Hydrogeological Drought Management Based on HDMI Multivariate Index

In this research, it has been tried to investigate the limitations of using the plain’s groundwater through using the monthly values ​​of discharge of agricultural wells, groundwater level and quality values ​​of electrical conductivity in areas of South Khorasan Province in 2013-2014. In this regard, a combination of groundwater resource index, modified standard electrical conductivity index and standardized well discharge index was used. Finally, hydrogeological drought management index (HDMI) was used to manage the groundwater drought and to investigate hydrogeological drought. HDMI is one of the useful and practical indices in this field that has been less studied. The results of groundwater resource index showed that in the study area, groundwater drought is clearly seen in the southeastern regions of the study area. Hydrogeological drought management index in the studied area showed that operation of groundwater in most of the studied area has a problem and operation of groundwater in these areas should be limited. About 86% of the studied areas are in limited operation condition, 10% are in problem-free operation condition and 4% are in non-operation condition. In general, the results of hydrological drought management index at the study area indicated that this index has high ability to provide an aquifer management and has a great help to water managers in the country. Relying on the results, it is possible to restrict water use at the aquifer and manage the water delivery

Flood routing via a copula-based approach

Abstract Floods are among the most common natural disasters that if not controlled may cause severe damage and high costs. Flood control and management can be done using structural measures that should be designed based on the flood design studies. The simulation of outflow hydrograph using inflow hydrograph can provide useful information. In this study, a copula-based approach was applied to simulate the outflow hydrograph of various floods, including the Wilson River flood, the River Wye flood and the Karun River flood. In this regard, two-dimensional copula functions and their conditional density were used. The results of evaluating the dependence structure of the studied variables (inflow and outflow hydrographs) using Kendall's tau confirmed the applicability of copula functions for bivariate modeling of inflow and outflow hydrographs. The simulation results were evaluated using the root-mean-square error, the sum of squared errors and the Nash–Sutcliffe efficiency coefficient (NSE). The results showed that the copula-based approach has high performance. In general, the copula-based approach has been able to simulate the peak flow and the rising and falling limbs of the outflow hydrographs well. Also, all simulated data are at the 95% confidence interval. The NSE values for the copula-based approach are 0.99 for all three case studies. According to NSE values and violin plots, it can be seen that the performance of the copula-based approach in simulating the outflow hydrograph in all three case studies is acceptable and shows a good performance

Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions

Abstract The complex hydrological events such as storm, flood and drought are often characterized by a number of correlated random variables. Copulas can model the dependence structure independently of the marginal distribution functions and provide multivariate distributions with different margins and the dependence structure. In this study, the conditional behavior of two signatures was investigated by analyzing the joint signatures of groundwater level deficiency and rainfall deficiency in Naqadeh sub-basin in Lake Urmia Basin using copula functions. The study results of joint changes in the two signatures showed that a 90–135 mm reduction in rainfall in the area increased groundwater level between 1.2 and 1.7 m. The study results of the conditional density of bivariate copulas in the estimation of groundwater level deficiency values by reducing rainfall showed that changes in values of rainfall deficiency signature in the sub-basin led to the generation of probability curves of groundwater level deficiency signature. Regarding the maximum groundwater level deficiency produced, the relationship between changes in rainfall deficiency and groundwater level deficiency was calculated in order to estimate the groundwater level deficiency signature values. The conditional density function presented will be an alternative method to the conditional return period

Impact of climatic parameters on the extent of mangrove forests of southern Iran

Mangrove forests play a valuable role in maintaining the coastal ecosystem. Global warming alongside human activities has caused reduced extent and health of these ecosystems in recent years. This study aimed to examine the variability of the extent of mangrove forests and the sea surface area in response to changes in climatic parameters in the south of Iran. To achieve this, the climatic data recorded at Bandar Abbas Synoptic Weather Station and Landsat series of satellite images were used. To detect the trends of meteorological parameters during 1987-2017, the modified Man-Kendall test and the Sen’s slope estimator were employed. We investigated the regression relationship between climatic parameters as well as the sea surface area and the mangrove forest extent. The results showed that mangrove forest extent was about 73.08 km2 in the first year of study (1987), which increased to 88.73 km2 (21%) in 2017. The minimum temperature (Z = 2.77, β = 0.0186), maximum temperature (Z = 2.066, β = 0.0362), and the extent of the mangrove forests (Z = 2.58, β = 0.0405) displayed significantly growing trends. In contrast, the mean temperature, precipitation, relative humidity, and the sea surface area had no significant trends during the study period. The minimum temperature presented the highest correlation coefficient with the mangrove forest extent (61%). It is expected, therefore, along with global warming and increasing minimum temperature, the extent of mangrove forests would have a growing trend in the south of Iran in the future. The results of this study can be used by natural resources and forest managers to determine the best place for afforestation in order to perform better protection of these forests

Comparison of the performances of GEP, ANFIS, and SVM artifical intelligence models in rainfall simulaton

