Major Sub-basins Research Articles

Unveiling the determinants of the spatial variability of nitrogen sources use in the Lake Victoria basin, East Africa

As nitrogen pollution increasingly threatens water quality in the Lake Victoria Basin, it is essential to investigate the spatial factors influencing nitrogen source use. Understanding these determinants is crucial to inform effective strategies to combat eutrophication, enhance nutrient management, ensure food security and promote sustainable ecological development. This study investigated spatial variation of N sources, the farmers' socio-demographic and farm characteristics factors influencing farmers' choice of nitrogen sources. Data was collected from 1500 farmers between October and December 2020. The farmers were randomly selected in Kenya, Rwanda, Uganda, Burundi and Tanzania country sub-basins within the Lake Victoria basin (LVB). Spatial autocorrelation was used to evaluate the spatial variation of the nitrogen sources while the Optimized Parameter Geographical Detector (OPGD) model was used to identify the factors influencing the choice or use of N sources. The OPDG results indicate that the country of residence and livestock ownership are the primary factors accounting for 31.9% and 22.1% of the variation in nitrogen sources across the entire Lake Victoria Basin, respectively. The major sub-basin factors influencing the use of N sources were both farm characteristics (types of crops grown and farm size) and social socio-demographic factors of the farmer (education). These findings highlight the need for tailored strategies, accounting for geographical variations, farm characteristics and socio-demographic factors are essential when formulating nitrogen management strategies and policies at local and regional scales within the LVB.

Assessment of monthly to daily streamflow disaggregation methods: A case study of the Nile River Basin

Study regionThe Nile River Basin Study focusThe lack of observed streamflow data at a short time scale poses a critical challenge for calibrating and validating hydrologic models. Therefore, many disaggregation methods were developed, resulting in various relative performances without a clear indication of the optimal choice. This study aims to iteratively assess eight monthly to daily streamflow disaggregation methods at 21 major subbasin outlets in the Nile River Basin (NRB) to identify the best-performing ones. These methods include one proportionality method and seven interpolation methods, i.e., linear, 2nd-order spline, 3rd-order spline, Piecewise Cubic Hermite Interpolating Polynomial (Pchip), Modified Akima (MAkima), mean preserved 2nd-order spline, and mean preserved 3rd-order spline. We assessed these methods using three metrics and visual investigations. New hydrologic insights for the regionThe results showed that the interpolation methods performed well, better than the proportionality method. However, their performances decreased at stations with high daily streamflow fluctuations. The interpolation methods’ performances were similar in mimicking the daily values but significantly different in preserving the mass balance. The mean preserving 3rd-order interpolation method (Lai 22) was the best in preserving the mass balance and capturing the low, moderate and high flows and, therefore, selected to generate the daily flow data in the NRB. The results of this study can guide a reliable method for obtaining daily streamflow data, which is important for the hydrologic and water management studies in the NRB.

Climate dynamics and the effect of topography on snow cover variation in the Indus-Ganges-Brahmaputra river basins

The river basins of the Indus-Ganges-Brahmaputra (IGB) are characterized by rapid population growth, increased water demand, and rapid snow/ice melting, all of which combine to make these catchments especially vulnerable to climate change. Here, we present a comprehensive dataset (2003−2020) for precipitation, temperature and snow cover area (SCA) to investigate the spatio-temporal variability of important climate factors in relation to elevation and across three major sub-basins of the IGB. Improved MODIS (Moderate Resolution Imaging Spectro-radiometer) Terra and Aqua snow cover data are used to estimate regional snow cover, while Climate Hazards Group InfraRed Precipitation with Station Data version 2.0 (CHIRPS-V2.0) is deployed for precipitation, and MODIS Land Surface Temperature (LST) data are used to estimate temperature. The analysis reveals significant changes in precipitation, LST and snow cover throughout the IGB region together with a strong degree of spatial coherence between SCA and regional surface topography (R2 = 0.82).Maximum changes were observed in the Brahmaputra basin at mean annual precipitation (13.9 mm/year) and reduced SCA (155.4 km2/year) over the period studied. Similar trends are also evident, albeit with relatively lower absolute values in the other two basins, whereby the Indus basin evidences a significant reduction in annual SCA, averaging 82.2 km2/year with increased precipitation (3.12 mm/year) and LST. This investigation further highlights the relative influence of climate change factors and regional human activities in determining high-elevation changes in SCA. Most of the middle elevation regions of the Ganges experienced a significant reduction in SCA, with human impact contributing 82% of the observed variation, while climate factors are more prominent in the Indus and Brahmaputra basins. Understanding the dynamics of snow cover and its regional influencing factors is crucial to the more accurate evaluation of water resources and to defining appropriate mitigation and adaptation strategies for the coming decades.

