Climate Variability Research Articles

Effect of Vegetation Growth, Agricultural Irrigation and Climatic Variability on Streamflow in Wujiang, China

Vegetation restoration and farmland irrigation are important environmental factors affecting the water cycle process in basins. Analyzing the impact of vegetation restoration and farmland irrigation on runoff is an international frontier and hot topic in current research, which is crucial for the management and protection of water resources, especially for the ecological protection and high-quality development of basins. Based on the normalized difference vegetation index (NDVI), effective irrigated area (EIA), and meteorology and hydrology data for Wujiang River (WJR), this research aims to quantitatively calculate the influence degree of vegetation recovery, agricultural irrigation, and climatic variability on discharge alteration in WJR. First, Mann–Kendall and Pettitt approaches were used for recognizing the mutation year of streamflow data at Wulong station from 1982 to 2015. Then, a corrective Budyko model was built by constructing multiple linear regression equations for the NDVI, climate factors, EIA, and Budyko parameters. Finally, the corrective Budyko model was adopted to reveal how vegetation restoration, agri-cultural irrigation, and climate variation influence discharge alteration in WJR. The results showed the following: (1) Both runoff depth (R) and rainfall (Pr) exhibited a non-significant de-clining tendency, while potential evapotranspiration (ET0) demonstrated a non-significant in-creasing tendency. The NDVI and EIA both demonstrated a notable upward tendency (p<0.01). (2) The mutation year of discharge in WJR was 2004. (3) The underlying surface parameters ω have a strong correlation with vegetation, agricultural irrigation, and climate factors. (4) The contribution of rainfall (Pr), potential evapotranspiration (ET0), NDVI, EIA, and human activities to runoff depth in WJR were 57.34%, 24.67%, -11.75%, 11.71%, and 18.02%, respectively. This re-search is helpful for elucidating the effects of ecological construction measures and agricultural irrigation on streamflow in WJR, and offers great scientific significance and practical value for understanding the evolution mechanism of water circulation and for managing regional water resources.

ECOLOGICAL NICHES AND IMPLICATIONS FOR REFORESTATION OF PTEROCARPUS ERINACEUS, KHAYA SENEGALENSIS AND ISOBERLINIA DOKA IN THE CLASSIFIED FORESTS OF NORTHERN COTE DIVOIRE

Deforestation in Cote dIvoire, particularly in the Pale and Pouniakele classified forests, threatens biodiversity and essential ecosystem services. This study explores the potential of Pterocarpus erinaceus, Khaya senegalensis, and Isoberliniadoka to restore degraded ecosystems through reforestation projects, using maximum entropy modeling. The objective is to analyze the ecological niches of these species in order to optimize reforestation strategies. The results show that Pterocarpus erinaceus has a high tolerance to climate variations, with an average AUC of 0.878, and a concentrated distribution in the central and northern savannahs. The main factors influencing its presence are the standard deviation of temperatures (47%) and humidity (15.4%). Khaya senegalensis, on the other hand, is well adapted to open environments with low vegetation cover and high climate variability. Its mean AUC of 0.893 indicates a robust prediction, with areas of presence in the central-eastern part of the country. Major contributions include temperature standard deviation (21.4%) and NDVI (18.5%). Finally, Isoberliniadoka shows greater sensitivity to stable climatic conditions, with a mean AUC of 0.848 and a presence limited to northern regions, where forest cover (32.1%) and mean temperature (39.7%) are the dominant variables. Ecological differences are observed between species, mainly influenced by climate variability and vegetation density. This implies the importance of adapting reforestation programs to the environmental specificities of each species. This study offers practical recommendations to improve the sustainability of reforestation projects in Cote dIvoire, taking into account local ecological challenges.