In this paper, evaluation the performances of GEP (gene expression programming), ANFIS ( adaptive fuzzy interference system), and SVM (support vector machine) artificial intelligence models in two scales of daily and monthly rainfall data from Urmia meteorological station (Iran) and monthly rainfall data from Diata meteorological station (India) was used in rainfall simulation. The correlation coefficient of observed and simulated values was evaluated by the R2 criterion, simulation error was evaluated by the root mean square error (RMSE), and MB criteria and model efficiency were evaluated by the Nash-Sutcliffe method. The results show that the correlation coefficients in the GEP model based on daily data from Urmia station and monthly data from Diata station are 23 and 58%, respectively, and R2 in simulation with GEP is estimated to be 55% lower than with the other two models. The R2 range in both ANFIS and SVM models varies from 91 to 93%. On average, the RMSE values in the GEP simulation are 50% and 55% higher than the ANFIS ratio for daily and monthly data at the two stations, respectively, and the RMSE values of ANFIS model are 1% and 3% higher than those of the SVM at Urmia and Diata stations, respectively. The bias values of the GEP model are 72% and 60% higher than those of ANFIS at Urmia and Diata stations, respectively. The GEP efficiency factors are 56% and 61% lower than those of ANFIS at Urmia and Diata stations, respectively. And the ANFIS efficiency ratio is 1 and 2% lower than SVM in Urmia and Diata stations, respectively. Therefore, rainfall simulation with the SVM model is associated with a lower error rate and better efficiency, the ANFIS model is close to the efficiency of SVM, and the GEP model is not suitable for rainfall simulation

Estimation of Dew Point Temperature in Different Climates of Iran Using Support Vector Regression

The prediction of global climate change using the values recorded in a statistical period requires a precise method that can accurately identify the fluctuations of these changes. By patterning these changes, the parameter values for the years or future periods are predicted, or the statistical gap can be eliminated. In this research, meteorological data of six stations in different climates of Iran were used to model and estimate the values of the dew point temperature (DPT). The stations studied are Ahvaz, Urmia, Kerman, Gorgan, Rasht, and Babolsar. In order to estimate the DPT values, support vector regression was used, and to optimize the parameters of the support vector regression model, the ant colony algorithm was used. In this study, four different input patterns of meteorological data have been investigated as input of the support vector regression model. Pattern I with seven inputs (monthly minimum, maximum, and average air temperatures, monthly precipitation, saturation vapor pressure, actual vapor pressure and relative humidity), Pattern II with three inputs (monthly average air temperature, saturation vapor pressure, and actual vapor pressure), Pattern III with two inputs (monthly minimum and maximum air temperatures), and Pattern IV with an input (monthly average air temperature) were used. It is recommended that if the number of inputs in the model is small, the model will be more user-friendly. Based on the results of analyzing different patterns, it can be concluded, that Pattern III is the suitable pattern for estimating DPT values at the stations studied in different climates of Iran based on the three criteria of root mean square error (RMSE), Nash–Sutcliffe model efficiency coefficient (NSE), and coefficient of determination (R2). Overall, the results showed that the selected pattern increases the accuracy of the model by up to 24% compared to the conventional model

Development of decomposition-based model using Copula-GARCH approach to simulate instantaneous peak discharge

Abstract Estimation of instantaneous peak discharge is important in the design of hydraulic structures and reservoir management. In this study, a new approach called CEEMD-Copula-GARCH is presented for simulating instantaneous peak discharge in the Qale Shahrokh basin, upstream of Zayanderood Dam, Iran. In the developed method, the Complementary ensemble empirical mode decomposition (CEEMD) algorithm was used to analyze the observed values and generate the intrinsic mode function values and residual series. For this purpose, the intrinsic mode function values were simulated based on vine copula and its tree sequence (C-vine, D-vine, R-vine and their independent and Gaussian modes), and the residual series of the CEEMD algorithm were simulated by the GARCH model. The results of simulating instantaneous peak discharge values (m3/s) using the CEEMD-Copula-GARCH approach in the study area showed that the amount of simulation error based on the RMSE statistic compared to the CEEMD-Copula model and simulation without decomposition has improved by about 20 and 70%, respectively. The model’s efficiency was also estimated based on the Nash–Sutcliffe efficiency in the proposed approach of 0.99, and the certainty of the proposed approach was also confirmed based on the presented violin plot. According to the presented results, the proposed approach has high accuracy and efficiency in the simulation of instantaneous peak discharge (m3/s), which can be used in the flood control system design and flood management. Using the methodology proposed in this study, multivariable models can be used in simulating univariate series with high accuracy

Analyzing the droughts in Iran and its eastern neighboring countries using copula functions

