Individual Stations Research Articles

Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station

The fast proliferation of electric vehicles (EVs) profoundly affects the stable operation of the power system. Since EV charging demand is affected by users volatile behaviors and many other environmental and social factors, their uncertainty is quite pronounced. Traditional deterministic forecasting methods which provide little information on the uncertainty may not be informative to the decision-making of power system with massive EVs. This paper proposes a novel feature-enhanced deep learning method (FEDM) for EV charging demand probabilistic forecasting of individual station, in which a two-stage feature selection model is constructed to introduce the Pearson correlation coefficient between charging demand and external features as a prior knowledge to reweight the initial weights. The performance is improved compared to which is without feature-enhanced. In the comparison with eight benchmarks, FEDM shows superiority in both deterministic and probabilistic evaluation metrics. With high computational efficiency, FEDM has low dependence on the historical data stock, which can still achieve good performance in the case of low data volume. It provides a fast and accurate forecasting scheme for the demand forecasting of individual stations under the limitation of low data stock.

Spatial and temporal variability of water transparency, its primary drivers, and other optical properties in the Hudson River Estuary

Water transparency, measured as the diffuse attenuation coefficient of photosynthetically-active (400–700 nm) radiation, KdPAR, was measured monthly from May–Oct 2020 at 73 locations along 250 km of the Hudson River estuary (HRE) between New York City, NY and Waterford, NY. Three primary optical drivers of KdPAR variability: chlorophyll concentration (CHL), turbidity, and light absorption by colored dissolved organic matter (aCDOM), were measured concurrently to assess their relative importance to spatial and temporal KdPAR patterns. KdPAR was higher and more variable in the brackish HRE relative to limnetic regions further north. KdPAR was also significantly higher nearshore and near sewage treatment plant outfalls relative to midchannel sites. Temporal variability at individual stations was generally lower than cross-channel or cross-region spatial variability. Turbidity was the most important factor in KdPAR variability in all regions of the HRE, followed by CHL, with aCDOM playing a lesser role. Although many water quality variables have changed in the HRE over recent decades, KdPAR measurements in the brackish regions overlapped with published observations from the 1970s–1990s. KdPAR in the limnetic regions was lower in 2020 than measurements made prior to the Zebra mussel invasion in the early 1990s, but similar to measurements in the limnetic regions made since the invasion. Results of this study can be used to optimize satellite-based remote sensing algorithms for synoptic water quality observations of the HRE. Proposed future alterations in the HRE, including dredging and the construction of storm-surge barriers, would likely change existing KdPAR patterns. This study therefore provides an updated baseline for optical parameters in the HRE and highlights the importance of understanding the impacts of any proposed alterations to upper-water-column turbidity in this system. Results also demonstrate that KdPAR, euphotic depths, and Secchi depth measurements were positively correlated throughout the HRE. Secchi depth measurements may therefore be useful for additional retrospective studies, and future monitoring (including by satellite-derived KdPAR) of water transparency in the HRE.

Mathematical formulations for the multi-period alternative fuel refueling station location problem with routing under decision-dependent flow dynamics

The refueling station location problem with routing considers vehicles’ ranges and drivers’ preferences about their routes to improve the alternative fuel station infrastructure. Comprehensive planning is necessary for developing a mature infrastructure to overcome budgetary constraints and spatial limitations. Hence, adopting a multi-period planning approach becomes crucial when taking into account the evolving demand for alternative fuel vehicles over time. The evolution of demand can be dependent on exogenous and endogenous factors. Although it is typical to account for exogenous demand growth in multi-period planning, a few studies also take into account an endogenous factor which is the refueling opportunity of drivers on their paths. In this study, in addition to the refueling opportunities, we consider the proximity of each individual station to the flow-based demands. We draw attention to the significance of considering the effects of individual station locations on demand evolution, as these strategic locations can play an important role in reducing the drivers’ range anxiety and increasing their acceptance of the technology. Hence, we introduce a multi-period alternative fuel refueling station location problem with routing under different vehicle flow evolution dynamics that employ various weights for the factors where the natural growth rate is exogenous and the decisions of station locations and flow coverage are endogenous to the problem. We propose three mixed integer linear programming formulations for different evolution dynamics. We carry out computational experiments on the real road networks of Belgium, California, and Turkey and present our findings on the performances of the proposed mathematical models and the gains that can be obtained by considering multi-period planning and incorporating the effects of decisions into the vehicle flow evolution.

