The aim of this study is to compare and calibrate of four different temperature based estimation methods in monthly time scale at Urmia Lake basin. The selected methods were Hargreaves (HG), Thornthwaite (TW), Blaney- Criddle (BC) and Linacre (Lin). For this purpose the information of weather parameters in the period 1986-2010 were used. Results of mentioned methods compared with the output of the FAO Penman- Monteith (PM56). Methods calibrated using the two distinct approaches. At first state only one calibration coefficient estimated along every year. At second state calibration coefficients estimated for each station and every month in a year. Performance of methods evaluated using the , RMSE, MBE and MAE statistics. The effectiveness of method’s calibration evaluated using the. Results showed that before calibration larger biases existed for selected methods comparing PM56. Calibration of methods considerably improved their performances. Results indicated that calibration of methods in the case of second state was very effective comparing the first state. The HG method was recognized as a best method either before or after calibration (in the first state) at the Urmia lake watershed. Linacre and Blaney- Criddle methods selected as the best methods following the Linacre method (after calibration at first state). Thornthwaite method had large error and therefore was not suitable method for estimation of at the study area. The Linacre method was known as the best method of estimation at Urmia Lake basin after calibration (at second state). HG, BC and TW were in the second to fourth rank orders, respectively.

Rainfall-runoff simulation models can be used in many water resources applications such as flood control, drought management. Although modeling is both continuous and single-event, continuous modeling has been less important in our country. In continuous models, more hydrological parameters are involved in comparison with single-event models, although this leads to more complicated modeling, but instead of a more realistic conditions of the hydrological system of the watershed will be illustrated and, in continuous systems, the surface water status can be monitored over a long period of time. Single-event models simulate only one incident, hence the moisture content between rainfall events is not considered, in contrast to continuous models of longer periods for estimating the response of the hydrologic information of the basin considered throughout the length of the rainfall events and between them (Lastoria, 2008). The American Hydrological Engineers Center (HEC), along with continuous hydrologic modeling, added the Soil Moisture Accounting (SMA) soil moisture content algorithm based on the PRMS model to the HMS software (Bennett, 1998). In this research, the aim is to provide an automatic calibration model based on the anion colony for the HMS-SMA soil moisture model. In this continuous model, the multiplicity of the considered parameters of the model, in addition to causing the difficulty of calibration by the method of trial and error, which also allows the automatic calibration of the software package to fail. For this purpose, in this research, by selecting a continuous HMS-SMA rainfall-runoff model, an external optimization program (Anion Cluster Algorithm (ACOR)) was used to overcome the weakness.

The successful application of a conceptual rainfall-runoff (CRR) model depends on how well it is calibrated. Generally, CRR model deals with many parameters that should be estimated through robust optimization tools. The degree of difficulty in solving a global optimization method is generally dependent on the dimensionality of the CRR model and certain characteristics of objective function. The purpose of optimization is to finalize the best set of parameters associated with a given calibration data set that optimize the evaluation criteria. In this study, a global optimization method known as the SCE (Shuffled Complex Evolution) has been developed for autocalibration of CRR parameters. This method is developed based on the nature of optimization problems in CRR models, combination of probabilistic and deterministic approaches, and clustering and competitive evolution. SCE method has shown promise as an effective and efficient optimization technique. Model verification and validation results indicated that automatic calibration was superior to other existing algorithms. Also, the proposed SCE algorithm was programmed via innovative ways to reduce the memory allocation and improve the speed of computations. In addition, a new method of storing very large matrices with small number of non-zero members was implemented. Thus, personal computers can also be used for automatic calibration of up to 35 parameters. In this study, the developed SCE technique has been applied for auto calibration of storage CRR model, namely NAM, in Gamasiab watershed within the greate Karkhe basin. The calibration and validation results and evaluating criteria shows the effectiveness and efficiency of this method for autocalibrating of CRR parameters.

The purpose of this study is to compare and evaluate five different methods of estimating the evapotranspiration of the reference plant on a nine-year daily data scale in Yazd province. Selected methods included Hargreaves-Samani (HS) , Priestley-Taylor (PT) , Turk (Turc) , Makkink (MK) and Dalton (D). For this purpose, data from ten synoptic meteorological stations were used covering a period of 9 years. The results of the mentioned methods were evaluated by FPM-56 method. Also, using FPM-56 method, the mentioned methods were calibrated for the studied stations. Also, using FPM-56 method, the mentioned methods were calibrated for the studied stations. RMSE, NS, SI, MAE, R^2 statistical criteria were used to evaluate the results. The results showed that before calibration the results of different methods are very different from FPM-56. The only acceptable model before calibration was the HS model. After calibration, the results of the models improved and model D, which was the worst model before calibration, was recognized as the best model for estimating evapotranspiration in Yazd province among the five selected methods. Mean values of Model D before calibration MAE = 3.83, R^2= 0.84, NS = -1.99, SI = 0.83, RMSE = 4.21 and after calibration MAE=0.83, R^2=0.86, NS=0.72, SI = 0.22, RMSE=1.02 was obtained. After calibration, Turc, PT and MK models were in the next categories of the best models.

