Land Surface Parameters Research Articles

Generalized spatio-temporal-spectral integrated fusion for soil moisture downscaling

Soil moisture (SM) is one of the key land surface parameters, but the coarse spatial resolution of the passive microwave SM products constrains the precise monitoring of surface changes. The existing SM downscaling methods typically either utilize spatio-temporal information or leverage auxiliary parameters, without fully mining the complementary information between them. In this paper, a generalized spatio-temporal-spectral integrated fusion-based downscaling method is proposed to fully utilize the complementary features between multi-source auxiliary parameters and multi-temporal SM data. Specifically, we define the spectral characteristic of geographic objects as an assemblage of diverse attribute characteristics at specific spatio-temporal locations and scales. Based on this, the SM-related auxiliary parameter data can be treated as the generalized spectral characteristics of SM, and a generalized spatio-temporal-spectral integrated fusion framework is proposed to integrate the spatio-temporal features of the SM products and the generalized spectral features from the auxiliary parameters to generate fine spatial resolution SM data with high quality. In addition, considering the high heterogeneity of multi-source data, the proposed framework is based on a spatio-temporal constrained cycle generative adversarial network (STC-CycleGAN). The proposed STC-CycleGAN network comprises a forward integrated fusion stage and a backward spatio-temporal constraint stage, between which spatio-temporal cycle-consistent constraints are formed. Numerous experiments were conducted on Soil Moisture Active Passive (SMAP) SM products. The qualitative, quantitative, and in-situ site verification results demonstrate the capability of the proposed method to mine the complementary information of multi-source data and achieve high-accuracy downscaling of global daily SM data from 36 km to 9 km.

Impact of Convective and Land Surface Parameterization Schemes on the Simulation of Surface Temperature and Precipitation Using RegCM4.7 During Summer Period Over the DPR Korea

Impact of Convective and Land Surface Parameterization Schemes on the Simulation of Surface Temperature and Precipitation Using RegCM4.7 During Summer Period Over the DPR Korea

Detailed height mapping of trees and buildings (HiTAB) in Chicago and its implications to urban climate studies

Abstract The vertical dimensions of urban morphology, specifically the heights of trees and buildings, exert significant influence on wind flow fields in urban street canyons and the thermal environment of the urban fabric, subsequently affecting the microclimate, noise levels, and air quality. Despite their importance, these critical attributes are less commonly available and rarely utilized in urban climate models compared to planar land use and land cover data. In this study, we explicitly mapped the height of trees and buildings (HiTAB) across the city of Chicago at 1 m spatial resolution using a data fusion approach. This approach integrates high-precision light detection and ranging (LiDAR) cloud point data, building footprint inventory, and multi-band satellite images. Specifically, the digital terrain and surface models were first created from the LiDAR dataset to calculate the height of surface objects, while the rest of the datasets were used to delineate trees and buildings. We validated the derived height information against the existing building database in downtown Chicago and the Meter-scale Urban Land Cover map from the Environmental Protection Agency, respectively. The co-investigation on trees and building heights offers a valuable initiative in the effort to inform urban land surface parameterizations using real-world data. Given their high spatial resolution, the height maps can be adopted in physical-based and data-driven urban models to achieve higher resolution and accuracy while lowering uncertainties. Moreover, our method can be extended to other urban regions, benefiting from the growing availability of high-resolution urban informatics globally. Collectively, these datasets can substantially contribute to future studies on hyper-local weather dynamics, urban heterogeneity, morphology, and planning, providing a more comprehensive understanding of urban environments.

Impacts of Topography‐Based Subgrid Scheme and Downscaling of Atmospheric Forcing on Modeling Land Surface Processes in the Conterminous US

AbstractThe effects of small‐scale topography‐induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). In this study, a new topography‐based subgrid structure referred to as topographic units (TGU) designed to better capture subgrid topographic effects, and methods to downscale atmospheric forcing to the land TGUs have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Effects of the subgrid scheme and downscaling methods on ELM simulated land surface processes are evaluated over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5‐degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS‐level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional‐level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small‐scale surface heterogeneity in ESMs and motivate future research to understand their effects on land‐atmosphere interactions, streamflow, and water resources management over mountainous regions.

