Karkheh Basin Research Articles

Extreme flash flood susceptibility mapping using a novel PCA-based model stacking approach

This study introduces an efficient methodology for model stacking, incorporating six diverse machine learning and statistical models alongside principal component analysis (PCA). The approach is applied for the flash flood susceptibility mapping within the Karkheh Basin in Iran. The selected models include random forest (RF), boosted regression trees (BRT), support vector machine (SVM), artificial neural networks (ANN), generalized additive model (GAM), and the least absolute shrinkage and selection operator (Lasso), with RF also serving as the meta-model for the stacking. The results revealed significant correlations among the predictions of the individual models, which could potentially impact the meta-model’s efficacy. To address this, PCA was applied to the model predictions to generate de-correlated components as inputs for the meta-model, thereby enhancing prediction accuracy and robustness. Evaluation based on the area under the receiver operating characteristic (AUROC) curve demonstrated that the GAM outperformed all other individual models with the highest accuracy score of 0.924. In contrast, the RF and ANN models had the lowest accuracy, both registering at 0.872. However, the performance disparity across models was minimal. Notably, the PCA-based stacking approach (0.936) surpassed both traditional model stacking (0.912) and the performances of all individual models, advocating for its use in enhancing predictive accuracy. These findings endorse the PCA-stacking method over conventional stacking techniques. Nonetheless, further research across varied applications is warranted to generalize its efficacy.

Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins

This study addresses the challenge of utilizing incomplete long-term discharge data when using gridded precipitation datasets and data-driven modeling in Iran’s Karkheh basin. The Multilayer Perceptron Neural Network (MLPNN), a rainfall-runoff (R-R) model, was applied, leveraging precipitation data from the Asian Precipitation—Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climatic Research Unit (CRU). The MLPNN was trained using the Levenberg–Marquardt algorithm and optimized with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Input data were pre-processed through principal component analysis (PCA) and singular value decomposition (SVD). This study explored two scenarios: Scenario 1 (S1) used in situ data for calibration and gridded dataset data for testing, while Scenario 2 (S2) involved separate calibrations and tests for each dataset. The findings reveal that APHRODITE outperformed in S1, with all datasets showing improved results in S2. The best results were achieved with hybrid applications of the S2-PCA-NSGA-II for APHRODITE and S2-SVD-NSGA-II for GPCC and CRU. This study concludes that gridded precipitation datasets, when properly calibrated, significantly enhance runoff simulation accuracy, highlighting the importance of bias correction in rainfall-runoff modeling. It is important to emphasize that this modeling approach may not be suitable in situations where a catchment is undergoing significant changes, whether due to development interventions or the impacts of anthropogenic climate change. This limitation highlights the need for dynamic modeling approaches that can adapt to changing catchment conditions.

A novel approach to predictor selection among large-scale climate indices for seasonal rainfall forecasting in small catchments

ABSTRACT Utilizing identical climate indices as predictors for all climate divisions within large basins may result in unreliable rainfall forecasts at the sub-basin scale. This study aimed to develop a new approach to identify the most effective predictors among large-scale climate indices for seasonal rainfall forecasting in small areas. The proposed approach combines a selective singular value decomposition method (SSVD) with a non-linear sequential forward selection method (NLSFS). Applying the new algorithm for seasonal rainfall forecasting within two climate divisions in Karkheh basin, Iran, indicated that the climate indices identified by the SSVD differed between the study areas. The combination of these indices exhibited a correlation with seasonal rainfall approximately 11% higher than those derived from the SVD method. Moreover, NLSFS significantly enhanced the forecast accuracy compared to the frequently employed linear sequential forward selection (SFS) method, and the optimal predictors chosen by the two methods differed across all seasons.

Hydrological Drought Monitoring Based on the Correct Calculation of Standardized Flow Index in the Karkheh Basin

Hydrological Drought Monitoring Based on the Correct Calculation of Standardized Flow Index in the Karkheh Basin

Comparison of the performances of the gene expression programming model and the RegCM model in predicting monthly runoff

Abstract Prediction of rainfall and runoff is one of the most important issues in managing catchment water resources and sustainable use of water resources. In this study, the accuracy and efficiency of the Gene Expression Programming (GEP) model and the Regional Climate Model (RegCM) to predict runoff values from monthly precipitation were investigated. For this purpose, monthly precipitation data of 48 synoptic stations, monthly temperature data of 21 synoptic stations, and also monthly runoff data of 40 hydrometric stations located in the Karkheh basin during 45 years (1972–2017) were used. Out of this statistical period, 40 years was used for calibration, and five years (1995–1999) for the validation of the model results. The results showed that the GEP model with an average R2 value of 0.948, average RMSE value of 19.4 m3/s, average NSE value of 0.91, and average SE value of 0.3, had a much more accurate performance than the RegCM model, which had an average R2 value of 0.04, average RMSE value of 298.2 m3/s, average NSE value of −0.64, and average SE value of 4.6 in predicting monthly runoff.

