Abstract. High Mountain Asia (HMA) is undergoing unprecedented warming, affecting the cryosphere – including permafrost (frozen ground) – and leading to various hazards. However, understanding the prevalence, distribution, and dynamics of these hazards and how they respond to a changing climate is challenging. Permafrost is extensive in HMA, and China makes up a significant portion of this. The permafrost area in China is about 1.6×106 km2, 66 % of which is on the Qinghai–Tibet Plateau. However, most of the scientific literature concerning permafrost in China is published in Chinese and, hence, remains largely unnoticed by the non-Chinese-speaking scientific communities. In this article, we used a systematic review to evaluate the Chinese scientific literature on permafrost-related hazards and found that the studied areas are concentrated in certain areas, especially on the Qinghai–Tibet Engineering Corridor (QTEC). The increasing amount of literature on permafrost hazards reflects the increased impact of climate warming on infrastructure built on permafrost. Not only is permafrost affecting infrastructure; these anthropogenic disturbances themselves also have amplified the occurrence of hazards around settlements and infrastructure. The literature shows the strong relationship between latitude and elevation with permafrost thickness. The permafrost classification system and nomenclature used by Chinese scientists is different to that used elsewhere, which is a potential source of confusion and deserves attention.

Abstract. Ground-based lidar data have proven extremely useful for profiling the convective boundary layer (CBL). Many groups have derived higher-order moments (e.g., variance, skewness, fluxes) from high-temporal-resolution lidar data using an autocovariance approach. However, these analyses are highly uncertain near the CBL top when the depth of the CBL (zi) is changing during the analysis period. This is because the autocovariance approach is usually applied to constant height levels and the character of the eddies is changing on either side of the changing CBL top. Here, a new approach is presented wherein the autocovariance analysis is performed on a normalized height grid, with a temporally smoothed zi. Output from a large eddy simulation model demonstrates that deriving higher-order moments from time series on a normalized height grid has better agreement with the slab-averaged quantities than the moments derived from the original height grid.

Abstract. Riparian areas serve as interfaces between terrestrial and aquatic (including glaciers and glacial lakes) ecosystems, playing a crucial role in shaping landscapes, supporting flora and fauna diversity, and supporting human communities. Thus, riparian areas maintain ecological, cultural, and socio-economic resilience, enriching communities dependent on these ecosystems. Riparian areas are of great ecological value and have immense potential for tourism. However, the touristic value of riparian zones has largely remained unexplored and is confined mainly to the area of river-based recreational activities. This paper proposes “riparian tourism” as a holistic and sustainable form of tourism that encompasses both consumptive and non-consumptive forms of tourism. The exploration of the subject and the conceptualization of this potentially globally appealing form of tourism have the potential to offer entrepreneurial and touristic opportunities, especially for local communities, thereby ensuring not only ecological but also socio-economic benefits. The paper delves into creating a conceptual framework for riparian tourism, e.g. cryo-tourism. The research in this sense contributes greatly to increasing the discourse on sustainable tourism and emphasizes the urgency to incorporate tourism and conservation actions in riparian areas which are greatly impacted by the changing climate.

Abstract. During the last few decades, a general increase in heavy rainfall events has been documented in many areas of the world. These events have caused changes in soil erosion rates and strongly affected the human activities in mountain areas by reducing the national income obtained from cultivated land. In this context, the use of fallout radiotracers can be an important tool for better understanding the consequences of climate change in these areas and proposing effective countermeasures in order to reduce soil loss. In this contribution, plot experiments carried out in southern Italy that involve the use of 137Cs and 210Pbex measurements were performed to estimate soil erosion rates during the last few decades. The overall results indicate an increase in soil erosion rates during the last 15–20 years and suggest the use of this technique to detect climate change in mountain areas.

Abstract. Nepal annually invests millions of dollars in hydropower development, with a substantial portion of these funds allocated to civil infrastructure. Climate change, which impacts geological and hydrological conditions, poses a threat to civil components throughout hydropower project construction and operation. This causes budget overruns, project delays, and adverse consequences for society and the environment, ultimately resulting in multimillion-dollar losses. The identification of risk factors and their underlying causes, stemming from the impacts of climate change, constitutes a fundamental aspect of this study. This critical analysis primarily draws upon extensive literature reviews to pinpoint these factors (risk factors arising from climate change), emphasizing their economic and human impacts. This paper highlights the risks to run-of-river hydropower infrastructures in Nepal. The findings of the study can be used to develop and implement adaptation strategies to mitigate the risks posed by climate change to Nepal's vital hydropower sector.

