Tag: Climate Change Downscaling Techniques

Evidence from temperature analog for traditional and economic cities in Nigeria: implications for sustainable city-related actions

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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).

Analyzing the Role of Changing Climate on the Variability of Intensity-Duration-Frequency Curve Using Wavelet Analysis

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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.

Analyzing the Role of Changing Climate on the Variability of Intensity-Duration-Frequency Curve Using Wavelet Analysis

Latest Results

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.

Developing climate change adaptation pathways in the agricultural sector based on robust decision-making approach (case study: Sefidroud Irrigation Network, Iran)

Latest Results

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.

Developing climate change adaptation pathways in the agricultural sector based on robust decision-making approach (case study: Sefidroud Irrigation Network, Iran)

Latest Results

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.

Assessment of historical and projected changes in extreme temperatures of Balochistan, Pakistan using extreme value theory

Latest Results

Abstract

The fundamental consequences of global warming include an upsurge in the intensity and frequency of temperature extremes. This study provides an insight into historical trends and projected changes in extreme temperatures on annual and seasonal scales across “Balochistan, Pakistan”. Historical trends are analyzed through the Mann Kendal test, and extreme temperatures (Tmax and Tmin) are evaluated using generalized extreme value (GEV) distribution for historical period (1991–2020) from the observational data and the two projected periods as near-future (2041–2070) and far-future (2071–2100) using a six-member bias-corrected ensemble of regional climate models (RCMs) projections from the coordinate regional downscaling experiment (CORDEX) based on the worst emission scenario (RCP8.5). The evaluation of historical temperature trends suggests that Tmax generally increase on yearly scale and give mixed signals on seasonal scale (winter, spring, summer, and autumn); however, Tmin trends gave mixed signals at both yearly and seasonal scale. Compared to the historical period, the return levels are generally expected to be higher for Tmax and Tmin during the both projection periods in the order as far-future > near-future > historical on yearly and seasonal basis; however, the changes in Tmin are more evident. Station-averaged anomalies of + 1.9 °C and + 3.6 °C were estimated in 100-year return levels for yearly Tmax for near-future and far-future, respectively, while the anomalies in Tmin were found to be + 3.5 °C and + 4.8 °C which suggest the intensified heatwaves but milder colder extreme in future. The findings provide guidance on improved quantification of changing frequencies and severity in temperature extremes and the associated impacts.

An exhaustive investigation of changes in projected extreme precipitation indices and streamflow using CMIP6 climate models: A case study

Latest Results

Abstract

This study draws attention to the better comprehension of spatio-temporal analysis of climate changes based on precipitation extremes and projection of future streamflow for efficient management of water resources in the Krishna River Basin (KRB), India. The concept of symmetric uncertainty (SU) is employed to select the top five Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to project future precipitation extreme indices under different Shared Socio-economic Pathways (SSPs). Grid-wise trend analysis reveals that there is more number of decreasing trends in extreme precipitation indices than increasing trends. From the results, it is observed that the percentage contributions of maximum one-day (RX1day) and five-day (RX5day) precipitation indices to the annual total precipitation indices are more important. In future periods, the precipitation extremes are expected to increase, especially the heavy precipitation indices such as R95p, R99p, RX1day, and RX5day, which are increasing significantly along with R50. The projection of future streamflow in the KRB is done using a Support Vector Machine (SVM) and is expected to increase under different SSPs. These precipitation extremes may increase the chance of hydrological calamities across the basin in the future.

Research highlights

  • Spatio-temporal analysis of extreme precipitation indices is carried out using CMIP6 climate model simulations over KRB.

  • One of the most efficient algorithm, symmetric uncertainty is employed to select best-performing GCMs to reduce the uncertainty in GCM selection.

  • The association between the extreme indices and discharge is carried out using Pearson correlation.

  • A significant increase is observed in projected extreme indices, especially very extreme indices such as 95th and 99th percentiles, RX1day and RX5day.

  • SVM regression is established between TOTPR and mean daily discharge to predict the future annual average streamflow under different SSP scenarios.

