Climate-induced Changes Research Articles

BioVars - A bioclimatic dataset for Europe based on a large regional climate ensemble for periods in 1971–2098

Ongoing ecological research is concerned with analysing climate-induced changes in species distribution. For this purpose, the projection must have high-quality bioclimatic variables from historical and future climatic periods for the projection. To date, there are many global bioclimatic variables on this topic. Nevertheless, a consistent dataset with identical model variables from historic and projected periods is rare. We present 26 bioclimatic variables that are calculated based on a large ensemble consisting of 70 bias-adjusted GCM-RCM simulations for 1971–2098. Both, the historic and the projection periods were calculated using the same models to ensure consistency between the periods. The variables are validated against E-OBS observations from which we calculated the same bioclimatic variables. For projection periods we chose 20 year ranges between 2021–2098. Here, we offer two versions of them: (1) variables separated into RCP 2.6, 4.5 and 8.5, including percentiles among the realisations and within the RCPs; and (2) variables per realisation separately. We then extracted the temporal 5th, 50th and 95th percentile per period as representing values.

Cryo-social dynamics: the interplay of glacial dynamics and socioeconomic conditions in the Shigar Valley, Karakoram, Pakistan

Mountain communities globally are experiencing increasing challenges as climate-induced glacier changes disrupt water resources and agricultural systems. The Hindukush-Karakoram-Himalayan region of Pakistan has been witnessing environmental changes over the last few decades with widespread impacts on different sectors of life. In the Shigar Valley of the Karakoram region, communities face socio-economic challenges partly attributed to changes in glacier dynamics, which directly affect water availability and agricultural productivity. This study investigates the impacts of glacial changes on community livelihoods by integrating glacier modeling with a comprehensive socioeconomic survey. Glaciological change was analyzed from 1970 to 2020 using the Open Global Glacier Model, with projections under SSP-126, SSP-370, and SSP-585 scenarios for 2020–2100. Glacier mass balance simulations driven by historical climatological data from 1970 to 2020 reveal an overall negative trend, despite shorter periods of glacier growth driven by regional hydroclimatic anomalies. Future projections indicate glacier volume could decline by over 60% under SSP-585 by 2100, compared to approximately 30% under SSP-126. A questionnaire survey was conducted across 20 villages, engaging 375 respondents to understand the extent of dependence on glacier-fed resources, migration dynamics, and adaptive strategies to socioenvironmental conditions. The findings reveal that while 42% of respondents reported high dependence on glacier-fed and snow melt, 46% are facing challenges due to irregular water supplies. These challenges have been affecting the socio-economic fabric of the community as 71% of respondents reported that migrations were driven by declining local productivity, with 66% of migration destinations being international. The communities are aware of the challenges, and they have been adopting various strategies to cope with it. These include rainfed farming (32%), adoption of drought-resistant crops (19%), and expansion into proglacial (recently deglaciated areas and areas closer to glaciers’ termini) lands (52%).

Climate change and staple grain acreage: Regional adaptation in China's agricultural cluster

The regionally adaptative use of cropland for staple grain production forms a critical foundation for agricultural clustering, however, the effects of climate change on grain acreage across regions have received insufficient attention. This study uses recent county-level data from 2000 to 2019 in China and employs an empirical framework that incorporates 10-year averages of weather variables to analyze regional variations in grain acreage in response to changes in temperature and precipitation. Our findings reveal significant regional heterogeneity: increases in temperature and precipitation generally lead to an expansion of staple grain acreage in initially colder or drier regions. These findings remain robust after accounting for a range of confounding factors. Mechanism analysis indicates that these climate-induced changes in grain acreage stem from variations in land productivity. Furthermore, the study identifies shifts in comparative advantages among staple grains across regions: rising temperatures favor paddy over corn in colder regions and corn over wheat in warmer areas, while increased precipitation enhances corn's advantage over wheat in arid regions. These findings have important implications for designing effective adaptation policies to sustain grain productivity in China's agricultural clusters and ensure food security targets.

High-resolution groundwater storage anomalies in the Middle and Lower Yangtze River Basin of China using machine learning fusion of in-situ wells, satellite gravity and hydrological model.

Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of groundwater storage is often challenging due to the limitations of observation techniques and mathematical methods. In this study, two machine learning (ML) algorithms, namely random forest (RF) and artificial neural networks (ANNs), were employed to estimate groundwater level anomaly (GWLA) and groundwater storage anomaly (GWSA) with a 0.25° resolution using multi-source datasets, including in-situ wells, the Gravity Recovery and Climate Experiment (GRACE), land surface models and hydrogeological parameters. The results indicated the ANN algorithm outperformed the RF algorithm in predicting the spatiotemporal variations of the shallow and deep GWLA in the Middle and Lower Yangtze River Basin (MLYRB). Hence, the ANN algorithm was used to construct a model for predicting the GWSA over the 2005-2017 period. The GWSA exhibited an increasing linear rate of about 0.77±0.30mm/yr in almost the entire area, except in the Han River Basin (HRB), where GWSA decreased by -1.18±0.38mm/yr due to decreased precipitation amounts. The occurrence of seasonal variations in the deep GWSA showed lead time effects compared with those in the shallow GWSA, ranging from 0 to 1 month and 1 to 2 months in the humid and relatively dry areas, respectively. It was found that the ANN-based model results showed pronounced responses of the GWSA to the drought events. The standard groundwater drought index (SGDI) was further calculated to assess the spatiotemporal characteristics of the groundwater drought events. The results revealed the occurrence of a severe drought event in 2011, as well as pronounced impacts of the El Niño-Southern Oscillation (ENSO) events on the GWSA. This study demonstrated the effectiveness of in-situ groundwater measurements in enhancing the reliability of ML-based GWSA prediction. Furthermore, the constructed high-resolution groundwater variation features in this study can provide water resource managers with enhanced information and valuable insights into climate-induced groundwater changes.

Multi-decade northward shift of loggerhead sea turtle pelagic habitat as the eastern North Pacific Transition Zone becomes more oligotrophic

The North Pacific Transition Zone (NPTZ) is known as a global marine hotspot for many endangered and commercially significant highly mobile marine species. In the last few decades, the region has undergone unprecedented physical and biological transformations in response to climate variability and change. Although it is anticipated that many highly mobile species will need to adapt and shift their distributions, current predictions have relied on short-term data sets or modeled simulations. This has left a critical gap in our understanding of long-term (decadal or longer) change and species’ responses within the NPTZ. Here, we integrate nearly 3 decades of satellite tracking data from a climate sentinel, the juvenile North Pacific loggerhead sea turtle (Caretta caretta), with concurrent observations of sea surface temperature (SST) and chlorophyll-a concentrations to examine higher trophic level response to climate-induced changes within the eastern bounds of the NPTZ. Between 1997–2024, the NPTZ has warmed by 1.6°C and experienced an approximately 19% decline in mean surface chlorophyll-a concentration, a proxy for reduced productivity, resulting in a 28% (1.65 million km2) increase in total oligotrophic habitat in the eastern NPTZ. Over the same period, the average latitude of loggerhead sea turtle foraging habitat in the NPTZ has shifted northwards by 450–600 km. This represents a distributional shift rate of 116–200km/decade. In most years both the southern and northern range limits for the loggerhead turtle have shifted northward in tandem, indicating a habitat range shift rather than a range expansion. Our findings reveal significant physical and biological change to the NPTZ over the last quarter century and the first empirical evidence illustrating the substantial spatial response of a highly mobile megafaunal species. As the NPTZ continues to become more oligotrophic, these insights can provide vital information for dynamic conservation and management strategies within this critically important ecosystem.

Changes in climate drove vegetation and land use dynamics at the onset of farming in Europe

Abstract Here we present the first high-resolution continuous palaeoecological study from Greece covering the Mesolithic–Neolithic transition at Limni Zazari, a small lake in western Macedonia. We study how interactions between vegetation and climate might have affected the introduction of agriculture to Europe ca. 8500 years ago. We found that mixed deciduous oak woodlands established around the lake once moisture availability began to increase at ~10,300 cal yr BP. Between 8600 and 8000 cal yr BP, climate change, causing drier conditions, led to the decline of the woodlands and the expansion of steppe and grassland vegetation. Concurrently, in agreement with the archaeological record, pollen indicative of arable and pastoral farming indicate the onset of Neolithic farming. After 8000 cal yr BP the forest composition changed, with a major expansion of pine forests and increases in disturbance-adapted trees like Ostrya and Fagus. This change might be linked to changes in moisture availability, but it is likely that land use also facilitated these shifts. We conclude that the introduction of Neolithic farming was advantaged by climate-induced vegetation changes. While the vegetation structure around Zazari was very sensitive to changes in moisture, early anthropogenic disturbances led to changes in the vegetation composition that are still important today.

