Tag: Climate Change Downscaling Techniques

Abstract

This research has mapped the Quaternary equilibrium line altitude (ELA) using White’s and Porter’s methods to reconstruct past and present temperatures using a regression model in the Gharaghom basin located in northeastern Iran. The potential impact of climate change on glaciers was detected by using an artificial neural network. A regression model between temperature and altitude based on available observation data and reconstructed data indicated that the present temperature lapse rate of − 0.41 °C would occur for every hundred meters of increasing altitude in stations. The reconstruction of the past temperature of the Pleistocene revealed an increase of about 10.36–10.5 °C for the mean annual temperature of the current temperature in the basin. The recent ELA rises from the Pleistocene 2236 m and 2200 m a.s.l. according to White and Porter’s methods, respectively. So, considering the present temperature lapse rate and the highest altitude of 3271 m, the maximum Pleistocene ELA depression would be about 800 m due to modern temperature increase of 10.36–10.5 °C compared to the past Pleistocene temperature. Based on the neural network projection model, linear growth in temperature would occur for the next three decades (2021–2051) in the Gharaghom basin. The minimum temperature has been much more affected than the maximum temperature, so global warming has caused an increase in the monthly minimum and maximum temperature in most seasons in different parts of the basin based on trend analysis. The average temperature rise of 0.19 °C for future decades would consequently affect the water resources.

Abstract

Climate change and human activity have increased fires in India. Fine particulate matter ( \(\hbox {PM}_{2.5}\) ) is released into the atmosphere by stubble burning in Punjab and Haryana and forest fires in the north-eastern and central areas of the country. Accurate short-term \(\hbox {PM}_{2.5}\) estimates are essential to protect human health and reduce acute air pollution. However, global air quality forecasting methods grapple with a persistent assumption of fire emissions. They use near-real-time fire emissions throughout the prediction cycle. Air quality forecasts are prone to inaccuracies and biases due to fire emissions’ dynamic nature. We employ spatiotemporal deep learning techniques, specifically ConvLSTM and ConvGRU, to forecast fire emission locations up to three days in advance. Through our evaluation, we find that ConvLSTM outperforms ConvGRU in terms of prediction accuracy and performance. The chosen model provides a very good correlation coefficient ( \(\approx 0.8\) ) for the 1st day forecast and a moderate value (0.5 \(-\) 0.55) for subsequent 2nd and 3rd days forecasts. The predictors NDVI, temperature, wind, surface pressure, and total cloud cover are included to our model training to improve these correlations. In Punjab-Haryana, wind input improves results. This fire burning location prediction method could improve air quality forecasting. Our deep learning model can improve forecasts by revealing the complex interactions of components and reflecting fire emissions’ dynamic nature. This research may help improve air quality forecasts in the face of rising fire events, protecting communities across the Indian subcontinent.

Abstract

In the present study, we used a bubble column photobioreactor to test carbon dioxide using Chlorella vulgaris RDS03 under laboratory conditions. The nutrient concentration of medium was optimized by Box-Behnken design through response surface methodology (RSM), and regression coefficient (R²) value of 0.999 was analyzed by analysis of variance (ANOVA). The microalga Chlorella vulgaris RDS03 was captured—98.86% of CO₂ analyzed by CO₂ utilization and biofixation kinetics, 310U mL⁻¹ of RuBisCO enzyme, 5.32 mg mL⁻¹ of biomass, 124 mg g⁻¹ of carbohydrate, 247.15 mg g⁻¹ of lipid, 4.1 mL g⁻¹ of bioethanol, and 4.9 mL g⁻¹ of biodiesel produced. The molecular weight of purified RuBisCO enzyme was analyzed as 54 kDa by 15% of SDS PAGE. The 3D homology structure of N-terminal amino acids sequence of RuBisCO was predicted with 415 amino acid residues. The biodiesel was subjected to functional group analysis using Fourier transform infrared spectroscopy (FTIR). The fifty biodiesel (FAME) compounds were identified by gas chromatograph-mass spectroscopy (GC–MS) analysis and major compounds viz., linolenic acid (C18:2), oleic acid (C18:2), stearic acid (C18:0), palmitic acids (C16:1), and myristic acid (C14:0). The produced bioethanol was confirmed using high-performance liquid chromatography (HPLC).

