Effective Resource Management Research Articles

Educational Sector Financing and Economic Wellbeing

This study examines the relationship between educational Sector financing and economic wellbeing in Nigeria, focusing on government funding, private domestic investment, and foreign expenditure in education. The study employs econometric techniques to analyze the long-term dynamics of educational financing on the Human Development Index (HDI). The findings reveal a significant increase in educational financing activities over time, particularly in domestic funding. However, despite the growth in funding, there is evidence of underemployment among educated individuals, suggesting a disconnect between educational investments and economic outcomes. Co-integration analysis indicates a long-term relationship between government educational financing and human development. The reliance on government funding for education underscores the need for effective resource management and targeted interventions to address gaps in the education system. Keywords: Educational financing, economic development, Nigeria, government funding, private investment.

A comparative analysis of deep-learning models for dam discharge forecasting for disaster management

ABSTRACT This paper represents a comprehensive study of dam water discharge prediction using various deep-learning models. The main aim of this paper is to provide a data-driven solution for effective water resource management and thereby controlling the effects of floods and droughts. The Malampuzha dam, which has a spillway, a right bank canal (RBC), and a left bank canal (LBC), is considered for the study. While the spillway helps to control the water level in the reservoir by allowing extra water to flow downstream during times of severe rainfall or high inflow, hence reducing the risk of floods, the LBC and RBC are intended to divert water for agriculture. Altogether, 10 years of consecutive meteorological and dam-related data are utilized for training and testing the models in forecasting water discharge through LBC, RBC, and the spillway. Different deep-learning models, namely, long short-term memory, Bi-directional long-short term memory, recurrent neural network, gated recurrent unit, one-dimensional convolutional neural network (1D-CNN), deep neural network, autoencoder, and residual networks are explored in predicting the accuracy of dam water discharge. After testing eight deep-learning models, it was discovered that 1D-CNN performed well in predicting the discharge of water.

Accurate Feature Extraction from Historical Geologic Maps Using Open-Set Segmentation and Detection

This study presents a novel AI method for extracting polygon and point features from historical geologic maps, representing a pivotal step for assessing the mineral resources needed for energy transition. Our innovative method involves using map units in the legends as prompts for one-shot segmentation and detection in geological feature extraction. The model, integrated with a human-in-the-loop system, enables geologists to refine results efficiently, combining the power of AI with expert oversight. Tested on geologic maps annotated by USGS and DARPA for the AI4CMA DARPA Challenge, our approach achieved a median F1 score of 0.91 for polygon feature segmentation and 0.73 for point feature detection when such features had abundant annotated data, outperforming current benchmarks. By efficiently and accurately digitizing historical geologic map, our method promises to provide crucial insights for responsible policymaking and effective resource management in the global energy transition.

Assessing Drought Patterns in Al-Baha: Implications for Water Resources and Climate Adaptation

Due to growing water demands and changing hydro-meteorological variables brought on by climate change, drought is becoming an increasingly serious climate concern. The Al-Baha region of Saudi Arabia is the subject of this study because it is susceptible to both agricultural and meteorological droughts. This study investigates how climate change affects patterns of drought in Al-Baha by analyzing four drought indices (Agricultural Standardized Precipitation Index (aSPI), the Standardized Precipitation Index (SPI), the Rainfall Deficiency Index (RDI), and the Effective Reconnaissance Drought Index (eRDI)) for the years 1991–2022. Analysis of rainfall data was carried out to classify drought events according to their duration, frequency, and severity. Results showed that severe droughts occurred in 2009, 2010, 2012, 2016, and 2022, with 2010 being the worst year. Results also indicated a notable decrease in precipitation, which has resulted in extended dry spells. Several indices indicate that this tendency has significant ramifications for agriculture, particularly in areas where farming is a major economic activity. In addition, the possible occurrence of hydrological drought was also observed based on the negative values for the Reservoir Storage Index (RSI) in Al-Baha. Projections for the future under two Representative Concentration Pathways (RCPs) showed notable variations in temperature and precipitation. Both the RCP4.5 (low emission) and the RCP8.5 (high emission) projection scenarios indicate that drought conditions will likely worsen further. Depending on the emission scenario, it is projected to show a temperature increase of 1–2 °C, whereas the variability in precipitation projections indicates significant uncertainty, with a reduction change in the range of 1.2–27% between 2050 and 2100. The findings highlight the urgent need for proactive adaptation strategies, effective water resource management, and the development of sophisticated drought prediction tools. Addressing these challenges is crucial for sustaining agriculture and managing water scarcity in Saudi Arabia in the face of increasing drought risk.

