Publications

@article{Pathak2026b,
title = {A multi-model statistical, machine learning, and deep learning framework for landslide susceptibility in Nepal’s mid-hills},
author = {Lalit Pathak},
url = {https://doi.org/10.1007/s44475-026-00029-0},
doi = {10.1007/s44475-026-00029-0},
issn = {3059-4936},
year = {2026},
date = {2026-04-29},
journal = {Discover Hazards},
volume = {2},
number = {1},
pages = {20},
abstract = {Landslides pose significant threats to biodiversity, life, and infrastructure in mountainous regions, making susceptibility mapping essential for disaster risk reduction. This study presents a comprehensive multi-model comparison of statistical, machine learning, deep learning, and ensemble approaches for landslide susceptibility mapping in the eastern Chure region of Nepal, characterized by fragile Sub-Himalayan lithology, intense monsoon precipitation, and expanding infrastructure. Ten models were evaluated: Frequency Ratio (FR), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, LightGBM, Artificial Neural Network (ANN), Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Twelve conditioning factors were analyzed, including soil type, topographic wetness index, elevation, slope, stream power index, aspect, lithology, distance to roads, land use, distance to rivers, and curvature. The dataset included 123 training and 53 testing landslide locations with balanced non-landslide samples. Random Forest showed the best performance (Test AUC = 0.928; Accuracy = 0.838), followed by XGBoost (Test AUC = 0.881) and LightGBM (Test AUC = 0.844). Deep learning models showed lower performance (ANN ≈ 0.773; CNN ≈ 0.766; DNN ≈ 0.765), while LSTM produced the weakest result (Test AUC = 0.622). The Top 3 ensemble (RF, XGBoost, LightGBM) produced coherent spatial patterns and strong class separability. Classification of ensemble outputs into five susceptibility zones indicated that 22.7% of the watershed falls within High and Very High susceptibility classes. These results demonstrate that ensemble machine learning approaches provide robust landslide susceptibility assessments and can support land-use planning and disaster risk management in landslide-prone regions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Sitaula2026,
title = {Chemical Characterization and Spatial Distribution of Microplastics in the Surface Water of the Arun River Basin, Central Himalaya, Nepal},
author = {Sagar Sitaula and Niru Basnet and Somy Bhattarai and Rupesh Bohara and Ramesh Raj Pant and Bishal Dahal and Alisha Dahal and Basant Giri and Bhanu Bhakta Neupane},
url = {https://doi.org/10.1007/s11270-026-09239-0},
doi = {10.1007/s11270-026-09239-0},
issn = {1573-2932},
year = {2026},
date = {2026-02-01},
journal = {Water, Air, & Soil Pollution},
volume = {237},
number = {9},
pages = {549},
abstract = {Glacier-fed Himalayan Rivers are vital lifelines for South Asia, sustaining ecosystems, agriculture, and millions of people across national boundaries. Although microplastic pollution is reported in several water systems, the transboundary rivers in the central Himalayas remain critically understudied. This research investigates the chemical characteristics and spatial distribution of microplastics in the surface water of the Arun River, a major transboundary tributary of the Ganges system, spanning China, Nepal, and India. Surface water samples (n = 28) were collected in duplicate from 14 sites across the Arun River Basin, Koshi Province, Nepal, selected based on land use and land cover, spatial distribution, and anthropogenic activities. Microplastics were detected in all samples, with an average concentration of 30 ± 3.2 particles per liter (range: 23–35 particles/L). Microplastic concentrations were notably elevated in proximity to cropland and built-up zones, with peak levels recorded at sites influenced by construction activities and agricultural runoff. Intriguingly, comparable concentrations were also detected at relatively undisturbed locations, indicating the potential for long-distance transport mechanisms. Most particles were black (78%), fragment-shaped (69%), and small-sized (32–100 μm; 95%). Dominant polymers included polyethylene, polyethylene terephthalate, polypropylene, and polystyrene. Multivariate analysis and land use mapping indicated higher microplastic loads in urbanized and construction-impacted zones, while tributary inflows contributed to dilution. Unlike global patterns of downstream accumulation, microplastic concentrations in the Arun River peaked in its middle reaches, reflecting localized land use dynamics. These findings highlight the influence of human activities and call for targeted interventions to reduce plastic pollution in transboundary Himalayan River Systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{PATHAK2026181239,
title = {Integrated multi-hazard assessment for climate-resilient watershed management: A transferable prioritization framework from Nepal's Mid-Hills},