Quantifying the spatiotemporal patterns and environmental impacts of surface coal mining in the Xilingol Steppe, Inner Mongolia

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Abstract

Surface coal mining is one of the most environmentally destructive human disturbances. This study provides the first comprehensive assessment of the speed and scale of coal mining and its major environmental impacts in the Xilingol Steppe, Inner Mongolia from 1990 to 2015, using remote sensing data and landscape metrics. Our results show that during this period the number of surface coal mining areas (SCMAs) increased from 40 to 504 (about 13 times), while the size of SCMAs increased from 3.21 km2 to 283.62 km2 (greater than 88 times). The rapid expansion of SCMAs greatly fragmented the steppe landscape, consumed huge amounts of water, damaged rivers and wetlands, and substantially reduced grassland productivity. We estimated that the amount of water consumed by coal mining increased from 2.35 million m3 in 1990 to 242.61 million m3 in 2015 (more than 103 times), negatively affecting all six major rivers and most wetlands in the region. About 222 km2 of steppes were eradicated, resulting in a grassland production loss of 7.17×1010 g C. Our findings indicate that surface coal mining has transformed the steppe landscape and devastated its ecosystem function and services, posing a major threat to the environment of the region. Future studies need to focus on more in-depth integrative assessments of environmental, economic, and social impacts of surface coal mining to seek sustainability solutions for the region.

Statistical downscaling of GCMs wind speed data for trend analysis of future scenarios: a case study in the Lombardy region

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Abstract

Near-surface wind speed is a key climatic variable, affecting many sectors, such as energy production, air pollution, and natural hazard. Lombardy region of Italy is among the European areas with lowest average wind speed, leading generally to low air quality and wind energy potential. However, it is also one of the most affected area by tornadoes in Italy. Here we investigate possible changes in wind circulation as due to prospective global warming. We analysed wind speed WS under future scenarios (SSP1-2.6 and SSP5-8.5) from six Global Climate Models (GCMs) until 2100, tuned against observed WS data. We employed a statistical downscaling method, namely Stochastic Time Random Cascade (STRC) to correct locally GCMs outputs. Three statistical tests, i.e. Linear Regression, Mann Kendall, Moving Window Average, were carried out to analyse future trends of: annual WS averages, 95th quantile (as an indicator of large WS), and the number of days of calm wind per year (NWC). The proposed STRC algorithm can successfully adjust the mean, standard deviation, and autocorrelation structure of the GCM outputs. No strong trends are found for the future. The chosen variables would all display non-stationarity, and the 95th percentile display a positive trend for most of the stations. Concerning NWC, notable discrepancies among GCMs are seen. The STRC algorithm can be used to successfully adjust GCMs outputs to reflect locally observed data and to then generate credible long-term scenarios for WSs as a tool for decision-making.

Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios

Latest Results

Abstract

One of the main challenges regarding the prediction of groundwater resource changes is the climate change phenomenon and its impacts on quantitative variations of such resources. Groundwater resources are treated as one of the main strategic resources of any region. Given the climate change phenomenon and its impacts on hydrological parameters, it is necessary to evaluate and predict future changes to achieve an appropriate plan to maintain and preserve water resources. In this regard, the present study is put forward by utilizing the Statistical Down-Scaling Model (SDSM) to forecast the main climate variables (i.e., temperature and precipitation) based on new Rcp scenarios for greenhouse gas emissions within a period from 2020 to 2060. The results obtained from the prediction of climate parameters indicate different values in each emission scenario, so the limit, minimum and maximum values occur in the Rcp8.5, Rcp2.6 and Rcp4.5 scenarios, respectively. Also, a model is developed by utilizing the GMDH artificial neural network technique. The developed model predicts the average groundwater level based on the climate variables in such a way that by implementing the climate parameters forecasted by the SDSM model, the groundwater level within a time period from 2020 to 2060 is predicted. The results obtained from the verification and validation of the model imply its proper performance and reasonable accuracy in predicating groundwater level based on the climate variables. The findings derived from the present paper indicate that compared to the years prior to the prediction period, the groundwater level of the Sahneh Plain has dramatically dropped so that based on the Rcp scenarios, the groundwater level values are in their lowest state within the period from 2046 to 2056. The findings of this paper can be used by managers and decision makers as a layout for evaluating climate change effects in the Sahneh Plain.