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Statistical downscaling of General Circulation Models (GCM) simulations is widely used for projecting precipitation at different spatiotemporal scales. However, the downscaling process is linked with different source of uncertainty including structural/parametric uncertainty of the model and output uncertainty. This research proposes a novel framework to assess the parametric uncertainty of downscaling model, and used this framework to assess the performance of different bias correction methods linked to the regression-based statistical downscaling model. The used downscaling framework in the current paper is Statistical Downscaling Model (SDSM). The conventional bias correction method linked with SDSM is the Variance InFlation method (VIF), this paper substitutes this method with three different bias correction methods including Local Intensity Scaling (LOCI), Power Transformation (PT), and Quantile Mapping (QM) to assess the associated parametric and global uncertainty of each method in different climate by using a new approach. The proposed method is applied to six different stations located in Iran and United States with different climate status. Average Relative Interval Length (ARIL), P-level, and Normalized Uncertainty Efficiency (NUE) are used as uncertainty indicators to evaluate the results. Results represent that in every assessed climate class, LOCI, and PT, work better than conventional VIF in both amount and occurrence modules of SDSM framework. More precisely, LOCI works better in station that has wet summer, while PT performs well in the stations where there is no or very limited precipitation in summer. Substituting LOCI with VIF, result in increasing the value of NUE by at least factor of 3 in occurrence and amount model which means the significant reduction in structural uncertainty. Also applying PT in arid regions improves the NUE indicator at least by factor 2 in occurrence and amount model and by factor 3 in output uncertainty assessment, and results in less parametric and output uncertainty. Results illustrate the important role of bias correction approaches in reducing structural, and output uncertainty and improving the statistical efficiency of the downscaling model. [ABSTRACT FROM AUTHOR]

This paper presents the results of studies on water resources management strategies, under the influence of climate change, in the Zarrineh River basin, providing some 41% of the total inflow into the Lake Urmia. Climate change simulation over the period 1992–2050 was performed by downscaling of seven global circulation models. The best model was then selected for modelling the hydrological behavior of the basin using a rainfall-runoff model while an allocation model was used to evaluate future supply and demands. A system dynamics model was further utilized to investigate the sustainability of the dynamic behavior of the basin. Five suggested water resources management strategies for two future climate scenarios were investigated. Results indicated that implementation of the strategy of 40% reduction in agricultural water demand would provide higher degrees of sustainability. Based on this, the average annual inflow to Lake Urmia in the years 2021 to 2050 would be 1029.75 MCM, which is equal to 81.02% of the Lake Urmia's environmental requirement from the river's share. [ABSTRACT FROM AUTHOR]

Given that accessible freshwater resources are becoming more scare, knowledge about the climate change effects on sources of primary salinity in water resource systems is getting more important. Most of limited preceding works on analysis of climate change induced salinization of the river ecosystems have employed simplistic empirical models. Accordingly, this paper deals with developing a conceptual module for primary salinity modelling in framework of a physically-based hydrological model, i.e. Soil Water Assessment Tool (SWAT), for analyzing climate change impacts on catchment-scale primary hydro-salinity. The developed methodology is applied to saline Shour River located south of Iran, which recently has been facing water shortage and restriction due to the river's salinity. The model is validated according to historical records of the river's discharge and salinity using Sequential Uncertainty FItting ver. 2 (SUFI2) algorithm, where acceptable Nash-Sutcliff values of 0.73 (0.76) and 0.56 (0.65) is respectively achieved for discharge and total dissolved solids (TDS) in calibration (validation) period. The calibrated model is then utilized for climate change scenarios analysis where climate time series projected under conditions of A2, B1 and A1B scenarios are introduced to the model. The results indicates how time series of river salinity increases for both near future, years 2046–2064, and far future, years 2081–2099, with respect to current condition. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to monitor and forecast hydrological drought in a probabilistic manner. The proposed approach deals with the supply and demand variables and the role of carryover in a system to estimate the probability of drought severity at different hydroclimatlogical conditions as well as different storage volume levels. This approach might be of significance when the supply and demand variables of a water resources system change considerably by climate variation. Major probability values and their mutual use in the proposed drought forecasting method are discussed. The presented approach is applied for the hydrological drought forecasting of Zayandeh-rud river basin in Iran. This probabilistic view of drought monitoring and forecasting is useful for risk-based decisions in water resources planning and management. The proposed index could be used to overcome the lack thereof in the existing surface water supply index. [ABSTRACT FROM AUTHOR]

