Topographic Attributes Research Articles

Application of Geographic Information System and Automated Guidance System in Optimizing Contour and Terrace Farming

Farming contour and terrace fields using automated guidance systems with global navigation satellite system (GNSS) receivers requires appropriate geographic features for effective guidance and soil and water conservation. The objective of this paper was to develop methodologies for improving and designing guidance features for operating guidance systems in contour and terrace fields. This study was conducted in the Texas High Plains where contour and terrace farming practices are prevalent in slope fields. Four case studies were used to demonstrate the application of a geographic information system (GIS) in optimizing guidance geographic features, including line smoothing, line extending and connecting, creating swath AB lines, and guide-to-line features. Line smoothing removes sharp angularities and curve oscillations on guidance line features, resulting in smooth and more effective guidance operations. The line extension and connection method creates a more convenient and simple guidance feature by combining multiple AB lines. Guide-to-line features derived from AB lines can eliminate confusions when using a guidance system with multiple AB lines in fields with complicated topographic attributes. A methodology was also developed to create guidance AB lines by processing the elevation data generated by a guidance system with a real-time kinematic (RTK) receiver. Guidance line features created in this study satisfy user requirements for effective guidance operations and soil and water conservation. Integrating the application of GIS spatial analysis capabilities and automated guidance systems can enhance farming operations by improving or creating guidance line features, as well as satisfying soil and water conservation needs. Parameter selection for enhancing or creating guidance line features needs to consider unique field conditions and user requirements for simple, convenient, and effective field operations.

Right-Angle Pattern of Minor Fluvial Networks from the Ionian Terraced Belt, Southern Italy: Passive Structural Control or Foreland Bending?

Morphometric analyses of both the topography and drainage network have been carried out in a large sector of the Ionian coastal belt of southern Italy in order to unravel the possible control of Late Quaternary thrust front activity on the evolution of the fluvial net. The study area extends in the southernmost sector of the Bradano Foredeep and is featured by several orders of uplifted marine terraces, ranging in age from Middle Pleistocene to Late Quaternary. The flight of the marine terraces is deeply cut by a trellis-type and regularly spaced minor fluvial network. Morphotectonic investigations based on field survey, photo-aerial interpretation, topographic attributes, morphometric indices, and analysis of longitudinal river profiles suggest a strong control on the drainage network arrangement by a pervasive orthogonal fracture system, produced and preserved into the brittle caprock of the terraces, made by conglomerate. Since a similar pervasive and orthogonal fracture pattern is typically generated by gentle folding of rocks, the development of the Ionian hydrographic networks could be attributed to a general—maybe still active—bending of the foredeep area due to the eastward propagation of blind thrusting of the Apennines orogenic chain.

Downscaling incidence risk mapping for a Colombian malaria endemic region

To map at a fine spatial scale, the risk of malaria incidence for the important endemic region is Urabá-Bajo Cauca and Alto Sinú, NW Colombia, using a new modelling framework based on GIS and remotely sensed environmental data. The association between environmental and topographic variables obtained from remote sensors and the annual parasite incidence (API) for the years 2013-2015 was calculated using multiple regression analysis; subsequently, a model was constructed to estimate the API and to project it to the entire endemic region in order to design the risk map. The model was validated by relating the obtained API values with the presence of the three main Colombian malaria vectors, Anopheles darlingi, Anopheles albimanus and Anopheles nuneztovari. Temperature and Normalized Difference Water Index (NDWI) showed a significant correlation with the observed API. The risk map of malaria incidence showed that the zones at higher risk in the Urabá-Bajo Cauca and Alto Sinú region were located south-east of the region, while the northern area presented the lowest malaria risk. A method was generated to estimate the API for small urban centres, instead of the used reports at the municipality level. These results provide evidence of the utility of risk maps to identify environmentally vulnerable areas at a fine spatial resolution in the Urabá-Bajo Cauca and Alto Sinú region. This information contributes to the implementation of vector control interventions at the microgeographic scale at areas of high malaria risk.