CORS station for synergistic monitoring of multivariate surface parameters in expansive soils

Expansive soils cause frequent surface deformation due to their expansion and contraction, which is a serious engineering hazard, and long-term subsidence monitoring is a prerequisite for preventing and controlling expansive soil disasters. Currently, the conventional monitoring methods for the above issue include Interferometric Synthetic Aperture Radar (InSAR) technology, but InSAR is not suitable for uninterrupted monitoring of surface deformation and has low sensitivity. Meanwhile, it can’t obtain multiple surface environmental parameters around the station. The Global Navigation Satellite System (GNSS), a system that can directly acquire surface deformation, has been widely used in landslide disaster monitoring, and in recent years, this technology has also been applied to the field of expansive soil disaster monitoring. At the same time, GNSS can also provide a constant stream of L-band microwave signals to obtain ground environmental information such as precipitable rainfall and soil moisture around the station. In previous studies of expansive soil hazards, GNSS technology has been mainly used to provide surface deformation information without exploring its potential to invert ground environmental information around stations. This paper proposes a ground-based GNSS remote sensing integrated monitoring system that integrates expanding land surface parameters such as “precipitable rainfall, soil moisture, and three-dimensional deformation” and analyses the ability of ground-based GNSS to be used for integrated monitoring of expanding soil hazards by combining ten years of consecutive observational data from GNSS stations along the coastal area of Houston. The experimental results show that the GNSS is capable of providing highly accurate time-series characterization of deformation, and inelastic subsidence in recent years has resulted in a cumulative permanent elevation loss of 2 cm along the Houston coast. The correlation coefficient between soil moisture extracted by the fifth-generation European reanalysis data (ERA5) and soil moisture inverted by ground-based GNSS is 0.514. At the same time, the GNSS was also able to monitor the zenithal precipitable water vapor (PWV) and soil moisture changes around the GNSS station and further analyze the response relationship among the three parameters, which could comprehensively evaluate the stability of expansive soils, avoiding the unreliability of relying on a single piece of monitoring information to assess the stability of expansive soils. We hope to construct a more comprehensive ground-based GNSS remote sensing monitoring system to better monitor expansive soil hazards.

Spatial-temporal patterns of ecological-environmental attributes within different geological-topographical zones: a case from Hailun District, Heilongjiang Province, China

The climate change and extension of human activities are shedding more stresses on ecosystems. Ecological zoning could help manage the ecosystem and deal with environmental problems more effectively. Geology and topography could affect the ecology primarily and are vital perspectives on ecological zoning. It is worth preliminarily understanding the spatial-temporal patterns of ecological-environmental attributes within various geological-topographical ecological zones (GTEZs). The objective of this study was to delineate GTEZs and present a spatial-temporal analysis on soil and land surface parameters within GTEZs. Firstly, Landsat imageries, high resolution satellite imagery products, digital elevation model, regional geological map, black soil thickness, soil bulk density, meteorological data, and ground survey were collected and conducted. Secondly, GTEZs in Hailun District were delineated according to geological and topographical background. Thirdly, soil composition, and monthly land surface temperature (LST), enhanced vegetation index (EVI), net primary productivity (NPP) were derived from ground survey and Landsat imageries. Finally, spatial-temporal patterns of various ecological-environmental attributes within different GTEZs were preliminarily revealed and analyzed. Results show that sand alluvial plain zone and silt-clay undulating plain zone mainly possess thick soil with fine-medium granule and higher bulk density, and are mainly covered by crops and grass, vegetation flourish the most in August with the highest monthly EVI and NPP. While the sand-conglomerate hill zone, sandstone hill zone, and granite hill zone possess relatively thin soil with medium-coarse granule and lower bulk density, and are mainly covered by forest, vegetation flourish the most in June and July, and has the highest yearly total NPP. With thinner soil thickness and higher NPP, hill zones tend to have more vulnerability to disturbance and more contribution to carbon neutrality target.

Impact of canopy environmental variables on the diurnal dynamics of water and carbon dioxide exchange at leaf and canopy level

