Qazvin Plain Research Articles

Investigation and prediction of maize water requirements in four growth stages under the influence of natural factors (Case study: Qazvin plain, Iran)

Since maize water requirement is different at different growth stages, so prediction and extraction of association rules related to water requirements of the plant were performed separately at initial, development, mid, and late season growth stages. Accordingly, information on water requirement of maize during 20 years (2000–2019) in Qazvin plain was used. First, the results of C5.0, CART, CHAID, and QUIST algorithms related to corn water demand forecast were evaluated. According to the results, CART algorithm at the initial growth stage and C5.0 algorithm at the development, mid and late season stages of growth, had the best performance in predicting water requirements of maize. The factors of air humidity and precipitation were the most important factors in predicting water requirements of maize at the initial stage of growth by CART tree algorithm. Also, according to the results of C5.0 algorithm, it was found that at the development and mid-season growth stages, precipitation and air temperature were the most important, while at the late season stage of growth, the two factors of sunny (sunshine) hours and wind speed were most important in predicting plant water requirements. Finally, using Apriori algorithm, association rules between water requirements and the factors affecting it were extracted at four growth stages of maize. The results of association rules were evaluated by indicators of confidence, support, and lift. According to the results of Apriori algorithm, precipitation at the initial and development growth stages as well as air temperature and wind speed factors at the mid and late season stages of growth, respectively, had the greatest relationship with water requirements of maize.

Nutritional Status Assessment of Pistachio Orchards in Qazvin Plain, Iran

ABSTRACT Plant analysis is used to optimize the fertilizer applications and diagnosis of the plant nutrition disorders. In the present study, the standard norms for pistachio orchards have been calculated and compared using two methods of Compositional Nutrient Diagnosis (CND) and Deviation from Optimum Percentage (DOP) in Qazvin Plain (Iran). The composite leaf samples were taken from 200 pistachio orchards during three years (2017–2019) and concentrations of Manganese (Mn), Zinc (Zn), Iron (Fe), Magnesium (Mg), Calcium (Ca), Potassium (K), Phosphorus (P), Nitrogen (N), Boron (B), and Copper (Cu) were measured, along with recording the orchards’ yield. The results of CND method showed that the most frequency percentage of the nutrients deficiency was respectively belonged to Mn, Mg, and P in the orchards. Same results were obtained in DOP method. The priority of the nutrient’s deficiency in DOP method was as follows: Mn > Mg > P > K> Ca. In general, it is concluded that Mn, Mg, and P are the most limiting nutrients, are which due to being calcareous, high K content and low organic matter content of the soil, these deficiencies are justified. Therefore, it is recommended to use organic matters, P, Mg, and Mn to improve the yield of pistachio orchards.

Digital Modeling of Three-Dimensional Soil Salinity Variation Using Machine Learning Algorithms in Arid and Semi-Arid lands of Qazvin Plain

Soil salinity, as one of the most important indicators of soil quality, has crucial roles in land use planning and land management in arid and semi-arid regions. The aim of this study was to model soil salinity at five standard depth (0-5, 5-15, 15-30, 30-60, and 60-100 cm) of global digital soil mapping project in 60,000 hectares of Qazvin plain with spatial resolution of 15m. Field studies included a sampling of 278 soil profiles and then the EC was measured in the laboratory. The recursive feature elimination (RFE) method was employed to select environmental covariates including parameters extracted from Landsat 8 image (OLI/TIRS) data, topography, and climatic parameters. Four machine learning algorithms as random forest (RF), cubist (CB), decision tree regression (DTr), and k-nearest neighbors (k-NN) were applied for predicting and mapping soil salinity. According to RFE, 10 covariates were chosen for each standardized depth. The results of modeling showed that the CB model at the depth of 0-5 and 15-30 cm with R2 values of 0.92 and 0.85 and RMSE 4.77 and 7.90 dS/m and the RF model at depths of 5-15, 30-60, and 60-100 cm with R2 values of 0.93, 0.94, 0.96 and RMSE 6.65, 5.10 and 3.20 dS/m, respectively, had the highest accuracy compared to two other models i.e., DTr and k-NN. Furthermore, the covariates extracted from RS data had more impact on topsoil salinity prediction while the climate and topographic attributes influence subsurface soil salinity. Generally, The RF and CB models along with appropriate environmental covariates were able to present salinity variation of study standard depths.