How to manage regime shifts: insights from a dryland social–ecological system

ABSTRACT Effective water resource management in the Zayandeh-Rud River Basin, a critical region in the Middle East, requires understanding system dynamics and anticipating regime shifts. This study uses system dynamics to model water consumption changes in the basin, analysing various scenarios (10% to 30% increase/decrease) in agricultural, urban, and industrial sectors. Results show that increased consumption harms water systems, while reductions, especially a 30% decrease, stabilize systems and improve water quality. Upstream reductions benefit downstream resources, highlighting the importance of integrated management. Proactive strategies can mitigate adverse impacts, ensuring a sustainable water future for the region’s inhabitants.

Impact of Land Use and Land Cover Changes on Soil Physico-Chemical Properties in Southern Tigray, Northern Ethiopia

Soil quality declines due to the conversion of natural vegetation into farmland and grazing areas. The response of soil properties to land use and land cover changes (LULC) exhibits both spatial and temporal variations. This study aimed to assess the effects of LULC changes on the physico-chemical properties of soil in the Wojic watershed. Soil samples were collected from natural forests, bushland, shrubland, and cultivated lands across three landforms of the watershed (upper, middle, and lower) to evaluate the physical and chemical properties of the soil associated with different LULC types. The LULC categories were compared using mean values and critical thresholds for selected physicochemical soil properties. Soil analysis was conducted using R software, employing one-way analysis of variance (ANOVA). A normality test was performed before the post hoc analysis, and Tukey’s honest significance difference (HSD) test was utilized for mean separation among the LULC types. Additionally, a normalized difference vegetation index (NDVI) was calculated for the years 1997 and 2017. A simple linear regression model was developed to estimate soil physico-chemical properties over the past 20 years, using laboratory soil parameters and the NDVI for 2017. The pH values showed a slight decrease in cultivated land (from 5.7 to 5.4), bushland (from 7 to 6.6), and shrubland (from 6.5 to 6.2) over the study period. The analysis indicated a declining trend in physico-chemical properties, attributed to changes in vegetation cover and management practices. Consequently, the government needs to enforce policies and regulations that promote effective land resource management and utilization, with particular emphasis on the proper management and conservation of forests, bushlands, and shrublands, as well as measures to prevent increased land resettlement.

Assessment of different interpolation algorithms for daily rainfall spatial distribution in the Var catchment, France

AbstractTo have effective water resource management, the distributed hydrological models are commonly applied for supporting the decision‐making processes. Among different inputs, the spatial distributed rainfall plays significant role in those model simulations. Many interpolation methods have been developed for generating distributed rainfall based on measurement samples. However, depending on the catchment characteristics and data availability, the suitable interpolation algorithm is case‐dependent. This paper presents one operational approach for determining the resonable interpolation algorithm in a complex large catchment (Var catchment, France). Based on the daily rainfall data (2008–2014) collected from 16 stations in the Var catchment, six different interpolation approaches including: inverse distance weight (IDW), spline, kriging with linear and spherical semi‐variogram models and geographically weighted regression considering elevation effects and the combined impacts of elevation and distance to the sea were tested. Integrated the results of statistical and modeling assessments, the 400 m resolution distributed rainfall generated by IDW algorithm shows high preference in generating distributed rainfall in the Var catchment. Moreover, the strategy described in the article also shows promising acceptability for other catchments.

