Tag: Climate Change Downscaling Techniques

Abstract

This study investigates the trend in the projected rainfall and temperature over undivided Sudan and its major cities of political, trade, and agricultural significance under two different Representative Concentration Pathways (RCPs; RCP2.6 and RCP8.5). Available high-resolution datasets from the Coordinated Regional Climate Downscaling Experiment- Coordinated Output for Regional Evaluations (CORDEX-CORE) at a resolution of 25 km along with their ensemble are considered. The study analyzes projected climate conditions, with a specific emphasis on the near future (2036–2060) and far future (2071–2095). The rainfall distribution is projected to decline across South Sudan (undivided Sudan) under RCP2.6 (RCP8.5). The projected temperature is significantly increasing while rainfall is decreasing across all cities, with these trends being more pronounced under the RCP8.5 scenario. These changes could potentially result in various climate extremes such as severe heatwaves, droughts, and wildfires, which could have significant impacts on the ecosystems, agriculture, public health and ultimately, the livelihood and socio-economic condition of the people. The findings of the study will assist the governments, local administration and town planners in formulating short-term and long-term strategies for adaptation and mitigation, aimed at reducing the impacts of climate change. The study suggests specific measures to address the extreme heat and water deficit at the local scale, hence making it a valuable policy document for addressing the changing climate in undivided Sudan.

Abstract

In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of deep convolutional neural network (CNN). The CNN has superior features for autonomous learning and expression, and feature extraction from original input data can be realized by means of training CNN models that match practical applications. Due to the rapid progress in deep learning technology, the structure of CNN is becoming more and more complex and diverse. Consequently, it gradually replaces the traditional machine learning methods. This paper presents an elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, fully connected layers, and output layers. On this basis, this paper gives a comprehensive overview of the past and current research status of the applications of CNN models in computer vision fields, e.g., image classification, object detection, and video prediction. In addition, we summarize the challenges and solutions of the deep CNN, and future research directions are also discussed.

Abstract

The two vast Patagonian icefields are a global hotspot for ice-loss. However, not much is known about the total ice volume they store - let alone its spatial distribution. One reason is that the abundant record of direct thickness measurements has never been systematically exploited. Here, this record is combined with remotely-sensed information on past ice thickness mapped from glacier retreat. Both datasets are incorporated in a state-of-the-art, mass-conservation approach to produce a well-informed map of the basal topography beneath the icefields. Its major asset is the reliability increase of thicknesses values along the many marine- and lake-terminating glaciers. For these, frontal ice-discharge is notably lower than previously reported. This finding implies that direct climatic control was more influential for past ice loss. We redact a total volume for both icefields in 2000 of 5351 km³. Despite the wealth of observations used in this assessment, relative volume uncertainties remain elevated.

Abstract

The two vast Patagonian icefields are a global hotspot for ice-loss. However, not much is known about the total ice volume they store - let alone its spatial distribution. One reason is that the abundant record of direct thickness measurements has never been systematically exploited. Here, this record is combined with remotely-sensed information on past ice thickness mapped from glacier retreat. Both datasets are incorporated in a state-of-the-art, mass-conservation approach to produce a well-informed map of the basal topography beneath the icefields. Its major asset is the reliability increase of thicknesses values along the many marine- and lake-terminating glaciers. For these, frontal ice-discharge is notably lower than previously reported. This finding implies that direct climatic control was more influential for past ice loss. We redact a total volume for both icefields in 2000 of 5351 km³. Despite the wealth of observations used in this assessment, relative volume uncertainties remain elevated.

Abstract

Background

In southern central rift valley of Ethiopia, maize is an important crop because of its adaptation to wider agro-ecologies and higher yield potential. However, most cultivars were not parameterized to include in the database of Decision Support System for Agro-technology Transfer (DSSAT). As a result simulation of growth and yield of those cultivars was not possible under changing climate.

