Tag: Climate Change Downscaling Techniques

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

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).

Abstract

Climate change has significantly influenced the occurrence of extreme events and their outcomes in developing countries, like Pakistan. This research investigates the impact of climate variability on the development of Intensity Duration Frequency (IDF) curves using wavelet analysis across two Pakistani cities i.e. Abbottabad and Islamabad. IDF curves are produced utilizing the Statistical Software Package (HEC-SSP) and Hydrological Engineering Center and Watershed Modeling System (WMS), where daily meteorological (i.e., rainfall, and the approx. temperature) data from 1960 to 2020 (60 years) at Abbottabad and Islamabad was gathered from Pakistan Meteorological Department (PMD). Initially, the relation between the observed data was extracted by applying the slope approaches of Mann-Kendall and Sen. Following the removal of serial correlation, generalized IDF curves are developed utilizing the time and turnaround for both stations. Finally, climate variability’s impact on IDF curves was studied using wavelet analysis applied to three different pairs of input data, i.e., maximum, minimum, and mean temperatures against the developed IDF curves. Results showed that wavelet analysis are extremely useful to monitor the climate variability’s influence/role on frequency and return periods of flood events (i.e., IDF curves). The developed IDF curves showed higher intensities during the monsoon period, whereas lower intensities of IDF curves are observed in other months against the 24 hours’ duration of different return periods. Results also depicted that Abbottabad has experienced higher intensity of rainfall as compared with Islamabad city, which might be linked due to the changing climate and the use of land in both cities and verified by the results obtained from wavelet analysis. Increased cloud cover and precipitation resulted from the orographic effect, coupled with the influence of lower temperatures at elevated altitudes. Wavelet analysis showed a strong impact of climate (i.e., temperature) on IDF curves, where significant changes in the period and frequency are observed between the two cities. Overall, this study will be useful to understand how IDF curves are affected by climate variability while predicting the future flood events and sustainable design of urban drainage system.

Abstract

Climate change has significantly influenced the occurrence of extreme events and their outcomes in developing countries, like Pakistan. This research investigates the impact of climate variability on the development of Intensity Duration Frequency (IDF) curves using wavelet analysis across two Pakistani cities i.e. Abbottabad and Islamabad. IDF curves are produced utilizing the Statistical Software Package (HEC-SSP) and Hydrological Engineering Center and Watershed Modeling System (WMS), where daily meteorological (i.e., rainfall, and the approx. temperature) data from 1960 to 2020 (60 years) at Abbottabad and Islamabad was gathered from Pakistan Meteorological Department (PMD). Initially, the relation between the observed data was extracted by applying the slope approaches of Mann-Kendall and Sen. Following the removal of serial correlation, generalized IDF curves are developed utilizing the time and turnaround for both stations. Finally, climate variability’s impact on IDF curves was studied using wavelet analysis applied to three different pairs of input data, i.e., maximum, minimum, and mean temperatures against the developed IDF curves. Results showed that wavelet analysis are extremely useful to monitor the climate variability’s influence/role on frequency and return periods of flood events (i.e., IDF curves). The developed IDF curves showed higher intensities during the monsoon period, whereas lower intensities of IDF curves are observed in other months against the 24 hours’ duration of different return periods. Results also depicted that Abbottabad has experienced higher intensity of rainfall as compared with Islamabad city, which might be linked due to the changing climate and the use of land in both cities and verified by the results obtained from wavelet analysis. Increased cloud cover and precipitation resulted from the orographic effect, coupled with the influence of lower temperatures at elevated altitudes. Wavelet analysis showed a strong impact of climate (i.e., temperature) on IDF curves, where significant changes in the period and frequency are observed between the two cities. Overall, this study will be useful to understand how IDF curves are affected by climate variability while predicting the future flood events and sustainable design of urban drainage system.

Abstract

Allocation of water in the situation of climate change presents various uncertainties. Consequently, decisions must be made to ensure stability and functionality across different climatic scenarios. This study aims to examine the effectiveness of adaptation strategies in the agricultural sector, including a 5% increase in irrigation efficiency (S1) and a shift in irrigation method to Dry-DSR (direct seeded rice) under conditions of climatic uncertainty using a decision-making approach. The study focuses on the basin downstream of the Sefidroud dam, encompassing the Sefidroud irrigation and drainage network. Initially, basin modeling was conducted using the WEAP integrated management software for the period 2006–2020. Subsequently, the impact of climate change was assessed, considering RCP2.6, RCP4.5, and RCP8.5 emission scenarios on surface water resources from 2021 to 2050. Runoff and cultivated area, both subject to uncertainty, were identified as key parameters. To evaluate strategy performance under different uncertainties and determine the efficacy of each strategy, regret and satisfaction approaches were employed. Results indicate a projected decrease in future rainfall by 3.5–11.8% compared to the base period, accompanied by an increase in maximum and minimum temperatures (0.83–1.62 °C and 1.15–1.33 °C, respectively). Inflow to the Sefidroud dam is expected to decrease by 13–28%. Presently, the Sefidroud irrigation and drainage network faces an annual deficit of 505.4 MCM, and if current trends persist with the impact of climate change, this shortfall may increase to 932.7 MCM annually. Furthermore, satisfaction indices for strategy (S2) are 0.77 in an optimistic scenario and 0.70 in strategy (S1). In a pessimistic scenario, these indices are 0.67 and 0.56, respectively. Notably, changing the irrigation method with Dry-DSR is recommended as a robust strategy, demonstrating the ability to maintain basin stability under a broad range of uncertainties and climate change scenarios. It is crucial to note that the results solely highlight the effects of climate change on water sources entering the Sefidroud dam. Considering anthropogenic activities upstream of the Sefidroud basin, water resource shortages are expected to increase. Therefore, reallocating water resources and implementing practical and appropriate measures in this area are imperative.