APPLICATION OF GLOBAL CIRCULATION MODELS (GCMS) TO PREDICT RAINFALL AND TEMPERATURE VARIABILITIES IN KANO STATE, NIGERIA

The study explored present and future climate scenarios of Kano using Global Circulation Models (GCMs). The metrological data from 12 locations were used for calibration of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The historical record of 10 years was used for both temperatures and rainfall data which represent a period of 2010 to 2019. The future climate projections were made for 2041 to 2060 centered in the 2050s under RCP2.6 and RCP8.5 emission scenarios. The spatial analysis was conducted using the Inverse Distance Weighted (IDW) tool in an ArcGIS environment. The result shows that the temperature of Kano has increased relative to baseline condition by 1.025oC (2.45%) and 4.6 oC (10.98%) under RCP2.6 and RCP8.5 scenarios respectively. However, the annual rainfall was found to decrease from the historical amount by -37.9 mm (5%), and -68.2 mm (9%) under RCP2.6 and RCP8.5 emission scenarios. Moreover, the spatial analysis indicated that there was climate variability across the width and breadth of the state. The study concludes that the climate change effect will cause the temperature of Kano to rise considerably in the future with the consequences of decreased rainfall amount. The findings recommend that policymakers should deploy adaptation strategies to reduce the adverse effects that might be experienced.

The Impact of Climate Variability on Cattle Heat Stress in Vanuatu

Heat stress is a climate extreme that impacts cattle health, fertility, feed intake, production, and well-being. In Vanuatu, the beef industry is crucial to local livelihoods and the nation’s economy, thus the objective of this study was to examine the impact of heat stress on cattle health and production. This study uses the Heat Load Index (HLI) and Accumulated Heat Load (AHL) as proxies to assess the impact of heat stress on cattle in Vanuatu over a 30-year period (1994–2023), using the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) data. The analysis examines historical patterns of heat stress in cattle across Vanuatu, identifying more instances of heat stress occurring during the wet season due to characteristically elevated temperatures, humidity, and low wind speeds. Findings also suggest that El Niño events may increase the intensity and duration of heat stress events. These insights inform the development of an Early Warning System for heat stress in cattle, establishing a crucial foundation for targeted adaptation strategies aimed at enhancing the resilience and sustainability of Vanuatu’s beef industry to climate variability and change.

Runoff Change Characteristics and Response to Climate Variability and Human Activities Under a Typical Basin of Natural Tropical Rainforest Converted to Monoculture Rubber Plantations

Climate variability and human activities are major influences on the hydrological cycle. However, the driving characteristics of hydrological cycle changes and the potential impact on runoff in areas where natural forests have been converted to rubber plantations on a long-term scale remain unclear. Based on this, the Mann–Kendall (MK) and Pettitt breakpoint tests and the Double Mass Curve method were employed to identify the variation characteristics and breakpoints of precipitation (P), potential evapotranspiration (ET0), and runoff depth (R) in the Wanquan River Basin (WQRB) during the 1970–2016 period. The changes in runoff attributed to P, ET0, and the catchment characteristics parameter (n) were quantified using the elastic coefficient method based on the Budyko hypothesis. The results revealed that the P and R in the WQRB exhibited statistically insignificant decreasing trends, while ET0 displayed a significant increasing trend (p < 0.05). The breakpoint of runoff changes in the Jiabao and the Jiaji stations occurred in 1991 and 1983, respectively. The runoff changes show a negative correlation with both the n and ET0, while exhibiting a positive correlation with P. Moreover, it is observed that P and ET0 display higher sensitivity towards runoff changes compared to n. The decomposition analysis reveals that in the Dingan River Basin (DARB), human activities account for 53.54% of the runoff changes, while climate variability contributes to 46.46%. In the Main Wanquan River Basin (MWQRB), human activities contribute to 46.11%, whereas climate variability accounts for 53.89%. The research findings suggest that runoff is directly reduced by climate variability (due to decreased P and increased ET0), while human activities indirectly contribute to changes in runoff through n, exacerbating its effects. Rubber forest stands as the prevailing artificial vegetation community within the WQRB. The transformation of natural forests into rubber plantations constitutes the primary catalyst for the alteration of n in the WQRB. The research findings provide important reference for quantifying the driving force of hydrological changes caused by deforestation, which is of great significance for sustainable management of forests and water resources.