Modeling the Impact of Climate Change on Streamflow in the Meghna River Basin: An Analysis Using SWAT and CMIP6 Scenarios

This study assesses future climate change impacts on the hydrologic response of the Upper Meghna River Basin (UMRB), a major river system in Bangladesh. Separate SWAT (Soil and Water Assessment Tool) hydrologic models were developed for the three major sub-basins of the UMRB, i.e., Barak, Meghalaya, and Tripura, considering their unique geographical, hydrological, and land-use characteristics. To evaluate the efficiency of multi-site modeling in providing better model performance, the SWAT models were calibrated at both single and multiple locations. Those models were then simulated to estimate future flows using climate projection data from thirteen CMIP6 General Circulation Models (GCMs) under moderate and extreme emission scenarios, SSP2-4.5, and SSP5-8.5. The results revealed that the annual maximum flow will keep increasing gradually with time. The outlets of the Meghalaya sub-basin will experience a more significant rise in future flow in the upcoming decades compared to the Barak and Tripura sub-basins. Results showed that dry season flows with increases of up to 31–50% would be less affected compared to the wet periods, which could experience increases of up to 47–66%) across the sub-basins by the end of the 21st century under extreme emission projections. Besides an increasing trend in the mean flow, future flows at several outlets also exhibited an escalation in the intensity and frequency of extreme flood events.

Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin

Predicting monthly streamflow is essential for hydrological analysis and water resource management. Recent advancements in deep learning, particularly long short-term memory (LSTM) and recurrent neural networks (RNN), exhibit extraordinary efficacy in streamflow forecasting. This study employs RNN and LSTM to construct data-driven streamflow forecasting models. Sensitivity analysis, utilizing the analysis of variance (ANOVA) method, also is crucial for model refinement and identification of critical variables. This study covers monthly streamflow data from 1979 to 2014, employing five distinct model structures to ascertain the most optimal configuration. Application of the models to the Zarrine River basin in northwest Iran, a major sub-basin of Lake Urmia, demonstrates the superior accuracy of the RNN algorithm over LSTM. At the outlet of the basin, quantitative evaluations demonstrate that the RNN model outperforms the LSTM model across all model structures. The S3 model, characterized by its inclusion of all input variable values and a four-month delay, exhibits notably exceptional performance in this aspect. The accuracy measures applicable in this particular context were RMSE (22.8), R2 (0.84), and NSE (0.8). This study highlights the Zarrine River’s substantial impact on variations in Lake Urmia’s water level. Furthermore, the ANOVA method demonstrates exceptional performance in discerning the relevance of input factors. ANOVA underscores the key role of station streamflow, upstream station streamflow, and maximum temperature in influencing the model’s output. Notably, the RNN model, surpassing LSTM and traditional artificial neural network (ANN) models, excels in accurately mimicking rainfall–runoff processes. This emphasizes the potential of RNN networks to filter redundant information, distinguishing them as valuable tools in monthly streamflow forecasting.

Recent Developments in Helium Exploitation in southern Saskatchewan and adjacent areas of Montana and Alberta

The following is a survey of a re-emerging helium exploration and production area located at the northwest rim of the Williston Basin located in southwest Saskatchewan, southeastern Alberta, and north central Montana. The Williston Basin is one of the two major sub-basins of the Western Canadian Sedimentary Basin; the other being the Alberta Basin foreland basin, east of the Cordillera. From a slow 2016 start, 21st century helium production from the area already exceeds the total from area’s 1960s and 70s production heyday. As mid-continent legacy helium storage and production decline, the importance of this area’s net-zero helium production can only grow.

