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Ice-wedge networks underlie polygonal terrain and comprise the most widespread form of massive ground ice in continuous permafrost. Here, we show that climate-driven thaw of hilltop ice-wedge networks is rapidly transforming uplands across Banks Island in the Canadian Arctic Archipelago. Change detection using high-resolution WorldView images and historical air photos, coupled with 32-year Landsat reflectance trends, indicate broad-scale increases in ponding from ice-wedge thaw on hilltops, which has significantly affected at least 1500 km2 of Banks Island and over 3.5% of the total upland area. Trajectories of change associated with this upland ice-wedge thermokarst include increased micro-relief, development of high-centred polygons, and, in areas of poor drainage, ponding and potential initiation of thaw lakes. Millennia of cooling climate have favoured ice-wedge growth, and an absence of ecosystem disturbance combined with surface denudation by solifluction has produced high Arctic uplands and slopes underlain by ice-wedge networks truncated at the permafrost table. The thin veneer of thermally-conductive mineral soils strongly links Arctic upland active-layer responses to summer warming. For these reasons, widespread and intense ice-wedge thermokarst on Arctic hilltops and slopes contrast more muted responses to warming reported in low and subarctic environments. Increasing field evidence of thermokarst highlights the inherent climate sensitivity of the Arctic permafrost terrain and the need for integrated approaches to monitor change and investigate the cascade of environmental consequences.

This paper documents the first comprehensive inventory of thermokarst and thaw-sensitive terrain indicators for a 2 million km2 region of northwestern Canada. This is accomplished through the Thermokarst Mapping Collective (TMC), a research collaborative to systematically inventory indicators of permafrost thaw sensitivity by mapping and aerial assessments across the Northwest Territories (NT), Canada. The increase in NT-based permafrost capacity has fostered science leadership and collaboration with government, academic, and community researchers to enable project implementation. Ongoing communications and outreach have informed study design and strengthened Indigenous and stakeholder relationships. Documentation of theme-based methods supported mapper training, and flexible data infrastructure facilitated progress by Canada-wide researchers throughout the COVID-19 pandemic. The TMC inventory of thermokarst and thaw-sensitive landforms agree well with fine-scale empirical mapping (69% to 84% accuracy) and aerial inventory (74% to 96% accuracy) datasets. National- and circumpolar-scale modelling of sensitive permafrost terrain contrasts significantly with TMC outputs, highlighting their limitations and the value of empirically-based mapping approaches. We demonstrate that the multi-parameter TMC outputs support a holistic understanding and refined depictions of permafrost terrain sensitivity, provide novel opportunities for syntheses, and inform future modelling approaches, which are urgently required to comprehend better what permafrost thaw means for Canada’s North.

Kurseong hill subdivision of the Darjeeling district, West Bengal, is associated with complex geomorphic set-up stemmed from form–process interaction on varied slope, relief, elevation, aspects, ridge–valley orientation, density and frequency of drainage, dissection, ruggedness and topographic wetness over different stratigraphy of geological rocks having different hardness. This complexity provides ample scope to evaluate the terrain and categorize the terrain into different sensitivity zones according to land utilization point of view so that different terrain segments can be classified into different grades and therefore can be rationally utilized and managed for sustainable use. Therefore, the present paper takes an attempt to evaluate the terrain sensitivity of the Kurseong hill subdivision based on multi-criteria analysis method to identify different grades of terrain according to their ability to hold and sustain the pressure of land utilization for settlements, agriculture and other land uses. Terrain sensitivity evaluation of the Kurseong hill subdivision has been done on the basis of the study of hard rock geology, some selected geomorphometric parameters derived from 3-arc-second SRTM digital elevation data as well as SOI toposheets having RF of 1:50,000 and the existing land use factors after necessary ground truthing on some selected sites. The procedure adopted for multi-criteria analysis is 1 km2 grid-based rating value assignment algorithm of the selected parameters and determination of different classes of sensitive terrain and their gradation according to the capacity of the terrain to withstand present pressure of land utilization. Some measures for rational land utilization and management have also been proposed for optimal land use planning for the study area.

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations.

