Topographic Slope Research Articles

Integrating groundwater pumping data with regression-enhanced random forest models to improve groundwater monitoring and management in a coastal region

Groundwater is essential for sustaining human life and ecosystems as a freshwater resource. However, intensive groundwater pumping (GWP) can deplete groundwater levels, and exacerbate issues such as sea-level rise and saltwater intrusion in coastal areas, further affecting the availability and accessibility of groundwater. To address these challenges, accurate monitoring and modeling of water table depth (WTD), a key indicator of groundwater storage, is useful for sustainable groundwater management. This work studies the implementation of a regression-enhanced random forest (RERF) model to predict WTD anomalies with pumping as a major input for New Jersey, a coastal state in the United States. The predicted WTD anomalies align well with observations, with a test Nash-Sutcliffe Efficiency (NSE) of 0.49, a test Pearson correlation coefficient (r) of 0.72, and a test root-squared mean error (RMSE) of 1.61 m. Based on a permutation feature importance, the most important input variables in the model for predicting WTD anomalies were long-term mean WTD, precipitation minus evapotranspiration (PME), and GWP. Using the trained RERF model, we generated 90 m spatial resolution WTD anomaly maps for New Jersey for January and July 2015, showing areas of increasing and decreasing WTD. We then inverted the RERF model to predict GWP using WTD anomalies, land cover, and a cross metric as additional inputs. This approach was less effective, yielding a test NSE of 0.40, a test r of 0.65, and a test RMSE of 15.44 million liters/month. A permutation feature importance revealed the most important input variables to be PME, long-term mean WTD, and topographic slope. Again we generated 90 m GWP maps for New Jersey for January and July 2015, offering finer resolution than the previous maps at the subwatershed level. Focusing on New Jersey, the study provides insights into the relationship between WTD anomalies and its critical input variables including GWP in coastal areas. Moreover, significant gaps in WTD observations persist in New Jersey, highlighting the need for comprehensive monitoring efforts. Thus, by employing ML techniques and leveraging available data, this study contributes to improving groundwater management practices and informing future decision-making.

Hybrid delineation of landforms: Case of Bystrytsia-Pidbuzka drainage basin

Landforms, together with geological substrate and geomorphic processes, are essential for delineation of geoecosystems, which are indispensable for environmental management at landscape and regional levels. Geomorphometry is a geographic information system technology, which affords quantitative land-surface analysis and landform delineation using digital elevation models (DEM). Application of geomorphometry has been fostered by the emergence of free global high-resolution DEMs. However, automated geomorphometric extraction of landforms for flat areas, like wide river valleys, may be problematic owing to insufficient accuracy of the DEM. We selected the Bystrytsia-Pidbuzka drainage basin of 500 km², which has a transitional location between low flysch External Carpathian Mountains and wavy denudation-alluvial plains of the Fore-Carpathian Upland in Lviv Oblast (Ukraine), to test a hybrid methodology of landform delineation – manual and automated geomorphometric. We considered regional landforms as orotectonic units (morphostructures), which are used to characterize ecoregions, and morpholithotopes as the smallest local landforms – mesorelief elements together with surface deposits and current geomorphic processes. FABDEM V1-2 with a resolution of 30*30 m was used as a primary geodataset of elevation data. Ecoregions and morpholithotopes of wide flat valley bottoms were delineated via manual interpretation of the DEM and ancillary data, while the morpholithotopes of the hilly and mountainous interfluves were delineated automatedly using three topographic variables: topographic position index, slope, and flow accumulation. Within the study area, we singled-out six microecoregions and four mesoecoregions: Upper Dnister Depression, Upper Dnister Upland, Marginal Beskydy, and Dnister Beskydy. There are 21 classes of morpholithotopes distinguished belonging to flat valley bottoms with alluvial and lacustrine deposits, narrow valleys and big gullies, as well as lower concave and upper convex slopes formed by colluvial and eluvial-colluvial deposits respectively, divided into four inclination categories, and two lithological groups. Each morpholithotope class is attributed with a drainage status and probable current geomorphic processes. The obtained dataset is oriented on further ecological application. Key words: morpholithotope; ecoregion; geomorphometry; FABDEM; the Carpathian Mountains; the Fore-Carpathian Upland.