Climate change and epidemiological trends of malaria and Lassa fever transmission in Nigeria (2015-2022): A systematic review and meta-analysis

Background: Climate change exacerbates infectious disease transmission in Nigeria, a country with a high burden of infectious diseases. Objective: To conduct a systematic review and meta-analysis examining the relationship between climate variability and infectious disease transmission in Nigeria. Methods: We systematically searched major databases, identifying 25 studies, with 11 studies meeting the inclusion criteria for meta-analysis using R version 4.4.1. Results: The meta-analysis revealed significant associations between temperature increases and malaria (pooled effect estimate = 1.034, 95% CI: 1.016-1.052) and Lassa fever (pooled effect estimate = 1.029, 95% CI: 1.015-1.043) transmission. Egger's funnel plot showed no significant publication bias, and sensitivity analysis confirmed the robustness of the findings. Conclusion: This study provides evidence for the association between climate variability and infectious disease transmission in Nigeria. This underscores the urgent need for targeted interventions to protect vulnerable populations, including children under 5, pregnant women, the elderly, people with comorbidity, and low-income communities.

Assessment of catchment water resources allocation under climate change in Luwombwa sub-catchment, Zambia

The uncertainty in climate change and high water demand pose pressure on the natural water resources supply. Not only does this require better understanding but also a call for immediate interventions, mitigation and adaptive measures. This study evaluates catchment water resources in the Luwombwa sub-catchment in Zambia through statistical analysis in the downscaling of past, present and future climatic variables from the CMIP6 climatic model. These variables are then integrated into WEAP - a semi-distributed hydrological and water evaluation model - to perform water demand and allocation scenario modelling. Mult-site calibration and validation were conducted on five selected micro-catchments within Luwombwa sub-catchment. The model performance was assessed usng the R2, NSE and PBIAS as the objective functions. Satisfactory values of 92 % for R2, 82 % for NSE and 6.9 % for PBIAS were achieved. This allowed for scenario modelling on water demand and allocation among competing users. Three future scenarios (2022–2050) were developed from the historical to baseline (1988–2022) and included state of water resources availability under climate change, expansion of irrigation area and impact of dam construction in the sub-catchment. The study reveals a decrease of 20 % in sub-catchment's water availability resulting from 9.3 % (equivalent to 4oC) rise in maximum temperature and 4.5 % reduction in rainfall within the entire sub-catchment. This is especially under the persistence of SSP370 climate variability scenario projections downscaled from four GCM models by the year 2050. The study further revealed that the change point for anticipated future climate extremes is likely to occur between 2027 and 2030. The results are indicative of downward trends in streamflow under climate change and socioeconomic development leading to increase in water value and water scarcity. The insights from the study are critical to inform formulation of effective catchment water resources management strategies such as the development of management plans and adapation measures in the face of climate change and the needs for different stakeholders involvement.

Simulation of Intra-seasonal Climatic Variability, Potential Yield and Yield Gap of Sugarcane for the South Gujarat Region, India

The validated CANEGRO (DSSAT v4.7) model was used to simulate the intra-seasonal climatic variation viz. temperature (-3oC to +3oC) and rainfall (-25% to +25%) variability in sugarcane yield and quantifying the yield gap of sugarcane at Navsari, Bharuch, Surat and Valsad districts of south Gujarat region under the various management options included dates of planting, irrigations and fertilizers application and compared with the reported yield of the districts. The cane yield increased with an increase in rainfall and a decrease in temperature. In contrast, the cane yield decreased with a decrease in rainfall and an increase in temperature. The extent of effect was found to vary with crop growth stages. The potential yield of sugarcane simulated by the model in different districts was found to vary between 107.0 to 114.7 t ha-1, while the reported yield in these districts varied between 68.1 to 72.0 t ha-1. This resulted in a total yield gap between 38.9 to 45.5 t ha-1 in different districts. The highest yield gap was in Navsari district while the lowest yield gap was found in Bharuch district.