Optimizing Water Productivity through the Conjunctive Use of Borewell and Canal Water for Enhancing Sheep Productivity in Arid Climate of Western Rajasthan, India

In the arid regions of Rajasthan, water scarcity poses a significant challenge to both agriculture and livestock productivity. Sustainable practices in such climates require effective management of limited water resources. This study focuses on optimizing water productivity through the conjunctive use of canal water and borewell water to address the problem of water scarcity while maximizing output per unit of water. The experiment involved eighteen healthy lambs of aged 2-3 months which were managed intensively and divided into three groups of six lambs each based on a randomized block design. All groups were fed Pennisetum glaucum (pearl millet) and Medicago sativa (lucerne) ad-libitum along with concentrates adjusted according to their body weights. The groups were offered three types of drinking water: canal water (G1), a combination of canal and borewell water (G2) and borewell water (G3) in a clean bucket twice a day. No significant effect on total feed intake and body weight of the lambs was found based on the drinking water source. However, G1 and G2 lambs, which were offered canal water, consumed significantly (p<0.05) less water (74.4 ± 1.84 liters) compared to the lambs in G3 (81.6 ± 1.91 liters). The virtual water requirement per kilogram of weight gain, calculated from both direct water intake and indirect intake through feed was lowest in G1 lambs (5771.34 liters) followed by G2 lambs (6129.00 liters) and G3 lambs (6312.00 liters) per kilogram of weight gain, respectively. These results indicate that water productivity was highest with canal water (G1) and lowest with borewell water (G3) per kilogram of weight gain. This study underscores the importance of efficient water management in maximizing productivity and resource use efficiency in water-limited arid regions.

Changing Rainfall Patterns in the Northeastern South Kivu Region, Democratic Republic of the Congo: A Detailed Analysis Using CHIRPS Rainfall Data (1981–2023)

AbstractUnderstanding changes in rainfall patterns is vital for effective water resource management and agricultural planning in climate-sensitive regions. In Northeastern South Kivu, Democratic Republic of the Congo, limited knowledge of rainfall dynamics poses challenges for local climate assessments. This study analyzed rainfall patterns using the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset from 1981 to 2023 to fill this knowledge gap. The findings revealed significant spatial and temporal changes in rainfall across the regions of Kabare, Bukavu, Kalehe, and Idjwi. Mean annual rainfall ranged from 1136.92 mm to 2048.85 mm, with coefficients of variation between 8.23% and 8.45%. Monthly rainfall fluctuations were substantial, ranging from 16 to 56.35%. Standardized rainfall anomalies indicated a shift from predominantly wet conditions in the 1980s to drier conditions in later decades, with over half the years from 1990 to 2023 recording below-average rainfall. Mann-Kendall trend analysis, supported by Innovative Trend Analysis (ITA), confirmed significant decreases in annual rainfall, with rates between − 3.53 to -5.72 mm/year. Additionally, rainfall intensity indices highlighted a decline in total annual precipitation, suggested fewer extreme events. Spatial variability was observed, with regions like Kalehe and Bukavu showed higher values for indices such as R95P and RX1day compared to Kabare and Idjwi. These results underscored the urgent need for adaptive strategies to mitigate water scarcity and manage changing rainfall patterns in the region.

Establishing an Interactive Sequence Database for Shiitake Cultivar Identification.

Shiitake mushrooms (Lentinula edodes) hold significant cultural and economic value, particularly in Asia where they are extensively cultivated. The diversification of shiitake cultivars, driven by the need to adapt to various climatic conditions and cultivation methods, has resulted in over 200 distinct cultivars. Reliable identification of these cultivars is crucial for breeding, intellectual property protection, and effective genetic resource management. Traditional morphological methods are inadequate due to their subjectivity and labor-intensive nature. This study leverages nanopore high-throughput sequencing to comprehensively analyze the rDNA regions (SSU, ITS, LSU, IGS) of 41 shiitake strains from Taiwan's Bioresource Collection and Research Center (BCRC), comprising 5 wild strains, 33 commercial strains, and 3 wild-commercial hybrids. Our results identified the IGS1 region as the most variable and suitable for cultivar differentiation. Consequently, we developed an interactive online database (https://github.com/Raingel/ShiitakeIGS1) that integrates 317 IGS1 sequences from Taiwan, Japan, and China. This platform allows users to upload their IGS1 sequences and identify similar cultivars through a user-friendly interface, enhancing the precision and efficiency of shiitake cultivar identification.

