Species Mixing Proportion and Aridity Influence in the Height–Diameter Relationship for Different Species Mixtures in Mediterranean Forests

Estimating tree height is essential for modelling and managing both pure and mixed forest stands. Although height–diameter (H–D) relationships have been traditionally fitted for pure stands, attention must be paid when analyzing this relationship behavior in stands composed of more than one species. The present context of global change makes also necessary to analyze how this relationship is influenced by climate conditions. This study tends to cope these gaps, by fitting new H–D models for 13 different Mediterranean species in mixed forest stands under different mixing proportions along an aridity gradient in Spain. Using Spanish National Forest Inventory data, a total of 14 height–diameter equations were initially fitted in order to select the best base models for each pair species-mixture. Then, the best models were expanded including species proportion by area (mi) and the De Martonne Aridity Index (M). A general trend was found for coniferous species, with taller trees for the same diameter size in pure than in mixed stands, being this trend inverse for broadleaved species. Regarding aridity influence on H–D relationships, humid conditions seem to beneficiate tree height for almost all the analyzed species and species mixtures. These results may have a relevant importance for Mediterranean coppice stands, suggesting that introducing conifers in broadleaves forests could enhance height for coppice species. However, this practice only should be carried out in places with a low probability of drought. Models presented in our study can be used to predict height both in different pure and mixed forests at different spatio-temporal scales to take better sustainable management decisions under future climate change scenarios.

Evaluating the Degradation of Natural Resources in the Mediterranean Environment Using the Water and Land Resources Degradation Index, the Case of Crete Island

Natural resources degradation poses multiple challenges particularly to environmental and economic processes. It is usually difficult to identify the degree of degradation and the critical vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators) may also describe these complex systems or phenomena. In this approach, the Water and Land Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the 1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope, and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic interventions and natural processes, as well as their vulnerability status. The results for Crete Island indicate that prolonged water resources shortages due to low average precipitation values or high water demand (especially in the agricultural sector), may significantly affect Water and Land degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra tool to assist policymakers to improve their decisions to combat Natural Resources degradation.

Water use pattern and related agrometeorological indices of rabi & kharif sunflower (Helianthus annuus L.) at Deccan plateau

The agrometeorological data pertinent to estimation of water use and related agrometeorological indices of KBSH - II (1988 to 1991) and MORDEN varieties of sunflower (1992 and 1993) cultivated both in rabi and kharif seasons, were used to understand the comparative water use pattern and agrometeorological indices for getting an idea about the crop condition at Bangalore. The study revealed that mean weekly water use was higher in almost all the years during the kharif season than during the rabi season except in 1990 and the consumptive water use increased with development of the vegetative cover of the crop reaching a peak value in the vegetative growth stage. The ARI (agroclimatic rainfall index) and cumulative YMI (yield moisture index) were always higher during the kharif season than correspondingly those during the rabi season and showed yearly and seasonal variability in different growth stages which was due to the moisture stress condition of the soil as well as prevailing weather conditions of the atmosphere. In case of AI (aridity index), high values were observed at early and late crop growth stages during the kharif season which showed that the crop experienced less aridity between vegetative to seed formation stage. The water use efficiency (WUE) of the crop also revealed wide variation due to variety and season.

Identifying Forest Structural Types along an Aridity Gradient in Peninsular Spain: Integrating Low-Density LiDAR, Forest Inventory, and Aridity Index

Forest structure is a key driver of forest functional processes. The characterization of forest structure across spatiotemporal scales is essential for forest monitoring and management. LiDAR data have proven particularly useful for cost-effectively estimating forest structural attributes. This paper evaluates the ability of combined forest inventory data and low-density discrete return airborne LiDAR data to discriminate main forest structural types in the Mediterranean-temperate transition ecotone. Firstly, we used six structural variables from the Spanish National Forest Inventory (SNFI) and an aridity index in a k-medoids algorithm to define the forest structural types. These variables were calculated for 2770 SNFI plots. We identified the main species for each structural type using the SNFI. Secondly, we developed a Random Forest model to predict the spatial distribution of structural types and create wall-to-wall maps from LiDAR data. The k-medoids clustering algorithm enabled the identification of four clusters of forest structures. A total of six out of forty-one potential LiDAR metrics were utilized in our Random Forest, after evaluating their importance in the Random Forest model. Selected metrics were, in decreasing order of importance, the percentage of all returns above 2 m, mean height of the canopy profile, the difference between the 90th and 50th height percentiles, the area under the canopy curve, and the 5th and the 95th percentile of the return heights. The model yielded an overall accuracy of 64.18%. The producer’s accuracy ranged between 36.11% and 88.93%. Our results confirm the potential of this approximation for the continuous monitoring of forest structures, which is key to guiding forest management in this region.

Optimal Selection of Empirical Reference Evapotranspiration Method in 36 Different Agricultural Zones of China

The Food and Agriculture Organization has proposed the current version of the Penman–Monteith method (FAO56-PM) as the standard for calculating reference evapotranspiration (ET0); however, high meteorological data requirements limit its application in many areas. There is thus an urgent need to identify the best alternative empirical method to accurately calculate ET0 in regions that lack sufficient meteorological data. In this study, three temperature-based methods and five radiation-based methods were evaluated using ET0 values generated using the FAO56-PM method in 36 agricultural zones in China based on meteorological data from 823 stations, measured between 2011 and 2020. The results showed that the optimal temperature-based method and radiation-based method differed for different agricultural zones, and no one temperature method or radiation method could be suitable for all agricultural zones. The eight empirical methods were regionally calibrated to improve the ET0 calculation accuracy in the different zones. The relationship between the optimal methods and climatic conditions showed that the most reliable empirical method could be selected according to the local annual mean temperature and aridity index. The results provide useful guidance for the selection of reliable empirical ET0 methods in agricultural zones outside China.