Graphical abstract

Abstract

Climate change is one of the main factors affecting the habitats and water resources of the country. These changes may sometimes create natural disasters like floods and droughts around the world have done huge damages to the Pakistan in recent decades. Universal climatic variables such as temperature and precipitation influence runoff, river flow, evapotranspiration etc. Therefore, various techniques and models for the analysis and simulation of hydroclimatic time series have been projected. To minimize time and cost of the analysis, a nonparametric singular spectrum analysis (SSA) method was used to predict hydroclimatic variables. The SSA method has proven to be an influential tool for hydroclimatic data to examine important information about constrained components and further analysis. It is one of the smooth time series methods that does not require any prior assumption, such as the stationarity of the series or the normality of the residuals. This paper briefly explains the main steps of the technique and performs an SSA output to calibrate and validate the monthly temperature, precipitation and river flow for the Upper Indus Basin (UIB) rivers of ​​Pakistan. The SSA prediction and forecasting results are compared to known parametric techniques of multiple linear regression (MLR) and vector autoregression (VAR) methods. It can be stated that SSA leads to better results for both the calibration period and the validation period.

Abstract

Rainfall is one of the most important meteorological phenomena that impacts many fields, including agriculture, energy, water resources management, and mining, among others. While machine learning (ML) models have shown great potential in rainfall forecasting as they perform well and sometimes better than some physical models, the complex physical processes involved in rainfall formation make single ML models insufficient for providing accurate rainfall estimates in most cases. Although there are comprehensive reviews of the performance evaluation of individual ML models in the literature, only a limited number of reviews exist that include hybrid models that specifically focus on rainfall forecasting. This paper presents an extensive review of the performance of hybrid ML models for rainfall forecasting. The vital information on the forecasting time scales, model inputs, and evaluation methods used for constructing these models has been analysed and discussed. The findings revealed that hybrid ML models composed by integrating data pre-processing techniques and optimisation algorithms may be a successful and efficient solution to enhance rainfall predictions at various timescales. Hybrid ML models used for rainfall predictions are capable of producing comparatively more accurate forecasts and reducing uncertainty for both short and longer lead times. Recent advances in physical-ML hybrid models for weather forecasting have also been highlighted. Overall, this review article provides useful information to researchers interested in developing early warning systems for precise and timely rainfall forecasting.

Abstract

Wetlands are important natural resources for humans and play an irreplaceable ecological function in the terrestrial ecosystem. To curb the continued loss of wetlands globally, international organizations and many countries have taken a series of major conservation and restoration measures. This work reviews these wetland conservation and restoration measures, interprets China’s wetland conservation and restoration management policies, and proposes that future research on wetland resources in China should be conducted from the aspects of international frontiers and national strategic plans, socioeconomics, and smart services. The results show that the 27 International Wetlands Days from 1997 to 2023 provided new goals and tasks for the protection and management of wetlands. The important topics and outcomes of the 14 Conferences of the Contracting Parties to the Convention on Wetlands from 1980 to 2022 provided new directions and new challenges for wetland development. In the future, we should enhance wetland ecological functions, promote sustainable wetland development, and overcome the technical bottleneck of fragile wetland ecosystem restoration. From 1992 to 2022, China embarked on a new phase of wetland protection and restoration. The overall experience of wetland protection and restoration in China has been formed through national strategic deployment, legal policy establishment, and project planning and implementation. The needs to provide for and plan the long-term protection of wetlands at the national level, to innovate restoration and management techniques and application systems, and to effectively address the complex issues of wetland protection and restoration through collaborative division of labor among multiple departments were emphasized. Research on the future trends of wetlands should be directed towards the exploration and practice of the United Nations Sustainable Development Goals and several international conventions in support of sustainable wetland development. Wetland protection, restoration, and management services should be promoted for national strategic needs and local, high-quality social and economic development. In addition, research on cross-integration and academic innovation should be enhanced for disciplinary development, global supervision, comprehensive assessment, and smart decision making.

