Karkheh River Basin Research Articles

Validation of physicochemical water quality results through cross-checking with biological indices

ABSTRACT Biological assessment of rivers is critical for evaluating ecosystem health, detecting ecological changes, validating physicochemical monitoring data, assessing the effectiveness of water treatment systems, conserving biodiversity, and guiding water resource management. In this study, the surface water quality of the Karkheh River basin was evaluated using the National Sanitation Foundation Water Quality Index (NSFWQI) and the Iranian Surface Water Quality Index (IRWQIsc) for qualitative analysis. For biological monitoring, the Biological Monitoring Working Party (BMWP) index, along with the Average Score per Taxon (ASPT) and the Hilsenhoff Biotic Index (HFBI), was employed to provide a comprehensive assessment. Water quality was analyzed across 18 sampling stations, revealing significant spatial variation. Upstream areas exhibited relatively good conditions, with NSFWQI and IRWQIsc values of 59 and 68, respectively. In contrast, downstream locations near urban and agricultural areas demonstrated poor water quality, with values of 27 and 33, respectively. Overall, approximately 70% of the river's length has been severely impacted, highlighting the need for targeted planning and management to mitigate pollution. The biological indices corroborated the physicochemical results, revealing severe ecological stress in polluted regions. The close alignment between the NSFWQI and biological indicators emphasizes the importance of a multifaceted approach in assessing aquatic health. Key factors contributing to the river's degradation include excessive water extraction, untreated wastewater discharge, and diminished self-purification capacity, particularly in downstream areas.

Water allocation sustainability assessment in climate change: a modeling approach using water footprint and just policy

Abstract Climate change has challenged water allocation for food production in water-scarce areas. This fact calls for water reallocation (RA) strategies in basins with dominant agriculture. This study develops a framework combining the SWAT model and water footprint (WF) to evaluate water resource sustainability and improve its indices by fair RA from agriculture. The Karkheh River Basin in Iran was chosen as a study area for verification. Deficit irrigation (DI) was a farm strategy to promote basin sustainability and maintain food security. DI was distributed according to the equality of resources, proposed by Ronald Dworkin, as a just allocation principle. It means irrigated water would be allocated based on an equal water ratio per hectare. Results showed that the basin is currently unsustainable regarding the groundwater (BkWS) and surface flow (BuWS). According to the SSP5-8.5 scenario, the BuWS in the basin increases from 1.12 to 1.22 (9%), and BkWS increases from 2 to 2.15 (7.5%), while GnWS remains relatively constant at 0.99. By Dworkin's principle, DI caused 21-48% reduction in water allocation among five provinces. RA improved the BuWS, GnWS, and BkWS and ensured environmental flow. Climate change reduces 3.5% of food production, with an extra 9% by RA. These reductions would not endanger food security.

Investigation of the effects of climate change on hydrological drought and pattern detection for severe climatic conditions using CCT and SWAT models in the semi-arid region—Case study: Karkheh river basin

Investigation of the effects of climate change on hydrological drought and pattern detection for severe climatic conditions using CCT and SWAT models in the semi-arid region—Case study: Karkheh river basin

Deep Insight on Land Use/Land Cover Geospatial Assessment through Internet-Based Validation Tool in Upper Karkheh River Basin (KRB), South-West Iran

Recently, the demand for high-quality land use/land cover (LULC) information for near-real-time crop type mapping, in particular for multi-relief landscapes, has increased. While the LULC classes are inherently imbalanced, the statistics generally overestimate the majority classes and underestimate the minority ones. Therefore, the aim of this study was to assess the classes of the 10 m European Satellite Agency (ESA) WorldCover 2020 land use/land cover product with the support of the Google Earth Engine (GEE) in the Honam sub-basin, south-west Iran, using the LACOVAL (validation tool for regional-scale land cover and land cover change) online platform. The effect of imbalanced ground truth has also been explored. Four sampling schemes were employed on a total of 720 collected ground truth points over approximately 14,100 ha. The grassland and cropland totally canopied 94% of the study area, while barren land, shrubland, trees and built-up covered the rest. The results of the validation accuracy showed that the equalized sampling scheme was more realistically successful than the others in terms of roughly the same overall accuracy (91.6%), mean user’s accuracy (91.6%), mean producers’ accuracy (91.9%), mean partial portmanteau (91.9%) and kappa (0.9). The product was statistically improved to 93.5% ± 0.04 by the assembling approach and segmented with the help of supplementary datasets and visual interpretation. The findings confirmed that, in mapping LULC, data of classes should be balanced before accuracy assessment. It is concluded that the product is a reliable dataset for environmental modeling at the regional scale but needs some modifications for barren land and grassland classes in mountainous semi-arid regions of the globe.

