Climate, Volume 10, Issue 12 (December 2022) – 15 articles

Ocean conditions influence the economies and climate of West Africa. Based on the 30-year daily Optimum Interpolation Sea Surface Temperature (OISST) dataset during May–October, upwelling surface variability and marine heatwaves (MHWs) at the northern coast of the Gulf of Guinea are investigated. The cooling surface decreases more rapidly around Cape Palmas than around Cape Three Points and extends eastward. MHWs variability exhibits a frequent occurrence of such events since 2015 that is consistent with the observed oceanic warming and the decrease in upwelling surface. The empirical orthogonal functions performed on the annual cumulated intensity of MHWs show four variability modes that include the whole northern coast, an east–west dipole between the two capes, a contrast between the northern coast at the two capes and the meridional section east of 5° E, and a north–south opposition. These patterns show 3-year, 6-year, and 8-year trends, and are related to coastal upwelling at the northern coast of the Gulf of Guinea. Similarly, surface ocean and atmospheric conditions are modified according to MHW periods. These changes take place before, during, and after MHW events. These results could be used to understand how this change influences the marine ecosystem, the local fisheries resources, and the extreme rainfall episodes in West Africa. Full article

An analysis of spatiotemporal variability of air temperature and precipitation in the Volga Federal District (European Russia) between 1966 and 2021 was carried out. Based on data from 20 meteorological stations, relatively evenly located on the territory under consideration, the spatial distribution of average monthly and average annual air temperatures and monthly and annual precipitation was assessed; some indicators of the temporal variability of these variables in the period under consideration were calculated and analyzed. It was revealed that throughout the Volga Federal District, there was a tendency of climate warming in all months, and a slight increase in annual precipitation, except for the southeast of the district, where the precipitation trend was negative. It is noted that in the period 1955–1998, the number of negative air temperature anomalies was approximately equal to the number of positive ones; however, in the later period 1999–2021, the number of positive anomalies significantly exceeded the number of negative ones. Based on reanalysis data, climatic maps of vaporization and runoff in the Volga Federal District during 1966–2021 were created. The dependence of air temperature fluctuations on the nature of atmospheric circulation was revealed using the NAO, AO, and SCAND indices. On the example of the central part of the district (Republic of Tatarstan), some increase in summer aridity of the climate was revealed by using Budyko’s dryness index, Selyaninov’s hydrothermal coefficient, and Sapozhnikov’s humidification coefficient. The indicators of runoff and evaporation were also calculated using the methods of Schreiber and Ivanov. Against the background of the positive trend in vaporization rates, favorable conditions for a decrease in runoff were noted. Full article

For this study on urban climatology, the study area is the city of Balneário Camboriú, belonging to the Brazilian state of Santa Catarina (SC), located at 26°59′42″ south latitude and 48°37′46″ west longitude. As it is the most vertical city in the entire Southern Hemisphere, Balneário Camboriú was selected as the study area for the development of this climate analysis. Then, this study was concerned with analyzing the formation of urban heat islands throughout the daytime and nighttime in the city of Balneário Camboriú, Santa Catarina, Brazil, on some days in October 2020, from the perspective of the local climatic zones. Seven fixed sampling points and one official weather station were selected for this research. These points were selected in order to facilitate analysis of the climatic behaviour of the urban area throughout the day, comparing it with the other points, and also to verify possible changes in the local climate in the most diverse types of LCZ. At these same points, the Sky View Factor (SVF) measurements were taken. to elaborate the map of LCZ of Balneário Camboriú, the WUDAPT method was used. There was a great variation of the SVF between the collection points, and different LCZs were mapped, which contributed to the formation of urban heat islands whose maximum magnitude was 10.8 °C and islands with freshnesses of magnitudes of −4.5 °C. Full article

In this study, the Acısu Basin—viz., the headwater of the Gediz Basin—in Turkey, was modelled using three types of hydrological models and three different calibration algorithms. A well-known lumped model (GR4J), a commonly used semi-distributed (SWAT+) model, and a skillful distributed (mHM) hydrological model were built and integrated with the Parameter Estimation Tool (PEST). PEST is a model-independent calibration tool including three algorithms—namely, Levenberg Marquardt (L-M), Shuffled Complex Evolution (SCE), and Covariance Matrix Adoption Evolution Strategy (CMA-ES). The calibration period was 1991–2000, and the validation results were obtained for 2002–2005. The effect of the model structure and calibration algorithm selection on the discharge simulation was evaluated via comparison of nine different model-algorithm combinations. Results have shown that mHM and CMA-ES combination performed the best discharge simulation according to NSE values (calibration: 0.67, validation: 0.60). Although statistically the model results were classified as acceptable, the models mostly missed the peak values in the hydrograph. This problem may be related to the interventions made in 2000–2001 and may be overcome by changing the calibration and validation periods, increasing the number of iterations, or using the naturalized gauge data. Full article

