Implications For Resource Management Research Articles

Water Quality Analysis Through Satellite Images

This project report presents a comprehensive study on water quality analysis using satellite images. The increasing global demand for water resources and the growing impact of human activities on water ecosystems underscore the need for efficient and scalable methods for water quality assessment. Leveraging remote sensing technology, particularly satellite imagery, provides a promising solution for monitoring large water bodies at a regional and global scale. The project focuses on the development and implementation of a robust methodology for water quality analysis through the utilization of satellite images. Key objectives include the extraction of relevant water quality indicators such as turbidity, chlorophyll-a concentration, and dissolved organic matter. Image processing techniques, including spectral analysis and machine learning algorithms, are employed to derive quantitative information from satellite data. The study employs a multi-sensor approach, utilizing data from various satellite platforms, to enhance the accuracy and reliability of the water quality assessments. Additionally, ground truth data collected from in situ measurements and laboratory analysis are integrated into the analysis to validate and calibrate the remote sensing results. The project contributes to the advancement of remote sensing applications in environmental monitoring, providing a cost-effective and timely method for water quality assessment. The findings of this research have implications for water resource management, environmental conservation, and policy formulation. Furthermore, the developed methodology can be applied to monitor changes in water quality over time, supporting sustainable water resource management practices. The report concludes with recommendations for further research and the potential integration of the developed methodology into existing water quality monitoring systems. Overall, the project highlights the significance of satellite-based approaches in addressing contemporary challenges related to water quality on a global scale.

Event-based evaluation of operational ENSO forecasting models in 2002–2020: Implications for seasonal water resources management

Given the practical implications of El Niño-Southern Oscillation (ENSO) forecasting for developing decision support systems and its importance in global climate prediction on seasonal to interannual scales, significant efforts have been made to enhance ENSO forecasts. This study presents an event-based evaluation of ENSO forecasts and discusses the implications of such forecasts to water resources management. Using ENSO forecasts for the period 2002–2020, this study evaluates and compares the results of two types of forecasting models, i.e., statistical and dynamical models, in predicting Sea Surface Temperatures (SSTs). Forecasting skills are evaluated for distinct target seasons via two metrics, spearman correlation and mean squared error. In addition, the performance of the two types of models at different lead times are also evaluated. Results reveal that the forecasting skills of these models are comparable, both of which exhibit higher forecasting skills for the boreal fall-winter season and lower skills for the boreal spring season. Event-based analyses show that dynamical and statistical models under-forecast SST anomaly at the onset month for El Niño events, although the forecasting error diminishes with a reduced forecasting lead for most occasions. For La Niña events, SST anomaly forecasting errors could be positive or negative. It is also difficult for the models to accurately predict the quick shift from one ENSO phase to another. Factors that contribute to such challenges are discussed. The implication of ENSO forecasts for water resources management, mainly streamflow forecasts, for a regional water supply utility in the southeastern United States is also discussed. Results from this study provide insights into ENSO forecasting skills and their practical implications.

Nonprofit Human Resources: Crisis Impacts and Mitigation Strategies

This study empirically evaluates the relationships between the state and human service nonprofits’ human resources during a crisis. We employ qualitative content analysis to critically assess the experiences of 31 nonprofits that experienced the 2015 to 2017 Illinois Budget Impasse. We evaluated the nonprofits’ strategic human resource management implications through a resource dependency lens at three levels: micro-, meso-, and macro-. Human service nonprofits pull from a toolbox of strategies in surprising ways. Strategy choices were intrinsically linked to the impacts experienced by the individual workers (micro-) and organization (meso-). Micro-level impacts included additional emotional labor and reduced benefits, while meso-level impacts included loss of capacity and short-term planning changes. Finally, the sector-level impacts included a multipronged brain drain of the nonprofit human resource industry. The findings are helpful for nonprofit employees, managers, policy-makers, and anyone concerned about the delivery of social services by nonprofits during crises.

