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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.

Ideal watersheds of simple geometries, characterized by dominant processes involved in rainfall–runoff transformation, can provide model users/developers with detailed insights into hydrological components. In the present paper, some models were evaluated against available laboratory data on impervious rectangular and V-shaped (open-book) watersheds. The HEC-HMS as a numerical hydrologic model was adopted along with analytical solution of the kinematic wave approximation. The numerical methods used in HEC-HMS model involve finite difference method. Simulations were performed for various cases formed through variation of watershed slope and rainfall intensity. The outputs of the models involved runoff hydrographs, time to peak discharge and maximum discharge. For the rectangular watershed, numerical solution of KW within HEC-HMS was applied while for the V-shaped case, the numerical solution of KW in HEC-HMS and DWSM models and nonlinear reservoir scheme in SWMM model were compared. According to the results, in the rectangular watershed case, numerical model of KW within HEC-HMS showed good performance with relative accuracy of 69.2% and 96.0% in estimation of time to peak discharge and maximum discharge, respectively. In the case of V-shaped watershed, all rainfall–runoff models provided satisfactory results so that the nonlinear reservoir scheme in SWMM model outperformed the KW model. Moreover, regarding the kinematic wave solution schemes, finite difference (KW in HEC-HMS) and shock fitting (KW in DWSM) were shown to be similar in efficiency with the Nash–Sutcliffe efficiency index of 0.94.

Changes in amount and frequency of runoff are caused by interaction of the climatic and anthropogenic factors in human-nature system. Those part of the runoff variations caused by anthropogenic loadings are more manageable compared with climate effects occurring over a long period. In this paper, we propose a framework to decompose the effect of anthropogenic and climatic time series on streamflow. To fulfill this task, anthropogenic time series attribution (ATSA) method is introduced. ATSA employs the output of climate elasticity (CE) method to extract human-affected time series and naturalizes the annual streamflow. In other words, ATSA makes it possible to exploit CE output in time series analysis. Furthermore, temporal downscaling process performed by a hybrid discrete wavelet transform and artificial neural network (DWT-ANN) enhances the temporal resolution of ATSA. We applied ATSA framework on Urmia Lake (northwest of Iran) inflow over the 1972–2010 period. CE application indicated that the contribution of climatic and anthropogenic factors in reduction of the mean inflow to the lake is about 60 and 40%, respectively. ATSA also revealed how human exploitation affected the time series of the lake inflow. Roughly, 14,000 MCM of the lake inflow reduction was attributed to the anthropogenic impacts between 1995 and 2010 such that the removal of anthropogenic loading could have led to rising water level up to 1275.5 meters above the sea level (i.e., 1.4 m above the ecological level) in 2010. Furthermore, the effect of de-trended inflow time series was investigated via DWT-ANN. Trend-free pre-whitening Mann-Kendall (TFPW-MK) test indicated significant increase in temperature, potential evapotranspiration, population, reservoir capacity, irrigated area, and the number of wells, over 39 years of the study period.

Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems. Recently, the Copernicus Climate Change Service (C3S) database has been releasing monthly forecasts for lead times of up to three months for public use. This study evaluated the ensemble forecasts of three C3S models over the period 1993–2017 in Iran’s eight classified precipitation clusters for one- to three-month lead times. Probabilistic and non-probabilistic criteria were used for evaluation. Furthermore, the skill of selected models was analyzed in dry and wet periods in different precipitation clusters. The results indicated that the models performed best in western precipitation clusters, while in the northern humid cluster the models had negative skill scores. All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons. Moreover, with increasing lead time, the forecast skill of the models worsened. In terms of forecasting in dry and wet years, the forecasts of the models were generally close to observations, albeit they underestimated several severe dry periods and overestimated a few wet periods. Moreover, the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models. In general, the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran. For the clusters considered in Iran and for the long-range system versions considered, the Meteo France model had lower skill than the other models.