On the relationship between surface pressure, terrain elevation, and air temperature. Part I: The large diurnal surface pressure range at Gale Crater, Mars and its origin due to lateral hydrostatic adjustment

The daily variation of surface pressure observed by the Curiosity Rover Environmental Monitoring Station (REMS) is both significantly larger than observed at other landing sites on Mars and larger than simulated for the Curiosity site by global circulation models (GCM). Mesoscale numerical models are able to simulate the large REMS daily pressure range, but only if they possess sufficiently high horizontal resolution (grid spacing <5 km); low resolution (120–500 km) GCM simulations typically generate daily ranges of about half the observed value. The pressure range in low resolution simulations corresponds to the large-scale thermal tides and the augmentation of this range in high resolution models is associable with mesoscale topographic and surface property variations in the Gale Crater region. We show that the augmentation is due to the lateral redistribution of mass required for the surface pressure distribution over topographic relief to remain approximately hydrostatic as the near-surface air temperature varies through the diurnal cycle. The physical origin and nature of this adjustment flow is explored. We provide a means of predicting the daily surface pressure due to lateral hydrostatic adjustment for any location and further show that this range is slightly reduced by the inability of the atmosphere to completely achieve hydrostaticity and by the thermal effects of induced flows.

LiDAR patch metrics for object-based clustering of forest types in a tropical rainforest

Tropical rainforests support a large proportion of the Earth’s plant and animal species within a restricted global distribution, and play an important role in regulating the Earth’s climate. However, the existing knowledge of forest types or habitats is relatively poor and there are large uncertainties in the quantification of carbon stock in these forests. Airborne Laser Scanning, using LiDAR, has advantages over other remote sensing techniques for describing the three-dimensional structure of forests. With respect to the habitat requirements of different species, forest structure can be defined by canopy height, canopy cover and vertical arrangement of biomass. In this study, forest patches were identified based on classification and hierarchical merging of a LiDAR-derived Canopy Height Model in a tropical rainforest in Sumatra, Indonesia. Attributes of the identified patches were used as inputs for k-medoids clustering. The clusters were then analysed by comparing them with identified forest types in the field. There was a significant association between the clusters and the forest types identified in the field, to which arang forests and mixed agro-forests contributed the most. The topographic attributes of the clusters were analysed to determine whether the structural classes, and potentially forest types, were related to topography. The tallest clusters occurred at significantly higher elevations (>850 m) and steeper slopes (>26°) than the other clusters. These are likely to be remnants of undisturbed primary forests and are important for conservation and habitat studies and for carbon stock estimation. This study showed that LiDAR data can be used to map tropical forest types based on structure, but that structural similarities between patches of different floristic composition or human use histories can limit habitat separability as determined in the field.

Late-glacial and Holocene history of the northeast Mediterranean mountain glaciers - New insights from in situ-produced 36Cl-based cosmic ray exposure dating of paleo-glacier deposits on Mount Olympus, Greece

In this study, we present a new glacial chronology based on 20 in situ-produced 36Cl-based cosmic ray exposure datings from moraine boulders and bedrock from the Throne of Zeus (TZ) and Megala Kazania (MK) cirques on Mount Olympus. The 36Cl derived ages of glacial landforms range from 15.6 ± 2.0 to 0.64 ± 0.08 ka, spanning the Late-glacial and the Holocene. The Late-glacial, recorded in both cirques, is partitioned in three distinct phases (LG1-3): an initial phase of moraine stabilization at 15.5 ± 2.0 ka with subsequent deglaciation starting at ∼14 ka (LG1), followed by a shift to marginal conditions for glaciation at 13.5 ± 2.0 ka (LG2), sustained by large amounts of wind-blown snow, despite regional warming. Glacial conditions returned at 12.5 ± 1.5 ka (LG3) and were characterized by low air temperatures and glacier shrinking. The Holocene glacial phases (HOL1-3) are recorded only in the MK cirque, likely due to its topographic attributes. An early Holocene glacier stillstand (HOL1) at 9.6 ± 1.1 ka follows the regional temperatures recovery. No glacier activity is observed during the mid-Holocene. The Late Holocene glacier expansions, include a moraine stabilization phase (HOL2) at 2.5 ± 0.3 ka, during wet conditions and solar insolation minima, while (HOL3) corresponds to the early part of the Little Ice Age (0.64 ± 0.08ka). Our glacial chronology is coherent with glacial chronologies from several cirques along the northeast Mediterranean mountains and in pace with numerous proxies from terrestrial and marine systems from the north Aegean Sea.

