Complex Topographic Features Research Articles

Implementation of Geodesign Framework for Development of Flood Resilient Hill Town: A Case Study

Flooding is a natural disaster characterized by the inundation of land areas due to the overflow of water from its usual channels. Various natural and man-made factors are responsible for the occurrence of floods and the damage caused by them. Floods have become more dangerous in the Himalayan region because of complex topographical features and extreme climatic conditions. In recent years, the Kullu district of Himachal Pradesh has experienced a number of devastating floods due to its proximity to the Beas River and the hilly terrain which caused collective damage to man-made as well as natural resources, leading to loss of lives and property. Recognizing the urgent need for flood resilience, the present study aims to develop a flood-resilient town through a geodesign framework that will reduce the impact of floods in hilly areas. The study uses a three-stage geodesign framework that involves data collection, data analysis, and geodesign-based flood-resilient plan modeling. In the first stage, the data pertaining to historical floods and geospatial data of the study area was collected. In the second stage, flood susceptibility mapping was done to identify vulnerable communities and assess critical infrastructure at risk based on the analytical hierarchy process (AHP). In the third stage, interactive three-dimensional (3D) modeling was done in ESRI CityEngine to develop a flood-resilient model for the study area which integrates geospatial data, flood simulation, land-use planning, and stakeholders’ engagement. The results of this study show that by focusing on flood resilience, this model offers a valuable insight and practical approach for shaping the development in hilly areas that can withstand and adapt to the challenges of increasing flood occurrences.

Frequency analysis of extreme rainfall over the Japanese archipelago by leveraging gauge-adjusted radar and satellite estimates

With the increasing frequency of extreme rainfall and difficulty in capturing their spatial variability by rain gauges, especially over regions with complex topographic and climatic features like Japan, there is a growing interest in using radar and satellite-derived precipitation estimates for building intensity–duration–frequency (IDF) curves. Such a statistical method provides references for studying extreme rainfall statistics at varying spatiotemporal scales. Yet, a comparative evaluation of the efficiency of these datasets to derive reliable IDFs over the Japanese archipelago is lacking. This study compares IDFs developed from four rain gauge-adjusted remotely sensed datasets (RSDs) derived from weather radars (R/A) and satellites (GsMAP_G, CMORPH_G, and IMERG_G) with those created from long-term rain gauge records (i.e., reference IDFs) for evaluating their reliabilities, particularly for 10- and 100-year return periods. A Bayesian Generalized Extreme Value model was adopted to estimate the IDFs. The results show that the four RSDs exhibited varying performances in deriving reliable IDFs within the 5–95% confidence bounds of the reference IDFs depending on rainfall duration and climate settings. While R/A performed better for one-hour rainfall return levels, particularly in the cold climate with warm summers, satellite-driven RSDs, especially IMERG_G, showed better performances for long-duration rainfall return levels. The performance discrepancies were primarily linked to the differences in rainfall estimation and bias adjustment schemes rather than the differences in record lengths and spatial resolutions among the tested RSDs. Thus, it is crucial to consider the rainfall duration and climate settings when using the derived IDFs for diverse practical applications.

Creating a spatially continuous air temperature dataset for Taiwan using thermal remote-sensing data and machine learning algorithms

Weather stations can provide accurate and high temporal resolution air temperature (Ta) measurements, but their limited spatial coverage due to sparse distribution poses an issue and challenge. However, satellite data can offer land surface temperature (LST) observations with high spatial coverage, which have a strong relationship with Ta, making them ideal for enhancing Ta estimation. This study uses satellite-derived and auxiliary data to create a monthly mean Ta dataset with a 1 km resolution over Taiwan from 2003 to 2020. We employed three machine learning (ML) algorithms and seven different datasets comprising 12 explanatory variables with LST obtained from the MODIS to find the optimal combination of algorithm and dataset for Ta estimation in Taiwan. We applied recursive feature elimination (RFE) to reduce the model complexity and overfitting issues. For model assessment, we used five-fold cross-validation to evaluate the ML models, and indicators such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square of error (RMSE) were employed. The results show that the XGB regressor performed the best among the three models with the highest accuracy. The RFE using the XGB model suggested eight selected variables, including nighttime LST, daytime LST, elevation, longitude, latitude, distance to the sea, month, and year. Based on the variance importance analysis, nighttime LST was the most crucial variable, followed by daytime LST and month. We found that the final monthly Ta dataset using the XGB model had an excellent five-fold cross-validated performance (R2 = 0.986, MAE = 0.477 °C, and RMSE = 0.639 °C). Furthermore, the XGB model not only performed well throughout all four seasons but also had high and consistent accuracy across months, years, and subsets, indicating its potential for accurately estimating Ta in Taiwan's complex topographic features with varying climate conditions. The resulting monthly Ta dataset created by our model can be an essential input for environmental studies.

