The drought has enormous adverse effects on agriculture, water resources and environment, and causes damages around the world. Drought risk assessment and prioritization of drought management can ...help decision makers and planners to manage the adverse effects of drought. This paper aims to determine the risk of drought in Iran. At the first stage, standardized precipitation index (SPI) was calculated for the period 1981–2016. Then the probability map of different drought classes or drought hazard probability map were prepared. After that the indicator-based vulnerability assessment method was used to determine the drought vulnerability index. Five indices including climate, topography, waterway density, land use and groundwater resources were chosen as the most critical factors of drought in Iran and followed by the analytical hierarchy process questionnaire, the weights of each index were obtained based on expert opinions. Fuzzy membership maps of each index and sub-index were prepared using ArcGIS software. The drought vulnerability map of Iran was plotted using these weights and maps of each indicator. Finally, the drought risk map of Iran was provided by multiplying drought hazard and vulnerability maps. According to the 43-completed questionnaires by experts, climate index has the highest vulnerability to drought. Climate does not have an important role in drought hazard index, but it is the most crucial factor to classified drought vulnerability index. The results showed that central, northeast, southeast and west parts of Iran are at high risks of drought. There are regions with different risks in Iran due to unusual weather and climatic conditions. We realized that the climate and the groundwater situation is almost the same in the central, east and south parts of Iran, because the land use plays a crucial role in the drought vulnerability and risk in these areas. The drought risk decreases from the center of Iran to the southwest and northwest.
This study investigates the linkage between agricultural drought and meteorological drought, using normalization different vegetation index (NDVI) and land surface temperature (LST) for the whole of ...Iran in four periods of 16 days of late March, April, May, and June between 2000 and 2017. LST and NDVI were obtained from the production of MODIS MOD13A2 and MOD11A2. In the next step, the basic time synchronization of LST and NDVI was performed by the mean of two 8-day LST images and their conversion to 16-day LST for each year. The vegetation health index (VHI) was calculated from the combination of temperature condition index (TCI) and vegetation condition index (VCI). Then, Iran’s map of standardized precipitation-evapotranspiration index (SPEI) was calculated on a 12-month timescale for 68 meteorological stations with the inverse distance weighted (IDW) method. By using ArcGIS 10.5, the Pearson correlation coefficient and the slope of linear regression were calculated between 12-month SPEI and VHI for all four periods in different climates: hyper dry, dry, semi-dry, semi-humid, and humid. Results showed that the correlation increased when the temperature increased. This increase occurred in dry and hyper-dry climates. As the temperature rose, the slope of the linear regression for 12-month standardized precipitation-evapotranspiration index (SPEI) on vegetation health index (VHI) increased. The highest and lowest average effects of the slope were observed in dry and climatic class, respectively. With increasing temperature, the need for water in plants will increase. Hence, there is a direct relation or a positive correlation between temperature and correlation strength and slope effect. Therefore, plants more seriously will face drought stress during the drought period, and this drought stress has a positive correlation with drought intensity. From this study, it was concluded that the highest correlation and the highest slope effect between the 12-month SPEI and VHI happened in the dry climatic class in June.
Drought is one of the important factors causing vegetation degradation. Determination of areas with vegetation more sensitive to drought can be effective in drought risk management. Considering the ...ability to describe vegetation conditions, vegetation health index (VHI) was used to determine the probability of vegetation vulnerability to drought and to provide the map of Iran showing sensitive areas to drought. This study tries to express the probability of vegetation vulnerability to drought in four main climatic classes including hyper-arid, arid, semi-arid and semi-humid, and humid in Iran. Temperature condition index (TCI) and vegetation condition index (VCI) were calculated using land surface temperature (LST) derived from the MOD11A2 product and normalized different vegetation index (NDVI) obtained from MOD13A2 product, MODIS sensor. Combining these two indices, VHI was calculated for late of March, April, May, and June during 2000–2017. VHI was classified into five classes representing the drought intensity. Then, the probability of occurrence (%) of each class was calculated and multiplied with weight of each class, varying from 0 to 40 based on drought intensity. Finally, probability of vegetation vulnerability index (PVVI) was calculated by summing of the values obtained for each class. The results showed that PVVI was higher in arid and hyper-arid areas than that in other areas in the four studied periods. The highest mean values of PVVI in humid as well as semi-arid and semi-humid classes were found in April as 59.87 and 62.4, respectively, while the highest mean values of PVVI in arid and hyper-arid classes were observed in May as 70.98 and 68.13, respectively. In total, our results showed that PVVI is affected by different climatic and topographic conditions, and it suggested that this index be used to determine the probability of vegetation vulnerability.