Prediction of Local Alterations in the Relative Amounts of Temperature and Precipitation Caused by Climate Change in Near and Far Future, and Drought Investigation Using SPI and SPEI Indices in Qazvin Plain, Iran

Prediction of Local Alterations in the Relative Amounts of Temperature and Precipitation Caused by Climate Change in Near and Far Future, and Drought Investigation Using SPI and SPEI Indices in Qazvin Plain, Iran

Evaluation of various meteorological datasets in estimation yield and actual evapotranspiration of wheat and maize (case study: Qazvin plain)

Precipitation (P) and temperature (T) are important factors in agricultural studies, water resources, and ecosystems. Therefore, their accurate measurement and estimation are very important. In this study, GLDAS-AgMERRA, GLDAS-CRU, GLDAS-AgCFSR networked meteorological datasets were evaluated in estimating the yield and actual evapotranspiration (ETa) of wheat and maize plants and comparing them with Qazvin Synoptic Station information. For this purpose, the Qazvin Synoptic Station information from 1980 to 2010 and the climatic information of the mentioned sets was extracted for six scenarios (S1: P and potential evapotranspiration (ETp) of GLDAS-T of CRU,S2: P and ETP of GLDAS-T of AgMERRA, S3: P of GLDAS- T and ETP of CRU, S4: P of GLDAS- T and ETP of AgMERRA, S5: P and ETP of GLDAS- T of AgCFSR, S6: P of GLDAS- T and ETp of AgCFSR). These dataset’s data quality was evaluated using R2, NRMSE, and ME statistical parameters with Aqua Crop model outputs. The calculated statistical parameters showed that in estimating the yield of the simulated wheat plant using Scenario 2 and 4, for maize, scenarios 1 and 3 were better correlated with other scenarios. In terms of ETa, Scenario 3 for maize and Scenario 6 for wheat were better correlated. The results show that accurately networked meteorological datasets can be used to estimate crop yields.

Geochemistry of quaternary sediments of Haji-Arab river catchment, Qazvin Plain, Northwestern Iran: provenance and source rock weathering

Geochemistry of quaternary sediments of Haji-Arab river catchment, Qazvin Plain, Northwestern Iran: provenance and source rock weathering

Evaluation of the effect of climate change on maize water footprint under RCPs scenarios in Qazvin plain, Iran

Climate affects agriculture and the main effect of climate change on agriculture largely depends on two variables: temperature (T) and precipitation (P). In this study, the effect of climate change on maize yield and water footprint (WF) in Qazvin plain was investigated. Three scenarios (RCP2.6, RCP4.5, and RCP8.5) for 2021–2040, 2041–2060, 2061–2080, and 2081–2100, respectively, were generated by the LARS-WG model and compared with the baseline period 1986–2015. Yield (Y), water requirement (WR), and evapotranspiration (ET) of maize were simulated using the Aqua Crop model for baseline and future periods. In this study, the results of the scenarios were compared with the observed data of Qazvin plain for maize crop by using the statistical error criteria including coefficient of explanation (R2), normal square root means error (NRMSE), and mean absolute error (MAE). The simulation results of the LARS-WG model in the baseline showed that the model has more accurate in the simulation of minimum temperature (Tmin) and maximum temperature (Tmin) than P. Also, Our findings have investigated that the T will increase in future periods. P changes were seen as both decreasing and increasing. The results showed that the yield decreased in future periods, whereas the WR and ET increased in most of the models used. Also, the results showed that the maize WF will increase in future periods. The results obtained and the proposed method will help water managers, users, and agricultural developers for preparing new water-saving strategies and achieving agricultural sustainability.

