Qazvin Plain Research Articles

Tracking of Infectious Diseases and Deadly Injuries through Signs Observed in Excavated Human Skeletons of 2000 BC/Iron Age in Iran.

Throughout history, many wars have occurred for various reasons, and many empires and kings have fallen or many people killed by wars. Wars were not always due to the conquest of the country. in the Iron Age, societies were governed by tribes at the head of the tribe, and war was only for to seize property, slaves, and food. Our research area is the same period as the Medes Kingdom, which included the union of small, large tribes, wars between tribes existed in that period, and their signs can be seen on the remains of the people of that period. Our research is related to human remains from Sagezabad cemetery, Qazvin plain, which dates back to 2000 BC (Iron Age 2 and 3) in Iran. The blows on the remains were very serious and caused death. We have discussed how to kill by "considering the injured body". Our investigation of how people were killed in war based on injury marks and bullet holes in bones, and simulating those injuries to body tissues and organs also, people who had bone cuts from the war and survived and had bone repair and died due to lack of nutrients and infection were also discussed.

Oral Infections in Ancient Human Skulls in 2000 BC/Iron Age, Iran.

Oral infections have been seen in humans since ancient times. Excessive penetration of this infection can cause human death. Most of these infections are gum cysts and abscesses. The cyst creates large hard lumps in the gums, which is causes loose, and protruding teeth and abscesses, causing cavities in the jawbone and teeth. In this article, we have discussed for this infectious disease in 4000 - year - old ancient humans from Qazvin Province, Iran. The bone remains of our research are related to Sagezabad ancient cemetery in Qazvin plain. We tried to use reliable international atlases to get detailed information about ancient oral infections. The bones were extracted from the 2019 excavation of the Ghara Tappe area of Sagezabad for the Iron Age 2nd and 3rd Qazvin plains of Iran. This cemetery belongs to the period of the Medes Kingdom (pre - Achaemenian kingdom) in Iran. We have discussed one of the ancient cemeteries with a large number of ancient populations. In this cemetery, there are signs of war and infectious diseases on the bones, which can be clearly seen. We have specially mentioned the abscess as the cause of oral infection from Sagezabad cemetery. Oral infection existed in Iran since 2000 BC. Of course, this infection was common in ancient times and even Paleolithic period, like Homo Heidelbergensis.

Assessment of Climate Change-Induced Water Scarcity Risk by Using a Coupled System Dynamics and Bayesian Network Modeling Approaches

The water scarcity risk induced by climate change is contributing to a sequence of hydrological and socioeconomic impacts. Certain numbers of related impacts are locked in already and are expected to be much greater in the future. So, there is still a lack of understanding of its dynamics, origin, propagation, and the mutual interaction of its drivers. In recent years, several model-based approaches have been introduced to tackle the complexity, dynamics, and uncertainty of water scarcity specifically. However, the coupled modeling while addressing different aspects of the risk of water scarcity under the climate change scenarios has been rarely done. For bridging this gap, in this research, the combination of complementary System Dynamics modeling and Bayesian Network was applied to Qazvin Plain in Iran with five AOGCM models under two Shared Socioeconomic Pathways (SSP) scenarios (126 and 585). Key findings of this research show: 1) Baseline risk assessment indicates a low probability of water scarcity; however, in the future 30-year time horizon with continuous change in hazard, vulnerability, and exposure for SSP126, the risk fell in the extreme category with an average probability of 41%. Under SSP585, the risk varies between extreme and high categories with an average probability of 47%. 2) Economic development, particularly regional gross domestic product (RGDP) in 2045–2054 in SSP585 can diminish the negative projected consequences of climate change and therefore investments in adaptation policies could offset negative consequences, highlighting the role of economic growth in climate resilience. 3) It is projected that crop yield and income will receive the largest negative effects due to cutting back the agriculture area. 4) Considering the interplay of climate change, economic development, and water extraction policies is essential for the design, operation, and management of water-related activities. The proposed integrated methodology provides a comprehensive framework for understanding climate change-induced water scarcity risks, their drivers, and potential consequences. This approach facilitates adaptive decision-making to address the evolving challenges posed by climate change.