Comparative assessment of empirical random forest family's model in simulating future streamflow in different basin of Sarawak, Malaysia

In Sarawak, a region highly vulnerable to climate change, the translation of climate-induced changes in rainfall to river flow is non-linear, presenting a challenge for water resource managers. This research investigates the impact of climate change on hydrological processes in Sarawak, Malaysia, with a specific focus on assessing future spatiotemporal variations in streamflow. The families of Random Forest (RF) empirical models based on data mining techniques were compared and utilized to develop a continuous hydrologic model. Then, by incorporating rainfall and evapotranspiration future projection prepared based on RF past performance statistical downscaling, the top performing RF empirical hydrological model was used for future streamflow projection. The results showed that despite an expected increase in rainfall, the RFR (Random Forest by Randomization) empirical hydrological model demonstrated a potential decrease in river runoff due to heightened evapotranspiration demands associated with rising temperatures. The examination of climate-induced alterations in both rainfall and evapotranspiration patterns revealed a consistent decrease in river discharges during the early to middle period across Sarawak, followed by a shift towards an increasing trend by the end of the 21st century. The central region along the Rajang basin exhibited a prevailing decrease in river discharge, with contrasting patterns in the last part of the century. The northern region displayed diverse trends, with some basins experiencing decreases in river runoff despite augmented rainfall, emphasizing the heterogeneity in response. By employing empirical models, and projecting future scenarios, the study contributes to a better understanding of climate change impacts on hydrology in the region, essential for effective water resource management and environmental conservation.

A Localized Evaluation of Surface Water Quality Using GIS-Based Water Quality Index along Satpara Watershed Skardu Baltistan, Pakistan

Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and examine water quality data to facilitate the identification of pollution hotspots, trend analysis, and knowledge-based decision-making for effective water resource management. This study aims to evaluate the physiochemical and bacteriological parameters of the Satpara watershed and to provide the spatial distribution of these parameters. This study endeavors to achieve Sustainable Development Goal 6 (SDG 6) by identifying localities with excellent and unfit water for drinking, sanitation, and hygiene. A total of fifty-one surface water samples were collected from various parts of the Satpara watershed during the fall season of 2023. Well-established laboratory techniques were used to investigate water for parameters like Electrical Conductivity (EC), pH, turbidity, total dissolved solids (TDSs), major cations (K+, Na+, Mg2+, Ca2+), major anions (Cl−, SO42−, NO3−, HCO3−), and bacteriological contaminants (E. coli). Spatial distribution maps of all these parameters were created using the Inverse Distance Weighted (IDW) technique in a GIS environment. A significant variation in the quality of water was observed along the study area. The level of Escherichia coli (E. coli) contamination is above the permissible limit at various locations along the watershed, making water unsafe for direct human consumption in these areas. Some regions showed low TDS values, which could adversely affect human health and agricultural yield. From the WQI valuation, 58.82% of the collected samples were “Poor”, 31.8% were “Very poor” and 9.8% were found to be “Unfit for drinking”. The research findings emphasize the pressing need for consistent monitoring and adoption of water management strategies in Skardu City to warrant sustainable soil and water use. The spatial maps generated for various parameters and the water quality index WQI offer critical insights for targeted intercessions.

Decoupling of sulfation reactions to enhance zinc extraction from electric arc furnace dust: Unveiling the significance of O2 activation and mechanistic insights