The aim of this study was investigating the effect of drought on groundwater storage and measuring the impact of future drought events under climate change conditions on Najafabad aquifer in Iran. A Support Vector Machine (SVM) model was developed to simulate groundwater storage by using the Particle Swarm Optimization (PSO) to optimize SVM parameters. Various meteorological and hydrological drought indices were considered as model inputs and the best combination of drought indices was selected by the Gamma Test (GT) method. The drought analysis revealed that the Groundwater Resource Index (GRI) and the Reconnaissance Drought Index (RDI) were the most effective indices in modelling the aquifer. To examine future drought events under climate change, the outputs of five ISI-MIP5 (Inter-Sectoral Impact Model Inter-comparison Project) General Circulation Models (GCMs) were derived under two Representative Concentration Pathways (RCPs). The results indicated that the most severe droughts will occur during the years 2035–2039 and 2033–2041 under RCP4.5 and RCP8.5, respectively. Continuing the current aquifer withdrawal policy, the groundwater storage will be affected by future drought events and decrease by 8.8% and 9.5% compared to the base period (1990–2016) under RCP4.5 and RCP8.5, respectively. [ABSTRACT FROM AUTHOR]

Gully erosion is one of the most complicated and destructive forms of water erosion. In order to prevent this erosion, the important factors controlling gully heads must be understood. This paper examines gully head advancement in the Hableh Rood Basin, Iran by (1) observing gully head advance between 1957 and 2005 using field studies, aerial photography and GIS analysis and: (2) applying and evaluating widely used experimental models including the, Thompson (Trans ASAE 7(1):54–55, ), SCS (I) and SCS (II) models, for estimating migrating headcuts over the study period. The results showed that the highest mean gully advancement (0.26 m year − 1) took place during the 1956–1967 period, with most gullies having lower and steady headcut retreat rates between 1967–2000 (0.21 m year − 1) and 2000–2005 (0.15 m year − 1). This suggests that the majority of gullies in the study area were still in the early stages of formation in the first study period and their formation may be linked to land use or climatic changes pre 1956. Analysis of the correlation between environmental characteristics of the study area and gully advancement indicated that the upslope area of head cuts and soluble mineral content of the soil were the two most important factors influencing the spatial and temporal variation of gully longitudinal development. Results of multiple regression revealed that the simple relation including upslope area and soluble minerals can explain 93% of total variance and relatively reflects the effects of runoff and waterfall process for headcut retreat. Application of statistical error analysis to evaluate the four gully advancement models showed that in comparison to other models, the second model of SCS has more reliable results for predicting longitudinal gully advancement in this study area and other similar regions. However, this study indicates that future modelling in the region should consider the role of soil soluble mineral content in predicting gully advancement. [ABSTRACT FROM AUTHOR]

In a changing climate, drought indices as well as drought definitions need to be revisited because some statistical properties, such as the long-term mean, of climate series may change over time. This study aims to develop a Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI) for reliable and robust quantification of drought characteristics in a changing climate. The proposed indicator is based on a non-stationary log-logistic probability distribution, assuming the location parameter of the distribution is a multivariable function of time and climate indices, as covariates. The optimal non-stationary model was obtained using a forward selection method in the framework of the Generalized Additive Models in Location, Scale, and Shape (GAMLSS) algorithm. The Non-stationary and Stationary forms of SPEI (i.e., NSPEI and SSPEI) were calculated using the monthly precipitation and temperature data of 32 weather stations in Iran for the common period of 1964–2014. The results showed that almost at all the stations studied, the non-stationary log-logistic distributions outperformed the stationary ones. The AICs of the non-stationary models for 97% of the stations were lower than those of the stationary models. The non-stationary models at 90% of the stations were statistically significant at the 5% significance level. While SSPEI identified the long-term and continuous drought and wet events, NSPEI revealed the short-term and frequent drought/wet periods at almost all the stations of interest. Finally, it was revealed that NSPEI, compared to SSPEI, was a more reliable and robust indicator of drought duration and drought termination in vegetation cover during the severest drought period (the 2008 drought). Therefore, it was suggested as a suitable drought index to quantify drought impacts on vegetation cover in Iran. [ABSTRACT FROM AUTHOR]

