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Due to the severe effects of climate change on the agricultural sector, urgent action is required on the part of farmers and is, indeed, critical to reducing climate change impacts. However, reports globally revealed farmers' engagement in climate change adaptation is still insufficient, ambivalent, and inconsistent and farmers do not consider adaptation to be urgent. Researchers have argued that this issue is rooted in psychological biases beside other factors. Therefore, the aim of this study is to evaluate how psychological distance determines climate change beliefs, risk perception and adaptation strategies among Iranian farmers. A cross-sectional paper-based survey was conducted in the Dasht-e Azadegan county of Khuzestan province in southwest Iran. The study sample consisted of 250 farmers selected through a multi-stage random sampling process. An expert panel review and a pilot study were conducted to confirm convergent validity and reliability of the scales. The results confirm that all four dimensions of psychological distance influence water management adaptation strategies and non-farm activities. Moreover, all psychological dimensions, except the temporal dimension, affect adaptation in farming management. Thus, making climate change more proximal to decision makers could be a strategic way of encouraging individuals to take adaptive actions. This study emphasizes that concepts of psychological distance can be applied to help organizations (e.g., agriculture extension services) to understand farmers' risk perceptions and responses to climate change impacts and improve risk communication to better engage farmers in climate action. [ABSTRACT FROM AUTHOR]

Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes' habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble approach using five distribution modelling methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy Modelling, Generalized Linear Models, and Random Forest was applied to produce a spatial snakebite risk model for Iran. To achieve this, four venomous snakes' habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and then multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. Multiplying habitat suitability models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi province) has the highest snakebite risk in the country. In addition, villages that were at risk of envenoming from the four snakes were identified. Results revealed that 51,112 villages are at risk of envenoming from M. lebetinus, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N. oxiana. Precipitation seasonality was identified as the most important variable influencing distribution of the P. persicus, E. carinatus and M. lebetinus in Iran. Precipitation of the driest quarter was the most important predictor of suitable habitats of the N. oxiana. Since climatic variables play an important role in shaping the distribution of the four venomous snakes in Iran, thus their distribution may alter with changing climate. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes' habitat suitability models as an indicating factor. Results of this study can be used in snakebite and human-snake conflict management in Iran. We recommend increasing public awareness of snakebite envenoming and education of local people in areas which identified with the highest snakebite risk.

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Inter-annual variation in weather conditions has been shown to affect the reproductive phenological patterns of many organisms. Because of their relatively small body size and dependence on ectothermic prey, temperate-zone insectivorous bats are particularly sensitive to adverse spring environmental conditions that affect the duration of gestation and timing of parturition in these animals. This study aimed to compare phenological recruitment, birth seasonality and synchrony and morphological changes during postnatal growth in Rhinolophuseuryale, Rhinolophusferrumequinum and Myotisemarginatus in two consecutive years representing a typical dry (2015) and an extremely wet climatic event (2016) in a nursing colony in Kerend cave, western Iran. Females of these three bat species arrived from their wintering cave to the nursing colony in late April to mid-May each year. Synchrony of parturition as defined by amount clustering of births within a year assessed by circular statistics showed that for R. euryale and R. ferrumequinum the angular variance in dry year were significantly (P < 0.05) lower than in wet year, indicating a low level of synchrony in 2016. Similar comparison showed that births from M. emarginatus were highly synchrony, and there were no significant differences in timing of births among years (P > 0.05). Generalized estimating equation (GEE) for R. euryale indicated that for body mass and forearm length tests of parallelism (interaction term or growth rate) and tests for equal intercepts (y-intercepts or group term) were significant (P < 0.001). In R. ferrumequinum, the initial (y-intercepts) forearm length and body mass were not significantly (P > 0.05) different between the 2 years, but the tests for parallelism showed a significant decrease in growth rates of body mass and forearm length in the wet year (P < 0.05). Similar comparison in M. emarginatus indicated that for body mass, tests of parallelism were significantly different (P = 0.004), while tests for equal intercepts were not (P = 0.23). Our results suggest that climate changes may have unequal effects on different bat species due to differences in foraging habitat, niche partitioning, reproductive requirements and foraging strategies. [ABSTRACT FROM AUTHOR]

Understanding the effects of climate change and anthropogenic activities on the hydrogeomorpholgical parameters in wetlands ecosystems is vital for designing effective environmental protection and control protocols for these natural capitals. This study develops methodological approach to model the streamflow and sediment inputs to wetlands under the combined effects of climate and land use / land cover (LULC) changes using the Soil and Water Assessment Tool (SWAT). The precipitation and temperature data from General Circulation Models (GCMs) for different Shared Socio-economic Pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) are downscaled and bias-corrected with Euclidean distance method and quantile delta mapping (QDM) for the case of the Anzali wetland watershed (AWW) in Iran. The Land Change Modeler (LCM) is adopted to project the future LULC at the AWW. The results indicate that the precipitation and air temperature across the AWW will decrease and increase, respectively, under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Streamflow and sediment loads will reduce under the sole influence of SSP2-4.5 and SSP5-8.5 climate scenarios. An increase in sediment load and inflow was observed under the combined effects of climate and LULC changes, this is mainly due to the projected increased deforestation and urbanization across the AWW. The findings suggest that the densely vegetated regions, mainly located in the zones with steep slope, significantly prevents large sediment load and high streamflow input to the AWW. Under the combined effects of the climate and LULC changes, by 2100, the projected total sediment input to the wetland will reach 22.66, 20.83, and 19.93 million tons under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The results highlight that without any robust environmental interventions, the large sediment inputs will significantly degrade the Anzali wetland ecosystem and partly-fill the wetland basin, resulting in resigning the wetland from the Montreux record list and the Ramsar Convention on Wetlands of International Importance. [ABSTRACT FROM AUTHOR]