Determining the accurate values of the return flow fraction from different uses is essential for optimal allocation, management and operation of water resources. The goal of this research was estimation of return flow coefficients from domestic, industrial and agricultural demands and also contribution of these return flows in surface flow. For this purpose, calibration technique and Genetic Algorithm were used. MODSIM is a river basin management decision support model that was used as a simulation model. In this research by Custom Run capability of MODSIM, i.e. coding in VB programming language into the model, the calibration of considered coefficients was provided. The calibration was done for 3 different objective function. Results showed that the most appropriate and logical coefficient was obtained in the 3rd calibration state, i.e. calibration with an objective function that included RMSE of surface flow discharge and RMSE of groundwater level. The return flow fractions of domestic, industrial and agricultural demands for Shian sub-basin in the western part of Iran are obtained 87, 76 and 18 percent, respectively.

The appropriate use of a conceptual rainfall-runoff model depends on how well its parameters are calibrated. Generally, rainfall-runoff models deal with numerous parameters that cannot be measured directly and should be estimated through optimization tools. The purpose of the optimization approach is to finalize the best set of parameters associated with a given calibration data set that optimize the evaluation criteria. In this paper, a continuous genetic algorithm calibration method has been used to estimate the NASH conceptual model parameters (n, k).The efficiency of the method was evaluated using the estimated parameters to simulate different rainfall - runoff events that happened in the Kasilian watershed in Mazandaran province during previous years. The calibration and validation results have shown that the suitability and efficiency of this model for auto calibrating of Nash conceptual rainfall-runoff model is more accurate and favorable.

One of the valid methods for simulation of the complex and non-linear process of rainfall-runoff is to use hydrological models. The purpose of this paper is to investigate the performance of three conceptual and lumped rainfall-runoff models; AWBM, Sacramento and SimHyd for simulating daily runoff at the outlet of the Amameh watershed using automatic calibration optimization genetic algorithm. Similar to other hydrological models, the range of parameters' variations is high in all three models and due to the difficulty of calibrating with trial and error-based methods, in this paper, the use of automatic calibration optimization methods for the hydrological models investigated. Preparation of required maps carried out by the GIS software version 10.4.1. Daily rainfall, potential evapotranspiration and observation runoff data of 2001-2005 used for calibration and 2006- 2007 data for simulations verificasion. The evaluation criteria including Nash-Sutcliff coefficient (NSE), coefficient of determination (R2) and root mean square error (RMSE) used to evaluate the proposed models. The statistical and graphical results of calibration and verification steps showed that SimHyd model performed better than the other two models with Nash-Sutcliff coefficient of 0.575 and 0.731, determination coefficient of 0.61 and 0.80 and the root mean square error of 1.033 and 0.829 respectively in the calibration and verification periods, using the automatic calibration optimization of the genetic algorithm and good graphical matching with the observational values. Also, AWBM and Sacramento models have satisfactory and desirable graphical and statistical results in the selected watershed and emphasize the good performance of automatic calibration optimization of the genetic algorithm.