Contribution of predictive factors of land degradation occurrence applying maximum entropy model

Land degradation (LD) is a significant environmental issue that needs to be spatially mapped in different areas. This research provides a framework to prioritize the geo-environmental and anthropogenic factors on LD in a semi-arid region, Qazvin Plain, in Iran based on the maximum entropy model for the first time and also to produce the LD hazard map. The LD inventory map that shows the occurrence points of LD was prepared based on the decreasing trend of net primary productivity (NPP) over 20 years. 120 random points were selected as degraded sites where NPP showed a decreasing trend. Several environmental and anthropogenic factors including temperature, rainfall, land use, normalized difference vegetation index (NDVI), vegetation soil salinity index (VSSI), visible and shortwave infrared drought index (VSDI), electrical conductivity (EC) and sodium adsorption ratio (SAR) of groundwater, groundwater table, annual depletion of groundwater resources, slope, aspect, and altitude were considered as predictive factors of LD and their relative contributions were determined using the maximum entropy model. According to the findings, 49.74% of the region was in the class of low degradation. Also, 22.57%, 17.87%, and 9.82% of the plain were related to the moderate, high, and very high degradation classes, respectively. The model specified altitude as the most significant variable influencing LD followed by land use, slope, and VSSI. The least important variable affecting LD in the study area was the aspect. The results of modeling LD also showed that the areas most sensitive to LD were frequently rangelands which are mostly found on steep slopes and highlands. Based on the UAC value, the model illustrated a very good efficiency with a value of 0.8. According to the results, the maximum entropy model successfully mapped the LD hazard in the Qazvin plain.

Assessment of water productivity improvement strategies using system dynamics approach

This study utilizes a system dynamics approach (SD) to assess the effects of water productivity improvement strategies on the Qazvin plain, Iran, and the uncertainty of the individual and interactive effects. The key indicators included in the important strategies are cropping pattern scenarios (CPS), deficit irrigation (DI), and modern irrigation systems development (MISD). Plain-scale results show that CPS 7, CPS 4, and CPS 8 had the highest physical water productivity (WPp) at 2.11, 1.99, and 1.95 kg/m3, respectively, representing a 21, 14, and 12 percent increase over CPS 1. Compared with CPS 1, CPS 4, CPS 6, and CPS 8 showed the highest values of WPe (5678, 5568, and 5503 Rials/m3, respectively). At the field scale, under DI, WPp increased for all crops (except corn, which was the most sensitive), but WPe is only increased for tomato, canola, pea, and barley and reduced for corn, potato, beans, lentils, and sugar beet. The WPe was affected by the DI, the irrigation system type, and the CPS. CPS 7 and CPS 6 had the highest and lowest water requirements, respectively, with 11,699 and 8207 m3/ha. Volume decline in aquifers is significantly affected by both CPS and DI. The CPS6, CPS8, and CPS2 were better than other scenarios. By modifying the cropping pattern, it is possible to prevent aquifer decline, thus improving the aquifer status (CPS5). MISD increased both field and plain WPp for all crops. The MISD improved groundwater resources and reduced demand by increasing efficiency to improve aquifer condition.

Crop monitoring and biomass estimation based on downscaled remote sensing data in AquaCrop model (case study: Qazvin Plain, Iran).

Ensuring food security requires monitoring crop growth conditions prior to harvesting. This research encompass three stages: remote sensing, crop modeling, and establishing a connection between the two. Besides, Fusion satellite images have been implemented to monitor and investigate fodder corn in three study areas. In the remote sensing stage, after implementing the downscaling algorithm and producing the leaf area index (LAI) time series, the results were compared with the estimated values from Landsat 8 and MODIS images, which were associated with overestimation in all cases. Furthermore, the results exhibited statistically significance with R2 > 95% and P-value < 0.05. The AquaCrop model was first calibrated and implemented in the crop modeling section at each growth stage based on the observational data measured in each field. The accuracy of the simulated model was checked according to the results of statistical indicators with high accuracy (NRMSE = 10% and RMSE = 0.03 (Ton/ha)) at significant level (95%) and was associated with underestimation. Using the SVM decision support algorithm, the relationship between downscaled LAI and calibrated CC (crop canopy) was estimated. This relationship was generated with 70% of the data and subsequently validated using the remaining 30% (R2 = 0.99, NRMSE = 0.01). On this basis, CC values were predicted. Finally, biomass values were compared with observed biomass values. According to the results of statistical indicators (RMSE = 0.19 (Ton/ha), NRMSE = 0.01, R2 = 0.96, P-value < 0.05), biomass estimation was highly accurate. These results demonstrate the reliability and accuracy of the model and the proposed method for simulating and estimating biomass before harvesting.

