Tag: Climate Change Downscaling Techniques

Abstract

In regions where gauging is lacking or limited, the development of a methodology for water balance assessment is a key challenge. Water balance assessment is a significant task in managing the sustainable use of water resources. This study aims to improve the accuracy of water balance components including actual evapotranspiration and groundwater recharge rate in the Hashtgerd study area, Iran. Groundwater extraction volumes are determined based on two-period data collection; while precipitation, evapotranspiration, and groundwater storage change are calculated from annual datasets. To address the data-scarce problem, as an additional measure of actual evapotranspiration, satellite measurements are used to improve recharge rate accuracy. To understand the impact of satellite data uncertainties on water resources studies, each estimated water balance component is compared to observation data, and the uncertainty of these components is quantified using statistical methods, variability, and standard error. The results show that the Hashtgerd aquifer receives 199 MCM from the main drains which is a major part of the water inflow. The extraction of wells is one of the most significant outflow components of the aquifer (i.e., 284 MCM); while water loss by evaporation is estimated to be 207 MCM of the outflow. The finding shows that satellite-based evapotranspiration can reduce recharge uncertainty which can improve the resolution of groundwater balance. This study supports that the aquifer is under severe environmental pressure. One of those concerns is that groundwater levels decreased by 0.92 m per year between 2000 and 2019.

Abstract

In regions where gauging is lacking or limited, the development of a methodology for water balance assessment is a key challenge. Water balance assessment is a significant task in managing the sustainable use of water resources. This study aims to improve the accuracy of water balance components including actual evapotranspiration and groundwater recharge rate in the Hashtgerd study area, Iran. Groundwater extraction volumes are determined based on two-period data collection; while precipitation, evapotranspiration, and groundwater storage change are calculated from annual datasets. To address the data-scarce problem, as an additional measure of actual evapotranspiration, satellite measurements are used to improve recharge rate accuracy. To understand the impact of satellite data uncertainties on water resources studies, each estimated water balance component is compared to observation data, and the uncertainty of these components is quantified using statistical methods, variability, and standard error. The results show that the Hashtgerd aquifer receives 199 MCM from the main drains which is a major part of the water inflow. The extraction of wells is one of the most significant outflow components of the aquifer (i.e., 284 MCM); while water loss by evaporation is estimated to be 207 MCM of the outflow. The finding shows that satellite-based evapotranspiration can reduce recharge uncertainty which can improve the resolution of groundwater balance. This study supports that the aquifer is under severe environmental pressure. One of those concerns is that groundwater levels decreased by 0.92 m per year between 2000 and 2019.

Abstract

In recent years, there has been an increased interest in understanding and predicting the weather using weather station data with Spatial-Temporal Graph Neural Networks (STGNN). However, it has large prediction errors as a result of the inherent non-linearities and the influence of dynamic spatio-temporal auto-correlation. Using a continuously-varying graph topology chronologically, while embedding domain knowledge to enforce validity, can effectively resolve the issue, but the implementation of such concept constitutes an interdisciplinary challenge for researchers. A Dynamic Graph Former (DGFormer) model is proposed to address this challenge. It combines a topology learner through a deep generative layer with domain knowledge enhancement inserted into the STGNN structure, where the derived physics-guided method allows for an efficient integration with the earth system. For capture of the optimal topology, we merge a node-embedding-based similarity metric learning and the superposition principle as physical assistants into the dynamic graph module. We evaluate our model with a real-world weather dataset on short-term (12 hours) and medium-range (360 hours) prediction tasks. DGFormer achieves outstanding performance with obvious improvements by up to 34.84% at short-term prediction and by up to 23.25% at medium-range prediction compared with the state-of-the-art methods. We also conducted detailed analyses for cities in three regions and visualized the dynamic graph, revealing the characteristics, advantages, and graph visualization of our model.