Remote sensing-based estimation of precipitation data (2000-2015) in Ecuador's Loja province

The primary climatic parameter frequently scrutinized in water balance assessments for water utilization is precipitation. Given its considerable variability across locations and over time, it is imperative to rely on high-quality statistical information to facilitate accurate analyses. This study aims to refine the estimation of precipitation data by enhancing information obtained from freely accessible satellite sensors with data collected from established observation stations. Monthly precipitation data spanning from 2000 to 2015 were gathered from 24 stations. Three distinct methodologies were employed to adjust individual station data to address missing data. Consistency analysis and data refinement were conducted for stations requiring adjustments, utilizing graphical analysis and non-parametric statistical techniques. The satellite products under evaluation correspond to the IMERG v6 algorithm. Subsequently, statistical metrics were used to compare observed and estimated data. A correction coefficient was computed by aligning monthly means between observed and calculated data to mitigate random and systemic errors. The IMERG algorithm demonstrates proficiency in accounting for altitude and seasonal variations, with the adjustment significantly enhancing its performance under these conditions.

Ant colony optimization-based solution to optimize load balancing and throughput for 5G and beyond heterogeneous networks

The escalating demand for data in wireless communication systems has posed significant challenges in recent years. This trend is predicted to continue, with explosive data usage and evolving quality of service demands from mobile users. The rapid increase in traffic demand, combined with the intricate nature of heterogeneous network (HetNet) scenarios, has significantly heightened the challenges confronting mobile network operators. These challenges encompass service quality, load distribution, coverage, and the overall user experience. Conventional approaches that prioritize maximum received power in the cell association mechanism tend to sustain network imbalances within the HetNets, making it difficult to cater for the diverse traffic requirements of mobile users. In this study, instead of focusing solely on enhancing individual user downlink rates, we maximize the number of users whose downlink needs are satisfied by integrating a cell range extension (CRE) technique with an ant colony optimization algorithm. Our proposed method considers both the workload of base stations and the signal to interference-plus-noise ratio of user devices to formulate an objective function aimed at calculating specific CRE bias values for individual small base stations. A comparative analysis of the proposed approach with existing techniques demonstrates its effectiveness. Simulation results underscore the success of our proposed strategy in meeting users’ throughput needs while reducing network imbalances and call drop rates.

Identifying Station Importance in Urban Rail Transit Networks Using a Combination of Centrality and Time Reliability Measures: A Case Study in Beijing, China

Time reliability (TR) is a critical factor that affects the efficiency and service quality of the urban rail transit network (URTN). However, previous studies have not incorporated TR into the evaluation of URTN station importance, focusing instead on basic centrality measures. Therefore, this paper proposes a new metric of station-based TR for evaluating and ranking URTN station importance. The new metric in combination with traditional centrality measures was used by the weighted Technique for Order of Preference by Similarity to Ideal Solution (weighted TOPSIS) to identify the combined significance level of individual URTN station importance and rank them accordingly. To investigate the performance of this method, we exploit deliberate attacks on the top-ranked stations through different methods. A case study of Beijing’s URTN during the morning peak hour showed that the proposed method is generally a better indicator for identifying station importance in maintaining network connectivity. The case study also demonstrated the feasibility and validity of the model. This study can provide recommendations for the planning and operation of rail transit systems and can inform the effective design of station protection strategies.

