Recommended daily rainfall-runoff model for Australian hydrology consulting

Among the different hydrological models used for water resources assessment, conceptual models are gaining popularity due to their simple structure and satisfactory performance. In this study, performance of two conceptual rainfall-runoff models, GR4J (Génie Rural) and AWBM (Australian Water Balance Model), working in Source modelling platform is evaluated at two catchments in the upper Godavari basin, Maharashtra, India. Source is an integrated water resource management tool, developed by eWater Ltd, Australia. GR4J is a four parameter two water store model and AWBM is an eight parameter three water store model. Gap-filled daily rainfall and potential evapotranspiration (PET) data measured from adjacent station is used as input to these models. The models are calibrated for the period 2000–2007 and validated for 2008–2011 using the observed streamflow data. A combination of Shuffle Complex Evolution (SCE) and the Rosenbrock equation is used to calibrate each model in Source framework. Statistical measures such as NSE (Nash–Sutcliffe Efficiency) and correlation coefficient (R2) are computed to evaluate the efficacy of model predictions. From the results obtained, it is found that GR4J model provides better streamflow predictions for the study catchments than AWBM. As GR4J is a simpler model, it is more adaptable for water balance modelling.

Assessment of the internal dynamics of the Australian Water Balance Model under different calibration regimes

In many regions where rainfall runoff models are required, there is a lack of streamflow data available to calibrate the model parameters. Along with streamflow data simply not being recorded, there are many reasons for a lack of a suitable data set for model calibration, such as significant modifications to catchment characteristics, or long periods of unseasonable rainfall producing unrepresentative relationships. Generally, for an ungauged catchment, it is desirable to implement a model with as few free parameters as possible, provided the conceptualization of the model is suitable for the catchment under consideration. The Australian Water Balance Model (AWBM) is a rainfall runoff model that is commonly used in Australia. Typically it has 7 parameters, however methods are available to determine the storage size and capacity parameters for the AWBM based on an estimate of the average annual runoff. This is a great advantage when applying the model to ungauged catchments. However, there are two AWBM parameters which cannot be determined by this approach, namely the baseflow index and baseflow recession constant. The aim of this paper is to develop regionalization relationships to allow these two parameters to be estimated for ungauged catchments, based on the characteristics of the catchment that can be easily identified. General Regression Neural Networks are used to identify the relationships between model parameters and catchment characteristics. The results indicate that by using only easily identifiable characteristics of an ungauged catchment, suitable estimates of the unknown AWBM parameter values can be obtained, thereby allowing reasonable rainfall-runoff models to be developed. While the relationships developed in this work are specific to Australian catchments, the methodology used can be easily adapted to develop relationships for other regions.

Accurate catchment level water resource assessment is the base for integrated river basin management. Due to the complexity in model structure and requirement of a large amount of input data for semi-distributed/distributed models, the conceptual models are gaining much attention in catchment modelling these days. The present study compares the performance of three conceptual models, namely GR4J, Australian Water Balance Model (AWBM) and Sacramento for runoff simulation. Four small catchments and one medium catchment in the upper Godavari river basin are selected for this study. Gap-filled daily rainfall data and potential evapotranspiration (PET) measured from the same catchment or adjacent location are the major inputs to these models. These models are calibrated using daily Nash–Sutcliffe efficiency (NSE) with bias penalty as the objective function. GR4J, AWBM and Sacramento models have four, eight and twenty-two parameters, respectively, to optimise during the calibration. Various statistical measures such as NSE, the coefficient of determination, bias and linear correlation coefficient are computed to evaluate the efficacy of model runoff predictions. From the obtained results, it is found that all the models provide satisfactory results at the selected catchments in this study. However, it is found that the performance of GR4J model is more appropriate in terms of prediction and computational efficiency compared to AWBM and Sacramento models.

Hydrologic models are important tools for accurate water resource assessment. Distributed models are commonly used for this purpose as its output provides detailed information about catchment hydrology. High data requirements and complex structure limit the application of the distributed models at the majority of locations. In comparison, conceptual models are simple and require minimum inputs for runoff generation. In this study, the performance of a physically-based spatially distributed model SHETRAN is compared with two simple lumped conceptual rainfall-runoff models, namely Australian Water Balance Model (AWBM) and GR4J, at Vamanapuram river basin in India. The results showed comparable performance of SHETRAN and AWBM. The study concludes that conceptual models are best suited for data-scarce regions, and the choice of distributed and lumped models for hydrologic studies are dependent on data availability and output requirementsKeywordsRiver basinRunoffSHETRANAWBMGR4JDistributed modelsConceptual models

