Australian Water Balance Model Research Articles

Improving the Applicability of Lumped Hydrological Models by Integrating the Generalized Complementary Relationship

AbstractLumped hydrological models (LHMs) are indispensable for water resource planning and environmental studies due to simple structures and robust performances. LHMs commonly focus on runoff processes with crude representations for other hydrological processes, such as evapotranspiration (E). Therefore, these models may yield unrealistic water balance partitioning. The challenge is to enhance the LHMs performance while retaining simplicity. The generalized complementary relationship (GCR) is a simple and robust method for estimating E. This study attempted to incorporate GCR into four widely used LHMs (Australian water balance model, GR2M, SIMHYD, and TANK) to test whether the integrated models (GCR‐LHMs) can improve runoff simulation at little cost to the model structure and data requirement. Original LHMs and integrated GCR‐LHMs were tested in 2112 catchments over various climatic conditions. Results show that the GCR‐LHMs outperform original LHMs in most catchments (77.7 ± 5.0%). In addition, the number of catchments that GCR‐LHMs have qualified performance (i.e., Kling‐Gupta coefficient [KGE] more than 0.5) increased by 10.7 ± 3.6% compared with LHMs. The performance of original and integrated models is dependent on the aridity index and normalized vegetation index. However, the improvement in model performance is less catchment characteristics dependent. These results indicate that incorporating GCR into the LHMs improves the model performance under different climatic and vegetation conditions and justifies the integration. GCR integration with LHMs can improve runoff estimation ability (with higher KGE and R‐Square) while retaining model simplicity and readily available input. These findings are valuable for improving the applicability and accuracy of LHMs.

Performance Evaluation of Rainfall-Runoff Models for Predictions of Inflows to Bhumibol Reservoir in Thailand

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.

Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China

Runoff prediction is of great significance to flood defense. However, due to the complexity and randomness of the runoff process, it is hard to predict daily runoff accurately, especially for peak runoff. To address this issue, this study proposes an enhanced long short-term memory (LSTM) model for runoff prediction, where novel loss functions are introduced and feature extractors are integrated. Two loss functions (peak error tanh (PET), peak error swish (PES)) are designed to strengthen the importance of the peak runoff's prediction while weakening the weight of the normal runoff's prediction. The feature extractor consisting of three LSTM networks is established for each meteorological station, aiming to extract temporal features of the input data at each station. Taking the upper Huai River Basin in China as a case study, daily runoff from 1960–2016 is predicted using the enhanced LSTM model. Results indicate that the enhanced LSTM model performed well, achieving Nash–Sutcliffe efficiency (NSE) coefficient ranging from 0.917–0.924 during the validation period (November 2005–December 2016), outperforming the widely used lumped hydrological models (Australian Water Balance Model (AWBM), Sacramento, SimHyd and Tank Model) and the data-driven models (artificial neural network (ANN), support vector regression (SVR), and gated recurrent units (GRU)). The enhanced LSTM with PES as loss function performed best on extreme runoff prediction with a mean NSE for floods of 0.873. In addition, precipitation at a meteorological station with a higher altitude contributes more runoff prediction than the closest stations. This study provides an effective tool for daily runoff prediction, which will benefit the basin's flood defense and water security management.

Streamflow simulation using conceptual and neural network models in the Hemavathi sub-watershed, India

Water is one of the most valuable natural resources and a major element of a state's and country's socio-economic growth. The world's water resources and India are under huge pressure because of rising demand and a limited supply. Proper water management is the only solution for ensuring a close gap between demand and supply. Hydrological modeling offers an answer to this issue by establishing relationships between different hydrological processes. Several models have been developed in the past decade to simulate the rainfall and runoff relations. Some models are simple conceptual models based on spatially distributed event-based or continuous and artificial intelligence (AI) models. This study aims to compare two conceptual daily-based models and one AI model developed for the Hemavathi sub-watershed in the Cauvery Basin (India). Two daily runoff models are implemented using conceptual models, i.e., Sacramento and the Australian water balance model (AWBM) using Rainfall-Runoff Library (RRL) tool and Feed forward Backpropagation neural network (FFBPNN) model. The models were calibrated for daily streamflow values from 1990 to 2006 and then validated from 2007 to 2015. The effectiveness of model runoff predictions is evaluated using statistical parameters such as Nash-Sutcliffe efficiency (NSE) and Correlation coefficient (CC) values. The NSE values for the FFBPNN is 0.88 (calibration) and 0.74 (validation), Sacramento model is 0.66 (calibration) and 0.48 (validation), and 0.63 (calibration) and 0.44 (validation) for the AWBM model. From the obtained results, the FFBPNN model performs well in terms of NSE and CC compared to Sacramento and AWBM models.