Due to uncertainty in the nature of indirect remotely sensed precipitation products, bias adjustment is a crucial step. In this research, bias adjustment of Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) snowfall estimates with 0.1° spatial resolution and half-hour temporal resolution was performed over a basin located in the Western United States based on Snowpack Telemetry (SNOTEL) ground station snow accumulation (SA) observations using the Gridded based Bias Adjustment using Copula (GBAC) approach. After dividing the study area into several boxes, usual and unusual pairs of cumulative distribution functions (CDFs) were separated in station pixels. Then, simulated biases were obtained using multivariate copula functions in station boxes. Next, based on the T copula function, bias adjustment bands were generated during the calibration period in station pixels. This step was followed by the validation stage. Also, bias adjustment bands were generated in pixels with no station. Based on calibration results, in boxes located west of 120° W, the range between the lower bound and the 10th percentile of the band had the most impact on satellite SA adjustment. Conversely, in boxes located east of 120° W, the middle range of the band (between 10th and 90th percentiles) was more effective. Thus, longitude was an effective factor in satellite SA bias adjustment. The validation results showed that in station boxes with a longitude over 120° west, the adjustment band was narrower. This is while in station boxes with longitude less than 120° west, the bias adjustment band better encompassed the ground SA data.

The main objective of the present study was to investigate runoff response to climate variables as well as landuse change over the past 30 years in Tajan River Basin, using the SWAT model. After the model calibration, four different scenarios were simulated and compared. Comparison of simulated runoff results determined from different scenarios indicated that climatic variables reduced the amount of runoff while the landuse change increased this amount in most months of the year. Simulated runoff under three landuse scenarios in all months demonstrated that the runoff achieved from scenario 1 was smaller than scenarios 2 and 4. In scenario 4, the runoff amount increased by 3–21% and 0.8–13% in Kordkheil station compared to those of scenario 1 and scenario 2, respectively. Furthermore, the increase in runoff for scenario 4 is 3–19% and 2–12% in Rig Cheshmeh station relative to those of scenario 1 and scenario 2, respectively. Nonetheless, the maximum change in runoff was only 6% under climatic variables. Hence, landuse had more significant impacts on the runoff compared to climatic variables.

Risk-based optimization is a framework that allows the designer to involve uncertainties in the decision-making process and determine the reliability of a hydraulic structure. This study was conducted to incorporate hydrological and hydraulic uncertainties in the probabilistic design of Karun-4 diversion system in southwestern Iran. The risk-based multi-objective optimization was developed for determining the effect of uncertainty sources on the characteristics of the diversion system. For this purpose, the time series of annual maximum peak flow and maximum flood volume for a period of 58 years were prepared. Archimedean copula function and non-dominated sorting genetic algorithm were adopted. The optimal values of upstream cofferdam height, downstream cofferdam height and the diameter of the first and second tunnels were estimated as 38, 11, 9.2 and 8.8 m, respectively, all corresponding to 25-year return period. The results suggest that the proposed framework could be valuable for decision makers when economic, hydraulic and hydrological uncertainties are expected.

Climate change, urbanization, and growing population have led to the rapid increase in the use of groundwater. Therefore, monitoring the groundwater (GW) changes is essential for water management and decision-makers. Due to frequent lack of reliable and sufficient in-situ information, remote sensing and hydrological models can be counted as the alternative sources for assessing GW storage changes on a regional and global scale. Here, we test such an approach for Qazvin Plain in Iran, one of the regions that recently have been facing severe drought conditions. The main purpose of this study is to downscale GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5-degree) to a finer spatial resolution (0.1-degree) using fine spatial resolution auxiliary datasets (0.1-degree) such as the evaporation, surface and subsurface runoff, snow depth, volumetric soil water, and soil temperature from the ERA5-Land model and precipitation from integrated multi-satellite retrievals for global precipitation measurement (IMERG). Different regression models were tested for the GWSA downscaling. Moreover, since different water budget components such as precipitation or storage are known to have temporal lead or lag relative to each other, the approach also includes a time shift factor among the components. The most suitable regression model with the highest skill score during the validation test was selected and applied to predict the 0.1-degree GWSA. The downscaled results showed a high agreement with the in-situ groundwater levels for Qazvin Plain in both interannual and monthly scales, with a correlation coefficient of 0.99 and 0.65, respectively. Moreover, the downscaled product clearly proves that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at higher spatial resolution. The major benefit of this method is in utilizing only the auxiliary data that are available with global coverage and free of cost, and that this method does not need in-situ GW records for training. Therefore, the proposed downscaling technique can potentially be applied to a global scale, other geographical regions, or aquifers.This study has received funding from the European Union’s Horizon 2020 research and innovation programme for G3P (Global Gravity-based Groundwater Product) under grant agreement nº 870353.