The Bhumibol reservoir in the Ping River basin is the largest reservoir in the Kingdom of Thailand. This reservoir has contributed to economic development of the country by supplying increased electricity and irrigation water demands as well as flood mitigation in riparian areas along the Ping and the Chao Phraya River. The prediction of inflows to the reservoir is crucial for the optimal management of water for irrigation, power generation and flood control. Properly customized rainfall-runoff models of the catchment could provide the basis for predicting the inflows to the reservoir. Hence, five lumped conceptual rainfall-runoff models were developed for the Ping River basin to simulate daily inflows to the Bhumibol reservoir. The rainfall-runoff models are Australian Water Balance Model (AWBM), Sacramento Soil Moisture Accounting Model, Simplified Hydrolog Model (SIMHYD), Soil Moisture Accounting and Routing Model (SMAR) and Tank Model. The evaluation of the performances of these models showed that all models are capable of predicting inflows. However, the SIMHYD, Sacramento, AWBM and Tank models perform better than SMAR model. Hence, these models could be employed for prediction of inflows to the reservoir with acceptable accuracy.

Embedding machine learning techniques into a conceptual model to improve monthly runoff simulation: A nested hybrid rainfall-runoff modeling

In this study, a quantitative assessment of uncertainty was made in connection with the calibration of Australian Water Balance Model (AWBM) for both gauged and ungauged catchment cases. For the gauged catchment, five different rainfall data sets, 23 different calibration data lengths and eight different optimization techniques were adopted. For the ungauged catchment case, the optimum parameter sets obtained from the nearest gauged catchment were transposed to the ungauged catchments, and two regional prediction equations were used to estimate runoff. Uncertainties were ascertained by comparing the observed and modelled runoffs by the AWBM on the basis of different combinations of methods, model parameters and input data. The main finding from this study was that the uncertainties in the AWBM modelling outputs could vary from −1.3% to 70% owing to different input rainfall data, −5.7% to 11% owing to different calibration data lengths and −6% to 0.2% owing to different optimization techniques adopted in the calibration of the AWBM. The performance of the AWBM model was found to be dominated mainly by the selection of appropriate rainfall data followed by the selection of an appropriate calibration data length and optimization algorithm. Use of relatively short data length (e.g. 3 to 6 years) in the calibration was found to generate relatively poor results. Effects of different optimization techniques on the calibration were found to be minimal. The uncertainties reported here in relation to the calibration and runoff estimation by the AWBM model are relevant to the selected study catchments, which are likely to differ for other catchments. The methodology presented in this paper can be applied to other catchments in Australia and other countries using AWBM and similar rainfall–runoff models. Copyright © 2014 John Wiley & Sons, Ltd.

The design of water resource structures needs long-term runoff data which is always a problem in developing countries due to the involvement of huge cost of operation and maintenance of gauge discharge sites. Hydrological modelling provides a solution to this problem by developing relationship between different hydrological processes. In the past, several models have been propagated to model runoff using simple empirical relationships between rainfall and runoff to complex physical model using spatially distributed information and time series data of climatic variables. In the present study, an attempt has been made to compare two conceptual models including TANK and Australian water balance model (AWBM) and a physically distributed but lumped on HRUs scale SWAT model for Tandula basin of Chhattisgarh (India). The daily data of reservoirs levels, evaporation, seepage and releases were used in a water balance model to compute runoff from the catchment for the period of 24 years from 1991 to 2014. The rainfall runoff library (RRL) tool was used to set up TANK model and AWBM using auto and genetic algorithm, respectively, and SWAT model with SWATCUP application using sequential uncertainty fitting as optimization techniques. Several tests for goodness of fit have been applied to compare the performance of conceptual and semi-distributed physical models. The analysis suggested that TANK model of RRL performed most appropriately among all the models applied in the analysis; however, SWAT model having spatial and climatic data can be used for impact assessment of change due to climate and land use in the basin.