Effects of Climate Change on Streamflow in the Godavari Basin Simulated Using a Conceptual Model including CMIP6 Dataset

Hydrological reaction to climate change anticipates water cycle alterations. To ensure long-term water availability and accessibility, it is essential to develop sustainable water management strategies and better hydrological models that can simulate peak flow. These efforts will aid in water resource planning, management, and climate change mitigation. This study develops and compares Sacramento, Australian Water Balance Model (AWBM), TANK, and SIMHYD conceptual models to simulate daily streamflow at Rajegaon station of the Pranhita subbasin in the Godavari basin of India. The study uses daily Indian Meteorological Department (IMD) gridded rainfall and temperature datasets. For 1987–2019, 70% of the models were calibrated and 30% validated. Pearson correlation (CC), Nash Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (CD) between the observed and simulated streamflow to evaluate model efficacy. The best conceptual (Sacramento) model selected to forecast future streamflow for the SSP126, SSP245, SSP370, and SSP585 scenarios for the near (2021–2040), middle (2041–2070), and far future (2071–2100) using EC-Earth3 data was resampled and bias-corrected using distribution mapping. In the far future, the SSP585 scenario had the most significant relative rainfall change (55.02%) and absolute rise in the annual mean temperature (3.29 °C). In the middle and far future, the 95th percentile of monthly streamflow in the wettest July is anticipated to rise 40.09% to 127.06% and 73.90% to 215.13%. SSP370 and SSP585 scenarios predicted the largest streamflow increases in all three time periods. In the near, middle, and far future, the SSP585 scenario projects yearly relative streamflow changes of 72.49%, 93.80%, and 150.76%. Overall, the findings emphasize the importance of considering the potential impacts of future scenarios on water resources to develop effective and sustainable water management practices.

A Comparative Evaluation of Conceptual Rainfall–Runoff Models for a Catchment in Victoria Australia Using eWater Source

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.

Assessment of rainwater harvesting potential of Rachuonyo North Sub-Catchment in Kenya using the Australian water balance model

Abstract Rainwater harvesting (RWH) is emerging as a promising alternative source of water in sub-Saharan Africa. It can be an alternative source of good-quality water to substitute other freshwater sources, to enable crop production beyond the growing season through supplemental irrigation as well as to improve the environment by minimizing the effect of drought and floods. The Rachuonyo North Sub-County of Kenya experiences low rainfall coupled with high population with limited access to reliable water sources. The study assessed the RWH potential of the Rachuonyo North Sub-Catchment with the aim of providing information on alternative water resources to meet the water demands for agriculture as well as domestic use in the region. The Australian water balance model (AWBM) was used to simulate the RWH potential of the Rachuonyo North Sub-Catchment using the area rainfall, evapotranspiration and river flow data. The calibration and validation of the model were performed with calibration and validation results yielding Nash–Sutcliffe efficiency (NSE) values of 0.503 and 1.00, respectively. Research findings indicated that the area has a potential for RWH with runoff harvest of between 104,496 and 43,646,142 m3/month, which can significantly support the residential and irrigation water demands for the area. Policymakers and development agencies in the region should pro-actively put in place measures to promote RWH interventions as a tool for increasing access to water. The methodology in the study is suitable for adaption for rainfall–runoff simulation in other sub-Saharan African regions where data are limiting.

Improving the performance of rainfall-runoff models using the gene expression programming approach

Abstract In this study, five hydrological models, including the soil and water assessment tool (SWAT), identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow (IHACRES), Hydrologiska Byråns Vattenbalansavdelning (HBV), Australian water balance model (AWBM), and Soil Moisture Accounting (SMA), were used to simulate the flow of the Hablehroud River, north-central Iran. All the models were validated based on the root mean square error (RMSE), coefficient of determination (R2), Nash-Sutcliffe model efficiency coefficient (NS), and Kling-Gupta efficiency (KGE). It was found that SWAT, IHACRES, and HBV had satisfactory results in the calibration phase. However, only the SWAT model had good performance in the validation phase and outperformed the other models. It was also observed that peak flows were generally underestimated by the models. The sensitivity analysis results of the model parameters were also evaluated. A hybrid model was developed using gene expression programming (GEP). According to the error measures, the ensemble model had the best performance in both calibration (NS = 0.79) and validation (NS = 0.56). The GEP combination method can combine model outputs from less accurate individual models and produce a superior river flow estimate.