Exploring cumulative probability functions for streamflow drought magnitude: Global scale analysis and parametric vs. non-parametric comparisons

Streamflow droughts pose significant challenges to water resource management and environmental sustainability. The accurate characterization of streamflow drought magnitude is essential for effective mitigation, assessment and adaptation strategies. In this study, we explore the implications of choosing different cumulative probability distribution functions (cdfs) on the determination of streamflow drought magnitude, as assessed by the z-score values of the standardized streamflow index (SSI) and return periods. Our investigation encompasses a comprehensive global-scale analysis, with the aim of identifying the most suitable cdfs for characterizing streamflow droughts. To accomplish this objective, we incorporate global simulated streamflow values from the model WaterGAP 2.2d and observed streamflow values from 280 Global Runoff Data Centre (GRDC) stations for the period 1981 – 2010. We evaluate the performance of 118 parametric cdfs for each of the 12 calendar monthly streamflow values at each model grid cell and GRDC station to determine the best fitting parametric cdf. Moreover, we go beyond the conventional scope of such studies by further assessing the best-fitting cdfs alongside non-parametric cdfs such as the Empirical cumulative distribution function (ecdf) and the cumulative distribution function derived from the Kernel density estimation function (kcdf). Among our findings, we establish a subset of 23 parametric cdfs that exhibit the best fit for the global streamflow values concerning all 12 calendar monthly streamflow values at individual GRDC stations or grid cells within the specified period. Furthermore, we observe that the accuracy of streamflow simulation and the length of the reference period directly influence the selection of the most suitable parametric cdf. Our comparative analysis reaffirms the existing understanding that opting for the best-fitting parametric cdf, rather than a non-parametric alternative, mitigates the risk of overestimating drought magnitudes. However, we emphasize the crucial importance of considering the intended purpose, approach-specific sensitivities, and uncertainties when choosing the most suitable parametric cdf for any calendar monthly streamflow values.

RECONSTRUCTION OF HEATING SYSTEMS IN RESIDENTIAL BUILDINGS WHILE MAINTAINING EXISTING HEAT SOURCES

The purpose of the article is to substantiate the need for real reconstruction of single-pipe vertical centralized heating systems, which are operated in most multi-story residential buildings in Ukraine, into apartment horizontal systems. The paper presents the main design shortcomings of heating systems in residential buildings built in the second half of the 20th century. The feasibility of using horizontal apartment systems is substantiated. For the first time, the heating system was reconstructed during the heating season without displacing residents and without stopping the existing heating system. A technical solution is proposed for supplying heat to apartments from an existing individual heating station with an elevator without its reconstruction. The article presents the results of a number of studies aimed at improving the heating system and determining the indicators of its economic and energy efficiency. Installation work can be carried out selectively in any number of apartments, and "old" and "new" heating systems can operate in parallel. The methodology for the reconstruction of heating systems presented in this paper can be used in the process of post-war restoration of the destroyed infrastructure of the housing stock of Ukraine. Modernizing in-building systems while maintaining the existing heat source is much cheaper than decentralizing heat supply by installing independent heating systems with a gas boiler in each apartment. The main conclusions of the article include that the reconstruction of existing heating systems is an objective necessity, without which the subscriber's influence on his heat consumption is impossible. The modernization described in this article is dictated by all the regulatory deadlines for major repairs of heating systems, which in most cases are more than twice as long.

A new soft clustering method for traffic prediction in bike-sharing systems

For the efficient management of bike-sharing systems (BSSs), accurate demand predictions are crucial to address the uneven distribution of bikes at various stations. Recent studies have explored a hierarchical prediction framework using cluster-level models to more accurately estimate demand at the station level. However, in frameworks based on hard clustering, where each station is exclusively assigned to one of several clusters, prediction accuracy tends to be lower for stations at the cluster boundaries. To improve accuracy for such stations, this study proposes a novel soft clustering algorithm for BSSs. The key idea is to allow stations to belong to multiple clusters, calculating the membership degree for each station based on transitions between stations and clusters obtained through hard clustering. This study also investigated the impact of restricting clusters to which individual stations belong based on distance or usage history. Two approaches, distance- and usage-based, were employed to determine the clusters to which each station belongs. Experimental results using Seoul Bike data demonstrate the effectiveness of the proposed method in enhancing traffic prediction accuracy within the hierarchical prediction framework. Notably, excluding clusters with minimal usage for each station using the usage-based approach yielded the best performance.

A Study of Precipitation Forecasting for the Pre-Summer Rainy Season in South China Based on a Back-Propagation Neural Network

In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change.

