Ecohydrology最新文献

Chromophoric dissolved organic matter (CDOM) in aquatic environments is an important component of the biogeochemical cycle and carbon cycle. The aim of this study is to investigate the long-term changes in CDOM in shallow and eutrophic Chaohu Lake, as well as its relationship with climate, environment and social factors. Using long time series Landsat image data and machine learning technology, the spatiotemporal evolution of Chaohu CDOM since 1987 was reconstructed. A total of 180 samples were collected, which were divided into three parts based on regional and hydrological characteristics. The results show that the water quality in different regions were significantly different, and TN may be the key factor driving the change of CDOM in Chaohu Lake. Machine learning algorithms including random forest (RF), support vector regression (SVR), neural network (NN), multimodal deep learning (MDL) model and Extreme Gradient Boosting (XGBoost) were used, among which XGBoost model performed best (R² = 0.955, mean absolute error [MAE] = 0.024 mg/L, root mean square error [RMSE] = 0.036 mg/L, bias = 1.005) and was used for CDOM spatiotemporal variation retrieval. The change of CDOM was seasonal, highest in August (0.67 m⁻¹) and lowest in December (0.48 m⁻¹), and the western lake is the main source of CDOM. Annual variability of the CDOM indicates that it began to decline after the completion of water pollution control in 2000. Temperature changes were closely related to CDOM (P < 0.01) and agricultural non-point source pollution plays an important role in Chaohu Lake. This study will provide feasible methods and scientific basis for the long-term remote sensing supervision of CDOM.

Vegetation plays an essential role in the atmospheric and hydrological processes, and vegetation responds differently to climate change in various regions, especially in extreme climates. Therefore, the use of static prescribed vegetation information from past years in numerical models can be a source of biases in hydrological simulations. However, previous studies have mainly focused on the effects of vegetation dynamics on hydrological processes in arid and semi-arid regions. It remains unclear how static or dynamic vegetation affects hydrological simulations in humid regions, especially under drought conditions. In this study, the Weather Research and Forecasting (WRF) model coupled with Noah-MP was used to assess the impact of vegetation dynamics on hydrological simulations in the East River basin (ERb) of China, which is a major water source for several major cities in the Pearl River Delta. The model was run with prescribed and dynamic vegetation conditions, respectively. Our model validation based on observed 2-m temperature (T2) and Leaf Area Index (LAI) showed that the model performance was improved when vegetation dynamics were considered. Our simulations with static or dynamic vegetation showed the impacts of vegetation dynamics on hydrological simulations under droughts. The model with vegetation dynamics simulated a wetter condition with higher soil moisture and runoff and lower T2, compared with the simulations of static vegetation. The results suggested that ignoring vegetation dynamics may overestimate the severity of drought in this humid basin, unlike arid and semi-arid regions. Therefore, consideration of vegetation dynamics in this humid basin will deepen our research on different types of zones and serve as a reference for other humid regions.

The ecological restoration (ER) project significantly affects the water retention function in the Taihang Mountain area. However, a comprehensive understanding of the water retention effects in different ER project areas still needs to be improved. In this study, we employed the integrated valuation of ecosystem services and trade-offs (InVEST) model to evaluate the differences in water retention among different ER project areas. Additionally, we used the structural equation model to explore the influence of various factors on water retention. The results showed the following: (1) The total amount of water retention in the Taihang Mountain area increased yearly from 2000 to 2020, with an 85.25% increase in 21 years. The water retention function showed a trend of transferring to a higher level. (2) The forest land restoration project showed the highest average water retention capacity, followed by the grassland restoration project, which together provided 61.12% of the water retention capacity in the ER areas. Forest land restoration project was found to have the most potential in improving water retention, while grassland restoration was more efficient. The water retention capacity of a 21-year-old artificial forest could only reach 70.92% of the natural forest. Cropland restoration mode increased the water retention by 22.85% compared with non-ecological engineering areas. (3) The enhancement of water retention function in the study area resulted from multiple factors, among which precipitation and root depth were the most critical variables. (4) According to the structural equation model, the impact of natural factors on water retention accounted for 74.33%, and ecological engineering had a greater impact on water retention in the hilly zone. The ER project significantly increased water retention capacity. The results provide scientific support for improving water retention function and optimizing ER projects in semi-arid areas of China.

