This study investigates the comparative performance of event-based and continuous simulation modelling of a stormwater management model (EPA-SWMM) in calculating total runoff hydrographs and direct runoff hydrographs. Myponga upstream and Scott Creek catchments in South Australia were selected as the case study catchments and model performance was assessed using a total of 36 streamflow events from the period of 2001 to 2004. Goodness-of-fit of the EPA-SWMM models developed using automatic calibration were assessed using eight goodness-of-fit measures including Nash–Sutcliff efficiency (NSE), NSE of daily high flows (ANSE), Kling–Gupta efficiency (KGE), etc. The results of this study suggest that event-based modelling of EPA-SWMM outperforms the continuous simulation approach in producing both total runoff hydrograph (TRH) and direct runoff hydrograph (DRH).

Personal Computer Stormwater Management Model (PCSWMM) was applied to investigate: (1) hydrological responses in the Myponga catchment as a result of land use changes; and (2) the possibility of adopting Water Sensitive Urban Design (WSUD) technologies (bio-retention cells) to manage resulting floods. Calibrated and validated models predicted the measured data with satisfactory accuracy and reliability. Different urbanization scenarios were tested. When the level of urbanization increased from 10% to 70%, mean discharge increased from 45% to 322%. Frequency of flood at 2-year Average Recurrence Interval (ARI) increased from 1 to 44 and frequency of floods at 100-year ARI increased from none to 8. At 70% urbanisation, trialled bio-retention facilities used as WSUD measures almost completely ameliorated 2-year ARI floods by reducing the frequency of such events from 44 to 2. Floods at smaller ARIs (2, 5, 10 and 20 years) were effectively managed by WSUD measures while floods at 50-and 100-year ARIs remained unchanged. The overall results improve understanding of the severity of the impacts of land use changes on the hydrology of a catchment and the ability of bio-retention cells to alleviate the risk of small to medium floods in the Myponga catchment.

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.

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Irrigation development necessitates suitable lands for higher yield production and the development of long-term irrigation systems. The purpose of this research was to identify appropriate irrigation lands for irrigation in the Minch Yekest watershed in West Amhara, Ethiopia. Geospatial and multi-criteria decision-making techniques were used in this study. For land suitability analysis for surface irrigation, slope, land use, altitude, distance from the water source, soil characteristics, and available water storage capacity parameters were used. To find the best location for surface irrigation, the values were weighted and combined using the weighted overlay tool. The irrigation land suitability of each physical land parameter was classified into four suitability classes (S1, S2, S3, and N) based on the Food and Agricultural Organization guideline. According to the findings, 63% of the watershed area is highly suitable, 6.25% is moderately suitable, 28.69% is marginally suitable, and 2.06% is not suitable for the aforementioned purposes. The methodological approach and study findings could help policymakers make better decisions when developing irrigation projects in Ethiopia.