Evaluation and optimization of hydrometric locations using entropy theory and Bat algorithm (case study: Karkheh Basin, Iran)

Hydrometric stations are important in most countries because of the application and importance of the data obtained from these stations. It is necessary to choose the best place for their establishment according to the cost of constructing hydrometric stations. The aim and innovation of this research are to optimize the location of hydrometric stations using Bat's meta-heuristic algorithm and interpolation methods, which information transfer entropy theory and Bat's algorithm were used to maximize the average amount of information transfer entropy. For this purpose, the data of 43 hydrometric stations of Karkheh basin in western Iran in period of 1991–2015 were used. In this research, two scenarios were investigated in order to improve the entropy of information transmission between stations. In the first scenario, using the kriging method to prepare the flow distribution map in the region and choosing normal kriging with spherical variogram as the best model to fit the average annual flow data and using the Bat algorithm to increase the correlation coefficient between the data and assuming no none of the available stations, 43 points were used to redeploy stations with higher average entropy in the region. The results of this scenario showed the concentration of new stations in the central and eastern areas of the basin. In the second scenario, the amount of entropy of information transfer at the regional level was calculated and 18 potential points were recommended for the establishment of new stations. The obtained variogram for the discharge of the basin showed that the range of influence is low and it is necessary to establish the stations at a close distance.

A framework for the forensic-engineering assessment of reservoir operation during floods based on a new standard operation policy

Floods inflict financial and human losses worldwide. The importance of assessing the effectiveness of flood management policies rises as the frequency and severity of floods increase. This study presents a novel forensic engineering framework for assessing the role of reservoir operation in achieving objectives under flood conditions. Specifically, this work develops two approaches, called the standard operation policy (SOP) and the ideal approach (IA) for the forensic assessment of reservoir-operation performance, which are compared with the historic management (HM) of reservoirs under flood conditions. This work introduces an innovative SOP for operation of reservoirs with the functions of flood control, meeting water demands, and hydropower generation. The genetic algorithm (GA) with the objective function of minimizing the maximum release from the most downstream reservoir in a multi-reservoir system is applied with the IA. The proposed framework is applied to evaluate the performance of the Seimare and Karkheh reservoirs system during floods in 2019 and 2020 in the Karkheh basin, Iran. The results show that in 2018–2019 and 2019–2020, on average, and under HM, the Seimare reservoir’s and Karkheh reservoir’s performance were respectively 28 and 68% worse than the SOP, and they were respectively 46 and 83% worse than the IA. This work’s forensic methods contribute to the practice of flood mitigation by means of reservoir operation.

Evaluation of widespread flooding of the Karkheh Basin in Iran using SWAT model and GLDAS database

Karkheh basin is a flood-prone region in Iran that was severely affected by devastating river floods in 2019. This study addressed some of the factors that affected on this event and were emphasized in the government inquiry committee. These include strategic questions such as the effect of prior precipitation in the basin and how the dams could mitigate the floods peak and volume. These questions as well as deficits in the rainfall data led to the application of the Soil and Water Assessment Tool model and the global land data assimilation system rainfall data to address the research questions. The results showed the initial managements of the dams prior to the occurrence of these floods was definitely affected by a decade continuous drought in the basin and the concerns about its continuation in 2019. The events occurred during March and April 2019, however, the precipitation occurred prior on October 2018 along with the resulted snowpack and soil saturation played a significant role in intensifying the floods. Although there were some limitations for the full operation of Seymareh Dam, the decision regarding its full operation could reduce the peak inflow to the Karkheh Dam from 8529 to 5447 m3/s. Finally, it is crucial to provide more accurate prediction systems, undertaking rapid and flexible responses and do not be misled by continuous droughts.

