Regional climate variability is generally controlled by atmospheric water vapor (WV), which sources and transport pathways are primarily driven by different large-scale teleconnection processes, ...particularly the El Niño-Southern Oscillation (ENSO). Hence, this study investigated spatial, temporal, and vertical variations in atmospheric WV during cold months of December through March in Iran over the period 2002–2020 and their relationships with the ENSO. Accordingly, we analyzed the atmospheric WV and cloud type data sets provided by the Atmospheric Infrared Sounder (AIRS) and the CloudSat, respectively, over different three atmospheric WV regions of Iran: Mountainous region (R1), the interior region as well as the Caspian Sea (R2), and southern seas (R3). Moreover, two of the strongest El Niño and La Nina events were selected for synoptic analysis and cloudiness monitoring over all three regions of R1, R2, and R3 in Iran. The results showed that atmospheric WV ranged from less than 8 kg/m
2
over the R1 to higher than 14 kg/m
2
over the R3. The atmospheric WV showed statistically insignificant (
p
> 0.05) decreasing trends across all three regions of R1, R2, and R3. The ENSO positively correlated with the atmospheric WV during both December in the R3 and March in the R2. The vertical atmospheric WV showed the highest anomalies at pressure levels of below 850 and above 500 hPa during the El Niño and La Nina events, respectively. The atmospheric WV over the mountainous regions in different longitudes of Iran was strongly affected by the La Nina events. However, across the southern seas, interior region, and mountains in eastern, central, and western Iran, respectively, the El Niño events influence the atmospheric WV. The Sudanese and the Eastern Mediterranean troughs (the Siberia and Arabia high-pressure systems) were highly contributed to atmospheric WV variability over Iran during the strong El Niño (La Nina) events in December 2015 (2010) and March 2016 (2011). The precipitable stratocumulus and nimbostratus clouds were mostly dominant over Iran (particularly across the R3) during the El Niño events in December. In conclusion, the warm (cold) phases of ENSO or the El Niño (La Nina) events effectively increase (decrease) the atmospheric WV over Iran during cold months, particularly both December and March.
•Influence of drought duration and severity on drought recovery period was explored.•MODIS-based vegetation health index was used for drought monitoring.•Drought recovery period was estimated using ...gross primary productivity.•Shrub lands and agriculture classes experienced elongated drought than forest.
Drought is a slow-onset phenomenon driven by the lack of precipitation, affecting the performance of plants and functionality of terrestrial ecosystems. In addition to the length and severity of drought, the period it takes for the plants to return to normal conditions is critical. Remote sensing data with appropriate spatial and temporal coverage facilitates monitoring drought and its consequences on local and global scales. This study investigated the influence of drought duration and severity on the drought recovery period (DRP) for different land use and land cover (LULC) types in Iran. The moderate resolution imaging spectroradiometer (MODIS)-based vegetation health index (VHI) was used to monitor drought in the period 2000–2020. The results identified 2000, 2001, and 2008 as drought years. DRP was estimated using gross primary productivity (GPP). The findings revealed that shrubland and cropland experienced more prolonged droughts than forests, which experienced the shortest drought duration. Similarly, shrublands and croplands had the most prolonged recovery, and forests had the shortest recovery time. A direct relationship was observed between drought severity and DRP in all LULC types, however the local correlation between drought duration and recovery time better revealed the heterogeneity of relationships. This study provides valuable information on the drought resilience of different LULC types for use in achieving better management and a deeper understanding of drought.
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•Local parameters (temperature and precipitation) affect δ18O and δ2H in the Middle East precipitation.•Regional parameters (teleconnection indices) affect δ18O and δ2H in the Middle ...East precipitation.•Most of the developed LMWLs had lower slopes and intercepts than the GMWL.•Simmr package was applied to calculate the contribution of the moisture sources to surface water recharge.
Located in semi-arid and arid regions in the world, the Middle East has always faced water shortage crisis. Therefore, water resources in this area should be studied by accurate methods such as the stable isotope technique. The results of the current isotopic study show the effects of the main local (temperature and precipitation) and regional parameters (teleconnection indices) on the δ18O values of precipitation and surface water resources across the Middle East. Plotting the values of δ18O vs δ2H in some of the major rivers in the Middle East on the local meteoric water lines shows that the surface water resources are dominantly recharged by local precipitation, while the deviation observed in some samples is due to evaporation. The contributions of various air masses (the precipitation events originating from the main air masses) in the recharge of the principal rivers have also been studied. The results of the mixing models demonstrate that the contributions of various air masses in the recharge of rivers vary significantly across the Middle East.