Taiwan regularly receives extreme rainfall due to seasonal mei-yu fronts that are modified by Taiwan’s complex topography. One such case occurred between 1 and 3 June 2017 when a mei-yu front contributed to flooding and landslides from over 600 mm of rainfall in 12 h near the Taipei basin, and over 1500 mm of rainfall in 2 days near the Central Mountain Range (CMR). This mei-yu event is simulated using the Weather Research and Forecasting (WRF) Model with halved terrain as a sensitivity test to investigate the orographic mechanisms that modify the intensity, duration, and location of extreme rainfall. The reduction in WRF terrain height produced a decrease in rainfall duration and accumulation in northern Taiwan and a decrease in rainfall duration, intensity, and accumulation over the CMR. The reductions in northern Taiwan are linked to a weaker orographic barrier jet resulting from a lowered terrain height. The reductions in rainfall intensity and duration over the CMR are partially explained by a lack of orographic enhancements to mei-yu frontal convergence near the terrain. A prominent feature missing with the reduced terrain is a redirection of postfrontal westerly winds attributed to orographic deformation, i.e., the redirection of flow due to upstream topography. Orographically deforming winds converge with prefrontal flow to maintain the mei-yu front. In both regions, the decrease in mei-yu front propagation speed is linked to increased rainfall duration. These orographic features will be further explored using observations captured during the 2022 Prediction of Rainfall Extremes Campaign in the Pacific (PRECIP) field campaign.

Emergency response to flood plain inundations requires real‐time forecasts of flow depth, velocity, and arrival time. Detailed and rapid flood inundation forecasts can be obtained from numerical solution of 2D unsteady flow equations based on high‐resolution topographic data and geomorphologically informed unstructured meshes. However, flow resistance parameters representing the effects of land surface topography unresolved by digital terrain model data remain uncertain. In the present study, flow resistance parameters representing the effects of roughness, vegetation, and buildings are determined hydraulically in real‐time using flow depth observations. A detailed numerical reproduction of a real flood has been largely corroborated by observations and subsequently used as a surrogate of the ground truth target. In synthetic numerical experiments, flow depth observations are obtained from a network of in‐situ flow depth sensors assigned to hydraulically relevant locations in the flood plain. Starting from a generic resistance parameter set, the capability of a tandem 2D surface flow model and Bayesian optimization technique to achieve convergence to the target resistance parameter set is tested. Convergence to the target resistance parameter set was obtained with 50 or fewer tandem flow + optimization iterations for each forecasting cycle in which the difference between simulated and observed flow depths is minimized. The flood arrival time errors across a 52 km2${\\text{km}}^{2}$flood plain inundation area were reduced by 3.13 hr with respect to results obtained without optimization from a fixed range of flow resistance parameters. Performance metrics like critical success index and probability of detection reach values above 90% across the flood plain. Plain Language Summary When a flood inundation occurs over a populated flood plain due, for instance, to a levee or dam failure, emergency response requires numerical solutions of 2D unsteady flow equations to provide real‐time forecasts of flow depth, velocity, and arrival time. These forecasts are important for supporting decisions regarding how emergency personnel resources should be allocated, and how evacuation of citizens and animals should be managed. As high‐resolution topographic data and terrain analysis methods have recently made possible detailed and efficient flood plain inundation models based on geomorphologically informed meshes, an effort can now be made to hydraulically determine flow resistance parameters from flow observations in real time. In this work, illustrated with a synthetic case study, flow observations are provided by an optimally designed network of in‐situ sensors that record flow depth over time. 2D flow model resistance parameters are determined with a Bayesian optimization technique based on observations collected by sensors with sufficient lead time for real‐time forecasting applications. With the proposed methods, flood inundations are numerically reproduced in real‐time in a hydraulically meaningful manner, and this has the potential to provide reliable flood forecasts and guidance for post‐event recovery. Key Points Flow resistance parameters representing the effects of land surface topography unresolved by digital terrain model data are determined hydraulically A sensitivity‐based flow depth sensor network design method is developed to capture in real‐time the full range of flood dynamics Bayesian optimization is found to enable reliable and rapid flow resistance parameter learning from simulated and observed flow depths

Through conducting dynamic and thermodynamic diagnoses as well as a series of numerical sensitivity simulations, we investigated the effects of the terrain, coastline, and cold pools on convection initiation (CI) and its subsequent upscale convective growth (UCG) during a case of heavy rainfall along the coast of South China. CI occurred at the vertex of the coastal concave mountain geometry as a combined result of coastal convergence, orographic lifting, and mesoscale ascent driven by the terminus of a marine boundary layer jet (MBLJ). In numerical simulations with the coastline or terrain of South China removed, the coastal CI does not occur or becomes weaker as the MBLJ extends farther north, suggesting that the coastline and terrain play a role in CI. In addition, local small-scale terrain can modulate the detailed location and timing of CI and UCG. When the coastal concave terrain and coastline near the CI are artificially removed or filled by additional mountains, the orographic lifting and the local convergence along the coast correspondingly change, which strongly affects the CI and UCG. From a thermodynamic perspective, the coastal concave terrain plays the role of a local moisture “catcher,” which promotes low-level moistening by blocking water vapor coming from an upstream moist tongue over the ocean. Furthermore, new convection is continuously generated by the lifting of low-level moist southerlies at the leading edges of cold pools that tend to move southeastward because of the blocking coastal mountains. Sensitivity experiments suggest that the MCS becomes weaker and moves more slowly when cold pools are weakened through a reduction of rain-evaporation cooling.