The Use of Artificial Neural Network and Advanced Statistics to Model Sediment Yield on a Large Scale: Example of Morocco

Morocco ranks among countries with the greatest achievements in the field of dams in Africa but is affected by the sedimentation phenomenon due to soil erosion in upstreams. The assessment of Sediment Yield (SY) and Suspended Sediment Yield (SSY) remains a challenging global issue, especially in Morocco, characterized by a great diversity of morphological, climatic, and vegetation cover. The main objective of this paper was to perform advanced statistics and artificial neural networks (ANN) in order to understand the spatial distribution of sediment yield and the factors most controlling it, including factors of the RUSLE model (Revised Universal Soil Loss Equation). In order to produce a model able to assess SY, we collected and analyzed extensive data of most variables that can be affecting SY using 42 catchments of the biggest and important dams of Morocco. Statistical analysis of the studied watersheds shows that SY is mainly related to the watershed area and the length of the drainage network. On the other hand, the SSY is higher in watersheds where gully erosion is abundant and lower in areas with no soil horizon. The SSY is mainly related to the altitude, aridity index, sand fraction, and drainage network length. In front of the complexity of preserving this phenomenon, the ANN was applied and gave very good satisfactory results in predicting the SSY (NSE=0.93, R2=0.93).

Requirement of heat unit and agrometeorological indices in selected wheat growing zones

Wheat growing ET -stations (viz., Jorhat, Varanasi, New Delhi, Ludhiana, Raipur, Jabalpur, Akola, Bellary, Banswara and Jodhpur) situated in arid to per humid climatic zones were selected. Heat unit and three agromeleorological indices, viz., ARI (agroclimatic rainfall index), YMI (yield moisture index) and AI (aridity index) were computed at various growth stages of wheat crop using latest available five years data for each of the stations. The study revealed that the crop degree days requirement varied from 1580 (at Jorhat) to 2350 (at Akola) with the maximum requirement at tillering and milk stages. All the stations (except Jorhat) recorded ARI values less than 25%, while for the stations in peninsular and western India, the values were even below 10%. Low cumulative YMI values were obtained in peninsular and western India, while high values were observed over eastern India. The wheat crop did not experience any aridity during tillering to flowering stages for all the stations (except Bellary and Banswara). High values of At were observed at early and late crop growth stages. Negative correlation was obtained between AI and ARI with the highest value (-0.89) observed at New Delhi. Depending upon this study, the wheat growing areas were divided into five zones.

Quantifying Groundwater Resources for Municipal Water Use in a Data-Scarce Region

Groundwater is a major source of drinking water worldwide, often considered more reliable than surface water and more accessible. Nowadays, there is wide recognition by the scientific community that groundwater resources are under threat from overexploitation and pollution. Furthermore, frequent and prolonged drought periods due to climate change can seriously affect groundwater recharge. For an appropriate and sustainable management of water systems supplied by springs and/or groundwater withdrawn from aquifers through drilling wells or drainage galleries, the need arises to properly quantify groundwater resources availability, mainly at the monthly scale, as groundwater recharge is influenced by seasonality, especially in the Mediterranean areas. Such evaluation is particularly important for ungauged groundwater bodies. This is the case of the aquifer supplying the Santissima Aqueduct, the oldest water supply infrastructure of the city of Messina in Sicily (Southern Italy), whose groundwater flows are measured only occasionally through spring water sampling at the water abstraction plants. Moreover, these plants are barely maintained because they are difficult to reach. In this study, groundwater recharge assessment for the Santissima Aqueduct is carried out through a GIS-based inverse hydrogeological balance methodology. Although this approach was originally designed to assess aquifer recharge at the annual scale, wherever a model conceptualization of the groundwater system was hindered by the lack of data, in the present study some changes are proposed to adjust the model to the monthly scale. In particular, the procedure for evapotranspiration assessment is based on the Global Aridity Index within the Budyko framework. The application of the proposed methodology shows satisfactory results, suggesting that it can be successfully applied for groundwater resources estimation in a context where monthly information is relevant for water resources planning and management.

Water and Heat-Triggered Changes of Bacterial Community Structure and Function in Biological Soil Crusts

Background and Aims：The outstanding ability of biological soil crusts (BSCs) in soil microenvironments regulation is mainly attribute to microorganisms that colonizing in biocrusts. We aimed to investigate the changes of bacterial community structure and function with biocrust succession, as well as their responses to climatic changes across large geographical scales.Methods: Algal BSCs and lichen BSCs were sampled along an aridity gradient on alpine grasslands. Bacterial communities in biocrusts were measured using high-throughput sequencing, and soil underlying biocrusts (0-5 cm) was collected for nutrients determination. Results: Our results indicated that compared with algal BSCs, bacterial community in lichen BSCs was characterized by lower diversity, more complex co-occurrence network and mutually beneficial relationships. The bacterial community assembly was governed mainly by stochastic processes for lichen BSCs, which was different from the almost equally important roles of stochastic and deterministic processes for algal BSCs. Geographical location had a significant effect on bacterial communities in both algal and lichen BSCs, while had a greater effect on lichen BSCs. It is noteworthy that the bacterial diversity of algal BSCs was positively correlated with aridity index, while that of lichens was negatively correlated with aridity index. Moreover, we determined lower soil pH and higher soil phosphorus content underlying lichen BSCs, implying their advantages in soil improvement. Conclusions: Aridity index was one of important driving factors of bacterial community in biocrusts, and its effects were biocrust type dependent. Lichen BSCs had greater effects on soil improvement than that of algal BSCs.