Simulating and quantifying legacy topographic data uncertainty: an initial step to advancing topographic change analyses

Rapid technological advances, sustained funding, and a greater recognition of the value of topographic data have helped develop an increasing archive of topographic data sources. Advances in basic and applied research related to Earth surface changes require researchers to integrate recent high-resolution topography (HRT) data with the legacy datasets. Several technical challenges and data uncertainty issues persist to date when integrating legacy datasets with more recent HRT data. The disparate data sources required to extend the topographic record back in time are often stored in formats that are not readily compatible with more recent HRT data. Legacy data may also contain unknown error or unreported error that make accounting for data uncertainty difficult. There are also cases of known deficiencies in legacy datasets, which can significantly bias results. Finally, scientists are faced with the daunting challenge of definitively deriving the extent to which a landform or landscape has or will continue to change in response natural and/or anthropogenic processes. Here, we examine the question: how do we evaluate and portray data uncertainty from the varied topographic legacy sources and combine this uncertainty with current spatial data collection techniques to detect meaningful topographic changes? We view topographic uncertainty as a stochastic process that takes into consideration spatial and temporal variations from a numerical simulation and physical modeling experiment. The numerical simulation incorporates numerous topographic data sources typically found across a range of legacy data to present high-resolution data, while the physical model focuses on more recent HRT data acquisition techniques. Elevation uncertainties observed from anchor points in the digital terrain models are modeled using “states� in a stochastic estimator. Stochastic estimators trace the temporal evolution of the uncertainties and are natively capable of incorporating sensor measurements observed at various times in history. The geometric relationship between the anchor point and the sensor measurement can be approximated via spatial correlation even when a sensor does not directly observe an anchor point. Findings from a numerical simulation indicate the estimated error coincides with the actual error using certain sensors (Kinematic GNSS, ALS, TLS, and SfM-MVS). Data from 2D imagery and static GNSS did not perform as well at the time the sensor is integrated into estimator largely as a result of the low density of data added from these sources. The estimator provides a history of DEM estimation as well as the uncertainties and cross correlations observed on anchor points. Our work provides preliminary evidence that our approach is valid for integrating legacy data with HRT and warrants further exploration and field validation

Physical geomorphometry for elementary land surface segmentation and digital geomorphological mapping

By interpretations related to energy, elementary land surface segmentation can be treated as a physical problem. Many pieces of such a view found in the literature can be combined into a synthetic comprehensive physical approach. The segmentation has to be preceded by defining the character and size of searched units to result from the segmentation. A high-resolution digital elevation model (DEM) is the key input for this task; it should be generalized to the resolution best expressing information about the searched units. Elementary land surface units can be characterized by various parts of potential gravitational energy associated with a set of basic geomorphometric variables. Elevation above sea level (z) represents Global Geomorphic Energy (GGE). Regional and Local Geomorphic Energy (RGE and LGE) are parts of GGE, represented respectively by relative elevation above the local base level (zrel) and local relief (elevation differential in a moving window Δz). Derivation (change) of elevation defines the slope inclination (S), determining the local Potential Energy of Surface (PES) applicable to mass flow. Normal slope line (profile) curvature (kn)s and normal contour (tangential) curvature (kn)c express change in the PES value (ΔPES(kn )s, ΔPES(kn )c), responsible for acceleration/deceleration and convergence/ divergence of flow. Mean curvature (kmean) determines the Potential Energy of Surface applicable to Diffusion (PESD). Energetic interpretation of basic geomorphometric variables enables their direct comparison and systematic evaluation. Consequently, the homogeneity of basic geomorphometric variables defines a hierarchy of states of local geomorphic equilibria: static equilibrium, steady state, and non-steady state dynamic equilibrium. They are local attractors of landform development reflected in the geomorphometric tendency to symmetry (horizontality, various types of linearity, and curvature isotropy, together expressed by gravity concordance). Nonequilibrium and transitional states can be characterized by the PES excess (PESe) determined by difference curvature (kd), by gravity discordant change of the PES characterized by twisting curvature (τg)c, and by Integral Potential Energy of Surface Curvature (IPESC) expressed by Casorati curvature (kC) (general curvedness). Excluding zrel and Δz, all these energy-related geomorphometric variables are local point-based. Local area-based and regional variables such as Glock’s Available Relief, Melton Ruggedness Number, Stream Power Index, Openness, Topographic Position Index, Topographic Wetness Index, and Index of Connectivity also have energetic interpretations although their definition is more complex. Therefore we suggest exclusive use of the local point-based variables in designs of elementary land surface segmentation. The segmentation should take notice of natural interconnections, the hierarchy of geomorphometric variables, elements of Local Geomorphic Energy, and (dis)equilibria states, so that elementary segments are clearly interpretable geomorphologically. This is exemplified by Geographic Object-Based Image Analysis (GEOBIA) segmentation of Sandberg, a territory on the boundary of the Carpathians and Vienna Basin with a complex geomorphic history and marked morphodynamics. Compared with expert-driven field geomorphological mapping, the automatic physically-based segmentation resulted in a more specific delineation and composition of landforms. Physical-geomorphometric characteristics of the elementary forms enabled the formulation of their system and subsequent improvement of the expert-based qualitative genetic analysis, with interpretation leading to a deeper understanding of the development and recent dynamics of the landscape

