Watershed prioritization using morphometric analysis by MCDM approaches

Erosion landscape characterization in the Himalayan basin: insights from geospatial data and multi-criteria evaluation

In regions characterized by mountainous landscapes, such as watersheds with high elevations, steep inclines, and rugged terrains, there exists an inherent susceptibility to water-induced soil erosion. This susceptibility underscores the importance of identifying areas prone to erosion to mitigate the loss of valuable natural resources and ensure their preservation over time. In response to this need, the current research employed a combination of four multi-criteria decision-making (MCDM) models, namely TOPSIS-AHP, VIKOR-AHP, ARAS-AHP, and CODAS-AHP, for the identification of areas susceptible to soil erosion within the Himalayan River basin of Nandakini, Uttarakhand, India. This identification was facilitated through the utilization of remote sensing and geospatial technologies. The study considered a total of 19 prioritization parameters that included morphological, topo-hydrological, climatic, and environmental factors specific to the Nandakini catchment for the purpose of prioritization modeling. The adoption of morphometric parameters in depicting the geological structures and hydrodynamic behavior of the river basin proves to be a crucial approach in locales where hydrological data may be scarce. The investigation delineated twenty watersheds within the catchment by employing SRTM DEM, SOI toposheets, and Geographic Information Systems (GIS), calculating the catchment's total area to be approximately 540.98 km2. The analysis determined that the catchment is classified as a 6th-order catchment, exhibiting mainly a sub-dendritic to dendritic drainage pattern. It was identified that the catchment is vulnerable to flooding and subsequent gully erosion due to the slow movement of surface runoff. Furthermore, the catchment's elongated shape and the compactness coefficient suggest a delayed peak runoff. The drainage texture ranged from very coarse to coarse, and the relief characteristics highlighted that the watersheds within the catchment possess a high relief ratio, thereby increasing their erosion vulnerability. Topo-hydrological indices revealed significant topographic variability and spatial differences in water availability and erosion potential across the basin. The efficacy of the MCDM models was evaluated through the Spearman's correlation coefficient test, alongside indices of intensity and percentage of change, to validate the findings. The ARAS-AHP and CODAS-AHP models were found to exhibit superior efficiency and higher accuracy relative to the other methods assessed. The insights gained from the ARAS-AHP and CODAS-AHP models are instrumental in the development of strategies for sustainable catchment management plans and inform decision-making processes regarding water resources management within the catchment.

A novel comprehensive approach to soil and water conservation: integrating morphometric analysis, WSA, PCA, and CoDA-PCA in the Naama sub-basins case study, Southwest of Algeria

This research paper presents a detailed investigation into the morphometric characteristics of sub-basins within the Naama region of Algeria, aiming to prioritize areas vulnerable to soil erosion and runoff risks. Focusing on five key sub-basins that collectively represent 75% of the Wilaya of Naama, the study employs a comprehensive methodological framework, integrating morphometric analysis (MA), weighted sum analysis (WSA), principal component analysis (PCA), and the novel approach of compositional data analysis (CoDA). Through the rigorous evaluation of sixteen distinct morphometric parameters selected based on their relevance to hydrological and geomorphological processes that influence erosion and runoff, this research provides a nuanced understanding of the factors influencing erosion susceptibility within each sub-basin. The analysis reveals a clear hierarchy of sub-basins based on their calculated compound parameters, effectively classifying them into high, moderate, and low priority categories for targeted intervention and resource allocation. The results highlight the Ain Sefra and Wadi Er Rosafa sub-basins as the highest priority areas, collectively encompassing 31.51% of the wilaya and posing the most significant threats of runoff and soil erosion. This identification allows for the prioritization of conservation efforts and the implementation of tailored management strategies in these critical areas. Furthermore, the integration of multiple prioritization approaches, including the innovative application of CoDA, ensures a robust and comprehensive assessment of the sub-basin landscapes. This multi-faceted approach provides a more nuanced understanding of the complex interplay between various morphometric parameters and their influence on erosion and runoff potential. The findings of this research have significant implications for sustainable land and water resource management within the Naama region. By identifying and prioritizing vulnerable sub-basins, the study provides a crucial foundation for informed decision-making, enabling stakeholders to implement targeted interventions and mitigate the detrimental impacts of soil erosion and excessive runoff. Moreover, the methodological framework presented in this research paper offers a valuable blueprint for similar studies in other regions facing comparable challenges. The cost-effective and time-efficient nature of the approach makes it a practical tool for prioritizing erosion and runoff risks in arid and semi-arid environments worldwide, contributing to the broader goals of environmental sustainability and land degradation neutrality.

