The demand for fresh water is on the increase, and the irrigation industry in Australia is looking to a future with less water. Irrigation consumes the bulk of the water extracted from various sources, and hence the efficiency of its use is of outmost importance. This paper reviewed the advancements made towards improving irrigation water use efficiency (WUE), with a focus on irrigation in Australia but with some examples from other countries. The challenges encountered, as well as the opportunities available, are also discussed. The review showed that improvements in irrigation infrastructure through modernisation and automation have led to water savings. The concept of real-time control and optimisation in irrigation is in its developmental stages but has already demonstrated potential for water savings. The future is likely to see increased use of remote sensing techniques as well as wireless communication systems and more versatile sensors to improve WUE. In many cases, water saved as a result of using efficient technologies ends up being reused to expand the area of land under irrigation, sometimes resulting in a net increase in the total water consumption at the basin scale. Hence, to achieve net water savings, water-efficient technologies and practices need to be used in combination with other measures such as incentives for conservation and appropriate regulations that limit water allocation and use. Factors that affect the trends in the irrigation WUE include engineering and technological innovations, advancements in plant and pasture science, environmental factors, and socio-economic considerations. Challenges that might be encountered include lack of public support, especially when the methods used are not cost-effective, and reluctance of irrigations to adopt new technologies.

Hydrological studies are useful in designing, planning, and managing water resources, infrastructure, and ecosystems. Probability distribution models are applied in extreme flood analysis, drought investigations, reservoir volumes studies, and time-series modelling, among other various hydrological studies. However, the selection of the most suitable probability distribution and associated parameter estimation procedure, as a fundamental step in flood frequency analysis, has remained the most difficult task for many researchers and water practitioners. This paper explains the current approaches that are used to identify the probability distribution functions that are best suited for the estimation of maximum, minimum, and mean streamflows. Then, it compares the performance of six probability distributions, and illustrates four fitting tests, evaluation procedures, and selection procedures through using a river basin as a case study. An assemblage of the latest computer statistical packages in an integrated development environment for the R programming language was applied. Maximum likelihood estimation (MLE), goodness-of-fit (GoF) tests-based analysis, and information criteria-based selection procedures were used to identify the most suitable distribution models. The results showed that the gamma (Pearson type 3) and lognormal distribution models were the best-fit functions for maximum streamflows, since they had the lowest Akaike Information Criterion values of 1083 and 1081, and Bayesian Information Criterion (BIC) values corresponding to 1087 and 1086, respectively. The Weibull, GEV, and Gumbel functions were the best-fit functions for the annual minimum flows of the Tana River, while the lognormal and GEV distribution functions the best-fit functions for the annual mean flows of the Tana River. The choices of the selected distribution functions may be used for forecasting hydrologic events and detecting the inherent stochastic characteristics of the hydrologic variables for predictions in the Tana River Basin. This paper also provides a significant contribution to the current understanding of predicting extreme hydrological events for various purposes. It indicates a direction for hydro-meteorological scientists within the current debate surrounding whether to use historical data and trend estimation techniques for predicting future events with issues of non-stationarity and underlying stochastic processes.

This study proposes a site location assessment model for citrus cropland using multi-criteria evaluation (MCE) and the combination of a set of factors for suitability mapping and delineating the suitable areas for citrus production in Ramsar, Iran. It defines an incorporated method for the suitability mapping of the most appropriate sites for citrus cultivars with an emphasis on the multi-criteria decision analysis (MCDA) process. The combination of geographic information system (GIS) and a modified version of the analytic hierarchy process (AHP) based on the ordered weighted averaging (OWA) technique is also emphasized. The OWA is based on two principles, namely: the weights of relative criterion significance and the order weights. Therefore, the participatory technique was employed to outline the set of standards and the important criterion. The results derived from the GIS–OWA technique indicate that the cultivation of citrus is feasible only in limited areas, which make up 6.7% of the total area near the Caspian Sea. This investigation has shown that the GIS–OWA model can be integrated into MCDA to select the optimal site for citrus production. The present research highlights how multi-criteria in GIS can play a considerable role in decision making for evaluating the suitability of selected sites for citrus production.

This study investigated temporal variabilities and trends of rainfall and discharges in Tana River Basin in Kenya using Mann–Kendall non-parametric test. Monthly rainfall data from ten stations spanning from 1967 to 2016 and daily streamflow data time series of observations from 1941 to 2016 (75 years) were analyzed with the aim of capturing and detecting multiannual and seasonal variabilities and monotonic trends. The results for the datasets suggested that the streamflow is largely dependent on increasing rainfall at the highlands. The rainfall trends seemed to have been influenced by altitudinal factors. The coefficient of variation of the ten rainfall stations ranged from 12% to 17% but 70% of rainfall stations showed negative monotonic trends and 30% show significant trends. The streamflow showed statistically significant upward monotonic trend and seasonal variability indicating a substantial change in the streamflow regime. Although the increasing trend of the streamflow during this period may not pose future risks and vulnerability of energy and irrigated agricultural production systems across the basin, variability observed indicates the need for enhanced alternative water management strategies during the low flow seasons. The trends and time series data indicate the potential evidence of climate and land use change and their impacts on the availability of water and sustainability of ecology and energy and agricultural production systems across the basin. Variability and trends of rainfall and streamflow are useful for planning studies, hydrological modeling and climate change impacts assessment within Tana River Basin.

This research reveals the magnitude of mangrove area loss and future impacts on mangrove species composition and distribution due to rise in Mean Sea Level (MSL). In this study, a geospatial model of potentially inundated areas was developed using Digital Elevation Model (DEM) data to assess the potential impacts of sea level rise (SLR) on the spatial distribution of mangrove species and estimate the potential inundation and subsequent mangrove area loss. The mangrove areas of 2646 ha, 9599 ha and 74,720 ha are projected to be inundated and subsequently lost by the end of the twenty first century for the low, medium and high SLR scenarios respectively under the net subsidence rate −2.4 mm/year relative to the baseline year 2000. All the major five mangrove species of the Bangladesh Sundarbans will be affected and this can potentially contribute to a change in the present species composition and biodiversity of the forest. Results suggest that, under the extreme scenario, inundation and subsequent loss of different mangrove species will be substantial and this can bring a massive change in the species composition and their spatial distribution in the Bangladesh Sundarbans.