An Integrated Approach to Benthic Habitat Classification of the North Eastern Qatar Marine Zone Using Remote Sensing, Geographic Information System, and in Situ Measurements

A key aspect in the conservation management of the coastal marine zone is mapping the benthic habitat, which is the focus of the work presented in this project. Multispectral Worldview-2 (WV2) satellite data acquired in April 2010 was used to classify and map the benthic habitat of the North-Eastern part of Qatar marine zone: A 35 km stretch of coastline, 7km wide, with water depth ranging from 0 to 11 m. Baseline field surveys of the area of study carried out in March-April 2010 identified 4 broad benthic types: Seagrass, Algae, live corals and sand. WV2 data was corrected for atmospheric and water column effects. Depth-invariant bottom indices were calculated and formed the basis for classification. Field survey data was used to implement the supervised classification and accuracy assessment. From the result of the classification, an overall accuracy of 81.8% was obtained. The gap in the available information on the benthic cover in the Qatari coastal marine zone makes the study useful to detect changes in the benthic cover over time

Impact of climate change on water resources in MENA countries: an assessment of temporal changes of land cover/land use and water resources using multi-temporal MODIS and Landsat data and GIS techniques

Water resources are crucial to food security and rural livelihood. Global climatic variation, particularly global warming and changes of precipitation patterns greatly affect the agricultural production and food security. The Middle East and North Africa (MENA) includes countries with poor economies and resources (e.g. Morocco, Yemen) as well as oil-rich economies of Gulf countries (e.g. Qatar, Kuwait, Saudi Arabia). Water resources are being increasingly scarce in the MENA countries and have great impact on the standard of living particularly in countries with poor economies. In addition to water scarcity, poor water management has also been contributing to the water issues. For example, the countries with the highest per capita water consumption (e.g. UAE) in the world are also found in the MENA countries while in some countries (Jordan, Syria) agriculture consumes more than 85% of water. Mapping water resources, monitoring the temporal changes of land cover and land use are the main ingredients in managing water resources. There are no better technologies than GIS and remote sensing to generate this information. Geospatial technologies, particularly GIS and remote sensing can be used to identify changes, vulnerable areas and potential areas for watershed development. Satellite data are available at varying level of detail ranging from 1km to 0.6m pixel size in spatial resolution supporting studies at global, regional and local levels. Once the vulnerable watersheds are identified, high resolution satellite and GIS data can be used to develop action plans at local levels. The purpose of this paper is to map and monitor water resources and land cover/use to identify vulnerable areas in the MENA region using two countries (Morocco and Yemen) for a comparative assessment. Both Morocco (North African country) and Yemen (Middle-East country) are poor countries and characterize water scarcity, poor water management, desertification and growing food security issues. The objectives are to: * Map water resources and catchments * Map land use and land cover in the region * Identify and map areas of potential hotspots or vulnerable areas The methods include developing a data base including satellite imagery and GIS data (e.g. elevation, climate, socio-economic data), use image processing techniques to extract land cover, land use and catchment information, and use GIS techniques to analyse data and modelling vulnerability. The outcome of the paper are useful in understanding the current status of water resources, production of an inventory of resources, understanding the potential areas of water resources as well as identifying vulnerable areas in selected countries

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.</p

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem\u27s carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers

coecms/ML-Drought-Prediction-Tutorial

&lt;p&gt;A tutorial for how to apply random forest to a drought prediction dataset. We use the Scikit-learn python module for the random forest model.&lt;/p&gt