Long-term analysis of reference evapotranspiration variations in the lower Bhavani basin

Evapotranspiration is a crucial component of the hydrological cycle and is profoundly influenced by climate change. Assessing regional evapotranspiration changes is essential for effective water resource management. The study investigated the dynamics of reference evapotranspiration in the Lower Bhavani Basin, a region with diverse landscapes increasingly vulnerable to climatic variability. Reference evapotranspiration data from the Food and Agriculture Organization using the AgERA5 dataset was analyzed to assess annual and seasonal trends from 2000 to 2022. The result showed that the mean yearly reference evapotranspiration in the basin is 1672.87 mm, with a significant decline of 3.53 mm per year. Seasonal analysis revealed a consistent decreasing trend across all seasons, with the southwest monsoon season showing the most significant reduction in evapotranspiration rates. The notable shift in the annual evapotranspiration rate was observed in 2016, highlighting the impact of climate change. Further, temperature, solar radiation and relative humidity were identified as the dominant factors influencing evapotranspiration rates in the basin. The relative moisture index indicated prevalent dry conditions, making the region susceptible to water stress. The findings provide critical insights for regional water resources management and accentuate the need for sustainable water management strategies to mitigate the impacts of climate change.

Carbon Sequestration in a Changing Climate: Management Techniques and Strategic Solutions

Carbon sequestration in agriculture plays a critical role in mitigating climate change by capturing and storing atmospheric carbon dioxide (CO2) in soil and biomass. This process involves various strategies and management practices aimed at enhancing soil organic carbon (SOC) levels while maintaining or improving agricultural productivity. Key strategies include no-tillage or reduced tillage, which minimizes soil disturbance and promotes residue retention, thus slowing down decomposition and enhancing carbon storage. Crop rotation and the use of cover crops further contribute to carbon sequestration by diversifying root structures and improving soil health. Organic farming practices, such as applying farmyard manure and compost, provide essential organic matter that enriches soil quality and increases carbon retention. Additionally, the incorporation of deep-rooted plants and biochar enhances carbon storage in deeper soil layers, making them more resilient to climate variability. The integration of these practices not only sequesters carbon but also improves soil fertility, water retention, and overall ecosystem health. Effective management of carbon sequestration in agricultural systems requires a holistic approach that considers local conditions, crop diversity, and sustainable land use practices. By adopting these strategies, agriculture can become a significant carbon sink, contributing to global efforts to reduce atmospheric CO2 concentrations while ensuring food security and environmental sustainability.

APPLICATION OF GEO-INFORMATICS IN THE MAPPING OF AREAS VULNERABLE TO SURFACE WATER STRESS IN KADUNA STATE, NIGERIA

The main purpose of this paper is to identify water-stressed areas as a response to land use/land cover (LULC), climate variability, and increasing population in Kaduna State using Remote Sensing and Geographic Information systems. To achieve this aim, the paper set out to identify/map out areas vulnerable to water stress in the study area. The Meteorological data for the study were sourced from twelve remotely sensed stations around Kaduna State for thirty-seven years (1984 to 2021), land use data from remotely sensed satellite imageries that covered thirty-seven years, and projected population for thirty-seven years respectively. There are several indices used to compute water stress and the paper opted for indices that could monitor climate variability and surface water resources. Thereafter, the six indicators were standardized, weighted, and aggregated in Microsoft Excel, and imported into the ArcGIS 10.2.1 environment, and a weightier vulnerability model analysis was used to determine the weights of each variable’s contribution to water stress. The result achieved indicated that: 54.41% of the entire study area is vulnerable to water stress, 15.80% is moderately vulnerable, 26.23% is less vulnerable and 3.56% is not vulnerable to water stress within the period under study. Furthermore, Birnin Gwari, Chikun, Igabi, Ikara, and Kajuru Local Government Areas (LGAs) were the most vulnerable areas in the State while not vulnerable areas are; Kauru, Kagarko, and Jaba LGAs. The paper recommends the implementation of water conservation measures which include the creation of wetlands and shelter belts in areas vulnerable to water stress.