Quaternary calcretes of Qazvin Plain (N Iran) based on multi-story geochemical and petrographic signatures

Quaternary calcretes of Qazvin Plain (N Iran) based on multi-story geochemical and petrographic signatures

The Effect of Drought Phenomenon on the Surface of Groundwater Aquifer in Qazvin Plain in Iran

As one of the most catastrophic atmospheric events, drought affects various aspects of the environment. Groundwater resources are one of the sectors that are influenced by long-term droughts and have received insufficient attention compared to other aspects of the environment. Rainfall data collected by 23 metrological stations for 20 years (2005, 2010, 2015, 2020) was used to investigate the drought event and its situation in Qazvin Plain, the central plateau of Iran. Drought characteristics are evaluated using the 24-month standardized precipitation index (SPI). The results of SPI indicated that insufficient precipitation, excessive use of groundwater for irrigation, and utilization of uncontrolled wells caused a significant reduction in groundwater aquifers from 2015 to 2020. To assess the performance of the SPI, a five-year moving average of available precipitation data was calculated, and the result confirmed the outcomes of the SPI. Appropriate geostatistical interpolation methods are used to generate maps of drought zoning. Based on the results of this investigation in the northeastern part of the study area, June and November had the highest and lowest rates of drought, respectively. The linear regression between the annual average of precipitation and the changes in groundwater aquifer level exposed a significant correlation of R2 = 0.4253. Furthermore, linear regression between 24-month SPI and groundwater aquifer level indicated a correlation of R2 = 0.614. Considering the results of this study, the reduction of groundwater aquifer levels in Qazvin Plain from 2015 to 2015 exposed a significant negative difference compared to previous years (2005 to 2010).

Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions

Predicting soil properties and evaluating their functions along with their related driving factors is useful for providing useful geographical information for soil management, which is especially important in arid and semi-arid regions. This study investigates the use of a structural equation modeling (SEM) approach for assessing the effects of soil forming factors through environ-mental proxies on three key soil properties, namely soil organic carbon (SOC), calcium carbonate equivalent (CCE) and clay content (clay) in an arid and semi-arid region of Iran. Using a set of 259 soil profiles collected over years 2016–2020 in the Qazvin plain, the cause-effect relationships were estimated between these soil properties and nine environmental factors derived from a digital elevation model and from satellite images. Focusing on two main horizons A and B, it was shown that normalized difference vegetation index, midslope position, elevation, multi-resolution valley bottom flatness, and saga wetness index are impacting these soil properties. Inside each horizon, the effect of CCE and clay on SOC was also evidenced, but to an extent that depends on the horizon. For each soil property, we were able to clearly identify the relationships between the two horizons. Although our SEM approach proved to be useful for identifying and estimating the cause-effect relationships, it failed to provide a good predictive model as required for a relevant digital soil mapping of these soil properties. However, as the SEM approach allows combining soil science knowledge inside a model that accounts for soil forming factors, external factors, and soil system at the same time, it permits an investigation of their potential cause-effect relationships in a rich theoretical framework. The SEM methodology is thus potentially useful for soil scientists that are studying various soil properties in other parts of the world, even if it cannot be advocated as an efficient digital soil mapping method in general.

Water shortage and optimal pattern of field cropping cultivation: Addressing economic and environmental concerns in Qazvin Plain, Iran