author = {Lalit Pathak and Kamana Joshi and Pradeep Ghimire and Rabin Malla},
url = {https://www.sciencedirect.com/science/article/pii/S0048969725028815},
doi = {https://doi.org/10.1016/j.scitotenv.2025.181239},
issn = {0048-9697},
year = {2026},
date = {2026-01-01},
journal = {Science of The Total Environment},
volume = {1012},
pages = {181239},
abstract = {Mountain watersheds face cascading multi-hazard risks that require integrated assessment frameworks to guide effective adaptation options, yet current approaches focus on single hazards or lack spatial precision for local-scale planning. Himalayan mid-hill watersheds exemplify this challenge, facing escalating hazards and soil erosion amid rapid land use transitions. This study integrated Maximum Entropy (MaxEnt) modeling, the Revised Universal Soil Loss Equation (RUSLE), and multi-temporal land use analysis within a spatially explicit Multi-Criteria Decision Analysis (MCDA) for watershed prioritization. Applied to the 703.36 km2 Marin watershed in central Nepal, this framework integrated multi-source datasets including 30 m SRTM DEM, ICIMOD LULC data (2000−2022), CHIRPS rainfall, ERA5 temperature, TerraClimate drought indices, and field-verified hazard inventories. MaxEnt models achieved robust performance for landslide (AUC = 0.828 ± 0.018) and fire susceptibility (AUC = 0.798 ± 0.025) mapping. Results revealed that 27.3 % of the watershed exhibits high/very high landslide susceptibility, 24.4 % faces elevated fire risk, 18.3 % lie in flood-prone zones, and 32.9 % severe erosion (>20 t/ha/yr).MCDA integration classified 24 % of the watershed requiring critical intervention, due to steep slopes (>30°), fragile lithology, and intensive land use transitions. Sensitivity analysis confirmed framework robustness with rank correlations r = 0.96–0.98 across ±20 % weight variations, while Monte Carlo simulation (n = 1000) provided uncertainty bounds for prioritization decisions (σ = 0.03–0.06). This transferable methodology addresses critical gaps in mountain watershed management by providing a scalable prioritization framework applicable to similar physiographic regions globally.},
keywords = {Climate adaptation, MaxEnt modeling, Mountain hydrology, Multi-hazard assessment, RUSLE, Watershed prioritization},
pubstate = {published},
tppubtype = {article}
}

@article{Uprety2026,
title = {Assessment of groundwater governance in the transboundary Cambodia-Mekong River Delta Aquifer System: Generic framework formulation and application},
author = {Tungish Uprety and Sangam Shrestha and Saurav KC and Mohanasundaram Shanmugam},
url = {https://doi.org/10.1007/s00267-026-02388-2},
doi = {10.1007/s00267-026-02388-2},
issn = {1432-1009},
year = {2026},
date = {2026-01-01},
journal = {Environmental Management},
volume = {76},
number = {3},
pages = {89},
abstract = {Groundwater, constituting approximately 30% of global freshwater reserves, is a critical resource for domestic, agricultural, industrial, and environmental needs in the Lower Mekong Region. The transboundary Cambodia-Mekong River Delta Aquifer System (CMRDAS), shared by Cambodia and Vietnam, plays a vital role in sustaining regional livelihoods but faces increasing pressures from urbanization, climate change, and weak governance arrangements. Despite its importance, a comprehensive quantitative framework for assessing groundwater governance in transboundary aquifer systems remains lacking. This study addresses this gap by developing a generic, indicator-based framework for assessing transboundary groundwater governance. The framework integrates four governance dimensions, namely Technical, Legal and Financial, Institutional, and Operational, operationalized through 27 indicators and synthesized into a Transboundary Groundwater Governance Index (TGGI) measured on a standardized scale from 0 to 3. The framework was validated through expert-based assessment and subsequently applied to the CMRDAS using a structured survey of groundwater governance stakeholders from the Lower Mekong Region. The results indicate that groundwater governance in the CMRDAS remains at an incipient stage, with a TGGI score of 0.84, reflecting limited progress in transboundary coordination. Significant governance gaps persist across operational, institutional, legal, and financial dimensions, particularly at the transboundary level compared to national contexts. Governance provisions and institutional capacity were consistently more developed in Vietnam than in Cambodia, highlighting asymmetries in governance maturity and implementation capacity. The study identifies key priorities for strengthening groundwater governance, including reallocating financial resources, enhancing institutional capacity, fostering inclusive and participatory policy frameworks, improving transparency and data sharing, and reinforcing local and transboundary coordination mechanisms. Overall, the framework provides a replicable and policy-relevant tool to support the diagnosis and improvement of groundwater governance in transboundary aquifer systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Joshi2026,
