Trend and Homogeneity Analysis of Precipitation in Iran

Analysis of the spatiotemporal changes in global tropospheric ozone concentrations from 1980 to 2020

Tropospheric ozone affects human health, ecosystems, and climate change. Previous studies on Tropospheric Column Ozone (TCO) have primarily concentrated on specific regions or global geographic divisions. This has led to insufficient exploration of the spatiotemporal characteristics and influencing factors of TCO in global and rational subregions. In this study, TCO is calculated using the Modern Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data and corrected using satellite data. Cluster analysis is conducted to explore the temporal characteristics of TCO variations in different regions. The results show that the global TCO is basically distributed latitudinally, with higher TCO in the northern hemisphere, which is related to atmospheric circulation, radiation, stratospheric transport, and the distribution of ozone precursors. Between 1980 and 2020, the global average annual TCO showed an increasing trend at 0.09 DU yr-1 due to rising anthropogenic emissions of ozone precursors (NOx at 589547.86 t yr-1 and NMVOC at 1070818.24 t yr-1), increasing tropopause height (-0.10 hPa yr-1), and the enhanced ozone flux at the tropopause (0.22 ppbv m s-2 yr-1). Cluster analysis reveals different trends in TCO changes across regions. The ocean south of 60°S and parts of West Antarctica (Region 2), the region from 30°N to 60°N and the western oceanic region of 30°S (Region 3), and the region from the equator to 60°S and the region north of 60°N (Region 5) exhibit increasing trends (with rates of 0.08 DU yr-1, 0.07 DU yr-1, and 0.11 DU yr-1, respectively), linked to the enhanced ozone flux at the tropopause, the rising tropopause height and increasing ozone p precursors. Conversely, the decreasing TCO trends in the equatorial Pacific (Region 1) and East Antarctica (Region 4) (with rates of -0.01 DU yr-1 and -0.02 DU yr-1) may be related to increased cloudiness and weakened photochemical reactions.

Energy consumption and thermal comfort of rock-cut and modern buildings

Energy consumption in the building sector justifies the necessity of knowing the thermal comfort perception of vernacular and modern architectural types, based on which a correct recognition was reached for the design of buildings suitable for the climatic conditions of each region. It should be determined that the different types of modern and traditional architecture are in the comfort level in harsh hot climate conditions and how much energy they consume to reach the comfort level. Despite consideration of energy consumption and thermal comfort in different buildings in Iran, there is no clear framework for evaluating these two parameters in different buildings and comparing them. This research aims to compare the indoor thermal comfort levels of vernacular architectural buildings and modern buildings in Iran's semi-hot and dry climate at the peak of summer heat and determine their energy consumption to reach the comfort level. This study has been accomplished by collecting field data, examining the indoor predicted mean vote (PMV) index of the buildings, and comparing them. It was found that rock-cut architecture buildings are in better thermal comfort conditions without energy consumption due to the use of groundmass temperature and low heat exchanges between the indoors and outdoors because of the thermal phase of the materials and the thickness of its layers. The indoor PMV average of rock-cut buildings in summer is -0.61; in modern buildings, it is 0.77, while these two building complexes are in the same climate and close. Also, the energy consumption to reach the comfort level in rock-cut buildings is zero, while modern buildings consume an average of 7.7 kW of electricity daily. The research results will lead to recognizing and modeling the climate design of vernacular architecture, which can be used in today's architecture to reduce energy consumption.

Analysis of extreme rainfall and drought events using statistical and fractal methods: A case study of Mauritius

Due to climate change, extreme rainfall and drought events are becoming more and more frequent in several regions of the globe. We investigated the suitability of employing statistical and fractal (or scaling) methods to characterise extreme precipitation and drought events. The case of the island of Mauritius was considered, for which monthly mean rainfall data for the period January 1950 to December 2016 were analysed. The generalised extreme value distribution was used to extract the 10- and 20-year return levels and the Standardised Precipitation Index (SPI) was used to identify anomalous wet and dry events. A log-term correlation analysis was also performed to characterise the relationship between maximum rainfall and its duration. The results indicate that the 10-year return level is approximately between 500 mm and 850 mm and the 20-year return level is between 600 mm and 1000 mm. Results also show that the extreme maximum rainfall events occur mostly during austral summer (November to April) and could be related to the effects of tropical cyclones and La Niña events, while anomalous dry events were found to be significantly persistent with very long periods of drought. Moreover, there was a strong correlation between maximum rainfall and its duration. The methodology used in this work could be very useful in similar studies for other Small Island Developing States.