The importance of eddy stirring in wind-driven coastal upwelling

Abstract Coastal upwelling systems play a key role in sustaining productive coastal ecosystems in the global ocean by transporting nutrients to surface waters. However, the fundamental mechanisms and pathways responsible for nutrient upwelling are not fully understood, largely due to the historically employed two-dimensional frameworks in which coastal upwelling systems have long been studied. Using both observations and idealized numerical simulations, we identify and quantify two primary routes of nutrient upwelling: the residual circulation, resulting from a significant cancellation between Eulerian mean and eddy-induced circulations, and along-isopycnal eddy stirring. Our analysis demonstrates that their relative contributions depend on two distinct parameters: 1) the slope Burger number S, defined here as S = αN/f, where α is the topographic slope angle and N and f are the buoyancy and Coriolis frequencies and 2) the surface nutrient uptake rate by biological activities. Specifically, we propose that wind forcing induces isopycnal tilting and surface outcropping, which creates favorable conditions for along-isopycnal nutrient gradients to develop in regions of strong biological activity at the surface. The magnitude of these gradients depends on both the slope Burger number (S), which influences the strength of the residual circulation bringing nutrients from depths, and the surface biological uptake rate, which consumes nutrients. Our diagnostics provide insights into the intricate pathways for nutrient upwelling and underscore the significance of eddy stirring in coastal upwelling systems.

Comprehend Analysis of Surface and Subsurface Sediment Distribution Using Underwater Acoustic Instruments

Abstract Ports play an important role in shipping and transporting while its fundamental challenge is sedimentation that occurs continuously at a location. A management system is required to respond to this siltation problem by dredging the underwater sediments parallel with Sustainable Development Goal 11 about sustainable cities and communities. Therefore, analyzing the area’s sediment types and distribution is necessary, especially using acoustic instruments. In this research, the dredging plan shows the relative depth distribution is around 4 to 18 meters with deep sloping topography on the north side. Furthermore, the overall result of the backscatter value of the multibeam echosounder ranges from −45.86 dB to −55.73 dB. According to the Angular Response Analysis, the sediment type of the seabed surface is clay with depths ranging up to 5.40 meters. The result of the sub-bottom profiler processing discovers step or bowl-like features where the curvature seems to have an unequal height. It considers the difference in sediment height around the area while the intensity is relatively low. Through acoustic processing analysis, it informs that the total area of 643872 m2 needs to be dredged with an estimated dredging volume of around 2181105 m3 both in surface and subsurface areas. The qualitative analysis of Multibeam Echosounder, and Sub Bottom Profiler data shows that the acoustic instrument’s products are accurate with validation data in the field.

Effect of the Variable Kuroshio Intrusion Pathway on Deep Flow in the Northern South China Sea

Abstract The effect of the variable Kuroshio intrusion pathway on deep flow in the northern South China Sea (NSCS) is investigated using observational and theoretical analyses. With significant seasonality in path variation, the leaking path is found to be most frequent, whereas the looping (leaping) path is transient and prefers to occur in winter (summer). Such multipath variability contributes greatly to the highest surface eddy kinetic energy in the NSCS, with the energy peaking for the looping path in winter, followed by the leaking and leaping paths. The information flow–based causality analysis suggests that the energetic surface perturbations in the Kuroshio intrusion region are causal to the intraseasonal variability in deep flow east of the Dongsha Islands, wherein the linkage is strongest within the Kuroshio Loop Current eddy shedding event related to the looping path. Therefore, the underlying dynamics for the vertical coupling is illustrated by this event, in which the eddy is highly compressed to the surface because of sloping topography and its energy is partially transferred downward through vertical pressure work to force the abyssal topographic Rossby waves. Moreover, the energetics and instability analyses reveal that barotropic instability dominates the energy transfer from background flow to eddies in the Kuroshio intrusion region, which energizes the surface perturbations most intensively for the looping path, followed by the leaking and leaping paths. The characteristic scales of the most unstable mode estimated by the reduced-gravity model are analogous to the observations and account for the rapid growth of surface perturbations in the Kuroshio intrusion region.