A synthesis of the diversity of freshwater fish migrations in the Amazon basin

AbstractIn the Amazon, the world's largest river basin, migrations within freshwater habitats are one of the predominant life history strategies for fishes. The flood pulse and the extensive river network provide aquatic organisms with temporal and spatial accessibility to a mosaic of freshwater habitats. Although migratory fish species are central to freshwater ecosystems and fisheries, the knowledge of species and migratory patterns has traditionally relied on anecdotal and scattered information, lacking a unifying methodological and conceptual framework. We quantitatively synthesize the evidence about this biological phenomenom in the Amazon basin through a systematic literature review. We constructed a reference database of migratory events in the Amazon basin, including species, life stages, purposes, direction, habitats and subbasins. We found that 223 species were documented in 90 references as performing migrations distributed across eight orders and 31 families. Migration is a conserved trait in the evolutionary history of Amazonian fish fauna, suggesting that ~41% of migratory species are likely unreported. We noted a geographical bias in the report of migratory events towards 13 of the 27 major subbasins of the Amazon. We found a significant association between the hydrological timing at the beginning and end of migrations across species, including reproduction as the most commonly reported purpose. However, most species lack the application of robust methods (e.g. telemetry, otolith microchemistry) to classify them as migratory, relying upon secondary sources of information (i.e. reviews or species checklists). Further, we discuss future opportunities and challenges to continue the study of fish migrations in the Amazon basin.

Comparative phylogeography informs community structure and assembly during and after Pleistocene Lake Bonneville

Abstract Dispersal is one of the major processes controlling both genetic diversity and species diversity and is frequently studied in both phylogeography and community ecology. As such, integrating these fields to uncover how both historical and contemporary dispersal have affected local community structure can provide greater insights into community assembly. We used comparative phylogeography to determine if freshwater fish species in the Bonneville Basin show evidence of geologically recent dispersal and gene flow, which would probably have occurred when the basin was inundated by Lake Bonneville in the late Pleistocene. We then used museum records to uncover patterns of contemporary community structure and relate them to the results of the phylogeographical analyses. We found evidence for late Pleistocene dispersal throughout the Bonneville Basin in most of the fish species studied, which would have homogenized ancient communities. However, modern communities show evidence of non-random community structure and dispersal limitation between major sub-basins and habitats. Together, these results suggest that the Bonneville Basin fish fauna assembled due to a combination of historical dispersal and contemporary habitat filtering and extinction dynamics following isolation. Further work should continue to combine different data types to achieve more accurate inferences regarding contemporary community assembly.

Mapping river turbidity at a large basin-scale with a spatially transferable ensemble model using Landsat 8 multispectral imagery

ABSTRACT Mapping river water quality at a basin scale with satellite image data represents a tremendous technical challenge, since traditional empirical remote sensing models calibrated for specific reaches of a river cannot be applied to all the rivers and streams in the basin with an adequate prediction accuracy. In this study, we developed a multi-predictor ensemble model to map and assess water turbidity for all rivers and streams in the Tombigbee River Basin with Landsat 8 multispectral images. Coincident with Landsat 8 satellite overpass on 2 May 2019, we conducted boat-based water quality data collection in the confluence area of Tombigbee River and Black Warrior River near the city of Demopolis, Alabama. Based on the coincident satellite image and field water samples, we calibrated and evaluated the multi-predictor ensemble model, which consists four component empirical models. Our evaluations suggest that the multi-predictor ensemble model can improve turbidity estimation accuracy by 79% in comparison with the best traditional empirical model. More importantly, our multi-predictor ensemble model can be applied to other river and stream reaches across three major sub-basins with a high prediction accuracy. With the multi-predictor ensemble model, we derived a turbidity map for all rivers and streams with a width greater than 90 m in the basin, by processing three Landsat 8 image scenes. This basin-wide turbidity map indicates that the turbidity of rivers and streams exhibits longitudinal gradients and distinct spatial trends within the basin, which were related to the basin land cover/land use, hydrometeorological condition, and the spatial distribution of dams. Owing to its strong spatial transferability and high prediction accuracy, the ensemble model, in conjunction with synoptic multispectral satellite image data, is recommended as an effective and efficient tool for mapping and monitoring river and stream water quality at a large basin or regional scale.

