Evaluating the Ecological Benefits of Management Actions to Complement Environmental Flows in River Systems.

Globally, many river systems are under stress due to overconsumption of water. Governments have responded with programmes to deliver environmental water to improve environmental outcomes. Although such programmes are essential, they may not be sufficient to achieve all desired environmental outcomes. The benefits of environmental water allocation may be improved using 'complementary measures', which are non-flow-based actions, such as infrastructure works, vegetation management and pest control. The value of complementary measures is recognised globally, but their ecological benefits are rarely well understood, either because there is limited experience with their application, or the importance of context- and location-specific factors make it difficult to generalise benefits. In this study, we developed an approach to evaluate complementary measures at different levels of detail as a mechanism to aid decision-making. For systems that require a rapid, high-level evaluation, we propose a score-based multi-criteria benefit assessment module. If more ecological detail is necessary, we outline a method based on conceptual models, expert elicitation and probability assessment. These results are used to populate a cumulative benefit assessment tool. The tool evaluates the benefits of proposed measures in the wider context by including variables such as flow, dependence on ongoing maintenance and additional ecological values. We illustrate our approach through application to the Murray-Darling Basin, Australia. As many water recovery programmes mature into their evaluation phases, there is an increasing need to evaluate the ecological benefits of including complementary measures in the toolkit available to policy makers.

Assessing climate change impacts on wetlands in a flow regulated catchment: A case study in the Macquarie Marshes, Australia.

Globally wetlands are increasingly under threat due to changes in water regimes as a result of river regulation and climate change. We developed the Exploring CLimAte Impacts on Management (EXCLAIM) decision support system (DSS), which simulates flow-driven habitat condition for 16 vegetation species, 13 waterbird species and 4 fish groups in the Macquarie catchment, Australia. The EXCLAIM DSS estimates impacts to habitat condition, considering scenarios of climate change and water management. The model framework underlying the DSS is a probabilistic Bayesian network, and this approach was chosen to explicitly represent uncertainties in climate change scenarios and predicted ecological outcomes. The results suggest that the scenario with no climate change and no water resource development (i.e. flow condition without dams, weirs or water license entitlements, often regarded as a surrogate for 'natural' flow) consistently has the most beneficial outcomes for vegetation, waterbird and native fish. The 2030 dry climate change scenario delivers the poorest ecological outcomes overall, whereas the 2030 wet climate change scenario has beneficial outcomes for waterbird breeding, but delivers poor outcomes for river red gum and black box woodlands, and fish that prefer river channels as habitats. A formal evaluation of the waterbird breeding model showed that higher numbers of observed nest counts are typically associated with higher modelled average breeding habitat conditions. The EXCLAIM DSS provides a generic framework to link hydrology and ecological habitats for a large number of species, based on best available knowledge of their flood requirements. It is a starting point towards developing an integrated tool for assessing climate change impacts on wetland ecosystems.

Characterisation of meteorological drought at sub-catchment scale in Afghanistan using station-observed climate data.

Droughts have severely affected Afghanistan over the last four decades, leading to critical food shortages where two-thirds of the country's population are in a food crisis. Long years of conflict have lowered the country's ability to deal with hazards such as drought which can rapidly escalate into disasters. Understanding the spatial and temporal distribution of droughts is needed to be able to respond effectively to disasters and plan for future occurrences. This study used Standardized Precipitation Evapotranspiration Index (SPEI) at monthly, seasonal and annual temporal scales to map the spatiotemporal change dynamics of drought characteristics (distribution, frequency, duration and severity) in Afghanistan. SPEI indices were mapped for river basins, disaggregated into 189 sub-catchments, using monthly precipitation and potential evapotranspiration derived from temperature station observations from 1980 to 2017. The results show these multi-dimensional drought characteristics vary along different years, change among sub-catchments, and differ across temporal scales. During the 38 years, the driest decade and period are 2000s and 1999-2022, respectively. The 2000-01 water year is the driest with the whole country experiencing 'severe' to 'extreme' drought, more than 53% (87 sub-catchments) suffering the worst drought in history, and about 58% (94 sub-catchments) having 'very frequent' drought (7 to 8 months) or 'extremely frequent' drought (9 to 10 months). The estimated seasonal duration and severity present significant variations across the study area and among the study period. The nation also suffers from recurring droughts with varying length and intensity in 2004, 2006, 2008 and most recently 2011. There is a trend towards increasing drought with longer duration and higher severity extending all over sub-catchments from southeast to north and central regions. These datasets and maps help to fill the knowledge gap on detailed sub-catchment scale meteorological drought characteristics in Afghanistan. The study findings improve our understanding of the influences of climate change on the drought dynamics and can guide catchment planning for reliable adaptation to and mitigation against future droughts.