Transport and deposition of terrestrial organic matter in marine littoral deltas: New evidence from flume experiments and 3D laser scanning

Terrestrial organic matter (TOM) serves as the primary parent material for source rocks in marine-terrestrial transitional environments. The transport and deposition process of TOM affects the development of high-quality source rocks in shallow marine areas. Based on the sequence stratigraphy of the Enping Formation (upper Eocene to lower Oligocene) in the Baiyun Sag and the petrological characteristics of the source rocks, simulation experiments of terrestrial dispersed organic matter deposition constrained by topography, provenance composition, sedimentation period and other conditions were conducted. Comparative experiments of individual constraints such as topographic slope, hydrodynamic conditions, water salinity and particle size of organic matter were also conducted. The results indicate that TOM enrichment in the littoral delta system initially increases with transport distance but subsequently decreases. Four TOM distribution patterns were identified: interrupted, interbedded, lenticular, and banded types. The interrupted, interbedded, and lenticular types predominantly occur in delta plains, whereas the banded type primarily develops in delta fronts and prodeltas. In the early stages of delta formation, gentle slope conditions are more conducive to the forward transport of TOM. As the delta develops, the sediment thickness increases, which reduces the slope gradient and thus weakens the effect on the transport distance of TOM. Higher flow intensity promotes TOM transport, however, wave action can impede forward transport, creating localized TOM enrichment areas on the delta plain. In shallow water, the increase in water salinity enhances the flocculation of TOM, thus reducing the transport distance of TOM. Meanwhile, Smaller TOM particle sizes correspond to greater transport distances and increased heterogeneity in its planar distribution within the delta-shallow sea sedimentary system.

GraphFlood 1.0: an efficient algorithm to approximate 2D hydrodynamics for landscape evolution models

Abstract. Computing hydrological fluxes at the Earth's surface is crucial for landscape evolution models, topographic analysis, and geographic information systems. However, existing formalisms, like single or multiple flow algorithms, often rely on ad hoc rules based on local topographic slope and drainage area, neglecting the physics of water flow. While more physics-oriented solutions offer accuracy (e.g. shallow-water equations), their computational costs limit their use in terms of spatial and temporal scales. In this contribution, we introduce GraphFlood, a novel and efficient iterative method for computing river depth and water discharge in 2D with a digital elevation model (DEM). Leveraging the directed acyclic graph structure of surface water flow, GraphFlood iteratively solves the 2D shallow-water equations. This algorithm aims to find the correct hydraulic surface by balancing discharge input and output over the topography. At each iteration, we employ fast-graph-theory algorithms to calculate flow accumulation on the hydraulic surface, approximating discharge input. Discharge output is then computed using the Manning flow resistance equation, similar to the River.lab model (Davy and Lague, 2009). The divergence of discharges iteratively increments flow depth until reaching a stationary state. This algorithm can also solve for flood wave propagation by approximating the input discharge function of the immediate upstream neighbours. We validate water depths obtained with the stationary solution against analytical solutions for rectangular channels and the River.lab and CAESAR-Lisflood models for natural DEMs. GraphFlood demonstrates significant computational advantages over previous hydrodynamic models, an with approximately 10-fold speed-up compared to the River.lab model (Davy and Lague, 2009). Additionally, its computational time scales slightly more than linearly with the number of cells, making it suitable for large DEMs exceeding 106–108 cells. We demonstrate the versatility of GraphFlood by integrating realistic hydrology into various topographic and morphometric analyses, including channel width measurement, inundation pattern delineation, floodplain delineation, and the classification of hillslope, colluvial, and fluvial domains. Furthermore, we discuss its integration potential in landscape evolution models, highlighting its simplicity of implementation and computational efficiency.

