Effects of different land-use systems (grazing and understory cultivation) on growth and yield of semi-arid oak coppices

The present study examines the extent of negative eff ects of traditional multiple land-use systems on oak coppices,from a forest management point of view.The study area was located in approximately 10,000 ha of hilly Brant’s oak(Quercus brantii Lindl.)woodlands in the central Zagros Mountains.In the same site-quality class,three land-use systems were compared:simple coppice(Co),coppice in conjunction with small ruminant grazing(CoG),and coppice with understory rain-fed wheat cultivation plus grazing(CoCG).Data on total wood volume of trunk and major branches,and annual ring growth,were collected and analyzed from 74 stands in 15 coppiced woodland patches.The results showed the advantage of Co over CoG and CoCG land-uses by 43 and 60 m 3 of mean accumulated wood volume per hectare,respectively.The diameter growth analysis also revealed an annual increase in wood production of trees in Co land-uses over 43 years,with an exception of the recent decade,when growth coincided with a severe drought.Using a back-extrapolation method,the minimum rotation age of woodlands in Co land-use was found to be 23.6 years,5 and 7 years shorter than those of CoG and CoCG land-uses,respectively.Unlike CoCG,woodlands located in Co and CoG land-use systems demonstrated a high level of agreement with self-thinning rule of−3/2.Values for the stand density index for coppiced oak woodlands were between more than 1000 for the least disturbed(Co)and less than 400 for the most disturbed woodlands(CoCG).The structure and growth rate of the coppiced oak woodlands were irreversibly disrupted by understory tillage plus grazing and in less extent by grazing alone.It was concluded that ending undergrowth cultivation in semi-arid oak coppices should be addressed as a priority by adopting minimum regulations.

Above- and below-ground biomass and carbon stocks of different tree plantations in central Iran

In arid and semi-arid lands using industrial wastewater for irrigating tree plantations offers a great opportunity to fulfill the purpose of Clean Development Mechanism by sequestering carbon in living tissues as well as in soil. Selection of tree for plantation has a great effect on the goal achievements, especially when the managers deal with afforestation projects rather than reforestation projects. The objective of this study was to quantify the above- and below-ground biomass accumulation and carbon storages of the 17-year-old monoculture plantations of mulberry (Morus alba L.), black locust (Robinia pseudoacacia L.), Eldar pine (Pinus eldarica Medw.) and Arizona cypress (Cupressus arizonica Greene) planted in central Iran. To assess the potential carbon storage, we destructively measured individual above- and below-ground tree biomass and developed and scaled models at stand level. Furthermore, carbon content at three soil depths (0–15, 15–30, 30–45 cm), the litter and the understory were assessed in sample plots. The results showed that the total amount of carbon stored by Eldar pine (36.8 Mg/hm2) was higher than those stored by the trees in the other three plantations, which were 23.7, 10.0, and 9.6 Mg/hm2 for Arizona cypress, mulberry and black locust plantations, respectively. For all the species, the above-ground biomass accumulations were higher than those of the below-ground. The root mass fractions of the deciduous were larger than those of the coniferous. Accordingly, the results indicate that the potential carbon storages of the coniferous were higher than those of the deciduous in arid regions.

Prioritizing woody species for the rehabilitation of arid lands in western Iran based on soil properties and carbon sequestration