Developing an efficient climate forecasting model for the spatiotemporal climate dynamics estimation and the prediction that fits the variable topography feature of the upper Blue Nile basin, Ethiopia

The spatiotemporal climate estimation and prediction are challenging and demanding for variable topography feature areas. Spatially, the upper Blue Nile basin (UBNB) is unsatisfactory owing to complex topographical features and the lack of accurate climate prediction and estimation models. Yet accurate information and reliable seasonal climate dynamics estimation and forecasting are essential in the region for controlling reservoir operation and flooding prevention. However, there is a lack of reports regarding the accuracy and predictability of the climate in the UBNB region. Therefore, this article aims to improve the Artificial Neural Network (ANN) model by adopting the Impulse Response Function (IRF) and comparing it to the Regional Climate Model (RCM) and European Centre of Medium-range Weather Forecast (ECMWF) models for spatiotemporal climate dynamics estimation and forecasting, which are reliant on the UBNB topography features. Different atmospheric parameter data are investigated from reanalysis models. A fast Fourier transform is applied to remove the redundancy of the data and avoid the computational cost. The RCM and ECMWF models are used to test the performance of ANN model prediction skills. The IRF model was applied to enhance the ANN model's climate prediction performance. A 12-month spatial variation of precipitation is analyzed. The ANN model showed a satisfactory prediction performance, better than the RCM and ECMWF models by 20 %. After increasing the ANN model performance by IRF, the prediction errors are reduced by 10.2 % for precipitation and by 7.9 % for temperature. Based on the model results, the temperature has increased over the past 40 years and is expected to continue for the coming three decades (30 years). In contrast, precipitation over the past 40 years has decreased, and a slight increment will be expected in the next eight years, from 2024 to 2029. Therefore, this model should be practiced across Ethiopia and the Globe for accurate prediction of climate patterns. Hence, clear awareness should be created for the local community by providing a scientific remedy for future climate conditions to reduce production risk.

Research on Landslide Trace Recognition by Fusing UAV-Based LiDAR DEM Multi-Feature Information

Landslide traces are crucial geomorphological features of landslides. Through the recognition of landslide traces, a better grasp of the topographical features of landslides can be achieved, thereby aiding in the enhancement of capabilities for the prevention, response, and management of landslides. Aiming at the complex topographic features of landslide traces, only using a single DEM product could provide a complete and comprehensive recognition of landslide traces. A method of landslide tracing recognition based on the fusion of multi-feature information from the Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-based LiDAR) Digital Elevation Model (DEM) is proposed. First, a high-precision DEM is constructed by using the LiDAR point cloud data. Based on the DEM, four multi-feature images that can enhance the landslide geomorphology are generated: hillshading, slope, positive openness, and sky-view factor. Furtherore, the DEM multi-feature images were fused using the Visualization for Archaeological Topography (VAT) method to obtain the DEM Multi-Feature Fusion Image (DEM-DFFI). Finally, the landslide traces were extracted from the DEM-DFFI based on fractal theory. The method presented in this paper makes full use of DEM multi-feature images and fuses them, which can accurately and clearly show the topographic and geomorphological features of landslides. Based on this, it helps improve landslide trace recognition accuracy.

The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale

Abstract. High-resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere–atmospheric processes. While this approach has yielded insightful results, studies over annual timescales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a high-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near-surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle–flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 594 times fewer computational resources than the Weather Research and Forecasting (WRF) atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data are often required at hectometer resolution for whole basins at seasonal timescales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver and coupling of the model with an intermediate-complexity snow model.