Land surface temperature trends derived from Landsat imagery in the Swiss Alps

Abstract. The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, all of which have far-reaching effects on ecosystems and societies. Landsat Collection 2 provides multi-decadal land surface temperature (LST) data, principally suited for large-scale monitoring at high spatial resolution. In this study, we assess the potential to extract LST trends using Landsat 5, 7, and 8 time series. We conduct a comprehensive comparison of both LST and LST trends with data from 119 ground stations of the Intercantonal Measurement and Information System (IMIS) network, located at high elevations in the Swiss Alps. The direct comparison of Landsat and IMIS LST yields robust satellite data with a mean accuracy and precision of 0.26 and 4.68 K, respectively. For LST trends derived from a 22.6-year record length, as imposed by the IMIS data, we obtain a mean accuracy and precision of −0.02 and 0.13 K yr−1, respectively. However, we find that Landsat LST trends are biased due to unstable diurnal acquisition times, especially for Landsat 5 and 7. Consequently, LST trend maps derived from 38.5-year Landsat data exhibit systematic variations with topographic slope and aspect that we attribute to changes in direct shortwave radiation between different acquisition times. We discuss the origin of the magnitude and spatial variation of the LST trend bias in comparison with modeled changes in direct shortwave radiation and propose a simple approach to estimate the LST trend bias. After correcting for the LST trend bias, the remaining LST trend values average between 0.07 and 0.10 K yr−1. Furthermore, the comparison of Landsat- and IMIS-derived LST trends suggests the existence of a clear-sky bias, with an average value of 0.027 K yr−1. Despite these challenges, we conclude that Landsat LST data offer valuable high-resolution records of spatial and temporal LST variations in mountainous terrain. In particular, changes in the mountain cryosphere, such as glacier retreat, glacier debris cover evolution, and changes in snow cover, are preserved in the LST trends and potentially contribute to improved prediction of permafrost temperatures with large spatial coverage. Our study highlights the significance of understanding and addressing biases in LST trends for reliable monitoring in such challenging terrains.

A 30 Arcsec Resolution VS30 Map for Mainland China Using Refined Topographic Slope-Based Cokriging (SCK) Model

ABSTRACT In 2022, we proposed a VS30 proxy model based on the Cokriging method, integrating local VS30 measurements with topographic slope data (SCK model, Zhou et al., 2022). Utilizing this model, a VS30 map for mainland China was developed based on 7797 local boreholes. However, the initial VS30 map was produced with a coarse grid resolution of 360 arcsec and did not comprehensively cover mainland China due to sparse distribution of boreholes in certain areas. In this study, we refined the SCK model by explicitly accounting for the influence of surrounding topographic slopes, incorporating more distant VS30 measurements, and improving the spatial distribution of VS30 measurements used in the model calculation. Tests based on virtual cases were conducted to evaluate the characteristics of the refined SCK model. In addition, 142 VS30 measurements from strong-motion stations in western China were added to enhance spatial coverage and model accuracy in these regions. Employing the refined SCK model and the expanded VS30 data set, a new VS30 map for mainland China was generated. This updated map features a higher grid resolution of 30 arcsec and covers the entirety of mainland China, showing reduced estimation errors and improved continuity. Comparisons with the U.S. Geological Survey Global VS30 Mosaic indicate that the results from the refined SCK model are more reasonable, as evidenced by local topography, VS profiles, lithologic profiles, and geology map, which better capture the impact of local geomorphological differences and climatic conditions on VS30 values.