Plants are an important component in many natural ecosystems. They influence soil properties, especially in arid ecosystems. The selection of plant species based on their adaptations to site conditions is essential for rehabilitation of degraded sites and other construction sites such as check-dams. Other factors to be considered in species selection include their effects on soil properties and their abilities to meet other management objectives. The purpose of this study was to assess the effects of native(Populus euphratica Oliv. and Tamarix ramosissima Ledeb.) and introduced(Eucalyptus camaldulensis Dehnh. and Prosopis juliflora(Swartz) DC.) woody species on soil properties and carbon sequestration(CS) in an arid region of Iran. Soil sampling was collected at three soil depths(0–10, 10–20 and 20–30 cm) at the sites located under each woody species canopy and in an open area in 2017. Soil physical-chemical property was analyzed in the laboratory. The presence of a woody species changed soil characteristics and soil CS, compared with the open area. For example, the presence of a woody species caused a decrease in soil bulk density, of which the lowest value was observed under E. camaldulensis(1.38 g/cm^3) compared with the open area(1.59 g/cm^3). Also, all woody species significantly increased the contents of soil organic matter and total nitrogen, and introduced species had more significant effect than native species. The results showed that CS significantly increased under the canopy of all woody species in a decreasing order of P. euphratica(9.08 t/hm^2)>E. camaldulensis(8.37 t/hm^2)>P. juliflora(5.20 t/hm^2)>T. ramosissima(2.93 t/hm^2)>open area(1.33 t/hm^2), thus demonstrating the positive effect of a woody species on CS. Although the plantation of non-native species had some positive effects on soil properties, we recommend increasing species diversity in plantations of native and introduced woody species to provide more diversity for the increased ecosystem services, resilience, health and long-term productivity.

Early differentiation in biomass production and carbon sequestration of white poplar and its two hybrids in Central Iran

We assessed the potential of white poplar（Populus alba L.） and its inter-sectional hybridization with euphrates poplar（P. euphratica Oliv.） for carbon storage and sequestration in central Iran. Trials were established at planting density of 2,500 trees per hectare in block randomized design with three replicates. After 6 years, we measured the above-ground biomass of tree components（trunk, branch, bark, twig and leaf）, and assessed soil carbon at three depths. P. alba 9 euphratica plantation stored significantly more carbon（22.3 t ha-1） than P. alba（16.7 t ha-1） and P. euphratica 9 alba（13.1 t ha-1）.Most of the carbon was accumulated in the above-ground biomass（61.1 % in P. alba, 72.4 % in P. alba 9 euphratica and 56.0 % in P. euphratica 9 alba）. There was no significant difference in soil carbon storage. Also, biomass allocation was different between white poplar P. alba and its inter-sectional hybridization. Therefore, there was a yield difference due to genomic imprinting, which increased the possibility that paternally and maternally inherited wood production alleles would be differentially expressed in the new crossing.

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.

Dieback intensity but not functional and taxonomic diversity indices predict forest productivity in different management conditions: Evidence from a semi-arid oak forest ecosystem

The relationships between different aspects of diversity(taxonomic,structural and functional)and the aboveground biomass(AGB)as a major component of global carbon balance have been studied extensively but rarely under the simultaneous influence of forest dieback and management.In this study,we investigate the relationships between taxonomic,functional and structural diversity of woody species(trees and shrubs)and AGB along a gradient of dieback intensity(low,moderate,high and no dieback as control)under two contrasted management conditions(protection by central government vs.traditional management by natives)in a semi-arid oak(Quereus brantii Lindl.)forest ecosystem.AGB was estimated and taxonomic diversity,community weighted average(CWM)and functional divergence indices were produced.We found that the aerial biomass was significantly higher in the intensively used area(14.57(±1.60)t/hm^(2))than in the protected area(8.70(±1.05)t/hm^(2))due to persistence of some large trees but with decreasing values along the dieback intensity gradient in both areas.CWM of height(H),leaf nitrogen content(LNC)and leaf dry matter content(LDMC)were also higher in the traditional managed area than in the protected area.In contrast,in the protected area,the woody species diversity was higher and the inter-specific competition was more intense,explaining a reduced H,biomass and LDMC.Contrary to the results of CWM,none of the functional diversity traits(FDvar)was affected by dieback intensity and only FDvar values of LNC,leaf phosphorus content(LPC)and LDMC were influenced by management.We also found significantly positive linear relationships of AGB with CWM and FDvar indices in the protected area,and with taxonomic and structural diversity indices in the traditional managed area.These results emphasize that along a dieback intensity gradient,the leaf functional traits are efficient predictors in estimating the AGB in protected forests,while taxonomic and structural indices provide better results in forests under a high human pressure.Finally,species identity of the dominant species(i.e.,Brant’s oak)proves to be the main driver of AGB,supporting the selection effect hypothesis.

