Forest Water Research Articles

Distribution of evapotranspiration components along vertical layers and their controls in dry days of larch plantation in the Liupan Mountains of northwest China

Precise quantification of forest evapotranspiration (ETf) including transpiration from tree (Tt), shrub (Ts), and herb (Th) layers, as well as evaporation from litter (El) and soil (Es) layers, and elucidating their responses to environmental conditions and stand structures are crucial for forest water management in water-limited forests. In this study, we observed the Tt, Ts, Th, El, and Es, reference evapotranspiration (ETo), soil volumetric water content (SWC), litter water content (LWC), leaf area index (LAI) in tree, shrub, and herb layers, and canopy shade (Ksc) from tree layer of the Larix principis-rupprechtii plantation during the dry days from May to October of 2021 and 2022 to elucidate the distribution of evapotranspiration along vertical layers and their environmental and structural determinants. The results indicated that the contributions of Tt, Ts, Th, El, and Es to ETf during the dry days in 2021 (2022) were 42.1 % (44.7 %), 9.2 % (8.1 %), 8.2 % (8.6 %), 15.0 % (13.1 %), and 25.5 % (25.5 %), respectively. Although Tt and Ts demonstrated quadratic relationships with ETo, Th, El, and Es exhibited linear relationships. All ETcs demonstrated a saturated exponential relationship with either SWC or LWC. Furthermore, Tt, Ts, and Th showed a saturated exponential relationship with their respective LAI, whereas El and Es exhibited a cubic relationship with LAI in tree layers. All understory ETcs (Ts, Th, El, and Es) decreased exponentially with the Ksc. The multi-factor models of evapotranspiration component (ETc) from different vertical layers, which coupled the impacts of environmental conditions and vertical structure, were developed, and provided superior accuracy (R2 = 0.78–0.88, NSE = 0.78–0.86, RSME = 0.03–0.16). Such insights deepened the understanding of vertical structural distribution and multi-factor responses of ETcs in forest ecosystems and hold the potential to inform and optimise forest water management strategies.

Advancing Knowledge in Forest Water Use Efficiency Under Global Climate Change Through Scientometric Analysis

Forests are critical in regulation of carbon and water cycles and mitigation of climate change. Forest water-use efficiency (WUE) refers to the ratio of biomass produced (or assimilated carbon) to the amount of water used by forests, which indicates how effectively a forest utilizes water to achieve productivity. Climate change and its impact on forest WUE are important research directions that explore the complex relationship between global environmental change and the forest ecosystem dynamics. The global intensification of climate change underscores the need for an inclusive understanding of forest water use and makes it crucial to know how forests balance carbon and water resources, which is essential for effective forest management and predicting ecosystem responses to climate change. This study aims to comprehensively and objectively analyze current research trends and future directions related to the response of forest WUE to climate change. Our database included 1755 research papers from the Web of Science Core Collection, spanning from 2000 to 2023. Our analysis included cooperative networks of countries, authors, and institutions, as well as the most frequently cited journals and articles, keyword co-occurrence analysis, and a keyword burst analysis. The results showed that the top cooperative country, author, and institution is PR China, Prof. Dr. Jesús Julio Camarero from the Consejo Superior de Investigaciones Científicas (CSIC), and the Chinese Academy of Sciences, respectively. The leading journal in this field is “Global Change Biology”. Critical research hot topics include gas exchange, modeling, altitudinal gradients, tree growth dynamics, net carbon exchange, global change drivers, tropical forests, nitrogen stoichiometry, Northern China plains, and extreme drought conditions. Frontier topics that have emerged in recent years include studies on China’s Loess Plateau, stable isotopes, radial growth, gross primary productivity, and Scots pine. The insights from this analysis are vital for researchers, decision-makers, and forestry professionals aiming to mitigate the impacts of climate change on forest WUE and overall ecosystem health and resilience. This study emphasizes the importance of sustained research efforts and global research collaboration in addressing the intricate challenges posed by climate change to forest ecosystems.