Improving estimates of the surface terrestrial water and energy budgets

Accurate estimates of the components of the surface terrestrial water and energy budgets are crucial to understand the interactions between land and atmosphere. These interactions play a critical role in modulating drought, heatwaves and other extreme events. In-situ measurements provide the most reliable direct estimates of these terms, however in many parts of the globe they are scarce or not available at all, and therefore cannot meet the spatial scale we need. The advancement of satellite technology and remote sensing retrieval algorithms has made it possible to estimate many fluxes from space, and led to the development of a suite of satellite-driven estimates of water and energy fluxes at the global gridded scale. These include entirely satellite-based estimates, reanalysis products, or model outputs driven by remotely-sensed surface properties. These global estimates have near complete spatial coverage, something lacking in in-situ observations. The aim of this thesis is to improve the current global gridded estimates of the water and energy fluxes by exploiting the accuracy of in-situ measurements and the coverage of existing, predominantly satellite-based gridded estimates, as well as physical relationships between them. A novel weighting technique is applied to derive hybrid estimates from a range of available satellite driven estimates of individual budget variables. It is an optimal merging method that accounts not only for performance differences between the participating products, but also the dependence of their errors, which has been ignored by the majority of similar attempts. This technique also provides rigorous estimates of uncertainty associated with the hybrid estimates that reflects their discrepancy with direct observations. We apply the merging technique to derive hybrid best estimates for net radiation, sensible, latent and ground heat flux and runoff. In a further post processing step, all the hybrid best estimates are further adjusted by enforcing the physical constraints of the water and energy balance.This process led to a novel suite of half degree gridded monthly hybrid estimates of water and energy budget terms. Component variables satisfy water and energy balance constraints simultaneously, provide uncertainty estimates consistent with ground-based observations, and compare more favorably with observations than any of the component products used to create them. In addition, the sensitivity of this approach to the employed datasets has given it a diagnostic utility which we use to evaluate multiple precipitation datasets and identify regions with less reliable estimates. An important finding is that physical constraints can complement in-situ observations to allow observation-based diagnostic evaluation of satellite-driven products in areas without direct observations

Toward the development of a remote sensing and field data framework to aid management decisions in the state of Qatar coastal environment

The benthic habitat of the Qatari nearshore marine environment plays a significant ecological role and environmental service. Areas of coral help provide protection and sustenance for a vast array of fish species and marine invertebrates, and represent a significant proportion of biodiversity in the region. Seagrass meadows which are prominent in northern Qatari waters are important to charismatic species such as the Hawksbill turtle and dugongs. Stands of macroalgae and even areas of coral rubble provide additional habitat for many marine species. These features, particularly the coral structures, also provide shoreline protection from high energy wave events and storm surges. As such, improving our understanding of these habitats as well as the need to protect and conserve the nearshore Qatari environment is a focus area for local researchers and regulatory authorities. Traditionally, the nearshore marine environment has been studied through intensive field measurement and monitoring. While valuable for collecting detailed site-specific information, this approach can be very resource intensive and also limiting in the scale at which habitats can be quantified and monitored for change. One way to address this is through remote sensing, which has been widely used around the globe to study and monitor benthic habitat cover. For example, data obtained from satellite imagery has been used to classify benthic habitats and in various coral reef remote sensing applications. In this paper, a framework is discussed for the remote sensing mapping of sensitive habitats in the Qatari nearshore marine environment, including field data collection and analysis. A case study from northeastern Qatar is highlighted, focusing on spring 2015 efforts to test the framework particularly for the purposes of mapping coral and seagrass habitats. Results suggest this approach may be a viable option for identifying sensitive areas in the region and monitoring longer-term ecosystem health under natural and anthropogenic stressors in a more safe and cost-effective manner. Limitations and lessons learned will be discussed as well as recommendations on the application of this approach to coastal environmental risk assessment and management planning in the State of Qatar

Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation

Dynamical downscaling (DD), and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia from grids of 12–48 km to 5 km, using the Australia Gridded Climate Data observations as the target. A separate MLP is developed for each coarse grid to predict the fine grid values within it, by combining coarse-scale time-varying meteorological variables with fine-scale static surface properties as predictors. The resulting predictions (on out-of-sample test periods) are more accurate than DD in capturing the rainfall climatology, as well as the frequency distribution and spatiotemporal variability of daily precipitation, reducing biases in daily extremes by 15%–85% with 12 km prediction fields. When prediction fields are coarsened, the skill of the MLP decreases—at 24 km relative bias increases by ∼10%, and at 48 km it increases by another ∼4%—but skill remains comparable to or, for some metrics, much better than DD. These results show that ML-based downscaling benefits from higher-resolution driving data but can still improve on DD (and at far less computational cost) when downscaling from a global climate model grid of ∼50 km