scenarios had little effect on vegetation growth from 2019 to 2030. Results from this study may provide basis for the protection of ecological environment in the Inner Mongolia Reach of the Yellow River Basin.

Contribution of nonpoint source pollution from baseflow of a typical agriculture-intensive basin in northern China.

Baseflow is an essential component of total surface runoff that is widely considered one of the most influential factors regarding water quality via nonpoint source (NPS) pollution. Previously, many researchers and policy makers have directed their efforts toward surface runoff pollution, largely ignoring nutrient delivery via baseflow. Taking a typical agriculture-intensive basin of northern China as an example, this study explored the spatiotemporal characteristics of baseflow and pollution load in relation to NPS pollution. Baseflow was quantified using digital filtering techniques, and the results together with observed pollution data were used to validate a physically based hydrological model, i.e., the Soil and Water Assessment Tool. Then, the spatial and temporal distribution characteristics of NPS and baseflow pollution were investigated using the modeling results. Results indicated that baseflow contribution to total runoff accounted for more than 70% during the studied years (2016-2018), and 84.15% of the basin area showed non-point source pollution dominated by baseflow pollution; both baseflow and its pollution load were greater in the nonflood seasons (spring, autumn, and winter) than in the flood season (summer); the spatial distribution of baseflow total nitrogen and total phosphorus pollution intensity showed higher values in the east and lower values in the west; the scaling effects of baseflow and its pollution load was found with increasing basin area. The results of our study highlighted the necessity for management of pollution load via baseflow in the river basin and provided reference information for improvement of NPS pollution management in other similar basins.

Spatiotemporal variations in evapotranspiration and its influencing factors in the semiarid Hailar river basin, Northern China.

Evapotranspiration (ET) is a critical variable in the world's water cycle, and plays a significant role in estimating the impact of environmental change on the regional hydrothermal cycle. Moreover, as an essential of eco-hydrological processes, changes in ET may exceptionally impact the local climate and provide indicative information on the eco-system's functioning. The Hailar River Basin (HRB), located in northern China, is one of the most sensitive areas to climate warming. Under the influence of climate change in recent years, the vegetation dynamics of the basin have been significant and have had profound effects on the regional water cycle conditions and hydrological processes. The HRB is located in a semiarid region and ET is the main mode of water consumption. The ET response to climate change and vegetation dynamics is the focus of research on ecohydrological processes in this basin. In this study, a distributed hydrological model, the BTOPMC model, is used to evaluate the actual ET in the HRB from 1981 to 2020, based on in situ meteorological data as well as LAI data obtained by satellite remote sensing. The seasonal, interannual and spatial dynamics of ET were characterized. The contribution of meteorological factors to ET was calculated by sensitivity analysis and multiple linear regression analysis, and the predominant elements influencing the difference in ET in the HRB were also discussed. The results show that: (1) estimated ET values can clarify over 85% of the seasonal variation in the observed values (R2= 0.79, P < 0.001; R2= 0.84, P < 0.001), which demonstrates that the model has a high precision. (2) Over the past 40 years, the annual ET has shown a clear increasing trend and a large spatial heterogeneity in its spatial distribution, which is consistent with the trend of vegetation. It mainly shows that the eastern forest area is larger than the central forest-grass transition area and the western meadow steppe area. (3) Sensitivity and influential factor contribution analyses show that the main factor driving interannual variability in ET is climate warming, followed by precipitation. At the same time, vegetation dynamics also play a crucial role in ET, especially in areas with different vegetation types and high coverage, while climatic factors also have a strong influence on ET indirectly through vegetation. Due to its special geographic location, the HRB is more sensitive to global climate change and is a typical ecologically fragile area. Therefore, this study has important scientific value and social significance for maintaining ecological security and the sustainable use of water resources.

Root-zone soil moisture estimation based on remote sensing data and deep learning.