Iran is currently suffering from a severe water shortage. Frequent droughts, along with over-abstraction of surface and groundwater, have pushed the country's water situation to crisis levels. To deal with this issue, changing the cultivation pattern has been suggested. The planners stated that other environmental objectives would be achieved by implementing changes to the cultivation pattern along with reducing water consumption. Concerning this issue, the present study aimed to identify the optimal water-saving cultivation pattern in the field cropping's subsector in Qazvin province. This province is particularly important in the production of field cropping, which has caused the groundwater level to drop. Therefore, 246 farmers were selected using a simple two-stage random sampling method, and the information was collected in 2019. According to the experts' ideas, a 15% increase in gross profit, a 10% decrease in agricultural water consumption, job stability, and a 15% decrease in chemical fertilizers and pesticides were considered the optimal goals. The findings showed that through the optimal allocation of the inputs to Low Water-Demand (LWD) and High Water-Demand (HWD) crops, the farmers could not only save water and different types of fertilizers and pesticides but also achieve their objectives in terms of their desired profit. Therefore, designing a system of allocation and distribution, establishing a standardization system, and monitoring and controlling farmers' behavior concerning the use of inputs can achieve the goal concerning farmers' income and goals for protecting water resources and the environment.

Downscaling WGHM-Based Groundwater Storage Using Random Forest Method: A Regional Study over Qazvin Plain, Iran

Climate change, urbanization, and a growing population have led to a rapid increase in groundwater (GW) use. As a result, monitoring groundwater changes is essential for water managers and decision-makers. Due to the lack of reliable and insufficient in situ information, remote sensing and hydrological models may be counted as alternative sources to assess GW storage changes on regional and global scales. However, often, these hydrological models have a low spatial resolution for water-related applications on a small scale. Therefore, the main purpose of this study is to downscale the GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5 degrees) to a finer spatial resolution (0.1 degrees) using fine spatial resolution auxiliary datasets (0.1 degrees), such as evaporation (E), surface (SRO), subsurface runoff (SSRO), snow depth (SD), and volumetric soil water (SWVL), from the ERA5-Land model, as well as the global precipitation (Pre) measurement (GPM-IMERG) product. The Qazvin Plain in central Iran was selected as the case study region, as it faces a severe decline in GW resources. Different statistical regression models were tested for the GWSA downscaling to find the most suitable method. Moreover, since different water budget components (such as precipitation or storage) are known to have temporal lead or lag relative to each other, the approach also incorporates a time shift factor. The most suitable regression model with the highest skill score during the training-validation was selected and applied to predict the final 0.1-degree GWSA. The downscaled results showed high agreement with the in situ groundwater levels over the Qazvin Plain on both interannual and monthly time scales, with a correlation coefficient of 0.989 and 0.62, respectively. Moreover, the downscaled product represents clear proof that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at a higher spatial resolution. The major benefit of the proposed method lies in the utilization of only the auxiliary data that are available with global coverage and are free of charge, while not requiring in situ GW records for training or prediction. Therefore, the proposed downscaling technique can potentially be applied at a global scale and to aquifers in other geographical regions.

Towards an integrated system modeling of water scarcity with projected changes in climate and socioeconomic conditions

Water scarcity is one of the major challenges in semi-arid and arid areas. Drivers for water scarcity include climate change, population growth, economic, and agricultural development. This paper presents a framework for assessing water scarcity under the impact of climate, industry, and socio-economic changes in the Qazvin Plain, Iran. A system dynamics model is developed and calibrated using historical data to evaluate the effects of the projected drivers on water scarcity in 2025–2054. A Bayesian averaging model was used to manage the uncertainty in the GFDL, INM, IPSL, MPI-ESM1-2, and MRI.MRI-ESM2-0 climate projections under the two future SSP126 and SSP585 (shared socioeconomic pathways) scenarios. The results demonstrate that the water scarcity index (with a minimum of 0 and a maximum of 1) is about 0.4 and 0.7 in SSP126 and SSP585, respectively, which may severely affect agricultural development. On the other hand, the industry, domestic, and service sectors are more resilient to these variations with no probable major effects on water scarcity. However, the stress on the agricultural sector may cause migration of the workforce to industry. Policymakers must focus on implementing appropriate adaptation strategies for the agricultural sector to prepare for unpredictable shocks to the system.

Evaluation and validation of salinity monitoring indices in the Qazvin plain

Evaluation and validation of salinity monitoring indices in the Qazvin plain

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

AbstractAlthough land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

Optimization of water and land allocation in salinity and deficit- irrigation conditions at farm level in Qazvin plain.