Palaeohydrology from the Northern Salado River, a lower Parana river tributary (Argentina)

Palaeofloods and drainage palaeostage can be approached from sedimentological, stratigraphical, geomorphological, geodetic, and geophysical information. This allows us to supply pre-instrumental and historical data and to assess a particular flood-prone area. It has been proved that the study of Late Holocene fluvial sediments is valuable source to estimate 1 × 102-1x103yr. scale occurrence and long-term recurrence of maximum events. The geological evidence of palaeofloods in lowlands in Central Argentina may reveal higher discharges likely occurred in the near past. In this work, we attempt to identify sedimentological evidence of past floods in Late Holocene sediments from the northern Salado River (NSR), an important tributary of the lower Paraná River basin (Chaco-Pampean plain region). In the yr. 2003, the lower reaches of the NSR recorded an extreme flood event that provoked a disaster in Santa Fe, a city of 500,000 inhabitants located at the river mouth. Considering the importance of this event, we developed a geomorphometry methodology for discriminating different levels of fluvial terraces and flood indicators in a representative area of the NSR, using multi-scale resolution Digital Elevation Model (DEM) data. Descriptions of flood-associated fluvial landforms and sedimentological stratigraphic attributes were performed in the field. High-resolution geodetic information and digital optical images were obtained from UAV photogrammetry. Ground Penetrating Radar (GPR) cross-sections were achieved and addressed to detect extreme flood evidence. A geomorphometric routine was applied to simulate the extreme flood scenarios, based on the data obtained from the field. The map resulting from the simulation was compared to satellite images recorded in the yr. 2003 extreme flood. A series of slackwater deposits and other palaeostage indicators (SWD-PSI) showed elevations higher than those reached over the yr. 2003 extraordinary flood (instrumentally recorded) and in the yr. 1914 historic flood event. The geomorphometric simulation of a flood event, calibrated from these diagnostic landforms, allowed us to extend the flood-prone area beyond the boundaries of the current active floodplain and channel. The integrative methodology enabled the mapping of areas potentially prone to flooding. The estimations of the discharges associated to the inferred palaeofloods could be 50–80% larger than the maximum events historically documented and instrumentally measured.Fil: Pedersen, Oscar Ariel. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; ArgentinaFil: Brunetto, Ernesto. Provincia de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Universidad Autónoma de Entre Ríos. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones Científicas y Transferencia de Tecnología a la Producción; ArgentinaFil: Krohling, Daniela Mariel Ines. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral; ArgentinaFil: Thalmeier, Maria Belen. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Autónoma de Entre Ríos; ArgentinaFil: Zalazar, Maria Cecilia. Universidad Autónoma de Entre Ríos; Argentin

Stream-Channel and Watershed Delineations and Basin-Characteristic Measurements using Lidar Elevation Data for Small Drainage Basins within the Des Moines Lobe Landform Region in Iowa, TR-692