Trends in Vegetation Seasonality in the Iberian Peninsula: Spatiotemporal Analysis Using AVHRR-NDVI Data (1982–2023)

Vegetation seasonality is a critical indicator of ecological responses to global climate change, especially in the Iberian Peninsula, where the intersection of human activity and climate variability amplifies these effects. Understanding these changes is vital for adopting ecogeographical sustainability and developing effective climate adaptation strategies. This study examines trends in vegetation seasonality in the Iberian Peninsula from 1982 to 2023, based on weekly AVHRR NDVI data (2184 images). By integrating Seasonal Trend Analysis (STA) with Robust Trend Analysis (RTA)—including the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test (α = 0.05), and false discovery rate (FDR) control—we identified significant phenological shifts and widespread vegetation greening. The results reveal a regional response to global patterns of climate change, with 94.2% of the study area exhibiting significant trends, particularly in the Mediterranean ecoregion, where earlier growing seasons are becoming increasingly common. These shifts highlight the urgent need for sustainable land and resource management in the face of accelerating global change. Our findings provide critical insights into the ecological dynamics of the Iberian Peninsula, offering a robust foundation for formulating policies that promote environmental sustainability and enhance resilience to climate change.

How does a change in climate variability impact the Greenland ice sheet surface mass balance?

Abstract. Given the long response time of ice sheets, simulations of the Greenland ice sheet typically exceed the availability of input climate data to reliably simulate the fast processes underlying surface mass balance. Strong feedback processes are known to make the mass balance sensitive to inter- and intra-annual variability. Even simulations with climate models do not always cover the full period of interest, motivating bridging these gaps using relatively coarsely resolved climate reconstructions or temporal interpolation methods. However, both of these approaches usually only provide information about the climatological average but not variability. We investigate how this simplification impacts the surface mass balance using the BErgen Snow SImulator. The model was run for up to 500 years using the same atmospheric climatology but different synthetic variabilities. While changing inter-annual variations has an impact of less than 5 % on the surface mass balance of the Greenland ice sheet, neglecting intra-annual variability by using a daily climatology causes a 40 % change in mass balance. Decomposing the total effect into contributions from different input variables, the biggest contributor is precipitation followed by temperature. Using a daily climatology, a small amount of snowfall every day overestimates the albedo and thus surface mass balance (SMB). We propose a correction that re-captures the effect of intermittent precipitation, reducing the SMB overestimation to 15 %–25 %. We conclude that simulations of the Greenland surface mass and energy balance should be forced with a transient climate, in particular for models that are calibrated with transient data.

Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables

Despite the implementation of various initiatives, dengue remains a significant public health concern in Malaysia. Given that dengue has no specific treatment, dengue prediction remains a useful early warning mechanism for timely and effective deployment of public health preventative measures. This study aims to develop a comprehensive approach for forecasting dengue cases in Selangor, Malaysia by incorporating climate variables. An ensemble of Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM), and Susceptible-Infected mosquito vectors, Susceptible-Infected-Recovered human hosts (SI-SIR) model were used to establish a relation between climate variables (temperature, humidity, precipitation) and mosquito biting rate. Dengue incidence subject to climate variability can then be projected by SI-SIR model using the forecasted mosquito biting rate. The proposed approach outperformed three alternative approaches and expanded the temporal horizon of dengue prediction for Selangor with the ability to forecast approximately 60 weeks ahead with a Mean Absolute Percentage Error (MAPE) of 13.97 for the chosen prediction window before the implementation of the Movement Control Order (MCO) in Malaysia. Extended validation across subsequent periods also indicates relatively satisfactory forecasting performance (with MAPE ranging from 13.12 to 17.09). This research contributed to the field by introducing a novel framework for the prediction of dengue cases over an extended temporal range.

EXPLORING CLIMATIC PATTERNS AMONG BRAZILIAN CAPITALS

This study aims to examine the patterns of climatic variables and delineate climatically homogeneous regions among Brazilian capitals using Principal Component Analysis (PCA) and clustering techniques. Annual climate data from 26 state capitals and the Federal District of Brazil were analyzed, covering the period from 1958 to 2022, representing different regions of the country. The PCA results revealed that the first three principal components accounted for 91.8% of the total data variability. Four climatically homogeneous regions were identified: the East region, the Northeast region, the North region and another region that comprises the rest of the country. Cluster Analysis (CA) also confirmed the formation of four homogeneous groups among municipalities based on climatic variables. The analysis of spatial distribution showed significant variations in annual climate variables between Brazilian capitals, with maximum values observed for maximum and minimum temperatures, precipitation, wind speed, relative humidity, evapotranspiration and soil moisture. These findings contribute to a deeper understanding of climate variability in Brazilian capitals and provide a basis for future studies on climate dynamics and regional impacts. Keywords: Principal Component Analysis, Cluster Analysis, Multivariate Analysis; Biplot

