Land Surface Factors Research Articles

Assessment of land surface temperature and factors influencing urban green space dynamics in Sapele, Delta State of Nigeria

Assessment of land surface temperature and factors influencing urban green space dynamics in Sapele, Delta State of Nigeria

Runoff variation in midstream Hei River, northwest China: Characteristics and driving factors analysis

Study regionThe midstream of the Hei River Basin (HRB). Study focusThe study investigated the changes in hydrological elements at different time scales in the basin from 1982 to 2020. We used the Budyko coupled balance equation to quantitatively assess the contributions of climate change and human activity-induced land surface changes to runoff during different periods. In addition, the driving factors of flow consumption within the basin were analyzed using wavelet coherence and generalized additive model (GAM). New hydrological insights for the regionPrior to 2001, there was a significant decrease in annual-scale runoff in the midstream. After that, a partial recovery was observed due to the influence of the Ecological Water Transfer Project (EWTP), while the NDVI trend exhibited an opposite pattern. The study indicates that human-induced modification of the underlying surface is the primary driving factor for the decrease in runoff, with land surface factors contributing 84.69% and 65.27% to the runoff reduction during two periods of change, respectively. Further research on the driving forces of runoff consumption reveals that vegetation cover and climate factors jointly regulate the variations in runoff consumption in the midstream, with multifactor effects surpassing those of individual factors, and NDVI emerges as the predominant controlling factor for runoff consumption. Moreover, all anthropogenic factors exhibit a high explanatory power for runoff consumption.

Response of Land Surface Temperature to Heatwave-Induced Bio-Geophysical Changes in Tropical Forests on Hainan Island from 2010 to 2022

Land surface temperature plays an important role in the water cycle and surface energy balance. Using data collected by a vorticity covariance tower from 2010 to 2022, the relative threshold method and TRM method were employed to study the land–atmosphere exchange of water and the heat flux of rubber forest ecosystems under heatwave and non-heatwave conditions. The results show that the latent heat flux, sensible heat flux, and incoming and outgoing radiation increase from non-heatwave to heatwave conditions. In addition, the multi-year average LST was 6.7 °C higher under HW conditions than under non-HW conditions at the 99% confidence level. Further attribution analysis demonstrates that heatwave-induced land surface temperature change is mainly governed by atmospheric factors rather than by land surface factors. Specifically, radiative forcing shows the largest positive contribution, which is partly offset by the negative contributions of air temperature and relative humidity. In particular, the contributions of radiative forcing, air temperature, relative humidity, and atmospheric pressure to LST were 14.70 K, −4.76 K, −5.86 K, and −0.04 K, respectively. Moreover, surface resistance contributed to LST by 2.42 K, aerodynamic resistance by −0.23 K, and soil heat flux by −0.91 K.

Air Temperature Modeling Based on Land Surface Factors by the Cubist Method (Case Study of Hamoun International Wetland)

The drying up of Hamoun International Wetland (HIW) and the loss of vegetation in this area have led to an increase in ambient temperature. This research examines the changes in the surface of HIW and its role in air temperature (Tair) using data on land surface temperature (LST), vegetation, wind speed, and relative humidity. The Cubist regression model (CRM) is used to simulate the effects of land surface factors (LSFs) on Tair. Four microsites with different plant cover percentages were selected for this purpose. After data collection, 75% of the data were used for modeling and 25% of the data were used for model testing. The results showed that CRM has adequate performance for estimating Tair. The assessment of the relationship between land surface temperature (LST) and Tair at 2 meter height showed that there was a high correlation coefficient between 0.86 and 0.91 in the different microsites. The results of using CRM for estimating Tair showed that this model can estimate air temperature from independent parameters of LST, wind speed, vegetation percentage, and relative humidity with a correlation coefficient of 0.98. In this model, the LST, relative humidity, and vegetation percentage were entered with values of 100%, 93%, and 83% respectively. Wind speed was not included in the model because the measurements were constant and less than 4 m/s throughout the period (no changes).