Drought-induced vulnerability and resilience of different land use types using time series of MODIS-based indices

Drought is a creeping and complex phenomenon that significantly impacts terrestrial ecosystem services by reducing the ecosystem's performance and upsetting the balance of the terrestrial carbon cycle. This study used Google Earth Engine (GEE) to quantify the effects of drought on the vulnerability and resilience of land use and land cover (LULC) types in the Karkheh basin, Iran's food basket and the third-largest basin in southwest Iran. Using MODIS data, we first extracted the vegetation health index (VHI) for drought monitoring during 2000–2020, and as a result, 2008 was designated as the drought year. Then, water use efficiency (WUE) was calculated for all LULC types during drought. In general, all LULC types experienced an increase in WUE, with the highest increase seen in the forest , which indicates the forest's high resistance to drought. To evaluate the ecosystems' resilience to drought, we computed the drought recovery period using the gross primary production (GPP). The results showed that recovery times varied among different LULC types, with the forest recovering in the shortest time, further indicating a higher resilience and greater importance for the absorption and stabilization of terrestrial carbon. Conversely, rainfed farming had the longest recovery time, indicating a higher vulnerability and, consequently, a greater need for the adoption of appropriate adaptive measures. The results of this study provide a better understanding of the response of different LULC types and their resilience to drought and can assist ecosystem conservation and LULC management.

PREDICTION OF FLOOD IN KARKHEH BASIN USING DATA-DRIVEN METHODS

Abstract. Flood causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Hence, flood prediction is highly significant for the mitigating municipal and environmental damage. The aim of this study was assessing the performance of different machine learning methods in predicting flood in Karkheh basin. To aim this, we used Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM), Feed Forward Back Propagation Neural Network (FFBPNN), and Radial Basis Function Neural Network (RBFNN) to simulate monthly streamflow in the study area. Furthermore, the performance of models was compared in predicting flood. All four models indicated good performance in simulating streamflow. However, LSSVM model had the highest accuracy compared with other models with R2 and RMSE of 85.89% and 30.02 m3/s during testing periods, respectively. Similarly, LSSVM model performed better in predicting annual maximum streamflow in comparison with other machine learning models.

Evaluating The hydrological response due to the changes in climate and land use on streamflow in the Karkheh basin, Iran

Abstract The Karkheh basin is one of Iran's largest and most waterlogged rivers. In this study, we aim to analyze the impact of Climate change and landuse change on the Karkheh basin using the Soil and Water Assessment Tool (SWAT). In this research, the considered land-use change is based on Iran's future policies, and climate change is studied by employing the RCP series and GCM in Mid-term (2040–2060) and Long-term (2080–2100). Firstly, changes in climate and land use are separately examined, and then the simultaneous impact of these two parameters is investigated. The results of the climate change study illustrate that the highest rate of change, which is a decrease of 14.3–22.8%, is achieved from the RCP 8.5 series in the long term. The results obtained from investigating the land-use change based on future policies in Iran show a maximum reduction of 7% in the average monthly runoff. The flow rate also decreases further when considering the simultaneous effects of both changes in the basin. In this case, the RCP 8.5 series shows a reduction of up to 39% in the long term. This study suggests that the effects of climate change are more significant than changes in landuse.

Performance evaluation of using Shannon’s entropy crossing time to monitor drought: a case study of the Karkheh

ABSTRACT Shannon’s entropy theory measures the average uncertainty in the outcomes of an event. Since drought monitoring is an important issue, it is imperative to develop more user-friendly, modern methods for it. The present study investigates the uncertainty of the difference between monthly mean precipitation and potential evapotranspiration utilizing Shannon’s entropy over short-, mid-, and long-term dynamic time scales in the Karkheh Basin of Iran. An entropy-based precipitation–evapotranspiration index (EPEI) is defined for drought classification. EPEI is compared with the standardized precipitation–evapotranspiration index (SPEI) in terms of spatiotemporal patterns. The results indicate a higher similarity between EPEI and SPEI under existing conditions with low average precipitation. Over short scales, both indices yield similar results in duration, frequency, and intensity. Over long scales, the consistency of the drought duration, frequency, and intensity results declines. The main difference between the two is the EPEI’s ability to determine the early onset of the drought event.

Optimized water allocation in persistent severe climatic conditions: A novel metaheuristic approach

With increasing global water tensions, the need for a comprehensive water management framework in persistent harsh conditions is crucial. A framework was designed to devise a comprehensive and detailed plan that can strike a balance between all water resources and demands. In contrast to previous studies, this framework can be adopted for long-lasting severe climatic conditions and suggests detailed cultivation patterns and reservoir management. The framework adopts a comprehensive, detailed, novel, multi-objective, and evolutionary-based approach for optimal water management during continuous extreme events in sectors including agriculture, industry, and the environment. Sustainability (ecological water demand and global warming potential), economic welfare, and equitability are allocation principles. This framework utilizes complex, metaheuristic algorithms that enable it to cope with big data and optimize complex systems with high precision. For the case study of the Karkheh basin, the results show that for a full recovery of the Hoor al-Azim wetland, the basin cannot bear the agricultural water demand, and such activities must be decreased drastically. It is also shown that a 10% increase of the agricultural net benefit will result in a 40% increase of the Gini coefficient.