Dynamics of Sandy Shorelines and Their Response to Wave Climate Change in the East of Hainan Island, China

Beach erosion and shoreline dynamics are strongly affected by alterations in nearshore wave intensity and energy, especially in the context of global climate change. However, existing works do not thoroughly study the evolution of the sandy coasts of eastern Hainan Island, China, nor their responses to wave climate change driven by climate variability. This study focuses on the open sandy coast and assesses shoreline evolutionary dynamics in response to wave climate variability over a 30-year period from 1994 to 2023, using an open-source software toolkit that semi-automatically identify the shorelines (CoastSat v2.4) and reanalysis wave datasets (ERA5). The shorelines of the study area were extracted from CoastSat, and then tidal correction and outlier correction were performed for clearer shorelines. Combining the shoreline changes and wave conditions derived from ERA5, the dynamics of the shorelines and their response to wave climate change were further studied. The findings reveal that the average long-term shoreline change rate along the eastern coast of Hainan Island is 0.03 m/year, with 44.8% of transects experiencing erosion and 55.2% showing long-term accretion. And distinct evolutionary patterns emerge across different sections. Interannual variability is marked by alternating erosion and siltation cycles, while most sections of the coast experiences clear seasonal fluctuations, with accretion typically occurring during summer and erosion occurring in winter. El Niño–Southern Oscillation (ENSO) cycles drive changes in parameters including significant wave height, mean wave period, wave energy flux, and mean wave direction, leading to long-term changes in wave climate. The multi-scale behavior of the sandy shoreline responds distinctly to the ongoing changes in wave climate triggered by ENSO viability, with El Niño events typically resulting in accretion and La Niña periods causing erosion. Notably, mean wave direction is the metric most closely linked to changes in the shoreline among all the others. In conclusion, the interplay of escalating anthropogenic activities, natural processes, and climate change contributes to the long-term evolution of sandy shorelines. We believe this study can offer a scientific reference for erosion prevention and management strategies of sandy beaches, based on the analysis presented above.

Quantifying Residual Soil Moisture through Empirical Orthogonal Functions Analysis to Support Legume-Based Cropping Systems

AbstractThis study investigates spatiotemporal variability of residual soil moisture during the OND (October-November-December) season in Ethiopia and its implications for crop productivity. Employing advanced statistical techniques, we analyze spatial and temporal distribution of soil moisture across Ethiopia from 1981 to 2020, focusing on selected crops including legumes: chickpea, field peas, common bean, soybean and alfalfa, to assess the potential of residual moisture to support post-rainy season cropping. Results indicate pronounced east-west moisture gradients, with eastern regions of Ethiopia exhibiting lower moisture levels (< 60 kg.m-2) compared to western regions (> 150 kg.m-2). The central highlands, which are pivotal for agricultural activities, demonstrate significant variability in moisture (standard deviations > 25 kg.m-2), with implications on agricultural sustainability. The northern and southeastern tips of the country are particularly vulnerable to prolonged drought, where climate change and frequent dry spells exacerbate moisture deficits, consequently impacting crop productivity. Despite these challenges, promising opportunities for future crop production emerge in the southeastern region, which is characterized by increasing moisture trend over time ($$\:\tau\:=0.59$$). Findings further indicate that residual moisture adequately meets and supports crop water requirements in the western, central, and southwestern Ethiopia. In these regions, residual moisture supports more than 90% of cropland water requirements of various crops during the initial and late-season growth stages, whereas water requirement coverage drops to less than 20% during the mid-season growth stage. Therefore, by utilizing residual soil moisture alongside supplemental irrigation, Ethiopian farmers can meet crop water needs for double cropping and enhance resilience to climate variability.

