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

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

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

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

Frequency Analysis of Precipitation Anomaly Percentage and Stream Flow Drought Using Copula Functions

The purpose of this research is to joint frequency analyze of precipitation anomaly percentage as a meteorological drought index and flow rate at Chalekhmaz station located in the Zarinerood basin at south of Lake Urmia in the period of 1995-2016, which is based on the duration of the mentioned indicators. The results of the analysis of investigated copula functions in Zarinerood basin showed that, based on evaluation criteria, Frank's copula function describes well the dependence between two variables of the duration of anomaly percentage and the duration of hydrological drought. In Chalekhmaz station, the expectation of drought duration of 4 to 7 months for the hydrological variable and 9 to 12 months for the meteorological variable in the coming years is not far from reality. The results of the study of the return period of drought characteristics showed that in the case of the frequency of the stream flow drought index, the return period also increases with the increase in the severity of the drought. The joint frequency analysis of drought characteristics shows how meteorological and hydrological drought characteristics can be determined simultaneously in one station by using joint probabilities. This can provide users and researchers with very useful information related to the probable behavior of drought characteristics in order to optimally use of surface water. For the duration of a certain meteorological drought in a station, the duration of the hydrological drought in the hydrometric station can be determined based on the conditional probability of occurrence and also certain return periods

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

Joint frequency analysis of rainfall and precipitation concentration index (PCI) at Birjand and Tabas meteorological stations, South Khorasan Province, Iran

In this study, copula functions and precipitation concentration index were used for the joint frequency analysis of the conditional probability of rainfall-rainfall and PCI-PCI at Birjand and Tabas meteorological stations in eastern Iran. Monthly rainfall data ​​in the statistical period 1969-2018 were considered in this regard. The results of PCI at the studied stations showed that at both stations the distribution of rainfall pattern is highly irregular, which was worse at Tabas meteorological station. By selecting the appropriate marginal distribution function and also confirming the correlation between rainfall- rainfall and PCI-PCI ​​at Tabas and Birjand meteorological stations, the Gumbel-Hougaard and Clayton copulas were selected for the pair variables, respectively. The results of conditional probability showed that with different probabilities, rainfall and PCI of each station can be estimated using the rainfall and PCI ​​of another station. For example, according to the presented curves, if the annual rainfall of Birjand meteorological station is 220 mm, with 80% probability, the annual rainfall of Tabas meteorological station will be about 110 mm. According to the presented curves and the use of copula functions in the joint analysis of the rainfall and rainfall pattern, it is possible to better water resources management and water harvesting in the region

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