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In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during search and rescue operations in disaster zones, and to genetic computer algorithms exploring parameter spaces. When looking for sparse targets in a homogeneous environment, a combination of ballistic and diffusive steps is considered optimal; in particular, more ballistic Lévy flights with exponent α ≤ 1 are generally believed to optimize the search process. However, most search spaces present complex topographies. What is the best search strategy in these more realistic scenarios? Here, we show that the topography of the environment significantly alters the optimal search strategy toward less ballistic and more Brownian strategies. We consider an active particle performing a blind cruise search for nonregenerating sparse targets in a 2D space with steps drawn from a Lévy distribution with the exponent varying from α = 1 to α = 2 (Brownian). We show that, when boundaries, barriers, and obstacles are present, the optimal search strategy depends on the topography of the environment, with α assuming intermediate values in the whole range under consideration. We interpret these findings using simple scaling arguments and discuss their robustness to varying searcher’s size. Our results are relevant for search problems at different length scales from animal and human foraging to microswimmers’ taxis to biochemical rates of reaction.

Heavy air pollution is strongly influenced by weather conditions and is thus sensitive to climate change. Especially, for the areas with complex topography such as the Sichuan Basin (SB), one of the most polluted areas of China, the synergistic effects of synoptic weather patterns and topography on air quality are unclear and warrant investigation. This study examined the typical synoptic patterns of SB in winter days of 2013–2017 and revealed their synergistic effects with topography on air quality. Three categories of synoptic patterns including dry low-trough, high-pressure, and wet low-vortex patterns accompanying heavy, medium, and slight air pollution, respectively, were identified. In particular, the dry low-trough patterns occur most frequently, accounting for around 62% of the total days. In the case of this pattern, westerly wind prevails over the SB and the aloft atmosphere is warmer than the Tibetan Plateau (TP) at the same height, which induces the cold air over TP moving eastward to the SB. Under the synergistic effects of the cold air eastward movement and TP, a strong descending motion (known as foehn) is observed on the leeward slope of the towering TP. This foehn warming causes a stable layer above the planetary boundary layer (PBL), which suppresses secondary circulation and PBL. These features restrict atmospheric pollutant dispersion, resulting in poor air quality. In contrast, for the high-pressure and wet low-vortex patterns, cold air masses from the north invade southward and cover the northwest SB. This invasion remarkably decreases the atmospheric stability of the lower troposphere, deepens the PBL, and enhances the height of secondary circulation, thereby facilitating air pollutant dispersion. Moreover, the wet low-vortex pattern is accompanied by frequent precipitation events (with 80% rainy days), further bringing down air pollution levels. These findings provide an insight for improving air pollution forecast in the complex terrain areas under global warming.

We review developments in the field of boundary-layer flow over complex topography, focussing on the period from 1970 to the present day. The review follows two parallel strands: the impact of hills on flow in the atmospheric boundary layer and gravity-driven flows on hill slopes initiated by heating or cooling of the surface. For each strand we consider the understanding that has resulted from analytic theory before moving to more realistic numerical computation, initially using turbulence closure models and, more recently, eddy-resolving schemes. Next we review the field experiments and the physical models that have contributed to present understanding in both strands. For the period 1970–2000 with hindsight we can link major advances in theory and modelling to the key papers that announced them, but for the last two decades we have cast the net wider to ensure that we have not missed steps that eventually will be seen as critical. Two important new themes are given prominence in the 2000–2020 period. The first is flow over hills covered with tall plant canopies. The presence of a canopy changes the flow in important ways both when the flow is nearly neutral and also when it is stably stratified, forming a link between our two main strands. The second is the use of eddy-resolving models as vehicles to bring together hill flows and gravity-driven flows in a unified description of complex terrain meteorology.

