Updated Land Research Articles

Kajian Pengembangan Lahan Pertanian Tanaman Pangan Berbasis Komoditas Unggulan di Kabupaten Pinrang, Sulawesi Selatan

Agriculture sector performs prominent role in the economy of Pinrang Regency. As the national food-estate area of South Sulawesi Province, RGDP agriculture sector of the regency increased from 4.72 to 7.94 trillion IDR in 2013-2017. This study aims to analyze food-crop agriculture land development based on primary commodities in Pinrang Regency, South Sulawesi. This study was conducted in twelve sub-districts in the regency’s area from September 2018 to June 2019. Application of geospatial technology was expected to present new approach with more accurate and easily updated land development planning in the area. The research data consisted of primary field survey data and secondary data of spatial pattern map of RTRW 2012-2032 from Bappeda and food-crop harvested area and production from BPS Pinrang Regency, characteristics and existing land use map from BBPPSDLP, and forest and water conservation area map from KLHK. Primary commodities was detemined based on Location Quotient and Shift Share Analysis. Potential land arrangement was referred to FAO Land Suitability Evaluation. Land development plan was formulated using selection and screening procedure based on Boolean logic. Five primary commodities was determined with 5,871 Ha potential land suitable for their development out of 7,324 Ha available land that consisted of land uses planned for paddy (3,529 Ha), cassava (1,398 Ha), corn (657 Ha), soybean (187 Ha), and sweet potato (100 Ha). The land development plan for these commodities was categorized into the first (147 Ha) and second (5,724 Ha) priority.

Classification of Urban Area Using Multispectral Indices for Urban Planning

An accelerating trend of global urbanization accompanying population growth makes frequently updated land use and land cover (LULC) maps critical. LULC maps have been widely created through the classification of remotely sensed imagery. Maps of urban areas have been both dichotomous (urban or non-urban) and entailing of discrete urban types. This study incorporated multispectral built-up indices, designed to enhance satellite imagery, for introducing new urban classification schemes. The indices examined are the new built-up index (NBI), the built-up area extraction index (BAEI), and the normalized difference concrete condition index (NDCCI). Landsat Level-2 data covering the city of Miami, FL, USA was leveraged with geographic data from the Florida Geospatial Data Library and Florida Department of Environmental Protection to develop and validate new methods of supervised and unsupervised classification of urban area. NBI was used to extract discrete urban features through object-oriented image analysis. BAEI was found to possess properties for visualizing and tracking urban development as a low-high gradient. NDCCI was composited with NBI and BAEI as the basis for a robust urban intensity classification scheme superior to that of the United States Geological Survey National Land Cover Database 2016. BAEI, implemented as a shadow index, was incorporated in a novel infill geosimulation of high-rise construction. The findings suggest that the proposed classification schemes are advantageous to the process of creating more detailed cartography in response to the increasing global demand.

Allometric constraints and competition enable the simulation of size structure and carbon fluxes in a dynamic vegetation model of tropical forests (LM3PPA‐TV)

Tropical forests are a key determinant of the functioning of the Earth system, but remain a major source of uncertainty in carbon cycle models and climate change projections. In this study, we present an updated land model (LM3PPA-TV) to improve the representation of tropical forest structure and dynamics in Earth system models (ESMs). The development and parameterization of LM3PPA-TV drew on extensive datasets on tropical tree traits and long-term field censuses from Barro Colorado Island (BCI), Panama. The model defines a new plant functional type (PFT) based on the characteristics of shade-tolerant, tropical tree species, implements a new growth allocation scheme based on realistic tree allometries, incorporates hydraulic constraints on biomass accumulation, and features a new compartment for tree branches and branch fall dynamics. Simulation experiments reproduced observed diurnal and seasonal patterns in stand-level carbon and water fluxes, as well as mean canopy and understory tree growth rates, tree size distributions, and stand-level biomass on BCI. Simulations at multiple sites captured considerable variation in biomass and size structure across the tropical forest biome, including observed responses to precipitation and temperature. Model experiments suggested a major role of water limitation in controlling geographic variation forest biomass and structure. However, the failure to simulate tropical forests under extreme conditions and the systematic underestimation of forest biomass in Paleotropical locations highlighted the need to incorporate variation in hydraulic traits and multiple PFTs that capture the distinct floristic composition across tropical domains. The continued pressure on tropical forests from global change demands models which are able to simulate alternative successional pathways and their pace to recovery. LM3PPA-TV provides a tool to investigate geographic variation in tropical forests and a benchmark to continue improving the representation of tropical forests dynamics and their carbon storage potential in ESMs.