The influence of malaria control interventions and climate variability on changes in the geographical distribution of parasite prevalence in Kenya between 2015 and 2020

BackgroundThe burden of malaria in Kenya was showing a declining trend, but appears to have reached a plateau in recent years. This study estimated changes in the geographical distribution of malaria parasite risk in the country between the years 2015 and 2020, and quantified the contribution of malaria control interventions and climatic/ environmental factors to these changes.MethodsBayesian geostatistical models were used to analyse the Kenyan 2015 and 2020 Malaria Indicator Survey (MIS) data. Bivariate models were fitted to identify the most important control intervention indicators and climatic/environmental predictors of parasitaemia risk by age groups (6–59 months and 5–14 years). Parasitaemia risk and the number of infected children were predicted over a 1 × 1 km2 grid. The probability of the decline in parasitaemia risk in 2020 compared to 2015 was also evaluated over the gridded surface and factors associated with changes in parasitaemia risk between the two surveys were evaluated.ResultsThere was a significant decline in the coverage of most malaria indicators related to Insecticide Treated Nets (ITN) and Artemisinin Combination Therapies (ACT) interventions. Overall, there was a 31% and 26% reduction in malaria prevalence among children aged < 5 and 5–14 years, respectively. Among younger children, the highest reduction (50%) and increase (41%) were in the low-risk and semi-arid epi zones, respectively; while among older children there was increased risk in both the low-risk (83%) and semi-arid (100%) epi zones. Increase in nightlights and the proportion of individuals using ITNs in 2020 were associated with reduced parasitaemia risk.ConclusionIncreased nightlights and ITN use could have led to the reduction in parasitaemia risk. However, the reduction is heterogeneous and there was increased risk in northern Kenya. Taken together, these results suggest that constant surveillance and re-evaluation of parasite and vector control measures in areas with increased transmission is necessary. The methods used in this analysis can be employed in other settings.

Water–Energy Nexus-Based Optimization of the Water Supply Infrastructure in a Dryland Urban Setting

Managing water supply systems is essential for developing countries to face climate variability in dryland settings. This is exacerbated by high energy costs for pumping, water losses due to aging infrastructures, and increasing demand driven by population growth. Therefore, optimizing the available resources using a water–energy nexus approach can increase the reliability of the water distribution network by saving energy for distributing the same water. This study proposes a methodology that optimizes the Water Distribution Network (WDN) and its management that can be replicated elsewhere, as it is developed in a data-scarce area. Indeed, this approach shows the gathering of WDN information and a model to save energy by optimizing pump schedules, which guarantee water distribution at minimal operational costs. The approach integrates a genetic algorithm to create pumping patterns and the EPANET hydraulic simulator to test their reliability. The methodology is applied for a water utility in the dryland urban setting of Lodwar, Turkana County, Kenya. The results indicate a potential reduction in energy costs by 50% to 57% without compromising the supply reliability. The findings highlight the potential of WEN-based solutions in enhancing the efficiency and sustainability of data-scarce water utilities in dryland ecosystems.

Mitigating flood risk at Shah Alam, Malaysia for sustainable development

The natural environment and infrastructure are severely damaged by floods, which are becoming frequent occurrences. It may not be able to stop floods given the impending climate change and variability of the weather. Yet mapping flood risk can help with both mitigation and prevention of flooding. The Shah Alam experiences flash floods yearly, which is why it was chosen. An interactive approach to decision-making under multi-criteria decision analysis (MCDA), the analytical hierarchy process (AHP), is used. The flood risk zone is divided into five categories very low-risk (32.38%), low-risk (22.25%), moderate-risk (23.17%), high-risk (18.87%), and very high-risk (3.33%) respectively. Based on the data gathered for this study, the economic losses are identified, low-risk areas (RM 2091), moderate-risk areas (RM 2343), and high-risk areas (RM 2728). The historical flood events occur in the Seksyen 36, 18, 17, and most of the southern part of Shah Alam, Malaysia. Rainfall and the Klang River have the biggest effects on these regions, with water drainage systems being critical in the Shah Alam area to reduce the risk of floods. When analysing flood risks in the Shah Alam region and throughout the nation, important stakeholders both state and non-state actors can benefit greatly from the flood susceptibility map provided by this study.