The Weather Research and Forecasting (WRF) model has been successfully used in weather prediction, but its ability to simulate precipitation over areas with complex topography is not optimal. Consequently, WRF has problems forecasting rainfall events over Chilean mountainous terrain and foothills, where some of the main cities are located, and where intense rainfall occurs due to cutoff lows. This work analyzes an ensemble of microphysics schemes to enhance initial forecasts made by the Chilean Weather Agency in the front range of Santiago. We first tested different vertical levels resolution, land use and land surface models, as well as meteorological forcing (GFS/FNL). The final ensemble configuration considered three microphysics schemes and lead times over three rainfall events between 2015 and 2017. Cutoff low complex meteorological characteristics impede the temporal simulation of rainfall properties. With three days of lead time, WRF properly forecasts the rainiest N-hours and temperatures during the event, although more accuracy is obtained when the rainfall is caused by a meteorological frontal system. Finally, the WSM6 microphysics option had the best performance, although further analysis using other storms and locations in the area are needed to strengthen this result.

The typical location and number of anemometer towers in the assessed area are the key to the accuracy of wind resource assessment in complex topography. As calculation examples, this paper used two typical complex topography wind farms in Guangxi, Yunnan province in China. Firstly, we simulated the wind resource status of the anemometer tower in the Meteodyn WT software. Secondly, we compared the simulated wind resource with the actual measured data by the anemometer tower in the same situation. Thirdly, we analyzed the influence of anemometer tower location and quantity in the accuracy of wind resource assessment through the comparison results. The results showed that the range which the anemometer tower can represent is limited (<5 kilometers), and the prediction error more than 5%. Besides, the anemometer towers in special terrain areas (such as wind acceleration areas) cannot be used as a representative choice. The relative error of the simulated average annual wind speed by choose different number of anemometer towers is about 4%, and the grid-connected power generation more than 6%. The representative effect of anemometer towers is of crucial for improving the accuracy of wind resource assessment in engineering applications.

Soil moisture data can reflect valuable information on soil properties, terrain features, and drought condition. The current study compared and assessed the performance of different interpolation methods for estimating soil moisture in an area with complex topography in southwest China. The approaches were inverse distance weighting, multifarious forms of kriging, regularized spline with tension, and thin plate spline. The 5-day soil moisture observed at 167 stations and daily temperature recorded at 33 stations during the period of 2010–2014 were used in the current work. Model performance was tested with accuracy indicators of determination coefficient ( R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and modeling efficiency (ME). The results indicated that inverse distance weighting had the best performance with R 2 , MAPE, RMSE, RRMSE, and ME of 0.32, 14.37, 13.02%, 0.16, and 0.30, respectively. Based on the best method, a spatial database of soil moisture was developed and used to investigate drought condition over the study area. The results showed that the distribution of drought was characterized by evidently regional difference. Besides, drought mainly occurred in August and September in the 5 years and was prone to happening in the western and central parts rather than in the northeastern and southeastern areas.

Local Climate Zoning (LCZ) appears as a crucial framework, intricately deciphering the nuanced climatic variations within urban landscapes, centering on the physical structure and surface attributes of cities. With 17 distinct classes standing for diverse features, LCZ provides a nuanced lens for studying urban microclimates. In this study, a meticulous classification of Tehran’s local climate classes was conducted using the Automated Geoscientific Analyses (SAGA) GIS software, complemented by sample verification through Google Earth. The findings illuminate Tehran’s diverse urban climate, revealing two predominant classes: dense semi-tall and denser, shorter categories. A granular district-wise analysis unfolds varying climatic scenarios: Central and Northern Districts (e.g., Districts 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14, and 17): Marked by densely built-up areas (LCZ1 and LCZ2). Southern and Western Districts (e.g., Districts 9, 15, 16, 18, 19, 20, 21, and 22): Hosting a mix of industrial zones and green expanses. While green spaces nestled among buildings are less conspicuous, predominantly confined to city parks (LCZ4 to LCZ6), the classifications strategically show areas with implications for high urban heat loads, pollution hotspots, best air circulation, and air quality. This insightful district-level analysis supplies a robust foundation for steering sustainable urban development. Field validation through on-site visits and meticulous photo documentation rigorously substantiates the accuracy of the output map. The research underscores the profound significance of LCZ, unraveling Tehran’s climatic intricacies at a district level. This comprehensive understanding proves essential for steering sustainable urban development, navigating the complexities within the diverse topography of Tehran’s districts.