Hydrological regionalisation based on available hydrological information for runoff prediction at catchment scale

Abstract. Regionalisation provides a way of transferring hydrological information from gauged to ungauged catchments. The past few decades has seen several kinds of regionalisation approaches for catchment classification and runoff predictions. The underlying assumption is that catchments having similar catchment properties are hydrological similar. This requires the appropriate selection of catchment properties, particularly the inclusion of observed hydrological information, to explain the similarity of hydrological behaviour. We selected observable catchments properties and flow duration curves to reflect the hydrological behaviour, and to regionalize rainfall-runoff response for runoff prediction. As a case study, we investigated 15 catchments located in the Yangtze and Yellow River under multiple hydro-climatic conditions. A clustering scheme was developed to separate the catchments into 4 homogeneous regions by employing catchment properties including hydro-climatic attributes, topographic attributes and land cover etc. We utilized daily flow duration curves as the indicator of hydrological response and interpreted hydrological similarity by root mean square errors. The combined analysis of similarity in catchment properties and hydrological response suggested that catchments in the same homogenous region were hydrological similar. A further validation was conducted by establishing a rainfall-runoff coaxial correlation diagram for each catchment. A common coaxial correlation diagram was generated for each homogenous region. The performances of most coaxial correlation diagrams met the national standard. The coaxial correlation diagram can be transferred within the homogeneous region for runoff prediction in ungauged catchments at an hourly time scale.

Predicting soil depth to bedrock in an anthropogenic landscape: a case study of Phewa Watershed in Panchase region of Central-Western Hills, Nepal

The Soil Depth to Bedrock (SDtB) parameter is highly variable over the landscape and is an important input for soil surface modeling including various types of mass movement that most often ignores the variability of soil depth. In landscapes that have been heavily modified by humans, SDtB parameters do not necessarily depend on natural processes, making it difficult to apply physically-based approaches. This study explored the possibility of using topographic attributes to model the SDtB in the complex topography of Phewa watershed in the Panchase region of Nepal. In June 2017, 865 SDtB points were surveyed along the excavated rural roads (approximately 300 km) within the watershed (111 km2). Topographic attributes such as slope, curvatures, altitude and compound terrain index were derived from a Digital Terrain Model along with Land use land cover attributes derived from recent Landsat remote sensing images. Utilizing these attributes as explanatory variables, we implemented simple kriging (SK) and multiple linear regressions (MLR) to predict SDtB for the watershed. The results showed that the MLR predicted SDtB map was statistically significant (R2 = 68%, EC = 0.93) over the SK (R2 = 12%, EC = -0.46). We concluded that the MLR approach in predicting SDtB for the complex geo-morphologic landscape such as the Phewa watershed in the Panchase region of Nepal can effectively be used, helping to inform the evolution of land-use as the region continues to develop.

Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping

Gully erosion has been identified as an important soil degradation process and sediment source, especially in arid and semiarid areas. Thus, it is useful to identify the spatial occurrence of this form of water erosion in the landscape and the most vulnerable areas. In this study, we explored the effects of different pixel sizes on some controlling factors extracted from a digital elevation model and remote sensing data when producing a gully erosion susceptibility map (GESM) of Ekbatan Dam Basin, Hamadan, Iran. An inventory map of the gully landforms was prepared based on global positioning system routes of the gullies, extensive field surveys, and visual interpretations of satellite images obtained from Google Earth. Five data sets with pixel sizes ranging from 2 to 30 m were obtained using topographic attributes and remote sensing data comprising the elevation, slope degree, slope aspect, catchment area, plan curvature, profile curvature, stream power index, topographic position index, topographic wetness index, land use, and normalized difference vegetation index, which can affect the distribution of gully erosion. For each data set, 70% and 30% of the data were selected randomly for calibrating and validating the models, respectively. The statistical relationships between the occurrence of gully erosion and controlling factors were calculated using four machine-learning models, i.e., generalized linear model, boosted regression tree (BRT), multivariate adaptive regression spline, and artificial neural network (ANN). Statistical tests comprising the kappa coefficient and the area under the receiver operating characteristic curve (AUC) were calculated for both the calibration and validation data sets to estimate the optimal pixel size. The results showed that among the data sets with different pixel sizes, the optimal pixel size was 10 m for each model. In addition, the capacity of the four techniques for modeling gully erosion occurrence was quite stable when the calibration and validation samples were changed in the data set. Finally, based on three changes of the calibration and validation data sets with a pixel size of 10 m, the BRT and ANN models obtained outstanding performance (AUC > 0.9), where they had the highest goodness-of-fit and predictive power, and thus the greatest robustness to changes in the calibration/validation data (i.e., lowest sensitivity to altering calibration/validation data). Our results demonstrate the importance of selecting a suitable pixel size when producing a GESM for soil and water management practices.