Rock glacier inventory and predictive modeling in the Mackenzie Mountains: predicting rock glacier likelihood with a generalized additive model

Rock glaciers have been the subject of extensive research in recent years due to their potential to serve as indicators of past and present climate conditions and their potential impacts on water resources. Location and descriptive rock glacier data within the Mackenzie Mountains were used to build a rock glacier inventory that will serve as a valuable resource for future research and monitoring efforts. Additionally, this study maps the likelihood of rock glacier presence using extracted variables in a generalized additive model (GAM). The model incorporates attribute data, including potential incoming solar radiation (PISR), topographic position index (TPI), slope, elevation, and lithology as controls for rock glacier development. Topographic data were compiled for three study regions of the Mackenzie Mountains from a 30 m digital elevation model (DEM). The analysis of the GAM showed that the most significant explanatory variables were PISR, elevation, slope, and TPI. The GAM model had an accuracy of 0.87 with a sensitivity of 0.92. This study provides important insights into the controls, distribution, and dynamics of rock glaciers in the Mackenzie Mountains, as well as both the limitations and the potential of statistical models in predicting their occurrence.

Physicochemical Characterization of Gasoline and Analysis of the Vulnerability and Criticality of Atmospheric Pollutants in N'Djamena

In this work we made an analysis for the physicochemical characterization of gasoline sold on the roadsides of the city of N'Djamena. For our first experience on the storage of gasoline in cans exposed to the sun, we noticed the formation of lumps and solid particles at the bottom of the cans. We then sampled a sample by district and subjected it to the analysis conditions. The analyses focused on Density at 15°C; ASTM distillation; Reid vapor pressure, Octane number; Determination of corrosion of copper blades as well as Determination of sulfur content. The results of the analyzed contraband gasolines are out of specifications under normal analysis conditions. Despite the many studies carried out on air pollution by particles, uncertainties remain. It is more or less complex to treat particulate pollution in urban areas, this is in fact due to the modifications resulting from different meteorological factors (wind, precipitation, etc.), topographical (slope, etc.) and urban morphological, which constitute limiting factors in the dispersion of these pollutants, thus, different maps have been studied according to the vulnerability and criticality of the accentuated situation relating to pollution throughout the city of N'Djamena. The results confirm the presence of particles that can affect human health. In addition to the presence of all the elements that we have been able to discover, there is on average the result of the presence of PM10 particles which is: 66.98016239 ug/m<sup>3</sup> well above the WHO guideline which recommends 50 μg/m<sup>3</sup>/day as well as the daily average for PM2.5 which is: 56.83616723 ug/m<sup>3</sup> well above the WHO guideline which recommends 25 μg/m<sup>3</sup>/day. The objective of this article is to clearly present the urgency of air pollution throughout the territory in order to take a binding decision on the current situation based on the proliferation and illicit sale of petroleum products in the city of N'Djamena, particularly gasoline.

Spatiotemporal variations of non-seismically fatal landslides in Northwest China: A case study from Shaanxi province

Landslides are one of the devastating geo-hazards that result in severe casualties in Northwest China (NW China) every year. However, a comprehensive landslide database is yet to be available for quantitatively assessing the distribution of fatal landslides in NW China. To investigate the spatiotemporal characteristics of non-seismically fatal landslides in NW China, we carried out a study using fatal landslides that occurred in Shaanxi province as an example to construct a new database by incorporating data of fatal landslides that occurred in the period between 1996 and 2018. A total of 332 non-seismically fatal landslides that claimed 1132 lives were compiled in this database. Additionally, a thorough study of spatial and temporal variations of fatal landslides reveals that anthropogenic landslides occur approximately evenly throughout the year. Natural fatal landslides, however, rise noticeably in July, August, and September. The spatial distribution analysis showed that fatal landslides in Shaanxi province of China were mainly distributed in the An’ kang, Yan’an, and Shangluo regions. Additionally, areas with relatively high relief (46.1 to 123 m), steep–slope topography (19.2 to 26.1°), and intense precipitation (465 to 937 mm) are more vulnerable to naturally-triggered landslides. The spatiotemporal analysis of the fatal landslides revealed increasing trends in zones such as Yan’an and Yulin cities (in northern part), and in An’kang city (in southern part). When combined with other information, the Fatal Landslide Database of Shaanxi Province can be used to provide a guide for risk assessment and spatial planning studies to mitigate geo-hazard risks.