Source Apportionment of Chemical Elements and Their Geochemical Baseline Values in Surface Water of the Parauapebas River Basin, Southeast Amazon, Brazil

The present work aims to evaluate the chemical quality and establish the geochemical baseline values of elements in the surface waters of the Parauapebas River basin (PRB), which is one of the major subbasins in the Itacaiúnas River watershed (IRW) located in the Brazilian Amazon. A total of 327 surface water samples were collected during the rainy and dry seasons in 2017. Results indicate that waters are slightly acidic to alkaline (pH 6 to 8), and there was a strong influence of the seasonal variation on water quality, with higher values of turbidity, Fe, Al, Mn, TDS, etc. in the rainy season. Two geochemical baseline types for the PRB were defined, i.e., ‘conservative baseline’ (CB), represented by the cumulative frequency curve, and the ‘environmental baseline’ (EB), comprising the sum of natural and diffuse anthropogenic contributions, represented by the 98th percentile. Except Fe, Mn and Al, the CB and EB values of various trace elements (Ba, Co, Cr, Cu, Ni, Rb, Sn, Sr, Ti, V and Zn) were lower than the recommended limits of CONAMA 357/05—class 2. The principal component analysis (PCA) identified the major geochemical association (Al-Ti-Cu-Cr-Ni-V), which is an imprint of the local geological setting. Ni and Cr showed enrichment at sites where mafic and ultramafic rocks are concentrated, while Cu concentration is mainly associated with the north and south mineralization belts. High concentrations of Fe and Mn are characteristic of the waters of this region and this is mostly influenced by specific land use activities and intense weathering/erosion of catchment materials. At the upper Parauapebas, anthropogenic contributions associated with soil use and occupation were also important along with the geogenic effects. The obtained results regarding sources of contaminants in some microbasins can be taken as a starting point for future studies on the environmental quality of the region’s water resources.

A storm-centered multivariate modeling of extreme precipitation frequency based on atmospheric water balance

Abstract. Conventional rainfall frequency analysis faces several limitations. These include difficulty incorporating relevant atmospheric variables beyond precipitation and limited ability to depict the frequency of rainfall over large areas that is relevant for flooding. This study proposes a storm-based model of extreme precipitation frequency based on the atmospheric water balance equation. We developed a storm tracking and regional characterization (STARCH) method to identify precipitation systems in space and time from hourly ERA5 precipitation fields over the contiguous United States from 1951 to 2020. Extreme “storm catalogs” were created by selecting annual maximum storms with specific areas and durations over a chosen region. The annual maximum storm precipitation was then modeled via multivariate distributions of atmospheric water balance components using vine copula models. We applied this approach to estimate precipitation average recurrence intervals for storm areas from 5000 to 100 000 km2 and durations from 2 to 72 h in the Mississippi Basin and its five major subbasins. The estimated precipitation distributions show a good fit to the reference data from the original storm catalogs and are close to the estimates from conventional univariate GEV distributions. Our approach explicitly represents the contributions of water balance components in extreme precipitation. Of these, water vapor flux convergence is the main contributor, while precipitable water and a mass residual term can also be important, particularly for short durations and small storm footprints. We also found that ERA5 shows relatively good water balance closure for extreme storms, with a mass residual on average 10 % of precipitation. The approach can incorporate nonstationarities in water balance components and their dependence structures and can benefit from further advancements in reanalysis products and storm tracking techniques.

Diversity of freshwater crabs (Crustacea: Decapoda: Brachyura) in the Subansiri River basin of Arunachal Pradesh and Assam, northeast India

Crabs are significant invertebrates in the Subansiri River basin, one of the major sub-basins of the Brahmaputra valley in northeast India. They have immense ecological and economic value and provide an alternative source of income for the local inhabitants. Following thirteen sites in the Subansiri River basin were surveyed to determine the availability and diversity of crab species in this region: Hatinala, Kalma river, Dikrong river, Senki river, Poma river, Harmuti, Ranganadi river dam site, Diju stream, Lower Subansiri river, Downstream Ranganadi, Pani gaon wetlands, Bosa gaon beel, and Bhelamora beel. Crab samples were collected using a random sampling method and identified using standard taxonomic keys. Seven species from two families were identified and distinguished by their morphology and habitat preferences: Sartoriana spinigera, Sartoriana trilobata, Maydelliathelphusa lugubris, Maydelliathelphusa harpax, Liotelphusa laevis (Gecarcinucidae) Lobothelphusa woodmasoni, and Acanthopotamon horai (Potamidae). These crabs are thought to be endemic to the Eastern Himalayan region due to their limited dispersibility. This is the first study aimed at contributing to the documentation and conservation of freshwater crabs in North East India. Additionally, the economic relevance of freshwater crabs is highlighted.