This paper aims to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) reanalysis precipitation data for the 2006–2015 period against ground-truth data over Iran. The ERA5 was first evaluated at daily, monthly, seasonal, and annual time scales, and then, the quantile mapping bias correction method was applied to the regional precipitation time series of Iran’s homogeneous precipitation regions. A set of categorical indicators and statistical indices were used to evaluate the ERA5 precipitation product against observations. The results showed that ERA5 enjoys favorable performance in most homogeneous precipitation regions of Iran, except in G3 and G6. The categorical indicators results indicated better performance of ERA5 in the rainy months with probability of detection (POD) > 0.7 and critical success index (CSI) > 0.4 than in the dry months. It is concluded that the ERA5 can accurately estimate precipitation amount but has moderate performance in estimating the number of rainy days and the days without rainfall. The RMSE (root mean square error), NSE (Nash–Sutcliffe efficiency), and mean absolute error (MAE) measures also indicate that the bias correction has significantly reduced the estimation errors. The results showed that, after bias correction, the MAE in G3, G6, and G8 regions decreased by 75%, 25%, and 25%, respectively. Moreover, the RMSE in G3, G6, and G8 regions decreased by 79%, 20%, and 12%, respectively. We found that after bias correction, the NSE metrics in the G3 region improved by approximately 200% (from − 0.8 to 0.85). In general, the ERA5 precipitation dataset can be considered as an alternative to ground-truth data over the whole territory of Iran.

In this study, 24-h forecasts of CMA, ECCC, ECMWF, KMA, NCEP and UKMO models were extracted from the TIGGE database and evaluated over selected stations in Iran within the 2010–2018 period. Daily forecast data were interpolated using the inverse distance method to the location of selected synoptic stations, while the frequency bias were further corrected using quantile mapping. The accumulation interval for precipitation was 24 h. Moreover, both raw and frequency-bias-corrected data were evaluated in deterministic and dichotomous modes at precipitation depth thresholds of 5, 15, 25, 35, and 45 mm. Forecasts were further partitioned and evaluated in different seasons in selected stations located in the margin of Alborz and Zagros Mountains. Best performing precipitation models did well over October–December season along the Alborz Mountains and over April–June along the Zagros Mountains. Results indicated that the forecasts were quite improved especially in high threshold after the frequency bias correction. Compared to other models, the ECMWF model provided best results in most stations. In contrast, NCEP performed poorly and could not provide a reliable medium to heavy precipitation forecasts. In general, none of the forecast models provided accurate estimates for precipitation depths over the 35-mm threshold. In fact, at higher precipitation thresholds, all models underpredicted the number of precipitation events.

Human activities (HA) and/or climate variability (CV) may be two major factors impacting natural flow regime (NFR). This study was conducted following two objectives. The first was to develop scenario-based hydrological modeling (SBHM) to disentangle the natural and human-induced impacts on flow regime. The second objective was to quantify the interaction between temperature and precipitation for the assessment of CV. To do so, six scenarios were defined to evaluate either the impact of HA, CV or both. Four major results were achieved: (1) the interaction between temperature and precipitation was more prominent in basin upstream areas, which reduced the streamflow by 9% in the entire simulation period; (2) when separating the effects of climatic and human factors, SBHM results in comparison with those of the climate elasticity analysis showed no significant differences; (3) HA were the main force driving the streamflow reduction in the study basin; (4) a 5 °C increase in air temperature in the future would lead to an increase of 1.6% in average annual streamflow, and 41% in peak runoff.

