Evaluating the impact of an agricultural land‐use change adaptation strategy on household crop production in semi‐arid Ghana

In this study, the Land Use Dynamic Simulator model was applied to investigate the impact of farm credit as an adaptation strategy to cope with effects of climate variability on agricultural land‐use change and crop production in the Vea watershed in Ghana. The authors identified the determinants of crop choices within the landscape (e.g., farm household and biophysical characteristics of farm plot). The crop choice sub‐model was then linked to the crop yield sub‐model to determine the yields of selected crops. In adapting to the impacts of climate variability, the maize credit adoption sub‐model under the maize cultivation credit scenario was integrated into decision‐making. This was simulated for a 20‐year period, and compared with the business‐as‐usual scenario. Under the simulated maize credit scenario, maize adopters increased from about 20 per cent to about 50 per cent and the area allocated for maize cultivation significantly increased by about 266 per cent. Consequently, the average annual aggregated household crop yield increased by 6.3 per cent higher than in the business‐as‐usual scenario. This simulation study shows that access to maize credit can significantly influence agricultural land‐use change and food availability in the study area. However, although access to farm credit may translate into food availability, the sustainability of this strategy is questionable.     

Effects of global irrigation on the near-surface climate

Irrigation delivers about 2,600 km³ of water to the land surface each year, or about 2% of annual precipitation over land. We investigated how this redistribution of water affects the global climate, focusing on its effects on near-surface temperatures. Using the Community Atmosphere Model (CAM) coupled to the Community Land Model (CLM), we compared global simulations with and without irrigation. To approximate actual irrigation amounts and locations as closely as possible, we used national-level census data of agricultural water withdrawals, disaggregated with maps of croplands, areas equipped for irrigation, and climatic water deficits. We further investigated the sensitivity of our results to the timing and spatial extent of irrigation. We found that irrigation alters climate significantly in some regions, but has a negligible effect on global-average near-surface temperatures. Irrigation cooled the northern mid-latitudes; the central and southeast United States, portions of southeast China and portions of southern and southeast Asia cooled by ~0.5 K averaged over the year. Much of northern Canada, on the other hand, warmed by ~1 K. The cooling effect of irrigation seemed to be dominated by indirect effects like an increase in cloud cover, rather than by direct evaporative cooling. The regional effects of irrigation were as large as those seen in previous studies of land cover change, showing that changes in land management can be as important as changes in land cover in terms of their climatic effects. Our results were sensitive to the area of irrigation, but were insensitive to the details of irrigation timing and delivery.     

Oxygen and silicon isotope ratios of the Luna 20 soil

The δ O¹⁸ and δ Si³⁰ analyses of the Luna 20 soil sample are +6.18 and +0.22, respectively, relative to the SMOW and Rose Quartz standards. However, an anomalous δ O¹⁸ value of +8.13 was obtained on one aliquot of the Luna 20 sample. Possible reasons for this apparently erroneous result are discussed.     

A Bayesian Nonparametric Approach to Unmixing Detrital Geochronologic Data

Mathematical Geosciences - Sedimentary deposits constitute the primary record of changing environmental conditions that have acted on Earth’s surface over geologic time. Clastic material is...     

Deep learning for dune pattern mapping with the AW3D30 global surface model

In this paper we present a deep learning (U-Net)-based workflow for classifying linear dune landforms based on the discrete Laplacian convolution of a new global elevation dataset, the AW3D30 digital surface model. Crest vectors were then derived for landscape pattern analysis. The U-Net crest classification model was trained and evaluated on sample data from dunefields across the Australian continent. The resulting crest vectors and dune defect placement were then evaluated in typical semi-arid and arid dune landscapes in eastern central Australia where high-resolution (5 m horizontal) digital elevation models are available (for three out of our four study sites) as a reference dataset. The method was applied to quantify dune pattern metrics for the entire Simpson Desert dunefield, Australia. The U-Net does a very good job of segmenting dune crests, even where dunes are less clear in the Laplacian map (intersection over union score ≈ 0.68). When crest vectors and dune defects (network nodes) were derived, the defect predictions were typically correct (0.4 to 0.79 correctness) but incomplete (0.02 to 0.64 completeness). Much of the residual error was traced to the resolution of the input data. Through the application to the Simpson Desert, we nevertheless demonstrated that our method can effectively be used for regional-scale dune pattern analysis. Furthermore, we suggest that the combination of morphological filtering and a convolutional neural network could readily be adapted to target other geomorphic features, such as channel networks or geological lineaments. © 2020 John Wiley & Sons, Ltd.     

Assessing the potential of drone‐based thermal infrared imagery for quantifying river temperature heterogeneity

Climate change is altering river temperature regimes, modifying the dynamics of temperature‐sensitive fishes. The ability to map river temperature is therefore important for understanding the impacts of future warming. Thermal infrared (TIR) remote sensing has proven effective for river temperature mapping, but TIR surveys of rivers remain expensive. Recent drone‐based TIR systems present a potential solution to this problem. However, information regarding the utility of these miniaturised systems for surveying rivers is limited. Here, we present the results of several drone‐based TIR surveys conducted with a view to understanding their suitability for characterising river temperature heterogeneity. We find that drone‐based TIR data are able to clearly reveal the location and extent of discrete thermal inputs to rivers, but thermal imagery suffers from temperature drift‐induced bias, which prevents the extraction of accurate temperature data. Statistical analysis of the causes of this drift reveals that drone flight characteristics and environmental conditions at the time of acquisition explain ~66% of the variance in TIR sensor drift. These results shed important light on the factors influencing drone‐based TIR data quality and suggest that further technological development is required to enable the extraction of robust river temperature data. Nonetheless, this technology represents a promising approach for augmenting in situ sensor capabilities and improved quantification of advective inputs to rivers at intermediate spatial scales between point measurements and “conventional” airborne or satellite remote sensing.