Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when nonstationarity is present in extreme rainfall events. A methodology consisting of threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and nonstationarity analysis, and stationary and non-stationary generalised Pareto distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on intensity-frequency-duration (IFD) relationships. The methodology developed was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, 3 h and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.

No abstract

Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when non-stationarity is present in extreme rainfall events. A methodology consisting of, threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and non-stationarity analysis, and stationary and non-stationary Generalized Pareto Distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on Intensity–Frequency–Duration (IFD) relationships. The developed methodology was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, and 3 and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.

No abstract

Abstract:The objective of this research is the assessment of the efficiency of a non-linear regression technique in predicting long-term seasonal rainfall. The non-linear models were developed using the lagged (past) values of the climate drivers, which have a significant correlation with rainfall. More specifically, the capabilities of SEIO (South-eastern Indian Ocean) and ENSO (El Nino Southern Oscillation) were assessed in reproducing the rainfall characteristics using the non-linear regression approach. The non-linear models developed were tested using the individual data sets, which were not used during the calibration of the models. The models were assessed using the commonly used statistical parameters, such as Pearson correlations (R), root mean square error (RMSE), mean absolute error (MAE) and index of agreement (d). Three rainfall stations located in the Australian Capital Territory (ACT) were selected as a case study. The analysis suggests that the predictors which has the highest correlation with the predictands do not necessarily produce the least errors in rainfall forecasting. The non-linear regression was able to predict seasonal rainfall with correlation coefficients varying from 0.71 to 0.91. The outcomes of the analysis will help the watershed management authorities to adopt efficient modelling technique by predicting long-term seasonal rainfall.

BACKGROUND Water footprint assessment is essential for the evaluation of water scarcity that considers both direct and indirect water consumption along the supply chain. This paper presents the estimation of water footprint for locally grown fruits and vegetables in Australia. Water footprint was calculated based on the framework developed in the Water Footprint Assessment Manual for the crops which are the most practicable to grow in Australia. Nine different crops (apples, grapes, tomatoes, oranges, peaches/nectarines, cherries, potatoes, carrots/turnips and almonds) in the agricultural industry were selected and identified as the most water‐consumptive crop and least water‐consumptive crop. For each type of crop, the three main water footprint components (blue, green, and grey water) were calculated. RESULTS It was found that almond had the highest water footprint (6671.96 m3 ton−1) and tomato had the lowest water footprint (212.24 m3 ton−1) in Australia. From the global comparison, it is revealed that total water footprint for Australian crops is much higher than the corresponding international average values, except for tomatoes, potatoes and almonds. Also, almonds had the highest water footprint among the nine crops investigated. CONCLUSION The study provides an insight into future sustainable cropping patterns in Australia, which suggest that tomatoes, carrots/turnips, potatoes and apples should continue to be grown in Australia, whereas stone fruit (e.g., almonds) should no longer be grown because of its high water footprint. © 2020 Society of Chemical Industry

No abstract

A green house field experiment of rice (Oryza sativa L) with arsenic amended irrigation water was conducted at Institute of Environmental Science of Rajshahi University to observe the trend of arsenic (As) accumulation into rice and soil. Sodium arsenate (Na 2 HAsO 4 ) amended irrigation water (0.0, 0.1, 0.5, 1.0, 2.0 and 4.0 mg/l As) was used for cultivating a popular HYV-aman rice variety named BR-11. Arsenic accumulation of rice straw, grain and soil were investigated. A significant (p ≤ 0.01) increasing trend of arsenic accumulation in straw, grain and soil was found with increase of arsenic in irrigation water. The highest level of arsenic in straw, grain and soil was observed in the treatment of 4.0 mg/l As containing irrigation water and lowest level in control treatment. Arsenic in irrigation water showed a strong positive correlations with arsenic accumulation into soil, straw and grain, and the trend of accumulation was found as water > soil > straw > grain.