This paper tries to introduce a time-series of temperature parameters as a potential method for studying the global warming. So, we investigated the spatial–temporal variations of warm-season ...temperature parameters (WSTP), including
start time
,
end time
,
length of season
,
base value
,
peak time
,
peak value
,
amplitude
,
large integrated value
,
right drive
, and
left drive
, using a database of 30 years’ period in different climates of Iran. We used daily temperature data from 1989 to 2018 over Iran to extract the parameters by TIMESAT software. We studied the trend analysis of WSTP through the Mann–Kendall method. Then, we considered the Pearson correlation coefficient to calculate the correlation between WSTP and time. We assessed the trends of the slope using a simple linear regression method. Then, we compared the results of the WSTP trend analysis in climatic zones. Our results accused the hyper-arid climatic zone has the longest warm season (194.89 days a year). The warm season in this region starts earlier than other regions and increases with moderate speed (left drive, 0.19 °C day
−1
). Then, it reaches a peak value (31.3 °C) earlier than the different climatic zones. On the other hand, the humid regions’ warm season starts with the shortest
length
and ends later than the other climatic zones (112.1 and 297.5 days a year for start and end times, respectively). We detected that the trend of the start time parameter has decreased by 98.02% of the study area during the last 30 years. The base value, length, and large integrated value parameters have an increasing trend of 66.47%, 80.11%, and 92.95% in Iran. The highest correlation coefficient with time was for start time and large integrated value parameters. Hence, the start time and large integrated value parameters have almost the most negative (< − 0.5) and positive (> 5) trend slope, among other parameters, respectively. In general, these results demonstrate that the studied region has faced global warming impacts over time by increasing the warm season and thermal energy, especially in arid and hyper-arid. We highlight the necessity of planning the land use under the high natural vulnerability of the studied local, especially in this new age of global warming.
To properly manage the terrestrial ecosystem, it is essential to understand the vegetation sensitivity to climate variations and human actions. The main target of this survey was to evaluate the ...spatiotemporal variation in vegetation cover, and its relationship with climate variations and to calculate the contributions of driving factors in Namak Lake basin, Iran, during 2001–2019. To this end, Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI) in 3, 6, 9, and 12-month time scales were used to assess vegetation dynamics and its reactions to climate variations based on coefficient of determination (R
2
) and Linear Regression (LR). The results presented that vegetation cover had an improving trend in 87.78% and a decreasing trend in 12.19% of the basin, while it was stable in 0.03% of areas. The correlation between VHI and different time scales of SPEI indicated that coverage was mainly affected by 3-month SPEI in more than half of the basin (53.74%). High correlations between VHI and SPEI were found in upland areas in the northeast and some areas in the east of the basin. These areas also had the highest slope of VHI changes in relation to climate factors. Climate variability affected about four-fifths (79.22%) of coverage, while 16.36% was influenced by human actions, and 4.42% by both factors. Moreover, more than 99% of the significant improvements and degradations in coverage were related to climate variations and mankind’s actions, respectively. The outcomes can serve as a foundation for initiating vegetation growth and protection in the Namak Lake basin.
