Evaluation of three daily rainfall generation models for SWAT

The Soil and Water Assessment Tool (SWAT) is a hydrologic model that was developed to predict the long-term impacts of land use change on the water balance of large catchments. Stochastic models are used to generate the daily rainfall sequences needed to conduct long-term, continuous simulations with SWAT. The objective of this study was to evaluate the performances of three daily rainfall generation models. The models evaluated were the modified Daily and Monthly Mixed (DMMm) model, skewed normal distribution (SKWD) model and modified exponential distribution (EXPD) model. The study area was the Woady Yaloak River catchment (306 km2) located in southwest Victoria, Australia. The models were assessed on their ability to preserve annual, monthly and daily statistical characteristics of the historical rainfall and runoff. The mean annual, monthly, and daily rainfall was preserved satisfactorily by the models. The DMMm model reproduced the standard deviation of annual and monthly rainfall better than the SKWD and EXPD models. Overall, the DMMm model performed marginally better than the SKWD model at reproducing the statistical characteristics of the historical rainfall record at the various time scales. The performance of the EXPD model was found to be inferior to the performances of the DMMm and SKWD models. The models reproduced the mean annual, monthly, and daily runoff relatively well, although the DMMm and SKWD models were found to preserve these statistics marginally better than the EXPD model. None of the models managed to reproduce the standard deviation of annual, monthly, and daily runoff adequately.

Inter-annual rainfall variations and suicide in New South Wales, Australia, 1964-2001

The suicide rate in New South Wales is shown to be related to annual precipitation, supporting a widespread and long-held assumption that drought in Australia increases the likelihood of suicide. The relationship, although statistically significant, is not especially strong and is confounded by strong, long-term variations in the suicide rate not related to precipitation variations. A decrease in precipitation of about 300 mm would lead to an increase in the suicide rate of approximately 8% of the long-term mean suicide rate.

Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R 2 and lower corrected Akaike information criterion (AICC). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R 2 and lower AICC. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.

Drought-rainfall cycles as potential environmental drivers of fowl plague and Newcastle disease uutbreaks in Australian poultry

Climatic conditions in Australia are erratic and characterised by periods of intense rainfall followed by periods of intense drought. This has considerable impact on the population dynamics and ecology of many Australian species of waterfowl, which are thought to form the reservoir of avian influenza viruses (AIV) but may also be important carriers (and possibly reservoirs) of other diseases (e.g. bursal disease, Newcastle disease). During the wet, waterfowl numbers increase with many serologically naive juveniles entering the population. During the subsequent period of drought, bird densities increase in the few remaining wetlands. We hypothesise that it is during this period of increasing densities of naive birds that the population’s viral prevalence of some infectious diseases may increase dramatically. Indeed, there exists a remarkable and suggestive coincidence between outbreaks of fowl plaque and Newcastle disease in Australian poultry farms and the periods of drought following a very wet period. In other words, we suspect a link between increased risk for disease outbreaks in poultry farms and the hypothesised high in the prevalences of the viruses causing these diseases in waterfowl. Given that poultry farms may provide ideal conditions for development of high-pathogenic strains, there is also a reciprocal risk for wildlife involved during these periods.

Spatial and temporal drivers of small mammal distributions in a semi-arid environment : the role of rainfall, vegetation and life-history

A key task in ecology is to understand the drivers of animal distributions. In arid and semi-arid environments, this is challenging because animal populations show considerable spatial and temporal variation. An effective approach in such systems is to examine both broad-scale and long-term data. We used this approach to investigate the distribution of small mammal species in semi-arid ‘mallee’ vegetation in south-eastern Australia. First, we examined broad-scale data collected at 280 sites across the Murray Mallee region. We used generalized additive mixed models (GAMMs) to examine four hypotheses concerning factors that influence the distribution of individual mammal species at this scale: vegetation structure, floristic diversity, topography and recent rainfall. Second, we used long-term data from a single conservation reserve (surveyed from 1997 to 2012) to examine small mammal responses to rainfall over a period spanning a broad range of climatic conditions, including record high rainfall in 2011. Small mammal distributions were strongly associated with vegetation structure and rainfall patterns, but the relative importance of these drivers was species-specific. The distribution of the mallee ningaui Ningaui yvonneae, for example, was largely determined by the cover of hummock grass; whereas the occurrence of the western pygmy possum Cercartetus concinnus was most strongly associated with above-average rainfall. Further, the combination of both broad-scale and long-term data provided valuable insights. Bolam's mouse Pseudomys bolami was uncommon during the broad-scale survey, but long-term surveys showed that it responds positively to above-average rainfall. Conceptual models developed for small mammals in temperate and central arid Australia, respectively, were not, on their own, adequate to account for the distributional patterns of species in this semi-arid ecosystem. Species-specific variation in the relative importance of different drivers was more effectively explained by qualitative differences in life-history attributes among species.

Dynamic evolving neural-fuzzy inference system for rainfall-runoff (R-R) modelling

Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. To the best of our knowledge, this is the first time that DENFIS has been used for rainfall-runoff (R-R) modeling. DENFIS model results were compared to the results obtained from the physically-based Storm Water Management Model (SWMM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Data from a small (5.6 km2) catchment in Singapore, comprising 11 separated storm events were analyzed. Rainfall was the only input used for the DENFIS and ANFIS models and the output was discharge at the present time. It is concluded that DENFIS results are better or at least comparable to SWMM, but similar to ANFIS. These results indicate a strong potential for DENFIS to be used in R-R modeling.

Performances of rainfall-runoff models calibrated over single and continuous storm flow events

Accurate parameter estimation is important for reliable rainfall-runoff modeling. Previous studies emphasize that a sufficient length of continuous events is required for model calibration to overcome the effect of initial conditions. This paper investigates the feasibility of calibrating rainfall-runoff models over a number of limited storm flow events. For a subcatchment having a moderate influence from initial soil moisture conditions, this study shows that rainfall-runoff models could still be calibrated reliably over a set of representative events provided that the events cover a wide range of peak flow, total runoff volume, and initial soil moisture conditions. This approach could provide an alternative calibration strategy for a small watershed that has a limited data length but consists of runoff events with a wide range of magnitudes. Compared to continuous-event calibration, event-based calibration appears to perform better in simulating the overall shape of hydrograph, peak flow and time to peak. However, continuous-event calibration was found to be more reliable in providing runoff volume, suggesting that continuous-event calibration should still be used when runoff volume is the main concern of a study.