Spatial variation in sediment connectivity of small watershed along a regional transect on the Loess Plateau

Sediment connectivity likely varies greatly over space, which further affects the processes of hydrology, erosion and sediment delivery of watershed. Small watershed is the basic geomorphologic unit for soil and water conservation. Few studies have been performed to quantify the spatial variation in sediment connectivity of small watershed at regional transect. Six typical small watersheds distributed from south to north on the Loess Plateau were selected as a regional transect to investigate the spatial variation in sediment connectivity and its influencing factors. In this study, the widely utilized index of connectivity (IC) was used to investigate the spatial variation of sediment connectivity in small watersheds. The source-sink landscape index reflecting the source or sink effect of landscape pattern on soil erosion process was utilized to compute the weights for IC. The redundancy analysis (RDA) was applied to identify the main influencing factors contributing to the spatial variation in IC. The results showed that IC increased from south to north (ranged from −2.02 to 1.76). A significant positive spatial autocorrelation was detected in IC of small watershed on the Loess Plateau (p < 0.05). The relationships between IC and environmental factors differed between different small watersheds. The results of RDA showed that the contributions of different influencing factors varied along the tested transect. The relative contributions of climate factors and vegetation factors to IC declined whereas that of land surface factors raised with latitude. Vegetation and climate were the main factors controlling the spatial variation in IC. The results are helpful to understand the spatial heterogeneity of soil erosion and evaluate the conservation benefit of vegetation restoration in semi-arid region.

Developing spatial model to assess agro-ecological zones for sustainable agriculture development in MENA region: Case study Northern Western Coast, Egypt

The development of Agro-Ecological Zones (AEZs) is a vital step in sustainable agricultural development. This study proposes a quantitative method for developing the AEZs and land suitability for Wadi El-Grawla, North-Western Coastal Region (NWCR), Egypt. Hundreds of surface soil samples and twenty soil profiles covering the variations in the study area were collected; then, the inverse distance weighted (IDW) interpolation technique was utilized to produce representative maps for different soil characteristics. Moreover, a digital elevation model (DEM) was generated from topographic maps to calculate various land surface factors such as slope, curvature, and relief density. In addition to benefiting from Sentinel-2 imagery in the separation process of different landform units with a classification accuracy of about 87%. Two soil orders were identified in the study area, namely, Aridisols and Entisols. Chemical analysis of soil samples revealed that soil pH range from 7.14 to 8.6. Total soluble salt changes widely from one location to another. Calcium carbonate (CaCO3) has a wide range of 3.5–31.5%. Organic matter (OM) ranges between 0.04 and 1.55%. A weighted overlay of obtained thematic maps resulted in three management zones in the Wadi El-Grawla area. Zone-1 represents the best location, with an area of about 2359.3 ha. At the same time, the regions of the area of zone-2 and zone-3 are 2526.4 ha and 1346.8 ha, respectively. Finally, suitability maps were produced for the most strategic crops cultivated in Wadi El-Grawla. The most suitable classes were S2 and S3 for all studied crops. The obtained results will support the decision-makers to put various development plans according to the conditions of the study area.

Attribution of the spatial heterogeneity of Arctic surface albedo feedback to the dynamics of vegetation, snow and soil properties and their interactions

The Arctic warming rate is triple the global average, which is partially caused by surface albedo feedback (SAF). Understanding the varying pattern of SAF and the mechanisms is therefore critical for predicting future Arctic climate under anthropogenic warming. To date, however, how the spatial pattern of seasonal SAF is influenced by various land surface factors remains unclear. Here, we aim to quantify the strengths of seasonal SAF across the Arctic and to attribute its spatial heterogeneity to the dynamics of vegetation, snow and soil as well as their interactions. The results show a large positive SAF above −5% K−1 across Baffin Island in January and eastern Yakutia in June, while a large negative SAF beyond 5% K−1 is observed in Canada, Chukotka and low latitudes of Greenland in January and Nunavut, Baffin Island and Krasnoyarsk Krai in July. Overall, a great spatial heterogeneity of Arctic land warming induced by positive SAF is found with a coefficient of variation (CV) larger than 61.5%, and the largest spatial difference is detected in wintertime with a CV > 643.9%. Based on the optimal parameter-based geographic detector model, the impacts of snow cover fraction (SCF), land cover type (LC), normalized difference vegetation index (NDVI), soil water content (SW), soil substrate chemistry (SC) and soil type (ST) on the spatial pattern of positive SAF are quantified. The rank of determinant power is SCF > LC > NDVI > SW > SC > ST, which indicates that the spatial patterns of snow cover, land cover and vegetation coverage dominate the spatial heterogeneity of positive SAF in the Arctic. The interactions between SCF, LC and SW exert further influences on the spatial pattern of positive SAF in March, June and July. This work could provide a deeper understanding of how various land factors contribute to the spatial heterogeneity of Arctic land warming at the annual cycle.