Determining the catchment design peak flow is crucial in hydrological practice. In this paper, the conceptual rainfall–runoff model EBA4SUB (Event-Based Approach for Small and Ungauged Basins) was ...

Ensemble precipitation forecast is effective in reducing the uncertainty and providing reliable probabilistic streamflow forecast. However, for operational applications, precipitation forecasts must go through bias correction in mean and spread. Although post-processing methods, such as BMA, have demonstrated good performance in ensemble-based calibration, the spatial correlation between stations may be altered after post-processing. In this research, ensemble precipitation forecasts of four NWP models, including ECMWF, UKMO, NCEP, and CMA within the TIGGE database, was bias-corrected and post-processed using quantile mapping and BMA for a case study basin in Iran. The ECC method was then used to recover the spatial correlation of ensemble forecasts. Subsequently, probabilistic streamflow forecast was conducted using post-processed precipitation forecasts. The results showed that the errors in the mean and spread of ensemble precipitation forecasts were corrected for each of the four NWP models while the ECC method was effective in maintaining spatial correlation. Furthermore, the results of probabilistic streamflow forecast showed that the performance of the forecast models improved after post processing, with the ECMWF model providing the best forecasts. More work is recommended to improve the impact of the ECC method on NWP models’ performance.

Prioritization of flood source areas (FSAs) is of paramount importance in flood management to adopt proportional measures within a watershed. Unit Flood Response (UFR) approach has been proposed to identify FSAs at subwatershed and/or cell scale. In this study, a distributed modified Clark (ModClark) model coupled with Muskingum flow routing method was used for hydrological simulations. Furthermore, SOMFCM clustering techniques involving Self-Organizing Feature Maps (SOFM) and Fuzzy C-Means algorithm (FCM) were used to identify Hydrologic Homogenous Regions (HHRs). The case studies were two semi-arid watersheds including Tangrah in northeastern Iran and eastern part of Walnut Gulch Experimental Watershed (WGEW) in Arizona. DEM-derived geomorphological and hydrological features were entered into Factor Analysis (FA) to determine the most effective variables in runoff generation. The optimum SOMFCM resulted in clustered HHRs map which was generally similar to that of the UFR-delineated FSAs at cell scale, especially in cases of maximum flood index values for both watersheds. Although clustering techniques, such as SOMFCM, cannot directly provide a map of FSAs involving absolute values of flood index, most dominant watershed physical features may be used to identify the most critical, or effective FSAs through clustered HHRs. Application of SOMFCM in two semi-arid watersheds demonstrated that SOMFCM provides a simple and useful tool to qualitatively identify the ranking of FSAs across a watershed. Therefore, the clustered HHRs involving higher ranks of FSAs that represent the most flood active regions, are expected to assist policymakers for effective management of floods.

Geoscientists are continuously confronted by difficulties involved in handling varieties of data formats. Configuration of data only in time or space domains leads to the use of multiple stand-alone software in the spatio-temporal analysis which is a time-consuming approach. In this paper, the concept of cellular time series (CTS) and three types of meta data are introduced to improve the handling of CTS in the spatio-temporal analysis. The data structure was designed via Python programming language; however, the structure could also be implemented by other languages (e.g., R and MATLAB). We used this concept in the hydro-meteorological discipline. In our application, CTS of monthly precipitation was generated by employing data of 102 stations across Iran. The non-parametric Mann–Kendall trend test and change point detection techniques, including Pettitt's test, standard normal homogeneity test, and the Buishand range test were applied on the generated CTS. Results revealed a negative annual trend in the eastern parts, as well as being sporadically spread over the southern and western parts of the country. Furthermore, the year 1998 was detected as a significant change year in the eastern and southern regions of Iran. The proposed structure may be used by geoscientists and data providers for straightforward simultaneous spatio-temporal analysis.