The water evaluation and planning (WEAP) approach and the invasive weed optimization algorithm (IWOA) are herein employed to determine the optimal operating policies in conjunctive (surface water/groundwater) systems for water supply in agricultural municipal/industrial (M&I) sectors under climate change. Climatic variables are simulated with atmospheric-ocean general circulation models (AOGCMs) under emission scenarios A2 and B2 during the baseline period 1971–2000 and the future periods 2040–2069 and 2070–2099 in the Khorramabad basin, Iran. The Hadley Centre Coupled Model, version 3 (HadCM3), and the Canadian Global Coupled Model, version 2 (CGCM2), produced superior temperature and rainfall projections, respectively, than other climate models. Under both emissions scenarios and during each future period, this study indicates an increase in temperature and a decrease in rainfall. Simulations of surface water with the IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) calibrated model shows a decrease in the future runoff. The Groundwater Modeling System (GMS) calibrated software projects a decrease in water level and a decrease in recharge under climate change scenarios. Simulation results from IHACRES and GMS are input to the Water Evaluation and Planning (WEAP) system to develop operational policies for the combined use of water resources., The water-allocation reliability of the system is estimated with the WEAP system for 24 scenarios reflecting climate change scenarios assuming increases in water demand, ranging from 10 to 60% in agriculture and from 20 to 30% in the municipal and industrial (M&I) sector. The IWOA is applied to optimize the conjunctive system of water resources (i.e., surface water and groundwater). The objective function is to maximize the system's water allocation reliability. The range of optimal water-allocation reliability changes is between 3 and 16%, with the lowest increase corresponding to the baseline period for agricultural water demand, and the highest rise corresponding to an increase of 50% in water demand under the B2 emissions scenario in 2040–2069 for the M&I water sector. [ABSTRACT FROM AUTHOR]

Climate as one of the key factors in water resources management affects the amount of water in the hydrological cycle, which subsequently impacts the level of water availability. Considering the challenges that the South Alborz Region, Iran is currently facing in supplying water for various consumers; in this study, the climate change adaptation scenarios are investigated for sustainable water supply and demand. This study uses a procedure in which five different adaptation approaches, under RCPs scenarios, were established using the WEAP model to assess the impacts of various adaptation strategies on increasing the balance between water supply and demand over current and 2020s accounts. The findings suggest an imbalance between supply and demand in the current situation with the greatest imbalance in domestic use while the minimum in the industrial sector. The results of assessing adaptive scenarios show that various scenarios have different effects on balancing the water supply and demand by different consumers; on the other hand, the scenarios that directly affect domestic water demand have the greatest effect on minimizing the gap between supply and demand in the region; therefore, the options for decreasing the population demand along with diminishing the losses in the domestic water distribution network are the most effective alternatives for balancing supply and demand under all of the climate scenarios. The findings of this research indicate that adaptive management with the focus on restricting demand helps actively management of water resources in the regions with scarce water resources. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions of the world, the occurrence of prolonged drought events (megadroughts) associated with climate change can seriously affect the balance between water supply and demand, thereby severely increasing the susceptibility of such regions to adverse impacts. In this study, a simple framework is introduced to estimate renewable water volumes (RW) to mitigate the challenges of megadrought events by managing the groundwater resources. The framework connects a weighted annual hydrological drought index (wSPEI) to RW, based on the short time-scale precipitation volume. The proposed framework, which was in a proof-of-concept case study applied to the Neishaboor watershed in the semi-arid part of Iran, showed that developing the weighted drought index can be valuable to estimate RW. The results suggested that the wSPEI, aggregating hydrological drought index (HSPEI) with the time scale k = 5 days and the regional coefficient s = 1.3 can be used to estimate RW with reasonable accuracy (R2 = 0.73, RMSE = 11.5 mm year−1). This indicates that in the Neishaboor watershed, the best estimation of RW can be determined by precipitation volumes (or the lack thereof) falling over 5-day aggregation periods rather than by any other time scales. The accuracy of the relationship was then investigated by cross validation (leave-one-out method). According to the results, the proposed framework performed fairly well for the estimation of RW, with R2 = 0.75 and RMSE = 12.2 mm year−1 for k = 5 days. The Overall agreement between the wSPEI, the RW derived from water balance calculations, and the estimated RW by the proposed framework was also assessed for a period of 34 years. It showed that the annual RW followed closely the wSPEI, indicating a reasonable relationship between wSPEI and the annual RW. Accordingly, the proposed framework is capable to estimate the renewable water of a given watershed for different climate change scenarios. [ABSTRACT FROM AUTHOR]