Regional Frequency Analysis of Drought Severity of SEI-SRI and SEI-SSI Indices in Common Duration Using Multivariate L-Moments and Copula Functions (Case Study: Karkheh River Basin)

Regional Frequency Analysis of Drought Severity of SEI-SRI and SEI-SSI Indices in Common Duration Using Multivariate L-Moments and Copula Functions (Case Study: Karkheh River Basin)

Improving long-term streamflow prediction in a poorly gauged basin using geo-spatiotemporal mesoscale data and attention-based deep learning: A comparative study

Precise long-term streamflow prediction has always been important in the hydrology field, and has provided essential information for efficient water-resource management and disaster prevention. Attention to this field has increased recently owing to water and climate crises. Despite the remarkable improvements in existing data-driven models, they still have weaknesses, particularly for multistep predictions in poorly gauged basins. The purpose of this study is to improve the multistep ahead prediction ability by considering mesoscale hydroclimate data as booster predictors and employing attention-based deep learning. Therefore, a meta-algorithm is proposed for analyzing time-series data in complex geo-spatiotemporal environments to assess the superiority of prediction with booster predictors through direct and direct-recursive hybrid strategies. In the subsequent stage, a novel, integrated neural network architecture is demonstrated that couples a deep convolutional neural network (CNN) with a deep attention network. In particular, the former network performs automatic feature engineering, and the latter focuses on lengthy sequence complexities. Four state-of-the-art combinations for 12-month ahead prediction are introduced, including pairs of TimeDistributed-CNN (TD-CNN) and 3D-CNN along with a Long- and Short-term Time-series network (LSTNet) or a Transformer network. Moreover, a base architecture was employed for model comparison that contains the ConvLSTM2D layer that is compatible with multidimensional time-series data. For this, all models were applied to the Karkheh River basin in the northeast of the Persian Gulf, where monthly historical records of streamflow are available from 1955 to 2021. The results revealed that the application of hydroclimate sea surface temperature data and mean surface level pressure along with local data increased the prediction accuracy. Moreover, the proposed integrated networks delivered more accurate long-term streamflow predictions than the base models through various evaluation criteria, including r, R2, mean absolute error, root-mean-square error, Kling–Gupta efficiency, Willmott's Index, Legates–McCabe's, and the Akaike information criterion. In summary, the 3D-CNN–Transformer achieved the best performance, followed by the TD-CNN–Transformer, TD-CNN–LSTNet, and 3D-CNN–LSTNet with R2 values equal to 0.952, 0.930, 0.900, and 0.837, respectively. This study demonstrates that the application of hydroclimate data with proposed integrated networks are particularly useful for poorly gauged basin. Thus, the proposed models can potentially improve multistep ahead streamflow prediction compared to univariate and equation-based models.

Comparison of bankruptcy methods in the operation management of the Karkheh River Basin to allocate more water to the Hawr-Al-Azim Wetland

Abstract Bankruptcy methods are straightforward practical methods to address the problem of allocating limited sources of water to different users in regions where claims exceed assets. In the present study, three levels of restoration for the Hawr-Al-Azim wetland (35, 50, and 100%) and two agricultural-related scenarios, maintaining the current cultivation area and decreasing the area under cultivation, were considered and assessed using classical bankruptcy methods. The results showed that, because of climatic conditions and agricultural demands, full wetland restoration was out of reach and led to minimum satisfaction levels for agricultural beneficiaries. The results also showed that the modified bankruptcy method led to the highest satisfaction levels for beneficiaries in the scenario of maintaining the current cultivation area. In addition, the percentage of the water supply was increased by applying the scenario of crop restriction in the conditions of the full restoration of the wetland; for example, in the Abbas Plain region, this increase was achieved by almost 10–15% in all methods. On the other hand, decreasing the area under cultivation shifted the allocation problem in the basin to a non-bankruptcy one.