A multi‐temporal satellite‐based risk analysis of archaeological sites in Qazvin plain (Iran)

AbstractIn recent decades, we have witnessed a dramatic increase in the risks and damages to archaeological sites and heritage places across the globe. By analysing a case study (the Qazvin plain) in northern Iran, we propose a classification of the types of risks and damages to archaeological sites and we assess the impact of each type over the last 40 years (1980s–2020s). Our research methodology consists of the cross‐correlated multi‐temporal analysis of different sets of satellite imagery with 167 archaeological sites in the Qazvin plain to determine the types, extension and evolution of the damages over the time. This methodology allowed to develop a three‐tier hierarchical framework based on the drivers, threats, and actions jeopardizing the cultural heritage of the region. The result of this study permits to overturn current narratives inferring that the most impacting risks to archaeological sites and heritage places are caused by natural hazards, looting and violent destruction rather than agricultural activity or construction. Consequently, based on this evidence, we draw a distinction between ‘silent’ but constant and pervasive and ‘loud’ but short‐living and circumscribed risks.

Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling

Flood spatial susceptibility prediction is the first essential step in developing flood mitigation strategies and reducing flood damage. Flood occurrence is a complex process that is not easily predicted through simple methods. This study describes optimization of support vector regression (SVR) using meta-optimization algorithms including the grasshopper optimization algorithm (GOA) and particle swarm optimization (PSO) for flood modeling at Qazvin Plain, Iran. Geospatial data including nine readily available geo-environmental flood conditioning factors (i.e., ground slope, aspect, elevation, planform curvature, profile curvature, proximity to a river, land use, lithology and rainfall) were derived. The information gain ratio (IGR) method was used to determine the relative importance of input variables. A historical flood inventory map for 43 locations was created from existing reports. The geospatial data and historical flood levels were used to construct the training and testing datasets. Then, the training dataset was used to generate flood-susceptibility maps using the optimized SVR model with the GOA and PSO algorithms. Finally, the predictive accuracy of the models was quantified using the statistical measures of root mean square error (RMSE), mean absolute error (MAE), and area under the receiver operating characteristic (ROC) curve (AUC). Although both the GOA and PSO algorithms improved SVR performance, the SVR-GOA model performed best (AUC = 0.959, RMSE = 0.31 and MSE = 0.098), followed by the SVR-PSO model (AUC = 0.959, RMSE = 0.33 and MSE = 0.11) and standalone SVR model (AUC = 0.87, RMSE = 0.35 and MSE = 0.12). Elevation, lithology and aspect had the highest IGR values and were identified as the most effective predictors of flood susceptibility.

System dynamics simulation of regional water supply and demand using a food-energy-water nexus approach: Application to Qazvin Plain, Iran

Understanding the complexity and feedbacks among food, energy, and water (FEW) systems is key to making informed decisions about sustainable development. This paper presents qualitative representation and quantitative system dynamics simulation of the water resources system in the Qazvin Plain, Iran, taking into account the energy intensity of water supply and interconnected water use sectors (e.g., urban, industrial, and agricultural). Qazvin Plain faces water resources challenges that are common to arid/semi-arid areas, including frequent droughts, declining surface water and groundwater, and increased urban and agricultural water demand. A system dynamics model is developed using historical data (2006–2016) to investigate the effects of anticipated dynamics of integrated water and energy sectors in the next two decades. The results of policy scenarios (2020–2039) demonstrate that the continuation of the existing management policies will cause severe damage to the water and energy sectors, pushing the system towards water resources limits to growth. An annual groundwater table decline of nearly 1 m is anticipated, indicating significant overshoot of the plain's natural recharge capacity, which may lead to the depletion of recoverable groundwater in the plain within the next three decades. The groundwater table decline will cause energy consumption of water supply to increase by about 32% (i.e., 380 GWh) to maintain irrigated agriculture. It is critical to implement a combination of water demand and supply management policies (e.g., net agricultural water savings and recycling treated wastewater) to delay the problem of water limits to growth in the region.