Landuse change and climate change are the main drivers of hydrological processes. The purpose of this study was to analyse the separate and combined future effects of climate and landuse changes on water balance components on different spatial and temporal scales using the integrated hydrological Soil and Water Assessment Tool model. The study focused on the changes and relationship between water yield (WYLD) and sediment yield (SYLD) in the heterogeneous Taleghan Catchment in Iran. For future climate scenarios, RCP 4.5 and RCP 8.5 of GFDL-ESM2M GCM were used for 2020–2040. A Markov chain model was used to predict landuse change in the catchment. The results indicated an increase in precipitation and evapotranspiration. The findings also showed that the relationship between WYLD and SYLD is direct and synergic. Climate change has a stronger effect on WYLD than landuse change, whereas landuse change has a stronger effect on SYLD. The conversion of rangelands to barren land is the most critical landuse change that could increase SYLD. The highest increase in WYLD and SYLD in scenario RCP4.5 resulted from the combined effects of climate and landuse change. We estimated WYLD of about 295 mm and SYLD of around 17 t/ha. The proposed methodology is universal and can be applied to similar settings to identify the most vulnerable regions. This can help prioritize management strategies to improve water and soil management in watersheds. [ABSTRACT FROM AUTHOR]

Climate changes and its undesirable impacts (such as melting glaciers, the occurrence of floods, hurricanes and droughts, etc.) are among the most important disasters that human beings have faced in recent decades. Considering the important role of the meteorological parameters on climate change, in this research the effectiveness rate of various meteorological parameters including the mean minimum and maximum annual temperature (Max-Temp and Min-Temp), the mean annual temperature (M-Temp), the mean annual sunshine (Sunshine), the mean annual relative humidity (Humidity), the mean annual wind speed (Wind) and the mean of annual precipitation (Rainfall) on United Nations Environmental Programme (UNEP) aridity index was assessed and prioritized using the Backward Multiple Ridge Regression (BMRR). In this study, the meteorological data series of 25 synoptic stations in Iran with different climate conditions during 1967–2017 was used. The results indicated that the BMRR method had a nice capability to predict the UNEP index using the above-mentioned meteorological parameters (the linear regression between observed and predicted the UNEP index had no difference with perfect reliable line (Y = X) at 0.05 significant levels and the R2 between two mentioned data series were significant at 0.01 levels at all stations). According to the results, Rainfall, Wind and Max-Temp parameters were the most effective parameters on the UNEP aridity index, respectively and the Min-Temp, M-Temp and Sunshine parameters were the least effective parameters on the UNEP index, respectively. Therefore, it is suggested that all human activities that have direct or indirect effects in the increasing temperature and reducing the rainfall must be revised and optimized based on the principles of sustainable development. [ABSTRACT FROM AUTHOR]