Changes in water and nitrogen availability can affect the structure and function of arid ecosystems. How these resources affect aboveground primary productivity (ANPP) remains far from clear. We examined the N and water limitation of ANPP from the species to the community level and the response of ANPP to annual precipitation in a Patagonian steppe. We conducted a 7-year field experiment with water addition (+W), nitrogen addition (+N) and +NW. Destructive methods for grasses and allometric relationships for shrubs were used to assess ANPP and vegetation indices (NDVI and MSAVI2) to estimate community ANPP. An increase in ANPP of one grass species (Papposstipa humilis) and a decrease of the grass Poa ligularis under +N were observed. Some shrub species exhibited mortality under nitrogen addition. Nitrogen exerted a positive effect on grass ANPP and amplified the sensitivity of grass ANPP to annual precipitation. However, +N had not effects on the shrub ANPP and shrub ANPP-precipitation relationship. Water addition by itself had no effect on ANPP for either shrubs or grasses. However, shrubs responded positively to an unusually wet year regardless of treatment and were also more sensitive to changes in annual precipitation than grasses. Total ANPP increased significantly in +N relative to the C and +W but without changes in the sensitivity to annual precipitation. The results suggest that the responses of grasses and shrubs to water inputs are driven by soil moisture redistribution and rooting depth and that grass and community ANPP are more limited by N than by water.

Actual evapotranspiration constitutes a vital component of the exchange of energy and water vapour between the soil-vegetation and atmospheric systems on terrestrial terrain. Nevertheless, the Tibetan Plateau, owing to its austere environmental conditions, harbours a scarcity of terrestrial monitoring stations. This circumstance presents a formidable challenge in attaining precise estimations of actual evapotranspiration. The complementary relationship method is a potential approach because it requires only routine meteorological data to estimate actual evapotranspiration on a regional or global scale. However, the suitability of the complementary relationship model across diverse ecosystems on the Tibetan Plateau necessitates further investigation. In this study, we scrutinized the simulation of daily and monthly actual evapotranspiration across 18 observation sites spanning eight distinct land use categories on the Tibetan Plateau. We employed the polynomial generalized complementary function introduced by Brutsaert (B2015), alongside its enhanced rendition proposed by Szilagyi (S2017) and Crago (C2018). The outcomes reveal that all three models adeptly replicate the fluctuations in actual evapotranspiration, irrespective of land use category or temporal scale—whether daily or monthly. This is true regardless of whether original or calibrated parameter values are applied. However, there exist significant variations in the performance of these models. In general, the C2018 model demonstrates superior performance across most ecosystems when original parameters are employed. Following parameter calibration, the simulation efficacy of the models experienced marked enhancement. Post parameter calibration, the B2015 model outperforms the other two models notably in desert and wetland environments. Furthermore, the simulation outputs from all three models display heightened sensitivity to parameter α, particularly in the context of the C2018 and S2017 models. These findings suggest that accurate estimation of parameter values is critical to improving the accuracy of estimating actual evapotranspiration. Calibrated parameter values, contingent on a fusion of vegetation, meteorology and surface roughness, exhibit variability across diverse ecosystems.

Hypoxic zones and associated nitrate pollution from farms, cities and industrial facilities is driving declines in water quality that affect ecosystems, economies and human health in major rivers and coastal areas worldwide. In the Mississippi River, the United States Environmental Protection Agency set a goal of reducing nitrogen loading 20% by 2025, but estimating progress towards this goal is difficult because data from in-stream gauges and laboratory samples are too sparse. Satellites have the potential to provide sufficient data across the Mississippi River, if a key methodological challenge can be overcome. Satellites provide data from visible light, but nitrates are only observable with ultraviolet light. We address this methodological challenge by using a two-step surrogate modelling procedure to link optical data and nitrates in the Lower Mississippi River. First, we correlate in situ nitrate measurements to common water quality parameters, particularly turbidity and chlorophyll, using data from water sensors installed at Baton Rouge, Louisiana, USA, and a long-term dataset from Louisiana State University. Second, we correlate these water quality data to satellite estimates of water quality parameters. We found a correlation between these water quality parameters and nitrate concentrations, as indicated by a coefficient of determination, when the relationship was viewed in nonlinear parameter space. The spatial extent of the correlation was tested with an upstream nitrate sensor 140 km north of the estimation location. These results provide proof of concept that we can develop models that use satellite data to provide large-scale monitoring of nitrates across the Mississippi River Basin and other impaired rivers, globally.