Trend and change-point detection analyses of rainfall and temperature over the Awash River basin of Ethiopia

Awash River basin (ARB) as a system is in a state of continuous change that requires successive studies to discern the changes or trends of climatic elements through time due to climate change/variability, and other socio-economical developmental activities in the basin. The livelihood of communities in the ARB is primarily based on rainfall-dependent agriculture. Effects of rainfall anomalies such as reduction of agricultural productivity, water scarcity, and food insecurity are becoming more prevalent in this area. In recent years, ARB has been experiencing more frequent rainfall anomalies that change-point detection test and trend analyses of basin rainfall associated with sea surface temperature is crucial in providing guidance to improve agricultural productivity in ARB. Change-point detection tests such as Pettit's, the von Neumann ratio (VNR), Buishand's range (BR) and standard normal homogeneity (SNH) plus trend analysis Mann-Kendall (MK) test of rainfall and temperature data from 29 meteorological stations in the ARB were carried out from 1986 to 2016. A significant increasing trend of annual and seasonal temperature was found. The temperature change-points for the annual and major rainy season (MRS) were detected in 2001, while for the minor rainy season (mRS) in 1997. A significant decreasing trend, shift, and high variability of rainfall were detected in the downstream part of the ARB. The BR and SNH results showed that the mRS rainfall change-point was in 1998, with a subsequent mean annual decrease of 52.5 mm. The increase (decrease) of rainfall in the annual and MRS was attributable to La Niña (El Niño) events. The significant decreasing trend and change-point of rainfall in the mRS was attributable to the steady warming of the Indian and Atlantic Oceans, local warming, and La Niña events. With this knowledge of the current trends and change-point for rainfall and temperature in the ARB, it is therefore essential that appropriate integrated water management and water-harvesting technologies are established, especially in the downstream areas. Moreover, early detection of El Niño episodes would provide invaluable warning of impending rainfall anomalies in the ARB and would enable better preparations to mitigate its negative effects.

Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

This paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning.

Vegetation response to changes in temperature, rainfall, and dust in arid environments

Climate change is responsible for changes in the world's vegetation. This study was aimed to investigate the effect of long-term variations in the air temperature, precipitation, and dust concentration on normalized difference vegetation index (NDVI) variations in the spring, summer, and growing season over arid regions of Iran. The results showed that the precipitation had a positive association with the NDVI in the spring and growing seasons (β > + 0.28; P < 0.05), while air temperature had a negative relationship with the NDVI changes in these seasons (β > - 0.34; P = 0.013). Our results also showed a negative relationship between the accumulated dust concentration (ADC) and NDVI in the spring (β = - 0.23; P = 0.09) and growing seasons (β = - 0.24; P = 0.003). Spatially, the strongest linkage between NDVI and climatic variables was observed in about two-thirds of the study region. In total, our findings indicate that the use of dust concentration, air temperature, and precipitation helps us to make a more accurate approach for evaluating the impacts of climate change on vegetation variations in the arid environments.