Assessment of the optimized scenarios for economic-environmental conjunctive water use utilizing gravitational search algorithm

Holding proper ratio between groundwater extraction and surface water allocation in conjunctive use of water resources may be a desirable way for simultaneously controlling the aquifer sustainability and meeting the increasing agricultural water demands. This paper focuses on maximizing the agricultural net benefits (NB) while avoiding the vital groundwater resources to excessively withdraw when utilized to supply the water demands and provide desirable net benefits. For this purpose, the gravitational search algorithm (GSA) is employed as the optimizer and linked to the artificial neural network (ANN) as the simulator of the groundwater level (GWL) variations. The study area is the west of the irrigation network of Qazvin plain in Iran. This area is divided into two zones: low-GWL-drawdown zone (zone 1); and high-GWL-drawdown zone (zone 2). For each zone, nine scenarios are defined considering three climatic years each of which is assigned three different crop patterns, totally resulting in 18 scenarios. The results show that the simulation-optimization models can decrease the groundwater drawdown in all scenarios by 0.77–1.84 m in zone 1, and by 1.28–1.97 m in zone 2. Furthermore, by replacing the existing crop pattern with two other ones, NB is increased by 35.5–53.7% in the zone 1 and by 24.9–59.7% in the zone 2. Additionally, the net benefit per unit water consumption volume (NBPD) is increased by 18.9–32.2% in zone 1 and by 9–52.6% in zone 2.

The Impact of Climate Change on Wheat, Barley, and Maize Growth Indices in Near-Future and Far-Future Periods in Qazvin Plain, Iran

The current noticeable climate change that has a significant impact on the environment and agricultural systems has become a serious concern for human society. Followed by the industrial activities of the global community and increased concentrations of greenhouse gases in the atmosphere, these changes are still considered as a threat to food safety and the environment. Changes in minimum and maximum temperature, rainfall amounts, and precipitation pattern, changes in CO2 concentrations, and complex interactions between these factors and different crops are studied in the form of different probabilistic scenarios to get a glimpse of the future cultivation situation. Agriculture in Qazvin Plain is based on the water transferred from Taleqan Dam and groundwater. Since today these resources are facing issues like restriction on transfer and draw off, which is because of growth of interest in city dwelling, inappropriate water consumption patterns and over-exploitation, agriculture is sensitive to probable changes in water requirements and it is necessary to predict these changes for better management in the future. This study was carried out to investigate the effect of climate change on growth indices for three crops including wheat, barley, and maize in Qazvin Plain with different possible climate change scenarios, in the form of five large-scale climate models and three greenhouse gas emissions scenarios in 30 years’ time from 2021 to 2050 as well as from 2051 to 2080. Results showed that biomass production, grain yield, and water use efficiency in three crops of wheat, barley, and maize have increased up to 20–40% in future periods in Qazvin region climate. Growing degree-days (GDD) of these three crops have increased by 20%. Transpiration has risen by 10% in wheat and maize; however, it was stable on average in the first period and slightly decreased in the second period in barley. The transpiration coefficient has reduced by 5% for wheat and barley and has increased by 15% for maize. Results are valid as long as the initial growing conditions of these three plants are done as before, and the irrigation scheduling is performed according to the present period in spite of climate change.

DRASTIC framework improvement using Stepwise Weight Assessment Ratio Analysis (SWARA) and combination of Genetic Algorithm and Entropy.

Hybrid and integrated techniques can be used to investigate intrinsic uncertainties of the overlay and index groundwater vulnerability assessment methods. The development of a robust groundwater vulnerability assessment framework for precise identification of susceptible zones may contribute to more efficient policies and plans for sustainable managements. To achieve an overall view of the groundwater pollution potential, the DRASTIC framework (Depth to the water table, net Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, and hydraulic Conductivity) can be used for intrinsic vulnerability assessment. However, the unreliability of this index is because of its inherent drawbacks, including the weight and rating assignment subjectivity. To modify the rating range, this study recommended a new DRASTIC modification using a recently introduced Multi-Criteria Decision-Making (MCDM) method, namely the Stepwise Weight Assessment Ratio Analysis (SWARA); in addition, the Entropy and Genetic Algorithm (GA) methods were employed to alter the relative weights of DRASTIC parameters. To improve the DRASTIC index, nitrate concentration data from 50 observation wells in the study site were used. To assess the models' overall performance, the datasets obtained from new observation wells, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were studied. The experiments were carried out in the aquifer of the Qazvin Plain in Iran. The results indicated the higher performance of the modified DRASTIC framework, manifested as an increase in the AUC value from 0.58 for generic DRASTIC to 0.68 for the SWARA-Ent framework and 0.74 for the SWARA-GA framework. The application of the SWARA technique, as an effective MCDM method, resulted in the DRASTIC rating system enhancement. The generic DRASTIC optimization by integrating SWARA and GA provided an effective framework to assess groundwater vulnerability to nitrate contamination in the Qazvin Plain.