Climate-Induced Changes in River Hydrological and Thermal Conditions in a Large Basin: Implications for Diadromous Fish Migration

Climate-induced changes in river conditions, such as water temperature and current velocity, can disrupt species migration patterns by altering the conditions encountered during migration. We assessed these impacts across the Loire River basin in Europe by compiling long-term fish passage data for three diadromous fish species at fishways, alongside reconstructed time series of water temperature and current velocity. We considered both water temperature and current velocity on days when migration was observed as ''suitable conditions'' for the species' upstream migration. Our findings for the 1963–2019 period revealed that trends in the frequency of suitable conditions for upstream migration were species-specific. For Atlantic salmon, particularly fall migrants, the frequency of suitable conditions has declined over time in certain areas. In contrast, the frequency of suitable migration conditions for both allis shad and sea lamprey has shown an overall increase across the basin. This study offers policy-makers and environmental managers valuable insights for developing effective, spatially explicit strategies to preserve diadromous fish populations amid ongoing climate change.

Legacy effects of precipitation change: Theories, dynamics, and applications.

The intensification of climate-induced precipitation change poses a dual challenge to terrestrial ecosystems: immediate effects on their structure and function, coupled with legacy effects that persist beyond the cessation of precipitation change. Quantifying these legacy effects accurately can greatly assist in assessing the long-term impact of precipitation change. However, their broader understanding is just beginning. Therefore, this review endeavors to synthesize the existing knowledge concerning the legacy effects of precipitation change, elucidating their nature, characteristics, driving factors, and implications, thereby fostering further advancements in this research domain. To begin, we define that precipitation legacies are carried by the information and/or material remnants arising from previous precipitation change, with the enduring impacts of these remnants (precipitation legacy carriers) on the current ecosystem being termed the precipitation legacy effects. To comprehensively investigate the performances of precipitation legacy effects, we introduce a multi-faceted characterization framework, encompassing magnitude, direction, duration, and spatial-temporal variability. This framework is complemented by a proposed sequential analysis approach, spanning the pre-, during, and post-precipitation change phases. Next, we emphasize that the nature of precipitation legacy carriers and the pattern of precipitation change jointly determine the characteristics of precipitation legacy effect. Subsequently, we elucidate the possible carriers of precipitation legacies across species, community, and ecosystem levels, as well as the linkages among these carriers and levels, thereby introducing the underlying formation mechanism of precipitation legacy effects. Lastly, from the perspective of ecosystem stability debt, we propose potential applications of precipitation legacy effects in future climate change research. The viewpoints and methodologies outlined in this review can deepen our comprehension of precipitation legacy effects, contributing to the comprehensive assessment of precipitation impact on soil-vegetation systems and providing guidance for formulating effective strategies to address future climate change.

The impact of climate change on Korea's agricultural sector under the national self-sufficiency policy.

Evolving environmental conditions due to climate change have brought about changes in agriculture, which is required for human life as both a source of food and income. International trade can act as a buffer against potential negative impacts of climate change on crop yields, but recent years have seen breakdowns in global trade, including export bans to improve domestic food security. For countries that rely heavily on imported food, governments may institute policies to protect their agricultural industry from changes in climate-induced crop yield changes and other countries' potential trade restrictions. This study assesses the individual and combined effects of climate impacts and food self-sufficiency policies in Korea, which is highly dependent on imports. We use the Global Change Analysis Model (GCAM), a global integrated assessment model, to explore (1) the direct impact of climate change on Korea's agricultural yields, (2) the full impacts of global climate change on agricultural production, including trade-induced changes due to yield changes in other regions, (3) the impacts of food self-sufficiency policy, and (4) the interactive impact of climate change and self-sufficiency policies. We find that, in Korea, the direct impact of climate change on agricultural yields would be overshadowed by the impact of global climate change due to changing trade patterns. Second, global climate change leads to a rise (rice and wheat) or a decline (soybeans) in Korean producer revenues, while simultaneously raising consumer expenditures on both staples and non-staples. Third, implementing self-sufficiency policies for wheat and soybeans in Korea boosts the nation's producer revenues, in conjunction with the effects of climate change, at the cost of additional increases in consumer expenditures for both staples and non-staples.