As a long-term water deficit condition, drought is a challenging issue in the management of water resources and has been known as a costly and less known natural disaster. Monitoring and predicting droughts, especially accurate determination of their beginning and duration are crucial in management of water resources and planning for mitigating the damaging effects of drought. In this study, the droughts in the southwestern region of Asia (Iran, Afghanistan, Pakistan, and Turkmenistan) were evaluated using the joint deficit index (JDI). Data of monthly and annual precipitation of 1392 downscaled rain gauge stations (by using the Bias Correction Spatial Disaggregation (BCSD method) within the statistical period of 1971-2014 were employed to calculate JDI. The results indicated that in recent years, the number of dry months in the studied region (especially in humid regions of Iran) has significantly increased, such that across all regions in Iran, the percentage of dry months has reached over 50%. The results also showed that in addition to scientific description of the general drought condition, JDI is also able to specify the time of beginning of droughts as well as long-term droughts, allowing investigation of the drought condition on a monthly scale. The results of investigating the trend of changes in the JDI values in the studied region revealed that the variations in these values have decreased on annual scale in the studied region. The extent of reduction in JDI and the increase in the number of dry months within the statistical period of 1971–2014 have been significant (at level of 5%) in Iran, suggesting increased drought in Iran, especially during winter. The values of monthly and annual precipitation in the studied region have been descending, where among the studied countries, Iran has experienced the maximum extent of reduction in precipitation

Spatial distribution of the daily, monthly, and annual precipitation concentration indices in the Lake Urmia basin, Iran

Investigations of the long-term observations of climate variables, as a practical approach to monitor climate changes, have attracted the interest of many researchers around the world. One of the important variables in this context is precipitation. The investigation of precipitation, one of the most important meteorological factors directly affecting accessibility to water resources, is of special importance. In every region, studies of precipitation on daily, monthly, or annual scales provide important information on the distribution, concentration, and dispersion of precipitation, as well as some conclusions about the associated hydrological problems. In this study, the precipitation concentration was calculated and zoned by means of the precipitation concentration index (PCI) in the basin of Lake Urmia, using monthly and annual rainfall data of 42 selected rain gauge stations, from which 24 stations located in the West Azerbaijan province (in the west of Lake Urmia) and 18 stations located in the East Azerbaijan province (in the east of Lake Urmia) during 1984–2013. The results of the studies of the precipitation concentration index over the basin of Lake Urmia showed that the dominant concentrations of spring, autumn, and winter precipitation were moderate, indicating a moderate distribution for the precipitation of the months in these seasons. In addition, in the period under study, uniform and regular precipitation concentrations (PCI<10) were observed only in winter and in the borders of the basin. In summer, almost the entire surface of the basin (excluding its northeastern part) faced a strongly irregular distribution of precipitation, indicating irregular rainfall in July, August, and September. Most of the basin of Lake Urmia is covered by an irregular distribution of precipitation on an annual scale. By investigating the precipitation distribution in the first and the last 10 years of the statistical period considered and by comparing them, it was revealed that the greatest increase in the precipitation concentration index was in autumn, it rose by approximately 20.55 percent. According to the results, on the basin scale, the concentration index showed that the daily rainfall of the Lake Urmia basin was neither in regular nor in strongly irregular conditions at any of the stations studied. All the stations studied were in fairly regular, moderate concentration and fairly irregular conditions of daily precipitation distribution. The results also showed that the moderate concentration includes most of the daily precipitation distributions throughout the basin

Application of vector autoregressive models to estimate pan evaporation values at the Salt Lake Basin, Iran

Thousands of billions of cubic meters of fresh water collected at great expense are evaporated annually from dams, and salts of evaporating water reduces water quality. In this study, the efficiency of the vector autoregressive model called VAR model has been examined on an annual scale using pan evaporation data in the salt lake basin, Iran, during the statistical period of 1996–2015. Since hydrologic modeling is concerned with the accuracy and efficiency of the model, therefore, we must try to evolve and improve the results of the models. In this study, VAR multivariable time series and nonlinear GARCH models have been used. The results of linear and nonlinear hybrid models in modeling the annual and monthly pan evaporation values of studied stations at the basin area of the salt lake indicated, that the pan evaporation values in the annual scale have the best fit with hybrid models. The results of the study of the accuracy of these models in modeling the pan evaporation values indicated, that the VAR-GARCH hybrid models have a high accuracy relative to the vector models and have been able to model the pan evaporation values with good accuracy and with the lowest error rate. Of the two models that have both annual nature (VAR and VAR-GARCH), the best model can be selected based on the estimation of the error values. In this study, we first examine the accuracy of the relatively new vector autoregressive model. The results of the estimation of error and efficiency of the model indicated the acceptable accuracy of this model in estimating the pan evaporation values in the annual scale. The 95% confidence interval confirmed the simulation results of the calibration step. Overall, the results showed that both VAR and VAR-GARCH models have high accuracy and correlation, and the model's performance criterion also confirms this. The percentage of improvement in the results from the model of the pan evaporation values in the annual scale using the VAR-GARCH model is about 4% relative to the VAR model. However, due to modeling the random section and reducing the uncertainty of the model, the results of modeling the pan evaporation values using the VAR-GARCH model are better than the VAR model. But due to the complexity of calculating the GARCH model, the VAR model can also be used