Exploring the Enablers of Organisational Inclusion: An Interpretive Structural Modelling Approach

This research paper aims to identify and structure the enablers that promote organisational inclusivity, fostering a workplace that values diversity and inclusion. Using Interpretive Structural Modelling (ISM), the study identifies the following four clusters of enablers: autonomous, linkage, dependent and driving. Furthermore, the research findings suggest that inclusive leadership acts as a key driving factor for promoting inclusivity in the organisation. The study offers practical implications for human resource managers to develop targeted strategies to create an inclusive culture and enhance overall organisational performance by recognising and prioritising the identified enablers.

Hydrological response to land use/land cover changes in Ethiopian basins: a review

Hydrological response to land use/land cover changes in Ethiopian basins: a review

Characterization of the future northeast monsoon rainfall based on the clustered climate zone under CMIP6 in Peninsular Malaysia

The Northeast monsoon (NEM) exerts a significant influence on the climate system of Peninsular Malaysia (PM) and has profound implications for water resource management, flood control, and disaster preparedness in the region. This study employed CHIRPS rainfall data from 1981 to 2022 at a spatial resolution of 0.25° to generate climate zones. The performance of global climate models (GCMs) in replicating monthly rainfall was evaluated using multiple statistical metrics, with the top-ranked ones being used for the generation of future projection downscaled ensemble models. The NEM months (November, December, January, February, and March) were used as input parameters to form clustered homogeneous groups using the Ward hierarchical method. The optimal number of climate zones was determined using the elbow method, silhouette width values, and spatial evaluation. Ultimately, a 7-cluster solution was chosen due to its consistency and meaningful cluster formation. The zones were categorized into two regions: the West Coast and the East Coast, based on their geographical location and distinctive monthly NEM rainfall patterns. The analysis of the probability distribution function (PDF) showed that rainfall in the West Coast region had a more spread-out pattern with varying amounts, while rainfall in the East Coast region was more concentrated and consistent. The Northeast climate zone received the highest rainfall, attributed to its proximity to the South China Sea, mountainous areas, and the NEM convergence zone. In contrast, the Northwest and Lower Central-West climate zones received the lowest rainfall due to the rain shadow effect, limited moisture availability, and distance from major moisture sources. The CMCC-ESM2, CMCC-CM2-SR5, EC-Earth3, and CanESM5 consistently ranked among the highest performing models. After linear scaling bias correction, validated GCMs showed significant improvements, with improvements in MAE, NRMSE, PBIAS, RSR, NSE, MD, CP, R2, and KGE by 11–45%, 15–47%, 96–100%, 15–47%, 10–79%, 5–37%, 68–113%, 11–73%, and 9–51%, respectively. Analysis of NEM season rainfall patterns across climate zones indicated consistent mid-future increases for the Northwest and Lower Central-West zones (12.4–19.8%) and the Upper Central-West and Southwest zones (0.2–5.4%) across all scenarios. In the far-future, the Southeast zone saw an overall increase in rainfall (4.9–11.4%), while the Central-West zone exhibited mixed results (−4.0 to 5.6%). The results also highlighted spatial variability and the need to consider specific climate zones when assessing the impacts of climate change on the NEM, emphasizing the importance of incorporating these projections into long-term planning and decision-making processes.

Climate Change Impacts on Precipitation Dynamics in the South Marmara Region of Turkey