title = {Monitoring chlorophyll-a in Phewa Lake, Nepal using satellite images and ensemble-based learning},
author = {Prayon Joshi and Richard Storey and Srijit Sharma and Bhogendra Mishra},
url = {https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1717629},
doi = {10.3389/fenvs.2026.1717629},
issn = {2296-665X},
year = {2026},
date = {2026-01-01},
journal = {Frontiers in Environmental Science},
volume = {14},
abstract = {Lakes in monsoon-dominated regions are highly vulnerable to climate change and eutrophication. Chlorophyll-a, a measure of phytoplankton biomass, is a critical indicator for detecting changes in trophic state. Readily available satellite images combined with machine learning techniques can enable long-term monitoring of chlorophyll-a in lakes. We evaluated 24 combinations of models and satellite images for Phewa Lake, Nepal (eight algorithms across three satellite combinations). An ensemble learning model combining a Support Vector Regression (SVR) and Random Forest (RF) based on Sentinel-2 imagery achieved the best relative performance amongst the tested models, although overall predictive accuracy was moderate. Although microwave imagery from Sentinel-1 can penetrate clouds, and therefore provide continuous monitoring during periods of persistent cloud cover, Sentinel-2 achieved higher accuracy (MAE = 0.2 mg/m3), due to the availability of high spectral resolution images and red-edge sensitivity. Analysis of Sentinel-2 images of Phewa Lake from 2018 to 2024 revealed relative seasonal patterns of chlorophyll-a consistent with limnological processes, with relatively higher concentrations during post-monsoon than other seasons. Model-generated maps showed relatively homogeneous spatial distributions of chlorophyll-a in post-monsoon, winter, and pre-monsoon, but highly heterogeneous and dynamic spatial patterns during monsoon, a season of high inflows and mixing. Remote sensing combined with machine learning offers a low-cost and scalable approach for freshwater monitoring that is particularly valuable in monsoonal and low-income countries. In Nepal, which has more than 5,000 lakes, such approaches have strong potential for national-scale monitoring and management. An effort to implement and validate machine learning models in other lakes can be beneficial for sustainable monitoring.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Pathak2026c,
title = {A multi-model statistical, machine learning, and deep learning framework for landslide susceptibility in Nepal’s mid-hills},
author = {Lalit Pathak},
url = {https://doi.org/10.1007/s44475-026-00029-0},
doi = {10.1007/s44475-026-00029-0},
issn = {3059-4936},
year = {2026},
date = {2026-01-01},
journal = {Discover Hazards},
volume = {2},
number = {1},
pages = {20},
abstract = {Landslides pose significant threats to biodiversity, life, and infrastructure in mountainous regions, making susceptibility mapping essential for disaster risk reduction. This study presents a comprehensive multi-model comparison of statistical, machine learning, deep learning, and ensemble approaches for landslide susceptibility mapping in the eastern Chure region of Nepal, characterized by fragile Sub-Himalayan lithology, intense monsoon precipitation, and expanding infrastructure. Ten models were evaluated: Frequency Ratio (FR), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, LightGBM, Artificial Neural Network (ANN), Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Twelve conditioning factors were analyzed, including soil type, topographic wetness index, elevation, slope, stream power index, aspect, lithology, distance to roads, land use, distance to rivers, and curvature. The dataset included 123 training and 53 testing landslide locations with balanced non-landslide samples. Random Forest showed the best performance (Test AUC = 0.928; Accuracy = 0.838), followed by XGBoost (Test AUC = 0.881) and LightGBM (Test AUC = 0.844). Deep learning models showed lower performance (ANN ≈ 0.773; CNN ≈ 0.766; DNN ≈ 0.765), while LSTM produced the weakest result (Test AUC = 0.622). The Top 3 ensemble (RF, XGBoost, LightGBM) produced coherent spatial patterns and strong class separability. Classification of ensemble outputs into five susceptibility zones indicated that 22.7% of the watershed falls within High and Very High susceptibility classes. These results demonstrate that ensemble machine learning approaches provide robust landslide susceptibility assessments and can support land-use planning and disaster risk management in landslide-prone regions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Pathak_Malla_Lamichhane_Dixit_2025,
title = {Resilience Under Threat: Climate Change Impacts and Adaptive Responses of Nepalese MSMEs},
