The study assesses the vulnerability of family farms to rainfall-induced flooding in the municipality of Kandi, northern Benin, using the IPCC vulnerability framework based on exposure, sensitivity, and adaptive capacity. Data were collected from 80 agricultural producers across ten villages through structured interviews and analyzed across five types of capital: human, physical, financial, social, and natural. Results reveal significant disparities between farm categories. Small farms (≤ 5 ha) exhibit very high vulnerability (4.5/5), marked by low education levels (38.9 % uneducated), limited mechanization (83.3 % without access), and poor credit availability (77.8 % excluded). Medium farms (5–20 ha) show moderate vulnerability (≈ 3.0/5), with 61% having access to mechanization and 46.3% to credit, but still constrained by low income diversification. Large farms (>20 ha) demonstrate low vulnerability (≈ 1.5/5), benefiting from strong assets: 76.2 % mechanized, 71.4 % with credit access, 90.5 % participating in cooperatives, and 57.1 % cultivating fertile soils. The analysis highlights an inverse correlation between farm size and flood vulnerability, reflecting structural inequalities in access to productive and adaptive resources. Strengthening human and financial capital among smallholders – through literacy, agricultural training, microfinance, and cooperative mechanization – is crucial to enhance resilience. This research contributes to climate vulnerability literature by integrating socio-economic and biophysical indicators into a composite framework, emphasizing the multidimensional nature of vulnerability and the need for inclusive adaptation policies in northern Benin.
In Benin, the Lower Ouémé Valley is a region of intense agricultural activity whose soil, hydrological, and climatic conditions are favourable to rice cultivation. Despite this potential, water management remains one of the challenges facing rice production in the area. The development of new land management approaches and agricultural practices is an ideal alternative for improving water management for agricultural production in a context of climate variability. Among these, the Smart-Valleys (SV) land management approach and the System of Rice Intensification (SRI) have been introduced to make rice production profitable in several regions of West Africa. This study aims to compare two land management approaches (Smart-valleys vs. Conventional) and three levels of irrigation, namely Low Variable Irrigation (IR1), Low Constant Irrigation (IR2), and Intermittent Irrigation (IR3) for efficient water management in rice production in the lower Ouémé valley. The experimental design consists of split plots with two replicates per treatment, where the management approach and irrigation levels represent the primary and secondary factors, respectively. The results of the experiment show that Smart-valleys (SV) management has a positive effect on yield (p-value = 0.01) and water productivity (p-value < 0.001). As for irrigation, the IR3 method yields the best performance in terms of water productivity (p-value < 0.001). In addition, the SV-IR2 combination maximizes paddy rice yields (8.5 t ha−1, an increase of 4.7 t ha−1), while the SV-IR3 combination optimizes water productivity (1.4 kg m−3, an increase of 1.06 kg m−3). This highlights the importance of an integrated approach that combines appropriate land management and optimal irrigation strategies to maximize water efficiency in rice production systems. An economic analysis of the different treatments will help identify the best approach to combine yields, water use, and profitability.
The Lake Nokoué and Porto-Novo Lagoon complex represents the most important Lagoon system in Benin and is influenced by saltwater intrusion during Low water period via Cotonou channel. The main objective was to determine the composition and structure of benthic macroinvertebrates based on their preferred habitat. Samples were collected from 15 stations between November 2022 and October 2023, following the fluctuations of the hydrological regime notably High water (October and November), Low water (January and March) and Slight rise water (June and August). The results reveal a total of 79 species across the entire studied Lagoon complex. For Lake Nokoué, the following abundances were obtained for: strictly freshwater taxa (4.27 %), strictly brackish taxa (44.58 %), and strictly saltwater taxa (11.86 %). In the Porto-Novo Lagoon, the following abundances were recorded for: strictly freshwater taxa (7.78 %), strictly brackish taxa (6.96 %), and strictly saltwater taxa (16.83 %). Regarding the hydrological regime, a predominance of strictly freshwater taxa was noted during the High water period in Lake Nokoué. Furthermore, this predominance was recorded during both High and Low water periods within the Porto-Novo Lagoon. Thus, the variation of these taxa was found to be more pronounced in Lake Nokoué (p<0.01) than in the Porto-Novo Lagoon (p>0.05).