Experience of Russian-Chinese industrial cooperation on the construction of the Moscow metro

The commissioning of the Big Circle Line in Moscow, some sections of which were laid jointly with Chinese underground builders, provided the first experience of interaction with foreign construction contractors on the Moscow Metro. The paper investigates this experience which is of value for the development of such works in the future. The analysis was carried out on the basis of scientific and technical documentation on certain facilities, the progress and results of construction and installation works, and information on other events. The paper describes the facilities where construction and installation works were carried out by joint efforts and shows the peculiarities of interaction between Russian and foreign (Chinese) metro builders on a number of sections of the Big Circle Line of the Moscow Metro. The achieved results are demonstrated by the example of individual underground stations. The study reveals the peculiarities of material and technical support of works on the sections constructed by Chinese construction contractors. The mutual exchange of experience, technologies, and work management practices along with the application of various tunnel boring machines from both countries (Russia and the PRC) has yielded fruitful results, demonstrated the possibility and efficiency of the direct engagement of foreign tunnel builders at Russian sites in close cooperation with their Russian counterparts, and revealed a scheme for rational division of labour, work management, and logistical support of works.

Deep Neural Networks Based Denoising of Regional Seismic Waveforms and Impact on Analysis of North Korean Nuclear Tests

AbstractWe test a deep learning based denoising autoencoder algorithm on regional and teleseismic seismological and hydroacoustic datasets, which we compile from the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organisation. We focus on stations which can be relevant to investigate North Korean nuclear tests. Denoising of waveform records using autoencoder techniques potentially enables improved signal detection and processing due to lowered signal-to-noise ratios. We train and compare the performance of several different denoising autoencoder models, for short- and long waveform periods, trained on the complete station network as well as on individual stations. We investigate if the denoised waveform signals are useful for seismic source analysis and if they can still be reliably used in downstream analysis for further inferences on the seismic source type, i.e. seismic moment tensor analysis. The declared North Korean nuclear tests are a suitable benchmark test set, as they have extensively been researched and their source type and location might be assumed known. Verification of the source type is of particular interest for potential nuclear tests under international law. We find that care needs to be taken using the denoised waveform data, as a slight bias is introduced in the seismic moment tensor analysis. However we also find promising results hinting at possible future use of the technique for standard analyses, as it improves the investigation of smaller events. Autoencoder based denoising techniques could be employed in future routine frameworks to increase earthquake catalog completeness and possibly aid in detecting smaller potential treaty relevant events.

Ensuring firefighter safety and resource preservation from contamination in demolition building fires

Research characteristics and define toxic substances that remain in emergency clothing after fires in abandoned and demolition buildings. As a basic component of the integrated rescue system, members of the Fire and Rescue Service may be primarily or secondarily exposed to the effects of dangerous substances that penetrate the body as part of intervention activities. Personal protective equipment (PPE) used by firefighters can be characterized as a potential source of hazardous exposure to toxic contaminants commonly found and released during fires. The research task was to detect these contaminants in individual samples based on gas chromatography (GC) and subsequently to determine the decontamination methods of firefighters and PPE after an intervention. Based on a real intervention in an abandoned and demolished building where there was a fire, firefighters from individual fire stations intervened, where, as stated in the research, they intervened in the mentioned protective equipment. The amount of dangerous substances was determined based on gas chromatography in the Control and Chemical Laboratory of the Ministry of the Interior in the village of Jasov. The stated procedure was used to determine the amount of residual hazardous substances on emergency clothing and the procedures for possible elimination of the hazard.