author = {Lalit Pathak and Rabin Malla and Binaya Kumar Lamichhane and Amod Mani Dixit},
url = {https://www.nepjol.info/index.php/jes/article/view/80594},
doi = {10.3126/jes.v11i1.80594},
year = {2025},
date = {2025-07-01},
journal = {Journal of Environment Sciences},
volume = {11},
number = {1},
pages = {53–62},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Pokhrel_KC_2025b,
title = {A Contextualized Groundwater Governance Framework for Sarlahi, Nepal},
author = {Ayushmita Pokhrel and Saurav KC},
url = {https://www.nepjol.info/index.php/injet/article/view/78658},
doi = {10.3126/injet.v2i2.78658},
year = {2025},
date = {2025-05-01},
journal = {International Journal on Engineering Technology},
volume = {2},
number = {2},
pages = {316–322},
abstract = {Effective groundwater governance is vital for sustainable resource management. This study presents a contextualized groundwater governance assessment framework applied to Barahathawa Municipality, Sarlahi, Nepal. The framework evaluates governance across four dimensions—technical, legal/institutional, cross-sector policy coordination, and operational—using 32 indicators to derive a composite Groundwater Governance Index (GGI). Barahathawa’s GGI of 1.03 reflects an early acceptable governance state. The technical dimension (midway between non-existent and basic) highlights gaps in groundwater data collection and dissemination. Legal and institutional mechanisms are approaching a basic level but are limited by the absence of comprehensive regulatory frameworks. Cross-sector policy coordination has reached an acceptable state, reflecting efforts to collaborate with local units/departments and sectoral entities, although these efforts are often informal and ad hoc. The operational dimension demonstrates initial progress toward an acceptable state, with advancements in transparency, conflict resolution, and community engagement in groundwater management discussions. However, further efforts are needed to enhance inclusivity and to establish a community-focused groundwater management action plan. This assessment framework thus provides a holistic and context-specific approach to identifying strengths and gaps in groundwater governance. By integrating insights from both experts and community stakeholders, it serves as an effective tool for understanding governance dynamics and guiding targeted improvements.},
keywords = {Governance assessment, Groundwater governance index, Groundwater management, Sustainability},
pubstate = {published},
tppubtype = {article}
}

@article{geosciences15040131,
title = {Landslides in the Himalayas: The Role of Conditioning Factors and Their Resolution in Susceptibility Mapping},
author = {Lalit Pathak and Badri Baral and Kamana Joshi and Dipak Raj Basnet and Danilo Godone},
url = {https://www.mdpi.com/2076-3263/15/4/131},
doi = {10.3390/geosciences15040131},
issn = {2076-3263},
year = {2025},
date = {2025-01-01},
journal = {Geosciences},
volume = {15},
number = {4},
abstract = {Landslides present remarkable hazards in the Himalayan region, particularly in areas with young and fragile topography. Mitigating vulnerability requires assessing susceptibility, which relies heavily on the accuracy of susceptibility maps generated through various approaches that consider different conditioning factors at various resolutions. This study, conducted in Jajarkot District within the Karnali Province of Nepal and covering 2230 km2, aims to identify suitable conditioning factors at appropriate resolutions. Sixteen factors, encompassing topography, hydrology, geology, and anthropogenic activities, were analyzed alongside a landslide inventory of 159 occurrences compiled from satellite imagery, the literature, and field surveys. A genetic algorithm (GA) was employed to determine the optimal set of conditioning factors, while Maximum Entropy (Maxent) modeling produced landslide susceptibility maps (LSM) at spatial resolutions ranging between 12.5 and 200 m. Resolution selection was guided by Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) analyses. Multicollinearity testing identified 15 influential factors, with land use ranking highest at 22.7%, followed by stream power index (SPI), drainage density, and aspect. The GA consistently highlighted land use and slope as effective factors across subset sizes. The results indicated resolutions finer than one hundred meters enhanced discrimination between landslide and non-landslide areas, emphasizing the need to balance resolution with computational resources and data availability. This study emphasizes the intricate interplay of conditioning factors, the GA’s efficacy in subset selection, and the crucial role of resolution in the improvement of susceptibility models. The findings provide practical insights for policymakers and disaster management authorities, aiding evidence-based decision making in the mitigation of landslide risk in Jajarkot and similar regions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Pathak2025,