Stand characteristics regulate forest water use efficiency in the Three-North Shelterbelt Forest Program region of China

Abstract The Three-North Shelter Forest Program (TNSFP) of China, one of the largest forestry ecological projects, serves as a nature-based solution to addressing ecological, social, and economic challenges and issues. This program, mainly situated in arid and semi-arid regions, critically focuses on improving water use efficiency (WUE) - maximizing carbon sequestration per unit of water consumed - as a key strategy for optimizing water resource utilization and ensuring the long-term success of vegetation restoration efforts. However, the regulatory mechanisms of forest WUE in this region remain unclear. Here, we adopted an interpretative machine-learning method to investigate the influence of main environmental elements, topographical conditions, and stand characteristics on forest WUE in the TNSFP region from 2001 to 2022 based on remote sensing products, ground monitoring data, and forest inventory data. Our study identified soil moisture (SM) as the primary factor influencing forest WUE across the TNSFP region, with higher SM levels generally leading to improved WUE in forests. However, stand characteristics strongly mediated their relationship. Specifically, forest WUE initially increases against forest density before peaking at about 1000 trees hm–2 for needle-leaved forests and 800 trees hm–2 for broad-leaved forests, respectively, then gradually declining due to water competition. When SM is relatively adequate, moderate thinning could significantly enhance forest WUE. Furthermore, implementing management strategies to improve WUE is crucial as needle-leaved forests mature. This study emphasizes the significant impacts of stand characteristics on forest WUE in the TNSFP region, offering essential insights for optimizing water resource management in managed forests across arid and semi-arid regions.

The Impact of Forest Management Inventory Factors on the Ecological Service Value of Forest Water Conservation Based on Machine Learning Algorithms

Based on forest management inventory data, this study applies machine learning algorithms to explore the relationships between forest water conservation capacity and forest management inventory factors, thus providing more extensive insights into forest water conservation services. By integrating the InVEST model and machine learning algorithms, this study identifies the key factors related to water conservation services based on forest management inventory factors and investigates the differences in and accuracy of forest water conservation models using the random forest algorithm. The results are as follows: (1) The determination coefficients (R2) of the three machine learning models range from 0.508 to 0.869, with root mean square errors (RMSEs) ranging from 28.380 to 69.339. The performance of these models is generally satisfactory, with the random forest algorithm showing superior results. (2) By leveraging the advantages of the three machine learning algorithms in handling categorical data, this study analyzes the contributions of forest management inventory factors, revealing the impact mechanisms of forest-type water conservation services. (3) The integration of machine learning algorithms allows for better processing of the scale and correlation of independent variables, providing more objective information on the main controlling factors of forest water conservation. (4) Predictions of water conservation capacity using machine learning are consistent with that of the InVEST model. The water conservation per unit area shows a variation trend as follows: slow-growing broadleaf forests > shrub forests > middle-growing broadleaf forests > cunninghamia lanceolata forests > fast-growing broadleaf forests > pine forests > bamboo forests. (5) Since this study considers only the factors available in the forest management inventory, which does not encompass all relevant influencing factors, it is difficult to fully address the complexities of how forest water conservation services interact with forest structure. Therefore, further research is needed to investigate the intrinsic mechanisms underlying the interactions between water conservation and forest management inventory factors.

Soil water dynamics and plant water uptake: Primeval mature vs. debris flow-developed half-mature subalpine fir stands in the eastern Tibetan Plateau

Natural disaster can disrupt soil structure and replace established vegetation with younger plants, altering the local hydrological processes. We used hydrogen and oxygen stable isotopes to examine soil water dynamics and plant water uptake patterns in two adjacent fir stands in the eastern Qinghai-Tibet Plateau: a primeval mature stand (MF, finer- textured soil) and a debris flow-developed half-mature stand (HMF, coarser-textured soil). Our results showed that the isotopic composition and soil gravimetric water content (SWC) in deep soil water in MF exhibited a more pronounced hysteresis pattern in response to precipitation compared to HMF, indicating lower turnover rate of soil water in MF. This was also confirmed by a smaller contribution of preferential flow to deep soil water in MF compared to HMF. The higher water storage (higher SWC values) and lower turnover rate of soil water suggest a higher soil water buffer capacity in MF. Additionally, both stands showed no significant difference in plant water sources, but plants in MF used more winter precipitation due to the lower soil water turnover rate. These differences suggest MF may be more vulnerable to water disasters, while HMF may be more susceptible to seasonal droughts under climate change. Our insights enhance understanding of hydrological processes linked to changing surface conditions and offer valuable information for managing forest water resources in mountainous regions.

Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm.