Hydrological Modelling of Bina River Basin in Madhya Pradesh, India

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.

Modelling Hydrological Processes and Identifying Soil Erosion Sources in a Tropical Catchment of the Great Barrier Reef Using SWAT

Study region: North Johnstone catchment, located in the north east of Australia. The catchment has wet tropical climate conditions and is one of the major sediment contributors to the Great Barrier Reef. Study focus: The purpose of this paper was to identify soil erosion hotspots through simulating hydrological processes, soil erosion and sediment transport using the Soil and Water Assessment Tool (SWAT). In particular, we focused on predictive uncertainty in the model evaluations and presentations—a major knowledge gap for hydrology and soil erosion modelling in the context of Great Barrier Reef catchments. We carried out calibration and validation along with uncertainty analysis for streamflow and sediment at catchment and sub-catchment scales and investigated details of water balance components, the impact of slope steepness and spatio-temporal variations on soil erosion. The model performance in simulating actual evapotranspiration was compared with those of the Australian Landscape Water Balance (AWRA-L) model to increase our confidence in simulating water balance components. New hydrological insights for the region: The spatial locations of soil erosion hotspots were identified and their responses to different climatic conditions were quantified. Furthermore, a set of land use scenarios were designed to evaluate the effect of reforestation on sediment transport. We anticipate that protecting high steep slopes areas, which cover a relatively small proportion of the catchment (4–9%), can annually reduce 15–26% sediment loads to the Great Barrier Reef.

Curve Number Calibrations on 204 Australian Catchments

AbstractThe Australian water balance model (AWBM) is automatically calibrated to 204 data sets from the main stream-gauging network in Australia. This daily rainfall-runoff model calculates surface...

Comparative evaluation of conceptual and physical rainfall\u2013runoff models

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.

Emergence and germination response of Sonchus oleraceus and Rapistrum rugosum to different temperatures and moisture stress regimes

AbstractSonchus oleraceus and Rapistrum rugosum are two rapidly emerging weeds of the northern grain region of Australia. To understand the ability of these weeds regarding their germination response to temperature and different soil moisture regimes, experiments were undertaken on the germination of these weeds at varying osmotic potential and temperature regimes. The experiment was conducted as a split‐plot design with alternating day/night temperature regimes (15/5, 20/10, 25/15 and 30/20°C) as a main plot and osmotic potential regimes (0.0, −0.1, −0.2, −0.4, −0.6, −0.8 and −1 MPa) as a subplot. At different temperature regimes, there was 65–91% germination of S. oleraceus in water (0 MPa). There was 0–4% germination at −0.8 MPa and no germination at −1.0 MPa. Osmotic potential values that can cause 50% reduction in germination of S. oleraceus based on a sigmoid regression model ranged from −0.38 to −0.48 MPa. There was 33–81% germination of R. rugosum in distilled water (0 MPa), 1–3% germination at −0.8 MPa and no germination at −1.0 MPa. Osmotic potential values that can cause 50% reduction in germination of R. rugosum based on a sigmoid model ranged from −0.26 to −0.54 MPa. Results of the study were related to the emergence pattern of weeds during field survey and soil moisture profiles estimated by the Australian Landscape Water Balance Model and explain the emergence of these weeds outside the normal seasonal window of prevalence as a response to changes in weather.

Towards the use of conceptual models for water resource assessment in Indian tropical watersheds under monsoon-driven climatic conditions

Water resource assessment is important for integrated water resources management of a basin to which rainfall is a vital component. Generally, semi-distributed to distributed models are used for water resource management studies. Higher data requirement and simulation time restrict the use of these models. Lumped conceptual models are drawing attention these days owing to their simplicity and minimum data requirement. In the present study, the performance of conceptual models Genie Rural a 4 parametres Journalie (GR4J) and Australian Water Balance Model (AWBM) was evaluated in comparison with semi-distributed Soil and Water Analysis Tool (SWAT) model. The selected study area, Upper Godavari River Basin, is in the windward side of Western Ghats in India, which receives heavy rainfall during south-west monsoon season. However, this region faces water scarcity issues in non-monsoon period due to lack of proper water management scenarios. Five catchments in upper Godavari basin are used for the analysis. Spatiotemporal analysis of rainfall is done to understand its effect on streamflow at the catchment outlet. The efficacy of model predictions has been analysed statistically in terms of Nash–Sutcliffe Efficiency, coefficient of determination (R2) and percentage bias. Flow duration curves and time series diagram of streamflow predictions are also compared to better understand the results. All three models predicted streamflow with reasonable accuracy. Considering structural simplicity, less data requirement and simplicity in calibration process, this study proposes conceptual models over SWAT in the regions facing data scarcity.