Methods

Two set of independent crop, management and soil data were used for calibration and validation of genetic coefficients of maize cultivars (BH-540, BH-546, BH-547, Shala and Shone) under condition of historic weather (1990–2020). Later, we simulated the growth and yield of maize using twenty multimodel climate ensembles across RCP 4.5 and 8.5 during early, medium and late century across Shamana, Bilate, Hawassa and Dilla clusters using DSSATv4.8 model.

Results

Cultivars BH-540, BH-546, BH-547, Shala and Shone produced yields of 5.7, 5.4, 5.2, 6.9 and 7.4 t ha⁻¹ with the corresponding error percentage of − 0.1, − 0.8, − 1.0, − 6.1 and 2.6%. The results of normalized root mean square were 1.14–4.2 and 3.0–3.9%, for grain yield during calibration and validation, respectively showing an excellent rating. The simulation experiment produced 5.4–9.2 t ha⁻¹ for grain yield of maize cultivars across the study areas, which is likely to fall close to 63.3% by 2070 if right adaptation options are not introduced necessitating switch in cultivars and production areas.

Conclusions

There is critical need for reduction of GHGs emissions, generation of innovative adaptation strategies, and development of drought and heat stress tolerant maize cultivars. Hence, researchers and policy makers shall act with utmost urgency to embark with breeding programs that target climate change adaptation traits in maize crop.

Abstract

Background

In southern central rift valley of Ethiopia, maize is an important crop because of its adaptation to wider agro-ecologies and higher yield potential. However, most cultivars were not parameterized to include in the database of Decision Support System for Agro-technology Transfer (DSSAT). As a result simulation of growth and yield of those cultivars was not possible under changing climate.

Methods

Two set of independent crop, management and soil data were used for calibration and validation of genetic coefficients of maize cultivars (BH-540, BH-546, BH-547, Shala and Shone) under condition of historic weather (1990–2020). Later, we simulated the growth and yield of maize using twenty multimodel climate ensembles across RCP 4.5 and 8.5 during early, medium and late century across Shamana, Bilate, Hawassa and Dilla clusters using DSSATv4.8 model.

Results

Cultivars BH-540, BH-546, BH-547, Shala and Shone produced yields of 5.7, 5.4, 5.2, 6.9 and 7.4 t ha⁻¹ with the corresponding error percentage of − 0.1, − 0.8, − 1.0, − 6.1 and 2.6%. The results of normalized root mean square were 1.14–4.2 and 3.0–3.9%, for grain yield during calibration and validation, respectively showing an excellent rating. The simulation experiment produced 5.4–9.2 t ha⁻¹ for grain yield of maize cultivars across the study areas, which is likely to fall close to 63.3% by 2070 if right adaptation options are not introduced necessitating switch in cultivars and production areas.

Conclusions

There is critical need for reduction of GHGs emissions, generation of innovative adaptation strategies, and development of drought and heat stress tolerant maize cultivars. Hence, researchers and policy makers shall act with utmost urgency to embark with breeding programs that target climate change adaptation traits in maize crop.

Abstract

The spatio-temporal variability of groundwater storage cannot be well understood without proper groundwater monitoring schemes. Since 2002, the launch of the Gravity Recovery and Climate Experiment Satellite (GRACE) mission has served to monitor Groundwater Storage Anomaly (GWSA) and filled the observational data gap on a regional scale. This study aimed to estimate the spatio-temporal GWSA in Ethiopia using GRACE satellite data. GWSA was calculated by disaggregating GRACE estimation of Terrestrial Water Storage Anomaly (TWSA) using auxiliary soil moisture and surface runoff data obtained from the Global Land Data Assimilation System. GWSA was decomposed using the Seasonal-Trend decomposition method, LOESS (STL). The results depicted an increasing variability of TWSA and GWSA over various regions of the country. Ethiopia experienced an increase in TWSA (3.8 mm yr⁻¹) and GWSA (4.6 mm yr⁻¹) between the years 2003 and 2021, with GWSA contributing primarily to the TWSA. Greater contributions to the rise in groundwater storage come from the Rift Valley, Omo Gibe, Baro Akobo, and a portion of the Genale Dawa, Awash, and Wabi Shebelle Basins. Except for the lowlands (Northwestern, Northeastern and Southeastern), most regions showed an average increase in GWSA per annum at varying rates. Precipitation, temperature, and evapotranspiration have a significant influence on the spatial variability of GWSA. The impact of precipitation on GWSA reached its maximum after a 2-month lag (correlation coefficient (R) = 0.62). GRACE captured the seasonal GWSA of Ethiopia reasonably well and can be used as a guide for a more detailed evaluation of the groundwater potential.