Application of Soft Computing Methods in Predicting Evapotranspiration

Exact prediction of evapotranspiration is necessary for study, design and management of irrigation systems. In this research, the suitability of soft computing approaches namely, fuzzy rule base, fuzzy regression and artificial neural networks for estimation of daily evapotranspiration has been examined and the results are compared to real data measured by lysimeter on the basis of reference crop (grass). Using daily climatic data from Haji Abad station in Hormozgan, west of Iran, including maximum and minimum temperatures, maximum and minimum relative humidities, wind speed and sunny hours, evapotranspiration was predicted by soft computing methods. The predicted evapotranspiration values from fuzzy rule base, fuzzy linear regression and artificial neural networks show root mean square error (RMSE) of 0.75, 0.79 and 0.81 mm/day and coefficient of determination of (R2) of 0.90, 0.87 and 0.85, respectively. Therefore, fuzzy rule base approach was found to be the most appropriate method employed for estimating evapotranspiration.

GIS Based Fuzzy Logic Approach for Identification of Groundwater Artificial Recharge Site

In recent decades population increasing and development of agriculture and also being mountainous and climatic characteristics of Sefieddasht plain and also nonuniform distribution of rainfall in study area have led to irregular use of groundwater resources in study area. This issue has led to critical condition of groundwater resources in Sefieddasht plain. This research was carried out to determine the suitable areas for artificial recharge in Sefieddasht plain. Four factors namely, alluvial quality, alluvial thickness, slope, and infiltration rate parameters were explored and maps produced and classified using GIS. Fuzzy logic model was used to determine the suitable areas for artificial recharge. Finally land use maps were used as a filter. Based on results 4.12% of region was recognized as suitable area for artificial recharge.

Evaluation of climate change effects on extreme flows in a catchment of western Iran

Investigation of the relationship between catchment hydrology with climate is essential for understanding of the impact of future climate on hydrological extremes, which may cause frequent flooding, drought, and shortage of water supply. The purpose of this study is to investigate the effects of climate change on extreme flows in one of the subcatchments of the Ilam dam catchment, Iran. The changes in climate parameters were predicted using the outputs of HadCM3 model for up to the end of the current century in three time periods including 2020s, 2050s, and 2080s. For A2 scenario, increases of 1.09℃, 2.03℃, and 3.62℃, and for B2 scenario rises of 1.18℃, 1.84℃, and 2.55℃ have been predicted. The results suggest that for A2 scenario, the amount of precipitation would decrease by 12.63, 49.13, and 63.42 and for B2 scenario by 47.02,48.51, and 70.26 mm per year. Also the values of PET for A2 scenario would increase by 51.18, 101.47 and 108.71 and for B2 scenario by 60.09, 89.86, and 124.32 mm per year. The results of running the SWAT model revealed that the average annual runoff would decrease by 0.11, 0.41, and 0.61 m3/s and for B2 scenario by 0.39, 0.47, and 0.59 m3/s. The extreme flows were then analyzed by running WETSPRO model. According to the results, the amounts of low flows for A2 scenario will decrease by 0.02, 0.21 and 0.33 m^3/s and for B2 scenario by 0.19, 0.26 and 0.29 m^3/s in the 2020s, 2050s and 2080s, respectively. On the other hand, the results show an increase of peak flows by 11.5, 19.1 and 48.7 m^3/s in A2 scenario and 11.12, 25.93 and 48.1 m^3/s in B2 scenario, respectively. Overall, the results indicated that an increase in return period leads to elevated levels of high flows and diminished low flows.