High-precision evaluations of water environment quality are highly important for improving the accuracy of early warning systems of regional water pollution risk and improving the regional water environment. This paper employs the chimp optimization algorithm (ChOA) to enhance the traditional random forest model, resulting in the chimp optimization algorithm-random forest (ChOA-RF) water quality assessment model for evaluating the Jiansanjiang area in Heilongjiang Province, China. The results show that the overall water environment in Jiansanjiang has the following characteristics: "The water quality of farms in the northwest is poor, and the quality of groundwater is better than that of surface water." Total nitrogen (TN) and total phosphorus (TP) in surface water and ammonium nitrogen (NH3-N), ferrum (Fe), and manganese (Mn) in groundwater are the main pollutants. The TP and TN in surface water and the NH3-N in groundwater exceeded the relevant standards, likely due to the excessive application of chemical fertilizers, especially nitrogen fertilizers. Additionally, Fe and Mn are harmful native substances. According to these findings, targeted improvement strategies, such as reducing nitrogen fertilizer application, plugging well, and increasing the surface water utilization rate, are proposed. Moreover, the ChOA-RF model is compared with the traditional empirical value model and the particle swarm optimization-random forest (PSO-RF) model. The results show that the ChOA-RF model can effectively reduce the root mean square error and mean absolute percentage error and improve the coefficient of determination. The running time and convergence ability are also better than those of the PSO-RF model, which is a more accurate and efficient machine learning model. The model can be used not only for high-precision evaluation of regional water environment quality but also for other machine learning fields.

Preserving isohydricity: vertical environmental variability explains Amazon forest water use strategies.

Increases in hydrological extremes, including drought, are expected for Amazon forests. A fundamental challenge for predicting forest responses lies in identifying ecological strategies which underlie such responses. Characterization of species-specific hydraulic strategies for regulating water use, thought to be arrayed along an 'isohydric-anisohydric' spectrum, is a widely used approach. However, recent studies have questioned the usefulness of this classification scheme, because its metrics are strongly influenced by environments, hence can lead to divergent classifications even within the same species. Here, we propose an alternative approach positing that individual hydraulic regulation strategies emerge from the interaction of environments with traits. Specifically, we hypothesize that the vertical forest profile represents a key gradient in drought-related environments (atmospheric vapor pressure deficit, soil water availability) that drives divergent tree water use strategies for coordinated regulation of stomatal conductance (gs) and leaf water potentials (ΨL) with tree rooting depth, a proxy for water availability. Testing this hypothesis in a seasonal eastern Amazon forest in Brazil, we found that hydraulic strategies indeed depend on height-associated environments. Upper canopy trees, experiencing high VPD, but stable soil water access through deep rooting, exhibited isohydric strategies, defined by little seasonal change in the diurnal pattern of gs and steady seasonal minimum ΨL. By contrast, understory trees, exposed to less variable VPD but highly variable soil water availability, exhibited anisohydric strategies, with fluctuations in diurnal gs that increased in the dry season along with increasing variation in ΨL. Our finding that canopy height structures the coordination between drought-related environmental stressors and hydraulic traits provides a basis for preserving the applicability of the isohydric-to-anisohydric spectrum, which we show here may consistently emerge from environmental context. Our work highlights the importance of understanding how environmental heterogeneity structures forest responses to climate change, providing a mechanistic basis for improving models of tropical ecosystems.

Regional patterns and drivers of modelled water flows along environmental, functional, and stand structure gradients in Spanish forests

Abstract. The study of the water cycle in the forest at large scales, such as countries, is challenging due to the difficulty of correctly estimating forest water flows. Hydrological models can be coupled with extensive forest data sources, such as national forest inventories, to estimate the water flow of forests over large extents, but so far the studies conducted have not analysed the role of stand structure variables or the functional traits of the forest on predicted blue and green water flows in detail. In this study, we modelled the water balance of Spanish forests using stand structure and species data from forest inventories to understand the effects of climate, stand structure, and functional groups on blue water flows. We calculated blue water and green water flows and expressed them relative to received precipitation. Relative blue water flow was mainly concentrated in the wetter regions (Atlantic and alpine biomes) of Spain (around 25 %) in comparison with the Mediterranean biomes (10 %–20 %) and during the autumn–winter season. The leaf area index (LAI) of the forest stand is the most important predictor of relative blue water, exhibiting a negative effect until it reaches a plateau at higher levels (around 2.5–3). Deciduous forests showed a greater relative blue water flow than evergreen functional groups (25 %–35 % and 10 %–25 %, respectively) primarily due to leaf fall during the autumn–winter season. This study highlights how green water is decoupled from blue water; namely, blue water depends on winter and autumn precipitation, while green water depends on the spring and summer water demand and how the species' functional traits (deciduous vs. evergreen) can influence blue water production.