Past studies reported a drastic growth in the wildland–urban interface (WUI), the location where man‐made structures meet or overlap wildland vegetation. Fighting fire is difficult in the WUI due to the combination of wildland and structural fuels, and therefore, WUI areas are characterized by frequent damage and loss of structures from wildfires. Recent wildland fire policy has targeted fire prevention, evacuation planning, fuel treatment, and home hardening in WUI areas. Therefore, it is important to understand the occurrence of wildfire events relative to the location of the WUI. In this work, we have reported the occurrences of wildfires with respect to the WUI and quantified how much of the WUI is on complex topography in California, which intensifies fire behavior and complicates fire suppression. We have additionally analyzed the relative importance of WUI‐related parameters, such as housing density, vegetation density, and distance to wildfires, as well as topographic factors, such as slope, elevation, aspect, and surface roughness, on the occurrence of large and small wildfires and the burned area of large wildfires near the WUI. We found that a very small percentage of wildfire ignition points and large wildfire‐burned areas (>400 ha or 1000 acres) were located in the WUI areas. A small percentage of large wildfires were encountered in WUI (3%), and the WUI area accounted for only 4% of the area burned, which increased to 5% and 56%, respectively, outside WUI (5‐km buffer from WUI). Similarly, 66% of fires ignited outside WUI, whereas only 3.6% ignited within WUI. Results from this study have implications for fuel management and infrastructure hardening, as well as for fire suppression and community response.

Cold‐air pooling is an important topoclimatic process that creates temperature inversions with the coldest air at the lowest elevations. Incomplete understanding of sub‐canopy spatiotemporal cold‐air pooling dynamics and associated ecological impacts hinders predictions and conservation actions related to climate change and cold‐dependent species and functions. To determine if and how cold‐air pooling influences forest composition, we characterized the frequency, strength, and temporal dynamics of cold‐air pooling in the sub‐canopy at local to regional scales in New England, USA. We established a network of 48 plots along elevational transects and continuously measured sub‐canopy air temperatures for 6–10 months (depending on site). We then estimated overstory and understory community temperature preferences by surveying tree composition in each plot and combining these data with known species temperature preferences. We found that cold‐air pooling was frequent (19–43% seasonal occurrences) and that sites with the most frequent inversions displayed inverted forest composition patterns across slopes with more cold‐adapted species, namely conifers, at low instead of high elevations. We also observed both local and regional variability in cold‐air pooling dynamics, revealing that while cold‐air pooling is common, it is also spatially complex. Our study, which uniquely focused on broad spatial and temporal scales, has revealed some rarely reported cold‐air pooling dynamics. For instance, we discovered frequent and strong temperature inversions that occurred across seasons and in some locations were most frequent during the daytime, likely affecting forest composition. Together, our results show that cold‐air pooling is a fundamental ecological process that requires integration into modeling efforts predicting future forest vegetation patterns under climate change, as well as greater consideration for conservation strategies identifying potential climate refugia for cold‐adapted species. To determine if and how cold‐air pooling influences forest composition, we surveyed tree composition and characterized the frequency, strength, and temporal dynamics of cold‐air pooling in the sub‐canopy at local to regional scales in New England, USA. We found that cold‐air pooling was frequent (19–43% seasonal occurrences) and that sites with the most frequent inversions displayed inverted forest composition patterns across slopes with more cold‐adapted species, namely conifers, at low instead of high elevations. Cold‐air pooling occurred across seasons and times of day, and, in some locations, was surprisingly more frequent during the daytime than the nighttime.

Soil loss is a crucial problem due to its importance in agricultural and livestock production, where intensive human activities have exacerbated erosive processes. In this framework, the RUSLE model emerges as a crucial tool for soil loss estimation, with the LS factor being a central component of the model. However, several research studies have shown that an inadequate selection of LS can overestimate soil erosion in complex topographies. This study assesses the overestimation of the RUSLE (Revised Universal Soil Loss Equation) model by slope length and steepness factor, focusing on a complex terrain characterized by abrupt changes in elevation and slope. Seven divergent equations were used to evaluate the LS factor and compared with the results of the RUSLE model. The findings revealed that the models proposed by Desmet and Govers and Moore and Brusch proved best suited to terrain with such characteristics, avoiding over-estimation. The meticulous selection of the LS model was highlighted as a crucial aspect, underlining the importance of an accurate methodology in soil loss measuring in topographically complex environments. This study contributes to the understanding and improvement of erosion models, providing valuable guidelines to address soil loss overestimation by LS-Factor.