Topography Dominates the Spatial and Temporal Variability of Soil Bulk Density in Typical Arid Zones

Soil bulk density is a crucial indicator for assessing soil matter storage and soil quality. Due to the complexity of sampling soil bulk density, particularly in deeper layers, it is essential to study the spatial distribution patterns of soil bulk density and their influencing factors. To address the gap in large-scale studies of vertical (from surface to deeper layers) and horizontal (across a broad area) variations in soil bulk density in arid regions, this study focuses on Changji Prefecture, located in the central northern slope of the Tianshan Mountains and characterized by typical vertical zonation. By integrating classical statistics, geostatistics, and geographic information systems (GISs), this study investigates the spatial distribution patterns and driving factors of soil bulk density. The results indicate that soil bulk density in Changji Prefecture increases with soil depth, with significantly lower values in the surface layer than in deeper layers. Spatially, despite minimal variation in latitude, there is considerable elevation difference within the study area, with the lowest elevations in the central region. Soil bulk density exhibits a spatial distribution pattern of higher values in the northeast (desert areas) and lower values in the southwest (forest areas). The nugget effect in the surface layer (0–20 cm) is substantial at 44.9%, while the deeper layers (20–100 cm) show nugget effects below 25%, suggesting that the influence of both natural and anthropogenic factors on deep soil bulk density is limited and mainly affects the surface layer. Stepwise regression analysis indicates that among topographic factors, slope and elevation are the primary controls of spatial variability in soil bulk density across layers. This research demonstrates that, in arid regions, soil bulk density is influenced primarily by natural factors, with limited impact from human activities. These findings provide valuable data support and theoretical guidance for soil management, agricultural planning, and sustainable ecosystem development in arid regions.

Perception of the Urban Space of the Waorani Nationality in Shell Township, Pastaza, Ecuador

The township of Shell in the Province of Pastaza, Ecuador was initially an exploration base and later, a missionary base for evangelization of the peoples and nationalities of the Amazon. It plays an important role in the Waorani nationality and its social, cultural and economic dynamics, especially in the Nuevo Amanecer neighborhood, which is home to approximately 43 Waorani families whose epicenter has become a strategic space for their territorial dynamics. The research, through ethnographic methodologies and field work, has allowed to establish the perception from the Waorani nationality to the urban space. During the development of this study two sources of information were applied: methodological bibliography and thematic content reference. The study of 120 members of 43 Waorani families of the Ecuadorian Amazon and a process of direct observation, as a participant observer during the years 2019-2020-2021-2022, resulted in a conceptual model Bailey (1996), where the Waorani use preferences of displacement, economic attributes of the area, location of residence, representation of the spatial-urban structure, space-action, time of stay, activity space and objective spatial structure of the urban environment; and a transect as a very effective method to recognize population movements, sectoral differences, topographic slopes and social dynamics presented in the territory from a plan and elevation view. Human beings adapt easily to new spaces, in spite of cultural and social differences, their assimilation processes will depend on their capacity to approach and accept the environment. The Nuevo Amanecer settlement has motivated families to move to peri-urban or urban spaces in intermediary cities, which has provoked a forced adaptation and assimilation due to the same processes involved in rural-urban relations. Received: 30 May 2024 / Accepted: 21 October 2024 / Published: 05 November 2024

Examining wildfire dynamics using ECOSTRESS data with machine learning approaches: the case of South‐Eastern Australia's black summer