Improper extraction of water from resources especially in arid and semi-arid regions leads to a decrease in the quality of water and soil resources. In such areas, management activities such as increasing water productivity in agricultural sector would be a key step towards sustainable development. Therefore, water resources management to improve the allocation of limited water supplies is essential. In this study, a non-linear programming optimization model have been combined with a AquaCrop model to determine the optimal water and land allocation considering the quality issues of both water and soil resources with focusing on enhancing agriculture water productivity. For this purpose, the spatial variations of chemical and physical properties of soil in the Qazvin plain were taken into account. The soil of study site was divided into three salinity classes, and three weather conditions were identified by Standardized Precipitation Index (SPI). Moreover, five irrigation strategies were modeled under each weather condition. To understand the response of major crops under cultivation to water and salinity, the AquaCrop model was calibrated and validated (2005-2020) and utilized in the objective function. Accordingly, the production functions of the different products were obtained, and the cultivation area as well as amount of water consumption of the crops were optimized by using the target functions of maximum net income and maximum water use efficiency. The results showed that the model is capable of simulating crop yield in salinity and water deficit conditions. The coefficient of determination (R2) for barley, wheat and maize was equal to 0.86, 0.92, and 0.96, respectively. Findings reveal that total irrigation water could be reduced by 20% on average without profit reduction when compared to the profit of the present situation. Total economic profit could be increased by 18% on average through the optimization of water allocation and cropping pattern with the same water supply amount as that of the current situation. Also, the water productivity increased between 12 to 30% under these conditions. Therefore, the proposed model can efficiently optimize the amount of irrigation water and cultivation area on a regional scale considering salinity conditions.

Reviewing the Effects of Guaranteed and Purchasing Price Policies on Cultivation Pattern of Agronomic Crops in Qazvin Plain, Iran

The effects of the guaranteed and purchasing price policy on agronomic crop cultivation patterns were investigated under three scenarios in the Qazvin plain of Iran. In all scenarios, the surface under cultivation of water-saving products, especially wheat, decreased and on the contrary, the surface under cultivation of water-taking products, such as alfalfa and tomato, increased. The average income of farmers in the region increases under all scenarios; the lowest increase is related to the second scenario in which farmers’ income rises by 7.5–7.7%, and the highest increase is related to the third scenario in which farmers’ income rises by 8.5%.

Classification, Study and Analysis of Animal Bones in Shirtel Area in Qazvin Plain from Bronze Age to Mid-Iron Age

Classification, Study and Analysis of Animal Bones in Shirtel Area in Qazvin Plain from Bronze Age to Mid-Iron Age

The effect of increasing water use efficiency on improving the status of groundwater resources using WEAP model in Qazvin Plain

The effect of increasing water use efficiency on improving the status of groundwater resources using WEAP model in Qazvin Plain

The Effect of Drought Phenomenon on the Surface of Groundwater Aquifer in Qazvin Plain in Iran

As one of the most catastrophic atmospheric events, drought affects various aspects of the environment. Groundwater resources are one the sectors that are influenced by long-term droughts and received insufficient attention compared to other aspects of the environment. Rainfall data collected by 23 metrological stations for 20 years (2005, 2010, 2015,2020) was used to investigate the drought event and its situation in Qazvin plain, the central plateau of Iran. Drought characteristics are evaluated using the 24-month standardized precipitation index (SPI). The results of SPI indicated that insufficient precipitation, excessive use of groundwater for irrigation, and utilization of uncontrolled wells caused a significant reduction in groundwater aquifers from 2015 to 2020. To assess the performance of the SPI, a five-year moving average of available precipitation data was calculated, and the result confirmed the outcomes of SPI. Appropriate geostatistical interpolation methods are used to generate maps of drought zoning. Based on the results of this investigation in the northeastern part of the study area, June and November had the highest and the lowest rate of drought, respectively. The linear regression between the annual average of precipitation and the changes of groundwater aquifer level exposed a significant correlation of R2 = 0.4253. Furthermore, linear regression between 24-month SPI and groundwater aquifer level indicated a correlation of R2 = 0.614. Considering the results of this study, the reduction of groundwater aquifer levels in Qazvin plain from 2015 to 2015 exposed a significant negative difference compared to previous years (2005 to 2010).

Modeling the Vertical Soil Calcium Carbonate Equivalent Variation by Machine Learning Algorithms in Qazvin Plain

Modeling the Vertical Soil Calcium Carbonate Equivalent Variation by Machine Learning Algorithms in Qazvin Plain