Impacts of Land Surface Conditions and Land Use on Dust Events in the Inner Mongolian Grasslands, China

Aeolian processes in temperate grasslands (TGs) are unique because the plant growth–decay cycle, soil water, and land-use interactions affect the seasonal and inter-annual changes in dust events. Land-use types in Inner Mongolian TGs are unique (settled grazing and grass mowing) compared with those in Mongolian TGs. Since 2003, land use has been controlled by grassland protection legislation, which is intended to prevent desertification and dust storms. In this study, we used process-based ecosystem (DAYCENT) and statistical modeling, along with dust event observations from March to June of 1981–2015, to (1) identify critical land surface factors controlling dust emissions (vegetation components, live grass, standing dead grass, litter, and soil moisture) at typical and desert steppe sites in Inner Mongolia and (2) estimate the impact of controlled land-use legislation on dust events. The DAYCENT model realistically simulated the dynamics of the observed vegetation components and soil moisture in 2005–2015. At both sites, similar significant correlations were obtained between spring dust events and wind speed or a combination of all surface factors that retained anomalies (memory) from the preceding year. Among the surface factors, vegetation was a critical factor that suppressed dust in Inner Mongolian TGs, similar to that in Mongolian TGs. In the desert steppe, standing dead grass had the strongest memory and was significantly correlated with dust events, whereas no significant correlations were observed in the typical steppe. This suggests that, in a typical steppe region, heavy grazing and mowing result in few dead grasses, thereby inhibiting the prevention of dust events. Moreover, the simulations of dust events under controlled (light grazing) and uncontrolled (heavy grazing) land-use conditions demonstrated that the grassland protection legislation reduced the occurrence of dust events in typical and desert steppe sites by 25 and 40%, respectively, since 2003.

Downscaling the GPM-based satellite precipitation retrievals using gradient boosting decision tree approach over Mainland China

Remote sensing brings unprecedented opportunities to estimate precipitation at regional, continental, and even global scales. Nevertheless, the spatial resolutions of current mainstream satellite precipitation estimates are still too coarse to be directly used in the hydrological and meteorological applications over the medium and small scales. The spatio-temporal distribution of precipitation is often affected by various land surface factors, such as geographical location, vegetational cover, topographic characteristics, and ground temperature. Based on the relationship between precipitation and multi-geospatial factors, this study proposed a new spatial downscaling approach named as gradient boosting decision tree (GBDT) to downscale the annual satellite precipitation estimates of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) from 0.1° to 0.01° gridded resolution over Mainland China from 2015 to 2018. As for the preprocessing of downscaling algorithm, the entire study area of Mainland China was separated into four different climate regions due to the spatial discrepancy of the relationship between precipitation and geospatial factors. To validate the effectiveness of GBDT approach, we compared it with other two types of downscaling methods based on random forests (RF) and support vector machine (SVM), respectively. Our evaluation results show that the geographical location (including both latitude and longitude) seems to be relatively more important and stable for GBDT modelling than other land surface factors across the four climate regions in Mainland China. In terms of large spatial scales, the GBDT and RF algorithms generally are superior to SVM for downscaling the IMERG precipitation, as SVM reduced the considerable accuracy of the downscaled results. Last but not least, GBDT exhibits more robust features in downscaling the satellite precipitation retrievals than RF over complex terrains, where the amount of precipitation has strong spatial heterogeneity.

Combination of GF-2 high spatial resolution imagery and land surface factors for predicting soil salinity of muddy coasts

The distribution of soil salinity in coastal wetlands determines gradient distribution and succession of vegetation communities. Due to the inaccessibility of mudflats in coastal areas, soil salinity prediction and mapping is necessary for biodiversity protection and ecological integrity. Yancheng National Nature Reserve (YNNR) is experiencing a salinity-induced invasion of Spartina alterniflora and degradation of native vegetation (Suaeda salsa) since the 1980s, with Suaeda salsa facing the dilemma of disappearing from YNNR. This study was aimed to establish comprehensive land surface factors, such as salinity indices, vegetation characteristics, location, terrain attributes and tidal creeks, for soil salinity mapping from 2015 to 2019. Multiple linear regression (MLR) and random forest (RF) linked land surface factors and soil salinity, and RMSE of the soil salinity predictions were 0.83 ds/m for MLR and 0.72 ds/m for RF. The RF model for soil salinity mapping was more suitable. The value of soil salinity varied from 0.9 ds/m to 5.2 ds/m and decreased year by year, with average values of 3.09 ds/m, 2.87 ds/m and 2.67 ds/m for RF in 2015, 2017 and 2019. The distance from sea and vegetation type were the two most important indicators to predict the distribution of soil salinity in these mudflats, thus better predictions on how to predict the response of vegetation communities to shifting soil salinity.