Abstract. Quantifying water vapor and carbon dioxide (CO2) exchange dynamics between the land and the atmosphere through observations and modeling is necessary in order to reproduce and project near-surface climate in coupled land–atmosphere models. The exchange of water and CO2 occurs at the leaf surface (leaf level) and in a net manner through exchanges at all the leaf surfaces composing the vegetation canopy and at the soil surface (canopy level). These exchanges depend on the meteorological forcings imposed by the overlying atmosphere (atmospheric boundary layer level). In this paper, we investigate the effect of four canopy environmental variables (photosynthetic active radiation (PAR), water vapor pressure deficit (VPD), air temperature (T), and atmospheric CO2 concentration (Ca)) on the local individual leaf exchange and canopy exchange of water and CO2 at hourly timescales. Additionally, we investigate the effect of atmospheric boundary layer (ABL) processes on the local exchange. To that end, we simultaneously investigated the exchanges of water and CO2 at leaf level and canopy level for an alfalfa field in northern Spain over 1 day in summer 2021. We used comprehensive observations ranging from stomatal conductance to ABL measurements collected during the Land Surface Interactions with the Atmosphere in the Iberian Semi-Arid Environment (LIAISE) experiment. To support the observational analysis, we used a coupled land–atmosphere model (CLASS model) that has representations at all levels considered. To relate how temporal changes of the four environmental variables modify the fluxes of water and CO2, we studied tendency equations of the leaf gas exchange. These mathematical expressions quantify the temporal evolution of the leaf gas exchange as a function of the temporal evolution of PAR, VPD, T, and Ca. To investigate the effects of ABL processes on the local exchange, we developed three modeling experiments that impose surface radiative perturbations by a cloud passage (which perturbed PAR, T, and VPD), entrainment of dry air from the free troposphere (which perturbed VPD), and advection of cold air (which perturbed T and VPD). The model results and observations matched the leaf gas exchange (r2 between 0.23 and 0.67) and canopy gas exchange (r2 between 0.90 and 0.95). The tendency equations of the modeled leaf gas exchange during the study day revealed that the temporal dynamics of PAR were the main contributor to the temporal dynamics of the leaf gas exchange, with atmospheric CO2 temporal dynamics being the least important contributor. From the three modeling experiments with ABL perturbations, the surface radiative changes induced by a cloud perturbed the CO2 exchange the most, whereas all of them perturbed the water exchange to a similar extent. Second-order effects on the dynamics of the leaf gas exchange were also identified using the tendency equations. For instance, the decrease in the net CO2 assimilation rate during the cloud passage caused by a decrease in surface radiation was further enhanced due to the decrease in air temperature also associated with the cloud. With this research we showcase that the proposed tendency equations can disentangle the effect of environmental variables on the leaf exchange of water and CO2 with the atmosphere, as represented in land–surface parameterization schemes. As such, this framework can become a useful tool with which to analyze these schemes in weather and climate models.

Comparative evaluation of meteorological inputs for improved storm surge modeling: A case study of tropical Cyclone Vayu

The selection of meteorological inputs in storm surge is crucial, with wind and pressure fields playing a significant role in energy transfer and the study area's bathymetry. While using observed track information for wind and pressure drop has been the standard approach for the past decade, recent studies have highlighted the need for atmospheric inputs from models like the Weather Research and Forecast model (WRF) for storm surge forecast. This study aims to compare the performance of a DELFT-3D FM storm surge model forced with inputs from IMD (India Meteorological Department) observed the best track and pressure drop (EXP-1) and wind and pressure fields from WRF (EXP-2) for Tropical Cyclone Vayu, which occurred in the southern Arabian Sea basin in June 2019. The study compares the simulated storm water levels and highlights the benefits of using time and space-varying wind and pressure input for improved surge representation. Results show that the WRF-DELFT setup outperforms the IMD-DELFT setup, particularly for tide gauge locations close to the storm eye. The simulated storm surge captures the intensified stage of Vayu and provides a more realistic representation than the model forced with IMD track data. However, biases and limitations, such as inadequate representation of land surface parameterization, are identified. The study suggests further exploring wave-induced effects on total water level and multiple cyclone scenarios to enhance wind speed and track displacement prediction accuracy and improved land-sea parameterization can help address these limitations.

Examining the relationship of major air pollutants with land surface parameters and its monthly variation in Indian cities using satellite data