Understanding the dynamics of precipitation patterns is crucial for effective water management strategies, especially in regions vulnerable to the impacts of climate change. This study investigates the projected changes in annual and seasonal precipitation across the South Marmara Region of Turkey by comparing the averages of the reference period (1971-2000) with those of the future period (2061-2090). Employing multiple climate models (GFDL, HADGEM, and MPI) and Representative Concentration Pathways (RCP4.5 and RCP8.5), the analysis includes Mann-Kendall trend tests and Sen's slope method to determine trends in precipitation patterns. Key findings reveal significant variability in precipitation projections among different models and scenarios, with implications for water resource management, agriculture, and ecosystem resilience in provinces such as Çanakkale, Balıkesir, Bursa, Bilecik, and Yalova. According to the annual rainfall change rates relative to the reference period, Balıkesir province stands out as the most resilient province against climate change with average rates of 8.81% and 7.09% under the HADGEM and MPI model simulations, respectively. Regarding seasonal variations, Bilecik province is expected to experience a significant decrease in rainfall, reaching up to -53.78% under the MPI RCP8.5 scenario. In terms of within-period changes in annual rainfall values, the strongest declining trend was identified with Z=-2.03 in Bilecik province under the MPI RCP8.5 scenario conditions by the Mann-Kendall test. On the other hand, for seasonal variations, Bursa province demonstrates the most robust decreasing trend under the GFDL RCP4.5 conditions (Z=-2.89). The study emphasizes the importance of considering spatially varying precipitation patterns and potential shifts in atmospheric circulation for sustainable water resource management amidst climate variability and change in the South Marmara region. These findings provide critical insights for policymakers and stakeholders involved in developing adaptive strategies to address the challenges posed by future climate scenarios.

Ethnobotanical study of medicinal plants by indigenous community of Aek Guo Village, Mandailing Natal District, Indonesia

Abstract. Rambey R, Nelasufa F, Athoriez APM, Solihin, Rahmawaty, Susilowati A, Afifuddin Y. 2024. Ethnobotanical study of medicinal plants by indigenous community of Aek Guo Village, Mandailing Natal District, Indonesia. Biodiversitas 25: 1046-1056. Aek Guo Village, Mandailing Natal District, Indonesia, falling under the jurisdiction of Forest Management Unit or Kesatuan Pengelolaan Hutan (KPH) IX Panyabungan, serves as a home for the Mandailing indigenous tribe, primarily engaged in agriculture with a significant dependence on the forest for sustenance. Within the cultural fabric of the Aek Guo community, there exists a profound tradition of employing medicinal plants as an alternative form of treatment, a practice deeply rooted in ethnobotanical knowledge passed down through generations. The study aimed to identify the diverse array of medicinal plants, their utilization practices, and key parameters such as Use Value (UV), Family Use Value (FUV), Plant Part Value (PPV), and Fidelity Level (FL) associated with their applications. A comprehensive approach that integrates qualitative and quantitative methods, the study involves semi-structured interviews and meticulous documentation. The findings reveal a rich repertoire of 66 plant species distributed across 36 families, constituting a vital component of the Aek Guo community. Noteworthy is the pre-eminence of ginger (Zingiber officinale) with the highest UV value (0.68), while paku gajah (Angiopteris evecta) being the lowest (0.01). Family Use Value (FUV) demonstrated variations, with Fabaceae leading the chart (0.39), and Simaroubaceae recording the lowest value (0.01). A closer examination of Plant Part Value (PPV) indicated a preference for leaves, comprising 56.06% of the utilized plant parts, while flowers contribute minimally at 1.51%. Fidelity Level (FL) values, crucial for assessing the significance of particular plants within the community, peak for kumis kucing (Orthosiphon aristatus) and ginger (Z. officinale) at 22.54%. In contrast, jerangau (Acorus calamus), pasak bumi (Eurycoma longifolia), salam (Syzygium polyanthum), and paku gajah (A. evecta) exhibit the lowest FL values at 1.41%. These findings contribute significantly to the broader understanding of ethnobotanical practices across different Mandailing communities, with potential implications for conservation, healthcare, and sustainable resource management.

Seasonal dynamics of water and sediment in the permafrost basin of the Qinghai‐Tibet plateau and its driving factors