Basin-characteristic measurements related to stream length, stream slope, stream density, and stream order have been identified as significant variables for estimation of flood, flow-duration, and low-flow discharges in Iowa. The placement of channel initiation points, however, has always been a matter of individual interpretation, leading to differences in stream definitions between analysts. This study investigated five different methods to define stream initiation using 3-meter light detection and ranging (lidar) digital elevation models (DEMs) data for 17 stream gages with drainage areas less than 50 square miles within the Des Moines Lobe landform region in north-central Iowa. Each DEM was hydrologically enforced and the five stream initiation methods were used to define channel initiation points and the downstream flow paths. The five different methods to define stream initiation were tested side-by-side for three watershed delineations: (1) the total drainage-area delineation, (2) an effective drainage-area delineation of basins based on a 2-percent annual exceedance probability (AEP) 12-hour rainfall, and (3) an effective drainage-area delineation based on a 20-percent AEP 12-hour rainfall. Generalized least squares regression analysis was used to develop a set of equations for sites in the Des Moines Lobe landform region for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEPs. A total of 17 streamgages were included in the development of the regression equations. In addition, geographic information system software was used to measure 58 selected basin-characteristics for each streamgage. Results of the regression analyses of the 15 lidar datasets indicate that the datasets that produce regional regression equations (RREs) with the best overall predictive accuracy are the National Hydrographic Dataset, Iowa Department of Natural Resources, and profile curvature of 0.5 stream initiation methods combined with the 20-percent AEP 12-hour rainfall watershed delineation method. These RREs have a mean average standard error of prediction (SEP) for 4-, 2-, and 1-percent AEP discharges of 53.9 percent and a mean SEP for all eight AEPs of 55.5 percent. Compared to the RREs developed in this study using the basin characteristics from the U.S. Geological Survey StreamStats application, the lidar basin characteristics provide better overall predictive accuracy

Scripting methods in topographic data processing on the example of Ethiopia

This study evaluates the geomorphometric parameters of the topography in Ethiopia using scripting cartographic methods by applying R languages (packages 'tmap' and 'raster') and Generic Mapping Tools (gmt) for 2D and 3D topographic modelling. Data were collected from the open source repositories on geospatial data with high resolution: gebco with 15 arc-second and etopo1 with 1 arc-minute resolution and embedded dataset of srtm 90 m in 'raster' library of R. The study demonstrated application of the programming approaches in cartographic data visualization and mapping for geomorphometric analysis. This included modelling of slope steepness, aspect and hillshade visualized using dem srtm90 to derive geomorphometric parameters of slope, aspect and hillshade of Ethiopia and demonstrate contrasting topography and variability climate setting of Ethiopia. The topography of the country is mapped, including Great Rift Valley, Afar Depression, Ogaden Desert and the most distinctive features of the Ethiopian Highlands. A variety of topographical zones is demonstrated on the presented maps. The results include 6 new maps made using programming console-based approach which is a novel method of cartographic visualization compared to traditional gis software. The most important fragments of the codes are presented and technical explanations are provided. The presented series of 6 new maps contributes to the cartographic data on Ethiopia and presents the methodology of scripting mapping techniques

Measuring and modelling soil erosion in agricultural systems: Evaluating the application of UAVs and SfM-MVS for soil erosion research