<p>Bucket-type conceptual hydrological models are widely popular, because of their relatively low data and computational demands. With the improved computational techniques and advances in computer sciences, web based hydrological modelling tools are becoming available too. Conceptual rainfall-runoff (CRR) models are designed to approximate the general physical mechanisms which govern the hydrologic cycle and found practical by many hydrologists and engineers. In this context, a web based, open-source, platform independent, easily accessible hydrological modelling tool <strong>Hidro-Odtu</strong> has been designed. Aiming at providing fast and accurate results, <strong>Hidro-Odtu</strong> utilize lumped and semi-distributed hydrological modelling capabilities. The design of the <strong>Hidro-Odtu</strong> contains pre-processing using the tools to automatically delineate the river network and basin boundaries, input the forcing data, lumped hydrological modelling with parameter calibration capability, hydrological overland flow routing and dynamic result visualization. Moreover, web-based technologies allow remotely prepare model input files, run model calculation and display model results for rainfall-runoff calculations. Bucket storage lumped, conceptual rainfall-runoff model is selected as core feature for hydrological model and it is enhanced to a semi-distributed model by including the Muskingum-Cunge flow routing method to simulate overland flow. Model results are evaluated by several performance indices such that Nash–Sutcliffe Efficiency Index (NSE), Sum of Square of Error (SSE) or Kling-Gupta Efficiency (KGE).</p><p>Hydrological modelling, calibration and routing algorithms have been implemented by using Python programming language for the back-end calculations and Node.js framework, html, JavaScript have been utilized for front-end side to handle data preparation and results visualization.</p><p>Hidro-Odtu have been evaluated with numerous data sets with different study areas and found successful to delineate sub basins and river network, to define rainfall-runoff relationship on the basis of the sub-basins. With this tool, it is aimed to obtain practical hydrological modelling results using web technologies.</p>

Added value of distribution in rainfall-runoff models for the Meuse basin

The Australian Water Balance model (AWBM) and the SimHyd rainfall–runoff model are conceptual models widely used for simulating daily flows in Australia. To evaluate their ability to model non-stationary daily flows, to quantify the effect of land disturbance, and to assess their performance in catchments outside Australia, these two models were applied to two small watersheds, the Fernow watershed No. 6 in West Virginia, USA, for the period 1959–2009, and the River Rimbaud watershed in the French Alps for the period 1968–2006. Both watersheds have experienced well documented disturbances as a result of clearing and fire, respectively. The modelling protocol followed was adopted for a workshop on hydrology under change, held during the 2013 IAHS Assembly in Göteborg, Sweden, which was based on split-sample tests. On balance, the AWBM worked marginally better than SimHyd for these two catchments, and neither model worked satisfactorily for the Fernow watershed where forest clearing, application of herbicide and changes in species composition had occurred. There is little difference in terms of model performance between periods when land disturbances occurred and other periods with relatively stable conditions. Conceptual models are better equipped to simulate climate-driven variations in the observed streamflow (e.g. the River Rimbaud), and inadequate in reproducing streamflow variability as a result of complex forest management practices.

Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters

Hydrological modelling at a catchment scale was conducted to investigate the impact of climate change and land-use change individually and in combination with the available streamflow in the Painkalac catchment using an eWater Source hydrological model. This study compares the performance of three inbuilt conceptual models within eWater Source, such as the Australian water balance model (AWBM), Sacramento and GR4J for streamflow simulation. The three-model performance was predicted by bivariate statistics (Nash–Sutcliff efficiency) and univariate (mean, standard deviation) to evaluate the efficiency of model runoff predictions. Potential evapotranspiration (PET) data, daily rainfall data and observed streamflow measured from this catchment are the major inputs to these models. These models were calibrated and validated using eight objective functions while further comparisons of these models were made using objective functions of a Nash–Sutcliffe efficiency (NSE) log daily and an NSE log daily bias penalty. The observed streamflow data were split into three sections. Two-thirds of the data were used for calibration while the remaining one-third of the data was used for validation of the model. Based on the results, it was observed that the performance of the GR4J model is more suitable for the Painkalac catchment in respect of prediction and computational efficiency compared to the Sacramento and AWBM models. Further, the impact of climate change, land-use change and combined scenarios (land-use and climate change) were evaluated using the GR4J model. The results of this study suggest that the higher climate change for the year 2065 will result in approximately 45.67% less streamflow in the reservoir. In addition, the land-use change resulted in approximately 42.26% less flow while combined land-use and higher climate change will produce 48.06% less streamflow compared to the observed flow under the existing conditions.

Rainfall-runoff models have been widely used through the last century all over the world. These models help to build a trustworthy relationship between the precipitation and runoff. The relationship between rainfall and runoff is essential in a catchment for hydrologic analysis and design. In the current paper, our main objective is to compute runoff by employing two hydrological models namely SCS CN and RRL toolkit Australian Water Balance Model (AWBM) and to check the which model is more suitable for runoff estimation in the Bina basin. The SCS CN method is an empirical approach for estimation of Direct Runoff.