The Groundwater Flow Behavior and the Recharge in the Nubian Sandstone Aquifer System during the Wet and Arid Periods

The Nubian Sandstone Aquifer System (NSAS) is made up of three major sub-basins: Kufra, Dakhla, and the N. Sudan Platform. It is one of the world’s largest groundwater systems. The aquifer’s hydrologic setting, connectivity of its sub-basins, and groundwater flow across these sub-basins are currently unclear. To address these issues, we used a combined approach that included: (1) a regionally calibrated groundwater flow model that mimics early (>10,000 years) steady-state conditions under wet climatic periods and later (<10,000 years BP–1960; 1960–2010) transient conditions under arid climatic periods; and (2) groundwater ages (36Cl, 81Kr) and isotopic (18O, 2H) data. The NSAS was recharged on a regional scale in previous wet climatic periods; however, in dry periods, its outcrops are still receiving local modest recharge. A progressive increase in 36Cl groundwater ages was found along groundwater flow directions and along structures that are sub-parallel to the flow direction. The NE–SW Pelusium mega shear zone is a preferential groundwater flow conduit from the Kufra to the Dakhla sub-basin. The south-to-north groundwater flow is hampered by the Uweinat–Aswan basement uplift. The findings provide useful information about the best ways to use the NSAS.

Climate vulnerability scenario of the agricultural sector in the Bicol River Basin, Philippines

This paper investigated the vulnerability of the agriculture sector and rural agriculture livelihoods in the Bicol River Basin (BRB) of the Philippines to projected changes in climate. The geographical characteristics of the BRB feature eight major sub-basins or watersheds consisting of Libmanan-Pulantuna, Ragay Hills, Thiris, Naga-Yabo, Pawili River, Waras-Lalo, Naporog, and Quinali. The study applied the combination of the participatory tools and the Climate Risk Vulnerability Assessment (CRVA) framework to gather information on local climate vulnerabilities and contexts. Briefly, the CRVA employed geospatial modeling and utilized several indicators which are presumed to affect vulnerability including exposure, sensitivity, and adaptive capacity which were aggregated to provide an index of vulnerability. This enabled us to identify areas of exposure and vulnerability and pointed areas of greatest need for strengthened adaptive capacity and risk management. Our findings revealed that vulnerability in the BRB was perceived to be relatively prevalent and that typhoons, flooding, and drought were identified to contribute significant impacts to rural livelihood. Furthermore, our findings in the CRVA suggested significant regional differences in vulnerability in the BRB. The majority of the towns in the central and northwestern portions of the BRB will largely experience increased vulnerability, particularly, in the Thiris sub-basin including some parts of Ragay Hills, Waras-Lalo, and the northwestern Libmanan-Pulantuna sub-basins. On the contrary, the entire Quinali region on the south is revealed to have the lowest vulnerability index. The clear policy implication of these accounts will be on how to mobilize developmental thrusts in both areas of disaster risk reduction and climate change adaptation at the sub-national level to reinforce local-based climate priority setting in adaptation interventions and policies.

How to Protect Free Flowing Rivers: The Bita River Ramsar Site as an Example of Science and Management Tools Working Together

The Orinoco river basin is the third largest river in the world by volume. Its catchment encompasses 27 major sub-basins including the Bita with a catchment area of about 825,000 ha, which originates in the Colombian high plains in the Llanos ecoregion. It has been recognized as a priority area for conservation through different gap analyses and overall determined to have good health according to the Orinoco report card 2016. The natural climate and hydrologic processes, and their synergies with flooded forests, savannas, wetlands, species diversity and local economic activities, are part of a dynamic and sensitive system. With the purpose of conserving the ecological, social and cultural benefits that it brings, the Colombian Government, with the support of regional and local civil society organizations, promoted the designation of a conservation area. Technical exercises were carried out including biological and socioeconomic surveys, local stakeholder consultations and future scenario modeling. In June 2018, the Bita River basin was designated as the largest Ramsar site in Colombia, providing a worldwide example of explicit protection of riverine systems. In order to maintain this free-flowing river, land use and fisheries management, in conjunction with other conservation actions, are being implemented and provide a model of protection for freshwater ecosystems that could be replicated elsewhere.

