Modeling Earth Systems and Environment

Knowledge of sediment transport is important to understand the transportation and recycling of elements and matter in the Earth system. Usually, sediment transport in rivers is characterized by suspended sediment concentration (SSC) and river discharge (Q). However, SSC measurements are often inadequate in many river systems, such as the Red River basin in Vietnam. In this study, we performed a Tributary-based Downstream gauge Estimation (TDE) machine learning (ML) approach to estimate SSC at Son Tay hydrological station based on Q and SSC monthly data from three upstream stations, one per tributary of the Red River over a 14-years period (2000–2013). A comparative analysis of four ML algorithms, including Multiple Linear Regression (MLR), Elastic Net (EN), Random Forest (RF), and Support Vector Machines (SVM) was conducted. Results showed that when using both Q and SSC of the three upstream stations, the SVM algorithm with linear kernel exhibited the highest accuracy (r2 = 0.87 and RMSE = 64.7 g m−3). The performance of the TDE-ML was seasonally dependent, with higher accuracy in the high-flow period. This approach also revealed that SSC measured at Yen Bai station (Thao River) had the highest contribution to the prediction of SSC at Son Tay station meanwhile Vu Quang station (Lo River) contributed the least to downstream SSC. Furthermore, new dams have been impounded during the 14-years period. Although the global performance of the RF method was slightly less than SVM with linear kernel, it was the only one able to fairly estimate SSC in the most recent 6-years period affected by new forcing.

A 13-member ensemble from CMIP5 is used to analyze the Iran future climate in terms of surface air temperature (TAS), identifying when anomalies of 1.5 ℃, 2.0 ℃ and 2.5 ℃ would be achieved respect to the preindustrial period (1861–1900) using the time sampling method. The global warming targets ( $${D}_{1.5}, {D}_{2.0}, {D}_{2.5}$$ ) are attained by the 2024, 2040 and 2056 under the RCP4.5 scenario, on the other hand, the respectively, attained years for Iran are 2023, 2036 and 2051, an evidence of a higher mean increase in surface air temperature respect to the global behavior. We identified important aspects regarding to temperature anomalies in the Iran region: (1) A mean warming of 1.0 ℃ for the 2001–2010 decade respect to the preindustrial baseline. (2) A mean warming of 0.5 ℃ for the 1960–2005 period respect to the preindustrial period. (3). For the 2006–2100 period under RCP4.5 scenario, was possible to identify a warming trend ( $$0.028 {^\circ{\rm C} } {\mathrm{year}}^{-1}$$ ), which is above double the trend observed in the period 1960–2005 ( $$0.012 {^\circ{\rm C} } {\mathrm{year}}^{-1}$$ ). 4) Largest trends over Iran occur for the 2006–2050 period (2.75 ± 0.74 to 4.72 ± 0.82 °C/century) respect to the 2006–2100 period (2.28 ± 0.36 to 3.39 ± 0.37 °C/century) with the most possible reason associated with the fact that under the RCP4.5 scenario, the emission rate increases toward 2040 and then stabilizes to the end of the century. All of these indicators evidence an intensification of the warming over the Iran region respect to the global trends. From the spatial analysis of surface air temperature trends over 5 regions of Iran for the 1850–2005, 1960–2005, 2006–2050 and 2006–2100 periods was possible to identify a significant increase in all the trends over Iran, but specially in the Southwestern of the country with a warming rate higher in warm regions than the cold climate regions. The present study reveled that further research should be development to explore renewable energy and create mitigation plans to minimize greenhouse gas emissions to overcome the increased risk of climate change effects.

The paper utilized Landsat 5 TM and Landsat 8 OLI for analyzing land use/land cover change and its impact on land surface temperature in Sundarban Biosphere Reserve, India. Split window algorithm and spectral radiance model were used for determining land surface temperature from Landsat 8 OLI and Landsat 5 TM, respectively. The land use land cover change analysis revealed phenomenal increase in the waterlogged areas followed by settlement and paddy and a decrease in open forest followed by deposition and water body. The distribution of average change in land surface temperature shows that water recorded highest increase in temperature followed by deposition, open forest and settlement. Overlay of the transect profiles drawn on land use/land cover change map over land surface temperature map revealed that the land surface temperature has increased in those areas which were transformed from open forest to paddy, open forest to settlement, paddy to settlement and deposition to settlement. The study demonstrated that increase in non-evaporating surfaces and decrease in vegetation have increased the surface temperature and modified the temperature of the study area.