Abstract. The Glacio-hydrological Degree-day Model (GDM) is a distributed model, but it is prone to uncertainties due to its conceptual nature, parameter estimation, and limited data in the Himalayan basins. To enhance accuracy without sacrificing interpretability, we propose a hybrid model approach that combines GDM with recurrent neural networks (RNNs), hereafter referred to as GDM–RNN. Three RNN types – a simple RNN model, a gated recurrent unit (GRU) model, and a long short-term memory (LSTM) model – are integrated with GDM. Rather than directly predicting streamflow, RNNs forecast GDM's residual errors. We assessed performance across different data availability scenarios, with promising results. Under limited-data conditions (1 year of data), GDM–RNN models (GDM–simple RNN, GDM–LSTM, and GDM–GRU) outperformed standalone GDM and machine learning models. Compared with GDM's respective Nash–Sutcliffe efficiency (NSE), R2, and percent bias (PBIAS) values of 0.80, 0.63, and −4.78, the corresponding values for the GDM–simple RNN were 0.85, 0.82, and −6.21; for GDM–LSTM, they were 0.86, 0.79, and −6.37; and for GDM–GRU, they were 0.85, 0.8, and −5.64. Machine learning models yielded similar results, with the simple RNN at 0.81, 0.7, and −16.6; LSTM at 0.79, 0.65, and −21.42; and GRU at 0.82, 0.75, and −12.29, respectively. Our study highlights the potential of machine learning with respect to enhancing streamflow predictions in data-scarce Himalayan basins while preserving physical streamflow mechanisms.

Abstract. Ocean liming has gained attention as a potential solution to mitigate climate change by actively removing carbon dioxide (CO2) from the atmosphere. The addition of hydrated lime to oceanic surface water leads to an increase in alkalinity, which in turn promotes the uptake and sequestration of atmospheric CO2. Despite the potential of this technique, its effects on the marine ecosystem are still far from understood, and there is currently no information on the potential impacts on the concentration and quality of dissolved organic matter (DOM), which is one of the largest, most complex and yet least understood mixtures of organic molecules on Earth. The aim of this study is to provide the first experimental evidence about the potential effects of hydrated lime addition on DOM dynamics in the oceans by assessing changes in its concentration and optical properties (absorption and fluorescence). To investigate the effects of liming on DOM pools with different concentrations and quality, seawater was collected from two contrasting environments: the oligotrophic Mediterranean Sea, known for its dissolved organic carbon (DOC) concentration comparable to that observed in the oceans, and the eutrophic Baltic Sea, characterized by high DOM concentration mostly of terrestrial origin. Hydrated lime was added to both waters to reach pH values of 9 and 10. Our findings reveal that the addition of hydrated lime has a noticeable effect on DOM dynamics in both the Mediterranean Sea and Baltic Sea, determining a reduction in DOC concentration and a change in the optical properties (absorption and fluorescence) of DOM. These effects, detectable at pH 9, become significant at pH 10 and are more pronounced in the Mediterranean Sea than in the Baltic Sea. These potential short-term effects should be considered within the context of the physicochemical properties of seawater and the seasonal variability.

Abstract. This study investigates the effectiveness of various calibration approaches within the Water Balance Simulation Model (WaSiM) to enhance the representation of hydrological variables. We assess the impact of three distinct configurations: Baseline (BL), Physical Groundwater Model (GW), and Physical Groundwater with Recharge Calibration (GW-RC) on the representation of hydrological variables. The analysis demonstrates that while traditional calibration primarily enhances streamflow prediction, integrating recharge and groundwater dynamics significantly refines the model’s ability to depict subsurface processes. The GW-RC configuration, with minimal emphasis on recharge in the objective function, shows a marked improvement in representing both the spatial and seasonal variability of groundwater recharge, suggesting that even small and targeted calibration adjustments can significantly enhance the accuracy and realism of model outputs. Although this approach may reduce the model’s flexibility in mirroring observed streamflow, it enhances the precision with which other hydrological processes are represented, providing a more accurate reflection of watershed dynamics. Our findings underscore the importance of multi-variable calibration frameworks, which incorporate both streamflow and internal hydrological variables, in developing robust models capable of adapting to anticipated hydrological shifts due to climate change. This approach provides a more accurate reflection of watershed dynamics and offers valuable insights for calibration strategies in hydrological modelling, water resource management and climate adaptation strategies.