AbstractClimate change has regulated cryosphere‐fed rivers and altered interannual and seasonal sediment dynamics, with significant implications for terrestrial material cycles and downstream aquatic ecosystems. However, there has been a notable scarcity of research focusing on the patterns of water–sediment transport within these permafrost zones. Integrating 6 years (2017–2022) of in‐situ observational data from FengHuoShan basin with the partial least squares‐structural equation modelling (PLS‐SEM) method, we analyse the driving factors, characteristics and seasonal patterns of the water–sediment transport process. We observed a gradual increase in both suspended sediment flux (SSF, Mt/yr) and runoff (Q, km3/yr) within the basin, with annual growth rates of 1.34%/yr and 0.75%/yr, respectively. It is worth noting that these growth rates exhibit seasonal variations, with the highest values observed in spring (SSF: 1.76%/yr, Q: 1.71%/yr). This indicates that the response to climate change is more pronounced in spring compared with summer and autumn. Through mathematical statistics and the PLS‐SEM model, we found that temperature plays a predominant role in the dynamics of water–sediment in both spring and autumn, whereas rainfall exerts a more significant impact during the summer. Most suspended sediment concentration (SSC, kg/m3) peak events throughout the year are primarily driven by rainfall. Affected by the freeze–thaw cycle of permafrost, SSC and discharge (Q, m3/s) exhibit distinct seasonality. SSC and Q demonstrate a clockwise trend; both Q and SSC begin to increase from May and peak in August before declining. The insights gleaned from this study hold significant implications for water resource management and soil conservation strategies in the region, particularly in the face of ongoing climatic changes characterized by warming and increased humidity.

Exploring potential trade-offs in outdoor water use reductions and urban tree ecosystem services during an extreme drought in Southern California

In Southern California cities, urban trees play a vital role in alleviating heat waves through shade provision and evaporative cooling. Trees in arid to semi-arid regions may rely on irrigation, which is often the first municipal water use to be restricted during drought, causing further drought stress. Finding a balance between efficient water use and maintaining tree health will be crucial for long-term urban forestry and water resources management, as climate change will increase drought and extreme heat events. This study aimed to quantify how urban tree water and carbon fluxes are affected by irrigation reductions, and how that relationship changes with tree species and temperature. We used an ecohydrologic model that mechanistically simulates water, carbon, and energy cycling, parameterized for 5 common tree species in a semi-arid urban area. We simulated a range of irrigation reductions based on average outdoor water use data from the city for a recent extreme drought as well as with warmer temperatures. We then analyzed the response of model outcomes of plant carbon fluxes, leaf area index (LAI), and water use. Results show that reducing irrigation up to 25%, a comparable amount as the California state mandate in 2014, has minimal effects on tree primary productivity and water use efficiency. We found that transpiration was linearly related to irrigation input, which could lead to a short-term loss of evaporative cooling with irrigation reductions during drought. However, primary productivity and LAI had a nonlinear response to irrigation, indicating shade provision could be maintained throughout drought with partial irrigation reductions. Results varied across tree species, with some species showing greater sensitivity of productivity to both irrigation reductions and potentially warmer droughts. These results have implications for water resources management before and during drought, and for urban tree climate adaptation to future drought.

Exploring the Role of HRM in Facilitating Collaboration and Knowledge Sharing in Decentralized Organizational Structures

This research investigates the role of human resources (HR) in facilitating collaboration and knowledge sharing in decentralized organizational structures. Through a systematic literature review approach, this study collects and analyzes various relevant literature from international databases. The research results highlight the importance of collaborative leadership, awareness, and organization of inter-organizational processes in facilitating collaboration in decentralized contexts. Additionally, factors such as individual motivation, management support, and technology also influence knowledge sharing practices within organizations. Empirical studies confirm that decentralized structures play an important role in facilitating collaboration and knowledge sharing, which in turn improves organizational performance and innovation. In conclusion, a deeper understanding of the dynamics of collaboration and knowledge sharing in decentralized structures has important implications for human resource management and organizational development.

