{"query_echo":{"q":"","filters":{"analysis_type":["model development"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'model development' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":5,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=model+development","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"climcent-000322","url":"https://climateinsightsdirectory.org/document/climcent-000322","title":"CoastalDEM v2.1: A high-accuracy and high-resolution global coastal elevation model trained on ICESat-2 satellite lidar","publisher":{"name":"Climate Central","organization":"climate-central"},"document_type":"research paper","language":"en","date_published":"2021-09-21","source_url":"https://assets.ctfassets.net/cxgxgstp8r5d/3f1LzJSnp7ZjFD4loDYnrA/71eaba2b8f8d642dd9a7e6581dce0c66/CoastalDEM_2.1_Scientific_Report_.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"CoastalDEM v2.1 is a high-resolution global digital elevation model (DEM) designed to improve the accuracy of coastal flood and sea level rise assessments. By employing a convolutional neural network (CNN) trained on NASA's ICESat-2 satellite lidar data and using NASADEM as a base, it significantly reduces the positive vertical bias common in satellite radar DEMs, particularly in densely populated urban areas where buildings often distort elevation readings. Validation against ICESat-2 and airborne lidar data from the US and Australia indicates that CoastalDEM v2.1 outperforms other leading global models like TanDEM-X and MERIT, especially in the critical elevation band below 5 meters.","ai_generated":true,"unit":"climcent-000322#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"CoastalDEM v2.1 demonstrates superior global accuracy compared to other publicly available global-scale models, particularly in low-lying and densely populated areas.","ai_generated":true,"unit":"climcent-000322#insight-1","date_modified":"2026-10-23"},{"text":"In the elevation band below 5 meters across all areas, the 30 m version of CoastalDEM v2.1 reduces global median bias to less than 0.01 m, with a root mean square error (RMSE) of 2.63 m and a 90th percentile linear error (LE90) of 2.99 m.","ai_generated":true,"unit":"climcent-000322#insight-2","date_modified":"2026-10-23"},{"text":"For coastal areas with moderate development (population density >1,000 people per km2) and elevations below 5 m, mean vertical bias improved from -0.5 m in version 1.1 to -0.1 m in version 2.1, representing a 91-95% bias reduction compared to other comparable DEMs.","ai_generated":true,"unit":"climcent-000322#insight-3","date_modified":"2026-10-23"},{"text":"CoastalDEM v2.1 shows consistent performance across different nations, with a lower median bias in 90% of countries and lower RMSE in at least 78% of countries when evaluating areas with population density >1,000 people per km2 and elevation <5 m.","ai_generated":true,"unit":"climcent-000322#insight-4","date_modified":"2026-10-23"},{"text":"Validation using airborne lidar data in the US and Australia confirms that CoastalDEM v2.1 has a median bias closer to zero and lower RMSE/LE90 values in the <5 m elevation band than competing global DEMs.","ai_generated":true,"unit":"climcent-000322#insight-5","date_modified":"2026-10-23"},{"text":"The model specifically improved accuracy in south Florida, particularly around Miami, where previous global DEMs exhibited multi-meter biases due to dense development and vegetation.","ai_generated":true,"unit":"climcent-000322#insight-6","date_modified":"2026-10-23"}],"climate_tags":["sea level rise","coastal flood","remote sensing"],"geography_discussed":["United States","Australia","Miami","Florida","Amsterdam","Dakar","Guayaquil","Jakarta","London","Shanghai"],"frame_perspective":[],"analysis_type":["model development","validation study"]},{"id":"sei-000809","url":"https://climateinsightsdirectory.org/document/sei-000809","title":"EconWEAP","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2016-11-02","source_url":"https://www.sei.org/publications/econweap-ca/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The paper introduces EconWEAP, a model that integrates the State Wide Economic Agricultural Production Model (SWAP) and the Water Evaluation and Planning (WEAP) system to better reflect physical and behavioral responses to climate change. By using a step function approximation of water demand curves from SWAP within an iterative search process, the model estimates crop land allocation based on economic value and water availability. Application of EconWEAP to California's Central Valley demonstrated a significant increase in agricultural revenues compared to using WEAP alone, achieved through the economically optimal allocation of water while maintaining the same hydrology.","ai_generated":true,"unit":"sei-000809#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The EconWEAP model is created by methodologically integrating the State Wide Economic Agricultural Production Model (SWAP), which is an agricultural economic optimization model, and Water Evaluation and Planning (WEAP), a climate-driven hydrological model.","ai_generated":true,"unit":"sei-000809#insight-1","date_modified":"2026-10-23"},{"text":"In tests conducted in California's Central Valley, the EconWEAP model showed a significant increase in revenues compared to the WEAP model by utilizing the economically optimal allocation of water without altering the hydrology.","ai_generated":true,"unit":"sei-000809#insight-2","date_modified":"2026-10-23"}],"climate_tags":["climate change","water management"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["model development","comparative analysis"]},{"id":"sei-000475","url":"https://climateinsightsdirectory.org/document/sei-000475","title":"A water system model for exploring electric energy alternatives in southeastern U.S. basins","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2013-09-13","source_url":"https://www.sei.org/publications/a-water-system-model-for-exploring-electric-energy-alternatives-in-southeastern-u-s-basins/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The document describes a water resource systems model for the