Managing Water Supply and Demand to Achieve Economic and Environmental Objectives: Application of Mathematical Programming and ANFIS Models

The integrated management of water supply and demand has been considered by many policymakers; due to its complexity the decision makers have faced many challenges so far. In this study, we proposed an efficient framework for managing water supply and demand in line with the economic and environmental objectives of the basin. To design this framework, a combination of ANFIS and multi-objective augmented ε-constraint programming models and TOPSIS were used. First, using hydrological data from 2001 to 2017, the rate of water release from the dam reservoir was estimated with the ANFIS model; afterwards, its allocation to agricultural areas was performed by combining multi-objective augmented ε-constraint models and TOPSIS. To prove the reliability of the proposed model, the southern Karkheh basin in Khuzestan province, Iran, was considered as a case study. The results have showed that this model is able to reduce irrigation water consumption and to improve its economic productivity in the basin.

Sustainable water resource planning at the basin scale with simultaneous goals of agricultural development and wetland conservation

Abstract Reducing the quantity of water in recent years has increased the competition between development projects and the environment. Wetlands are increasingly under pressure due to human activities. The most serious threats to wetlands are excessive agriculture and the diversion of water for irrigation. In recent years, due to water shortage and drought, wetland dryness in Iran has caused many problems, including the dust crisis. Therefore, planning at the basin scale is necessary to achieve sustainable development, which emphasizes the employment of mathematical models. In this study, using a reliability-based simulation–optimization approach, development planning in the Karkheh basin with the following two objectives is investigated: (1) total area under cultivation of agricultural development sectors and (2) supply reliability of the environmental flow requirement. The Water Evaluation and Planning (WEAP) model is used for the simulation of water resources and the multi-objective particle swarm optimization (MOPSO) algorithm is employed for optimization. The results show that in addition to significantly improving the supply reliability of the wetland requirement (from 55 to 79%), the design of agricultural development projects has been optimized. The reliability-based model has prevented unsustainable developments in the basin. Also, the average supply reliability of agricultural demands has increased from 51% (in previous studies) to 72%.

Regional frequency analysis of drought severity and duration in Karkheh River Basin, Iran using univariate L-moments method.

Drought is one of the natural disasters that causes a great damage to human life and natural ecosystems. The main differences are in the gradual effect of drought over a relatively long period, impossibility of accurately determining time of the beginning and end of drought, and geographical extent of the associated effects. On the other hand, lack of a universally accepted definition of drought has added to the complexity of this phenomenon. In the last decade, due to increasing frequency of drought in Iran and reduction of water resources, its consequences have become apparent and have caused problems for planners and managers. So in this research, regional frequency analysis using L-moments methods was performed to investigate severity and duration of Standardized Precipitation Index (SPI), Standardized Evapotranspiration Index (SEI), Standardized Runoff Index (SRI), and Standardized Soil Moisture Index (SSI) and to study of meteorological, agricultural, and hydrological droughts in Karkheh River Basin in Iran. Using K-means clustering method, basin was divided into four homogeneous areas. Uncoordinated stations in each cluster were removed. The best regional distribution function was selected for each homogeneous region, and it was found that Pearson type (3) has the highest fit on the data set in the basin. Based on Hosking and Wallis heterogeneity test, Karkheh Basin with H1 < 1 was identified as acceptable homogeneous in all clusters. The results showed that hydrological drought occurs with a very short time delay in Karkheh River Basin after the meteorological drought, and two indicators show meteorological and hydrological drought conditions well. Agricultural drought occurs after meteorological and hydrological drought, respectively, and its severity and duration are less than the other indicators. Meteorological, hydrological, and agricultural droughts do not occur at the same time in all of the years. In general, the SPI drought index shows the most severe droughts compared with the other three indices. By this way, in 5- to 20-year return period with severity of 3SPI and in 20- to 100-year return period with severity of 7SPI, region IV or the western and northwestern areas of the basin has been affected by severe meteorological drought. By using the regional standardized quantities, it is possible to estimate the probability of drought in any part of the catchment that does not have sufficient data for hydrological studies.