title = {Influence of knowledge sources on climate change awareness and adaptation priorities in Central Nepal},
author = {Lalit Pathak and Kamana Joshi},
url = {https://doi.org/10.1007/s44274-025-00299-3},
doi = {https://doi.org/10.1007/s44274-025-00299-3},
issn = {2731-9431},
year = {2025},
date = {2025-01-01},
journal = {Discover Environment},
volume = {3},
number = {1},
pages = {95},
abstract = {Climate change perceptions are shaped by the interplay between formal knowledge—acquired through formal education and mass media—and lived experiences of direct environmental impacts. This study investigates how these complementary knowledge sources influence climate awareness, risk perception, and pro-environmental behavior among diverse socio-professional groups in Central Nepal. A mixed-methods survey of 300 respondents across three districts (Kathmandu, Sindhupalchowk, and Chitwan) reveals an epistemological divide: professionals emphasize global-scale risks (e.g., glacier melt) while farmers focus on immediate local impacts (e.g., crop failure, erratic rainfall). Regression analysis shows that internalized personal norms (β = 1.01, p < 0.001) are the strongest predictors of pro-environmental behavior, with formal education playing a statistically non-significant role (β = −0.007},
keywords = {Climate change perceptions, Learned knowledge, Lived experience, Nepal Himalaya, Pro-environmental behavior},
pubstate = {published},
tppubtype = {article}
}

@article{KC2025,
title = {Nexus governance in practice: a stakeholder-driven framework for groundwater sustainability in Barahathawa Municipality, Madhesh Province},
author = {Saurav KC and Sumitra KC and Ayushmita Pokhrel and Subodh Paudel and Anuj Mishra and Marlene Buchy and Manohara Khadka and Anil Aryal},
url = {https://doi.org/10.1007/s00550-025-00579-9},
doi = {10.1007/s00550-025-00579-9},
issn = {2948-1627},
year = {2025},
date = {2025-01-01},
journal = {Sustainability Nexus Forum},
volume = {33},
number = {1},
pages = {20},
abstract = {Groundwater, a critical resource in the water-food-energy-ecosystem (WEFE) nexus, underpins food security and livelihoods globally and regionally. This study applies a contextualized framework – co-developed with local stakeholders – to assess groundwater governance in Barahathawa Municipality, Madhesh Province of Nepal, where 85% of irrigation and domestic needs rely on this resource. The framework evaluates 32 indicators across technical, legal and institutional, cross-sector policy, and operational dimensions, synthesizing findings into a Groundwater Governance Index (GGI). Results reveal a transitional governance system (GGI: 1.03, "early acceptable'' stage) with fragmented technical capacity (midway between non-existent and basic) due to unmonitored extraction, unmapped recharge zones, and sparse hydrogeological data. Legal and institutional gaps such as absence of permitting systems, unenforced pollution controls, and inequitable access highlight systemic risks to sustainability. Cross-sector coordination ("acceptable'' state) and operational transparency (initial "acceptable'' state) reflect growing synergies between agriculture, urban planning, and community actors, yet marginalized groups remain underrepresented. Lens-based analysis underscores lagging "state'' governance relative to the "community'' and "market'' lens, necessitating prioritized investments in participatory hydrogeological mapping, localized regulations, and inclusive decision-making. The framework guides the management of competing needs by offering practical solutions such as better irrigation practices, gender-sensitive budgeting, and partnerships with local drillers. By bridging technical, legal, and social gaps, this approach offers a replicable model for agrarian-urbanizing regions in the western Terai belt of the country, emphasizing adaptive governance, stakeholder synergy, and data-driven policies to balance socio-economic development with groundwater resilience in the face of climate and demographic pressures.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Shrestha2023cb,
title = {Household preparedness for emergencies during COVID-19 pandemic among the general population of Nepal},
author = {Salina Shrestha and Rabin Malla and Sadhana Shrestha and Pallavi Singh and Jeevan B. Sherchand},
doi = {https://doi.org/10.1371/journal.pgph.0003475},
year = {2024},
date = {2024-09-12},
journal = {PLOS Global Public Health},
volume = {4},
number = {9},
pages = {1-16},