The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a ...result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an intensive level in the area. This research study investigates the LCLU conditions upstream of this watershed for the years 1972, 1986, 2000 and 2014, using Landsat MSS, TM, ETM+ and OLI/TIRS images. LCLU maps for 1972, 1986, and 2000 were produced using pixel-based classification methods. For the 2014 LCLU map, Geographic Object-Based Image Analysis (GEOBIA) in combination with the data-mining capabilities of Gini and J48 machine-learning algorithms were used. The accuracy of the maps was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. The overall accuracy ranged from 89% to 95%, quantity disagreement from 2.1% to 6.6%, and allocation disagreement from 2.1% for 2014 to 2.7% for 2000. The results of this study indicate that a significant amount of change has occurred in the region, and that this has as a consequence affected ecosystem services and human activity. This knowledge of the LCLU status in the area will help managers and decision makers to develop plans and programs aimed at effectively managing the watershed into the future.
OpenStreetMap (OSM) is a volunteered platform designed to provide up-to-date and freely available geographic information data. OSM is known as one of the most extensively used instances of ...Crowdsourcing Geographic Data (CSGD)/Crowdsourced Geographic Information (CGI), Volunteered Geographic Information (VGI), or the Neogeography paradigm. Economically, OSM development can be beneficial for governments, especially in developing countries such as Iran, where financial support is limited. This paper analyzes the spatial pattern, evolution, density, and diversity of OSM road (OSMr) networks in Iran between 2008 and 2016 and looks to find casual relations between the OSM and census statistics. This is due to the fact that OSMr completeness reflects the importance of OSM data in human life. The diversity of OSM roads further reflects the concerns, requirements, and worthiness of clients about different roads’ information. The results show that the road network in Iran considerably increased from 2008 to 2016, with road length increasing to 489,400 km in 2016 from 4300 km in 2008. In addition, road density grew while road diversity and evenness declined, which could be due to an increase in main roads. These results propose a more considerable need for a more comprehensive approach using VGI to supplement gaps in authoritative data in developing countries.
•We analyzes the spatial pattern, evolution, density, diversity of OSM road networks and its relationships with census data.•Road density grew while road diversity and evenness declined.•Mapping direction extended from big cities to medium/small-sized ones.•Western counties located in mountainous regions are still not very active.•Top active counties producing VGI data are mostly populated by urban citizens.
In northern regions, like Finland, peak river discharge is principally controlled by maximum snowmelt runoff during spring (March–May). Global warming and climate change extensively influence both ...the quantity and temporal characteristics of peak discharge in northern rivers by altering snowpack accumulation and melt processes. This study analyzed peak spring flood discharge (PSFD) magnitude (PSFDM) and timing (PSFDT) in four natural rivers (Simojoki, Kuivajoki, Kiiminkijoki, and Temmesjoki) across northern Finland, in terms of long-term (1967–2011) variability, trends, and links to large-scale climate teleconnections. The PSFDM significantly (p < 0.05) declined in the Simojoki, Kuivajoki, and Kiiminkijoki rivers over time. Both the Simojoki and Kuivajoki rivers also experienced significant decreasing trends of about −0.33 and −0.3 (days year−1), respectively, in the PSFDT during 1967–2011. In these two rivers, the less and earlier PSFDs were principally attributable to the warmer spring seasons positively correlated with the North Atlantic Oscillation (NAO) in recent decades. Moreover, daily precipitation time series corresponding to the PSFD events showed no considerable effects on PSFDM and PSFDT changes in all the natural rivers studied. This suggests that less and earlier historical PSFDs in natural rivers at higher latitudes in northern Finland were primarily induced by warmer springtime temperatures influencing snowpack dynamics.