Low flow analyzing from upstream to downstream in Karkheh river basin

Recognizing and analyzing the behavior of flow indices is a prerequisite for water resource management and cropping pattern planning in arid and semi-arid regions. The purpose of this study was to analyze flow in order to provide regional relations and identifying vulnerable areas.. In this study, 26 stations with a common statistical period were selected by examining the daily stream flow data of hydrometric stations in Karkheh basin. The daily stream flow time series was prepared for each year. Then, 3, 5, 7, 10, 15, 30 and 60 days’ flow and average annual discharge were extracted for each year and each station. A new index was defined as the low flow index. Then the trend detection of flow index and its regional analysis was performed. Single and multiple regression between independent parameters of height, area, slope, distance to the outlet and related flow indices extracted and results were analyzed. The accuracy of simulation was also estimated through The coefficient of determination. The results showed that by increasing the distance from the outlet of Karkheh basin upwards, the trend of the index was higher and in the branches, the obtained index was less than the downstream. This indicates that the branches were more vulnerable and should be focused on better water resources management in the branches

Optimal Development of Agricultural Sectors in the Basin Based on Economic Efficiency and Social Equality

The limitation of freshwater resources and the growing demand for water, make the issue of water resource development planning and water allocation among stakeholders even more important. Ideally, water allocation should be economically efficient and socially equitable. In this study, a water allocation model is presented in an integrated framework that considers the interaction of water supply and demand according to economic and social factors. To achieve this, a reliability-based multi-objective optimization - simulation approach has been employed. The objective functions of the problem are: 1) maximizing GDP from agricultural sectors and 2) maximizing social equality in different provinces of the basin (measured using the Williamson coefficient). The fair development and allocation among the shared provinces in the basin can reduce conflicts in the region. Karkheh basin has been considered as a case study and decision variables of the problem are area under cultivation of agricultural development sectors in different provinces. The results show that, without harming the income of the agricultural sector, the spatial distribution of development projects can be done in such a way that equality (according to income level and the number of people working in each province) is achieved. One of the solutions of Pareto front compared to previous studies shows that, in addition to an increase of about 12% of the objective function 1 (GDP), the value of the objective function 2 (Williamson coefficient) decreased from 1.19 to 0.98. This indicates a decrease in income inequality among the provinces of the basin.

Recent trends in atmospheric evaporative demand in Southwest Iran: implications for change in drought severity

This study evaluates trends in climatic variables, atmospheric evaporative demand (AED) and meteorological drought between 1988 and 2018 over the southwest of Iran, where the Karkheh Basin is located. For this purpose, we used monthly time series of meteorological variables (precipitation, air temperature, wind speed and sunshine duration). The AED was estimated by the FAO-56 Penman-Monteith equation and drought severity by the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). There have been significant changes in the climate over the study domain, including a large increase in air temperature and wind speed and a decrease in relative humidity, which have driven a positive trend in the AED. The SPEI for the region has fallen, with the decrease being larger than the magnitude of change observed with the SPI. This suggests an increase in drought severity mostly associated with the rise in AED. The implications for water availability and climate change processes in the region are discussed.

Projected climate change in the Karkheh Basin, Iran, based on CORDEX models

Climate change is one of the most important challenges facing watersheds. In this study, the effects of climate change in the Karkheh River Basin (KRB), located in the Iranian plateau, have been investigated. The temperature and precipitation, as significant climatic parameters, have been investigated using the observed data (1980–2017), as a baseline period, and the projected data (2020–2090) which can be regarded as the output of the Coordinated Regional Climate Downscaling Experiment (CORDEX) model based on RCP8.5 and RCP4.5 scenarios. The status of the snow cover changes was evaluated using MODIS TERRA satellite sensors. The results show that there has been a downward trend in the precipitation amount in most of the KRB, at 95% confidence level. The projected results also show that the amount of annual precipitation will decrease with the deviation from the baseline period. According to RCP8.5 and RCP4.5 scenarios, the amount of annual precipitation will decrease by 88.2 and 56.1 mm, respectively, as compared with the baseline period. Moreover, the air temperature trend in the future period will be increased by deviation from the baseline period. Thus, by the end of this century and according to RCP8.5 and RCP4.5 scenarios, the minimum temperatures will rise by 4.5 and 2.5 °C, respectively. Evaluation of the snow cover status of the study area, based on the MODIS sensor over the last two decades, shows that the amount of snow covers has been decreasing, reflecting global warming in recent years. Therefore, long-term planning and water resources management, considering the climate change condition, are considered significant in this basin.