Apalachicola–Chattahoochee–Flint, Alabama–Coosa–Tallapoosa, and Tombigbee River basins. The model integrates electricity generation, agricultural, and municipal water use to assess impacts on streamflow and water temperatures, specifically addressing thermal pollution from power plant cooling. Validation against 1980–2010 data shows the model robustly represents monthly average streamflow and water temperatures.","ai_generated":true,"unit":"sei-000475#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The developed model represents various water users, including municipal users, agricultural users, and power plants, while incorporating data on local population, per-capita use estimates, and population growth.","ai_generated":true,"unit":"sei-000475#insight-1","date_modified":"2026-10-23"},{"text":"The model was calibrated and validated using observed managed flows in the three target basins, with flow calibration based on hydraulic conductivity of soil horizons, water capacity, and land cover, while temperature calibration focused on slope properties and channel width.","ai_generated":true,"unit":"sei-000475#insight-2","date_modified":"2026-10-23"},{"text":"Based on goodness-of-fit statistics, the model accurately represents water temperatures and monthly average streamflow under water use levels observed between 1980 and 2010.","ai_generated":true,"unit":"sei-000475#insight-3","date_modified":"2026-10-23"}],"climate_tags":["electricity generation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["model development","model calibration","model validation"]},{"id":"sei-000584","url":"https://climateinsightsdirectory.org/document/sei-000584","title":"Modelling of soil moisture to determine ozone flux to European forest trees","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2012-04-30","source_url":"https://www.sei.org/publications/do3se-modelling-of-soil-moisture-to-determine-ozone-flux-to-european-forest-trees/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","analysis_type"],"summary":{"text":"The paper details a new soil moisture module for the DO3SE model, designed to address a limitation in regional risk assessments where soil water deficits were previously assumed not to limit ozone flux. The module uses the Penman-Monteith energy balance method and empirical data on pre-dawn leaf water status to estimate the biological control of transpiration, testing four different methods ranging from simple soil water content relations to complex models incorporating hydraulic resistance and plant capacitance.","ai_generated":true,"unit":"sei-000584#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The DO3SE (Deposition of O3 for Stomatal Exchange) model is used to estimate ozone deposition, stomatal flux, and vegetation impacts across Europe and is integrated into the EMEP photochemical model to link vegetation damage risk to O3 precursor emission scenarios for policy use.","ai_generated":true,"unit":"sei-000584#insight-1","date_modified":"2026-10-23"},{"text":"A significant gap in regional flux-based risk assessments has been the assumption that soil water deficits do not limit ozone flux, caused by a lack of evaluated methods for modelling these deficits and their effect on stomatal conductance (gsto).","ai_generated":true,"unit":"sei-000584#insight-2","date_modified":"2026-10-23"},{"text":"The developed soil moisture module employs the Penman-Monteith energy balance method for water cycling and tests four methods for estimating the biological control of transpiration, varying from simple soil water content relations to complex models involving plant capacitance and hydraulic resistance.","ai_generated":true,"unit":"sei-000584#insight-3","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":[],"frame_perspective":[],"analysis_type":["model development","model evaluation"]},{"id":"sei-000983","url":"https://climateinsightsdirectory.org/document/sei-000983","title":"The MILLENNIA peat cohort model: predicting past, present and future soil carbon budgets and fluxes under changing climates in peatlands","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2011-01-03","source_url":"https://www.sei.org/publications/the-millennia-peat-cohort-model-predicting-past-present-and-future-soil-carbon-budgets-and-fluxes-under-changing-climates-in-peatlands/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The authors developed the MILLENNIA model to address shortcomings in global soil models, specifically the lack of long-term non-equilibrium soil organic carbon accumulation and dynamic hydrological processes. Unlike traditional C-pool models, MILLENNIA uses peat cohorts and variable water table depth to drive carbon dynamics. Validation against a UK peatland site demonstrates that the model's predictions for soil organic carbon accumulation and peat ages align well with observations.","ai_generated":true,"unit":"sei-000983#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Current global soil models are considered unreliable for climate-soil carbon feedback predictions because they fail to accurately represent peat carbon stocks and dynamics.","ai_generated":true,"unit":"sei-000983#insight-1","date_modified":"2026-10-23"},{"text":"Existing C-pool models suffer from two primary deficiencies: they lack representation of long-term, non-equilibrium soil organic carbon accumulation (peat cohorts) and they fail to include dynamic hydrological processes, such as changes in water table depth, which regulate carbon turnover.","ai_generated":true,"unit":"sei-000983#insight-2","date_modified":"2026-10-23"},{"text":"The MILLENNIA model, which incorporates variable water table depth and peat age-cohorts, shows high sensitivity to runoff, precipitation, decomposition rates, and litter quality; its predictions for peat ages and soil organic carbon accumulation were validated using data from a UK peatland site that is currently a weak net carbon source.","ai_generated":true,"unit":"sei-000983#insight-3","date_modified":"2026-10-23"}],"climate_tags":["peatland","carbon sequestration"],"geography_discussed":["United Kingdom"],"frame_perspective":[],"analysis_type":["model development","model validation"]}]}