Soil erosion in agricultural systems is a pressing issue for agricultural sustainability. Accelerated rates of soil erosion from conventional agricultural practices continues to outpace the rate of natural soil regeneration, and the continued expansion of agriculture into highly erodible landscapes coupled with the threat of more intense precipitation events from a warming climate indicate that soil erosion will continue to be a serious environmental problem throughout the 21st century. While the processes driving soil erosion are well understood, the distributed and small-scale nature of erosional processes makes it difficult to quantify the severity of the erosion problem. Conventional measurement methodologies lack the spatial and temporal resolution to characterize soil erosion events at the farm-field scale. Our inability to accurately measure soil erosion events has resulted in soil erosion estimates being primarily based on modelling without field-based evidence to evaluate and validate modelling outcomes. To address this research gap, we explore a new state-of-the-art workflow for measuring distributed erosion processes using automated photogrammetric workflows (i.e., Structure-from-Motion Multi-View Stereo [SfM-MVS]) and optical imagery from an unmanned aerial vehicle (UAV). We experientially investigated the accuracy of the UAV SfM-MVS workflow for recreating the topography of an agricultural field using different aerial surveying techniques. Our results demonstrated that for a standard parallel-axis nadir UAV image acquisition, an RTK-GNSS ground control survey, a sufficiently dense deployment of ground control points, and the use of a self-calibrating bundle adjustment in an SfM-MVS software application, the vertical accuracy (RMSE) of pointclouds converges on 2–3× the ground-sampling-distance of the optical imagery with a practical upper limit of 0.01 m. Our nadir aerial surveys had ground-sampling-distances of between 0.011 – 0.018 m, which resulted in pointclouds with a range in vertical accuracies of 0.021 – 0.039 m. This vertical accuracy constrained our workflow to measuring deep rill erosion, ephemeral gully erosion, and depositional zones; small-scale sheet and rill erosion processes could not be directly measured with our presented workflow. Applying the UAV SfM-MVS workflow to an agricultural field in Ontario, Canada, we were able to measure semi-distributed soil erosion processes using down-slope depositional zones as a proxy for up-slope erosion processes. Over the course of one year, 159.52 t of sediment was deposited down-slope, corresponding to an erosion rate of 18.83 t ha−1 yr−1; 86% of the total volume of eroded material was a result of intense storms during the corn growing season, with the majority of erosion associated with spring storms immediately following cultivation. During the winter months, despite the soil surface being barren after a moldboard plow, very little sediment was deposited down-slope. Soil erosion measurements collected using the UAV SfM-MVS workflow were then used to evaluate the predictions of the Universal Soil Loss Equation (USLE) and Water Erosion Prediction Project (WEPP). Model evaluations demonstrated that the WEPP had more accurate short-term predictions (i.e., 1-year annual and sub-annual) for a year of corn production. Long-term modelling with the WEPP for our agricultural study site predicted an average of 6.4 days per year with soil erosion events and 14.1 days per year with runoff events. Winter events and snowmelt constituted 70% of the average long-term runoff but winter runoff events were rarely associated with soil loss, which matched our in-situ observations and measurements. To further explore the spatial variability in distributed erosion processes, we used a series of very-high resolution DEMs derived from the UAV SfM-MVS workflow and a simple hydrology model to explore the impact of microtopography on surface runoff. Modelling results demonstrated that the orientation of tillage lines, surface slope, and maximum depression storage, all had a statistically significant impact on surface runoff. Our agricultural study site was at the highest risk of surface runoff and soil loss in the spring immediately following cultivation since the smoothed soil surface facilitated a high degree of landscape connectivity. Based on these results, we used our experiential knowledge of field-scale hydrology and erosion processes to additionally explore an up-scaled model implementation of the USLE for the entire watershed in which our agricultural study site was situated. We evaluated how different model user’s design choices and spatial conceptualizations of an agricultural systems affect predictions of soil erosion. We found a high degree of variability in soil erosion estimates at the watershed-scale, e.g., changing the implementation of a single USLE factor led to a range in model outcomes from 3.04 to 11.02 t ha-1 yr-1. This variability exemplifies the uncertainty associated with watershed-scale implementations of erosion models in the absence of a standardized and accredited model setup

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application's objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models' principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems

Geomorphometry 2020. Conference Proceedings

Geomorphometry is the science of quantitative land surface analysis. It gathers various mathematical, statistical and image processing techniques to quantify morphological, hydrological, ecological and other aspects of a land surface. Common synonyms for geomorphometry are geomorphological analysis, terrain morphometry or terrain analysis and land surface analysis. The typical input to geomorphometric analysis is a square-grid representation of the land surface: a digital elevation (or land surface) model. The first Geomorphometry conference dates back to 2009 and it took place in Zürich, Switzerland. Subsequent events were in Redlands (California), Nánjīng (China), Poznan (Poland) and Boulder (Colorado), at about two years intervals. The International Society for Geomorphometry (ISG) and the Organizing Committee scheduled the sixth Geomorphometry conference in Perugia, Italy, June 2020. Worldwide safety measures dictated the event could not be held in presence, and we excluded the possibility to hold the conference remotely. Thus, we postponed the event by one year - it will be organized in June 2021, in Perugia, hosted by the Research Institute for Geo-Hydrological Protection of the Italian National Research Council (CNR IRPI) and the Department of Physics and Geology of the University of Perugia. One of the reasons why we postponed the conference, instead of canceling, was the encouraging number of submitted abstracts. Abstracts are actually short papers consisting of four pages, including figures and references, and they were peer-reviewed by the Scientific Committee of the conference. This book is a collection of the contributions revised by the authors after peer review. We grouped them in seven classes, as follows: • Data and methods (13 abstracts) • Geoheritage (6 abstracts) • Glacial processes (4 abstracts) • LIDAR and high resolution data (8 abstracts) • Morphotectonics (8 abstracts) • Natural hazards (12 abstracts) • Soil erosion and fluvial processes (16 abstracts) The 67 abstracts represent 80% of the initial contributions. The remaining ones were either not accepted after peer review or withdrawn by their Authors. Most of the contributions contain original material, and an extended version of a subset of them will be included in a special issue of a regular journal publication