Development of high‐resolution monthly precipitation datasets for geomorphological applications in the Tianshan Mountains from 2000 to 2050

AbstractPrecipitation has a fundamental influence on landscape evolution and erosion rates. Accurate predictions of precipitation changes are crucial for landscape evolution modelling and erosion modelling. The Tianshan Mountains are known as the ‘Water Tower in Central Asia’, where precipitation is the main water supply for the surrounding lakes and rivers and also supports the population and economic development in the plain areas. This study developed a global climate model (GCM) and digital elevation model (DEM)‐based downscaling method and simulated high‐resolution monthly precipitation in Tianshan from 2000 to 2050, with a resolution of 90 m. The datasets developed from 30 surface observation stations had a high degree of precision and simulated the precipitation processes well in the historical period. The simulation results indicated that precipitation will increase at a rate of 11 mm/decade under the SSP245 scenario and 5 mm/decade under the RCP4.5 scenario from 2022 to 2050. Spatially, precipitation was projected to increase in the central and eastern regions and decrease in the western region. In the Kaidu, Urumqi and Manas river basins in the central Tianshan Mountains, precipitation was projected to increase at a rate of 20 mm/decade. The datasets developed here have important theoretical and practical implications for water resource management, and sustainable development in Central Asia.

Effect of Transplanting Date and Harvesting Period on Bulb Production of Winter Onion (Allium cepa L.)

Climate change has brought about unpredictable temperature fluctuations and altered rainfall patterns, which directly impact onion cultivation. Adjustment in planting and harvesting schedules in the context of changing climate and environmental factors is not only academically significant but also holds practical implications for agriculture, resource management, climate adaptation, economic development, and food security in Bangladesh. The present study was carried out to observe the influence of the transplanting and harvesting time on maximizing onion production. The experiment followed a RCB design with three replications, involving the cultivation of BARI Piaz-1 as the test crop with varying transplanting dates on November 1st, November 15th, and December 1st, and harvesting periods at 120, 130, and 140 days after planting (DAP). The study's results indicated that the choice of transplanting date and harvesting period had significantly influenced onion yield and yield attributes. Improved performances, including increase in plant height, number of leaves per plant, neck diameter, bulb diameter, bulb length, bulb weight, and overall yield were observed when onions were transplanted on November 15th and harvested after 130 days from planting. The highest yields, specifically 10.32 t/ha in 2018 and 11.33 /ha in 2019, were achieved under this conditions. Conversely, the lowest yields 6.28 t/ha in 2018 and 6.70 /ha in 2019 were obtained when onions were transplanted on December 1st and harvested after 120 days from planting. Additionaly, early planting (November 1st) exhibited a higher incidence of bolting, while late planting (December 1st) showed a higher disease incidence. Therefore, optimizing transplanting dates to align with November 15th and a 130-day harvesting period proved to be the most effective strategy for maximizing onion yield and other attributes, while late planting and early harvesting adversely affected these key attributes.

The Literature Review Analysis of The Human Resources Development in The Industry Era 4.0 Towards The Era of society 5.0

Since the beginning of the First Industrial Revolution (Industry 1.0), humans have realized the potential of technology as a tool for sustainable progress from time to time. During the Second Industrial Revolution (Industry 2.0), manufacturers began to switch to using electricity for assembly lines and mass production, which started in the 1870s. Then, with the application of electronic and information technology (IT), the concept of automation in the manufacturing industry was introduced during the Third Industrial Revolution (Industry 3.0) in the 1970s. In the context of the Fourth Industrial Revolution (Industry 4.0), the rapid progress of digital technology has changed the industrial world significantly, giving birth to what is called Industry 4.0. At this time human nature is increasingly complex in adapting to rapid change. However, along with advances in technology, new challenges have emerged in managing human resources effectively. Along with the Industrial Revolution, a new concept emerged known as Society 5.0, which encourages the integration of technology and social life outside of society. In the Society 5.0 era, society is expected to be able to quickly adapt to technological advances while maintaining human dignity in their professional lives. Therefore, it is important to understand how human evolution can adapt to this new paradigm. This research aims to explore the development of human resources in the context of the transition from Industry 4.0 to Society 5.0. This research uses the Systematic Literature Review (SLR) research method, which explores the development of human resources in facing the era of society 5.0, which is then prepared as a basis for determining strategic management of human resources using the PRISMA pattern. The research results show that there are implications for human resource management, organizational leadership, and job development in facing the challenges of Industry 4.0 and maximizing the opportunities provided by Society 5.0.