Climate variabilities over the period of 80 years (1930–2010) are analyzed by the combined use of divergence measures and rank correlation. First, on the basis of a statistical linguistics procedure, the m-th order differences of the monthly mean precipitations and temperatures on the globe are symbolized according to a binary coding rule. Subsequently, the annual 12-bit binary sequence for a station is divided into twelve 6-bit sequences by scanning it over a year. Computed results indicate that there is an optimal order of differences with which one can reveal the variabilities most distinctly. Specifically, it is found that for the analysis of precipitations, the second differences (m = 2) are most useful, whereas, for the temperatures, the third differences (m = 3) are preferable. A detailed comparison between the information-theoretic and the ranking methods suggests that along with the stability and coherence, owing to its ability to make an appeal to the eyes, the latter is superior to the former. Full article

Natural disasters have become more frequent and intense over the last decade mainly as a result of poor water and land management. Cultural sites and monuments are extremely vulnerable to natural disasters, particularly floods, while mitigation measures and protective infrastructure are difficult to construct within such areas. In the present study, the precipitation trends of the recent past and over the next 80 years were analyzed for the old town of Corfu (UNESCO World Heritage Site) in order to identify potentially significant changes that may affect the flood risk of the area. Moreover, a multi-criteria analysis using GIS software was used to identify high flood hazard zones in this living monument in order to propose specific mitigation measures that are in line with the characteristics of the site. The main effort in this study was to find a methodological approach for a fast but reliable assessment of future changes in the flood risk of historic monuments without the need for a hydrodynamic model and with a limited amount of locally based data. With the selected approach, a good indication of the potential changes in flood risk was provided, according to climate scenarios and simple, physically-based geostatistical models. The results indicate that no significant changes in the flood risk were found for the future climatic conditions, and the identified flood-prone areas will remain approximately the same as today in this particular historic monument. The uncertainty that is included in this output originates mainly from the inherent errors in climate modeling and from the non-high temporal resolution of the data. Full article

The Sahel is one of the regions with the highest rates of food insecurity in the world. Understanding the driving factors of agricultural productivity is, therefore, essential for increasing crop yields whilst adapting to a future that will be increasingly dominated by climate change. This paper shows how meteo-climatic variables, combined with fertilizers’ application rates, have affected the productivity of two important crops in the Sahel region, i.e. maize and millet, over the last three decades. To this end, we have applied a specifically designed neural network tool (optimised for analysis of small datasets), endowed with feed-forward networks and backpropagation training rules and characterised by generalised leave-one-out training and multiple runs of neural network models in an ensemble strategy. This tool allowed us to identify and quantify the impacts of single drivers and their linear and nonlinear role. The variables analysed included temperature, precipitation, atmospheric CO₂ concentration, chemical and organic fertilizer input. They explained most of the variance in the crop data (R² = 0.594 for maize and R² = 0.789 for millet). Our analysis further allowed us to identify critical threshold effects affecting yields in the region, such as the number of hours with temperature higher than 30 °C during the growing season. The results identified heat waves and fertilizer application rates playing a critical role in affecting maize and millet yields in this region, while the role of increasing CO₂ was less important. Our findings help identify the modalities of ongoing and future climate change impacts on maize and millet production in the Sahel. Full article

The number of people owning a private weather station (PWS) and sharing their meteorological measurements online is growing worldwide. This leads to an unprecedented high density of weather observations, which could help monitor and understand small-scale weather phenomena. However, good data quality cannot be assured and thorough quality control is crucial before the data can be utilized. Nevertheless, this type of data can potentially be used to supplement conventional weather station networks operated by National Meteorological & Hydrological Services (NMHS), since the demand for high-resolution meteorological applications is growing. This is why EUMETNET, a community of European NMHS, decided to enhance knowledge exchange about PWS between NMHSs. Within these efforts, we have collected information about the current interest in PWS across NMHSs and their experiences so far. In addition, this paper provides an overview about the data quality challenges of PWS data, the developed quality control (QC) approaches and openly available QC tools. Some NMHS experimented with PWS data, others have already incorporated PWS measurements into their operational workflows. The growing number of studies with promising results and the ongoing development of quality control procedures and software packages increases the interest in PWS data and their usage for specific applications. Full article