Towards a collective opinion generation approach with multiple objectives for evaluating rail transit station accessibility in urban areas

Urban rail transit can alleviate traffic congestion if sufficient ridership is achieved. The accessibility to rail transit stations largely determines public willingness to utilize urban rail transit. The current studies on the accessibility to rail transit stations tend to concentrate solely on individual rail transit lines or stations, neglecting the impact of the external macro-environment. Consequently, the outcomes of these measurements cannot accurately reflect the overall accessibility level of the region, nor do they furnish decision-makers with references for devising plans for rail transit development. This study introduces a novel approach for evaluating the accessibility to urban rail transit stations from a macro-level standpoint, utilizing expert knowledge. The present study conducts an analysis of the impact of political, economic, social, and technological factors on the accessibility to rail transit stations in urban areas. Subsequently, a comprehensive evaluation indicator system is developed based on the aforementioned analysis. Then, experts are summoned to provide their subjective evaluations for every indicator, which are depicted as probability distribution functions. The collective evaluation is derived through the aggregation of individual evaluations utilizing a bi-objective optimization approach that factors in both the collective fairness utility and the confidence level. Finally, the quantile average method is employed to consolidate the collective assessment outcomes of individual indicators, thereby deriving the level of accessibility to rail transit stations in urban areas. We design a small-scale application experiment and attempt to evaluate the accessibility to rail transit stations in Wuchang District, Wuhan with the proposed approach in order to demonstrate the feasibility of the approach.

Enriching the GEOFON seismic catalog with automatic energy magnitude estimations

Abstract. We present a seismic catalog (Bindi et al., 2024, https://doi.org/10.5880/GFZ.2.6.2023.010) including energy magnitude Me estimated from P waves recorded at teleseismic distances in the range 20°≤Δ≤98° and for depths shorter than 80 km. The catalog is built starting from the event catalog disseminated by GEOFON (GEOFOrschungsNetz), considering 6349 earthquakes with moment magnitude Mw≥5 occurring between 2011 and 2023. Magnitudes are computed using 1 031 396 freely available waveforms archived in EIDA (European Integrated Data Archive) and IRIS (Incorporated Research Institutions for Seismology) repositories, retrieved through the standard International Federation of Digital Seismograph Networks (FDSN) web services (https://www.fdsn.org/webservices/, last access: March 2024). A reduced, high-quality catalog for events with Mw≥5.8 and from which stations and events with only few recordings were removed forms the basis of a detailed analysis of the residuals of individual station measurements, which are decomposed into station- and event-specific terms and a term accounting for remaining variability. The derived Me values are compared to Mw computed by GEOFON and with the Me values calculated by IRIS. Software and tools developed for downloading and processing waveforms for bulk analysis and an add-on for SeisComP for real-time assessment of Me in a monitoring context are also provided alongside the catalog. The SeisComP add-on has been part of the GEOFON routine processing since December 2021 to compute and disseminate Me for major events via the existing services.

Time transfer via single‐record TDoA measurements of GNSS satellites using direct cross‐correlation and relative pilot code phases

AbstractRadar systems are evolving towards distributed receiver networks. As individual stations might be separated too far to install a cable link, novel methods are required to synchronise individual data records in the time domain. State‐of‐the‐art GNSS receivers disciplining a highly stable oscillator are able to output a timing signal with several nanosecond accuracy solely using non‐proprietary signals. However, they typically require a stable environment and become a major cost factor for receiver networks with a high number of nodes. A method is presented to passively synchronise data records via GNSS raw signals in a single record requiring only a GNSS antenna, an analogue‐to‐digital converter and computation hardware. The clock bias is estimated via the common view method with either full raw signal correlation or software‐based code correlation of individual signals from the GPS, Galileo and BeiDou constellation with sub‐nanosecond precision.

Assessment of Passive Geodetic Control Network in Anambra State, Nigeria

Anambra State is facing a number of environmental challenges that require geodetic infrastructure for effective solutions. Unfortunately, much remains to be done in this area as many control stations have been removed from their original locations, leaving some of their pillars in disrepair. To address this issue, a study was conducted to assess the reliability of the geodetic control network in Anambra for various applications. The study involved identifying the location of control stations within the state, assessing the status of the geodetic control network, and confirming the status of individual control stations. Using a handheld GNSS receiver, the control stations were located and critically assessed for their physical condition. In addition, in situ checks were carried out to determine if they were still maintaining their original positions. The results of the study showed that 36% of the visited and located control stations are still intact in their original positions, while 56% have been removed and 8% require maintenance. There should be continuous sensitization of the public on the importance of geodetic controls and the dangers of their destruction.

Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia

AbstractVolcanoes produce a variety of seismic signals and, therefore, continuous seismograms provide crucial information for monitoring the state of a volcano. According to their source mechanism and signal properties, seismo‐volcanic signals can be categorized into distinct classes, which works particularly well for short transients. Applying classification approaches to long‐duration continuous signals containing volcanic tremors, characterized by varying signal characteristics, proves challenging due to the complex nature of these signals. That makes it difficult to attribute them to a single volcanic process and questions the feasibility of classification. In the present study, we consider the whole seismic time series as valuable information about the plumbing system (the combination of plumbing structure and activity distribution). The considered data are year‐long seismograms recorded at individual stations near the Klyuchevskoy Volcanic Group (Kamchatka, Russia). With a scattering network and a Uniform Manifold Approximation and Projection (UMAP), we transform the continuous data into a two‐dimensional representation (a seismogram atlas), which helps us to identify sudden and continuous changes in the signal properties. We observe an ever‐changing seismic wavefield that we relate to a continuously evolving plumbing system. Through additional data, we can relate signal variations to various state changes of the volcano including transitions from deep to shallow activity, deep reactivation, weak signals during quiet times, and eruptive activity. The atlases serve as a visual tool for analyzing extensive seismic time series, allowing us to associate specific atlas areas, indicative of similar signal characteristics, with distinct volcanic activities and variations in the volcanic plumbing system.

Design of the New Production Line in the Automotive Company

Innovation is one of the key competitiveness strategies for small, medium, and large companies. Production line innovations using Lean methods are currently one of the most important ways to change the shape of the production line in a positive direction. Emphasis must be placed on the resulting innovation and the form of the production line, which should be as efficient and functional as possible and meet the required parameters of productivity and quality assurance. The primary goal of production line innovations using Lean methods is the elimination of bottlenecks in the production process that do not add value and the balancing of individual assembly stations. The aim of the contribution was to design a semi-automatic line for the completion and testing of seat locks under the name D3P2. The result of the solution is the creation of a robotic workplace that will ensure a higher production capacity and the related packaging of finished products. On the assembly line, an analysis of line balancing was carried out using the Yamazumi graph to eliminate unnecessary assembly process activities, Cycle time and Tact time. An analysis of the measurements of individual production activities and places was carried out on the line, and the proposal for a new production line was subsequently shared. Its main goal was to meet all customer requirements through high flexibility. The result of the analysis and applied measures was a reduction in the cycle time of the new line, balancing of individual posts and economic benefits.

Climate and Land Use/Land Cover Changes within the Sota Catchment (Benin, West Africa)

Climate and land cover changes are key factors in river basins’ management. This study investigates on the one hand 60-year (1960 to 2019) rainfall and temperature variability using station data combined with gridded data, and on the other hand land cover changes for the years 1990, 2005, and 2020 in the Sota catchment (13,410 km2, North Benin, West Africa). The climate period is different from the chosen land use change period due to the unavailability of satellite images. Standardized anomaly index, break points, trend analysis, and Thiessen’s polygon were applied. Satellite images were processed and ground truthing was carried out to assess land cover changes. The analyses revealed a wet period from 1960 to 1972, a dry period from 1973 to 1987, and another wet period from 1988 to 2019. The annual rainfall decreases from the south to the north of the catchment. In addition, rainfall showed a non-significant trend over the study period, and no significant changes were identified between the two normals (1960–1989 and 1990–2019) at catchment scale, although some individual stations exhibited significant trends. Temperatures, in contrast, showed a significant increasing trend over the study period at catchment scale, with significant break points in 1978, 1990, and 2004 for Tmax, and 1989 for Tmin. An increase of 0.4 °C and 1.2 °C is noted, respectively, for Tmax and Tmin between the two normals. The study also revealed increases in agricultural areas (212.1%), settlements (76.6%), waterbodies (2.9%), and baresoil (52%) against decreases in woodland (49.6%), dense forest (42.2%), gallery forest (21.2%), and savanna (31.9%) from 1990 to 2020. These changes in climate and land cover will have implications for the region. Appropriate adaptation measures, including Integrated Water Resources Management and afforestation, are required.