Downscaling of the flood discharge in a probabilistic framework

Many modeled and observed data are in coarse resolution, which are required to be downscaled. This study develops a probabilistic method to downscale 3-hourly runoff to hourly resolution. Hourly data recorded at the Poldokhtar Stream gauge (Karkheh River basin, Iran) during flood events (2009–2019) are divided into two groups including calibration and validation. Statistical tests including Chi-Square and Kolmogorov–Smirnov test indicate that the Burr distribution is proper distribution functions for rising and falling limbs of the floods’ hydrograph in calibration (2009–2013). A conditional ascending/descending random sampling from the constructed distributions on rising/falling limb is applied to produce hourly runoff. The hourly-downscaled runoff is rescaled based on observation to adjust mean three-hourly data. To evaluate the efficiency of the developed method, statistical measures including root mean square error, Nash–Sutcliffe efficiency, Kolmogorov-Smirnov, and correlation are used to assess the performance of the downscaling method not only in calibration but also in validation (2014–2019). Results show that the hourly downscaled runoff is in close agreement with observations in both calibration and validation periods. In addition, cumulative distribution functions of the downscaled runoff closely follow the observed ones in rising and falling limb in two periods. Although the performance of many statistical downscaling methods decreases in extreme values, the developed model performs well at different quantiles (less and more frequent values). This developed method that can properly downscale other hydroclimatological variables at any time and location is useful to provide high-resolution inputs to drive other models. Furthermore, high-resolution data are required for valid and reliable analysis, risk assessment, and management plans.

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.

Model-based water accounting for integrated assessment of water resources systems at the basin scale

Agricultural activities in the concept of integrated water resources management play a vital role. Especially in dry and semi-dry regions, agricultural activities have the largest share of water consumption. By employing a model-based approach using modified Soil and Water Assessment Tool (SWAT agro-hydrological model), this study has prepared Water Accounting Plus (WA+) framework requirements to investigate different conditions of supply and demand in wet (1985–2000) and dry (2001–2015) periods in a semi-dry basin (Karkheh River Basin) in Iran. Our assessments based on WA+ show decreasing 10% (21.65 to 19.29 Billion Cubic Meters (BCM)/year) of precipitation in the dry period caused a 4% (0.13 BCM/year) decline in natural evapotranspiration. However, the basin experienced a 24% increment in evapotranspiration from agricultural activities at the same period, and runoff was approximately halved (2.45 BCM/year). Therefore, especially in downstream parts, surface water withdrawal has decreased by 18%. These new conditions have put pressure on groundwater resources. The aquifer extraction and total withdrawal for irrigation have grown by about 17% and 4%, respectively. Finally, it is evident that the manageable water has diminished due to climate change; not only the managed water consumption in the basin has not reduced, but it has also highly risen. The current study results help water authorities arrange new hydrological and climatic conditions strategies.

Sensitivity of non-conditional climatic variables to climate-change deep uncertainty using Markov Chain Monte Carlo simulation

There is substantial evidence suggesting climate change is having an adverse impact on the world’s water resources. One must remember, however, that climate change is beset by uncertainty. It is therefore meaningful for climate change impact assessments to be conducted with stochastic-based frameworks. The degree of uncertainty about the nature of a stochastic phenomenon may differ from one another. Deep uncertainty refers to a situation in which the parameters governing intervening probability distributions of the stochastic phenomenon are themselves subjected to some degree of uncertainty. In most climatic studies, however, the assessment of the role of deep-uncertain nature of climate change has been limited. This work contributes to fill this knowledge gap by developing a Markov Chain Monte Carlo (MCMC) analysis involving Bayes’ theorem that merges the stochastic patterns of historical data (i.e., the prior distribution) and the regional climate models’ (RCMs’) generated climate scenarios (i.e., the likelihood function) to redefine the stochastic behavior of a non-conditional climatic variable under climate change conditions (i.e., the posterior distribution). This study accounts for the deep-uncertainty effect by evaluating the stochastic pattern of the central tendency measure of the posterior distributions through regenerating the MCMCs. The Karkheh River Basin, Iran, is chosen to evaluate the proposed method. The reason for selecting this case study was twofold. First, this basin has a central role in ensuring the region’s water, food, and energy security. The other reason is the diverse topographic profile of the basin, which imposes predictive challenges for most RCMs. Our results indicate that, while in most seasons, with the notable exception of summer, one can expect a slight drop in the temperature in the near future, the average temperature would continue to rise until eventually surpassing the historically recorded values. The results also revealed that the 95% confidence interval of the central tendency measure of computed posterior probability distributions varies between 0.1 and 0.3 °C. The results suggest exercising caution when employing the RCMs’ raw projections, especially in topographically diverse terrain.