Stability and Changes in the Spatial Distribution of China’s Population in the Past 30 Years Based on Census Data Spatialization

As the world’s most populous country, China has experienced massive population growth and dramatic regional migration over the past 30 years. From 1990 to 2020, the national population increased by 24.4%, the urban population tripled, and the rural population declined by 41.0%. Combined with complex topographic features, unique characteristics of the population distribution have emerged. Many studies have examined changes in the spatial distribution of the population. However, few studies have examined the stability of certain aspects of this distribution over the last 30 years, particularly at the raster scale, which may provide important information for future research and development plans. Based on land use maps and nighttime light images, China’s census data from 1990 to 2020 was scaled down to a resolution of 1 km using a method called multiple linear regression based on spatial covariates. The results show that there were some striking features of both stability and change in the spatial distribution of China’s population over the past three decades. The population shares divided by the Hu line, the Qinling-Huaihe line, and the three-step staircase have remained almost unchanged. In contrast, the population share of the coastal region has risen from 23.7% to 29.0% during the study period. The urban areas have expanded by 1.35 times and their population has doubled. In addition, for every 1 km2 increase in the urban areas, an area of 29.4 km2 has been depopulated on average. This suggests that urbanization can alleviate population pressure in larger areas. However, the coastal regions and urban and peri-urban areas were the main areas of population density growth, so they required a great deal of attention for ecological protection.

Source apportionment of ambient PM2.5 in an industrialized city using dispersion-normalized, multi-time resolution factor analyses

Ambient fine particulate matter (PM2.5) data were collected in the lower City of Hamilton, Ontario to apportion the sources of this pollutant over an 18-month period. Hamilton has complex topographical features that may result in worsened air pollution within the lower city, thus, dispersion-normalized, multi-time resolution factor analysis (DN-MT-FA) was used to identify and quantify contributions of factors in a manner that reduced the influence of local meteorology. These factors were secondary organic aerosols type 1 (SOA_1), particulate nitrate (pNO3), particulate sulphate (pSO4), primary traffic organic matter (PTOM), Steel/metal processing and vehicular road dust emissions (Steel & Mobile) and, secondary organic aerosols type 2 (SOA_2) with origins ranging from mainly regional to mainly local. Factors that were mainly local (PTOM, Steel & Mobile, SOA_2) contributed up to 17% of the average PM2.5 mass while mixed local/regional factors (pNO3, pSO4) made up 43% on average, indicating the potential for further reduction of harmful PM concentrations locally. Of particular interest from a health protection perspective, was the composition of PM2.5 on days when an exceedance of the 24-hr WHO air quality guideline for this pollutant was observed. In general, SOA_1 was found to drive summer exceedances while pNO3 dominated in the winter. During the summer period, SOA_1 was attributable to wildfires in the northern parts of Canada while local traffic sources in winter contributed to the high levels of pNO3. While local, industrial factors only had minor relative mass contributions during exceedances, they are high in highly oxidized organic species (SOA_2) and toxic metals (Steel & Mobile). Thus, they are likely to have more impacts on human health. The methods and results described in this work will be useful in understanding prevalent sources of particulate matter pollution in the ambient air in the presence of complex topography and meteorological effects.

Landscape, orientation and celestial phenomena on the ‘Coast of Death’ of NW Iberia

This paper investigates the land- and sky-scapes surrounding the dolmens of Costa da Morte (Coast of Death), Galicia. Having uncovered previously that the location of megalithic monuments in this coherent area of the south-eastern side of the European Atlantic Façade connects to complex topographical features, we now show how this chosen topography connects to astronomical phenomena. We will see how the detailed shape of the horizon coincides with specific risings and settings of the Sun and Moon, providing further support for the notion that the creators of these monuments selectively drew upon a variety of features found in their natural world.

Performance of New Near-Real-Time PERSIANN Product (PDIR-Now) for Atmospheric River Events over the Russian River Basin, California

Abstract Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

АНГИОФИБРОМА СТОПЫ (КЛИНИЧЕСКОЕ НАБЛЮДЕНИЕ)

Abstract. Relevance. Angiofibroma (angiomyofibroblastoma) is a rare soft tissue benign tumor. There are no descriptions of foot angiofibrom observations in the Russian specialized literature, which makes publications of this kind of materials relevant. The aim of the study was to analyze the experience of treatment of large locally advanced angiofibroma of the foot. Material and methods of research. A 17-year-old patient, according to ultrasound scanning and MRI, had a significant soft-tissue encapsulated tumor of the right foot. On 06.09.2018, a tumor removal operation was performed from three anatomical approaches - medial, posterior and plantolateral - with the maximum possible compliance with the principles of oncosurgery. Results and discussion. There were no complications, the wound healed by primary tension. Conclusion of histological examination of the extracted fragments of the tumor – angiofibroma of the foot. There is no data on tumor recurrence at the moment – more than three years after surgery. Conclusion. Summing up the data of various authors and our own experience, it can be argued that angiofibroma (angiomyofibroblastoma) is a benign slowly expansively growing tumor, more often localized in the area of the external genitalia mainly in women, but also affecting the foot. An exhaustive preoperative examination using ultrasound scanning and MRI, careful preoperative planning based on the principles of oncosurgery, taking into account the complex topographic and anatomical features of the foot and the full implementation of this plan in a specialized hospital allows you to avoid complications and achieve a long relapse-free period in the surgical treatment of large locally advanced angiofibroma of the foot.