Climate change is evident around the globe, which requires bold actions now to achieve UN-SDGs and Paris Agreement. The water sector is dominated by public finance and is almost subsidised. In addition, there is an increased risk perception surrounding climate investments in developing countries. Pricing climate risks is a daunting challenge for investors and the private sector, who must estimate the likelihood of various climate scenarios and their implications for physical, liability and transition risks at the firm, project, national, and regional scales. In addition, there is a building momentum to scale up global climate response. To translate this momentum into action will require significantly greater investments, investments in a different set of inclusive assets that address water security, mobilise the private sector and provides sector-based or economy-wide co-benefits to direct and indirect beneficiaries, e.g., job creation, health benefits, improved resilience and scaling knowledge and harmonise data and methodologies. Notably, climate–water finance is facing a dual challenge. It will have to both reduce the present water infrastructure financing gap and ensure that this new infrastructure/asset is low-carbon, resilient to climate change, and meets the goals of the UNFCCC and the Paris Agreement. Therefore, there is a need for a paradigm shift in the way how water asset is defined, developed, and financed. This paper presents this novel approach concept and its content and financial structure that enable treating water as a new asset class to enable private sector investment and ensure providing water for domestic, municipal, and industrial purposes and allows municipalities to scale their water reuse, sanitation, and desalination projects in partnership with the private sector and/or governments. It is increasingly important to treat water as a new asset class, particularly as nations around the world (particularly developing countries) are set to experience an anticipated 40% shortfall in water by 2030 due to climate change, economic recovery and growth, population growth and resource competition. Investment in water could be one of the ways of tackling this deficit by treating water as a new asset class. Full article

In Peninsular India, the Krishna River basin is the second largest river basin that is overutilized and more vulnerable to climate change. The main aim of this study is to determine the future projection of monthly streamflows in the Krishna River basin for Historic (1980–2004) and Future (2020–2044, 2045–2069, 2070–2094) climate scenarios (RCP 4.5 and 8.5, respectively), with the help of the Soil Water and Assessment Tool (SWAT). SWAT model parameters are optimized using SWAT-CUP during calibration (1975 to 1990) and validation (1991–2003) periods using observed discharge data at 5 gauging stations. The Cordinated Regional Downscaling EXperiment (CORDEX) provides the future projections for meteorological variables with different high-resolution Global Climate Models (GCM). Reliability Ensemble Averaging (REA) is used to analyze the uncertainty of meteorological variables associated within the multiple GCMs for simulating streamflow. REA-projected climate parameters are validated with IMD-simulated data. The results indicate that REA performs well throughout the basin, with the exception of the area near the Krishna River’s headwaters. For the RCP 4.5 scenario, the simulated monsoon streamflow values at Mantralayam gauge station are 716.3 m³/s per month for the historic period (1980–2004), 615.6 m³/s per month for the future1 period (2020–2044), 658.4 m³/s per month for the future2 period (2045–2069), and 748.9 m³/s per month for the future3 period (2070–2094). Under the RCP 4.5 scenario, lower values of about 50% are simulated during the winter. Future streamflow projections at Mantralayam and Pondhugala gauge stations are lower by 30 to 50% when compared to historic streamflow under RCP 4.5. When compared to the other two future periods, trends in streamflow throughout the basin show a decreasing trend in the first future period. Water managers in developing water management can use the recommendations made in this study as preliminary information and adaptation practices for the Krishna River basin. Full article

The assessment of the vulnerability to drought hazards in smallholder farming systems dependent on rain-fed agriculture has recently gained global popularity, given the need to identify and prioritize climate hotspots for climate adaptation. Over the past decade, numerous studies have focused on vulnerability assessments with respect to drought and other meteorological hazards. Nonetheless, less research has focused on applying common measurement frameworks to compare vulnerability in different communities and the sources of such vulnerability. Yet, the crucial question remains: who is more vulnerable and what contributes to this vulnerability? This article is a case study for assessing the vulnerability to drought of smallholder farmers in two wards in Chivi district, Masvingo Province, Zimbabwe. This study is timely, as climate change is increasingly affecting populations dependent on rainfed agriculture. This assessment has been conducted by calculating the Livelihood Vulnerability Index (LVI) and Livelihood Vulnerability Index of the Intergovernmental Panel on Climate Change (LVI-IPCC). This empirical study used data from 258 households from the two wards and triangulated it through Key Informant Interviews and Focus Group Discussions. To calculate the LVI, twenty-six subcomponents made up of seven major components, including socio-demographic variables; livelihood strategies; social capital; access to food, health, and water; and exposure to drought, were considered. To calculate the LVI-IPCC, we combined the three contributing factors of vulnerability (exposure, sensitivity, and adaptive capacity). Our results indicate that the LVI forward 14 is statistically higher than for ward 19 (F = 21.960; p ≤ 0.01) due to high exposure to drought, food insecurity, and compromised social networks. Concerning the LVI-IPCC, ward 14 was significantly more vulnerable to the impacts of drought than ward 19 (F = 7.718; p ≤ 0.01). Thus, reducing exposure to drought through early warning systems, building diversified agricultural systems, and social networks are of high priority to reduce the vulnerability of the farmers. Full article