The relationship of land surface parameters with the air pollutants is worth exploring. The present study investigates the relation of Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) with six air pollutants such as Aerosol Index (AI), Carbon monoxide (CO), formaldehyde (HCHO), Sulphur Dioxide (SO2), Nitrogen dioxide (NO2) and ozone (O3) from January to December month of year 2022 in four cities situated at varying climatic zones of India. Except for O3, which exhibits lower concentration in winter and greater in summer, the air pollutants concentration showed lower values during monsoon season and higher during summer and post-monsoon seasons. The relationship of LST, NDBI and NDVI with different air pollutants were found to vary throughout the year in the four cities. The magnitude of correlation coefficient (R) was found greater for AI and NO2 as compared to other pollutants depicting greater impact of land surface parameters on concentration of AI and NO2. The surface urban cool island effect in Bikaner showed strong negative relation of LST with NO2 with magnitude of R greater than 0.41. The surface urban heat island formation in Varanasi showed strong positive correlation with the air pollutants such as AI, CO, NO2 and O3 with magnitude of R value greater than 0.61, 0.31, 0.59 and 0.32 for AI, CO, NO2 and O3, respectively. Even though, the correlation of LST with air pollutants varied with seasons and cities, NDVI showed negative correlation with most of the air pollutants in the cities except in Bikaner where vegetation content is negligible. Thus, increasing the amount of vegetation in a city can improve the air quality by lowering the quantity of air pollutants there.

Recent Developments to the SimSphere Land Surface Modelling Tool for the Study of Land–Atmosphere Interactions

Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth’s system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model’s usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models’ inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years.

Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain

Abstract. Widespread afforestation has been proposed internationally to reduce atmospheric carbon dioxide; however, the specific hydrological consequences and benefits of such large-scale afforestation (e.g. natural flood management) are poorly understood. We use a high-resolution land surface model, the Joint UK Land Environment Simulator (JULES), with realistic potential afforestation scenarios to quantify possible hydrological change across Great Britain in both present and projected climate. We assess whether proposed afforestation produces significantly different regional responses across regions; whether hydrological fluxes, stores and events are significantly altered by afforestation relative to climate; and how future hydrological processes may be altered up to 2050. Additionally, this enables determination of the relative sensitivity of land surface process representation in JULES compared to climate changes. For these three aims we run simulations using (i) past climate with proposed land cover changes and known floods and drought events; (ii) past climate with independent changes in precipitation, temperature, and CO2; and (iii) a potential future climate (2020–2050). We find the proposed scale of afforestation is unlikely to significantly alter regional hydrology; however, it can noticeably decrease low flows whilst not reducing high flows. The afforestation levels minimally impact hydrological processes compared to changes in precipitation, temperature, and CO2. Warming average temperatures (+3 °C) decreases streamflow, while rising precipitation (130 %) and CO2 (600 ppm) increase streamflow. Changes in high flow are generated because of evaporative parameterizations, whereas low flows are controlled by runoff model parameterizations. In this study, land surface parameters within a land surface model do not substantially alter hydrological processes when compared to climate.

Global 1 km land surface parameters for kilometer-scale Earth system modeling

Abstract. Earth system models (ESMs) are progressively advancing towards the kilometer scale (“k-scale”). However, the surface parameters for land surface models (LSMs) within ESMs running at the k-scale are typically derived from coarse-resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1 km land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31 % to 54 % of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSM and ESM modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The 1 km land surface parameters are publicly available at https://doi.org/10.5281/zenodo.10815170 (Li et al., 2024).

The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application

Abstract. The interactions between the terrestrial biosphere, atmospheric chemistry, and climate involve complex feedbacks that have traditionally been modeled separately. We present a new framework that couples the Yale Interactive terrestrial Biosphere (YIBs) model, a dynamic plant-chemistry model, with the RegCM-Chem model. RegCM-Chem–YIBs integrates meteorological variables and atmospheric chemical composition from RegCM-Chem with land surface parameters from YIBs. The terrestrial carbon flux calculated by YIBs is fed back into RegCM-Chem interactively, thereby representing the interactions between fine particulate matter (PM2.5), ozone (O3), and carbon dioxide (CO2). For testing purposes, we carry out a 1-year simulation (2016) at a 30 km horizontal resolution over East Asia with RegCM-Chem–YIBs. The model accurately captures the spatio-temporal distribution of climate, chemical composition, and ecological parameters. In particular, the estimated O3 and PM2.5 are consistent with ground observations, with correlation coefficients (R) of 0.74 and 0.65, respectively. The simulated CO2 concentration is consistent with observations from six sites (R ranged from 0.89 to 0.97) and exhibits a similar spatial pattern when compared with carbon assimilation products. RegCM-Chem–YIBs produces reasonably good gross primary productivity (GPP) and net primary productivity (NPP), showing seasonal and spatial distributions consistent with satellite observations, and mean biases (MBs) of 0.13 and 0.05 kg C m−2 yr−1. This study illustrates that RegCM-Chem–YIBs is a valuable tool to investigate coupled interactions between the terrestrial carbon cycle, atmospheric chemistry, and climate change at a higher resolution on a regional scale.