Electric arc furnace dust (EAFD) is a hazardous solid waste generated during steelmaking, boasting significant concentrations of zinc and iron. Currently, a large amount of EAFD is disposed of in landfills, causing severe environmental pollution and resource wastage. To address this issue, the sulfation roasting followed by water leaching process was proposed, offering efficient zinc extraction, iron separation, low cost, and environmental friendliness advantages. This study presented a novel experimental design that decoupled the sulfation process for EAFD into gas–solid and solid–solid processes to uncover the sulfation mechanisms. During these processes, Zn-bearing phases were successfully converted into zinc sulfate (ZnSO4), while Fe-bearing phases transformed into iron oxide (Fe2O3). The resulting compounds were then separated using a water leaching process, achieving a high zinc extraction rate of 97.21% and a low iron residue of 0.61%, respectively, for the gas–solid sulfation process, and 98.76% and 0.81% for the solid–solid sulfation process. Comprehensive characterizations, in situ diffusion Fourier transform infrared spectroscopy (DRIFTS) experiments, and density functional theory (DFT) calculations provide a deeper understanding of the sulfation restructuring mechanisms in gas–solid and solid–solid roasting processes, highlighting the significant role of O2 activation. In the solid–solid sulfation process, Fe2(SO4)3, derived from the oxidation of FeSO4 by O2, was identified as the predominant compound responsible for sulfation. In the gas–solid sulfation process, sulfite species (SO32-) played a critical role as intermediates, eventually transforming into sulfate species (SO42-) with the assistance of O2. Moreover, adsorbed SO2 underwent oxidation and activation by adsorbed O2, forming the sulfate functional group. Migration of this functional group to the adjacent Zn atom through a transition state, established a Zn-SO4 bond, disrupting the Zn-O bond on ZnFe2O4 and completing the sulfation of ZnFe2O4. This study provides a fresh perspective on the application of sulfation roasting for the extraction of valuable metals, contributing to resource sustainability and effective management of zinc-bearing resources.

Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%.

Daily Runoff Forecasting Using Novel Optimized Machine Learning Methods

Accurate runoff forecasting is crucial for effective water resource management, yet existing models often face challenges due to the complexity of hydrological systems. This study addresses these challenges by introducing a novel bio-inspired metaheuristic algorithm, Artificial Rabbits Optimization (ARO), integrated with various machine learning (ML) models for runoff forecasting in the Carson and Chehalis River basins. The ARO-ML models are rigorously evaluated, demonstrating their superior ability to enhance forecasting accuracy compared to widely used ML techniques, including gradient boosting (GB), multi-layer perceptron (MLP), and K-nearest neighbor regression (KNN). In the Carson River, the GB model achieves the highest forecasting accuracy, which is significantly improved by ARO, resulting in a 24.8% reduction in root mean square error (RMSE). The MLP model also benefits notably from ARO, with RMSE improvements of 4.8% and a substantial 48.9% reduction in mean absolute error (MAE). The ARO-optimized KNN model shows exceptional performance, surpassing the baseline KNN during testing. In the Chehalis River Basin, ARO integration leads to substantial improvements in both GB and MLP models across various performance metrics during training and testing. Crucially, the results indicate that in both river basins, rainfall in preceding days has the most significant influence on streamflow forecasts, followed by air temperature. This study advances hydrological forecasting by validating the efficacy of ARO-ML models, offering a robust framework for enhanced predictive accuracy and providing deeper insights into the key drivers of streamflow in diverse hydrological contexts.

Multi-Scenario land cover changes and carbon emissions prediction for peak carbon emissions in the Yellow River Basin, China

Research on future land cover changes and carbon emissions is essential for effective land resource management and developing feasible carbon mitigation strategies. This study focused on the Yellow River Basin and employed the Future Land Use Simulation (FLUS) and Autoregressive Integrated Moving Average (ARIMA) models to project future land cover and carbon emissions. Additionally, bivariate spatial autocorrelation was utilized to analyze the relationship between them. Key findings are as follows: 1) Historically, the Yellow River Basin has experienced an expansion in construction land, forests, grasslands, and water, while cropland and unused land have diminished. Notably, construction land displayed the most significant changes, whereas grasslands showed minimal modification. Looking ahead, both the ecological protection and inertial development scenarios exhibit consistent trends with historical patterns across the land type categories. In contrast, the economic priority development scenario forecasts an increase in construction land, cropland, and grasslands, indicating a distinct shift compared to the other scenarios. However, the ecological protection scenario proves to be more sustainable. 2) In the absence of intervention, the simulated carbon emissions from construction land throughout the basin display a linear increase across various scenarios, with provincial-level variations showing an increase from southwest to northeast. However, Henan and Sichuan are expected to experience slower reductions in carbon emissions, compared to other projections. There is a notable positive correlation between carbon emissions and the comprehensive index, indicating that regions with high emissions typically experience substantial land and economic development. 3) Energy consumption projections for 2030 and 2060 indicate that to align with China’s carbon goals, it is essential to reduce energy consumption and adjust the fossil to non-fossil fuel ratio to reduce carbon emissions. Substituting coal with clean energy and enhancing energy efficiency will be more effective for achieving low-carbon emission targets. In summary, this study provides significant guidance for China’s ecological conservation, low-carbon emission strategies, and global carbon emission control efforts.