In an effort to improve their quality of life and battle poverty, many urban residents are turning to agriculture as an alternative source of income, employment, and food security. However, climate-related hazards such as heatwaves, floods, and droughts have had an effect on urban agriculture. The purpose of this study was to determine how climate change-related hazards affected the urban livestock industry in Masvingo City. These researchers administered a structured questionnaire on urban livestock farmers, the results of which were triangulated with in-depth interviews with livestock stakeholders. The results show that the urban livestock industry is significantly impacted by climate-related hazards. Farmers lose livestock to diseases, poor pastures, and extreme weather conditions. Furthermore, the hazards badly affect the storage and distribution of livestock products, the labour supply and productivity, and the profitability of livestock enterprises. This study contributes to the body of knowledge on the urban livestock industry and climate change-related hazards. The results are significant to policy makers and livestock stakeholders to understand climate change effects on the urban livestock sector so as to formulate mitigation, adaptation, and coping strategies against any adverse effects. This paper is a foundation for future studies and these researchers suggest that future studies be on location-specific adaptation strategies. Full article

This perspective paper highlights the potentials, limitations, and combinations of openly available Earth observation (EO) data and big data in the context of environmental research in urban areas. The aim is to build the resilience of informal settlements to climate change impacts. In particular, it highlights the types, categories, spatial and temporal scales of publicly available big data. The benefits of publicly available big data become clear when looking at issues such as the development and quality of life in informal settlements within and around major African cities. Sub-Saharan African (SSA) cities are among the fastest growing urban areas in the world. However, they lack spatial information to guide urban planning towards climate-adapted cities and fair living conditions for disadvantaged residents who mostly reside in informal settlements. Therefore, this study collected key information on freely available data such as data on land cover, land use, and environmental hazards and pressures, demographic and socio-economic indicators for urban areas. They serve as a vital resource for success of many other related local studies, such as the transdisciplinary research project “DREAMS—Developing REsilient African cities and their urban environMent facing the provision of essential urban SDGs”. In the era of exponential growth of big data analytics, especially geospatial data, their utility in SSA is hampered by the disparate nature of these datasets due to the lack of a comprehensive overview of where and how to access them. This paper aims to provide transparency in this regard as well as a resource to access such datasets. Although the limitations of such big data are also discussed, their usefulness in assessing environmental hazards and human exposure, especially to climate change impacts, are emphasised. Full article

Climate change makes the climate system of a given region unpredictable and increases the risk of water-related problems. GCMs (global climate models) help in understanding future climate conditions over a given region. In this study, 12 GCMs from the CMIP6 (coupled model intercomparison project six) were evaluated and ranked based on their abilities to describe the historical observed series. The ensemble mean of bias-adjusted best five models of average annual precipitation showed an increment with an uncertainty range of (2.0–11.9) and change in the mean of 6.4% for SSP2-4.5 and (6.1–16.1) 10.6% for SSP5-8.5 in 2040–2069 relative to the historical period. Similarly, for 2070–2099, increments of (2.2–15.0) 7.9% and (11.8–29.4) 19.7% were predicted for the two scenarios, respectively. The average annual maximum temperature series showed increments of (1.3–2.0) 1.6 °C for SSP2-4.5 and (1.7–2.3) 2.0 °C for SSP5-8.5 in 2040–2069. At the same time, increments of (1.7–2.3) 2.0 °C and (2.8–3.2) 3.0 °C were predicted for 2070–2099. Furthermore, it was predicted that the average annual minimum temperature series will have increments of (1.6–2.3) 2.0 °C and (2.2–2.9) 2.5 °C for 2040–2069 and (2.1–2.7) 2.4 °C and (3.7–4.2) 4.0 °C for 2070–2099 for the two scenarios, respectively. An increase in precipitation with increased land degradation in the sub-basin results in a higher risk of flood events in the future. Improved soil and water conservation practices may minimize the adverse impacts of future climate change on the loss of agricultural productivity. Full article