Static and dynamic source identification of trace elements in river and soil environments under anthropogenic activities in the Haraz plain, Northern Iran

Unsustainable human activities have disrupted the natural cycle of trace elements, causing the accumulation of chemical pollutants and making it challenging to determine their sources due to interwoven natural and human-induced processes. A novel approach was introduced for identifying the sources and for quantifying the contribution of trace elements discharge from rivers to soils. We integrated fingerprinting techniques, soil and sediment geochemical data, geographically weighted regression model (GWR) and soil quality indices. The FingerPro package and the state-of-the-art tracer selection techniques including the conservative index (CI) and consensus ranking (CR) were used to quantify the relative contribution of different upland sub-watersheds in trace element discharge soil. Our analysis revealed that off-site sources (upland watersheds) and in-site sources (land use) both play an important role in transferring trace elements to the Haraz plain (northern Iran). The unmixing model's results suggest that the Haraz sub-watersheds exhibit a higher contribution to trace elements transfer in the Haraz plain, and therefore, require greater attention in terms of implementing soil and water conservation strategies. However, it is noteworthy that the Babolroud (adjacent to Haraz) exhibited a better performance of the model. A spatial correlation between certain heavy metals, such as As and Cu, and rice cultivation existed. Additionally, we found a significant spatial correlation between Pb and residential areas, particularly in the Amol region. Our result highlights the importance of using advanced spatial statistical techniques, such as GWR, to identify subtle but critical associations between environmental variables and sources of pollution. The methodology used comprehensively identifies dynamic trace element sourcing at the watershed scale, allowing for pollutant source identification and practical strategies for soil and water quality control. Tracer selection techniques (CI and CR) based on conservatives and consensus improve unmixing model accuracy and flexibility for precise fingerprinting

Cartografía geomorfológica de deslizamientos en el escarpe yesífero de la margen izquierda del río Ebro en el entorno de Zaragoza

El área de estudio esta localizada en la margen izquierda del río Ebro 11 Km aguas abajo de Zaragoza entre las localidades de Alfajarin y Osera de Ebro. El área esta caracterizada por la existencia de un prominente escarpe yesífero afectado por movimientos de masa. Se nos presentan dos litologias: Materiales evaporíticos (Fm. ZAragoza) susceptibles de procesos karsticos de la formación Zaragoza y una capa basal de arcillas correspondiente a la formación Sariñena en la base del escarpe con propiedades plásticas a favor de las cuales se desarollan movimientos de masas. Esta unidad arcillosa aparece por primera vez al pasar la localidad de Nuez de Ebro definiendo diferentes diferentes procesos de formación de los movimientos. Estos movimientos estan condicionados por una familia de diaclasado orientado No-SE que incrementa la inestabilidad del escarpe. Estos movimientos pueden generar nuemerosos riesgos para la población

The impacts of exceptional rainfall on phosphorus mobilisation in a mountain agroforestry catchment (NE, Spain)

Erosion causes significant soil and nutrient losses that can reach streams and degrade habitats. Phosphorus (P) is among the nutrients of greatest concern for water pollution. Due to the increase in the number of storm events over the last decade, which could rise further under climate change scenarios, a more in-depth analysis of the effect of rainfall on the behaviour of P in fragile environments is needed. Little is known about the mobilisation and export of P in mountainous Mediterranean agroecosystems. To contribute to this knowledge, this research analysed the variability of P in the sediments of streambeds of different orders in an agroforestry area of the Northern Ebro Basin (Arag ' on, Spain) following an exceptional rainfall event; the implications of different land uses were also explored. Sediment composition was assessed before and after the rainfall event in three nested subcatchments and then related to soil properties. Phosphorus was mostly linked to the mineral fraction (mainly to silicates), while the links between P with clay and organic matter (P-clay, P-OM) were very weak. The P-OM links occurred only in the soils of forested areas. Agricultural lands, which are prone to erosion and had the highest P concentrations, contribute to P release. However, the streambeds and the lateral erosion of channel banks by floods triggered by the rainfall event should be considered as the main contributors to the export of P. The high intensity rainfall event led to 35% and 60% reductions in clay and OM, respectively, and to an enrichment of P in the sediments, the concentrations of which were lower in the headwaters than downstream. This means that the P in streambeds remains exposed in relatively high concentrations following extreme rainfall events with implications for the P cycle and water pollution.This research is part of the Project I+D+i PID2019-104857RB-I00, funded by the MCIN/AEI/10.13039/501100011033/

Remote sensing for monitoring the impacts of agroforestry practices and precipitation changes in particle size export trends