Addressing the need for improved land cover map products for policy support

The continued increase of anthropogenic pressure on the Earth’s ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products – and there are many available – are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users’ specific needs would fundamentally change the relationship between users and land cover products – requiring more government support to make these systems a reality.

RECALCULATION OF FOREST INCREMENT, MORTALITY AND HARVEST RATE IN LATVIA ACCORDING TO UPDATED LAND USE DATA

National forest inventory (NFI) is an efficient way to monitor forest resources, including increment, mortality, and harvest rate. It provides accurate statistical information about land use data, land use change and biomass stock change and enables to tie the specific increment, mortality and harvest data to defined land use category, dominant tree species, site types etc. Recently there have been introduced changes in land use calculation method using NFI data in Latvia. New calculation method takes into account present land use data and land use data from two previous cycles considerably reducing uncertainty of the estimates and takes into account possible land management practices which may alter the land use category in long-term, also usage of auxiliary data has been introduced to increase accuracy of determination of final land-use category. Changes in land use calculation method directly affects distribution of data on increment, morality and harvests between land use categories. The aim of the study is to determine increment, mortality and harvest rate in Latvia between the first and third NFI cycle (2004-2018) using updated land use data and to compare obtained result with data from the most recent greenhouse gas (GHG) inventory report.

Remote Sensing and Multi-Criteria Evaluation for Malaria Risk Mapping to Support Indoor Residual Spraying Prioritization in the Central Highlands of Madagascar

The National Malaria Control Program (NMCP) in Madagascar classifies Malagasy districts into two malaria situations: districts in the pre-elimination phase and districts in the control phase. Indoor residual spraying (IRS) is identified as the main intervention means to control malaria in the Central Highlands. However, it involves an important logistical mobilization and thus necessitates prioritization of interventions according to the magnitude of malaria risks. Our objectives were to map the malaria transmission risk and to develop a tool to support the Malagasy Ministry of Public Health (MoH) for selective IRS implementation. For the 2014–2016 period, different sources of remotely sensed data were used to update land cover information and substitute in situ climatic data. Spatial modeling was performed based on multi-criteria evaluation (MCE) to assess malaria risk. Models were mainly based on environment and climate. Three annual malaria risk maps were obtained for 2014, 2015, and 2016. Annual parasite incidence data were used to validate the results. In 2016, the validation of the model using a receiver operating characteristic (ROC) curve showed an accuracy of 0.736; 95% CI [0.669–0.803]. A free plugin for QGIS software was made available for NMCP decision makers to prioritize areas for IRS. An annual update of the model provides the basic information for decision making before each IRS campaign. In Madagascar and beyond, the availability of the free plugin for open-source software facilitates the transfer to the MoH and allows further application to other problems and contexts.

Land Trusts as Conservation Boundary Organizations in Rapidly Exurbanizing Landscapes: A Case Study from Southern Appalachia

Exurban development is occurring in many formerly rural areas nationwide, often outpacing the ability of institutions to update land use regulations. These pressures can negatively impact local ecosystems and natural resources, including reduced biodiversity and degraded water quality. Local nongovernmental organizations play an important role in promoting conservation in exurban landscapes, where there is relatively little regulatory and institutional infrastructure. Here, we draw on boundary organization theory to discuss how land trusts can function as boundary organizations, by using boundary objects and working as a bridge between community members, scientists, and governments to navigate complex conservation challenges. Mainspring Conservation Trust in southern Appalachia serves as a case study to explore methods for engaging and connecting diverse stakeholders. We show that land trusts can provide a flexible and necessary alternative to regulations for meeting conservation objectives by working at the boundary between science and local action.

