Ecohydrology最新文献

Plant surface water storage (PWS) greatly affects canopy rainfall interception and thus has a significant impact on water availability for plants under drought conditions. However, it remains unclear whether the responses of PWS characteristics of dioecious plants exhibit sexual dimorphism under drought stress and how these responses relate to plants' drought resistance. To this end, the impact of drought on the PWS characteristics, related leaf traits and biomass accumulation of Spinacia oleracea males and females was investigated. Under normal water conditions, PWS per unit total dry weight and per unit leaf area of females were 52.8% and 42.1% lower than those of males, respectively, indicating a potentially higher PWS efficiency of males. Under drought conditions, PWS per unit total dry weight and per unit leaf area in males decreased significantly by 24.9% and 62.7%, respectively, while those in females remained unchanged, implying greater sensitivity of males' PWS efficiency to drought. The PWS capacities of the stem, leaves and entire plant were similar between males and females under normal water conditions, but the males exhibited a greater reduction under drought. Female plants showed a 20.4% lower reduction in leaf dry weight and a stronger increase in leaf density under drought than males, reflecting stronger drought resistance in females. Analysis based on leaf hydraulic characteristics suggested that both sexes might enhance their foliar water uptake, as indicated by larger contact area of water drop on leaf surface due to improved leaf wettability (lower leaf contact angles) and better structural basis for foliar water absorption due to higher leaf succulence indexes under drought. However, females might benefit more from these adaptations due to their undiminished PWS efficiency. These results suggested that deriving greater benefits from foliar water uptake through maintaining stable rather than decreased PWS efficiency may be one of the reasons why female S. oleracea plants exhibit stronger drought resistance than the males.

These days, Indian rivers are under tremendous stresses as increasingly larger quantities of water are withdrawn to meet ever-increasing demands caused by population and industrial growth, flow regulation and fragmentation, land-use changes, sediment mining and changing climate. One way to keep rivers healthy is through environmental flows (Eflows), whereby time- and space-varying quantities of water are released into the river to maintain its ecosystem. This paper discusses the Eflows concept and estimation methods. Selected case studies from India have been described in which the methods used include hydrological, hydrology + hydraulics and Building Block Method. A detailed analysis of Eflows assessment for the Ganga and Yamuna rivers has been presented keeping in view the practical aspects. I suggest that Eflows assessments should consider physical processes such as river–aquifer interaction, particularly in the case of alluvial rivers. Water quality aspects may also be considered if water quality degradation is due to changes in flow regimes. Keeping in view the large size of India, variability and the number of rivers, there is a need to determine Eflows requirements for different river reaches in the country. The paper has also discussed barriers to Eflows assessment and implementation, technical and policy-related challenges and follow-up actions for wider Eflows implementation. For successful Eflows implementation, the general public needs to be associated with environmental preservation.

Freshwater ponds are prevalent globally and provide critical ecosystem functions (e.g., water storage and groundwater recharge), yet little is known of their hydrological features immediately following formation. We analysed stable isotopes of water (δ²H, δ¹⁸O) to characterize spatio-temporal hydrologic variation in ponds created by the Mount St. Helens eruption. We also examined how climate and landscape features interact to regulate local hydrology. Ponds were sampled for isotopic analysis in spring (2015, 2017, 2018) and summer (2018). Mass balance models characterized the water balance of ponds (evaporation:inflow; E:I), as well as water sources (rain, snow). Other variables were measured in situ (temperature, conductance), collected from data sources (meteorology) or quantified with remote sensing (vegetation). Bayesian estimates of standard ellipse areas (SEA_B) were used to compare isotopic values among years, whereas linear models were used to examine local and regional drivers of E:I, as well as intra-annual isotopic shifts. We observed high interannual variability (as SEA_B), suggesting that snow was the main water source in wet years but that the proportion of rain and snow varied among sites in dry years. Spring E:I was negatively correlated with total precipitation, whereas the importance of evaporation in summer varied with pond morphology, with large shallow ponds exhibiting the greatest evaporation. Evaporation regulated the hydrology of ponds with higher dissolved organic carbon (DOC; as residuals of DOC and chlorophyll). We show that simple metrics of basin morphometry can predict seasonal variability in pond hydrology, allowing managers to better estimate pond sensitivity to future climate conditions.