Nutrient leaching potential along a time series of forest water reclamation facilities in northern Idaho

Forest water reclamation is a decades-old practice of repurposing municipal reclaimed water using land application on forests to filter nutrients and increase wood production. However, long-term application may lead to nutrient saturation, leaching, and potential impairment of ground and surface water quality. We studied long-term effects of reclaimed water application on nutrient leaching potential in a four-decade time series of forest water reclamation facilities in northern Idaho. Our approach compared reclaimed water treated plots with untreated control plots at each of the forest water reclamation facilities. We measured soil nitrifier abundance and net nitrification rates and used tension lysimeters to sample soil matrix water and drain gauges to sample from a combination of matrix and preferential flow paths. We determined nutrient leaching as the product of soil water nutrient concentrations and model-estimated drainage flux. There was more than 450-fold increase in nitrifier abundance and a 1000-fold increase in net nitrification rates in treated plots compared with control plots at long-established facilities, indicating greater nitrate production with increased cumulative inputs. There were no differences in soil water ammonium, phosphate, and dissolved organic nitrogen concentrations between control and effluent treatments in tension lysimeter samples. However, concurrent with increased nitrifier abundance and net nitrification, nitrate concentration below the rooting zone was 2 to 4-fold higher and nitrate leaching was 4 to 10-fold higher in effluent treated plots, particularly at facilities that have been in operation for over two decades. Thus, net nitrification and nitrifier abundance assays are likely indicators of nitrate leaching potential. Inorganic nutrient concentrations in drain gauge samples were 2 to 11-fold higher than lysimeter samples, suggesting nutrient losses occurred predominantly through preferential flow paths. Nitrate was vulnerable to leaching during the wet season under saturated flow conditions. Although nitrogen saturation is a concern that should be mitigated at long-established facilities, these forest water reclamation facilities were able to maintain average soil water nitrate concentrations to less than 2 mg L−1, so that nitrogen and phosphorous are effectively filtered to below safe water standards.

Improving Otsu Method Parameters for Accurate and Efficient in LAI Measurement Using Fisheye Lens

The leaf area index (LAI) is an essential indicator for assessing vegetation growth and understanding the dynamics of forest ecosystems and is defined as the ratio of the total leaf surface area in the plant canopy to the corresponding surface area below it. LAI has applications for obtaining information on plant health, carbon cycling, and forest ecosystems. Due to their price and portability, mobile devices are becoming an alternative to measuring LAI. In this research, a new method for estimating LAI using a smart device with a fisheye lens (SFL) is proposed. The traditional Otsu method was enhanced to improve the accuracy and efficiency of foreground segmentation. The experimental samples were located in Gansu Ziwuling National Forest Park in Qingyang. In the accuracy parameter improvement experiment, the variance of the average LAI value obtained by using both zenith angle segmentation and azimuth angle segmentation methods was reduced by 50%. The results show that the segmentation of the front and back scenes of the new Otsu method is more accurate, and the obtained LAI values are more reliable. In the efficiency parameter improvement experiment, the time spent is reduced by 17.85% when the enhanced Otsu method is used to ensure that the data anomaly rate does not exceed 10%, which improves the integration of the algorithm into mobile devices and the efficiency of obtaining LAI. This study provides a fast and effective method for the near-ground measurement of forest vegetation productivity and provides help for the calculation of forest carbon sequestration efficiency, oxygen release rate, and forest water and soil conservation ability.