Extracting association rules from changes in aquifer drawdown in irrigation areas of Qazvin plain, Iran

Proper planning for better management of water resources, especially groundwater, is of particular importance due to water deficits and reductions in aquifer drawdown depth. The Qazvin plain has been exposed to variations of water levels in the aquifer drawdown during recent years. The present study aims to evaluate the dependency among the variations of aquifer drawdown groundwater levels and different effective factors using data mining association rules and the Apriori algorithm. Information on changes in aquifer drawdown depth and the affecting factors (2004–2018) were considered to extract the association rules. The results of association rules were evaluated by confidence, support, and lift indices. Based on the results, increasing the volume of the discharged water from agricultural wells with more than 0.8 million cubic meters had the highest correlation with the aquifer drawdown with more than 1 m. In addition, the extracted rules demonstrated that the aquifer drawdown was less than 0.3 m when the volume of water delivered to the irrigation network and discharged from agricultural wells was more than 0.5 and less than 0.2 million cubic meters, respectively. Further, the air temperature and water demand of plants had a direct relationship with the volume of the aquifer drawdown in most extracted rules while the precipitation volume and percentage of air humidity had reverse relations with the aquifer drawdown. Finally, due to the unfavorable conditions of the Qazvin plain, further use of irrigation networks and suitable cropping patterns is strongly recommended regarding improving aquifer conditions.

The effect of urbanization on agricultural water consumption and production: the extended positive mathematical programming approach.

One of the most important factors in the agricultural sector is the supply of water. There are numerous factors affecting the supply of water in the agricultural sector. One of these factors is urban development. After the agricultural and industrial revolution, urban revolution is one of the most important issues in the economic development of countries. Urban development has two implications for agricultural water use; this both increases the demand for agricultural crops and the consumption of water in the household sector, which both lead to a reduction in available water in the agricultural sector. Therefore, determination of the impact of urbanization on water consumption in agriculture is necessary. In this study, we have examined the extended positive mathematical programming method on the agricultural sector of Qazvin Province. The results of the study showed that 1% increase in urbanization would reduce water consumption in agriculture by 0.639mm3 compared to the base year. Also, household water consumption will reach 92.92-761.9mm3 in base year. On the other hand, the cultivated area and the production of some irrigated crops (wheat, barley, rapeseed, sunflower, other oilseeds, alfalfa) will decrease to 86.36 hectare and 346.6 tons. In the indirect effect of increasing urbanization, per each percentage urbanization, the household water consumption will decrease by 2.19mm3, and after a 20-year horizon, the total capacity of the Taleghan Dam will be allocated to Tehran's household water,. So that, 288mm3 of agricultural water will be reduced, and it will reduce the cultivated area for 28,250 (ha). Also, farmers' gross profits will be 10.5 percent lower than the base year which is a threat for Qazvin plain farming. Solutions were proposed to reduce the negative effects of the urban development phenomenon, including the use of modern irrigation systems in the agricultural sector will significantly increase irrigation efficiency. So that only 1% increase in irrigation efficiency can completely neutralize the direct and indirect negative impacts of urbanization on a 12-year mid-term horizon.