Algae-based diets offer a sustainable approach to enhancing mussel aquaculture, with potential benefits for ecohydrological systems through improved biofiltration and nutrient cycling. This study evaluates the effects of three algal feed types—powdered Spirulina platensis, freeze-dried Ulva rigida and frozen Nannochloropsis sp.—on the growth performance and fatty acid composition of Mediterranean mussels (Mytilus galloprovincialis Lamarck 1819) across three size classes (small, medium, large) under controlled conditions. A total of 810 mussels were fed for 28 days, and biometric and lipid parameters were measured. Mussels fed with Spirulina exhibited the highest growth rates and docosahexaenoic acid (DHA) levels, with small individuals increasing by 14.7% in shell length and 25% in body weight. Nannochloropsis led to significant enrichment of eicosapentaenoic acid (EPA), particularly in medium mussels, while Ulva resulted in lower growth and higher saturated and omega-6 fatty acids. The results demonstrate clear size-dependent responses to algal diets and emphasize the nutritional and ecological advantages of Spirulina and Nannochloropsis. It is well established that mussels respond to algal diets in terms of growth and lipid composition, and the present findings confirm these diet-dependent differences. These algae-based feeds not only improve mussel quality but also support the development of sustainable aquaculture systems by enhancing filter-feeding efficiency and reducing the environmental footprint of bivalve farming. The study provides evidence that algae-integrated diets can play a key role in sustainable water quality management and the design of multifunctional aquaculture systems aligned with ecosystem-based approaches. These findings not only enhance mussel quality but also contribute to water quality regulation through improved particle clearance and filtration, reinforcing the role of algae-based diets in the design of ecohydrological aquaculture systems that integrate biological processes with hydrological functions. These improvements in growth and lipid profile also support enhanced filtration activity and nutrient assimilation, indicating that algae-based diets may strengthen the ecohydrological functionality of mussels in integrated aquaculture systems.

The plant communities of Poyang Lake, constituting the foundational element of the wetland ecosystem, are integral to crucial ecological processes including energy flow, biodiversity sustenance, water purification and hydrological regulation. Consequently, they serve an irreplaceable function in preserving the stability and ecosystem services of the region. This study uses Landsat 8, Sentinel-2 optical images and Sentinel-1 SAR images as data sources to extract spectral reflectance, index features and texture features from optical images as well as radar backscattering features from SAR images, constructing a multidimensional feature dataset. The Recursive Feature Elimination algorithm is employed to perform feature optimization on the dataset. Three classification schemes with different feature combinations are designed, and based on the random forest classifier, the impacts of multisource data fusion and feature optimization on the accuracy of plant community identification are investigated. The results demonstrate that the feature optimization-based classification scheme attains the highest accuracy, reaching 93.42% overall accuracy with a Kappa coefficient of 0.93. Meanwhile, the optical-SAR data fusion scheme shows significantly superior performance compared with the optical-only scheme, delivering a 13.04% enhancement in overall classification accuracy. This study provides a scientific reference for remote sensing classification of wetland plant communities and supports biodiversity conservation and ecological management in the Poyang Lake wetland.

Hydropower development significantly impacts the fragile fish habitats in river reaches of the Tibetan Plateau. To support the conservation of fish resources in these reaches, this study developed a physical habitat evaluation model for spawning grounds based on the ecological requirements of key fish species. An artificial neural network (ANN) model was employed to fit the response relationships between spawning ground indicators and environmental factors. Results indicate that water temperature is a critical factor influencing spawning grounds. In natural river reaches, suitable spawning periods occur primarily in the afternoon. In contrast, water temperature in dam-downstream reaches is significantly affected by hydropower operations, leading to distinct differences in spawning rhythms compared to natural reaches. The Weighted Usable Area (WUA) and Patch Number (PN) of spawning grounds initially increase and then decrease with rising flow. The ANN model effectively fits the response relationships between environmental factors and WUA and PN (R² > 0.87). Water temperature exhibits a stronger influence, while flow primarily affects WUA and PN by altering suitable substrate area. This study presents the development and application of physical and ANN models for fish spawning grounds in hydropower-affected river reaches of the Tibetan Plateau. The findings reveal the distribution patterns of spawning grounds and identify key environmental factors. These results provide methodological references and scientific evidence for the evaluation and conservation of fish resources, supporting the sustainable management of native fish populations in plateau rivers.

Snowmelt from montane forests is a critical water resource in the western United States. Forest managers use treatments like selective thinning to encourage resilient ecosystems for wildfire mitigation and wildlife habitat. There is also interest in managing forests to optimize snowpack retention to improve water resources in a changing climate, but detailed studies and management recommendations are limited. We explore the controls on snowpack accumulation using a newly developed light detection and ranging (lidar) point-cloud filtering method and a local open-reference area approach using data collected over gradients in forest structure across multiple snow seasons in the Sagehen Creek Basin (SCB) in the central Sierra Nevada, California, USA. Consistent with previous studies with much more limited snow and vegetation measurements, we show there is ~25% more snow accumulation in open areas relative to forested areas. Random forest (RF) outputs indicate that forest structure metrics explain a greater amount of accumulation variance than terrain metrics, and the greatest potential to increase snow accumulation via thinning occurs when the fraction of vegetation (fVEG) is > 30%. Our results suggest that considering both coarse (e.g., fVEG) and fine-scale (e.g., the arrangement of canopy) canopy information is integral to predict snowpack response to canopy disturbance at many relevant management scales (i.e., 100 m² to 100 km²). The corresponding simple decision support tool, developed with data from SCB, can assess the utility of completed and planned forest restoration over a larger spatial extent to strategically target areas with the highest potential snowpack response. Our new lidar processing methods are easily transferrable to other areas where they could improve snowpack management from forest restoration.