To gain a deeper understanding of the water balances of Alpine grassland ecosystems, it is crucial to know the abiotic and biotic drivers of evapotranspiration. The abiotic drivers are very heterogeneous in mountain regions because elevation, slope and aspect control incoming R_s, and atmospheric layering affect air temperature, humidity and wind distribution. In a study with 24 lysimeter plots distributed over a study area of approx. 300 km² in the Eastern Alps, we covered a wide range of topographic conditions. We investigated the effects of abiotic drivers on evapotranspiration by measuring evaporation from a free-water body (E_w). For the biological modulation of crop evapotranspiration (ET_c), we analysed two different grassland types (at peak biomass and at low biomass) and calculated the respective crop coefficients (K_c). Results showed that primarily physical drivers such as the accumulated solar radiation from sunrise to measurement (R_s_acc), followed by atmospheric pressure (P), wind speed (u) and vapour pressure deficit (VPD) influence both E_w and ET_c. Moreover, ET_c is also significantly influenced by standing biomass and the grassland type (i.e., resource use strategies of the vegetation types) and by the geographic location along the valley (i.e., entrance, middle and head of the valley). We suppose plant stress and/or ground winds to be the underlying factor for the significance of the geographic location, yet further research is needed. The current study helps towards a better understanding of the water balance in alpine grassland ecosystems, but we also show that some spatial drivers cannot yet be adequately addressed.

Because of the confluence of human activities and climate change, the hydrological regime in the Han River basin has substantially evolved, necessitating a multi-faceted, quantitative analysis of the causative factors. Employing cross-wavelet analysis, we examined nonlinear relationships between runoff and meteorological variables. Additionally, we assessed hydrological indicators via the IHA index and RVA, then quantified the drivers of runoff variations across different time scales using the Budyko hypothesis and a Generalized Regression Neural Network (GRNN) model. The findings reveal the presence of sustained resonance periods within the climate-runoff system, notably concentrated in 9- to 15-month intervals during the years 1985–1994, 1995–2012, and 2014–2018, with a confidence level of 95%. Overall, the basin exhibited moderate change (41.66%), with 15 indicators displaying varying degrees of moderate to high transformation. These shifts underscore significant ecosystem transformations. The influence of driving factors on runoff varies across temporal scales. On an annual scale, human activities predominantly shape runoff changes (52.35%), while meteorological factors contribute significantly (47.65%). Conversely, at the monthly scale, climate change emerges as the dominant influence on runoff patterns in June and September, with human activities maintaining a principal role in other months, notably exceeding 90% even in November.

Temporarily consented tracks made from high-density polyethylene (HDPE) mesh have been used to mitigate both the physical and ecological impacts on peatlands from low-frequency vehicle usage. However, the impacts of mesh track removal or abandonment at the end of the consented period remain poorly understood. Over a 2-year period, we studied replicate sections of abandoned mesh track which, at the start of the experiment, had been unused for approximately 5 years, on a UK blanket bog. Some sections were removed (using two treatment methods – vegetation mown and unprepared), whereas others were left in situ. Metrics were compared both between treatments and to undisturbed reference areas. Significant differences in surface soil moisture were found between abandoned and removed tracks depending on season. Control areas had higher volumetric soil moisture than track locations. Compaction was significantly higher across all track locations in comparison to controls (p < 0.001), but rarefaction was not recorded post-removal, suggesting long-term deformation. Overland flow events were recorded in rut sections for a mean of 16% of the time, compared to <1% in control areas. Sediment traps on the tracks collected 0.406 kg compared to 0.0048 kg from the control traps, equating to a per trap value of 7.3 g from track samplers and 0.17 g from control samplers. Erosion and desiccation features occurred on both removed and abandoned track sections. Both abandonment and removal of mesh tracks have a wide range of impacts on the physical properties of peatlands, suggesting that only where access is a necessity should such a track be installed.

In recent years, landscape fragmentation has become increasingly serious due to the impact of human activities. The ecological network can ensure the ecological function of the region through linearly connecting ecological corridors and effectively solving the problem of landscape fragmentation that occurs in the evolution of landscape patterns. Based on MSPA and MCR models, the article analyses the distribution and changes of landscape types in the Haihe River basin and constructs an ecological network in the study area by combining the local ecological characteristics of the basin. The network structure is optimized in the end by selecting three large stepping stones, constructing 69 internal ecological stepping stones, identifying 212 fracture points, and adding seven ecological corridors to build a point–line surface network structure with an internal and external double-loop structure. The connectivity of the network structure is evaluated by using complex network method. It is calculated that the network connectivity performance is improved by 13.95% after optimization, which means the species exchange in the study area is closer.