Soil moisture dynamics and dominant controls at different spatial scales over semiarid and semi-humid areas

Soil moisture dynamics plays an active role in ecological and hydrological processes, and it depends on a large number of environmental factors, such as topographic attributes, soil properties, land use types, and precipitation. However, studies must still clarify the relative significance of these environmental factors at different soil depths and at different spatial scales. This study aimed: (1) to characterize temporal and spatial variations in soil moisture content (SMC) at four soil layers (0–40, 40–100, 100–200, and 200–500 cm) and three spatial scales (plot, hillslope, and region); and (2) to determine their dominant controls in diverse soil layers at different spatial scales over semiarid and semi-humid areas of the Loess Plateau, China. Given the high co-dependence of environmental factors, partial least squares regression (PLSR) was used to detect relative significance among 15 selected environmental factors that affect SMC. Temporal variation in SMC decreased with increasing soil depth, and vertical changes in the 0–500 cm soil profile were divided into a fast-changing layer (0–40 cm), an active layer (40–100 cm), a sub-active layer (100–200 cm), and a relatively stable layer (200–500 cm). PLSR models simulated SMC accurately in diverse soil layers at different scales; almost all values for variation in response (R2) and goodness of prediction (Q2) were >0.5 and >0.0975, respectively. Upper and lower layer SMCs were the two most important factors that influenced diverse soil layers at three scales, and these SMC variables exhibited the highest importance in projection (VIP) values. The 7-day antecedent precipitation and 7-day antecedent potential evapotranspiration contributed significantly to SMC only at the 0–40 cm soil layer. VIP of soil properties, especially sand and silt content, which influenced SMC strongly, increased significantly after increasing the measured scale. Mean annual precipitation and potential evapotranspiration also influenced SMC at the regional scale significantly. Overall, this study indicated that dominant controls of SMC varied among three spatial scales on the Loess Plateau, and VIP was a function of spatial scale and soil depth.

Water harvesting techniques based on terrain modification enhance vegetation survival in dryland restoration

To successfully restore drylands, where the scarcity of water is one of the main limiting factors for plant survival, water inputs should be enhanced as much as possible. A specific type of water harvesting that concentrates runoff generated in bare areas upslope (runoff source areas) in a planting area downslope (runoff sink area) is an effective technique that, unlike traditional restoration, does not entail much over-cost. The objective of this study was to analyze the effectiveness of terrain modification techniques, aimed at water harvesting, on a degraded hillslope at a quarry restoration site in an arid area (Almeria, southeast Spain). Two different terrain modifications, to harvest surface runoff and increase soil water retention, were applied at two different hillslope sectors: a) micro-catchments were constructed on the upper section of the hillslope, where the slope gradient was high and b) narrow terracing was done in the middle part of the hillslope, which was characterized by moderate slope gradients. An additional sector, not subjected to terrain modification treatment, was used as a control, and was set in the lower part of the hillslope where the slope gradient was the lowest. A similar planting system was adopted for all experimental treatments; planting was done in 12 fertility island patches. Within each patch 12 different autochthonous plant species were planted in a staggered pattern with 80 cm separation. All plant species, which were from a nursery, were planted within a 1 L container made from organic fibers and containing compost from plant residues enriched with zeolites. To determine the microtopographical modifications required in the analysis of the optimal water harvesting strategy, a digital elevation model, with 0.2 m spatial resolution, was created from the data generated using a terrestrial laser scanner. Runoff flow direction and contribution areas, as well as a set of topographical attributes strongly related to the terrain water retention capacity (wetness index, slope gradient, aspect, USLE LSF factor, etc.), were calculated. Results indicated that micro-catchments had the highest plant survival rates and vegetation coverage values because they were the terrain treatment areas with the largest capacity to retain run-on from contributing areas. The areas without terrain modification (control areas) showed the highest plant mortality.

Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data

Abstract. Arctic tundra ecosystems will play a key role in future climate change due to intensifying permafrost thawing, plant growth and ecosystem carbon exchange, but monitoring these changes may be challenging due to the heterogeneity of Arctic landscapes. We examined spatial variation and linkages of soil and plant attributes in a site of Siberian Arctic tundra in Tiksi, northeast Russia, and evaluated possibilities to capture this variation by remote sensing for the benefit of carbon exchange measurements and landscape extrapolation. We distinguished nine land cover types (LCTs) and to characterize them, sampled 92 study plots for plant and soil attributes in 2014. Moreover, to test if variation in plant and soil attributes can be detected using remote sensing, we produced a normalized difference vegetation index (NDVI) and topographical parameters for each study plot using three very high spatial resolution multispectral satellite images. We found that soils ranged from mineral soils in bare soil and lichen tundra LCTs to soils of high percentage of organic matter (OM) in graminoid tundra, bog, dry fen and wet fen. OM content of the top soil was on average 14 g dm−3 in bare soil and lichen tundra and 89 g dm−3 in other LCTs. Total moss biomass varied from 0 to 820 g m−2, total vascular shoot mass from 7 to 112 g m−2 and vascular leaf area index (LAI) from 0.04 to 0.95 among LCTs. In late summer, soil temperatures at 15 cm depth were on average 14 ∘C in bare soil and lichen tundra, and varied from 5 to 9 ∘C in other LCTs. On average, depth of the biologically active, unfrozen soil layer doubled from early July to mid-August. When contrasted across study plots, moss biomass was positively associated with soil OM % and OM content and negatively associated with soil temperature, explaining 14–34 % of variation. Vascular shoot mass and LAI were also positively associated with soil OM content, and LAI with active layer depth, but only explained 6–15 % of variation. NDVI captured variation in vascular LAI better than in moss biomass, but while this difference was significant with late season NDVI, it was minimal with early season NDVI. For this reason, soil attributes associated with moss mass were better captured by early season NDVI. Topographic attributes were related to LAI and many soil attributes, but not to moss biomass and could not increase the amount of spatial variation explained in plant and soil attributes above that achieved by NDVI. The LCT map we produced had low to moderate uncertainty in predictions for plant and soil properties except for moss biomass and bare soil and lichen tundra LCTs. Our results illustrate a typical tundra ecosystem with great fine-scale spatial variation in both plant and soil attributes. Mosses dominate plant biomass and control many soil attributes, including OM % and temperature, but variation in moss biomass is difficult to capture by remote sensing reflectance, topography or a LCT map. Despite the general accuracy of landscape level predictions in our LCT approach, this indicates challenges in the spatial extrapolation of some of those vegetation and soil attributes that are relevant for the regional ecosystem and global climate models.

Combining Digital Elevation Data, Expert Knowledge and GIS for Geomorphological Mapping; The Case Study of Mount Hymettus, Athens, Greece

Abstract This study presents a geomorphological map for Mount Hymettus (Athens, Greece). The geomorphological content was produced by processing DEM derived topographic attributes, hydrography and geology. In particular, the backbone of this procedure was the definition of the appropriate criteria for landform identification by validating conditional statements for the processed data in a GIS environment. Extended fieldwork and photo-interpretation verified the outputs. Following the assessment, the derived landforms were grouped into the following geomorphological units: the main alpine metamorphic mass, the foot slopes and the coastal area. A custom layout regarding symbology, colouring, and generalization was designed in order to highlight the captured geomorphological content. The results indicate that the DEM derived topographic attributes combined with the geological setting and the river network generate successfully a large number of landforms under certain circumstances.