Appraisal of Spatiotemporal Variations in Snow Cover Dynamics using Geospatial Techniques: A paradigm from Gilgit-Baltistan, Pakistan

Snow and glaciers in the mountain ranges of Hindu-Kush, Karakoram, and Himalayas (HKH), are supposed to be shrinking. Consequently, serious significances occur in respect of water accessibility for inhabitants of down streams. This investigation is an effort o identify the temporal variation in snow covers of Gilgit Baltistan (GB), northern Pakistan. For the calculation of the Normalized Difference Snow Index (NDSI) and estimation of snow-covered area (SCA), MODIS images for the period of 2000 to 2020 were used. Hunza, Astore, Gilgit, Diamir, Shyok, Ghanche, Skardu and Shigar are major sub-basins lying in GB with Hunza and Skardu being the mostly glaciated areas. Annual fluctuation in snow cover within GB was 10 to 80%. In accumulation season, from December to February, snow cover was almost 80% while in melting season, from July to September this area was reduced to 65-75%. The maximum and minimum Snow-Covered Area (SCA) detected in 2009 was almost 57687.85 km2 and 12083.40 km2 respectively. These results show fluctuations in SCA within the time duration of the last two decades.

Statistical Downscaling and Hydrological Modeling-Based Runoff Simulation in Trans-Boundary Mangla Watershed Pakistan

The economy of Pakistan relies on the agricultural sector which mainly depends on the irrigation water generating from the upper Indus river basin. Mangla watershed is a trans-boundary basin which shares borders of India and Pakistan, it comprises five major sub-basins, i.e., Jhelum, Poonch, Kanshi, Neelum and Kunhar. The runoff production of this basin is largely controlled by snowmelt in combination with the winter precipitation in the upper part of the basin and summer monsoon. The present study focusses on the application of a statistical downscaling method to generate future climatic scenarios of climatic trends (temperature and precipitation) in Mangla watershed. Statistical Downscaling Model (SDSM) was applied to downscale the Hadley Centre Coupled Model, version 3, Global Climate Model (HadCM3-GCM) predictions of the A2 and B2 emission scenarios. The surface water analyst tool (SWAT) hydrological model was used for the future projected streamflows based on developing climate change scenarios by SDSM. The results revealed an increasing trend of annual maximum temperature (A2) at the rates of 0.4, 0.7 and 1.2 °C for the periods of 2020s, 2050s and 2080s, respectively. However, a consistent decreasing trend of temperature was observed at the high-altitude region. Similarly, the annual minimum temperature exhibited an increasing pattern at the rates of 0.3, 0.5 and 0.9 °C for the periods of 2020s, 2050s and 2080s, respectively. Furthermore, similar increases were observed for annual precipitation at the rates of 6%, 10%, and 19% during 2020, 2050 and 2080, respectively, for the whole watershed. Significant increasing precipitation trends in the future (2080) were observed in Kunhar, Neelum, Poonch and Kanshi sub-basins at the rates of 16%, 11%, 13% and 59%, respectively. Consequently, increased annual streamflow in the future at the rate of 15% was observed attributing to an increased temperature for snow melting in Mangla watershed. The similar increasing streamflow trend is consistent with the seasonal trends in terms of winter (16%), spring (19%) and summer (20%); however, autumn exhibited decreasing trend for all periods.

Geomorphology of the Sulden River basin (Italian Alps) with a focus on sediment connectivity

ABSTRACT An area-wide digital geomorphological map consisting of 12,180 non-overlapping polygons was created for the Sulden river basin (South Tyrol, Italian Alps) with the purpose to carry out a GIS-based sediment connectivity analysis. Thirty-one landform types were defined with respect to their role within sediment cascades. As such, the classification and the related symbology partly differ from a traditional geomorphological map where several areal objects are frequently represented by scaled and rotated point symbols. The catchment (∼130 km²), exhibits a high geomorphological variability as well as relatively large glacierized areas. We used the geomorphological map for a first qualitative estimate of the main differences between the two major sub-basins concerning the components of the sediment cascades: while the Trafoi sub-catchment exhibits a high number of small landslides and debris flow channels (i.e. source and transport landforms), the Sulden sub-catchment is rather characterized by large proglacial and talus landforms (i.e. temporary storage landforms).