Lake Toho is one of the important freshwater ecosystem in southern Benin, under intense human activities. This study aims to assess the spatial and temporal variations of water physicochemical parameters in Lake Toho. A total of 54 water samples were taken during three campagns at 6 stations from February to June 2024. Water depth and Secchi Disk Depth (SDD) were recorded in situ using a Secchi disk. Turbidity was determined using a turbidimeter. Parameters such as temperature, pH, dissolved oxygen, oxygen saturation, salinity, Electrical Conductivity (EC), and Total Dissolved Solids (TDS) were measured using a probe multiparameter. Nutrient concentrations were quantified using specific reagents and a spectrophotometer. Analyses of variance revealed spatial and temporal significant variations (p < 0.05) for water depth (1.71–2.24 m), SDD (21.33–26.44 cm), pH (7.06–7.68), temperature (30.4–32.25 °C) and suspended solids (22.56–27.22 mg L−1). EC (470.56–508.72 µS cm−1), salinity (0.22–0.24 PSU), nitrate (7.29–10.66 mg L−1), ammonium (0.18–0.28 mg L−1) and orthophosphate (0.08-0.13 mg L−1) were showed only very significant variation between months (p < 0.001). Significant correlations (p < 0.001) were observed between SDD and turbidity, between nitrate and pH, water depth, salinity, EC and TDS. The nutrient enrichment observed in this lake could be responsible for organic pollution, likely driven by anthropogenic inputs. This study constitutes a preliminary assessment of the ecosystem, and further investigations are needed to better understand its biogeochemical functioning and the nature of the disturbances affecting it.
In centenary celebration of IAHS,
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.
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.
The development and integration of the spatial and temporal probabilities of landslides are required for complete landslide hazard mapping at any location. Under changing climate, the computation of the temporal probability of landslides with rainfall magnitude alone is inaccurate. This research proposes a framework based on copula functions to develop a landslide probability map using multi-site rainfall data by accounting for the rainfall variables of intensity and duration using a joint-probability approach. The proposed technique is used for Wayanad District, Kerala, India, considering extreme rainfall events in 2018. Firstly, the landslide susceptibility map of the district was developed using a robust random forest (RF) model. Based on regional geology, geomorphology, and climate, different regions of Wayanad have varying rainfall thresholds assessed according to the intensity and duration of the rainfall. Then, the temporal probability of landslides was developed, accounting for the intensity and duration of rainfall events using the joint-probability estimation using copula. Through the integration of the landslide spatial probability map with the temporal probability, landslide hazard maps (LHMs) for Wayanad were developed for time periods ranging from 1 to 50 years. The results of the study indicate the need for bi- or multi-variate landslide probability modeling in studies on regional landslide hazard assessments.
Snow-induced disasters pose significant risks in mountainous regions. Accurate visibility assessment is crucial for informed decision-making and disaster preparedness. This paper presents an innovative approach that integrates theoretical literature with practical application using augmented reality (AR) to address this challenge. The proposed system, named Him-Drishti, harnesses the established correlation between snowfall intensity and visibility to create a predictive model. By incorporating user-input snowfall intensity predictions, the Him-Drishti AR application dynamically overlays real-time visibility simulations onto the physical environment. Moreover, the use of smartphones further amplifies the practicality of this innovative solution, making the AR application a handy tool, even in the most challenging terrain. This intuitive visualization empowers stakeholders with a comprehensive understanding of potential risks, facilitating informed decisions and optimized disaster response strategies. Through AR visualization, stakeholders, emergency responders, and local authorities gain valuable insights into visibility conditions, enabling them to make informed decisions, take proactive measures, and allocate resources effectively. This study not only enhances the understanding of snow-related disasters but also demonstrates the capacity of AR in disaster management and risk reduction.
The backwater effect can be caused by a number of factors, including the bridge's pier width and deck level. In lowland regions, the backwater effect can be particularly problematic, as it can lead to flooding and inundation of farmlands and houses. This study examines the impact of bridge structure on the backwater effect in the lowland region of Shreekhandapur, Kavre, Nepal. The study area is characterized by a hill slope and inner river valley, with a gentle river profile that results in low flow velocity and increased deposition. The study compares the backwater effect of an old demolished bridge with four piers of 1.2 m width and abutments on both sides to a new bridge with a single pier of 0.4 m width and a deck level that is 1 m higher. HEC-RAS software was used to analyse the steady flow and create a flood hazard map. The results show that the new bridge with a reduced pier width and increased deck level significantly reduces the backwater effect. The flood hazard map shows that the new bridge along with floodwalls reduces the potential hazard-prone areas by up to 50 %. The findings of this study can be applied to disaster mitigation and bridge design in lowland regions. By reducing pier width and increasing deck level, bridges can be designed to minimize the backwater effect and reduce the risk of flooding.