Effects of topography and soil properties on soil selenium distribution and bioavailability (phosphate extraction): A case study in Yongjia County, China

Selenium (Se) is an essential trace element for humans. In order to investigate how soil Se is influenced by topography and soil properties, we selected Yongjia County, an area with mountainous topography, as a study area. This study used cultivated soil data to comprehensively analyze the effects of topography and soil properties on Se mobility and bioavailability and to identify the key factors influencing Se distribution in the environment. Factors considered in this study were elevation, slope, topographic wetness index, the coefficient of weathering and eluviation, pH, organic matter, and Fe2O3. The concentration of total soil Se (0.382±0.123mgkg−1) was far higher than the background value of soil in China, and 98% of the soil samples were classified as having moderate Se levels (>0.175mgkg−1), indicating Yongjia County is a Se-rich region in China. Phosphate extracted Se accounted for an average of 9% of the total Se and was significantly associated with soil total Se, Fe2O3, pH, and the coefficient of weathering and eluviation. Fe2O3 primarily controlled Se adsorption, fixation, and availability in soil. Under the geo-environmental conditions in the study area, the total Se in the soil increased first and then decreased with increases in elevation, slope, and the topographic wetness index, and the phosphate extracted Se showed similar patterns except for the elevation. The findings showed that topographical attributes and soil physicochemical properties synthetically influenced the distribution and bioavailability of Se in soil.

Building a Flood-Warning Framework for Ungauged Locations Using Low Resolution, Open-Access Remotely Sensed Surface Soil Moisture, Precipitation, Soil, and Topographic Information

Soil moisture (SM) plays an important role in determining the antecedent condition of a watershed, while topographic attributes define how and where SM and rainfall interact to create floods. Based on this principle, we present a method to identify flood risk at a location in a watershed by using remotely sensed SM and open-access information on rainfall, soil properties, and topography. The method consists of three hydrologic modules that represent the generation, transfer, and accumulation of direct runoff. To simplify the modeling and provide timely warnings, the flood risk is ascertained based on frequency of exceedance, with warnings issued if above a specified threshold. The simplicity of the method is highlighted by the use of only three parameters for each watershed of interest, with effective regionalization allowing use in ungauged watersheds. For this proof-of-concept study, the proposed model was calibrated and tested for 65 hydrologic reference stations in the Murray–Darling Basin in Australia over a 35-year study period by using satellite-derived surface SM. The three model parameters were first estimated using the first ten-year data and then the model performance was evaluated through flood threshold exceedance analyses over the remaining 25-year study period. The results for estimated parameters and skill scores showed promise. The three model parameters can be regionalized as a function of watershed characteristics, and/or representative values estimated from neighboring watersheds, allowing use in ungauged basins everywhere.

Comparison of SRTM-V4.1 and ASTER-V2.1 for Accurate Topographic Attributes and Hydrologic Indices Extraction in Flooded Areas

Comparison of SRTM-V4.1 and ASTER-V2.1 for Accurate Topographic Attributes and Hydrologic Indices Extraction in Flooded Areas

Automatic approach to deriving fuzzy slope positions

Fuzzy characterization of slope positions is important for geographic modeling. Most of the existing fuzzy classification-based methods for fuzzy characterization require extensive user intervention in data preparation and parameter setting, which is tedious and time-consuming. This paper presents an automatic approach to overcoming these limitations in the prototype-based inference method for deriving fuzzy membership value (or similarity) to slope positions. The key contribution is a procedure for finding the typical locations and setting the fuzzy inference parameters for each slope position type. Instead of being determined totally by users in the prototype-based inference method, in the proposed approach the typical locations and fuzzy inference parameters for each slope position type are automatically determined by a rule set based on prior domain knowledge and the frequency distributions of topographic attributes. Furthermore, the preparation of topographic attributes (e.g., slope gradient, curvature, and relative position index) is automated, so the proposed automatic approach has only one necessary input, i.e., the gridded digital elevation model of the study area. All compute-intensive algorithms in the proposed approach were speeded up by parallel computing. Two study cases were provided to demonstrate that this approach can properly, conveniently and quickly derive the fuzzy slope positions.