Evapotranspiration fusion and attribution analysis in the upper and middle reaches of the Yellow River Basin

Study regionThe Upper and Middle Reaches of the Yellow River Basin Study focusTaking the upper and middle reaches of the Yellow River as the research area, 15 types of models developed by the fusion of four multi-source products of GLDAS-Noah V2.1, ERA5-Land, GLEAM v3.6a and GLEAM v3.6b were assessed. Based on the fusion results of actual evapotranspiration(ET), the key factors that drive the spatial variation in actual evapotranspiration was explored, the land surface parameters of Fu formula in different watersheds were estimated, the relationship between land surface parameters and ecological construction measures was established, and the contribution rate of precipitation, potential evapotranspiration, NDVI and check-dam control area to ET change was quantified. New hydrological insights for the regionThe multi-year average actual evapotranspiration from 2003 to 2018 in the watersheds controlled by Tangnaihai, Lanzhou, Toudaoguai, Longmen and Tongguan is 378, 368, 346, 368 and 410 mm/year, respectively. The optimal fusion of the actual evapotranspiration is employing the Extreme Learning Machine to fuse the GLDAS-Noah V2.1 and GLEAM v3.6b products. The long-term actual evapotranspiration estimated from the Fu formula incorporating the ecological construction measures have high agreement with the fusion results. The impact of soil moisture on the spatial variation of actual evapotranspiration is significant. The contribution rate of precipitation to ET change is the highest, followed by NDVI, potential evapotranspiration and check-dam control area.

Improving the Asian dust storm prediction using WRF-Chem through combinational optimization of physical parameterization schemes

This study aims to enhance the accuracy of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in forecasting Asian dust storms (ADSs) by using the micro-Genetic Algorithm (μGA). We developed an optimization system---the WRF-Chem-μGA system---to seek the optimal combination of the planetary boundary layer (PBL) and land surface parameterization schemes, which are crucial for numerical forecast of dust storms. The optimization was conducted concerning meteorological and air quality variables, i.e., aerosol optical depth, PBL height, 2 m temperature, 2 m relative humidity, and 10 m wind speed, simultaneously for three ADS cases over the optimization domain, including South Korea. Among a total of 32 available combinations of physical parameterization scheme options (8 from PBL and 4 from land surface schemes), the optimized set through the WRF-Chem-μGA system consists of the Asymmetrical Convective Model version 2 (ACM2) for the PBL scheme and the Noah land surface model with Multiple Parameterization options (Noah-MP) for the land surface scheme. The optimized set showed an improvement ratio of up to 22.5 % in terms of the normalized RMSE for all meteorological and air quality variables, compared to various non-optimized sets of physical parameterization schemes for two additional ADS cases. The optimal set proposed in this study can be used comprehensively in numerical forecasts of various meteorological and air quality problems in the East Asian region, using the WRF-Chem model.

A Machine Learning-Based High-Resolution Soil Moisture Mapping and Spatial–Temporal Analysis: The mlhrsm Package

Soil moisture is a key environmental variable. There is a lack of software to facilitate non-specialists in estimating and analyzing soil moisture at the field scale. This study presents a new open-sourced R package mlhrsm, which can be used to generate Machine Learning-based high-resolution (30 to 500 m, daily to monthly) soil moisture maps and uncertainty estimates at selected sites across the contiguous USA at 0–5 cm and 0–1 m. The model is based on the quantile random forest algorithm, integrating in situ soil sensors, satellite-derived land surface parameters (vegetation, terrain, and soil), and satellite-based models of surface and rootzone soil moisture. It also provides functions for spatial and temporal analysis of the produced soil moisture maps. A case study is provided to demonstrate the functionality to generate 30 m daily to weekly soil moisture maps across a 70-ha crop field, followed by a spatial–temporal analysis.

High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China

Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R2 of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions.