An increase in retrogressive thaw slump (RTS) activity has been observed in the Arctic in recent decades. However, a gap exists between observations in high Arctic polar desert regions where mean annual ground temperatures are as cold as −16.5 °C and vegetation coverage is sparse. In this study, we present a ∼30 year record of annual RTS observations (frequency and distribution) from 1989 to 2018 within the Eureka Sound Lowlands, Ellesmere and Axel Heiberg Islands. Record summer warmth in 2011 and 2012 promoted rapid RTS initialization, increasing active slumps from 100 in a given year or less to over 200 regionally and promoting RTS initiation in previously unaffected terrain. Differential GPS and remote sensing observations of 12 RTSs initiated during this period (2011-2018) provided a mean headwall retreat rate for all RTSs of 6.2 m yr−1 and for specific RTSs up to 26.7 m yr−1. To better understand the dynamics of climate and terrain factors controlling RTS headwall retreat rates we explored RTS interactions by correlating headwall retreat with climate factors (thawing degree days, annual rainfall and annual snowfall) and terrain factors (aspect and slope). Our findings indicate a sensitivity of cold permafrost in the high Arctic to climate-driven thermokarst initiation, but the decoupling of RTS dynamics from climate appears to occur over time for individual RTS as terrain factors take on a greater role controlling headwall retreat. Detailed observations of thermokarst development in a high Arctic polar desert permafrost setting are important as it demonstrates the sensitivity of this system to changes in summer temperatures and highlight differences to changes occurring in other Arctic permafrost regions.

This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They confirmed that the most significant differences in annual mean wind speed at 100 m a.g.l. (above ground level) mostly coincide with areas of high surface roughness length and not with the location of the domains or maximum wind speed. Then an ensemble of more than 50 simulations with different setups for a single year was carried out for one domain covering northern Europe for which tall mast observations were available. We varied many different parameters across the simulations, e.g. model version, forcing data, various physical parameterizations, and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes in the wind climate in the boundary layer are mostly due to using different physical parameterizations, especially the planetary boundary layer scheme, the representation of the land surface, and the prescribed surface roughness length. Also, the setup of the simulations, such as the integration length and the domain size, can considerably influence the results. We assessed the degree of similarity between winds simulated by the WRF ensemble members and the observations using a suite of metrics, including the Earth Mover's Distance (EMD), a statistic that measures the distance between two probability distributions. The EMD was used to diagnose the performance of each ensemble member using the full wind speed and direction distribution, which is essential for wind resource assessment. We identified the most realistic ensemble members to determine the most suitable configuration to be used in the final production run, which is fully described and evaluated in the second part of this study .

This study aims to capture groundwater potential zones integrating deep neural network and groundwater influencing factors. The present work was carried out for Gopi khola watershed, mountainous terrain in Nepal Himalaya as the watershed mainly relies upon the groundwater assets; it is a need to explore groundwater potential for better management of the aquifer framework. Ten groundwater influencing factors were collected such as elevation, slope, curvature, topographic positioning index, topographic roughness index, drainage density, topographic wetness index, geology, lineament density, and land use thematic layers. Among those influencing factors, topographic roughness index was removed because of multicollinearity issue to reduce the dimension of the dataset. A spring inventory map of 145 spring locations was prepared using field survey method and an equal number of spring absence points were randomly generated. The 70% of spring and spring absence pixels were used as training dataset and remaining as test dataset. The final map was created based on predicted probabilities ranging from 0 to 1. The validation was done using the receiver operating characteristic curve, which shows that the area under the curve is 76.1% for the training dataset and 82.1% for the test dataset. The sensitivity analysis was performed using Jackknife test which shows that the lineament density is the most important factor. The experimental results demonstrated that deep neural network is highly capable to capture groundwater potential zone in mountainous terrain. The present study might be useful and preliminary work to exploit the groundwater. The consequences of the current study may be valuable to water administrators to settle on appropriate choices on the ideal utilization of groundwater assets for future arranging in the basic investigation zone.