Despite its potential for drinking, domestic/non-domestic use, and agricultural development in Ethiopia, groundwater is underutilized. By employing a geospatial tools analytical hierarchy approach and statistical models, this study evaluated the potential groundwater zone of the Mensa River watershed in the Omo River Valley, Ethiopia. This study identified seven significant groundwater potential influencing parameters and mapped them based on remote sensing, field and secondary data: geology, lineament density, land use/land cover, slope, drainage density, soil type, and rainfall. Based on the significance of the seven thematic maps as judged by professional judgment and a review of the pertinent literature, weights were assigned to each in the AHP model. The weighted thematic factors were integrated to create a composite map of potential groundwater zones using a weighted linear combination approach. According to the results of the analytical hierarchy process models, 7% and 21% of the research region contain zones with very high and high groundwater potential, respectively. This zone consists of low drainage, high lineament, and medium trachyte rocks in the study area's central, western, and southern regions. An analysis of the spatial relationships between well/spring sites and seven groundwater conditioning parameters was conducted using a frequency ratio model. According to the frequency ratio model, 4% and 10% of the research area have very high and high groundwater potentials, primarily in the northern and northwest regions, with limited coverage in the centre. Using the area under-curve approach, the analytical hierarchy process and frequency ratio models identified likely groundwater zones with 70.5% and 60% accuracy. According to the sensitivity analysis results, groundwater’s potential relies mainly on LU/LC, precipitation, and soil type. The integrated approach of the present study's outcome may be helpful for hydrogeologists, planners, and administrators in the study area for managing groundwater resources sustainably.

Symphyotrichum lanceolatum (Willd.) G. L. Nesom is an alien invasive species in Europe, where it presents a potential threat to natural habitats. Its rapid expansion in recent decades raises questions and concerns about the causes and consequences of its spread in Slovakia. We investigated natural and anthropogenic habitats along with topographic and environmental factors, including changing climatic conditions such as air temperature and precipitation totals to adjust prediction models of the species distribution. Using 19 various algorithms, the models for the past, present, and future were calculated based on 395 octoploid populations selected by flow cytometry. The models revealed the potential species distribution along rivers and in human settlements and its increasing during the period 1970–2060 from 23.6 to 53.85% of the territory as a result of climatic change. A conditional inference tree indicates that the expansion can be limited by a mean annual air temperature below 8 °C and a pH of soil less than 5.5. Therefore, there is a high probability of the further spread of S. lanceolatum across Slovakia.

The subsurface of a suspected fault zone at Ojirami Southwestern Nigeria was investigated using the four-electrode array model of the electrical resistivity method of geophysical prospecting. Measurements were taken along four traverses using the 1-D and 2-D electrical resistivity field techniques. The Schlumberger and dipole–dipole electrode configurations were respectively utilized for each of the techniques. The 1-D data were interpreted by partial curve matching and computer iteration using the IPI2Win® software. The data obtained from the 2-D ERI were processed and migrated using the Dipro for Windows® 4.0 inversion software. Inversion was done using the finite element modelling (FEM) method. The second-order smoothness constraint was used and five iterations were carried out on each data set. Three 2-D resistivity models and one geoelectric section were generated from the inverted resistivity data. A resistivity depth slice map of the depth range of 2.5–5 m was also generated for the assessment of the spatial configuration of the suspected fault zone. Geoelectric units which include the topsoil/outcrop, weathered/faulted bedrock and the fresh bedrock were delineated by the 2-D resistivity models and the geoelectric section. The suspected fault zone manifested as zone of comparatively low resistivity (8–81 Ω-m) within two resistive (800 to > 5000 Ω m) basement bedrocks on the resistivity images and geoelectric section. The resistivity depth slice map also revealed the fault zone to be a low resistivity discontinuity within two flanks of fresh bedrocks. The study concluded that the study area is indeed a fault zone.