Cadastral Boundary Extraction and Image Classification Using OBIA and Machine Learning for National Land Records Modernization Programme in India

The present work is based on the dynamic approach for the extraction of cadastral boundaries and image classification using machine learning algorithms. The efforts are focused on easing the map digitization process in the country. The Large Scale Mean Shift Segmentation algorithm was used for the delineation of cadastral boundaries from two different types of study regions taken up for study, based on their landforms-hills and plains. The quality of segmentation was measured by AssesSeg software. Models using classifiers-Random Forest and Support Vector Machines were trained and their efficiency was tested on multiple images. The behavior of models was observed based on the landforms. The error matrices were generated based on the reference data. We tested these models as demonstrator for updating old maps through image analysis and on the basis of their performance, considered the potential of using them to update land records data in the country. This research shows the possibility of adapting the supervised machine learning methods for the extraction and classification of geographical features using satellite imagery.

Entre la expansión urbana y la producción de alimentos

La falta de políticas de ordenamiento territorial adecuadas, las deficiencias en su aplicación y los múltiples intereses de los actores vinculados al periurbano productivo en el Partido de La Plata, entre otros factores, han delineado el actual uso insustentable del territorio. Recientemente, han surgido experiencias novedosas de extensión y de investigación-acción participativa, que incorporan lineamientos surgidos como demandas de distintos actores del territorio. En este trabajo buscamos aportar nuevos conocimientos sobre los usos y coberturas del suelo actualizados, sobre las relaciones espaciales entre ellos y con variables físicas, y formular consideraciones para colaborar en las estrategias de planificación colectiva en el territorio. El mapa de usos/coberturas se realizó a partir de imágenes Landsat 8 (2015) aplicando “support vector machine” (ENVI). Éste se integró con mapas de aptitud del suelo, modelo de drenaje y límites de delegaciones municipales. Se identificaron 11 usos/coberturas. Entre ellas, las coberturas semi-naturales se encontraron mucho menos representadas en la zona hortícola. Un 67,93% de los invernáculos se hallaron a menos de 1000 m de zonas urbanas y el 51,16% del área urbana se asentó sobre suelos aptos para la agricultura. La delegación con mayor concentración de invernáculos fue Abasto (23,5%). Por último, el 45,06% de los invernáculos se hallaron a menos de 200 m de arroyos y un 10,19% a menos de 50 m. Esta información es de gran utilidad para un futuro proceso de planificación territorial sustentable, y proporciona una base útil para nutrir el debate acerca de los modelos de agricultura periurbana en Argentina, y en particular en la región de La Plata.

MONITORING OF AGRICULTURAL LANDS CONDITION IN VLADIMIR REGION

The concept of state land monitoring development, adopted by Government decree No. 1292-r dated 07/30/2010, provides for the formation of information resources about lands, including a qualitative as-sessment of land fertility. In connection with it, a need arose to update land fertility data carried out by soil surveys institutes Rosgiprozem and Roszeproekt in the 60-80s. On the maps of administrative division of Vladimir region and natural-agricultural zoning, an administrative-agricultural zoning of soils of Vladimir region was carried out. Based on the synthesis of data from the Unified State Register of Soil Resources of Russia, administrative-agricultural zoning, the spatial distribution of t totality of soil differences is deter-mined and a system of soil-territorial allotments is formed. Based on the system of soil-territorial divisions of Vladimir region, geographical coordinates of soil samples selection were established, for which in 2018 monitoring of agricultural lands in Vladimir region was carried out. Our analysis of the dynamics of soil fertility showed that over the past years (from 1961 to 1995) there have been significant changes. The course on the chemicalization of agriculture in the 70-80s of the last century led to a significant increase in humus in the soil, macrocells and a decrease in acidity. Liberal agricultural reform in the 90s of the last century led to a significant decrease in sown areas. A significant part of the area of arable land passed into a deposit or overgrown with forest. It led to the conservation of previously intensively used sown areas, to the conservation and increase of organic matter and nutrients in the soil.

Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia

The Weather Research and Forecasting (WRF) model is one of the regional climate models for dynamically downscaling climate variables at finer spatial and temporal scales. The objective of this study was to evaluate the performance of WRF model for simulating temperature and rainfall over Lake Tana basin in Ethiopia. The WRF model was configured for six experimental setups using three land surface models (LSMs): Noah, RUC and TD; and two land use datasets: USGS and updated New Land Use (NLU). The performances of WRF configurations were assessed by comparing simulated and observed data from March to August 2015. The result showed that temperature and rainfall simulations were sensitive to LSM and land use data choice. The combination of NLU with RUC and TD produced very small cold bias (0.27 °C) and warm bias (0.20 °C) for 2m maximum temperature (Tmax) and 2m minimum temperature (Tmin), respectively. WRF model with RUC and NLU captured well the observed spatial and temporal variability of Tmax, while TD and NLU for Tmin. Moreover, rainfall simulation was better with NLU; especially NLU and Noah configuration produced the smallest mean bias (2.39 mm/day) and root mean square error (6.6 mm/day). All the WRF experiments overestimated light and heavy rainfall events. Overall, findings showed that the application of updated land use data substantially improved the WRF model performance in simulating temperature and rainfall. The study would provide valuable support for identifying suitable LSM and land use data that can accurately predict the climate variables in the Blue Nile basin.

Assessment of coupled regional climate model (RegCM4.6–CLM4.5) for Indian summer monsoon

A model experiment with a regional climate model (RegCM4.6) coupled with an updated land–surface scheme (Community Land Model; CLM4.5) is integrated for 34 years to study the Indian summer monsoon (ISM). An eastward shift (towards Bay of Bengal; BoB) in simulated monsoon features due to the secular shift of the moist convective systems is observed. Further diagnostics show lower temperature over India as compared to BoB; while the Findlater jet is significantly weakened over the Arabian Sea. The low-pressure drags the southwesterly towards BoB through India. The Bowen ratio and evaporation reflect less soil moisture availability over the land. The analysis of simulated omega, moist static energy and Hadley circulation illustrates that an atmospheric condition dominates over land which further suppresses the convection and hence the rainfall. However, favorable atmospheric condition supporting convection and moisture availability leads to heavy precipitation over BoB and hence causes the eastward shift in simulated ISM rainfall. The convective fraction contributes the major share to the total precipitation. An evaluation of appropriate land surface spin-up period in order to investigate the optimum stabilization period for CLM4.5, indicates a possible weak coupling between land and atmosphere components in model simulation. This led to the improper energy and moisture exchanges between land and atmosphere in the simulation. The present work also suggests that, the simulated mean rainfall is not predominantly influenced by the length of different spin-up periods (up to 25 years) in case of weak coupling of land and atmosphere.

Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches

Land development in sub-urban areas is more frequent than in highly urbanized cities, causing land prices to increase abruptly and making it harder for valuers to update land values in timely manner. Apart from this, the non-availability of sufficient reliable market values forces valuers to use alternatives and subjective judgement. Land value is critical not only for private individuals but also for government agencies in their day-to-day land dealings. Thus, mass appraisal is necessary. In other words, despite the importance of reliable land value in all aspects of land administration, valuation remains disorganized, with unregulated undertakings that lack concrete scientific, legal, and practical foundations. A holistic and objective way of weighing geospatial factors through expert consultation, legal reviews, and evidence (i.e., news) will provide more realistic results than a regression-based method that does not comprehend valuation factors (i.e., physical, social, economic, environmental, and legal aspects). The analytic hierarchy process (AHP) enables these factors to be included in the model, hence providing a realistic result. The innovative land valuation model (iLVM), developed in this study, is an inclusive approach wherein experts are involved in the selection and weighing of 15 factors through the AHP. The model was validated using root mean squared error (RMSE) and compared with multiple regression analysis (MRA) through a case study in Baybay City, Philippines. Based on the results, the iLVM (RMSE = 0.526) outperformed MRA (RMSE = 1.953).