Land subsidence from interferometric SAR and groundwater patterns in the Qazvin plain, Iran

ABSTRACTQazvin plain as one of the important plains in northwest Iran in terms of industrial and agricultural activities, reveals evidence of land subsidence in the last decade. We used a descending ENVISAT dataset during 2003–2006 and two ascending and descending Sentinel-1 tracks acquired during 2014–2016 to study the subsidence in the area. Our time-series analysis of the ENVISAT data reveals that the area is subsiding at an average rate of 40 mm yr−1 with a maximum value of 50 mm yr−1 in the line of sight direction. Moreover, our result for the Sentinel-1 ascending and descending tracks shows that the average subsidence rate reaches 65 mm yr−1 in the line of sight direction. Comparing the obtained mean velocity maps of the two datasets indicates the growing subsidence pattern in the northwest-southeast direction inside the plain. The investigation of the water source reveals that the overexploitation of aquifers caused an approximately 17 m decline in the groundwater level from 2001 to 2011. This drastic drop in the groundwater level has resulted in progressive subsidence over time. The comparison between the extent of subsidence from the image processing and the water level depletion contour map in the Qazvin plain depicts that subsidence is occurring in areas with a high decrease in underground water level. This study suggests that, besides decreasing the number of deep wells as well as their outflow water, the types of agricultural production related to water consumption and irrigation methods in an area such as Iran can play an important role in decreasing the stress on aquifers.

Modeling prediction of dispersal of heavy metals in plain using neural network

Today, the supply of safe drinking water is one of the most important problems in societies. In the present research, using a neural network, a method to determine the dispersal trend of groundwater pollutants was provided through a case study of heavy metals, including lead, zinc and arsenic in Qazvin plain. Then, using a sensitivity analysis, the actual significance of each parameter was determined in the model and by plotting graphs and response levels, the effects of abstraction, discharge, electrical conductivity, temperature, hydraulic gradient, lifetime, groundwater level and depth from surface to well screen on the concentration of metals were studied individually and two by two. The model was applied to predict the situation of the plain in the coming years, and only if the abstraction is reduced to a half rate, the plain condition would remain stable and the concentration of the metals would not be increased.

DIGITAL SOIL MAPPING USING GEOMORPHOMETRIC ANALYSIS AND CASE-BASED FUZZY LOGIC APPROACH

Abstract. In low relief region such as plains, applied digital soil mapping has a controvertible issue, therefore, this study was aimed to digital mapping of soil classes at family levels by appropriate Geomorphometric variables along with fuzzy logic with area of 16,600 hectares in Qazvin Plain. Based on the geomorphologic map, the plain and pen plain are dominant landscape units. In this regards, 61 soil profiles were dogged. According to the expert’s opinion, covariates including diffuse insolation, standardized height, catchment area, valley depth and multiresolution valley bottom flatness (MrVBF) had the most important in order to generating soil map. Also, 19 fuzzy soil class maps were generated through using sample-based in ArcSIE software. Validation were carried out using achieved overall accuracy (OA) and Kappa index through error matrix. Subsequently, both ignorance and exaggerating uncertainty of hardened soil map were also done. The results showed that 19 soil families class were found. Accordingly, OA and the Kappa index were 54% and 46% respectively. The uncertainty of ignorance and exaggeration were obtained from 0 to 0.64 and 0 to 1, respectively. Moreover, the results indicated that exaggerated uncertainty was the highest in the northern and the lowest in the southern regions. Generally, applied geomorphometric parameters had the specific importance in the low relief areas for mapping of soils that have not been assessed properly so far.

The study of environmental and human factors affecting aquifer depth changes using tree algorithm

In recent years, more attention has been paid to water resources due to the development of agriculture and proper planning for better management of aquifers. Considering the effect of different factors on aquifer depth changes, in this study, human and environmental factors affecting the depth of aquifer changes in Qazvin plain were used. The Classification And Regression Tree (CART) algorithm was adopted to investigate and predict changes in aquifer depth. According to the results, the highest probability of the aquifer drop observed in July, August and September was 86.5%, and the highest probability of uprising aquifer depth arisen in December, January, February and March was 71.2%. According to sensitivity analysis by CART algorithm, the most important human and environmental factors affecting the number of aquifer depth changes in Qazvin plain were groundwater withdrawal from the agricultural abstraction well and the air temperature, respectively. Therefore, predicting the amount of aquifer depth changes by CART model, planning and managing groundwater resources is possible.

Determination of Economic Value of Agricultural Water in Greenhouse Cultivation of Qazvin Plain

Determination of Economic Value of Agricultural Water in Greenhouse Cultivation of Qazvin Plain