In the realm of hydraulic modeling for water distribution networks, the calibration process plays a pivotal role. Calibration involves precise adjustments to align a model with observed data. However, when the measured data is scarce, the calibration process becomes challenging. In such cases, laboratory models prove valuable for simulating real-world conditions. Variability in parameters like pipe dimensions, length, roughness coefficients, and nodal demand as well as nodal elevations often leads to disparities between computer-based model outcomes and reality. Despite extensive research on computer-based models, laboratory water distribution network models and their calibration have received relatively limited attention due to the challenges and costs associated with them. In this study, a laboratory model of a water distribution network was constructed and subjected to hydraulic calibration. Roughness coefficients and minor head losses within the network were determined using a meta-heuristic method. Then, pipe roughness coefficients for polyethylene pipes were assessed and compared with values from scientific references. In the following, hydraulic validation of the network was conducted using the water quality simulation of a conservative substance. This approach illustrates the level of concurrence of flow ratios in the network pipes between the model and reality.The laboratory network was made of PE40 and consists of a square looped system with 1-meter pipe lengths, employing a 1000-liter tank to maintain a constant water head. This research was conducted in three stages. In the first stage, network calibration was performed using piezometric pressure and output flow data. Roughness coefficient and pipe minor head loss coefficients were selected as decision variables. The objective function was defined to minimize the total weighted difference in piezometric head and discharge between the model and reality. In the second stage, validation was performed based on pressure and output flow data. In the third stage, the network's hydraulic validation with respect to pipe flow rates was performed through the modeling of a conservative substance. This is because the dissolution of a conservative substance occurs solely due to mixing at nodes and flow division, allowing it to represent the correspondence of flow ratios in network pipes between the model and reality. In this research, pressure data was recorded using piezometers, and salt concentration was calculated using TDS (Total Dissolved Solids) sensor. After performing the optimization, a value of 0.008 was obtained for the Darcy-Weisbach friction coefficient (ɛ), This value aligns well with the assumption of new pipes in the network, in agreement with previous research (e.g., 0.050 by The Plastics Pipe Institute, 2008, and 0.070 by Padilla et al., 2013). Also, values of 1.20 and 0.89 were obtained for the minor loss coefficients of 0.5 and 1-meter pipes, respectively. Furthermore, the optimized minor loss values effectively reflect differences attributed to the number of connections in the 0.5 and 1-meter pipes, falling within recommended scientific ranges. Notably, unlike previous field studies, this research uniquely focuses on a laboratory model.After hydraulic calibration and validation using pressure and output flow data, further validation was conducted using the water quality model. The saltwater solution was injected at a specific point in the network, and the TDS quality parameter was measured at the two points in the network. Subsequently, utilizing the TDS values and the established relationship between TDS and salt concentration calculated in the laboratory, the salt concentration at the location of two sensors was determined. It's worth mentioning that the network's water supply contained dissolved solids. Subsequently, initial and injection concentrations were applied to the model and, the simulation was performed. A comparison of salt concentration results at two sensor locations revealed an 8.5% error in the first experiment and 2.5% in the second, confirming excellent agreement between the laboratory and computer-based network hydraulic model.

Planning and management of water resource and river basins needs use of conceptual hydrologic models which play a significant role in predicting basins response to different climatic and meteorological processes. Evaluating watershed response through mathematical hydrologic models requires finding a set of parameter values of the model which provides thebest fit between observed and estimated hydrographs in a procedure called calibration. Asmanual calibration is tedious, time consuming and requires personal experience, automaticcalibration methods make application of more significant CRR models which are based onusing a systematic search procedure to find good parameter sets in terms of at least oneobjective function.

Conceptual hydrologic models play a significant role inpredicting a basin’s response to different climatic and meteorological processes within natural systems. However, these models require a number of estimated parameters. Model calibration is the procedure of adjusting the parametervalues until the model predictions match the observed data. Manual calibration of high-fidelity hydrologic (simulation) models is tedious, time consuming and sometimesimpractical, especially when the number of parameters islarge. Moreover, the high degrees of nonlinearity involved in different hydrologic processes and non-uniqueness ofinverse-type calibration problems make it difficult to find asingle set of parameter values. In this research, the conceptual HEC-HMS model is integrated with the Particle Swarm Optimization (PSO) algorithm.The HEC-HMS model was developed as areplacement for HEC-1, which has long been considered as astandard model for hydrologic simulation. Most of thehydrologic models employed in HEC-HMS are event-basedmodels simulating a single storm requiring the specificationof all conditions at the beginning of the simulation. The soil moistureaccounting model in the HEC-HMS is the onlycontinuous model that simulates both wet and dry weatherbehavior.Programming of HEC –HMS has been done by MATLAB and techniques such as elite mutation and creating confusion have been used in order to strengthen the algorithm and improve the results. The event-based HEC-HMS model simulatesthe precipitation-runoff process for each set of parameter values generated by PSO. Turbulentand elitism with mutation are also employed to deal with PSO premature convergence. The integrated PSO-HMS model is tested on the Kardeh dam basin located in the Khorasan Razavi province.