The objective of the present paper is to propose a framework for development of an optimal cropping pattern aimed at ground water recovery using an agent based approach. In the proposed agent-based model (ABM), the agents’ learning from each other as well as their self-learning from their own behavioral feedback was studied through simulation of the behavior of agricultural agents using fuzzy inference system (FIS). Moreover, the agents’ behavior were determined using linear programming in order to maximize the farmers’ income. The governmental agent regulated the interactions between agricultural and environmental agents by imposing its policies in the form of scenarios. The efficiency of the presented methodology was evaluated using hydrological data of Najaf Abad region, located in Iran’s central plain, on the basis of three hydrological scenarios (wet, normal, and dry) subject to governmental policy of aquifer recovery. The results showed that in a normal scenario with current groundwater withdrawal, the water level reduced by an average of 0.18 m per year. In contrast, the water level increased by an average of 0.48 m under aquifer recovery scenario. Furthermore, despite 17% reduction in water rights of agricultural agents in the study area, the total long-term agricultural income declined only by less than 4%, and in the planning horizon, their average annual income associated with these two management scenarios were estimated at 123.5 and 119 million US dollars, respectively.

Hydrological drought forecasting is considered a key component in water resources risk management. As sustained meteorological drought may lead to hydrological drought over time, it is conceptually feasible to capitalize on the dependency between the meteorological and hydrological droughts while trying to forecast the latter. As such, copula functions are powerful tools to study the propagation of meteorological droughts into hydrological droughts. In this research, monthly precipitation and discharge time series were used to determine Standardized Precipitation Index (SPI) and Standardized Hydrological Drought Index (SHDI) at different time scales which quantify the state of meteorological and hydrological droughts, respectively. Five Archimedean copula functions were adopted to model the dependence structure between meteorological/hydrological drought indices. The Clayton copula was identified for further investigation based on the p-value. Next, the conditional probability and the matrix of forecasted class transitions were calculated. Results indicated that the next month's SHDI class forecasting is promising with less than 10% error. Moreover, extreme and severe meteorological drought classes lead to hydrological drought condition with a more than 70% probability. Other classes of meteorological drought/wet conditions lead to normal hydrological (drought) condition with less than 50% probability and to wet hydrological condition with over 20% probability.

Satellite products, like all datasets, are subject to errors and uncertainties. Due to inherent biases embedded in satellite precipitation estimates, we present an error-adjustment approach based on the statistical differences between satellite precipitation products and in-situ observations (observed errors) employing two widely-used error models, namely that additive and the multiplicative error models, in an attempt to assess their suitability for the error correction of satellite-based daily precipitation estimates over northeast Austria. An error-adjustment technique based on the concept of the copula is adopted and applied to correct the supplied precipitation fields. It was found that IMERG precipitation estimates improved after error adjustment when compared to original satellite precipitation estimate (OSPE). The additive error model resulted in a better improvement by fitting the entire range of data when compared with the multiplicative error model. Moreover, the additive error model extracted the error with more accuracy and produced a better estimation of their characteristics, while the method based on the multiplicative error was less robust. However, the overall spatial dependence of the observed errors is reasonably preserved as that of the generated errors by copula. In addition, the validation results implied that the simulated realizations error-adjusted band, encompassed the observed data reasonably. Moreover, the copula-based simulations associated with the additive error model performed much better in comparison to the multiplicative error model. Overall, by using the t-copula model with an emphasis on the additive error model and imposing the simulated error fields on the OSPE, one may generate multiple realizations of precipitation fields.

Prediction of drought severity class/state can provide useful insight into preparedness actions. In this study, hydrological drought class, as determined by the standardized hydrological drought index (SHDI), was predicted. SHDI1 and SHDI3 drought classes were determined in seven and nine drought class systems. Support vector regression (SVR), support vector classification (SVC), and rough set theory (RST) were tested and compared as forecast modelling tools. Different combination of historic monthly streamflow and precipitation time series, or values/classes of SHDI and SPI, were considered as predictors to tackle the following question; is it possible to achieve the same or better accuracy by directly predicting the drought class, instead of predicting the streamflow (or drought index values) first and subsequently estimating the qualitative severity (class) of the drought? The results were more accurate in case of considering drought classes as inputs/output. However, the number of drought/wet classes may affect prediction accuracy. Prediction in case of fewer drought/wet classes leads to more accurate results. In addition, the SHDI3 prediction was more accurate than the SHDI1 prediction. RST showed slightly better accuracy than SVC and SVR. In case of nine-class forecast, the overall accuracy of RST model in correctly predicting the exact class, or with maximum one class shift, was 90.6% for SHDI1 and 96.5% for SHDI3. These values were 92.9% for SHDI1 and 98.8% for SHDI3 in seven-class system. Overall, direct classification methods (SVC and RST), in addition to simplicity, were more successful than the indirect method (SVR) in determination of severe dry/wet classes.