A Discriminative Spectral-Spatial-Semantic Feature Network Based on Shuffle and Frequency Attention Mechanisms for Hyperspectral Image Classification

Due to end-to-end optimization characteristics and fine generalization ability, convolutional neural networks have been widely applied to hyperspectral image (HSI) classification, playing an irreplaceable role. However, previous studies struggle with two major challenges: (1) HSI contains complex topographic features, the number of labeled samples in different categories is unbalanced, resulting in poor classification for categories with few labeled samples; (2) With the deepening of neural network models, it is difficult to extract more discriminative spectral-spatial features. To address the issues mentioned above, we propose a discriminative spectral-spatial-semantic feature network based on shuffle and frequency attention mechanisms for HSI classification. There are four main parts of our approach: spectral-spatial shuffle attention module (SSAM), context-aware high-level spectral-spatial feature extraction module (CHSFEM), spectral-spatial frequency attention module (SFAM), and cross-connected semantic feature extraction module (CSFEM). First, to fully excavate the category attribute information, SSAM based on a “Deconstruction-Reconstruction” structure is designed, solving the problem of poor classification performance caused by an unbalanced number of label samples. Considering that deep spectral-spatial features are difficult to extract, CHSFEM and SFAM are constructed. The former is based on the “Horizontal-Vertical” structure to capture context-aware high-level multiscale features. The latter introduces multiple frequency components to compress channels to obtain more multifarious features. Finally, towards suppressing noisy boundaries efficiently and capturing abundant semantic information, CSFEM is devised. Numerous experiments are implemented on four public datasets: the evaluation indexes of OA, AA and Kappa on four datasets all exceed 99%, demonstrating that our method can achieve satisfactory performance and is superior to other contrasting methods.

Estimating the effects of driving forces on ecosystem services and their responses to environmental conditions.

Understanding the factors driving ecosystem service (ES) change is essential for maintaining ES functions and achieving sustainable development. Although research on the spatial variations in the effects of driving forces on ESs provides guidance for regional ecological management, the responses of driving forces to environmental conditions have not been adequately investigated, especially in regions with high spatial heterogeneity. By using remote sensing images and socioeconomic data, this paper aims to fill this gap by estimating the spatial distribution characteristics of the effects of driving forces on ESs and their responses to different environmental conditions in Sichuan Province, China. First, the biophysical values of soil conservation (SC) and water yield (WY) were evaluated using ecological simulation models. Second, the spatial distribution of the effects of four driving forces on two services was explored using the geographically weighted regression (GWR) model. Finally, the responses of driving forces to environmental conditions were quantified by using scatter plots. The results revealed that the spatial patterns of SC and WY showed spatial heterogeneity. The effects of driving forces on ESs varied with space. Both positive and negative effects of driving forces were observed in Sichuan Province. Under different biophysical and socioeconomic conditions, the effects of driving forces on ESs showed different change trends, characterized by fluctuating trends and obvious thresholds. In our study area, urban sprawl, impervious surfaces, agricultural expansion, intensive human activities, and complex topographic features contributed to the variations in the effects of driving forces. Our results suggest that the responses of driving forces to different land-use coverage, topographical, NDVI, and socioeconomic conditions should be considered in ecological decision-making. Such research results are expected to manage the driving forces of ESs and serve as a practical reference for local management in order to maintain the functions of ESs and attain sustainable development.

Diurnal variations of rainfall affected by complex topography based on high-density observation in Chongqing over southwest China

Located in the eastern edge of the Sichuan Basin (SCB) in the southwest China, Chongqing is a mountainous region with typical complex topographic features. Using the hourly rainfall observation data of high-density 1686 meteorological stations in Chongqing during warm season from 2009 to 2016, we investigated the diurnal characteristics of precipitation affected by complex topography. The complex mountainous terrain has a significant impact on diurnal variations and distinct regional features of rainfall amount, frequency, and intensity. The stations located in the higher complex mountainous areas have greater rainfall amount, frequency, and intensity than those in the lower surrounding areas. In addition, the detailed characteristics of the rainfall amount and frequency in the four study regions further show that the rainfall amount and frequency significantly increase with the rise of elevation, especially in the area that terrain height sharply increases along the mountain extending direction. The diurnal variation of the rainfall amount is characterized by a bimodal structure with a dominant early-morning peak occurring at approximately 0700 LST (23 UTC) and a weaker secondary late-afternoon peak at approximately 1600 LST (08 UTC), while the rainfall frequency has a single early-morning peak. The terrain height has a significant impact on the proportions of the early-morning rainfall. With the elevation increasing in the four study regions, the proportions of rainfall amount (frequency) that occurs during early-morning period decrease.