Abstract

In recent years, there has been an increased interest in understanding and predicting the weather using weather station data with Spatial-Temporal Graph Neural Networks (STGNN). However, it has large prediction errors as a result of the inherent non-linearities and the influence of dynamic spatio-temporal auto-correlation. Using a continuously-varying graph topology chronologically, while embedding domain knowledge to enforce validity, can effectively resolve the issue, but the implementation of such concept constitutes an interdisciplinary challenge for researchers. A Dynamic Graph Former (DGFormer) model is proposed to address this challenge. It combines a topology learner through a deep generative layer with domain knowledge enhancement inserted into the STGNN structure, where the derived physics-guided method allows for an efficient integration with the earth system. For capture of the optimal topology, we merge a node-embedding-based similarity metric learning and the superposition principle as physical assistants into the dynamic graph module. We evaluate our model with a real-world weather dataset on short-term (12 hours) and medium-range (360 hours) prediction tasks. DGFormer achieves outstanding performance with obvious improvements by up to 34.84% at short-term prediction and by up to 23.25% at medium-range prediction compared with the state-of-the-art methods. We also conducted detailed analyses for cities in three regions and visualized the dynamic graph, revealing the characteristics, advantages, and graph visualization of our model.

Abstract

The study investigated the impact of climate and land cover change on water quality. The novel contribution of the study was to investigate the individual and combined impacts of climate and land cover change on water quality with high spatial and temporal resolution in a basin in Turkey. The global circulation model MPI-ESM-MR was dynamically downscaled to 10-km resolution under the RCP8.5 emission scenario. The Soil and Water Assessment Tool (SWAT) was used to model stream flow and nitrate loads. The land cover model outputs that were produced by the Land Change Modeler (LCM) were used for these simulation studies. Results revealed that decreasing precipitation intensity driven by climate change could significantly reduce nitrate transport to surface waters. In the 2075–2100 period, nitrate-nitrogen (NO₃-N) loads transported to surface water decreased by more than 75%. Furthermore, the transition predominantly from forestry to pastoral farming systems increased loads by about 6%. The study results indicated that fine-resolution land use and climate data lead to better model performance. Environmental managers can also benefit greatly from the LCM-based forecast of land use changes and the SWAT model’s attribution of changes in water quality to land use changes.

Graphical abstract

Abstract

The need for renewable energy sources is recently necessitated by attaining sustainability and climate change mitigation. Accordingly, the use of renewable energy sources has been growing rapidly during the last two decades. Yet, the potentials of renewable energy sources are generally influenced by several climatic factors that either determine the source of energy such as wind speed in the case of wind power or affect the performance of system such as the reduction in solar PV power production due to temperature increase. This highlights the need for assessing climate change impacts on renewable energy sources in the future to ensure their reliability and sustainability.

This paper is intended to assess impacts of climate change on wind and solar potential energy in Egypt by the year 2065 under RCP 8.5 scenario. For this purpose, a GIS-based methodology of three main steps was applied. The results revealed that solar energy potential in Egypt is expected to be relatively less vulnerable to climate change compared to wind energy. In this respect, it was found that while wind energy potential was estimated to range ± 12%. By the year 2065 under RCP 8.5 scenario, PV module power is expected to decrease by about 1.3% on average. Such assessment can assist in developing more sustainable and flexible renewable energy policy in Egypt.

Abstract

Context

Urban parks are essential for maintaining aesthetics within cities and keeping their its energy balance by helping mitigate the Urban Heat Island (UHI) effect through controlling ambient and land surface temperature (LST).

Objectives

To investigate the impact of cooling in terms of distance by variously configured urban parks of a humid subtropical city, using landscape metrics and open-source data.

Methods

Land use (LU) was obtained through maximum likelihood classification of 3 m resolution aerial RGB-NIR imagery supported by ground control points and park boundaries collected during field survey. LST at matching resolution was obtained through downscaling of Landsat-8 LST at 30/100m resolution, calculated with the Radiative Transfer Equation (RTE). Landscape metrics for patches of parks were calculated using landscapemetrics R library and related to neighbourhood distances over built-up land use (LU).