Lessons Learned from Flood Management in Iran

Iran has a longstanding challenge in supplying water during prolonged drought periods. This has drawn considerable attention towards the dam industry over the past four decades, leading to the study, construction and operation of several large dams. These dams played a critical role in controlling the massive floods of 2019 and 2020, among others. Nevertheless, due to the increased intensity and frequency of extreme events because of climate change, the downstream regions of these large storage dams still face significant damages. This is mainly attributed to the insufficient dredging of rivers and tributaries, lack of rule curve and operation guideline for some storage dams, inaccurate prediction of flood volume, violation of land-use and water management action plans, promotion of industries with high water need, and floodplain encroachment. In this study, we aim to evaluate the performance of several large dams in the Karkheh and Karoon river basin, located in southwestern Iran, in managing the floods took place in the aforementioned periods. We also discuss the challenges and the lessons learned, with suggestions for improving the flood management in the country.

Reservoir management under different operating water levels, operation policies and climate change conditions

Water resources/reservoir management in developing countries attracts considerable attention due to growing human requirements and environmental concerns. The Seimare–Karkheh hydropower reservoir cascade, in the Karkheh River basin southwest of Iran, was studied. The impacts of changing reservoir operating water levels on hydropower generation and downstream environmental requirements were evaluated under different climate change conditions. For several years, the operating water level of Seimare reservoir was 704.5–720.0 metres above sea level (masl) (H1). Decision makers then adopted a different policy, with the operating range changed to 704.5–723.0 masl (H2). More recently, decision makers reduced the normal and minimum water levels so the reservoir now operates at 695.0–704.5 masl (H3). It was found that, for the period 2006–2050, based on H3, hydropower production would be reduced by 2.3–12.1% and 2.4–12.6% compared with policies H1 and H2, respectively. In 2051–2100, these reductions were found to be 5.8–11.2% and 7.7–11.3%, respectively. Furthermore, the results demonstrated that the current policy would substantially affect downstream hydrological alteration: 60–72% in Seimare River and 48–66% in Karkheh River for the period 2006–2050. The issue was found to be more pronounced in 2051–2100, with hydrological alteration of 68–73% in Seimare River and 59–66% in Karkheh River.

A robust multiple-objective decision-making paradigm based on the water\u2013energy\u2013food security nexus under changing climate uncertainties

From the perspective of the water–energy–food (WEF) security nexus, sustainable water-related infrastructure may hinge on multi-dimensional decision-making, which is subject to some level of uncertainties imposed by internal or external sources such as climate change. It is important to note that the impact of this phenomenon is not solely limited to the changing behavior patterns of hydro-climatic variables since it can also affect the other pillars of the WEF nexus both directly and indirectly. Failing to address these issues can be costly, especially for those projects with long-lasting economic lifetimes such as hydropower systems. Ideally, a robust plan can tolerate these projected changes in climatic behavior and their associated impacts on other sectors, while maintaining an acceptable performance concerning environmental, socio-economic, and technical factors. This study, thus, aims to develop a robust multiple-objective decision-support framework to address these concerns. In principle, while this framework is sensitive to the uncertainties associated with the climate change projections, it can account for the intricacies that are commonly associated with the WEF security network. To demonstrate the applicability of this new framework, the Karkheh River basin in Iran was selected as a case study due to its critical role in ensuring water, energy, and food security of the region. In addition to the status quo, a series of climate change projections (i.e., RCP 2.6, RCP 4.5, and RCP 8.5) were integrated into the proposed decision support framework as well. Resultantly, the mega decision matrix for this problem was composed of 56 evaluation criteria and 27 feasible alternatives. A TOPSIS/Entropy method was used to select the most robust renovation plan for a hydropower system in the basin by creating a robust and objective weighting mechanism to quantify the role of each sector in the decision-making process. Accordingly, in this case, the energy, food, and environment sectors are objectively more involved in the decision-making process. The results revealed that the role of the social aspect is practically negligible. The results also unveiled that while increasing the power plant capacity or the plant factor would be, seemingly, in favor of the energy sector, if all relevant factors are to be considered, the overall performance of the system might resultantly become sub-optimal, jeopardizing the security of other aspects of the water–energy–food nexus.