abstract = {The COVID-19 pandemic has negatively impacted the global economy affecting numerous people’s livelihoods. Despite preventive behaviors and advancements of vaccination, the risk of infection still exists due to the emergence of new variants of concern and the changing behavior of the SARS CoV-2 virus. Therefore, preparedness measures are crucial for any emergency. In such situations, it is important to understand preparedness behavior at the household level, as it aids in reducing the risk of transmission and the severity of the disease before accessing any external support. Our study aimed to evaluate household preparedness level for emergencies during the COVID-19 pandemic and its relationship with socio-demographic characteristics among the general population of Nepal. Data was collected through a questionnaire survey. Descriptive statistics, a Chi-square test, and logistic regression model were used for analysis. The study demonstrated that 59.2% had a good preparedness level. Good preparedness was observed among the respondents living in urban areas, those who were married, had white-collar occupations, high-education with graduate and above and high-income levels with monthly income >NPR 20,000, and were young-aged. The study findings underscore the need to develop tailored programs on preparedness prioritizing vulnerable population. It further highlights the importance of proper and consistent information flow, resources distribution, capacitating human resources and better health surveillance.},
keywords = {and Police, Critical care and emergency medicine Graduates, Medical risk factors, Mental health and psychiatry, Nepal, OVID 19, Pandemics},
pubstate = {published},
tppubtype = {article}
}

@article{NGUYEN2023101077,
title = {Assessing decentralized groundwater governance performance in the lower Mekong region: The case of Khon Kaen province, Thailand},
author = {Thi Phuoc Lai Nguyen and Preeyaporn Muenratch and Sangam Shrestha and Ashim Das Gupta and Saurav K.C.},
url = {https://www.sciencedirect.com/science/article/pii/S2352801X23001789},
doi = {https://doi.org/10.1016/j.gsd.2023.101077},
issn = {2352-801X},
year = {2023},
date = {2023-12-30},
journal = {Groundwater for Sustainable Development},
pages = {101077},
abstract = {Decentralized groundwater management has been implemented in various forms in the Lower Mekong Region (LMR) aiming to improve the sustainability and equity of groundwater (GW) management. However, fragmented implementation and unclear delegation of responsibilities have hindered the success of these approaches. The Royal Thai government has been a pioneer in introducing comprehensive groundwater policies and has implemented formal measures for GW management for over five decades, including the decentralized GW management model introduced in 2011. This study examined the performance of GW governance (GWG) in the LMR through the case of Khon Kaen province, Thailand, and using the OECD water framework with contextualized indicators. Sen's slope estimation and the Mann-Kendal test were also utilized to look into the patterns of groundwater extraction data in the study area from 2000 to 2019 in relation to the policy timeline analysis. The findings revealed a significant increase in GW extraction for business usage after the implementation of decentralization, driven by urbanization, and economic and tourist development. The research also found that the rise of new water actors with divergent interests, marginalization, the absence of non-governmental actors, and a lack of scientific and technical capacities prevent the effective GWG. However, the study discovered that the majority of GWG principles are favorably related to one another, with regulatory frameworks, defined roles and duties, and coherent policy being closely associated with most GWG principles. On the other hand, trade-offs across sectors and users were seen to have a detrimental effect on other GWG principles. The findings contribute to evolving the GWG Theory of Change (TOC), centering on GW users. This challenges the conventional idea of addressing water shortage solely through increased infrastructure, emphasizes institutional pluralism and scientific evidence for market-based mechanisms, and addresses GW use disparities.},
keywords = {Groundwater extraction, Groundwater marginalized groups, Groundwater policy, New groundwater actors, OECD water governance framework, Theory of change},
pubstate = {published},
tppubtype = {article}
}

@article{ARYAL2023101590,
title = {Spatio-temporal variability of streamflow in major and medium rivers of Nepal},
author = {Anil Aryal and Manish Shrestha and Sharad Aryal and Surabhi Upadhyay and Manisha Maharjan},
url = {https://www.sciencedirect.com/science/article/pii/S221458182300277X},
doi = {https://doi.org/10.1016/j.ejrh.2023.101590},
issn = {2214-5818},
year = {2023},
date = {2023-12-01},
journal = {Journal of Hydrology: Regional Studies},
volume = {50},
pages = {101590},
abstract = {Study region
We selected six (three on each) major and medium river basins of Nepal as a study domain for the analysis. The study areas were so selected that they represent the river basins across the country from East (Kankai basin) to West (Karnali basin).