Wildfires are increasing in risk and prevalence. The most destructive wildfires in decades in Australia occurred in 2019–2020. However, there is still a challenge in developing effective models to understand the likelihood of wildfire spread (susceptibility) and pre‐fire vegetation conditions. The recent launch of NASA's ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution of 70 m by measuring ecosystem stress and drought conditions preceding wildfires. We incorporated ECOSTRESS data, vegetation indices, rainfall, and topographic data as independent variables and fire events as dependent variables into machine learning algorithms applied to the historic Australian wildfires of 2019–2020. With these data, we predicted over 90% of all wildfire occurrences 1 week ahead of these wildfire events. Our models identified vegetation conditions with a 3‐week time lag before wildfire events in the fourth week and predicted the probability of wildfire occurrences in the subsequent week (fifth week). ECOSTRESS water use efficiency (WUE) consistently emerged as the leading factor in all models predicting wildfires. Results suggest that the pre‐fire vegetation was affected by wildfires in areas with WUE above 2 g C kg−1 H₂O at 95% probability level. Additionally, the ECOSTRESS evaporative stress index and topographic slope were identified as significant contributors in predicting wildfire susceptibility. These results indicate a significant potential for ECOSTRESS data to predict and analyze wildfires and emphasize the crucial role of drought conditions in wildfire events, as evident from ECOSTRESS data. Our approaches developed in this study and outcome can help policymakers, fire managers, and city planners assess, manage, prepare, and mitigate wildfires in the future.

Mixed neural operator learning on the solitary wave propagation over slope topography and inverse problem

This study proposes the mixed neural operator (MNO) learning framework, which further combines with the particle swarm optimization (PSO) to address challenges of solitary wave propagation over topography. The forward problem is defined as the evolution prediction of the solitary wave propagating over topography, while the inverse problem is defined as an optimization to identify the topography parameter based on the solitary wave elevation. Both the forward and inverse problems can be considered within a single framework and the dataset are provided by the classical Korteweg–de Vries (KdV) equation. The MNO framework is shown to simulate the evolution of solitary waves over topography, accurately capturing the wave elevation under different topographical conditions. By comparing with different neural operators, it is found that the U-shape neural operator is the most suitable for the KdV equation simulation. The coefficient of determination for the inverse problem based on the combination of MNO and PSO can reach 0.992, showing great potential of the approach in topography recognition. Finally, the proposed learning framework is preliminary applied to the prediction of the tsunami runup onto a complex beach, and a good agreement is also achieved between the direct simulation and the learning framework prediction.

Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers

Abstract Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity ( S r ) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified S r and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale S r with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of S r globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with S r globally, the relation between precipitation and S r varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and S r for precipitation of up to 3 mm d − 1 , above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and S r . Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of S r , which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate S r on a global scale has the potential to be readily and easily integrated into the parameterization of S r in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.

Investigating Sampling Bias in Seismological Research Through Secondary Variable: Insights from VS30 Datasets of Mainland China, Japan, Türkiye, and Taiwan

Abstract Sampling bias is an inevitable issue in seismological research, especially in collecting seismic site condition data due to operational constraints. It impacts data representativeness and subsequent model performance. Previous researches give insufficient consideration to this issue. Some researches worked on raw data directly. Some others used conventional declustering methods that rely on data spatial distribution and proved to be ineffective. This study investigates sampling bias in seismological research by employing a debiasing method that incorporates secondary variables, focusing on VS30 datasets from mainland China, Japan, Türkiye, and Taiwan. Quantifying analysis showed that, when considering topographic slope as the secondary variable, the sampling biases in the mainland China and Taiwan dataset are more pronounced. When examining the secondary variable of geology age, the sampling biases in the Japan and Türkiye datasets that are not readily discernible through visual inspection of spatial distributions become apparent. By investigating the sampling bias of the Türkiye and mainland China dataset using semivariogram as the secondary variable, this study reveals hidden bias within the Türkiye dataset, despite its well-distributed appearance. This finding further illustrates the limitations of relying solely on spatial distribution to detect sampling bias. In addition, the study examined the impact of sampling bias on resulting models. The topographic slope-based VS30 proxy models of debiased data in the four regions demonstrate significant effects of debiasing on modeling outcomes. Notably, the debiased models exhibit a homogenized trend comparing to original models in the low topographic slope range, indicating the possibility of a globally consistent relationship.