Abstract

Deep neural networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However, collecting, storing and—in the case of supervised learning—labelling the data is expensive and time-consuming. Additionally, assessing the networks’ generalization abilities or predicting how the inferred output changes under input transformations is complicated since the networks are usually treated as a black box. Both of these problems can be mitigated by incorporating prior knowledge into the neural network. One promising approach, inspired by the success of convolutional neural networks in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve that affect the output in a predictable way. This promises an increased data efficiency and more interpretable network outputs. In this survey, we try to give a concise overview about different approaches that incorporate geometrical prior knowledge into neural networks. Additionally, we connect those methods to 3D object detection for autonomous driving, where we expect promising results when applying those methods.

Abstract

Responding to the threats of climate change by cities requires taking relevant actions that will communicate future conditions in reliable and effective manner for sustainable and transformational climate actions. We used the analog approach to assess the geographical shifts and changes in average temperature conditions for six traditional and economic cities under different climate scenarios (Mitigated and Unmitigated scenarios). We calculated the similarity in temperature between each pixel for the current (2021–2050) and future (2041–2070) conditions of the cities, with every pixel globally in the historical (1971–200) period. Our analysis revealed that; (1) the temperature of the cities in the current and future periods will be similar to conditions of another place on the globe during the historical period; (2) Kano city will experience even more drastic changes because of the low level of similarity to other places; (3) the new places found with similar temperature conditions are generally to the south of the corresponding cities thus indicating warming. The overall results show that the analogues of the cities are within the domain of the global tropical zone which occurs around the equator. Drawing from the interaction between cities and their analogues, we highlighted sustainable city related actions such as the incorporation of urban designs and policies to enhance human thermal comfort as adaptation and solution strategies. While future research might apply qualitative studies and additional data to support the analog results, our findings can guide the understanding and application of the analog approach into environmental issues in Nigeria and other West African countries in accordance to sustainable city goals (SDG 11).

Abstract

Responding to the threats of climate change by cities requires taking relevant actions that will communicate future conditions in reliable and effective manner for sustainable and transformational climate actions. We used the analog approach to assess the geographical shifts and changes in average temperature conditions for six traditional and economic cities under different climate scenarios (Mitigated and Unmitigated scenarios). We calculated the similarity in temperature between each pixel for the current (2021–2050) and future (2041–2070) conditions of the cities, with every pixel globally in the historical (1971–200) period. Our analysis revealed that; (1) the temperature of the cities in the current and future periods will be similar to conditions of another place on the globe during the historical period; (2) Kano city will experience even more drastic changes because of the low level of similarity to other places; (3) the new places found with similar temperature conditions are generally to the south of the corresponding cities thus indicating warming. The overall results show that the analogues of the cities are within the domain of the global tropical zone which occurs around the equator. Drawing from the interaction between cities and their analogues, we highlighted sustainable city related actions such as the incorporation of urban designs and policies to enhance human thermal comfort as adaptation and solution strategies. While future research might apply qualitative studies and additional data to support the analog results, our findings can guide the understanding and application of the analog approach into environmental issues in Nigeria and other West African countries in accordance to sustainable city goals (SDG 11).