Assessing the Sensitivity of Snow Depth Simulations to Land Surface Parameterizations within Noah-MP in Northern Xinjiang, China

Snow cover plays a crucial role in the surface energy balance and hydrology and serves as a key indicator of climate change. In this study, we conducted an ensemble simulation comprising 48 members generated by randomly combining the parameterizations of five physical processes within the Noah-MP model. Utilizing the variance-based Sobol total sensitivity index, we quantified the sensitivity of regional-scale snow depth simulations to parameterization schemes. Additionally, we analyzed the spatial patterns of the parameterization sensitivities and assessed the uncertainty of the multi-parameterization scheme ensemble simulation. The results demonstrated that the differences in snow depth simulation results among the 48 scheme combinations were more pronounced in mountain regions, with melting mechanisms being the primary factor contributing to uncertainty in ensemble simulation. Contrasting mountain regions, the sensitivity index for the physical process of partitioning precipitation into rainfall and snowfall was notably higher in basin areas. Unexpectedly, the sensitivity index of the lower boundary condition of the physical process of soil temperature was negligible across the entire region. Surface layer drag coefficient and snow surface albedo parameterization schemes demonstrated meaningful sensitivity in localized areas, while the sensitivity index of the first snow layer or soil temperature time scheme exhibited a high level of sensitivity throughout the entire region. The uncertainty of snow depth ensemble simulation in mountainous areas is predominantly concentrated between 0.2 and 0.3 m, which is significantly higher than that in basin areas. This study aims to provide valuable insights into the judicious selection of parameterization schemes for modeling snow processes.

Evaluating the Topographic Factors for Land Suitability Mapping of Specialty Crops in Southern Ontario

Climate change research identifies risks to agriculture that will impact agricultural land suitability. To mitigate these impacts, agricultural growing regions will need to adapt, diversify, or shift in location. Various machine learning algorithms have successfully modelled agricultural land suitability globally, predominantly using climate and soil features. Topography controls many of the environmental processes that impact agriculture, including soils, hydrology, and nutrient availability. This research evaluated the relationship between specialty crops and topography using land-surface parameters extracted from a 30 m DEM, soil features, and specialty crop presence/absence data derived from eight years of previous land classifications in southern Ontario, Canada. Using random forest, a model was developed for each specialty crop where feature permutation importance, Matthew’s correlation coefficient, and the area under the precision-recall curve was calculated. Elevation relative to watershed minimum and maximum, direct radiation on Day 172, and spherical standard deviation of normals were identified as the mean most important topographic features across all models and beet crops were found to have the highest association with topographic features. These results identify locations of agricultural expansion opportunities if climate becomes more favourable. The importance of topography in addition to climate and soils when identifying suitable areas for specialty crops is also highlighted.

Scaling artificial heat islands to enhance precipitation in the United Arab Emirates

Abstract. Potential for regional climate engineering is gaining interest as a means of solving regional environmental problems like water scarcity and high temperatures. In the hyper-arid United Arab Emirates (UAE), water scarcity is reaching a crisis point due to high consumption and over-extraction and is being exacerbated by climate change. To counteract this problem, the UAE has conducted cloud-seeding operations and intensive desalination for many years but is now considering other means of increasing water resources. Very large “artificial black surfaces” (ABSs), made of black mesh, black-painted, or solar photovoltaic (PV) panels have been proposed as a means of enhancing convective precipitation via surface heating and amplification of vertical motion. Under the influence of the daily UAE sea breeze, this can lead to convection initiation under the right conditions. Currently it is not known how strong this rainfall enhancement would be or what scale of black surface would need to be employed. This study simulates the impacts at different ABS scales using the WRF-Noah-MP model chain and investigates impacts on precipitation quantities and underlying convective processes. Simulations of five square ABSs of 10, 20, 30, 40, and 50 km sizes were made on four 1 d cases, each for a period of 24 h. These were compared with a Control model run, with no land use change, to quantify impacts. The ABSs themselves were simulated by altering land cover static data and prescribing a unique set of land surface parameters like albedo and roughness length. On all 4 d, rainfall is enhanced by low-albedo surfaces of 20 km or larger, primarily through a reduction of convection inhibition and production of convergence lines and buoyant updrafts. The 10 km square ABS had very little impact. From 20 km upwards there is a strong scale dependency, with ABS size influencing the strength of convective processes and volume of rainfall. In terms of rainfall increases, 20 km produces a mean rainfall increase over the Control simulation of 571 616 m3 d−1, with the other sizes as follows: 30 km (∼ 1 million m3 d−1), 40 km (∼ 1.5 million m3 d−1), and 50 km (∼ 2.3 million m3 d−1). If we assume that such rainfall events happen only on 10 d in a year, this would equate to respective annual water supplies for > 31 000, > 50 000, > 79 000, and > 125 000 extra people yr−1 at UAE per capita consumption rates. Thus, artificial heat islands made from black panels or solar PV offer a means of enhancing rainfall in arid regions like the UAE and should be made a high priority for further research.