Study focus
This study focuses on the long-term hydrologic alteration in the river flow of Nepal's medium and major rivers of different river basins. The overarching objective of the study is to evaluate the spatio-temporal change in flow magnitude, duration, frequency, timing, and rate of change in the major and medium rivers in Nepal. With continuity in the development activities, it is imperative to analyze the potential impact of human activities in the hydrologic regimes. For this, we used a set of 33 indicators from the indicator of hydrologic alteration (IHA) developed by The Nature Conservancy for the pre and post-impact period. The pre and post-impact period is defined here as the time before and after which the substantial alteration occurred, possibly due to multiple reasons. The pre and post impact periods were determined using the Pettitt statistical test carried out at the most downstream of the hydrological gauge station of each river basin. Further, the trends in the annual flow were estimated using the Mann-Kendall test, and the slope of the trend was estimated using Sen’s slope.
New hydrological insights for the region
The results showed that in the post-impact period, the mean annual discharge in major and medium rivers of Nepal was found to decrease by 5.86% and 7.94%, respectively. Except for the West Rapti River (−14.3%), the hydrologic change of 1-day maximum flow is observed to have increased in the medium rivers and decreased in the major rivers. Moreover, except for the Kankai River (+14.29%), the hydrologic change of 1-day minimum flow is observed to be reduced in both the major and medium rivers. In major rivers, the overall degree of alteration ranges from 28.7% to 38.0%, which resembles the low to mid variability range. Similarly, the result of the hydrologic alteration showed that for the medium rivers, the overall degree of alteration varied from 35.8% to 46.7%, representing the medium range of variability. This suggests that the river systems undergoing moderate hydrologic alteration have experienced moderate alteration. These rivers might be capable of sustaining a healthy ecosystem, however, could be more susceptible to other stressors like drought.},
keywords = {Change point, Climate variability, Ecosystem services, IHA, Major and medium rivers},
pubstate = {published},
tppubtype = {article}
}

@article{talampas2023,
title = {At the crossroad: Stakeholders’ perspectives from Thailand and Lao PDR towards a transboundary groundwater cooperation in the Khorat Plateau aquifer},
author = {Wendell D Talampas and Sangam Shrestha and S Mohanasundaram and Ho Huu Loc and Ashim Das Gupta and Saurav KC},
doi = {https://doi.org/10.1016/j.gsd.2023.101010},
year = {2023},
date = {2023-11-01},
journal = {Groundwater for Sustainable Development},
volume = {23},
pages = {101010},
publisher = {Elsevier},
abstract = {To understand stakeholder perceptions from Thailand and Lao PDR in promoting and supporting transboundary groundwater cooperation, a stakeholder workshop was conducted to look into the role of scientific information, groundwater resource use, institutional and legal frameworks and challenges and opportunities within the Khorat Platea transboundary aquifer. Results showed that (1) stakeholders perceived scientific information as pivotal in exposing potential problems and facilitate transformative change among users and policy makers across border, (2) groundwater resource use in the plateau area is limited to domestic and agricultural applications but increasing resource abstraction portrays a forbidding future scenario, (3) Thailand and Lao PDR have made significant strides in the decentralization of laws and policies pertaining to groundwater use but remains weak as local authorities lack the knowledge to manage and protect groundwater resources, and (4) Thailand and Lao PDR are at the crossroad of groundwater competition and cooperation and should work together to manage and protect the Khorat Plateau transboundary aquifer system. While anticipation for a transboundary cooperation may still need more efforts to take, interests and initiatives from all stakeholders must continue as initial elements for transboundary cooperation have already been achieved.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{10.2166/wcc.2023.120,
title = {Influences of teleconnections on climate variables in northern and northeastern Thailand},
author = {Arisara Nakburee and Sangam Shrestha and S Mohanasundaram and Ho Huu Loc and Manisha Maharjan},
url = {https://doi.org/10.2166/wcc.2023.120},