Plains Zebras Prioritize Foraging Without Sacrificing Social Bonds During a Severe Drought.

Anthropogenically induced climate change has significantly increased the frequency of acute weather events, such as drought. As human activities amplify environmental stresses, animals may be forced to prioritize survival over behaviors less crucial to immediate fitness, such as socializing. Yet, social bonds may also enable individuals to weather the deleterious effects of environmental conditions. We investigated how the highly social plains zebra (Equus quagga) modify their activity budgets, social networks, and multimodal communication during a drought. Although animals prioritized feeding and the number of social interactions dramatically decreased in the late drought period, social associations remained robust. We observed age/sex class-specific changes in social behavior, reflecting the nutritional needs and social niche of each individual. Stallions devoted more time to greeting behaviors, which could mitigate harassment by bachelor males and facilitate grazing time for the females of the harem. Juveniles significantly increased time spent active socializing, despite mothers showing the greatest decrease in the number of social interactions. Instead, unrelated, nonlactating females served as social partners, accommodating both juveniles' social needs and lactating mothers' nutritive requirements. Using a network-based representation of multimodal communication, we observed a decrease in the number of signals used during the drought. Individuals used less diverse multimodal combinations, particularly in the costly context of aggression. These findings illustrate how social roles and differential responses to acute environmental stress within stable social groups may contribute to species resilience, and how communication flexibly responds to facilitate both survival and sociality under harsh environmental conditions.

Two-Stage Planning for Smart Buildings With Flexible Heating Load Considering Climate Change Induced Heat Waves

Two-Stage Planning for Smart Buildings With Flexible Heating Load Considering Climate Change Induced Heat Waves

Late Holocene cooling drove drastic decreases in cladoceran diversity in a subarctic lake

Subarctic lakes are sentinels of climate change, showing responses in their physical, chemical, and biological properties. However, climate-induced changes in invertebrate diversity and their underlying mechanisms are not fully understood. We explored the relationship between past climate change and taxonomic composition of subfossil cladocerans in a subarctic lake during the last ca. 5700 years. The Cladocera community shifted from specialist to generalist species at ca. 3500 cal years BP, corresponding to the long-term cooling period between the Holocene Thermal Maximum and the Late Holocene. Taxonomic diversity declined driven by the collapse of the keystone herbivorous Daphnia longispina group, pelagic and littoral predators, and phytophilous benthic species, therefore resulting in a simplification of the food web and a reduction of trophic levels. Furthermore, the shift in cladoceran composition was associated with the decline of aquatic primary producers and the development of birch forest, suggesting a potential causal link between dissolved organic carbon dynamics and cladoceran community composition. This study provides empirical evidence of the response of cladocerans to climatic fluctuations and their underlying mechanisms through catchment-mediated processes and direct temperature-induced changes.

Edge Computing Architecture for the Management of Underwater Cultural Heritage

Underwater cultural heritage (UCH) is a valuable resource that preserves humanity’s historical legacy, offering insights into traditions and civilisations. Despite its significance, UCH faces threats from inadequate regulatory frameworks, insufficient conservation technologies, and climate-induced environmental changes. This paper proposes an innovative platform combining the internet of underwater things and edge computing technologies to enhance UCH’s real-time monitoring, localisation, and management. The platform processes data through a central unit installed on a buoy near heritage sites, enabling efficient data analysis and decision making without relying on cloud connectivity. Integrating acoustic communication systems, LoRa technology, and nonterrestrial networks supports a robust multilayered communication infrastructure for continuous operation, even in remote maritime areas. The platform’s edge node deploys artificial intelligence models for real-time risk assessment, focusing on key environmental parameters to predict and mitigate corrosion rates and climate-related threats. A case study illustrates the system’s capabilities in underwater localisation, demonstrating how edge computing and acoustic triangulation techniques enable precise tracking.