The quantitative understanding of vegetation vulnerability as a major example of terrestrial ecosystems under hydrometeorological stress is essential for environmental risk preparedness and ...mitigation strategies. The aim of this study was to develop a new quantitative vegetation vulnerability map using benchmark and standalone machine learning (ML) algorithms (e.g., RF, SVM and Maxent), as well as influencing variables (evaporation, rainfall, maximum temperature, slope degree, elevation, topographic wetness index, distance from river, aspect, land use), in the South Baluchistan basin, Iran. An ensemble model was developed based on selected standalone ML algorithms. A vegetation vulnerability index (VVI), based on remote sensing indices (NDVI, VCI, LST, and TCI), was used to monitor vegetation conditions and changes. Five evaluation metrics for the confusion matrix (accuracy, precision, bias, Probability of Detection (POD), False Alarm Ratio (FAR)) and ROC-AUC were used to measure the predictive performance of the ensemble model and VVI. The optimum values for accuracy, precision, bias, POD, FAR, and ROC-AUC were obtained as 0.89, 0.88, 1.02, 0.91, 0.11, and 0.946, respectively for the ensemble model. Based on remote sensing data, VVI achieved a 0.923 prediction rate in vegetation vulnerability mapping (the efficiency of the ensembled model was somewhat better than VVI). Based on the results obtained from the ensemble model, precipitation (PRD = 20.61), maximum temperature (PRD = 12.31), evaporation (PRD = 5.53), and distance from the river (PRD = 2.62) were found to be the most important variables. The methodology as presented in this study provides valuable information in a large area and can be easily modified for other case studies by adding different influencing variables.
Display omitted
•Vulnerability maps were produced using machine learning and remote sensing.•An ensemble model was developed to monitor vegetation vulnerability.•Confusion metrics were used to measure the performance of the models.•Machine learning resulted in improved predictions compared to remote sensing techniques.
Due to the complex physics of both snow and snowmelt, particularly in mountainous topographic regions, studying the dynamics and variations of snow cover (SC) has been a very challenging task, and ...therefore, its relationship with snowline elevation (SLE) mobility has not been well documented. The spatiotemporal dynamics of SC in the Haraz Watershed, where streamflow is snowmelt-dominated, is of great importance, particularly for monitoring ecosystem processes, irrigation practices, and water management in the region. In the current study, due to the lack of a ground-based station, the remotely sensed eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) images were considered in order to assess the dynamics of SC through investigating the monthly-normalized difference snow index (NDSI) during 2001 to 2018. Additionally, the SLE mobility was inferred through representing and assessing three indices related to SC, including variability in the number of snowy pixels and variations of the minimum and mean elevation in snowy pixels over time. According to the results, generally, 99.49% of the study regions showed NDSI declines, and 56.85% of these pixels showed significant trends. Variations of SC frequency showed that 32% of the study area has a moderate to very high snow existence probability. The trend of minimum and average elevation in snowy pixels indicates that January and December had significant increases, meaning that SLE increases during that time and moves towards higher elevations. The rate of changes in the average elevation of snowy pixels indicated an increasing rate of SLE in the months of January, February, March, August, and December, respectively equal to 8.18, 0.69, 2.51, 22.59, and 5.82 m per year. Further, results indicated that the percentage of the significant decrease in SC is highest on the slope aspects of southeast, south, and southwest (respectively equal to 66.81%, 62.35%, and 62.35%), meaning that SLE increases faster on these slope aspects.
Display omitted
•The spatiotemporal dynamics of snow cover was investigated.•Snowline Elevation (SLE) mobility was detected using snowy pixels and their elevations.•SLE varied from 2890 to 5300 m from the coldest to warmest months.•The maximum increase rate of SLE was in August, equal to 22.59 m per year.•SLE increased faster on the slope aspects of southeast, south, and southwest.
Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the semi-arid basins. The excessive withdrawal of groundwater, as well as the other underground natural ...resources, has been introduced as the significant causing of land subsidence and potentially, the earth fissuring. Fissuring is rapidly turning into the nations’ major disasters which are responsible for significant economic, social, and environmental damages with devastating consequences. Modeling the earth fissure hazard is particularly important for identifying the vulnerable groundwater areas for the informed water management, and effectively enforce the groundwater recharge policies toward the sustainable conservation plans to preserve existing groundwater resources. Modeling the formation of earth fissures and ultimately prediction of the hazardous areas has been greatly challenged due to the complexity, and the multidisciplinary involved to predict the earth fissures. This paper aims at proposing novel machine learning models for prediction of earth fissuring hazards. The Simulated annealing feature selection (SAFS) method was applied to identify key features, and the generalized linear model (GLM), multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and support vector machine (SVM) have been used for the first time to build the prediction models. Results indicated that all the models had good accuracy (>86%) and precision (>81%) in the prediction of the earth fissure hazard. The GLM model (as a linear model) had the lowest performance, while the RF model was the best model in the modeling process. Sensitivity analysis indicated that the hazardous class in the study area was mainly related to low elevations with characteristics of high groundwater withdrawal, drop in groundwater level, high well density, high road density, low precipitation, and Quaternary sediments distribution.
•Machine learning (ML) prediction of earth fissure hazard.•Key features selection using the simulated annealing (SA) method.•Good performance of the ML models (Accuracy >86%; Precision >81%).•The worst and best models respectively were GLM (as a linear model) and RF.
Groundwater resources are important sources of fresh water for the agricultural sector in Tashk-Bakhtegan and Maharloo basin in Fars province, Iran. In this study, data were collected from 420 ...groundwater samples to assess the sustainability of groundwater for irrigation using hydrochemical properties. The groundwater quality was evaluated using 15 hydrogeochemical indices, namely Sodium Adsorption Ratio (SAR), Magnesium Hazard (MH), Salinity Hazard (SH), Chloride (Cl-), Permeability Index (PI), Total Dissolved Solids (TDS), Potential Salinity (PS), Total Hardness (TH), Kelley’s Ratio (KR), Sodium Percentage (SP), Chloro-Alkaline Index I (CAI-I), Residual Sodium Bicarbonates (RSBC), Synthetic Harmful Coefficient (K), and Base Exchange Index (r_1), along with Meteoric Genesis Index (r_2). The results of these indices indicated that the GWQ was totally different in the north and south of the study area. It is mainly acceptable for irrigation based on SAR, MH, SH, Cl-, TDS, PS, TH, KR, SP, CAI-I, and K indices in the northern parts, while it has limitations for use in the agricultural sector in most parts of the southern areas. Based on PI and RSBC indices, GWQ is entirely acceptable for irrigation all over the basin. According to the results of r_1 and r_2 indices, GWQ belongs to Na+-HCO3- and shallow water percolating types in the northern parts, while it belongs to Na+-SO42- and deep water percolating types in the southern parts. Regarding the Land Use/ Land Cover map, agricultural lands and rangelands are mainly located in center toward north of the basin, where the GWQ is more suitable for irrigation.
Although land degradation is a worldwide challenge and a destructive phenomenon, little studies have been done on the application of new numerical methods (data mining and statistically), for spatial ...simulation of this phenomenon and identification of areas sensitive to land degradation. The aim of this study is to spatially simulate land degradation in the Qazvin plain using the frequency ratio model to identify areas prone to land degradation. For this purpose, using the trend of changes in net primary production during the years 2001 to 2020, the points of occurrence of land degradation in the Qazvin plain were determined. Approximately 70% and 30% of the points were used to prepare the land degradation vulnerability map and validate the model's efficiency, respectively. For this research, 15 parameters affecting land degradation (directly and indirectly) including temperature, rainfall, slope, aspect, elevation, EC and SAR of ground water, ground water level, annual ground water decline, land use, normalized difference vegetation index, normalize difference salinity index, vegetation soil salinity index, normalized difference moisture index, and visible and shortwave infrared drought index, were introduced into the model as predictors factors or independent parameters. Finally, using the area under the ROC curve, the effectiveness of the frequency ratio model for spatial simulation of land degradation was assessed. The map of land degradation susceptibility shows that the areas prone to degradation are located in the northeast, north, northwest, west, southwest, and south of the Qazvin plain, which mainly includes good, moderate and poor rangelands. For the land use parameter, the highest frequency ratio was associated with the sum of good, moderate, and poor rangeland (5.66). The value of AUC = 0.7 indicates the good performance of the frequency ratio model in spatial simulation of land degradation.
PDF