Application of electrical resistivity tomography for groundwater evaluation in Yirgacheffe Town and its environs, Main Ethiopian Rift

Exploring groundwater is crucial in areas with poor surface water quality, such as Yirgacheffe Town and its surroundings. This study aims to assess aquifer materials and groundwater potential using Electrical Resistivity Tomography (ERT) in the region,within the Main Ethiopian Rift. Four electrical resistivity profiles were gathered using a Wenner array with a WDA-1 digital resistivity and induced polarization meter from Chongqing, China. Each profile extended 236 m with 4-m electrode spacing. Data were analyzed and inverted with Res2DinV software and validated against lithological logs. The ERT inversion revealed three subsurface geoelectric layers: (1) massive rhyolite ignimbrite and basalt (>2000 Ωm); (2) weathered rhyolite ignimbrite and basalt (200–2000 Ωm); and (3) topsoil (4–200 Ωm). These results are key for effective water resource management and highlight ERT's utility in similar geological settings.

A hybrid technique to enhance the rainfall-runoff prediction of physical and data-driven model: a case study of Upper Narmada River Sub-basin, India

Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments.

Combining residual convolutional LSTM with attention mechanisms for spatiotemporal forest cover prediction

Understanding spatiotemporal variations in forest cover is crucial for effective forest resource management. However, existing models often lack accuracy in simultaneously capturing temporal continuity and spatial correlation. To address this challenge, we developed ResConvLSTM-Att, a novel hybrid model integrating residual neural networks, Convolutional Long Short-Term Memory (ConvLSTM) networks, and attention mechanisms. We evaluated ResConvLSTM-Att against four deep learning models: LSTM, combined convolutional neural network and LSTM (CNN-LSTM), ConvLSTM, and ResConvLSTM for spatiotemporal prediction of forest cover in Tasmania, Australia. ResConvLSTM-Att achieved outstanding prediction performance, with an average root mean square error (RMSE) of 6.9% coverage and an impressive average coefficient of determination of 0.965. Compared with LSTM, CNN-LSTM, ConvLSTM, and ResConvLSTM, ResConvLSTM-Att achieved RMSE reductions of 31.2%, 43.0%, 10.1%, and 6.5%, respectively. Additionally, we quantified the impacts of explanatory variables on forest cover dynamics. Our work demonstrated the effectiveness of ResConvLSTM-Att in spatiotemporal data modelling and prediction.

Tropical cyclone-induced rainfall variability and its implications for drought in Taiwan: Insights from 1981 to 2022

This study analyzes the interplay between tropical cyclones (TCs) and drought in Taiwan over the period from 1981 to 2022, leveraging data from CHIRPS, FLDAS, IBTrACS, and Sentinel and Landsat satellite imagery. Our findings reveal a significant decline in TC-induced rainfall over the last decade, with a turning point in 2017. We establish a low to moderate positive correlation between TC-induced rainfall and subsequent spring soil moisture levels, with April exhibiting the strongest connection. This relationship strengthens in transitions from agricultural to forested areas. Additionally, our research identifies a robust correlation (r = 0.77) between TC-induced rainfall and reservoir water levels, underscoring the consistent link between TCs and hydrological drought. However, intriguing disparities emerge in water availability patterns. In 2007 and 2020, reservoirs maintained normal levels despite reduced typhoon rainfall in prior years. Conversely, in 2019, water shortages occurred despite typical TC-induced rainfall. These cases suggest that water availability is influenced by factors beyond TCs, including water demand, infrastructure, monsoon and spring rainfall, air temperature, and water management strategies. Furthermore, we uncover a symmetrical relationship between TCs and monsoon-induced rainfall, indicating their potential to balance reservoir water supply. Nevertheless, both TCs and the monsoon exhibit declining trends in the past decade, heightening the risk of spring season water scarcity in Taiwan. Effective water resource management demands consideration of natural and anthropogenic factors, encompassing land use changes, deforestation, production activities, water pollution, and policy strategies. These measures are essential to mitigate water scarcity and promote sustainability in Taiwan.