Influence of land-use types and topographic slopes on the physico-chemical characteristics of soils in Northwestern Ethiopia

AimsThis study examines the effects of land-use types and slope position on selected soil physico-chemical properties in the Ageza Watershed, Northwestern Ethiopia, focusing on soil fertility depletion and its impact on agricultural productivity.Materials and methodsThe experiment utilized a randomized complete block design (RCBD) with three replications, involving three land-use types and slope positions. A total of 27 soil samples (both disturbed and undisturbed) were collected, prepared, and analyzed for selected physico-chemical properties following standard laboratory procedures. Data were analyzed using two-way ANOVA with SAS software.ResultsSignificant variations in most soil physico-chemical properties were observed across land-use types and slope positions. Soil bulk density, organic carbon (OC), total nitrogen (TN), available phosphorus (Av. P), cation exchange capacity (CEC), and exchangeable bases significantly (P ≤ 0.001) differed among the land-use types and slope positions. The main effects showed significant (P ≤ 0.05) differences in sand, silt, and clay contents across the land-use types. However, pH did not show significant differences (P > 0.05). Soil bulk density, silt content, Av. P, exchangeable bases, and CEC all showed significant (P ≤ 0.01) differences among the slope positions. Forest land had the highest values for clay (46.11%), OC (6.08%), Av. P (20.60 mg/kg), CEC (33.89 cmol (+) kg-¹), and exchangeable cations: Ca²+ (23.72 cmol (+) kg-¹), Mg²+ (4.40 cmol (+) kg-¹), K+ (1.18 cmol (+) kg-¹), and Na+ (0.77 cmol (+) kg-¹). Grazing land exhibited higher silt and bulk density, while cultivated land had higher sand content. Lower slope positions recorded the highest values for clay (47.33%), silt (20.77%), pH (4.90), Av. P (16.61 mg/kg), Ca²+ (20.10 cmol (+) kg-¹), Mg²+ (3.73 cmol (+) kg-¹), K+ (1.00 cmol (+) kg-¹), Na+ (0.66 cmol (+) kg-¹), and CEC (28.71 cmol (+) kg-¹), while bulk density was higher in the upper slope position.ConclusionLand-use types and slope positions significantly influence variations in soil physico-chemical properties and overall soil fertility status. Forest preservation and management are essential to improve the soil fertility in this region.

Uncertainty in model estimates of global groundwater depth

Abstract Knowing the depth at which groundwater can be found below the land surface is critical for understanding its potential accessibility by ecosystems and society. Uncertainty in global scale water table depth (WTD) limits our ability to assess groundwater’s role in a water cycle altered by changing climate, land cover, and human water use. Global groundwater models offer a top–down pathway to gain this knowledge, but their uncertainty is currently poorly quantified. Here, we investigate four global groundwater models and reveal steady-state WTD disagreements of more than 100 m for one-third of the global land area. We find that model estimates of land areas with shallow groundwater at <10 m depth vary from 10% to 71% (mean of 23%). This uncertainty directly translates into subsequent assessments, as land areas with potential groundwater accessibility for forests, population, and areas equipped for irrigation, differ substantially depending on the chosen model. We explore reasons for these differences and find that contrary to observations, 3 out of 4 models show deeper water tables in humid than in arid climates and greatly overestimate how strongly topographic slope controls WTD. These results highlight substantial uncertainty associated with any global-scale groundwater analysis, which should be considered and ultimately reduced.