Analysis of the Spatio-Temporal Variability of Precipitation and Drought Intensity in an Arid Catchment in South Africa

Water deficit is high and precipitation varies spatio-temporally in arid areas. This study was conducted to analyse the spatio-temporal variability of precipitation and drought intensity in an arid catchment in South Africa. The Soil and Water Assessment Tool (SWAT) was used to estimate the spatio-temporal precipitation where nine meteorological stations were used as input to the model. The model was calibrated and validated by regionalization with a physical similarity approach. SWAT only predicts precipitation at sub-basin level. Hence, the mean precipitation was further interpolated by using the inverse distance weighted method (IDW). The Mann–Kendall trend test shows that there was no trend in annual precipitation whereas in the monthly precipitation there was a 0.01 mm decrease. Daily precipitation varied from 0.1 to 4 mm whereas in a monthly basis, it varied from 6 mm (September) to 43.4 mm (February). The annual precipitation varied from 169 mm (1983) to 415 mm (2003) with a long-term mean of 280.8 mm. Precipitation is also highly variable in space throughout the catchment. Generally, annual precipitation decreased from north to south; however, during the winter season, the reverse was true due to the influence of rain-bearing condition from the south- western direction. Based on the aridity index (AI), the catchment is categorized as arid. The SPI shows that the 1983 drought was the worst whereas the 2003 and 2004 years were relatively wet. The results from this study provide baseline information for further research in climate change adaptation and environmental monitoring programs in the region.

Streamflow Alterations, Attributions, and Implications in Extended East Rapti Watershed, Central-Southern Nepal

Streamflow alteration and subsequent change in long-term average, seasonality, and extremes (e.g., floods and droughts) may affect water security, which is a major concern in many watersheds across the globe. Both climatic and anthropogenic activities may contribute to such changes. Therefore, this study assesses: (i) Streamflow and precipitation trends to identify streamflow alterations in the Extended East Rapti (EER) watershed in central-southern Nepal; (ii) relationship of the alterations to climatic and anthropogenic sources; and (iii) implications of streamflow changes to the socio-environmental system. The trends in streamflow were analyzed for pre-and post-1990 periods considering the abrupt shift in temperature trend in 1990. Results showed a general decreasing trends in discharge post-1990 in the EER watershed. Human activities have significantly contributed in altering streamflow in the EER. Human-induced streamflow alterations have affected the water availability, food security, river health, aquatic biodiversity, and groundwater abstraction in the EER watershed.

Discontinuities in the Ozone Concentration Time Series from MERRA 2 Reanalysis

Artificial discontinuities in time series are a great problem for trend analysis because they influence the values of the trend and its significance. The aim of this paper is to investigate their occurrence in the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA 2) ozone concentration data. It is the first step toward the utilization of the MERRA 2 ozone data for trend analysis. We use the Pettitt homogeneity test to search for discontinuities in the ozone time series. We showed the data above 4 hPa are not suitable for trend analyses due to the unrealistic patterns in an average ozone concentration and due to the frequent occurrence of significant discontinuities. Below this layer in the stratosphere, their number is much smaller, and mostly, they are insignificant, and the patterns of the average ozone concentration are explainable. In the troposphere, the number of discontinuities increases, but they are insignificant. The transition from Solar Backscatter Ultraviolet Radiometer (SBUV) to Earth Observing System (EOS) Aura data in 2004 is visible only above 1 hPa, where the data are not suitable for trend analyses due to other reasons. We can conclude the MERRA 2 ozone concentration data can be used in trend analysis with caution only below 4 hPa.

Drought and Desertification in Iran

Iran has different climatic and geographical zones (mountainous and desert areas), mostly arid and semi-arid, which are suffering from land degradation. Desertification as a land degradation process in Iran is created by natural and anthropogenic driving forces. Meteorological drought is a major natural driving force of desertification and occurs due to the extended periods of low precipitation. Scarcity of water, as well as the excessive use of water resources, mainly for agriculture, creates negative water balances and changes in plant cover, and accelerates desertification. Despite various political measures having been taken in the past, desertification is still a serious environmental problem in many regions in Iran. In this study, drought and aridity indices derived from long-term temperature and precipitation data were used in order to show long-term drought occurrence in different climatic zones in Iran. The results indicated the occurrence of severe and extremely severe meteorological droughts in recent decades in the areas studied. Moreover, the De Martonne Aridity Index (IDM) and precipitation variability index (PVI) showed an ongoing negative trend on the basis of long-term data and the conducted regression analysis. Rapid population growth, soil salinization, and poor water resource management are also considered as the main anthropogenic drivers. The percentage of the rural population in Iran is decreasing and the urban area is growing fast. Since the 1970s, the usage of groundwater in Iran has increased around fourfold and the average annual decrease in the groundwater table has been around 0.51 m. The results of the study provide a better ex-post and ex-ante understanding of the occurrence of droughts as key driving forces of the desertification in Iran. Additionally, they can enable policymakers to prepare proper regional-based strategic planning in the future. Desertification cannot be stopped or managed completely, but could be mitigated by the adoption of some proposed sustainable land management strategies.