Hydroclimate risk to electricity balancing throughout the U.S

Abstract Although hydropower produces a relatively small portion of the electricity we use in the United States, it is a flexible and dispatchable resource that serves various critical functions for managing the electricity grid. Climate-induced changes to water availability will affect future hydropower production, and such changes could impact how the areas where the supply and demand of electricity are balanced, called balancing authority areas, are able to meet decarbonization goals. We calculate hydroclimate risk to hydropower at the balancing authority scale, which is previously underexplored in the literature and has real implications for decarbonization and resilience-building. Our results show that, by 2050, most balancing authority areas could experience significant changes in water availability in areas where they have hydropower. Balancing areas facing the greatest changes are located in diverse geographic areas, not just the Western and Northwestern United States, and vary in hydropower generation capacity. The range of projected changes experienced within each balancing area could exacerbate or offset existing hydropower generation deficits. As power producers and managers undertake increasing regional cooperation to account for introducing more variable renewable energy into the grid, analysis of risk at this regional scale will become increasingly salient.

Unraveling climate change-induced compound low-solar-low-wind extremes in China.

China's pursuit of carbon neutrality targets hinges on a profound shift towards low-carbon energy, primarily reliant on intermittent and variable, yet crucial, solar and wind power sources. In particular, low-solar-low-wind (LSLW) compound extremes present a critical yet largely ignored threat to the reliability of renewable electricity generation. While existing studies have largely evaluated the impacts of average climate-induced changes in renewable energy resources, comprehensive analyses of the compound extremes and, particularly, the underpinning dynamic mechanisms remain scarce. Here we show the dynamic evolution of compound LSLW extremes and their underlying mechanisms across China via coupling multi-model simulations with diagnostic analysis. Our results unveil a strong topographic dependence in the frequency of compound LSLW extremes, with a national average frequency of 16.4 (10th-90th percentile interval ranges from 5.3 to 32.6) days/yr, when renewable energy resources in eastern China are particularly compromised (∼80% lower than that under an average climate). We reveal a striking increase in the frequency of LSLW extremes, ranging from 12.4% under SSP126 to 60.2% under SSP370, primarily driven by both renewable energy resource declines and increasingly heavily-tailed distributions, resulting from weakened meridional temperature (pressure) gradient, increased frequency of extremely dense cloud cover and additional distinctive influence of increased aerosols under SSP370. Our study underscores the urgency of preparing for significantly heightened occurrences of LSLW events in a warmer future, emphasizing that such climate-induced compound LSLW extreme changes are not simply by chance, but rather projectable, thereby underscoring the need for proactive adaptation strategies. Such insights are crucial for countries navigating a similar transition towards renewable energy.

Machine learning prediction of tipping in complex dynamical systems

Anticipating a tipping point, a transition from one stable steady state to another, is a problem of broad relevance due to the ubiquity of the phenomenon in diverse fields. The steady-state nature of the dynamics about a tipping point makes its prediction significantly more challenging than predicting other types of critical transitions from oscillatory or chaotic dynamics. Exploiting the benefits of noise, we develop a general data-driven and machine-learning approach to predicting potential future tipping in nonautonomous dynamical systems and validate the framework using examples from different fields. As an application, we address the problem of predicting the potential collapse of the Atlantic meridional overturning circulation, possibly driven by climate-induced changes in the freshwater input to the North Atlantic. Our predictions based on synthetic and currently available empirical data place a potential collapse window spanning from 2040 to 2065, in consistency with the results in the current literature. Published by the American Physical Society 2024

Modeling time series of vegetation indices in tallgrass prairie using machine and deep learning algorithms