doi = {10.2166/wcc.2023.120},
issn = {2040-2244},
year = {2023},
date = {2023-10-01},
journal = {Journal of Water and Climate Change},
volume = {14},
number = {10},
pages = {3460-3483},
abstract = {Teleconnection events can influence normal regional weather patterns and affect weather forecast accuracy. To improve the forecast ability, the relationship between main teleconnections such as El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Madden–Julian Oscillation (MJO), and climate variables (rainfall, maximum and minimum surface temperature, vertical mixing ratio, and vertical maximum temperature) was established using lag correlation coefficient and t-test methods. The results reveal moderately significant correlations between El Niño, positive IOD and rainfall, and vertical mixing ratio, which can be associated with lower-than-usual rainfall. The coincidence between El Niño and positive IOD events can worsen drought. Even though the MJO and regional weather correlations were significant, the magnitude of correlation coefficients was negligible. In addition, the spatiotemporal distribution of ENSO shows that the strong El Niño has more influence on rainfall anomalies in the post-1980s. Since there are insufficient studies on the association between teleconnections and climate variables, especially vertical mixing ratio, our findings can benefit prediction development for teleconnection-induced regional climate anomalies for extreme events and water management preparations in northern and northeastern Thailand.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Shrestha2023c,
title = {Household preparedness for emergencies during COVID-19 pandemic among the general population of Nepal},
author = {Salina Shrestha and Rabin Malla and Sadhana Shrestha and Pallavi Singh and Jeevan B. Sherchand},
url = {https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0003475},
doi = {https://doi.org/10.1371/journal.pgph.0003475},
year = {2023},
date = {2023-09-12},
journal = {PLOS Global Public Health},
volume = {4},
number = {9},
pages = {1-16},
abstract = {PLOS Global Public Health},
keywords = {and Police, Critical care and emergency medicine Graduates, Medical risk factors, Mental health and psychiatry, Nepal, OVID 19, Pandemics},
pubstate = {published},
tppubtype = {article}
}

@article{10.2166/wcc.2023.313,
title = {Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin},
author = {Quyen Nguyen and Sangam Shrestha and Suwas Ghimire and Mohana S Sundaram and Wenchao Xue and Salvatore G P Virdis and Manisha Maharjan},
url = {https://doi.org/10.2166/wcc.2023.313},
doi = {10.2166/wcc.2023.313},
issn = {2040-2244},
year = {2023},
date = {2023-08-01},
journal = {Journal of Water and Climate Change},
volume = {14},
number = {8},
pages = {2902-2918},
abstract = {This paper aims to evaluate two machine learning (ML) algorithms, namely, convolutional neural network (CNN) and long short-term memories (LSTM) deep learning algorithms, to predict the hydrological regime of the 3S River Basin under various climate change scenarios. Climate models CMCC-CMS, HadGEM-AO2, and MIROC5 were used to predict future climate and streamflow for three future periods: near-future (2020–2050), mid-future (2050–2080), and far-future (2080–2100) under two Representative Concentration Pathways (RCPs) 4.5 and 8.5. The future projection shows an increase in mean annual temperature from 0.08 to 4.3 °C by CMCC-CMS, from 0.13 to 4.4 °C by HadGEM-AO2, and −0.07 to 4.2 °C MIROC5 models. Similarly, the annual precipitation is projected to fluctuate from 13.3 to 62.5% by CMCC-CMS, from −12.4 to 26.1% by HadGEM-AO2, and from 6.9 to 49% by the MIROC5 model. The 3S River Basin expects an increasing trend in streamflow in the Srepok and Sesan Rivers, while the Sekong is projected to have reduced streamflow. ML models predicted the increasing flood risk in the Sekong and Sesan catchments with the increase of the Q5 index in the future but a decrease in the Srepok.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Pathak_Joshi_Ghimire_2023,
title = {Estimation of soil erosion using the Revised Universal Soil Loss Equation (RUSLE) in Relation to Landslides in Mid-hills of Nepal},
author = {Lalit Pathak and Kamana Joshi and Pradeep Ghimire},
url = {https://www.nepjol.info/index.php/jes/article/view/56483},
doi = {10.3126/jes.v9i1.56483},
year = {2023},
date = {2023-07-14},
journal = {Journal of Environment Sciences},
volume = {9},
number = {1},
pages = {82–93},
keywords = {Landslides, Mid-hills, RUSLE, Soil erosion},
pubstate = {published},
tppubtype = {article}
}