Soil moisture in the root zone is the most important factor in eco-hydrological processes. Even though soil moisture can be obtained by remote sensing, limited to the top few centimeters (<5 cm). Researchers have attempted to estimate root-zone soil moisture using multiple regression, data assimilation, and data-driven methods. However, correlations between root-zone soil moisture and its related variables, including surface soil moisture, always appear nonlinear, which is difficult to extract and express using typical statistical methods. The artificial intelligence (AI) method, which is advantageous for nonlinear relationship analysis and extraction is applied for root-zone soil moisture estimation, but by only considering its separate temporal or spatial correlations. The convolutional long short-term memory (ConvLSTM) model, known to capture spatiotemporal patterns of large-scale sequential datasets with the advantage of dealing with spatiotemporal sequence-forecasting problem, was used in this study to estimate root-zone soil moisture based on remote sensing-based variables. Owing to limitation of regional soil moisture observation data, the physical model Hydrus-1D was used to generate large and spatiotemporal vertical soil moisture dataset for the ConvLSTM model training and verification. Then, normalized difference vegetation index (NDVI) etc. remote sensing-based factors were selected as predictive variables. Results of the ConvLSTM model showed that the fitting coefficients (R2) of the root-zone soil moisture estimation significantly increased compared to those achieved by Global Land Data Assimilation System products, especially for deep layers. For example, R2 increased from 0.02 to 0.60 at depth of 40 cm. This study suggests that a combination of the physical model and AI is a flexible tool capable of predicting spatiotemporally continuous root-zone soil moisture with good accuracy on a large scale.

Modification and upscaling of S-W model based on vertical distributions of soil moisture and vegetation root biomass.

Soil water is the dominant factor controlling evapotranspiration (ET) in arid and semi-arid regions. However, the widely used ET simulation models, such as the Shuttleworth-Wallace model (S-W model), are insufficient in simulating the direct influence of soil moisture (SM), especially in the root zone. Based on SM and ET field data, we found that the influence of SM on ET increased with soil depth in the grassland. Evaporation in the S-W model was optimized by SM at 0-5 cm as the root mean square error (RMSE) decreased from 1.4 to 0.17, while transpiration was optimized by SM at 10-20 cm as the RMSE decreased from 0.26 to 0.07. The modified S-W model incorporating SM was called the S-W-Ï´ model. To up-scale application and to verify the accuracy of the S-W-Ï´ model under watershed water balance, we replaced the ET simulation module based on the S-W model with our S-W-Ï´ model and the Block-wise TOPMODEL with Muskingum-Cunge routing method (BTOPMC) model that we used as the basis of our simulation. The influence of SM was determined by the proportion of root biomass of different vegetation types at different depths, and each depth interval was assigned a weighting reflecting its degree of influence. The results showed that the S-W-Ï´ model improved accuracy with all the modification schemes tested. The modification scheme determined by the vegetation root distribution pattern had the greatest effect, providing a 4% accuracy improvement. The modified ET and hydrological models have the potential to support water basin management to a greater extent.

Vertical variations of soil water and its controlling factors based on the structural equation model in a semi-arid grassland.

Soil water content (SWC) of a vertical profile plays an important role in the soil-plant-atmosphere continuum system through eco-hydrological process, which was controlled by multiple factors. Previous studies ignored soil water from a systematic perspective because of the lack of suitable methods to deal with interrelated factors. We developed a meta-model based on structural equation model (SEM) to identify the factors contributing to soil water, and the interactions among these factors, in a semi-arid grassland system. The model was based on the hypothesis that soil water is affected by hydrological variables (precipitation: P, evapotranspiration: E and underground water: GW), vegetation (vegetation coverage: VC and above ground biomass: AGB), and soil parameters (soil organic matter: SOM and bulk density: BD). E and AGB decrease soil water content, while VC and SOM help to retain soil water content. The proportion of explained variation in soil water increased with depth due to increasing stability. The most important contributors were AGB (r∂ = -0.15) and VC (r∂ = 0.39), and their contributions were opposite because their mechanisms differed. The accumulation of AGB in the growth season consumed soil water through root uptake. The contribution of AGB increased with depth, inferring that grassland species are xerophytes with deep roots to access soil water during drought. Coverage positively contributed to soil water, but its influence decreased with depth because its main effects (intercepting rainfall and providing shade) were at the surface. This systematic perspective of how hydrological, vegetation, and soil properties affect soil water will be useful to guide the management of semi-arid grasslands.

Non-point source pollution risks in a drinking water protection zone based on remote sensing data embedded within a nutrient budget model.

A simple, transparent and reliable method for evaluating non-point source pollution (NPSP) risks to drinking water source areas lacking observational data is proposed herein. The NPSP risks are assessed by using nutrient budget models for total nitrogen and total phosphorus, making the best use of remote sensing and field survey data. We demonstrate its potential using a case study of the Chaihe Reservoir in northeastern China. Fertilizer inputs and crop-uptake outputs were estimated based on normalized difference vegetation index, which is derived from remote sensing as indicators of crop growth and production. The nutrient balances for this area showed surpluses of both N and P within the soil system. Estimated imbalances per unit area were consistent with statistical relationships derived from all Chinese counties, demonstrating that the proposed method is reliable. The surplus P amounts were higher than the standard threshold for NPSP risks, indicating the existence of a potential contamination risk of P to this drinking water source.