Vegetation Change on an Alaska Estuary after the 1964 Great Alaska Earthquake

Chickaloon Flats, Kenai National Wildlife Refuge, is a 10,000 ha tidal mudflat complex on the Kenai Peninsula in southcentral Alaska. It is a protected coastal estuary stopover area along the Pacific Flyway, covering 7% of the total estuarine intertidal area from Cook Inlet to Prince William Sound. Because Chickaloon Flats is historically an important avian migratory stopover and has relative regional importance in terms of estuarine intertidal area, there was a need to evaluate the current vegetation and determine any changes since the last ground study in 1972. We collected land cover data at a sample of ground-truthed points on Chickaloon Flats during 2009–2010 to evaluate existing vegetation communities as compared to historic 1970–71 values. A maximum likelihood supervised classification was performed on 2005 Landsat TM imagery to create an updated land cover classification of Chickaloon Flats. We used two different analytical approaches to show that the most notable changes in land cover types from 1975–2005 were the increase in early successional communities and a decline in unvegetated mud. Additionally, inland land cover types, which represent the more stable marsh-like communities, decreased in area as a whole. The documented changes in vegetation composition and structure over the four decade study period are attributable to elevation changes from sediment accretion and post-1964 Earthquake subsidence. As a result, evidence points to reduced shorebird use of the Flats.

Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam

Rapid land use change has taken place in many neighboring provinces of the capital of Vietnam such as Thai Nguyen province over the past 2 decades due to urbanization and industrialization. Deriving accurate and updated land cover and land-use change information is essential for the environmental monitoring, evaluation and management. In this study, a robust classification algorithm, Random Forest (RF) was employed in R programming to map and monitor temporal and spatial characteristics of urban expansion and land-use change in Thai Nguyen province, Vietnam. The results showed that there has been a substantial and uneven urban growth and a significant loss of forest and cropland between 2000 and 2016. Most of the conversion of agriculture and forest into built-up and mining uses were largely detected in rural regions and suburbs of Thai Nguyen. Further GIS analysis revealed that rapid urban and industrial expansion was spatially occurred in the southern rural portions and central area of the province. This study also demonstrates the potential of Landsat data and combination of R programming language and GIS to provide a timely, accurate and economical means to map and analyze temporal land cover and land use changes for future national and local land development planning.

An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support

Land-use/land-cover information is the basis of global-change research and regional governmental management. Automatic approaches are always required to update land maps for large-scale areas, and change detection techniques are the most important component of land-updating methods. Previous research has confirmed that simple change detection based on Landsat images from two different years with two different phenophases yields unsatisfactory results and may induce many misclassifications and pseudo-change identifications because of the phenological differences between remote sensing images. With the support of the Google Earth Engine (GEE), we propose a land-use/land-cover type discrimination method based on a classification and regression tree (CART), apply change-vector analysis in posterior probability space (CVAPS) and the best histogram maximum entropy method for change detection, and further improve the accuracy of the land-updating results in combination with NDVI timing analysis, which indicates the annual growth of ground vegetation. In the case study, we select western China as the research area and obtain a 2014 land map based on the ESA GlobCover 2009 dataset. The results confirm that the accuracy of the land-renewal results based on the CART-CVAPS-NDVI method reach 78.6–88.2%, which is 4–10% higher than that of the CART-CVPAS method without NDVI timing analysis. The CART-CVAPS-NDVI method has more detailed and accurate resolutions for land-change detection.

GIS-Enabled Culvert Design: A Case Study in Tuscaloosa, Alabama

A GIS-enabled culvert design module is presented. This module employs Python programming to combine a proposed culvert location, topography, land use, and rainfall data to automatically design a culvert. The module is embedded within ESRI ArcGIS 10.4 software, providing a seamless single platform that eliminates error propagation associated with cross-platform data transfer as well as providing 95% time savings over traditional calculation methods. The module uses United States Geological Survey digital elevation data to analyze watershed topography. Runoff coefficients are determined from data available through the National Land Cover Database. Rainfall data are retrieved from the National Oceanic and Atmospheric Administration and combined with watershed and land use information to calculate peak discharge using the rational method. Peak discharge is then combined with culvert design parameters to design a single-barrel culvert. The module was used to redesign ten existing culverts along a highway in Tuscaloosa, Alabama, resulting in designs for updated land cover and rainfall conditions. Results from the techniques developed herein can be used for planning purposes and to highlight vulnerabilities in the existing infrastructure. The automation methods may be extended to other hydrologic objectives and runoff mitigation design such as open-channel design and detention or retention ponds.