The combination of Multi-Criteria Decision-Making (MCDM) and morphometric parameters for prioritizing the erodibility of sub-watersheds in the Ouljet Es Soltane basin (North of Morocco)

Preserving water and soil resources ranks among the top priorities outlined in the national water strategy. Indeed, the integrated management of water resources in vulnerable territories, particularly in Morocco, requires a deep knowledge of the hydrological functioning and use of water resources in these regions. The diverse hydroclimatic and morphological features within the Ouljet Es Soltane watershed, which is a sub-basin of the extensive Oued Sebou watershed, present significant challenges in managing its water and soil resources. Identifying areas susceptible to soil erosion is crucial for implementing preventive measures in the Ouljet Es Soltane basin and ensuring its sustainable development. Morphometric analysis plays an important role in the effective management and sustainable utilization of the basin's resources. This study used four MCDM models, including the CF (Compound Factor), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and SAW (Simple Additive Weighing), to prioritize 20 sub-watersheds of the Ouljet Es Soltane watershed. Based on the sub-watershed prioritization results obtained from the VIKOR, TOPSIS, and SAW models, sub-watershed 16 achieved scores of 0, 0.59, and 0.8, respectively, positioning it as the first rank. These findings highlight that sub-watershed 16 exhibits a high susceptibility to erosion and is classified as one of the most vulnerable areas in terms of erosion risk. Based on the results obtained from the VIKOR, TOPSIS, and SAW models, the susceptibility of the sub-watersheds to erosion can be classified into four categories: low, moderate, high, and very high. On the other hand, the CF model only has two categories: low and moderate susceptibility. Overall, the findings suggest that morphometric parameters are highly effective in identifying areas at risk of erosion. Furthermore, the VIKOR, TOPSIS, and SAW methods exhibit greater predictive accuracy compared to the CF model. The comparison of these models involved the use of Spearman correlation coefficient test (SCCT). The findings of this study can provide valuable insights for making informed decisions in developing an effective framework for soil erosion control strategies.

Improving the accuracy of air relative humidity prediction using hybrid machine learning based on empirical mode decomposition: a comparative study

This paper proposes a hybrid air relative humidity prediction based on preprocessing signal decomposition. New modelling strategy was introduced based on the use of the empirical mode decomposition, variational mode decomposition, and the empirical wavelet transform, combined with standalone machine learning to increase their numerical performances. First, standalone models, i.e., extreme learning machine, multilayer perceptron neural network, and random forest regression, were used for predicting daily air relative humidity using various daily meteorological variables, i.e., maximal and minimal air temperatures, precipitation, solar radiation, and wind speed, measured at two meteorological stations located in Algeria. Second, meteorological variables are decomposed into several intrinsic mode functions and presented as new input variables to the hybrid models. The comparison between the models was achieved based on numerical and graphical indices, and obtained results demonstrate the superiority of the proposed hybrid models compared to the standalone models. Further analysis revealed that using standalone models, the best performances are obtained using the multilayer perceptron neural network with Pearson correlation coefficient, Nash-Sutcliffe efficiency, root-mean-square error, and mean absolute error of approximately ≈0.939, ≈0.882, ≈7.44, and ≈5.62 at Constantine station, and ≈0.943, ≈0.887, ≈7.72, and ≈5.93 at Sétif station, respectively. The hybrid models based on the empirical wavelet transform decomposition exhibited high performances with Pearson correlation coefficient, Nash-Sutcliffe efficiency, root-mean-square error, and mean absolute error of approximately ≈0.950, ≈0.902, ≈6.79, and ≈5.24, at Constantine station, and ≈0.955, ≈0.912, ≈6.82, and ≈5.29, at Sétif station. Finally, we show that the new hybrid approaches delivered high predictive accuracies of air relative humidity, and it was concluded that the contribution of the signal decomposition was demonstrated and justified.