A comparative study of conceptual rainfall-runoff models GR4J, AWBM and Sacramento at catchments in the upper Godavari river basin, India

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.

Performance evaluation of conceptual rainfall-runoff models GR4J and AWBM

ABSTRACT 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 Future Water Resources Availability under Climate Change Scenarios in the Mékrou Basin, Benin

This work aims to evaluate future water availability in the Mékrou catchment under climate change scenarios. To reach this goal, data from Regional Climate Models (RCMs) were used as the input for four rainfall-runoff models which are ModHyPMA (Hydrological Model based on Least Action Principe), HBV (Hydrologiska Byråns Vattenbalansavdelning), AWBM (Australian Water Balance Model), and SimHyd (Simplified Hydrolog). Then the mean values of the hydro-meteorological data of three different projected periods (2011–2040, 2041–2070 and 2071–2100) were compared to their values in the baseline period. The results of calibration and validation of these models show that the meteorological data from RCMs give performances that are as good as performances obtained with the observed meteorological data in the baseline period. The comparison of the mean values of the hydro-meteorological data of the baseline period to their values for the different projected periods indicates that for PET there is a significantly increase until 2100 for both Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 scenarios. Therefore, the rate’s increase of potential evapotranspiration (PET) under the RCP8.5 scenario is higher than that obtained under the RCP8.5 scenario. Changes in rainfall amounts depend on the scenario of climate change and the projected periods. For the RCP4.5 scenario, there is a little increase in the annual rainfall amounts over the period from 2011 to 2040, while there is a decrease in the rainfall amounts over the other two projected periods. According to the RCP8.5 scenario, the contrary of changes observed with the RCP4.5 scenario are observed. At a monthly scale, the rainfall amounts will increase for August and September and decrease for July and October. These changes in rainfall amounts greatly affect yearly and monthly discharge at the catchment outlet. Over the three projected periods and for both RCP4.5 and RCP8.5, the mean annual discharge will significantly increase related to the baseline periods. However, the magnitude of increases will depend on the projected period and the RCP scenario. At a monthly scale, it was found that runoff increases significantly from August to November for all projected periods and the climate change scenario.

Validation of CLIGEN Parameter Adjustment Methods for Southeastern Australia and Southwestern Western Australia

Abstract Global climate models (GCMs) are usually used for future climate projections. Model output from GCMs needs to be downscaled and stochastic weather generators such as Climate Generator (CLIGEN) are tools to downscale GCM output and to produce synthetic weather sequences that are statistically similar to the observed weather data. Two methods of adjusting CLIGEN parameters were developed to reproduce precipitation sequences for southeastern Australia (SEA), where significant changes in annual precipitation had occurred, and for southwestern Western Australia (SWWA), where the precipitation has shown a significant decreasing trend since the 1920s. The adjustment methods have been validated using observed precipitation data for these regions. However, CLIGEN outputs ultimately will be used as input to other simulation models. The objective of this research was to further validate the methods of CLIGEN parameter adjustment using conceptual hydrological models to simulate streamflow and to compare the streamflow using observed and CLIGEN-generated precipitation data. Six precipitation sites from SEA and SWWA were selected and synthetic time series of daily precipitation were generated for these sites. Conceptual hydrological models, namely, the Australian Water Balance Model and SimHyd, were used for flow simulation and were calibrated using recorded daily streamflow data from six gauging stations in SEA and SWWA. Both monthly and annual streamflow show statistically similar patterns using observed and CLIGEN-generated precipitation data. The adjustment methods for CLIGEN parameters are further validated and can be used to reproduce the significant changes, both abrupt and gradually decreasing, in streamflow for these two climatically contrasting regions of Australia.

Runoff Estimation by Australian Water Balance Model: A Review

Runoff Estimation by Australian Water Balance Model: A Review

A comparative assessment of AWBM and SimHyd for forested watersheds

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.