Community assembly influences plant trait economic spectra and functional trade-offs at ecosystem scales

Plant functional traits hold the potential to greatly improve the understanding and prediction of climate impacts on ecosystems and carbon cycle feedback to climate change. Traits are commonly used to place species along a global conservative-acquisitive trade-off, yet how and if functional traits and conservative-acquisitive trade-offs scale up to mediate community and ecosystem fluxes is largely unknown. Here, we combine functional trait datasets and multibiome datasets of forest water and carbon fluxes at the species, community, and ecosystem-levels to quantify the scaling of the tradeoff between maximum flux and sensitivity to vapor pressure deficit. We find a strong conservative-acquisitive trade-off at the species scale, which weakens modestly at the community scale and largely disappears at the ecosystem scale. Functional traits, particularly plant water transport (hydraulic) traits, are strongly associated with the key dimensions of the conservative-acquisitive trade-off at community and ecosystem scales, highlighting that trait composition appears to influence community and ecosystem flux dynamics. Our findings provide a foundation for improving carbon cycle models by revealing i) that plant hydraulic traits are most strongly associated with community- and ecosystem scale flux dynamics and ii) community assembly dynamics likely need to be considered explicitly, as they give rise to ecosystem-level flux dynamics that differ substantially from trade-offs identified at the species-level.

Estimating Drainage from Forest Water Reclamation Facilities Based on Drain Gauge Measurements

A growing human population requires sustainable solutions to regulate and dispose of municipal wastewater. Water treatment facilities in northern Idaho are permitted to apply reclaimed wastewater to forest land during the growing season at specified monthly hydraulic loading rates. We assessed the spatial and temporal variability of drainage below the rooting zone between non-irrigated (control) and irrigated (effluent) stands during the growing and dormant seasons in 2021. No drainage was observed during the two months of annual seasonal drought, but large magnitudes of drainage were recorded during the dormant season (38–94 cm), which was consistent with seasonal precipitation. The overall effect of effluent treatment on the drain gauge measurements did not differ from the controls, as effluent only increased the drainage at some facilities. The measured drainage averaged from 35 to 62 cm among facilities. We then used the drainage measurements to calibrate hydrological models (Hydrus-1D and Water Erosion Prediction Project [WEPP]) and predict the drainage in 50 measurement plots distributed evenly among five forest water reclamation facilities. As with the observed drainage, there were no statistically significant growing season differences in the predicted monthly drainage during the growing season between the effluent and control plots, suggesting the successful use of hydrologic models to support the measured drainage findings. While both models struggled to accurately predict the quantity of drainage during the dormant season, they both successfully predicted that drainage would continue through May. WEPP also successfully predicted that the treated plots began to drain in September and October following late-season irrigation at some facilities. The models showed that the prescribed crop coefficient used by the Idaho Department of Environmental Quality was adequate in avoiding drainage during the peak summer months.

The Ecosystem as Super-Organ/ism, Revisited: Scaling Hydraulics to Forests under Climate Change.

Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth's ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity.

Ecohydrological assessment of the water balance of the world's highest elevation tropical forest (Polylepis)

Polylepis trees grow at elevations above the continuous tree line (3000–5000 m a.s.l.) across the Andes. They tolerate extreme environmental conditions, making them sensitive bioindicators of global climate change. Therefore, investigating their ecohydrological role is key to understanding how the water cycle of Andean headwaters could be affected by predicted changes in environmental conditions, as well as ongoing Polylepis reforestation initiatives in the region. We estimate, for the first time, the annual water balance of a mature Polylepis forest (Polylepis reticulata) catchment (3780 m a.s.l.) located in the south Ecuadorian páramo using a unique set of field ecohydrological measurements including gross rainfall, throughfall, streamflow, and xylem sap flow in combination with the characterization of forest and soil features. We also compare the forest water balance with that of a tussock grass (Calamagrostis intermedia) catchment, the dominant páramo vegetation. Annual gross rainfall during the study period (April 2019–March 2020) was 1290.6 mm yr−1. Throughfall in the Polylepis forest represented 61.2 % of annual gross rainfall. Streamflow was the main component of the water balance of the forested site (59.6 %), while its change in soil water storage was negligible (<1 %). Forest evapotranspiration was 54.0 %, with evaporation from canopy interception (38.8 %) more than twice as high as transpiration (15.1 %). The error in the annual water balance of the Polylepis catchment was small (<15 %), providing confidence in the measurements and assumptions used to estimate its components. In comparison, streamflow and evapotranspiration at the grassland site accounted for 63.7 and 36.0 % of the water balance, respectively. Although evapotranspiration was larger in the forest catchment, its water yield was only marginally reduced (<4 %) in relation to the grassland catchment. The substantially higher soil organic matter content in the forest site (47.6 %) compared to the grassland site (31.8 %) suggests that even though Polylepis forests do not impair the hydrological function of high-Andean catchments, their presence contributes to carbon storage in the litter layer of the forest and the underlying soil. These findings provide key insights into the vegetation-water‑carbon nexus in high Andean ecosystems, which can serve as a basis for future ecohydrological studies and improved management of páramo natural resources considering changes in land use and global climate.