Climate change and population growth have influenced social and physical water scarcity in many regions. Accordingly, the future performance of water storage reservoirs, as one of the fundamental elements in the water resource management, are anticipated to be affected by climate change. This study reports on a framework that can model Reliability-Resiliency-Vulnerability (RRV) measures of water reservoirs in the context of climate change. The framework first develops a hydrological model of a reservoir system using its historical data. The model is then optimised to minimise the water deficit and flooding around the catchment area of the reservoir. The resulting optimal policies are simulated back to the model considering the GCMs. Finally, RRV indices are calculated. RRV indices are effective measures for defining the performance of reservoir systems. Reliability is defined as the probability of the failure of the system, Resiliency is defined as the time needed for the system to go back to its satisfactory state once it entered the failure state, and Vulnerability is defined as the "magnitude of the failure" of a system. The proposed framework has been applied to a reservoir system located in the south-west of Iran on the Dez river. The results show climate change may increase the reliability and resiliency of the system under study while increasing its vulnerability. Therefore, the output of this framework can also provide supplementary information to authorities and decision-makers to inform future water management and planning policies. [ABSTRACT FROM AUTHOR]

In many parts of the world, especially in metropolitan areas with dry climates, shortages of freshwater resources have become a significant challenge in water resources management. Rapid population growth and climate change in these areas impose additional burdens on water consumption, especially on surface water resources, which are increasingly becoming scarce. In this study, a change from the Standard Operating Policy (SOP) to the Modified Linear Decision Rule (MLDR) policy is considered as a solution to mitigate the impacts of climate change on dams and reservoir operation. The two policies were compared by using five reservoir system performance indices (reliability, resiliency, vulnerability, sustainability and meeting demands) and three hydropower energy generation indices. This study pertained to the Karaj hydropower dam reservoir in Iran and speculated on 2020–2039. Changing the operation policy from current SOP to the MLDR policy can mitigate the adverse effects brought by climate change. According to the results, the SOP policy works best for reservoir operation if there is enough water (optimistic scenario). In the case of pessimistic scenarios (shortage of water), operating the reservoir system with SOP policy would turn into a crisis in supplying drinking water. On the other hand, the MLDR policy prevents the complete draining of the reservoir. In all water-demand scenarios, the MLDR policy reduces the amount of time (by over 90%) in which the reservoir remains empty, compared to the SOP policy. The final results demonstrate how the MLDR policy can mitigate the undesirable effects of climate change. [ABSTRACT FROM AUTHOR]

Water crisis is one of the main global risks that has different impacts on the society. This work uses available data and tools to track water storage changes in Iran, where lack of observations limits hydroclimatological studies and thus causes disasters. Data from Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiment (GRACE) are combined to analyze water storage changes (LWE) in Iran. GRACE signals indicate a large reduction of the water storage in North of Iran along the coast of the Caspian Sea (the largest global inland water body), where the water level has been oscillating significantly. In addition, results show the largest reduction of the water storage that occurred in the karstic and alluvial aquifers in Zagros, where groundwater is overused and many dams are constructed across the rivers. In addition to anthropogenic forcing, climate change has short- and long-term impact on the water storage. The highest correlation between LWE and climatological variables including temperature and precipitation is at 3- and 2-month lags, respectively. Natural and anthropogenic forcing caused the maximum rate of LWE reduction (maximum average reduction) in Kermanshah province, where a sequence of earthquakes occurred (in 2017, 2018, and 2019). Results highlight the main role of water storage monitoring in management plans and decision-making processes to conserve natural resources and reduce hazards. [ABSTRACT FROM AUTHOR]

Reservoir operation cannot be carried out without due heed to surface water and groundwater resources, since neglecting either will have irreversible consequences. Optimal operation of the Zayandehrood Dam which supplies water into the Zayandehrood River basin in the central plateau of Iran is a case in point which warrants due consideration paid to both dam operation and the climate conditions in the region suffering from a history of successive droughts. The main objective of the present research is to develop operation rules for the Zayandehrood reservoir through a combined perspective of both surface and ground water resources using the fuzzy inference system, and adaptive neuro-fuzzy inference system. The objective is to determine the share of the Zayandehrood reservoir in meeting downstream water demands. For this purpose, the water shortage and the dramatic groundwater drawdown in the Zayandehrood River basin faced with in recent years have been studied in an attempt to develop operation models capable of controlling groundwater drawdown. The models indicate that not only can groundwater drawdown be controlled, but that it is also possible to establish a greater sustainability. Different operation models have been compared in terms of their operation criteria. Results show that the ANFIS model composed of optimal data enjoys a higher sustainability compared to others. [ABSTRACT FROM AUTHOR]