Results

Urban parks with homogenous cores and less complex shape provide distinctly higher cooling of neighbouring built-up LU of circa 2.55 °C over the distance of 18 m from park boundaries. Four metrics: contiguity index (CONTIG), core area index (CAI), fractal dimension index (FRAC) and perimeter-area ratio (PARA) represent significant relationship between spatial configuration of parks and their cooling distance. No cooling capacity of parks regardless of their shape and core was observed beyond the distance of 18 m, which remained constant with small fluctuations in the range of 0.5 °C up to the distance of 600 m.

Conclusions

The study concludes that cooling distance of urban parks in their neighbourhood extends up to 18 m, which is shorter than suggested by other studies.

Abstract

Internal variability changes in soil surface temperature and reflective radiation drove climate to change and vice versa over all time scales. This study investigates the heat flux components and the climate variables that drive the change in surface soil temperature down to 15.0 cm. We analyze the correlations of energy and radiation components to climate variables by coding principle component analysis (PCA) and finding the radiative forcing of atmosphere-energy-climate-soil continuum systems using datasets derived from ERA5 and NCEP/NCAR projections. The vectors contributing to the continuum were the shortwave, net solar radiation, and sensible flux are the main drivers. The average 72-year shortwave in the study locations was − 190.63 W/m². Because of the upwelling radiation to the atmosphere, the longwave flux did not exceed the shortwave over the study’s location. The sensible heat flux was the lowest in the northwestern highlands (approximately 32 Watt/m²) and the highest range during summer was 150–180 Watt/m² over the country. This variability in net radiation partitioning led to changes in surface warming and the responding climate. This study found that the average monthly soil surface temperature bound was 10–20 °C from November to March at all locations except for Amman and Ruwaished. The calculated soil heat storage is positive all year in the Dead Sea with an annual average of 76.53 W/m². The lowest storage heat was in Amman with an annual average of − 44.42 W/m². The anomalies of annual ERA5 reanalysis of main climate contributors extended from (− 5.46 to + 5.53 °C), (− 5.66 to + 4.36 °C), (− 1.3 to 2.87 mm/day), and around (− 25.97% to a maximum of 20.99%) for maximum and minimum near-surface air temperatures, daily precipitation, and relative humidity, respectively. The long-term mean evaporation was very low approximately − 1.85 × 10–7 mm. Mean monthly wind speed illustrates low-frequency variability by − 0.06 m/s. PCA represented the correlation coefficients of the climate variables that affected soil temperature the most: near-surface air temperature, maximum, and minimum (> 0.95). Soil–water content, precipitation, and humidity played a secondary negative role to a certain extent by regulating and slowing down the soil heat transfer − 61, − 64, and − 91%, respectively. This study enhances the understanding of energy partitioning and incorporates satellite products and climate simulations to recognize key influencing factors of energy changes and climate footprints toward soil heat flux that affect the biosphere, humans, and energy use.

Abstract

Background

Malaria is one of the major vector-borne diseases most sensitive to climatic change in West Africa. The prevention and reduction of malaria are very difficult in Benin due to poverty, economic insatiability and the non control of environmental determinants. This study aims to develop an intelligent outbreak malaria early warning model driven by monthly time series climatic variables in the northern part of Benin.

Methods

Climate data from nine rain gauge stations and malaria incidence data from 2009 to 2021 were extracted from the National Meteorological Agency (METEO) and the Ministry of Health of Benin, respectively. Projected relative humidity and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4).

A structural equation model was employed to determine the effects of climatic variables on malaria incidence. We developed an intelligent malaria early warning model to predict the prevalence of malaria using machine learning by applying three machine learning algorithms, including linear regression (LiR), support vector machine (SVM), and negative binomial regression (NBiR).

Results

Two ecological factors such as factor 1 (related to average mean relative humidity, average maximum relative humidity, and average maximal temperature) and factor 2 (related to average minimal temperature) affect the incidence of malaria. Support vector machine regression is the best-performing algorithm, predicting 82% of malaria incidence in the northern part of Benin.

The projection reveals an increase in malaria incidence under RCP4.5 and RCP8.5 over the studied period.

Conclusion

These results reveal that the northern part of Benin is at high risk of malaria, and specific malaria control programs are urged to reduce the risk of malaria.

Abstract

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China’s near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China’s eastern region, with population exposure risks mostly ranging from 0.8 to 5.