On the uncertainties introduced by land cover data in high-resolution regional simulations

We investigate the impact of implementing an up-to-date and detailed land cover dataset in high-resolution regional climate simulations. We used the Weather Research Forecast (WRF) model version 3.6.1 on a high horizontal resolution of 5 km × 5 km, with 29 vertical levels, covering mainland Europe. We performed simulations within the year 2050, using future Representative Concentration Pathway 8.5 mid-century projections, for 2 winter (January, February) and the 2 summer months (June, July) to investigate the seasonal dependency of the impact of the land cover datasets on and their interaction with the different meteorological conditions prevailing in summer and winter. We compare simulations using the CORINE Land Cover dataset (100 × 100 m) and the standard United States Geological Survey (USGS) (~ 1 × 1 km) land use data for the same periods. Our analysis shows that simulated meteorological variables (temperature at 2 m, wind speed, sensible and latent heat fluxes and PBL heights) differ significantly between the WRF simulations, linked to the land cover parameterization. We quantify and discuss the modelling uncertainties arising from surface-type classifications and motivate the use of high resolution, and continuously updated land use inventories in climate modelling, especially for future projections. Our findings are particularly important for the summer season and over large urban centers, and we strongly recommend the use of high-quality resolution land use data in modelling experiments studying heat waves in synergy with the urban heat island phenomenon and land–surface interactions.

Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection

The water balance in a watershed can be disrupted by forest disturbances such as harvests and fires. Techniques to accurately and efficiently map forest cover changes due to disturbance are evolving quickly, and it is of interest to ask how useful maps of different types of disturbances over time can be in the prediction of water yield. We assessed the benefits of using land cover maps produced at annual vs. five-year intervals in the prediction of monthly streamflows across 10 watersheds contained entirely within the US National Forest System. We found that annually updating land cover maps with forest disturbance data significantly improved water yield predictions using the Soil and Water Assessment Tool (SWAT; p < 0.01 improvement for both the Nash–Sutcliffe efficiency measure and the ratio of the root mean square error to the standard deviation of the measured data). Improvement related to using annually updated land cover maps was directly related to the amount of disturbance observed in a watershed. Our results lay a foundation to apply new high-resolution disturbance datasets in the field of hydrologic modeling to monitor ungauged watersheds and to explore potential water yield changes in watersheds if climate conditions or management practices were to change forest disturbance processes.

Evaluation of Agricultural Land Cover Representations on Regional Climate Model Simulations in the Brazilian Cerrado

AbstractExamining interactions between large‐scale land cover and land use change and regional climate in areas undergoing dynamic land transformations, like the Brazilian Cerrado, is crucial for understanding tradeoffs between human needs and ecosystem services. Yet regional climate models often do not include accurate land cover data of these complex landscapes. We use National Center for Atmospheric Research's Weather Research and Forecasting (WRF) model coupled to the Noah‐Multiparameterization (Noah‐MP) land surface model to run 10‐year climate simulations across Brazil to assess (1) whether an accurate, regionally validated land cover data set with two, new agricultural land cover classifications improves model simulation results; (2) the ability of Noah‐MP's dynamic vegetation option to model vegetation growth; and (3) the sensitivity of the model output to scale. The results of the simulations with the updated land surface perform better over intensive agricultural areas for precipitation, evapotranspiration, and temperature, especially during the wet‐to‐dry season transition months. Evapotranspiration is overestimated during the start of the rainy season across all model simulations, which is likely due to the soil moisture model. We also find that using the Noah‐MP dynamic vegetation significantly degrades agricultural leaf area index phenology simulations in Brazilian agricultural regions. Lastly, improving the model's resolution did not improve model output when compared to observational data. Incorporating more accurate representations of the landscape into regional climate models is essential for quantifying potential changes in climatological seasonality in dynamic, human‐modified regions and making informed land use decisions.