Salinisation of drinking water ponds and groundwater in coastal Bangladesh linked to tropical cyclones

Salinity is a widespread problem along the Asian coast, mainly in reclaimed lands where most people live. These low-lying areas are vulnerable to impacts from tropical cyclone induced storm surges. The role of such surges on the long-term salinity of water resources, particularly the salinisation of drinking water ponds, a key water resource, requires further investigation. Here we show, using high-resolution measurements of pond hydrology and numerical modelling, that episodic inundation events cause the widespread salinisation of surface water and groundwater bodies in coastal areas. Sudden salt fluxes in ponds cause salinity build-up in the underlying sediments and become a source of salinity. Rapid clean-up of drinking ponds immediately after a surge event can significantly minimize these salinity impacts, which are likely to increase under climate change. Our study has implications for coastal land use and water resources management in tropical deltas.

Hydrological responses to historical and predicted land use/land cover changes in the Welmel watershed, Genale Dawa Basin, Ethiopia: Implications for water resource management

Study regionWelmel watershed, Genale-Dawa Basin, Ethiopia. Study focusThis study aimed to analyze the hydrological impacts of land use/land cover (LULC) changes in the Welmel watershed for 1990–2020 and to predict the LULC change impact on the hydrological status in the year 2050. The hybrid land use classification technique for classifying Landsat images; Cellular-Automata Markov (CA-Markov) for prediction of the 2035 and 2050 LULC; and the Soil and Water Assessment Tool (SWAT) for hydrological modeling were employed in the analyses. New hydrological insights for the regionThe findings revealed that from 1990 to 2020, significant alterations in the hydrological regime occurred due to historical LULC changes in the Welmel watershed, which are expected to continue in the 2035 and 2050 periods. Forest cover loss and expansion of agricultural areas, which occurred from 1990 to 2020, have resulted in increased annual flow (6.18%), wet seasonal flow (10.21%), and surface runoff (25.32%), and reduced dry seasonal flow (6.34%), groundwater flow (17.17%, and evapotranspiration (0.36%). The 2035 and 2050 LULC indicate further increased annual flow, wet seasonal flow, and surface runoff, and reduced dry seasonal flow, groundwater flow, and evapotranspiration. The study also stated the hydrological impacts of the LULC changes at the sub-watershed scales. More noticeable changes in hydrological components have been observed at the sub-watershed scale, mainly associated with the uneven spatial distribution of LULC changes. The findings provide valuable insights for policymakers, water resource managers, and local communities in formulating strategies to enhance water resource management practices in the Welmel watershed and similar regions. It could also be for researchers and Academia who can be benefited and it can be used to supplement global goals like Sustainable Development Goal 6 (SDG 6): ensure the availability and sustainable management of water and sanitation for all.

High-resolution land surface modeling of the irrigation effects on evapotranspiration over the Yellow River basin

Accurate estimation of irrigation-induced evapotranspiration (ET) is critical for understanding the terrestrial water cycle. However, current estimation has large uncertainties due to the model limitations and the lack of accurate water use data. In this study, a water-energy balanced irrigation scheme is incorporated into the Conjunctive Surface-Subsurface Process version 2 (CSSPv2) model to improve the hydrological simulations over the Yellow River basin (YRB) where irrigation is extensive. The coupled model (CSSPv2-IRR) is calibrated against long-term water use data from provincial survey, and it is used to perform a 6-km resolution simulation over the YRB during 1991–2017. The results show that the coupled model can well reproduce hydrological processes within the basin, and the simulated irrigation amount, ET and surface soil moisture largely agree with the validation datasets. Specifically, the annual irrigation amount and irrigation-induced ET changes from the CSSPv2-IRR are more reasonable than ISIMIP2a and reanalysis products, with a reduction in bias by 79.5–97.3 % and 81.2–98.8 % respectively. The CSSPv2-IRR model simulation suggests that irrigation-induced annual ET change is 26.4 mm/year during 1991–2017, contributing to an 8.9 % increase in annual ET over the YRB, and the increase can reach 44.3 % over major irrigation districts. In addition, the contribution of irrigation to long-term increasing trend in ET over the irrigation districts in the upper reach is greater than the contribution of climate change. This study suggests a growing control of irrigation on the ET over the YRB, which has implications for regional water cycle and water resources management.

