Month: February 2024

Abstract

The variation of land surface temperature (LST) has a vital impact on the energy balance of the land surface process and the ecosystem stability. Based on MDO11C3, we mainly used regression analysis, GIS spatial analysis, correlation analysis, and center-of -gravity model, to analyze the LST variation and its spatiotemporal differentiation in China from 2001 to 2020. Furthermore, we employed the Geodetector to identify the dominant factors contributing to LST variation in 38 eco-geographic zones of China and investigate the underlying causes of its pattern. The results indicate the following: (1) From 2001 to 2020, the LST climate average in China is 9.6°C, with a general pattern of higher temperatures in the southeast and northwest regions, lower temperatures in the northeast and Qinghai-Tibet Plateau, and higher temperatures in plains compared to lower temperatures in mountainous areas. Generally, LST has a significant negative correlation with elevation, with a correlation coefficient of −0.66. China’s First Ladder has the most pronounced negative correlation, with a correlation coefficient of −0.76 and the lapse rate of LST is 0.57°C/100 m. (2) The change rate of LST in China during the study is 0.21°C/10 a, and the warming area accounts for 78%, demonstrating the overall spatial pattern a “multi-core warming and axial cooling”. (3) LST’s variation exhibits prominent seasonal characteristics in the whole country. The spatial distribution of average value in winter and summer differs significantly from other seasons and shows more noticeable fluctuations. The centroid trajectory of the seasonal warming/cooling area is close to a loop shape and displays corresponding seasonal reverse movement. Cooling areas exhibit more substantial centroid movement, indicating greater regional variation and seasonal variability. (4) China’s LST variation is driven by both natural influences and human activities, of which natural factors contribute more, with sunshine duration and altitude being key factors. The boundary trend between the two dominant type areas is highly consistent with the “Heihe-Tengchong Line”. The eastern region is mostly dominated by human activity in conjunction with terrain factors, while the western region is predominantly influenced by natural factors, which enhance/weaken the change range of LST through mutual coupling with climate, terrain, vegetation, and other factors. This study offers valuable scientific references for addressing climate change, analyzing surface environmental patterns, and protecting the ecological environment.

Abstract

Harnessing energy from the sun is crucial for locations battling with energy poverty and generation, especially in Africa, where equity in energy distribution and generation is a daily challenge. However, the evaluation and analysis of solar radiation has been limited by the paucity of atmospheric data in the African region. This study used monthly downward surface solar radiation (SSRD) from ERA5 as reference data to evaluate simulations of solar radiation from CORDEX, CMIP5 and CMIP6 models spanning the period 1990−2020 (present-day), mid-future (2020−2050), and far-future (2070−2100) across 4 climatic zones (Coastal, Forest, Guinea and Sahel) in Nigeria. Solar radiation were found to be overestimated in the Guinea and Sahel zones of the country, but fairly good performance were made in the Coastal and Forest zones. CMIP5, CMIP6 and CORDEX individual models all exhibit strong agreement in the projection of solar dimming across the four climatic zones in the mid- and far-future under both RCP4.5/SSP5 \(-\) 4.5 and RCP8.5/SSP5 \(-\) 8.5 scenarios. However, under the RCP8.5/SSP5 \(-\) 8.5 the greatest magnitude of dimming ( \(-\,35 W/m^2\) ) was found in CMIP6 models in the far-future and ( \(-12 W/m^2\) ) in the mid-future. The projected solar dimming was also predominant in all climatic regions under SSP5 \(-\) 4.5 for CORDEX, CMIP5, and CMIP6 models but at a much lower magnitude.

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

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

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

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 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.