Identification of Water Biota in the Mangrove Forest Water Area in Aek Horsik Village, Central Tapanuli District

Identification of Water Biota in the Mangrove Forest Water Area in Aek Horsik Village, Central Tapanuli District

A Qualitative Study of Water Quality Using Landsat 8 and Station Water Quality-Monitoring Data to Support SDG 6.3.2 Evaluations: A Case Study of Deqing, China

Facing the challenge of the degradation of global water quality, it is urgent to realize the Sustainable Development Goal 6.3.2 (SDG 6.3.2), which focuses on improving global water quality. Currently, remote sensing technology is widely used for water quality monitoring. Existing water quality-monitoring studies have been conducted based on quantitative water quality inversion. It requires a high degree of the synchronization of the time and location of the collection of station monitoring data and remote sensing data (air–ground spatiotemporal synchronization), which can be resource intensive and time consuming. However, policymakers and the public are more interested in the quality of water (good or poor) than in the specific values of the water quality parameters, as evidenced by the emergence of SDG 6.3.2. In this study, we change the traditional idea of quantitative water quality research, focus on water quality qualitative research combined with the characteristics of water pollution, propose a remote sensing water quality sample enhancement method under the condition of “air–ground spatiotemporal asynchrony”, and construct a remote sensing water quality sample library. On the basis of this sample library, a random forest water quality classification model was constructed to classify water quality qualitatively. We obtained the distribution of good water bodies in Deqing County, China, for example, from 2013 to 2022. The results show that the model has high accuracy (Kappa = 0.6004, OA = 0.8387), and we found that the water quality in Deqing County improved in the order of “major rivers, lakes, and tributaries” during the period from 2013 to 2015. This also verifies the feasibility of using this sample enhancement method to conduct qualitative research on water quality. Based on this water quality classification model, a set of spatial-type evaluation processes of SDG 6.3.2 based on image elements was designed. The evaluation results show that the water quality situation in Deqing County can be divided into two stages: there is a trend of substantial improvement from 2013 (evaluated value of SDG 6.3.2 = 63.25) to 2015 (evaluated value of SDG 6.3.2 = 83.16); and it has remained stable and fluctuating after reaching the good environmental water quality since 2015. This study proposes a simple method for rapidly evaluating SDG 6.3.2 via utilizing easily accessible Landsat 8 and water quality-monitoring data to classify water quality. The method can directly obtain water quality category information without the need for additional sampling, thus saving costs. It is a very simple process that is easy to implement, while also providing a high level of accuracy. This significantly reduces the barriers to evaluating SDG 6.3.2, supports the realization of the sustainable management of water resources globally, and is highly generalizable.

Declining groundwater storage expected to amplify mountain streamflow reductions in a warmer world

Groundwater interactions with mountain streams are often simplified in model projections, potentially leading to inaccurate estimates of streamflow response to climate change. Here, using a high-resolution, integrated hydrological model extending 400 m into the subsurface, we find groundwater an important and stable source of historical streamflow in a mountainous watershed of the Colorado River. In a warmer climate, increased forest water use is predicted to reduce groundwater recharge resulting in groundwater storage loss. Losses are expected to be most severe during dry years and cannot recover to historical levels even during simulated wet periods. Groundwater depletion substantially reduces annual streamflow with intermittent conditions predicted when precipitation is low. Expanding results across the region suggests groundwater declines will be highest in the Colorado Headwater and Gunnison basins. Our research highlights the tight coupling of vegetation and groundwater dynamics and that excluding explicit groundwater response to warming may underestimate future reductions in mountain streamflow.