The vegetation phenology of tallgrass prairie varies yearly, depending on climatic conditions, plant species composition, and location. Modeling time series of vegetation indices (VIs) using climate data can be useful for understanding and predicting how tallgrass prairie will respond to future climate scenarios and for identifying and managing areas of tallgrass prairie that are particularly susceptible to climate-induced changes. Machine or deep learning algorithms can be well-suited to model VIs for phenology studies by identifying patterns and relationships between climatic factors and VIs using historical data. This study evaluated the performance of 12 machine and deep learning algorithms, encompassing a diverse range of algorithmic families, in modeling patterns of the Moderate Resolution Imaging Spectroradiometer-derived enhanced vegetation index (EVI, greenness index) and land surface water index (LSWI) in native tallgrass prairie. The models include linear regression, Bayesian ridge, elastic net, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), support vector regression (SVR), K-nearest neighbors (KNN), artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM). Air and soil temperatures showed the highest correlations with EVI (r ≥ 0.77) and LSWI (r ≥ 0.56). The low correlation (r ≤ 0.23) of EVI and LSWI with contemporaneous rainfall or soil moisture suggests vegetation's delayed response to these factors. The results indicated that ensemble methods like XGBoost and random forest performed best across all three datasets (i.e., training, testing, and validation) for modeling EVI and LSWI. Deep learning models showed varying performance across datasets, and their performance was sub-optimal compared to XGBoost and random forest. The linear regression also showed a moderate performance, while the decision tree performed the weakest overall. The strong performance of XGBoost and random forest highlights the intricate and nonlinear relationship of prairie vegetation with climatic factors. These models' strength lies in capturing such complexities. This study provides insights into the key climatic factors and underlying processes that control the vegetation dynamics of tallgrass prairie ecosystems. Our machine learning models can be a valuable tool for developing new strategies to manage tallgrass prairie ecosystems in the face of climate change.

Modeling the Responses of Blue Carbon Fluxes in Mississippi River Deltaic Plain Brackish Marshes to Climate Change Induced Hydrologic Conditions

Modeling the Responses of Blue Carbon Fluxes in Mississippi River Deltaic Plain Brackish Marshes to Climate Change Induced Hydrologic Conditions

Mapping the Future: Climate-Induced Changes in Aboveground Live-Biomass Carbon Density Across Mexico’s Coniferous Forests

Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to assess how climate change will affect the carbon density of aboveground biomass (cdAGB) in Mexico’s coniferous forests for 2050 and 2070. We used cdAGB data from the National Forest and Soils Inventory (INFyS) of Mexico and 19 bioclimatic variables from WorldClim ver. 2.0. The best predictors of cdAGB were obtained using machine learning techniques with the “caret” library in R. The model was trained with 80% of the data and validated with the remaining 20% using Generalized Linear Models (GLMs). Current cdAGB prediction maps were generated using the best predictors. Future cdAGB was calculated with the average of three general circulation models (GCMs) of future climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5), under four Representative Concentration Pathways (RCPs): 2.6, 4.5, 6.0, and 8.5 W/m2. The results indicate cdAGB losses in all climate scenarios, reaching up to 15 Mg C ha−1, and could occur under the RCP 8.5 scenario by 2070 in the central region of the country. Temperature-related variables are more important than precipitation variables. Bioclimatic variables can explain up to 20% of the total variance in cdAGB. The temperature in the study area is expected to increase by 2.66 °C by 2050 and 3.36 °C by 2070, while precipitation is expected to fluctuate by ±10% relative to the current values, which could geographically redistribute the cdAGB of the country’s coniferous forests. These findings underscore the need for forest management to focus not only on biodiversity conservation but also on the carbon storage capacity of these ecosystems.

Influence of global warming and human activity on mercury accumulation patterns in wetlands across the Qinghai-Tibet Plateau.

Wetlands in the Qinghai-Tibet Plateau are a unique and fragile ecosystem undergoing rapid changes. We show two unique patterns of mercury (Hg) accumulation in wetland sediments. One is the 'surface peak' in monsoon-controlled regions and the other is the 'subsurface peak' in westerly-controlled regions. The former is attributed to the combined effects of increasing anthropogenic emissions and climate-induced changes in the cryosphere and wetland hydrology in the last 100-150 years. The climate changes in westerly-controlled regions in the last 50-70 years led to a fluctuation in hydrology and Hg peak in the sediment subsurface. The increase in legacy Hg input from soil erosion has largely enhanced the Hg accumulation rate in wetlands since the 1950s, especially in the proglacial wetlands. We highlight that accelerated glacier melting and permafrost thawing caused by global warming have altered geomorphology and hydrology, and affected Hg transport and accumulation in wetlands.