Assessment of Remotely Sensed Digital Elevation Models (DEMs) Compared with DGPS Elevation Data and Its Influence on Topographic Attributes

Accuracy and quality of DEM are of a great interest for a wide range of applications. In this study, quality of ASTER GDEM and SRTM DEMs were assessed in comparison with DGPS measurements. Impact of DEM resolution upon the accuracy of terrain representation and topographic attributes was also discussed. The study deduced that vertical error has a strong effect on error propagation and this highly obvious in higher elevations as the absolute standard error (SE) ranges between is ±0 - 2.5 and ±0 - 2.4 m for ASTER GDEM and SRTM respectively. This is reflected on slope and aspect as the vertical errors increase and uncertainty is relatively high in flat and low areas. Error propagation in low lands influenced drainage extraction and resulted in isolated and truncated water courses.

Geocode-based Aquatic Habitats in Hierarchical System of the Yellow River Basin

Riverine systems are primary habitats for aquatic species. Great efforts have been made to understand interaction between aquatic organism and various environmental factors in specific reaches of a river, yet little was known about how to identify habitats in river networks. In this paper, a comprehensive approach was proposed for identification of aquatic habitats in hierarchical river systems in terms of integrated geocodes synthesized through over-layer of multiple factors such as geologic periods, climatic conditions, water sources, geomorphic characteristics and river patterns. Using this approach in the Yellow River basin over an area of 758,600 km2, six groups of aquatic habitats were classified by clustering of 85 geocodes, and 313 sub-habitats were further identified by considering both surface composition (e.g. lithological features and soil texture) and topographic property (e.g. elevation and slope) in the loess geomorphic regions. Consequently, the contribution of the headwater streams to diversity of aquatic habitats among rivers and mid-sized rivers to the diversity within a river has been revealed by coupling of the geocode-based habitats and the river networks. The present study is of significance to efficient capture of habitat diversity and better understanding of biodiversity in rivers of different stream orders.

Selection of optimal scales for soil depth prediction on headwater hillslopes: A modeling approach

Landform attributes derived from digital elevation models (DEMs) are the most commonly used factors to predict soil depths on hillslopes. However, the selection of an appropriate algorithm to calculate terrain attributes and identify an optimal DEM resolution remains ambiguous. In this study, we propose a method to use high-resolution DEM and spatial soil depth data to obtain terrain attributes and identify the optimal DEM resolution to predict soil depths at hillslope scales. A geophysical method (ground penetrating radar, GPR) was used to investigate the reasons for the optimal DEM resolution findings. Point-scale soil depth from 116 sites and elevation data were collected from two adjacent headwater hillslopes (H1: 0.42ha and H2: 0.31ha) at the Hemuqiao hydrological experimental station in Southeast China. The elevation datasets were collected using a total station at a variable spacing level and were then used to derived DEMs at nine spatial resolutions: 0.25, 0.50, 0.75, 1.00, 2.00, 3.50, 5.00, 7.50 and 10.00m. Nine primary and secondary topographic attributes using the nine spatial resolution DEMs were then derived. Two different algorithms (D8 and D∞) for calculating contributing areas and related secondary topographic attributes were compared. We used and compared both linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN) models for soil depth prediction. Results demonstrated that the two models performed well for predicting soil depth. Specifically, MLR performed better than the non-linear model of ANNs. Additionally, we found that the multiple-direction algorithm (D∞) allowed flow divergence and avoided abrupt changes in soil depth predictions (orphan cells) and should be adopted for model construction. The D∞ algorithm performed better in divergent areas, such as ridges and side slopes, and the D∞ algorithm also worked well in convergent areas, such as valleys. Moreover, our results demonstrated that moderate (e.g., 2.00m) resolution topographic attributes, instead of the finest resolution, achieved the best prediction with the lowest root mean square error (RMSE) and mean absolute error (MAE) and the highest values of the coefficient of determination (R2). Moreover, the GPR results indicated that the valley accumulated more soils than side-slope areas, and a sharp increase in soil depth was found in areas adjacent to the valley. Comparing the optimal DEM resolution and valley width obtained by GPR, we found that average valley width (AVW) should be considered a good measure for choosing the optimal DEM resolution for soil depth prediction.