Impact of Land Surface Processes on a Record‐Breaking Rainfall Event on May 06–07, 2017, in Guangzhou, China

AbstractA record‐breaking extreme rainfall event with a maximum rainfall amount over 24 h of 524.1 mm occurred in Guangzhou, China, on May 06–07, 2017. To study the impact of land surface processes on this extreme rainfall, two 21‐member convective‐permitting ensemble forecasts over South China were performed based on two land surface models (LSMs), Noah and Community Land Model (CLM), and 21 forecasting members of the Global Ensemble Forecast System (GEFS). The results showed that, in general, members using the Noah LSM could better simulate urban heat islands (UHIs) and urban convection than members using the CLM LSM in this case. By investigating the ensemble member that most resembled the observations, it was found that the high temperature center in the urban area caused a thermal low in the early stage. As the southerly winds strengthened, the low‐level convergence line continued moving northward and eventually triggered convection in the mountainous region. A sensitivity experiment showed that the impact of land surface heterogeneity on precipitation could be reflected on a finer scale, and heavy rainfall was very sensitive to the changes in small‐scale land surface forces, including terrain and land use. Slight variations in small‐scale land surface conditions caused great responses in the total precipitation, indicating that for the occurrence of such quasi‐stationary extreme rainfall, a subtle balance between different atmospheric and land surface factors may be required.

Horizontal and vertical distributions of estuarine soil total organic carbon and total nitrogen under complex land surface characteristics

Soil total organic carbon (TOC) and total nitrogen (TN) in estuarine areas show distinct heterogeneities in horizontal and vertical distributions due to complex influencing factors, and clarifying the horizontal and vertical distributions and their influencing factors is of great significance. In this study, the eastern Chongming Island in the Yangtze River estuary in China was selected as the study area, and the horizontal and vertical distributions of soil TOC and TN in different layers of 0–20 cm, 20–40 cm, and 40–60 cm were exhibited in maps and analyzed in different community types. Then, a total of 17 potential factors in aspects of soil, vegetation, atmosphere, landscape, and position were adopted and their single and synergistic effects on the spatial patterns of TOC and TN were identified from different perspectives. Results indicated that the mean values of TOC and TN in the entire study area decreased from surface to bottom layers. TOC and TN showed distinct spatial heterogeneities in different sampling sites and community types; they were generally high in the inner part and low in the alongshore areas in surface soils and showed opposite spatial characteristics in bottom soils; and woodland showed the highest TOC and TN in surface soils and the lowest values in bottom soils of different community types. TOC and TN in different layers, as well as the variations of TOC and TN across layers, exhibited specific and different spatial patterns under the complex factors. In soil factors, pH and available phosphorus showed the closest correlations with TOC and TN in different layers; in land surface factors, plant diversity and growth condition contributed the most to the horizontal distribution while plant growth condition and bare soil index exerted the highest influences on the vertical distribution.

A quantitative model to simulate the vertical errors of SRTM3 DEM V4 data at the pixel level in the Shanbei Plateau of China

ABSTRACT The Shanbei Plateau is located in the central Loess Plateau of China, which exhibits typical loess geomorphology and suffers serious soil and water erosion. Numerous studies are conducted here using digital topographic analysis methods based on digital elevation model (DEM) datasets. Most of these topographic analyses are conducted at the pixel level; thus, it is meaningful to determine the vertical error distributions of the DEM datasets at that scale. Taking the 14th product of the Geoscience Laser Altimeter System on the Ice, Cloud, and land Elevation Satellite (ICESat/GLA14) as the benchmark, the aim of this research is to construct a quantitative model to simulate the vertical error distributions of SRTM3 (Shuttle Radar Topography Mission with a spatial resolution of 3”) DEM V4 data for the Shanbei Plateau. Through comparison, the multiple linear regression (MLR) model is chosen and the vertical error surface is determined based on the MLR model and the land surface factor data. Moreover, the rectified SRTM3 DEM V4 data are developed based on the original data version and the vertical error surface. Through accuracy assessment, the root mean squared error (RMSE) value for the rectified version is found to be 9.6 m. Compared to 20.6 m for the original version, this is a clear improvement. Meanwhile, the MLR model has an R 2 (the coefficient of determination) value of 0.484, which shows that it is able to represent the relationships between the vertical error distributions and the land surface factors. This research is not only meaningful for digital topographic analysis and its relevant fields of application but also provides a reference for accuracy assessments of other DEM datasets in different regions.

Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis

Due to the limitation in the availability of airborne imagery data that are high in both spatial and temporal resolution, land surface temperature (LST) dense in both space and time can only be obtained through downscaling of frequently acquired LST with coarse resolution. Many conventional downscaling techniques are only feasible in an ideal situation, where land surface factors as LST predictors are continuously available for downscaling the LST. These techniques are also applied only at large scales ignoring sub-regional variations. Based upon unmixing based approaches, this study presents an LST downscaling workflow, where only the coarse resolution of 1 km LST image at the prediction time is required. The conceptual backbone of the study is assuming that the LST patterns are governed by thermal behaviors of a fixed set of temperature sensitive land surface components. In operation, the study focuses on central Netherlands covering an area of 90 × 90 km. The MODIS and Landsat imagery acquired simultaneously are used as a coarse-fine resolution pair to derive downscaling mechanism which is then applied to coarse imagery at a time with missing fine resolution imagery. First, an optimal number of thermal components are extracted at fine resolution through the application of the non-negative matrix factorization (NMF). These components are assumed to possess unique temperature change patterns caused by combined effects of land cover change, radiance change, or both. Given the LST change and thermal components at coarse resolution, the LST change load of each component can then be obtained at the coarse resolution by solving a system of linear equations encoding thermal component-LST relationship. Such LST change load of thermal components is further unmixed to fine resolution and linearly weighted by the component distribution at fine resolution to obtain the fine resolution LST change. During the process, the coarse LST data is used directly without any resampling practice as shown in previous studies. Thus the technique is less time consuming even with a large downscaling factor of 30. The downscaled fine resolution LST represents an R-squared of over 0.7 outperforming classic downscaling techniques. The downscaled LST differentiates temperature over major land types and captures both seasonal and diurnal LST dynamics.

Regional spatial variability of root-zone soil moisture in arid regions and the driving factors — a case study of Xinjiang, China

Soil moisture is a key element of the hydrological cycle, and it significantly impacts the surface water and energy fluxes. However, a knowledge gap exists on the spatial variability of root-zone soil moisture at the regional scale in arid and hyperarid regions. Thus, soil moisture measurements at 142 sites were taken in Xinjiang (northwest China), and the relationships between soil moisture and 19 environmental factors were analyzed. The results showed that both absolute gravitational soil water content (SWC) and relative extractable water (REW) increased with increasing soil depth in the 0–100 cm soil profile. It generally decreased in the order of cropland &gt; forestland &gt; grassland &gt; shrubland &gt; bare land. Semivariograms suggested that SWC had moderate spatial dependence over a large range of 473–558 km, and REW was more randomly distributed at the regional scale in Xinjiang. Redundancy analysis suggested that environmental factors could explain 47.5%–50.9% of the variability of soil moisture, which was more strongly driven by land surface factors (p &lt; 0.01) than by climatic factors (p &gt; 0.05). Soil properties and other local variables explained, respectively, 40.7% and 32.3% of the variability of soil moisture in the 0–100 cm soil profile. Soil properties independently accounted for 12.8% and 28.1% of the variability in soil moisture in the 0–50 and 50–100 cm soil layers, respectively. Soil texture, field capacity, wilting point, organic carbon, bulk density, land use, and normalized difference vegetation index were the dominant factors influencing soil moisture variations.

Causal relationship in the interaction between land cover change and underlying surface climate in the grassland ecosystems in China

Land-climate interactions are driven by causal relations that are difficult to ascertain given the complexity and high dimensionality of the systems. Many methods of statistical and mechanistic models exist to identify and quantify the causality in such highly-interacting systems. Recent advances in remote sensing development allowed people to investigate the land-climate interaction with spatially and temporally continuous data. In this study, we present a new approach to measure how climatic factors interact with each other under land cover change. The quantification method is based on the correlation analysis of the different order derivatives, with the canonical mathematical definitions developed from the theories of system dynamics and practices of the macroscopic observations. We examined the causal relationship between the interacting variables on both spatial and temporal dimensions based on macroscopic observations of land cover change and surface climatic factors through a comparative study in the different grassland ecosystems of China. The results suggested that the interaction of land-climate could be used to explain the temporal lag effect in the comparison of the three grassland ecosystems. Significant spatial correlations between the vegetation and the climatic factors confirmed feedback mechanisms described in the theories of eco-climatology, while the uncertain temporal synchronicity reflects the causality among the key indicators. This has been rarely addressed before. Our research show that spatial correlations and the temporal synchronicity among key indicators of the land surface and climatic factors can be explained by a novel method of causality quantification using derivative analysis.

Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation

Soil enthalpy (H) contains the combined effects of both soil moisture (w) and soil temperature (T) in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method. Results indicate that T generally makes positive contributions to H, while w exhibits different (positive or negative) impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts, w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer (i.e., the fifth layer), H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation (P) prediction, the Huanghe–Huaihe Basin (HHB) and Southeast China (SEC), with similar sensitivities of H to w and T, are selected. Analyses show that, despite similar spatial distributions of H–P and T–P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer (June) P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors (e.g., T and w).

Quantifying the contribution of land use change to surface temperature in the lower reaches of the Yangtze River

Abstract. Anthropogenic land use has a significant impact on climate change. Located in the typical East Asian monsoon region, the land–atmosphere interaction in the lower reaches of the Yangtze River is even more complicated due to intensive human activities and different types of land use in this region. To better understand these effects on microclimate change, we compare differences in land surface temperature (Ts) for three land types around Nanjing from March to August, 2013, and then quantify the contribution of land surface factors to these differences (ΔTs) by considering the effects of surface albedo, roughness length, and evaporation. The atmospheric background contribution to ΔTs is also considered based on differences in air temperature (ΔTa). It is found that the cropland cooling effect decreases Ts by 1.76° and the urban heat island effect increases Ts by 1.25°. They have opposite impacts but are both significant in this region. Various changes in surface factors affect radiation and energy distribution and eventually modify Ts. It is the evaporative cooling effect that plays the most important role in this region and accounts for 1.40° of the crop cooling and 2.29° of the urban warming. Moreover, the background atmospheric circulation is also an indispensable part in land–atmosphere feedback induced by land use change and reinforces both these effects.

Attenuating the surface Urban Heat Island within the Local Thermal Zones through land surface modification

Inefficient mitigation of excessive heat is attributed to the discrepancy between the scope of climate research and conventional planning practice. This study approaches this problem at both domains. Generally, the study, on one hand, claims that the climate research of the temperature phenomenon should be at local scale, where implementation of planning and design strategies can be more feasible. On the other hand, the study suggests that the land surface factors should be organized into zones or patches, which conforms to the urban planning and design manner. Thus in each zone, the land surface composition of those excessively hot places can be compared to the zonal standard. The comparison gives guidance to the modification of the land surface factors at the target places.Specifically, this study concerns the Land Surface Temperature (LST) in Wuhan, China. The land surface is classified into Local Thermal Zones (LTZ). The specifications of temperature sensitive land surface factors are relative homogeneous in each zone and so is the variation of the LST. By extending the city scale analysis of Urban Heat Island into local scale, the Local Surface Urban Heat Islands (LSUHIs) are extracted. Those places in each zone that constantly maintain as LSUHI and exceed the homogenous LST variation are considered as target places or hotspots with higher mitigation or adaptation priority. The operation is equivalent to attenuate the abnormal LST variation in each zone. The framework is practical in the form of prioritization and zoning, and mitigation strategies are essentially operated locally.

Land surface memory effects on dust emission in a Mongolian temperate grassland

AbstractAeolian processes in temperate grasslands are unique in that the plant growth‐decay cycle, soil moisture/snowpack dynamics, and induced grazing interactively affect seasonal and interannual variations of dust emission. This study uses process‐based ecosystem model DAYCENT and unique saltation flux measurements to (1) identify primary land surface factors that control dust emission with soil moisture and vegetation components (live grasses, standing dead grasses, and litter) in a Mongolian grassland and (2) test the dead‐leaf hypothesis proposed by previous observational studies that correlates plant biomass in summer and dust events the following spring. In general, the DAYCENT model realistically simulates seasonal and interannual variations of the vegetation components and soil moisture that were captured by field observations during 2003–2010. Then, the land surface components are correlated with measured daily saltation flux in the springs of 2008–2009 and the frequency of monthly dusty days during March–June 2002–2010. Results show that dust emission had a similar amplitude of significant correlation with wind speed and a combination of all land surface components, which demonstrates a memory of the preceding year. The memory analysis reveals that vegetation and soil moisture anomalies during spring dust emission are significantly autocorrelated with the preceding year's (autumn) corresponding anomalies, which were controlled by rainfall during a given summer. Most importantly, of the vegetation components, the standing dead grasses had the strongest memory and simultaneous correlation with spring dust emission, suggesting the validity of the dead‐leaf hypothesis.