A Comparison of Continuous and Event-Based Rainfall–Runoff (RR) Modelling Using EPA-SWMM

This study investigates the comparative performance of event-based and continuous simulation modelling of a stormwater management model (EPA-SWMM) in calculating total runoff hydrographs and direct runoff hydrographs. Myponga upstream and Scott Creek catchments in South Australia were selected as the case study catchments and model performance was assessed using a total of 36 streamflow events from the period of 2001 to 2004. Goodness-of-fit of the EPA-SWMM models developed using automatic calibration were assessed using eight goodness-of-fit measures including Nash–Sutcliff efficiency (NSE), NSE of daily high flows (ANSE), Kling–Gupta efficiency (KGE), etc. The results of this study suggest that event-based modelling of EPA-SWMM outperforms the continuous simulation approach in producing both total runoff hydrograph (TRH) and direct runoff hydrograph (DRH).

The Role of Moisture Sources and Climatic Teleconnections in Northeastern and South-Central Iran’s Hydro-Climatology

Iran faces climate disparities due to extreme topographic anomalies, the Caspian Sea and the Persian Gulf water bodies, influences from diverse air masses and moisture sources, and its considerable area. FLEXPART model has been utilized to determine the main marine and continental moisture sources for south-central (Shiraz box) and northeastern (Mashhad box) parts of Iran. The marine moisture sources directly influenced extreme drought and wet conditions in Shiraz and Mashhad boxes during the wet period, while no correlation was observed during the dry period. In addition to local components, extreme drought and wet conditions have also been influenced by the climatic teleconnections. Extreme drought conditions mainly occurred during the La Niña phase, while wet conditions mainly occurred during the El Niño phase. Scrutinizing the effect of marine moisture sources on the hydrology of water resources demonstrated that the moisture contribution from the Arabian Sea directly influenced the discharges of Chenar-rahdar (in the Shiraz box) and Kardeh (in the Mashhad box) rivers during the wet period. However, the Red Sea inversely correlated with the discharges of both rivers during the dry period. Hydrogeologists, hydrologists, and meteorologists can utilize the outputs of this survey to develop climatology and hydrology models in the future.

The Identification of Iran’s Moisture Sources Using a Lagrangian Particle Dispersion Model

Iran has faced many water shortage crises in the past. Iran’s moisture sources for precipitation were identified by Lagrangian approach using the FLEXible PARTicle dispersion model (FLEXPART) v9.0 model. The results demonstrate that Iran receives its moisture from both continental and oceanic sources. During the wet season, moisture uptake from the Arabian Sea, the Persian Gulf, and the Mediterranean Sea is dominant, while during the dry season, the role of the Red Sea, the Caspian Sea, and the Persian Gulf is intensified. Studying drought conditions by comparing 1-month, 6-month, and 12-month standardized precipitation index (SPI) with (E-P) values of oceanic and continental moisture sources (E stands for the evaporation and P the precipitation) using multiregression model demonstrates that among oceanic sources the Arabian Sea, the Persian Gulf, the Mediterranean Sea, and the Indian Ocean affect SPI values and among continental sources, moisture from bare grounds and cultivated lands influences SPI values during wet season. However, no correlation exists between oceanic and continental (E-P) and SPI values during the dry season. The results obtained by this study can be used by meteorologists and hydrology scientists for future water management programmes in Iran.