The polyphase rifting and inversion of the Danube Basin revised

Abstract To track the evolution and formation of one of the major sub-basins of the Pannonian back-arc basin system, we re-evaluate pre-rift, syn-rift, post-rift, and inversion stages of the Danube Basin. This synthesis builds upon a new compilation of (i) geophysical measurements, (ii) current data about the substratum of the basin, (iii) recent advances in stratigraphic architecture of the basin fill, (iv) subsidence history, (v) geomorphological evolution, and (vi) new biostratigraphic and geochronological data.The history of the basin begins with the pre-rift stage represented by the pre-Neogene basement, which belongs to an Eo-Alpine mountain range uplifted at the end of the Cretaceous. The core of the mountain range was formed by pre-Alpine high-grade metamorphic complexes, with a dome-like structure. The crystalline complexes were overlain by late Paleozoic and Mesozoic cover and nappes, which were later eroded during the early Cenozoic. These pre-rift processes were triggered by (i) the late Cenozoic collision of the Alpine-Carpathian orogenic wedge with the European platform, (ii) the crustal extension resulting from the northeastward lateral extrusion of the Alpine-Carpathian-Pannonian lithospheric microplate, and (iii) the subsequent updoming of the lithospheric mantle. This stage was followed by the polyphase rifting of the basin associated with extensive volcanic activity. This syn-rift stage consists of four phases. The first syn-rift phase dated to the early Badenian (~15.2–13.8 Ma), with subsidence documented in the Blatne and Želiezovce depressions, was characterized by accommodation rates up to ~900 m·Ma-1. The second syn-rift phase dated to the late Badenian (~13.8–12.6 Ma), exhibited accelerated subsidence with accommodation rates up to ~2300 m·Ma-1, and mainly affected the Blatne depression. The third and fourth syn-rift phases took place during the Sarmatian (~12.6–11.6 Ma) and early Pannonian (~11.6–9.5 Ma) and the accommodation rates reached ~900 m·Ma-1 and ~1000 m·Ma-1, respectively. They were documented in the Risňovce, Komjatice and Gabcikovo-Győr depressions, confirming the eastward subsidence migration, up to the external zone of the middle Miocene mantle upheaval. The late Miocene post-rift stage, commonly refered to as a thermal subsidence stage (~9.5–6.0 Ma), was associated with formation of a widespread planation surface called the “Mid-mountain level” on the margins of the basin. The history of the Danube Basin terminates with the inversion stage which began after ~6.0 Ma and is still ongoing. It includes the continuous uplift of the basin margins and the subsidence of the central depocenters. The fluvial depositional systems did not behave steadily, because (i) lateral channel mobility and area of deposition increased progressively until ~4–3 Ma and caused onlaps on the base of the succession, followed by (ii) gradual confinement of the streams to the central part of the basin by uplifting basin margins, recorded by formation of river terrace staircases.

Fabric and internal architecture of Permian Basin turbidites indicated by unsupervised machine learning analysis of P-P and SV-P images

The Permian Basin of west Texas spans two major subbasins — the Midland Basin and the Delaware Basin. Both basins contain Wolfcampian- to Leonardian-age turbidites that form thick sections of prolific unconventional reservoirs. For the past several years, the most active drilling targets in the United States have been the Wolfberry turbidite interval of the Midland Basin and the Wolfbone turbidite interval in the Delaware Basin. We have used two new technologies to examine the internal architecture and fabric of a thick interval of these unconventional drilling targets with seismic reflection seismology. First, we used seismic interpretation software that uses unsupervised machine learning (ML), so that a higher level of detail could be extracted from seismic images. Second, we complemented our P-P imaging of Wolfberry turbidites with a new seismic imaging option, that being SV-P (or converted-P) imaging. Because vertical vibrators, particularly arrays of vertical vibrators, produce downgoing P and downgoing SV illuminating wavefields, SV-P reflections can usually be extracted from the same vertical-geophone responses as are P-P reflections. The combination of these two images essentially doubles the amount of information that can be extracted from data generated by P sources and recorded with vertical geophones. SV-P imaging with P sources has been ignored by reflection seismologists for decades, so we felt an obligation to illustrate the value of this ignored seismic mode. These two new tools — SV-P imaging and interpreting P-P and SV-P images via unsupervised ML — expanded our insights into the internal architecture and fabric of Wolfberry turbidites. Our work provides interpreters a much-needed example of applying unsupervised ML technology in a joint interpretation of P- and S-wave data.