A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate

Gyirong serves as an important channel to Chine-Nepal Economic Corridor, which is also the only land route for China-Nepal trade since the 2015 earthquake. However, the Gyirong corridor suffers from glacier-related debris flow from every April to September because of the complex topographic features and the changing climate. Therefore, a susceptibility map in response to precipitation and temperature change is timely, not only to ensure the safe operation of this corridor, but also to provide decision-makers a guidance for hazard mitigation and environmental remediation. Conventional method is difficult to consider and link the meteorological factors (e.g. temperature and precipitation), topographies, ecological, geological conditions all together to produce the susceptibility map, as such, machine learning is utilised to conduct the analysis. Logistic Regression (LR) and Support Vector Machine (SVM) were firstly applied to evaluate their efficiency and effectiveness of the performance of producing the susceptibility map. In order to improve the fitting and prediction accuracy (ACC), genetic algorithm - support vector machine (GA-SVM) and certainty factor - genetic algorithm - support vector machine (CF-GA-SVM) were conducted based on the initial analysis results of receiver operating characteristics curve (ROC) and ACC. Through the analysis, it can be seen that over 61% of the study areas have a high susceptibility to debris flow, requiring an intensive attention from the local government. To further optimise the computational time, when dealing with small amounts of sample data, SVM is more efficient than LR, but CF-GA-SVM can achieve the highest AUC (Area Under Curve) and ACC values, 0.945 and 0.800, respectively. Overall, CF-GA-SVM model presents a relatively high robustness according to sensitivity analysis.

Linkage between anomalies of pre-summer thawing of frozen soil over the Tibetan Plateau and summer precipitation in East Asia

The Tibetan Plateau (TP) is sensitive to climate change in the land–atmosphere coupling mechanism due to its complex topographic features and unique geographic location. This study explored the teleconnection between pre-summer thawing of frozen soil over the TP and summer precipitation in East Asia in the Meiyu–Baiu rainy season (June, July) from 1981 to 2019 using maximum covariance analysis (MCA). The precipitation fields forced by thawing of frozen soil were calculated by the coupled manifold technique. The variations in East Asian precipitation are significantly impacted by thawing of frozen soil over the TP, with a variance explained ratio in the surrounding middle and lower reaches of Yangtze River (MLYR) of around 10%–20%. The MCA analysis also revealed that the thickness of pre-summer frozen soil thawing had a positive relationship with summer precipitation in the MLYR and southern Japan (fraction = 0.59, correlation ≈ 0.99). To find out the possible mechanism, composite analyses were conducted on atmospheric and surface components with reanalysis products. The analysis results suggested that more (less) frozen soil thawing would increase (decrease) the sensible heat and land surface temperature with enhanced (weakened) surface diabatic heating over the TP. Then, the positive (negative) surface diabatic heating would result in an enhanced (weakened) South Asia High extending eastward followed by stronger (weaker) upper troposphere (200 hPa) westerlies, as well as the West North Pacific Subtropical High extending westward. As a result, the northeastward movement of the integrated vapor transport intensified (weakened) with the westward extension (eastward retreat) of the Meiyu–Baiu rain belt, leading to more (less) precipitation in the MLYR and southern Japan. It is thus suggested that pre-summer thawing of frozen soil over the TP could play a vital role in regulating East Asian summer precipitation and movement of the Meiyu–Baiu rain belt.

Effective rural electrification via optimal network: Optimal path-finding in highly anisotropic search space using multiplier-accelerated A* algorithm

United Nations’ 7th Sustainable Development Goal envisions the availability of modern energy for everyone by 2030. While the progress has been satisfactory in the last few years, further rural electrification is increasingly challenging. The current mainstream approach of electrifying villages individually is becoming cost-ineffective due to uncertainties in both resource availability and energy demand for small, difficult-to-reach, residences. A networked rural electrification model, i.e. a cost-optimized network connecting villages and generation facilities, could improve resources utilization, reliability and flexibility. However, determining optimal paths with common search algorithms is extremely inefficient due to complex topographic features of rural areas. This work develops and applies an artificial intelligence search method to efficiently route inter-village power connections in the common rural electrification situation where substantial topological variations exist. The method is evolved from the canonical A* algorithm. Results compare favorably with optimal A* results, at significantly reduced computational effort. Furthermore, users can adaptively trade-off between computation speed and optimality and hence quickly evaluate sites and configurations at reasonable accuracy, which is impossible with classical methods.