Recent land use changes, the absence of soil protection between crop periods, and extreme precipitation events have been highlighted as major influential factors in the fluctuations of sediment export in the last decades at the catchment scale worldwide. In this regard, soil erosion and fine-particle export are two of the major concerns of soil nutrient loss and water-quality decrease (e.g., increasing turbidity and vector of chemicals). However, while rainfall effects have been well-monitored, recent land use changes and management need additional approaches to evaluate their effect. In Mediterranean mountainous environments, in addition to forest management, agricultural practices during different cropland stages likely increase sediment and particle-bound chemicals in the drainage system. Moreover, most catchments lack instrumentalization. Thus, there is a gap in the knowledge on the processes influencing the sediment exported in ungauged catchments. To evaluate the processes involved, remote sensing and seasonal sampling of suspended sediments were examined for 5 years in a representative agroforestry system in three sub-catchments (SBCs) with different proportions of land uses. Temporal trends of NDVI, EVI, MSAVI, SAVI, and NDWI indices were analyzed for monitoring the vegetation status. With this information, we attempt to evaluate the soil response in terms of particle size export to land use change, vegetation status, and precipitation distribution in fine-grained sediment-reaching streams. Our findings not only highlight the significant effect of heavy precipitation events and vegetation cover on the grain-size fraction of the exported sediment but also reveal the existence of more complex factors influencing the export dynamics. A silt-increasing trend due to the increase of individual heavy precipitations from 2017 onward despite the total precipitation amount not increasing was detected. It is shown that indices such as NDVI and NDMI help detect small changes in vegetation cover, while EVI, SAVI, and MSAVI are more robust for detecting general patterns in large vegetated areas and preventing the appearance of artefacts in the data. Results from this study suggest that land use changes combined with short-scale changing trends of rainfall likely explain most of the possible effects observed in terms of sediment export changes.This research is part of the Project I+D+i PID 2019-104857RB-I00, funded by the MCIN/AEI/10.13039/501100011033/. This work represents a contribution to CSIC Interdisciplinary Thematic Platform (PTI) Teledetección (PTI-TELEDETECT). The contribution of IL was partially supported by the Research Foundation-Flanders (FWO, mandate 12V8622N)

Effects of rainfall intensity and slope on sediment, nitrogen and phosphorous losses in soils with different use and soil hydrological properties

The aim of this research was to analyse the effect of rainfall intensity and slope on soil and nutrient losses by hydric erosion in soils with different hydrological characteristics. This research was carried out on soils collected from slopes with different land uses/covers (LU/LC) -forest, scrub, agricultural, afforested and barren land-, from a mountain area (Sierra de Santo Domingo in the South Pyrenean region), where intensive farming and land use changes including land abandonment and changes in soil cover have occurred. Soils were placed on erosion boxes (30 cm × 20 cm) and compacted to a bulk density similar to that measured in the field (slope values ranged between 10 and 20%). Soil properties such as organic matter content, soil texture and N and P contents were analysed (values used as concentration in the original soil). Soils were subjected to simulated rainfall with intensity values usually recorded in the area during storms. Runoff volumes were collected at 10 min intervals from the time that runoff was generated. The steady infiltration rate as well as the average runoff rates and soil losses were evaluated for each land use. In the runoff samples, sediment concentration and nutrients (N and P) were analysed using different aliquots. The comparative analysis of the results obtained under simulated rainfall in plots with soils from different land uses allowed determining the differences in contribution of each land use to soil and nutrient losses when they are subjected to similar rainfall intensities. The results showed that the maximum runoff rates were reached in agricultural soil and barren land after 40 min at low intensity and after about 20 min at high intensity. However, in soils under forest, scrub and afforestation, runoff rates were much lower for the same rainfall intensity and duration period. Soil sealing was the main factor reducing infiltration in the agricultural and in barren LU/LC soils, while in the other cases runoff was mainly produced after saturation. Soil losses were more than 10xtimes higher in barren land and in agricultural soils than in the other land uses. Nitrogen losses in agricultural soils were about 3 times higher than in forest, and scrub or in afforested LU/LC. Under high intensity rainfall, there was an enrichment ratio (ER) of nitrogen in the sediment in relation to the original soil, which was higher in scrub and agricultural lands (up to 1.33 and 1.32, respectively) than in the rest of land uses (1.1 on average). Phosphorous losses were mainly associated with soil particles and the land uses that gave rise to higher P losses was agricultural under any intensity, while P losses increased significantly in forest and afforested LU/LC at high intensity. The enrichment ratio (ER) was higher in agricultural soils (up to 1.82, increasing with intensity), forest and afforested LU/LC (1.33 and 1.16, respectively under high intensity) than in scrub (1.22) and barren lands (near 1). Information gained in this research can be of interest to manage mountain agroecosystems to limit N and P supply from headwaters to hydrological systems

Towards robust smart data-driven soil erodibility index prediction under different scenarios

Soil erosion is a major cause of damage to agricultural lands in many parts of the world and is of particular concern in semiarid parts of Iran. We use five machine learning techniques—Random Forest (RF), M5P, Reduced Error Pruning Tree (REPTree), Gaussian Processes (GP), and Pace Regression (PR)—under two scenarios to predict soil erodibility in the Dehgolan region, Kurdistan Province, Iran. Our models are based on a variety of soil properties, including soil texture, structure, permeability, bulk density, aggregates, organic matter, and chemical constituents. We checked the validity of the models with statistical metrics, including the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), T-tests, Taylor diagrams, and box plots. All five algorithms show a positive correlation between the soil erodibility factor (K) and silt, sand, fine sand, bulk density, and infiltration. The GP model has the highest prediction accuracy (R2 = 0.843, MAE = 0.0044, RMSE = 0.0050). It outperformed the RF (R2 = 0.812, MAE = 0.0050, RMSE = 0.0061), PR, (R2 = 0.794, MAE = 0.0037, RMSE = 0.0052), M5P (R2 = 0.781, MAE = 0.0043, RMSE = 0.0053), and REPTree (R2 = 0.752, MAE = 0.0045, RMSE = 0.0056) algorithms and thus is a useful complement to studies aimed at predicting soil erodibility in areas with similar climate and soil characteristics

Capability of Sentinel-2 MSI Data for Monitoring and Mapping of Soil Salinity in Dry and Wet Seasons in the Ebinur Lake Region, Xinjiang, China

Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 − B7) / (B12 + B7)] and three-band index (TBI4) [(B12 − B3) / (B3 − B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2 V, RMSEV, and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area