Evaluation of the geomorphological scenario of Shimsha River Basin, Karnataka, India

Drainage morphometric analysis is very substantial in determining the characteristics of a river basin. It is performed through spatial analysis, which helps study the various hydrological interactions and responses in the watershed. In this research, the authors have tried to study the geomorphological scenario of the Shimsha River basin using the remote sensed data, toposheets, and geographic information systems tools. In the current study, linear, aerial, and relief parameters are derived and analysed to evaluate the runoff and erosion characteristics of the basin. The stream pattern of the Shimsha River is mostly dendritic with a sixth-order stream and a drainage density of 0.56 km/km². According to the morphometric characteristics, the study area appears to be in the equilibrium stage of development, slightly elongated, with moderate to low flow rates and reduced sensitivity to erosion. The hypsometric curve and hypsometric integral value of the Shimsha River basin show the mature phase of the geomorphic evolution of the basin and imply that runoff will be moderate to high. The asymmetry factor of the Shimsha River basin is 49.3, which specifies that the basin is slightly tilted towards the right. The study's results clarify the phenomena of runoff and erosion, which is crucial for watershed management initiatives.

Using fluorescence index (FI) of dissolved organic matter (DOM) to identify non-point source pollution: The difference in FI between soil extracts and wastewater reveals the principle

Traceability and quantification of agricultural non-point source pollution are of great significance to water pollution management in watersheds. In this study, fluorescence components and indices of dissolved organic matter (DOM) in the river, wastewater and soil extracts from different land use types were analyzed to screen indicators that can identify non-point source pollution in 15 independent small watersheds located at the southern Qinling piedmont (China). The results showed that DOM fluorescence components in soil extracts among different land uses didn't have significant differences. The values of humification index (HIX) did not vary obviously between soil extracts and wastewater, with the mean values ranging from 3.4 to 3.9. However, the average value of fluorescence index (FI) of effluent wastewater was about 2.1 and did not change significantly through treatment. The FI values of soil extracts were generally between 1.5 and 1.7. The FI values in most river waters were just between the FI values of wastewater and soil extracts. This phenomenon indicated that FI could be used as an indicator to distinguish point source and non-point source pollution. Besides, the correlation analysis showed a significant positive relationship between the non-point source pollution calculated by FI and δ¹⁵N. The relationship was different in January and July, but further confirmed the reliability of using FI to quantify non-point source pollution. This study demonstrated the feasibility of using FI to identify non-point source pollution. When combined with handheld fluorescence spectrometers and unmanned aerial vehicle-mounted fluorescence spectrometers, this method may be adopted more widely.

Individual and combined impacts of urbanization and climate change on catchment runoff in Southeast Queensland, Australia

Assessing the impacts of climate change and land-use change is of critical importance, particularly for urbanized catchments. In this study, a novel framework was used to examine and quantify these impacts on the runoff in six catchments in Southeast Queensland, Australia. For each catchment, temporal variations in impervious areas were derived from six satellite images using a sub-pixel classification technique and incorporated into the SIMHYD hydrological model. This model was satisfactorily calibrated and validated with daily runoff observations (0.63 ≤ Nash-Sutcliffe efficiency coefficient ≤ 0.94, percent bias ≤ ±18 %) and was used to produce baseline runoff for 1986-2005 in these six catchments. The projected population increase was used to predict future imperviousness based on the linear relationship between the two. The projected rainfall and evapotranspiration were derived from the ensemble means of the eight general circulation models. Catchment runoff was projected under two climate change scenarios (RCP4.5 and 8.5), three urbanization scenarios (low, medium, and high), and six combined scenarios for two future periods (2026-2045 and 2046-2065). Comparing with the baseline, it was found that (1) climate change alone would lead to a -3.8 % to -17.6 % reduction in runoff among the six catchments, for all scenarios and both future periods; (2) a 11.8 % to 78 % increase in runoff was projected under the three urbanization scenarios, and (3) a decrease in runoff due to climate change would moderate the increase in runoff caused by urbanization. For example, the combined effect would be a 54 % increase in runoff, with a -17.2 % decrease due to climate change and 78 % increase due to urbanization. Overall, runoff in the six catchments may be significantly affected by urban expansion. From this study, decision makers could gain a better understanding of the relative importance of the effects of climate and land-use change, which can be applied when developing future long-term water management plans at the catchment scale.