Temporal cluster-based local deep learning or signal processing-temporal convolutional transformer for daily runoff prediction?

Water scarcity poses a major obstacle to sustainable development, and precise discharge prediction plays a vital role in enabling effective water resource management. This study investigated improved prediction techniques for nonstationary time series. The study evaluated the effect of signal processing techniques and blending approaches on the performance of deep learning models for daily discharge prediction. It also compared the performance of cluster-based local modeling with hybrid signal processing-deep learning approaches. Two robust deep learning methods, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), along with a powerful signal processing approach called discrete wavelet transform, were utilized for prediction of daily discharge. Three blending approaches were assessed: 1) decomposing both inputs and target, 2) decomposing only the target, and 3) decomposing only the inputs and then blending them with deep learning models. Also, a new hybrid deep learning based model namely discrete wavelet transform-Temporal Convolutional Transformer (DWT-TCT) was developed. The results showed that a single-output wavelet transform-deep learning model (3rd blending approach) outperformed multi-output models, demonstrating a relative enhancement of up to 56% for the LSTM model and 51% for the CNN model. Furthermore, temporal cluster-based local modeling displayed promising performance, resulting in an improvement of up to 18% in NRMSE compared to the wavelet transform-deep learning model, while also requiring less computational cost. The successful results of the temporal cluster-based local modeling approach provide a beneficial alternative to hybrid signal processing-deep learning models. In addition the results showed that the proposed DWT-TCT model outperformed all other models with NRMSE ranges from 6.8% to 16.2% in the study areas. The results have implications for hydrology and water resources management, as they can be used to develop more precise and effective models for predicting discharge in view of nonstationarity.

Providing Enhanced Insights into Groundwater Exchange Patterns through Downscaled GRACE Data

The measurement of groundwater exchange between neighboring regions is a critical topic in water resource management and can usually be achieved through a combination of field investigations and the use of groundwater flow models. In this study, we employed the water balance and Darcy’s law methods, utilizing downscaled Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) data to assess groundwater exchange patterns in the Beijing-Tianjin-Hebei (BTH) region of China. Additionally, we determined the contributions of human activities and climate factors to the observed variations via residual analysis. The results revealed a consistent decrease in groundwater storage in the study area since 2008, especially in the spring and summer months. The groundwater exchange rates calculated by 1° and 0.05° groundwater storage anomalies (GWSAs) were basically consistent, and the downscaled GWSAs could better reflect the small-scale groundwater exchange characteristics. The groundwater exchange rate showed a decreasing trend from the Piedmont plain to the coastal areas. A notable trend of declining groundwater exchange between the Taihang Mountains and Piedmont plains was observed, and the downward trend gradually intensified from north to south between 2003 and 2007. After 2008, there was an increasing trend, and coastal areas exhibited the smallest amount of groundwater exchange. Human activities emerged as the predominant factor accounting for more than 90.9% of the overall reduction in groundwater storage, while climate change imposed a minimal influence on groundwater storage variations. The insights obtained in this study hold significant implications for groundwater resource planning and management in the region.

Are People More Horrible Than Ghosts? Workplace Haters and Employee Creativity

Based on the cognitive-affective personality system theory and social network theory, this study examines how three common workplace haters influence employee well-being and creativity. It also explores the negative moderating effects of personal social capital and organizational pride. The study utilized 496 valid supervisor-employee dyads (comprising 286 direct supervisors and 496 employees) from 13 companies in Taiwan. The findings revealed that: (1) Workplace haters negatively affect employee well-being; (2) Employee well-being positively affects employee creativity; (3) Employee well-being mediates the relationship between workplace haters and employee creativity; (4) Personal social capital moderates the relationship between workplace haters and employee well-being; and (5) Organizational pride moderates the relationship between employee well-being and employee creativity. Implications for behavioral researchers and human resource managers are discussed.