In recent decades, the Tsho Rolpa glacial lake has witnessed a significant increase in its surface area. Situated in the Eastern Himalayas at an elevation of 4552 m a.s.l. (meters above sea level), this lake has drawn attention due to the potential risks of glacial lake outburst floods (GLOFs). To comprehensively study and prepare for future GLOF events from Tsho Rolpa, advanced modeling techniques were employed, utilizing the National Weather Service BREACH (NWS-BREACH) and Hydrologic Engineering Center's River Analysis System (HEC-RAS) models. The assessment included the evaluation of dam breach scenarios, especially 20 and 40 m breaches, using the NWS-BREACH model to estimate breach hydrographs. This analysis revealed peak flow rates ranging from 8198 to 26 662 m3 s−13 s−1
Understanding the hydrology of the Himalayan region and its response to current and future climate scenarios is crucial for identifying the region's future water availability for infrastructural development. For this, the hydropower potential and the impact of future change on the hydrology of the Bheri River basin (BRb) and the Marsyangdi River basin (MRb) were analysed. The Glacio-hydrological Degree-Day Model version 2.0 (GDM V2.0), developed in the PCRaster dynamic modelling framework, was used to simulate the river runoff. Geospatial tools and different criteria were used to assess topographic features, identify suitable places for run-of-river (ROR) hydropower development, and estimate power potential. Eight scenarios with various combinations of temperature and precipitation changes were used to assess the hydropower potential. Increases in temperature by 0.5 and 1 °C (assumed for the near-term and mid-term future) and changes in precipitation of ±10 % and ±20 %, respectively, are used to conduct a sensitivity analysis for the hydropower potential. A total of 116 and 83 suitable sites were identified, and 4242 and 2823 MW power potentials were estimated in the BRb and the MRb, respectively. All the sensitivity scenarios show an increase in hydropower production, except for one with a drier scenario and less precipitation. The integration of a geographic information system (GIS) and a hydrological model helps us to understand the hydrological response to climate variables and its impact on hydropower in the Himalayan region.
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.
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
Rivers in the Himalayan and adjacent mountain regions are the lifelines of over 1 billion people and are the backbone of civilizations therein. The short gauge records of Nepal do not provide a sufficient time window to understand the natural variations in river discharge from a long-term climate perspective. By developing a network of over 100 tree-ring chronologies across Nepal, we checked their hydrological sensitivity for long-term streamflow reconstruction. This shows huge potential for long-term annual or seasonal streamflow reconstructions in different river basins of Nepal. A robust reconstruction model was developed between tree growth and streamflow, capturing 56 % of the variance in the actual data, and was used to reconstruct the March–July monthly average streamflow of Sinja Khola (river) at Diware from AD 1700 to 2013. The reconstruction revealed several dry and pluvial periods with the recent decline in the streamflow in the Sinja River. We found short- (2 to 8.7 years) to medium-term (35.2 years) periodicities in the reconstruction that are likely to be associated with climatic oscillations, such as the El Niño–Southern Oscillation (ENSO), the Atlantic Multidecadal Oscillation (AMO), and the North Atlantic Oscillation (NAO), along with the influence of local circulation patterns. Since Sinja Valley is related to the origin of the Nepali language and civilization, the information on long-term streamflow will be beneficial for water resource management in the context of rapid climate change and for preparedness for water-induced disasters in the region.
We have seen a surge in glacio-hydrological modeling efforts in the past few decades. This form of modeling is also being carried out in the Himalayan river basins, but a comprehensive high-resolution simulation software that can be effective with a limited number of hydrometeorological data is recommended. In this regard, an open-source, scalable, flexible, and distributed modeling system called PyGDM has been developed by fully coupling the Glacio-hydrological Degree-day Model with PCRaster Python software. To evaluate the potential of using this model in the Himalayan river basins, we calibrated and then validated the model for Trishuli River basin using geographical data and the existing hydrometeorological data. The tests showed a promising result with respect to the effective application of the model in the entire Himalayan region. The PyGDM source code was optimized and adapted to the process models of glacier melting and hydrological processes in Himalayan basins. It increased the speed of the simulation, made the model highly scalable to accommodate new submodels, and enhanced the flexibility of the model to ingest various types of input data and parameters. Hence, the PyGDM model strives to simulate the glacio-hydrological processes of the entire Himalayan region.
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.
This study examines the characteristics (magnitudes, trends, and frequency of occurrences) of extreme floods in Nepal, a country that is at significant risk from floods. Daily discharge data from 1980 to 2015 of three gauging stations (Chisapani of Karnali Basin, Devghat of Narayani Basin, and Chatara of Koshi Basin) were used to assess the largest 1 % of flows, the annual top five high flows, and floods of different return periods (2-, 5-, 10-, 20-, and 100-year). In addition, temporal trend analysis of the flood peaks was carried out using the Mann–Kendall test and Sen's slope estimates. Results show that the magnitudes of the largest 1 % flows range from 6310 to 17 900, from 6967 to 12 100, and from 6080 to 9610 m3 s−1