In this study, snow accumulation (SA) estimates of the Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) and Multi-Radar/Multi-Sensor (MRMS) products were eva...

Precipitation is an important and difficult climate variable to predict. Skillful sub-seasonal precipitation forecast can provide useful information for agriculture and water resources management communities. Nevertheless, sub-seasonal forecasts have been given less attention compared with forecasts of shorter/longer time horizons. Recently, the S2S database has made sub-seasonal to seasonal forecasts/reforecasts from 11 operational centers available to researchers. In this work, reforecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) spanning over a 20-year period were evaluated. Raw and post-processed precipitation forecasts were put against observed precipitation series of a number of synoptic stations with different precipitation regimes for all months of the year. By comparison, precipitation forecasts were more skillful in wet months. Raw forecasts in December and February were better than that in other months, while the weakest results were detected in August. There was no significant relationship between precipitation regime and prediction skill. Furthermore, quantile mapping (QM), Bayesian model averaging (BMA), and QM_BMA combination were adopted for post-processing. BMA outperformed QM and QM_BMA through improving the correlation coefficient between observations and average of ensemble forecasts; however, BMA performed weaker in other evaluation criteria, in particular in humid regions. It is concluded that the application of post-processing techniques greatly improved the results of ensemble precipitation forecasts. However, in a number of stations/months, the forecast results were not acceptable even after post-processing.

Identification of hydrologic homogeneous regions (HHR) facilitates prioritization of watershed management measures. In this study, a new methodology involving a combination of self-organizing features maps (SOFM) method and fuzzy C-means algorithm (FCM), designated as SOMFCM, is presented to identify HHRs. The case study region is Walnut Gulch Experimental Watershed (WGEW) located in Arizona. The input data consisted of a number of factors that influence runoff generation processes, including ten surface features as well as various rainfall values corresponding to 25, 50, and 100 years return periods. Factor analysis (FA) was applied for the selection of effective surface features along with rainfall value, used in the clustering algorithm. Validation procedure indicated that the best clustering scenario was achieved through merging three layers including TPI (topographic position index), CN (curve number), and P50 (50-year rainfall). The optimum number of clusters turned out to be six while the fuzzification parameter became 1.6. The presented methodology may be proposed as a simple approach for identifying HHRs.

Increasing salinity in Urmia Lake, located in the north-west of Iran, has turned into a critical issue, particularly because the lake is the habitat of a unique multi-cellular organism called Artemia Urmiana. During the past decades, several anthropogenic changes have taken place in the lake, which have resulted in increased salinity. This study introduces a reduced-order framework based on MIKE3 simulation model and proper orthogonal decomposition (POD) to simulate salinity patterns in Urmia Lake. Spatio-temporal variations of salinity in the lake firstly were simulated by MIKE3, and close matches were observed between salinity estimates from MIKE3 and those of the field data. Thereafter, 365 daily snapshots were taken from MIKE3 simulations, and subsequently 365 POD basis modes were computed. Due to high percentage of conserved energy of the lake system (salinity of lake) within the first ten POD basis modes, these modes were considered to develop a reduced-order salinity model (ROSM). Finally, results from MIKE3 were compared with the ROSM. It was shown that the first ten modes (among 365 modes) obtained by the POD conserved approximately more than 99.8% of the energy of the system. Moreover, using the first ten modes resulted in an error in magnitude of less than 0.01. Therefore, the ROSM could successfully capture the variations of salinity in the lake via its first ten modes.