Monitoring Snow Cover in Typical Forested Areas Using a Multi-Spectral Feature Fusion Approach

Accurate snow cover monitoring is greatly significant for research on the hydrology model and regional climate variation, especially in Northeast China where forests cover almost forty percent of the total area. However, effectively monitoring snow cover under the forest canopy is still challenging with either in situ or remote sensing observations. The global SNOWMAP algorithm pertinent to the fixed normalized difference snow index (NDSI) threshold is, therefore, no longer applicable in a typical forested region of Northeast China. In order to achieve the goal of improving the accuracy of monitoring snow cover in areas with forest, utilizing MOD09GA and MOD13A1 products, a new approach of snow mapping was developed in this study, and it exploits the fusion and coupling of spectral features by integrating and analyzing the normalized difference forest snow index (NDFSI), the normalized difference vegetation index (NDVI), and the NDSI index. Then, Landsat 8 OLI images of high resolution were used to evaluate snow cover mapping precision. The experimental results indicated that the NDFSI index combined with the NDVI index showed great potential for extracting the snow cover distribution in forested regions. Compared with the snow distribution obtained from the Landsat 8 images, the average bias and FAR (false alarm ratio) values of snow cover mapping obtained by this algorithm were 1.23 and 13.54%, which were reduced by 1.98 and 29.36%, respectively. The overall accuracy of 81.31% was reached, which is improved by 20.19%. Thus, the snow classification scheme combining multiple spectral features from MODIS data works effectively in improving the precision of automatic snow cover mapping in typical forested areas of Northeast China, which provides essential support and significant perspectives for the next step of establishing a runoff model and rationally regulating forest water resources.

Increased tree water use with the development of a dense understory layer in a North American hardwood forest

AbstractThe formation of a single species, recalcitrant understory vegetation layer can limit tree regeneration and, in the long term, modify the composition of forests. Few studies have investigated how recalcitrant vegetation influences competition for water resources although the formation of a dense understory is likely to modify the forest water balance. In eastern North American hardwood forests, the development of a dense understory layer of American beech (Fagus grandifolia) has been observed in stands dominated by sugar maple (Acer saccharum), a phenomenon that shares many characteristics associated with recalcitrant vegetation. Given that water availability is generally negatively correlated with stand density, we hypothesized that the formation of a dense understory beech layer increased competition for water resources, thus leading to reduced water use by sugar maple trees in beech‐dominated stands. Using thermal dissipation sensors, we measured sap flux density (Fd) of two sugar maple trees at three beech‐dominated sites and three control sites. During the growing season, Fd of sugar maple trees was significantly larger at beech‐dominated sites compared to control sites, indicating a greater rate of water use by sugar maples in stands with a dense understory beech layer. We provide two hypotheses to explain our results at the tree scale: (i) reduced cover by forest floor vegetation could limit transpiration by this layer, thus allowing increased water availability to supply transpiration by overstory trees, or (ii) increased tree transpiration rate could be a mechanism to satisfy nutrient requirements in beech‐dominated stands often associated with lower soil fertility.

Modelling the spatial abundance of invasive deer and their impacts on vegetation at the landscape scale

Deer populations and their impacts on forest ecosystems are increasing globally. Given the imperative and expense to mitigate impacts of invasive deer, we aimed to elucidate critical drivers of (i) deer density, (ii) deer impacts, and (iii) the relationship between them, to facilitate targeted management. We used quantile regression forests to model deer density (faecal pellet counts at 1948 locations) and impacts (browsing and other impacts on > 23,000 woody plants at 343 locations) across a mosaic of agricultural and forested ecosystems in Victoria, Australia (12,775 km2). Climate, topography, vegetation cover, and distance to water features were included as model covariates. Modelled deer density (r2 = 0.71, MAE = 0.56 pellets/m2) was most influenced by distance to waterbodies (> 10 ha, 31.2%), elevation (14.3%) and woody vegetation cover (12.9%). Modelled deer impact (r2 = 0.32, MAE = 6.9%) was most influenced by deer density (21.0%), mean annual precipitation (12.8%) and elevation (12.2%). Deer density was typically highest near large waterbodies, at low elevation, and with intermediate tree cover (40–70%). Impacts increased steadily with deer density up to ~ 2 pellets/m2. Our study demonstrates the importance of forest water and forest agricultural interfaces for both deer density and impacts. Deer are likely to be most abundant near waterbodies due to the availability of high-quality forage and water, and prefer lowland locations that have access to both open and forested habitats. Spatial models can be used to predict deer density and associated impacts to facilitate targeted invasive deer management.