Input parameters of hydrologic models are seldomknown with certainty. Therefore, they are not capable ofdescribing the exact hydrologic processes. Input data andstructural uncertainties related to scale and approximationsin system processes are different sources of uncertainty thatmake it difficult to model exact hydrologic phenomena.In automatic calibration, the parameter values dependon the objective function of the search or optimization algorithm.In characterizing a runoff hydrograph, threecharacteristics of time-to-peak, peak of discharge and totalrunoff volume are of the most importance. It is thereforeimportant that we simulate and observe hydrographs matchas much as possible in terms of those characteristics. Calibration was carried out in single objective cases. Model calibration in single-objective approach with regard to the objective function in the event of NASH and RMSE were conducted separately.The results indicated that the capability of the model was calibrated to an acceptable level of events. Continuing calibration results were evaluated by four different criteria.Finally, to validate the model parameters with those obtained from the calibration, tests perfomed indicated poor results. Although, based on the calibration and verification of individual events one event remains, suggesting set is a possible parameter.

All events were evaluated by validations and the results show that the performance model is not desirable. The results emphasized the impossibility of obtaining unique parameters for a basin. This method of solution, because of non-single solutions of calibration, could be helpful as an inverse problem that could limit the number of candidates. The above analysis revealed the existence of differentparameter sets that can altogether simulate verificationevents quite well, which shows the non-uniqueness featureof the calibration problem under study. However, the methodologyhas benefited from that feature by finding newparameter intervals that should be fine-tuned further inorder to decrease input and model prediction uncertainties.The proposed methodology performed well in the automatedcalibration of an event-based hydrologic model;however, the authors are aware of a drawback of the presentedanalysis – this undertakingwas not a completely fair validationprocedure. It is because validation events represent possiblefuture scenarios and thus are not available at the time ofmodel calibration. Hence, an event being selected as a validationevent should not be used to receive any morefeedback for adjusting parameter values and ranges.However,this remark was not fully taken into consideration, mostlybecause of being seriously short of enough observed eventsin this calibration study. Therefore, the proposed methodology,although sound and useful, should be validated inother case studies with more observed flood events.

Hydrologic models are useful tools for simulating water resources and flux(contain Runoff and evaporation) variations. Determining hydrological model parameters is primary condition for good simulating of hydrological process. With the estimated values of this parameters via the calibration process, the model can well simulate the natural system. The success of the existing models to simulate reality does not depend on the complexity and number of parameters. The structure of the model, identify influential parameters and calibration method can have a significant impact on improving the model outputs.
Optimization methods are from the useful automatic calibration methods. Practical experiences of hydrological models calibration have shown that single objective optimization methods often not enough to measure all important structures of observational data. In this research four different evolutionary multi-objective optimization algorithms are used for calibration and estimating main 4 parameters of GR4J (in French, mod`ele du G´enie Rural `a 4 param`etres au pas de temps Journalier) hydrological model over Danube basin. GR4J is a simple rainfall-runoff model and belongs to the family of hydrological models that focus on the soil moisture compartment.
All the four used evolutionary optimization techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-objective Particle Swarm Optimization (MPSO), the Pareto Envelope-Based Selection Algorithm II (PESA-II) and the Strength Pareto Evolutionary Algorithm II (SPEA-II) applied in the mode of two objective function. Both objective functions of these algorithms are regarded base on Nash–Sutcliffe model efficiency coefficient which can be calculated as follow:where and are observed and simulated values, respectively, and stands for the temporal mean of observations (, runoff or GRACE dTWS anomalies).
First objective is base on daily observed and simulated runoff and the other is according to daily Total Water Storage (dTWS) changes derived from the Gravity Recovery And Climate Experiment (GRACE) and its corresponding simulated daily total water storage using GR4J model. For the study basin, GR4J-derived Total Water Storage (TWS) at time t was calculated as the sum of amount of four water comportments which extracted from different simulation steps of GR4J as:TWSt = St + Rt + Vt + Wt
where S and R are the amount of water stored in the production store and routing store at the end of each day respectively. V and W are the remaining water in unite hydrographs (UH1 and UH2) during the simulation process at the end of each day, respectively. Finally, basin averaged TWS anomalies (dTWS) are computed as a difference between daily TWS (TWSt) and the temporal mean of TWS () during the study period as:GR4J parameters are estimated by maximizing the objective functions through each algorithm process. Some quality measures including Number of Pareto Solution(NPS), Generation Distance(GD), Spacing(SP) and Maximum Spread(MS) are used to compare the calibration results. The results indicate that NPS of all methods at the last iteration are the same. NSGA-II has better SP and MS criteria rather than other techniques for calibrating GR4J using GRACE dTWS and in-situ runoff data. MPSO show better response in all criteria respect to PESA-II and SPEA-II method and its GD is also better than NSGA-II. The calculated Nash-Sutcliffe model efficiency coefficient in calibration and evaluation periods show that GR4J simulation results can be judged as satisfactory results.