Geomorphic covariance structure of a confined mountain river reveals landform organization stage threshold

AbstractSignificant growth in mountain rivers research since 1990 has promoted the concept that canyon‐confined mountain rivers have complex topographic features nested from base‐ to flood‐stages due to canyon structure and abundant large bed elements. Nesting means literally structures inside of structures. Mathematically, nesting means that multiple individual features and repeating patterns exist at different frequency, amplitude, and phasing, and can be added together to obtain the complete structure. Until now, subreach‐scale landform structure, including nesting, has not been quantified sufficiently to understand morphodynamic mechanisms that control and respond to such organization. Geomorphic covariance structure analysis offers a systematic framework for evaluating nested topographic patterns. In this study, a threshold stage in mountain river inundation was hypothesized to exist. Above this stage landform structure is organized to be freely self‐maintaining via flow convergence routing morphodynamics. A 13.2 km segment of the canyon‐confined Yuba River, California, was studied using 2944 cross‐sections. Geomorphic covariance structure analysis was carried out on a meter‐resolution topographic model to test the hypothesis. River width and bed elevation had significantly less variability than previously reported for lower slope, partially confined gravel/cobble river reaches. A critical stage threshold governing flow convergence routing morphodynamics was evident in several metrics. Below this threshold, narrow/high “nozzle” and wide/low “oversized” were the dominant landforms (excluding “normal channel”), while above it wide/high “wide bar” and narrow/low “constricted pool” were dominant. Three‐stage nesting of base‐bankfull‐flood landforms was dictated by canyon confinement, with nozzle–nozzle–nozzle nesting as the top permutation, excluding normal channel.

An intelligent swath tool to characterize complex topographic features: Theory and application in the Teton Range, Licking River, and Olympus Mons

Swath analysis has been useful for linking topography to geomorphic and tectonic processes in a variety of landscapes. Established swath methods have been limited to rectangular or simplified curvilinear sampling blocks despite most geologic structures having complex boundaries and orientations. Moreover, swath boundaries and intervals are commonly defined by arbitrary boundaries and without defined corner points, which makes reproducibility difficult. To address these shortcomings we developed PyOSP (Python Library for Object-oriented Swath Profile), an open-source library that can objectively characterize complex geomorphic boundaries using elevation, slope angle, topographic position index, or other raster calculations by intelligently assimilating geoprocessing information into swath analysis. In addition, PyOSP has post-processing tools (cross-swath, data reclassification, slice, histogram analysis, etc.) and the ability to communicate easily with other GIS software. To evaluate the utility of PyOSP, we used it in three case studies (Teton Range in Wyoming, Licking River in Kentucky, and Olympus Mons on Mars) that illustrate the library's robustness, from objective quantitative description of the topography to improved insights with post-processing capabilities.

Revisiting Surface-Subsurface Exchange at Intertidal Zone with a Coupled 2D Hydrodynamic and 3D Variably-Saturated Groundwater Model

A new high-performance numerical model (Frehg) is developed to simulate water flow in shallow coastal wetlands. Frehg solves the 2D depth-integrated, hydrostatic, Navier–Stokes equations (i.e., shallow-water equations) in the surface domain and the 3D variably-saturated Richards equation in the subsurface domain. The two domains are asynchronously coupled to model surface-subsurface exchange. The Frehg model is applied to evaluate model sensitivity to a variety of simplifications that are commonly adopted for shallow wetland models, especially the use of the diffusive wave approximation in place of the traditional Saint-Venant equations for surface flow. The results suggest that a dynamic model for momentum is preferred over diffusive wave model for shallow coastal wetlands and marshes because the latter fails to capture flow unsteadiness. Under the combined effects of evaporation and wetting/drying, using diffusive wave model leads to discrepancies in modeled surface-subsurface exchange flux in the intertidal zone where strong exchange processes occur. It indicates shallow wetland models should be built with (i) dynamic surface flow equations that capture the timing of inundation, (ii) complex topographic features that render accurate spatial extent of inundation, and (iii) variably-saturated subsurface flow solver that is capable of modeling moisture change in the subsurface due to evaporation and infiltration.