Fish farming plays a key role in food security and local economic development in Benin. However, production systems remain largely dependent on manual management practices that constrain productivity, resource optimization, and operational efficiency. This study focuses on the design, development, and functional assessment of a semi-automated above-ground fish tank management system intended for small-scale aquaculture applications. The proposed system is based on a modular low-cost architecture integrating multiple sensors for the real-time monitoring of key water quality parameters, including temperature, pH, dissolved oxygen, and turbidity, along with an automated feeding mechanism and an autonomous corrective control strategy. System control and communication are ensured through a microcontroller-based platform combining Arduino MEGA and ESP8266 modules, while a dedicated web-based interface enables remote data acquisition, visualization, and basic decision support. A functional prototype was developed and evaluated through laboratory and operational tests focusing on system responsiveness, sensor data reliability, communication performance, and control execution. Although no experimental comparison with conventional management systems was conducted, the results demonstrate the technical feasibility and operational stability of continuous monitoring, and automated control under controlled conditions. Overall, the proposed platform offers a scalable, adaptable, and affordable technological framework that lays the groundwork for future large-scale experimental validation and the deployment of smart aquaculture solutions in resource-constrained environments.
This study aims to improve river flow prediction in a region where hydrological data are limited, which is essential for water management and flood preparedness. We combined a traditional rainfall–runoff model with data-driven learning methods to correct systematic simulation errors. Results show that the combined approach predicts river flow more accurately than the traditional model alone. These findings highlight a practical way to improve water resource planning in data-limited regions.
Accurate streamflow forecasting is essential for water resource management, yet remains challenging in data-scarce regions. This study develops and evaluates a hybrid modeling framework for the Ouémé River basin (Benin) that combines the conceptual GR4J hydrological model with machine learning (ML) via a residual-correction strategy. The approach integrates GR4J with three ML techniques Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks to correct systematic errors in simulated streamflow. Models were trained on daily data from 1996–2012 and validated on an independent period (2013–2020) using performance metrics including Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). Results show that all hybrid models (GR4J–RF, GR4J–XGB, and GR4J–LSTM) outperform the standalone GR4J model. The tree-based hybrids (GR4J–RF and GR4J–XGB) achieved the highest performance gains, with validation KGE values of 0.80 and 0.79, respectively, compared to 0.75 for GR4J alone. While the GR4J–LSTM hybrid also improved baseline results, it exhibited comparatively lower performance, attributed to the data-intensive nature of deep learning in a limited-data context. The study demonstrates that hybrid residual-correction frameworks, particularly those employing efficient tree-based ML, offer a robust and practical pathway for enhancing streamflow prediction in data-scarce catchments.
Understanding hydroclimatic variability and climate teleconnections in a warming world is crucial for drought-prone regions like West Africa, where economies heavily depend on rain-fed agriculture. This study investigates the spatiotemporal dynamics of hydrological and meteorological droughts in the Beninese Part of the Niger River Basin (BPNRB). Using the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Consecutive Dry Days Index (CDD), and the Streamflow Drought Index (SDI), the study assessed drought variability on 3- and 12-month timescales. Statistical methods were employed to explore drought patterns. The findings reveal a recovery phase in the 1990s following severe droughts in the 1970s and 1980s. Significant trends include an increase in CDD values during the rainy season, with an average of 18 dry spell days, and a pronounced upward trend in SDI, indicating intensifying hydrological droughts. These changes suggest growing pressure on both surface and groundwater resources. These findings provide critical guidance for water resource management, promoting efficient irrigation practices and developing preparedness plans. This research offers a robust scientific foundation for designing proactive drought management strategies and enhancing water and food security in Northern Benin.
This study focus on maps soil loss in the Zou catchment in central Benin to help protect farmland. Using satellite images, rainfall records and soil data, we identified areas where soil lost is most at risk. Most of the basin shows low soil loss risk, while small zones on steep slopes are more exposed. The results show the important for better land care and future work with higher-resolution satellite data.
Soil degradation constitutes a major constraint for sustainable agricultural development, particularly in extension zones where land resources are under increasing pressure. This study evaluates soil loss and erosion sensitivity within the Zou watershed at the Atchérigbé outlet. The analysis integrated SRTM Digital Elevation Models (30 m, 2023), Landsat OLI TIRS 09 imagery (scenes 192-054 and 191-055, from 2018 to 2023), rainfall data from 1986 to 2021 provided by METEO BENIN, and FAO soil data. Soil loss was quantified using the Universal Soil Loss Equation (USLE), which combines rainfall erosivity (R), soil erodibility (K), the topographic factor (LS), and the vegetation cover factor (C) derived from supervised land cover classification. Five classes of erosion sensitivity were identified with in average 54 t ha−1 yr−1. Very low and low sensitivity zones dominate the watershed, covering 97 % and 2 % of the area, respectively. Medium-sensitivity areas (0.21 %) form a longitudinal band across the basin, while high-sensitivity areas (0.29 %) occur mainly in the western sector. Very high sensitivity zones (0.013 %) are localized in slope failure sites. These spatial patterns underscore the heterogeneous nature of erosion processes and their potential impacts on agricultural productivity, biodiversity, and ecosystem stability. While the USLE provides a robust framework for large-scale erosion assessment, its application remains constrained by sensitivity to input data accuracy and limited suitability under extreme conditions. Nevertheless, the results offer critical insights for watershed management. To mitigate ongoing land degradation, the study emphasizes the necessity of compensatory reforestation and the adoption of sustainable land management practices.
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,
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
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.
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
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.
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.
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.
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.