This study investigates the impact of precipitation on Middle Eastern countries like Iran using precise methods such as stable isotope techniques. Stable isotope data for precipitation in Tehran were ...obtained from the Global Network of Isotopes in Precipitation (GNIP) station and sampled for two periods: 1961–1987 and 2000–2004. Precipitation samples were collected, stored, and shipped to a laboratory for stable isotope analyses using the GNIP procedure. Several models, including artificial neural networks (ANNs), stepwise regression, and ensemble machine learning approaches, were applied to simulate stable isotope signatures in precipitation. Among the studied machine learning models, XGboost showed the most accurate simulation with higher R2 (0.84 and 0.86) and lower RMSE (1.97 and 12.54), NSE (0.83 and 0.85), AIC (517.44 and 965.57), and BIC values (531.42 and 979.55) for 18O and 2H compared to other models, respectively. The uncertainty in the simulations of the XGboost model was assessed using the bootstrap technique, indicating that this model accurately predicted stable isotope values. Various wavelet coherence analyses were applied to study the associations between stable isotope signatures and their controlling parameters. The BWC analysis results show coherence relationships, mainly ranging from 16 to 32 months for both δ18O–temperature and δ2H–temperature pairs with the highest average wavelet coherence (AWC). Temperature is the dominant predictor influencing stable isotope signatures of precipitation, while precipitation has lower impacts. This study provides valuable insights into the relationship between stable isotopes and climatological parameters of precipitation in Tehran.
The Middle East is located in a semiarid and arid region and is faced with an intense water shortage crisis. Therefore, studying the hydrochemical characteristics of precipitation as a main part of ...the water cycle has great importance in this region. The hydrochemical analyses showed that the quality of precipitation was mainly affected by dust particles originating from terrestrial environments, while marine and anthropogenic sources had a minor role. The statistical studies showed that the dissolution of evaporative and carbonate minerals mainly controlled the hydrochemistry of precipitation. Precipitation had an acidic nature in some stations and a nonacidic nature in others. Ca2+ was the major acid-neutralizing cation in the Middle East precipitation. Various machine learning methods were also used to simulate the TDS values in precipitation. The accuracy of the developed models was validated, showing that the model developed by the Gboost method was more accurate than those developed by other machine learning techniques due to its higher R2 values. To conclude, the hydrochemistry of precipitation showed significant variations across the Middle East. The dissolution of particles with terrestrial origins dominantly controlled the hydrochemistry of precipitation, while marine and anthropogenic sources had minor roles.
This study analyses spatio-temporal trends in precipitation, temperature, and river discharge in the northeast of Iran during recent decades (1953–2013). The Pettitt, SNHT, Buishand, Box-Pierce, ...Ljung-Box, and McLeod-Li methods were applied to examine homogeneity in time series studied. The nonparametric Mann-Kendall and Sen’s slope estimator tests were used to detect possible significant (
p
< 0.05) temporal trends in hydrometeorological time series and their magnitude, respectively. For time series with autocorrelation, the trend-free pre-whitening (TFPW) method was used to determine significant trends. To explore spatial distributions of trends, their magnitudes were interpolated by the inverse distance whitening (IDW) method. Trend analysis shows that for daily, monthly, and annual precipitation time series, 12.5, 19, and 12.5 % of the stations revealed significant increasing trends, respectively. For mean temperature, warming trends were found at 38, 23, and 31 % of the stations on daily, monthly, and annual timescales, in turn. Daily and monthly river discharge decreased at 80 and 40 % of the stations. Overall, these results indicate significant increases in precipitation and temperature but decreases in river discharge during recent decades. Hence, it can be concluded that decreasing trends in river discharge time series over the northeast of Iran during 1953–2013 are in response to warming temperatures, which increase the rate of evapotranspiration. Differences between the results of our comprehensive large-scale study and those of previous researches confirm the necessity for more model-based local studies on climatic and environmental changes across the northeast of Iran.
Alpine habitats are characterized by a high rate of range restricted species compared to those of lower elevations. This is also the case for the Irano-Anatolian global biodiversity hotspot in ...South-West Asia, which is a mountainous area harbouring a high amount of endemic species. Using two quantitative approaches, Endemicity Analysis and Network-Clustering, we want to identify areas of concordant species distribution patterns in the alpine zone of this region as well as to test the hypothesis that, given the high proportion of endemics among alpine species, delimitation of these areas is determined mainly by endemic alpine species, i.e., areas of concordant species distribution patterns are congruent with areas of endemism. Endemicity Analysis identified six areas of concordant species distribution patterns irrespective of dataset (total alpine species versus endemic alpine species), whereas the Network-Clustering approach identified five and four Bioregions from total alpine species and endemic alpine species, respectively. Most of these areas have been previously identified using the endemic flora of different elevational zones. The identified units using both methods and both datasets are strongly congruent, proposing that they reveal meaningful distribution patterns. Bioregionalization in the Irano-Anatolian biodiversity hotspot appears to be strongly influenced by the endemic alpine species, a pattern likely to hold in alpine regions outside the Irano-Anatolian hotspot.
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