Extended periods of hydro-climate extremes with excessive or scarce rainfall associated with high or low temperatures have resulted in an imbalanced water cycle and inefficient socio-economic systems in several regions of Iran. However, there is a lack of comprehensive investigations on short-term to long-term variations in timing, duration, and temperature of wet/dry spells. This study bridges the current gap through a comprehensive statistical analysis of historical climatic data (1959–2018). Results indicated that the negative tendency of the accumulated rainfall (− 0.16/ − 0.35 mm/year during the past 60/30 years) in 2- to 6-day wet spells had made significant contributions to the ongoing downward trend in annual rainfall (− 0.5/ − 1.5 mm/year during the past 60/30 years) owing to a warmer climate condition. Warmer wet spells are likely responsible for precipitation patterns changes in snow-dominated stations since their wet spells temperature has more than threefold growth with increasing distance to coasts. The most detected trends in climatic patterns have started in the last two decades and become more severe from 2009 to 2018. Our results confirm the alteration of precipitation features across Iran due to anthropogenic climatic change, and suggest expected increase in air temperature would likely result in further dry and warm conditions over the coming decades. [ABSTRACT FROM AUTHOR]

The aim of the current study is to consider farmers' perceptions regarding the impacts of climate change on water resources and their intention toward adaptation in southwestern Iran. To this end, this study applied the theory of reasoned action and the norm activation model as well as these two models in combination. A descriptive quantitative research study was designed and conducted using cross-sectional survey methods among 250 farmers in Khuzestan province in southwestern Iran, selected through multistage sampling methods. Research data were collected through a structured questionnaire whose validity was confirmed by a panel of experts; scale reliability of the questionnaire was approved through a pilot study. Structural equation modeling analysis revealed that the norm activation model, the theory of reasoned action, and a model integrating the two can predict 32, 42, and 47%, respectively, of changes in farmers' intention toward performing climate-change adaptation activities. In the combined model, personal norm, subjective norm, and attitude were able to influence the farmers' intention to perform adaptive behaviors. Attitude towards adaptation is the most powerful predictor in explaining intention to adaptation. Subjective norm is the most important predictors of moral norms which is the logical confirmation behind the combination of the two models. In addition, the combined model has better predicting powerful that each model separately. The research findings hold valuable implications for policymakers seeking to increase the intention of farmers to implement adaptation activities against a background of harsh climate change and water scarcity in this region of Iran. [ABSTRACT FROM AUTHOR]

The Ardabil plain, with an approximate area of 1097.2 km2 in northwestern Iran, has experienced land subsidence due to intensive groundwater withdrawal and long seasons of drought in recent years. Different techniques have been used to investigate and evaluate subsidence in this region including: Global Positioning Systems (GPS), Levelling, and Geotechnical methods. These methods are typically expensive, time-consuming, and identify only a small fraction of the areas prone to subsidence. This study employs an Interferometric Synthetic Aperture Radar (InSAR) technique to measure the long-term subsidence of the plain. An open-source SAR interferometry time series analysis package, LiCSBAS, that integrates with the automated Sentinel-1 InSAR processor (COMET-LiCSAR) is used to analyze Sentinel-1 satellite images from October 2014 to January 2021. Processing of Sentinel-1 images shows that the Ardabil plain has been facing rapid subsidence due to groundwater pumping and reduced rainfall, especially between May 2018 to January 2019. The maximum subsidence rate was 45 mm/yr, measured at the southeastern part of the plain. While providing significant advantages (less processing time and disk space) over other InSAR processing packages, implementation of the LiCSBAS processing package and its accuracy for land subsidence measurements at different scales needs further evaluation. This study provides a procedure for evaluating its efficiency and accuracy for land subsidence measurements by comparing its measurements with the results of the GMTSAR and geotechnical numerical modeling. The results of geotechnical numerical modeling showed land subsidence with an average annual rate of 38 mm between 2006 and 2020, which was close to measurements using the InSAR technique. Comparison of the subsidence measurements of the Ardabil plain using the LiCSBAS package with results obtained from other techniques shows that LiCSBAS is able to accurately detect land deformation at large scales (~ km). However, they may not be optimized for more local deformations such as infrastructure monitoring. [ABSTRACT FROM AUTHOR]