Solute transport and solutional denu-dation rate of carbonate karst in the semi-arid Zagros region (southwes-tern Iran)

We conducted a one-year-long study of solute load measured three times per month in three neighboring subwatersheds (Alashtar, Khorram Abad, and Biranshahr) located in the Karkheh River basin in the Zagros region of southwestern Iran. Research was focused on the chemical composition of water (solute load), karst denudation rate, spatial and temporal variability, as well as comparison of solute load with suspended load. Results show that Ca-Mg-HCO3 is the dominant water type that reflects the lithological characteristics of the catchment areas. Lack of seasonal fluctuation of solute load and absence of dilution during high water levels but evident seasonal course of discharge defines the highest solute flux during the annual maximum of discharge in spring months. The highest solute flux is related to flood events. High annual variation of Na1 concentration compared to conservative Cl2 as well as Chloro-Alkali indexes (CAI and CAI2) suggests that Na1 adsorption and desorption during ion-exchange reactions occur in the regolith. This Na+ variability, to some extent, explains weak Ca21 and Mg21 dilution effect during high water levels. During the measurement period (2014-2015), solute flux calculated per catchment area amounted to 49-69 t km-2 a-1 (tons per km per year). The chemical composition of water and discharge shows by far the highest chemical denudation of limestones and dolomites (87−89 %), while dissolution of gypsum is of minor importance (11−13 %). As a result, the carbonate karst solutional denudation rate is between 0.010 and 0.040 mm a21 , where the higher values are more probable for longer periods due to the relatively low discharge during the spring of 2015. Comparison of dissolved and suspended loads indicates that the transport of suspended load is an order of magnitude less than transport of the dissolved load; the only exception is one flash flood event when suspended load exceeded the dissolved load. Besides a small decrease in solute flux as well as carbonate karst dissolution rates from NW to SE, no large hydrochemical differences between the three subwatersheds were detected.

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

Forecastability of a heavy precipitation event at different lead-times using WRF model: the case study in Karkheh River basin

Forecastability of a heavy precipitation event at different lead-times using WRF model: the case study in Karkheh River basin

Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation

This paper presents a novel framework comprising analytical, hydrological, and remote sensing techniques to separate the impacts of climate variation and regional human activities on streamflow changes in the Karkheh River basin (KRB) of western Iran. To investigate the type of streamflow changes, the recently developed DBEST algorithm was used to provide a better view of the underlying reasons. The Budyko method and the HBV model were used to investigate the decreasing streamflow, and DBEST detected a non-abrupt change in the streamflow trend, indicating the impacts of human activity in the region. Remote sensing analysis confirmed this finding by distinguishing land-use change in the region. The algorithm found an abrupt change in precipitation, reflecting the impacts of climate variation on streamflow. The final assessment showed that the observed streamflow reduction is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19% of the total basin area), reduction of forests (from 11 to 3%), and decreasing annual precipitation has substantially reduced the streamflow rate in the basin. The developed framework can be implemented in other regions to thoroughly investigate human vs. climate impacts on the hydrological cycle, particularly where data availability is a challenge.

Assessing hydrologic drought risk using multi-dimensional copulas: case study in Karkheh River basin

Assessing hydrologic drought risk using multi-dimensional copulas: case study in Karkheh River basin

Assessment of the uncertainties of global climate models in the evaluation of standardized precipitation and runoff indices: a case study

ABSTRACT Uncertainties in climate change projection can originate from various sources and cause challenges. Thus, two specific approaches were developed in this study, for use in the selection of global climate models and in the assessment of drought occurrence. Considering the bias-corrected data, the performance of global climate models was evaluated using statistical methods, and the 14 best-ranked models were selected. These climate scenarios were used in the Long Ashton Research Station (LARS) downscaling model to obtain the precipitation and temperature time series. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) was used to model the runoff time series. Standardized precipitation and runoff indices were considered to assess the probability of meteorological and hydrological droughts. Finally, the Bayesian method was used to analyse the uncertainty assessment of drought occurrence. This methodology was applied in the Karkheh River basin and presented the moderate drought condition as the most probable state.