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

To accurately manage water resources, a precise prediction of reference evapotranspiration (ET_ref) is necessary. The best empirical equations to determine ET_ref are usually the temperature-based Baier and Robertson (BARO), the radiation-based Jensen and Haise (JEHA), and the mass transfer-based Penman (PENM) ones. Two machine learning (ML) models were used: least squares support vector regression (LSSVR) and ANFIS optimized using the particle swarm optimization algorithm (ANFPSO). These models were applied to the daily ET_ref at 100 synoptic stations for different climates of Iran. Performance of studied models was evaluated by the correlation coefficient (R), coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), scatter index (SI) and the Nash-Sutcliffe efficiency (NSE). The combination-based ML models (LSSVR4 and ANFPSO4) had the lowest error (RMSE = 0.34-2.85 mm d^-1) and the best correlation (R = 0.66-0.99). The temperature-based empirical relationships had more precision than the radiation- and mass transfer-based empirical equations.

Spatial-temporal characteristics of corrected total phosphorus pollution loads from agricultural non-point sources in Tuojiang River watershed, Sichuan Province of southwestern China

Traditional method of estimating pollution loads may neglect the internal spatial heterogeneity of socio-economic driving factors, which can result in overestimate and underestimate of pollution loads. In this study, the corrected approach to estimating total phosphorus (TP) pollution load was proposed to explore its future variation to develop effective phosphorus pollution control strategies for water environment management. As the first-class tributary of the Yangtze River, the TP out of limits in the Tuojiang River is serious. Thus, based on the presently related basic datasets related to TP pollution load estimation, we firstly adopted the GM (1,1) model to predict their varied trends from 2021 to 2025. We then used the pollution emission coefficient method to calculate the TP pollution load. Moreover, considering the temporal and spatial heterogeneity of the pollutant generation coefficient, we further introduced population and GDP factors to further modify the pollutant generation coefficient to correct TP pollution load. Finally, we employed the exploratory spatial data analysis (ESDA) method to explore spatial distribution characteristics and spatial autocorrelation of TP pollution load from diverse pollution sources in 2025. The results showed that the total TP pollution load from diverse pollution sources will increase from 12,194.92 t in 2021 to 12,461.26 t in 2025, an increase of 2.18%. More concretely, the TP pollution load from rural domestic sewage, rural domestic waste and livestock, and poultry pollution sources will separately decrease by 94.24 t, 77.9 t, and 86.52 t. However, the TP pollution load from agricultural runoff and agricultural solid wastes pollution sources will increase by 74.52 t and 451.49 t, respectively. The contribution of TP pollution load from diverse pollution sources to total TP pollution load will be as follows: livestock and poultry (63.49%) > agricultural solid wastes (16.72%) > agricultural runoff (12.26%) > rural domestic sewage (4.12%) > rural domestic waste (3.41%). The difference in the spatial distribution of TP pollution load from diverse pollution sources in 2025 will be prominent. TP pollution from rural domestic sewage and rural domestic waste pollution sources is more serious in the Xindu and Longquanyi districts, and that from agricultural runoff and agricultural solid wastes pollution sources is more prominent in the midstream and downstream. TP pollution load from livestock and poultry pollution source is higher in the Renshou, Anyue, Rongxian, Luxian counties, and Jiangyang district. Additionally, TP pollution load from rural domestic sewage, rural domestic waste, agricultural runoff, and agricultural solid wastes pollution sources in 2025 will show a clear spatial correlation, but the spatial correlation of TP pollution load from livestock and poultry pollution source will be weak. The study is effective to eliminate the influence of temporal and spatial variation of pollutants generates coefficients on TP pollution load estimation. The method can reflect the actual condition of pollution loads in watersheds more objectively, which can be applied to estimate other pollution loads of similar watersheds with intensive socio-economic activities. The findings in this study can provide a critical reference for the stakeholders to balance water environment conservation and socio-economic development.