Spatial Variability and Optimal Number of Rain Gauges for Sampling Throughfall under Single Oak Trees during the Leafless Period

Contrasting effects of low-severity fire on stemflow production between coexisting pine and oak trees

As climate change intensifies, fires events are predicted to increase in forest ecosystems. Fire alters the ecosystem structure and consequently, the hydrological cycle. However, little is known about the impacts of forest fire on stemflow. A field experiment was conducted to evaluate the short-term response of stemflow production to low-severity fire in a coniferous and broadleaved mixed forest. Results demonstrated low-severity fire changed stemflow yield and had insignificant effect on the correlation between stemflow efficiency and rainfall or plant morphological variables. In unburned site Quercus acutissima and Pinus massoniana and in burned site Q. acutissima and P. massoniana, stemflow percentage averaged 3.86, 0.37, 1.20, and 0.47 %, whereas funneling ratio averaged 38.8, 4.2, 11.4, and 5.1, respectively. Fire substantially decreased the stemflow percentage and funneling ratio of Q. acutissima (P < 0.05) and slightly enhanced P. massoniana (P > 0.05). The responses of stemflow production to fire differed significantly between oak and pine trees. Fire made Q. acutissima become less effective in funneling rain to the forest ground, which is attributed to that the scaly bark was burned to highly furrowed bark that delivers less water to tree base. Burned P. massoniana was more productive in draining stemflow relative to unburned trees and is attributed to the bark which was still flaky regardless of. Additionally, the higher canopy openness allows more rain to funnel to the trunk. Stemflow efficiency was reduced in response to fire and limited the transfer of water and nutrients from canopy to soil and can reduce the competitiveness of Q. acutissima after fire disturbance.

Experimental partitioning of rainfall into throughfall, stemflow and interception loss by Haloxylon ammodendron, a dominant sand-stabilizing shrub in northwestern China

Rainfall partitioning by the vegetation canopy represents a significant component of the local hydrological cycle by reshaping the amount and spatial distribution of rainfall. Measuring the components of rainfall partitioning, however, has been a challenging task due to laborious- and time-consuming field experiments. In this study, to probe the influences of long-term afforestation on dynamic patterns of rainfall partitioning, the dominant sand-stabilizing shrub Haloxylon ammodendron at three different ages was selected for field measurements during the 2020-2021 growing season. The throughfall percentage for young H. ammodendron (YH, 75.9 %) was significantly higher than that for middle-aged H. ammodendron (MAH, 63.4 %) and mature H. ammodendron (MH, 62.4 %) (p < 0.05 for all cases). However, the interception loss percentage of YH (22.3 %) was significantly lower than that for MAH (35.0 %) and MH (36.5 %) (p < 0.05 for all cases). No significant difference was found for stemflow percentage among YH (1.8 %), MAH (1.5 %) and MH (1.1 %). Smaller rainfall events contributed to a higher interception loss percentage and a lower net rainfall percentage for all ages. Both throughfall and stemflow percentage first showed increasing trends and then tended to be stable with increasing rainfall amount and duration, whereas interception loss percentage showed the opposite patterns. Rainfall partitioning was significantly correlated with the plant area index, stem basal area and canopy height (p < 0.05 for all cases), which may account for significant differences in rainfall partitioning patterns, as all shrubs experienced the same weather conditions. The average funneling ratio was 56.6, 26.7 and 17.9 for YH, MAH and MH, respectively. These results suggested that H. ammodendron afforestation can have a significant impact on rainfall partitioning by reducing net rainfall reaching the soil and may have some implications for local water budget and ecosystem management in oasis-desert ecotones.

The Influence of Data Density and Integration on Forest Canopy Cover Mapping Using Sentinel-1 and Sentinel-2 Time Series in Mediterranean Oak Forests

Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accuracy on FCC mapping. The three-year time series increased the ability of all ML models to classify FCC classes, in particular the sparse forest class, which was not distinguished well by the one-year dataset. Class-level accuracy assessment results showed a remarkable increase in F-1 scores for sparse forest classification by integrating S-1 and S-2 (10.4% to 18.2% increased for the CART and SVM ML models, respectively). In conclusion, the synergetic use of S-1 and S-2 spectral temporal metrics improved the classification accuracy compared to that obtained using only S-2. The study relied on open data and freely available tools and can be integrated into national monitoring systems of FCC in Mediterranean oak forests of Iran and neighboring countries with similar forest attributes.