Abstract

The diurnal cycle of rainfall in the Lake Victoria Basin of East Africa results from the super positioning of regional circulations driven by lake/land temperature differences and topography in the presence of the large-scale flow. Analysis of a triple-nested regional model simulation with a convective-permitting inner domain shows how these elements combine to produce the observed regionality of precipitation, including diurnal cycling, in boreal fall. A single diurnal rainfall peak occurs throughout the basin, but the time of maximum rainfall varies within the basin. The rainy period over the lake begins with precipitation over the northern part of the lake near 02Z (0500 LT), so it is not simply nighttime rain driven by lake/land breezes. Onset of the rainy period occurs only when low geopotential heights over the relatively warm lake cause a southward branch of the Turkana Jet to form. The formation of the jet depends on nighttime cooling over the Eastern Rift Mountains, which acts to direct the large-scale, moist flow around the topography. Topography also plays a role in the daytime rainy period over land in the Lake Victoria Basin. Moist divergence over the lake supports convergence and precipitation over the shore regions. Precipitation rates are twice the magnitude over the eastern shore compared with the western shore because daytime warming of the high elevations of the Eastern Rift Mountains allows the large-scale easterly flow to go over the mountains. This easterly flow converges with the lake/land circulation and doubles precipitation rates over the eastern shore.

Abstract

This study assesses the performance of 37 global climate models (GCMs) from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) in simulating extreme precipitation in Madagascar. In this study, six extreme precipitation indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) were used, namely consecutive dry days (CDD), heavy precipitation days (R10MM), very heavy precipitation days (R20MM), maximum 5-day precipitation (RX5DAY), extremely wet days (R99P), and simple daily intensity (SDII). The performance of the model was evaluated from 1998 to 2014 against the Tropical Rainfall Measuring Mission 3B42 (TRMM) and the Global Precipitation Climatology Project (GPCP). The results show that most of the models in CMIP6 reasonably reproduce the annual precipitation cycle of the study area. The results also suggested that models from CMIP6 tend to underestimate extreme precipitation indices such as CDD, R20MM, SDII, and R99P. However, for R10MM and RX5DAY, the performance of individual models varies remarkably. By looking at the performance metrics, not a single model consistently performed well. Model performance changes with the reference data, the extreme precipitation indices, and the performance metrics. However, the findings of this study indicate that overall, multi-model ensemble mean outperforms most individual models. The study lays the basis for the analysis and projection of future extreme precipitation in Madagascar but also provides scientific evidence for the choice of models.

Abstract

High-resolution weather data is crucial for assessing future climate change impacts on local environments, yet downscaling low-resolution Global Climate Models (GCMs) outputs and addressing associated uncertainty remain significant challenges. In this study, we propose a novel spatial weather generator using generative networks, specifically a numerical conditional deep convolutional generative adversarial network (cDCGAN), as a promising solution. The cDCGAN generates high-resolution weather data from low-resolution GCM outputs and was applied to four case areas in China under four Shared Socio-economic Pathway (SSP) scenarios. The results demonstrate the cDCGAN's accuracy, consistency, and stability, with low uncertainties. The model performs optimally in low-elevation plains and tropical regions. The cDCGAN offers advantages in uncertainty analysis over traditional downscaling methods, serving as a valuable tool for climate change analysis, response estimation, and environmental management decision-making within the spatial statistics domain.

Abstract

The Indo-Gangetic Plain (IGP), the source of grains for around 40% Indian population, is known as the breadbasket of India. The Indian Summer Monsoon Rainfall (ISMR) plays a vital role in the agricultural activities in this region. The rapid urbanization, land use and land cover change have significantly impacted the region’s agriculture, water resources, and socioeconomic facets. The present study has investigated the observed and regional modeling aspects of ISMR characteristics, associated extremes over the IGP, and future perspectives under the high-emission RCP8.5-scenario. Future projections suggest a 10–20% massive decrease during pre-monsoon (March–May) and earlier ISM season months (i.e., June and July). A significant 40–70% decline in mean monsoon rainfall during the June–July months in the near future (NF; 2041–2060) has been projected compared to the historical period (1986–2005). An abrupt increase of 80–170% in mean monsoon rainfall during the post-monsoon (October–December) in the far future (FF; 2080–2099) is also projected. The distribution of projected extreme rainfall events shows a decline in moderate or rather heavy events (5 or more) in NF and FF. Further, an increase in higher rainfall category events such as very heavy (5–10) and extremely heavy rainfall (5 or more) events in NF and FF under the warmer climate is found. However, the changes are less prominent during FF compared to the NF. The mean thresholds for extremely heavy rainfall may increase by 1.9–4.9% during NF and FF. Further, the evolution patterns of various quantities, such as tropospheric temperature gradient (TG), specific humidity, and mean sea level pressure, have been analyzed to understand the physical processes associated with rainfall extremes. The strengthening in TG and enhanced atmospheric moisture content in NF and FF support the intensification in projected rainfall extremes over IGP.