{"query_echo":{"q":"","filters":{"analysis_type":["technical validation"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'technical validation' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":2,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=technical+validation","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-000320","url":"https://climateinsightsdirectory.org/document/climcent-000320","title":"CoastalDEM v2.1 A New, 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/Ygz6c7lz0MaEneXJfE1xT/c9e07d21aa917dc2d7b77d4ba13e7395/CoastalDEM2.1-White-Paper_.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"CoastalDEM v2.1 is a near-global digital elevation model (DEM) developed by Climate Central to provide more accurate land elevation data for coastal flood risk assessments. By utilizing a sophisticated convolutional neural network trained on NASA's ICESat-2 satellite lidar data, the model corrects the positive vertical bias common in satellite radar-derived DEMs, which often mistake building rooftops for ground level. Testing shows that CoastalDEM v2.1 virtually eliminates global median bias in the critical 0 to 5 m elevation band, significantly outperforming other publicly available global DEMs such as NASADEM, TanDEM-X, MERIT, and AW3D30, particularly in densely populated urban areas.","ai_generated":true,"unit":"climcent-000320#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"CoastalDEM v2.1 significantly reduces vertical bias compared to other global digital elevation models (DEMs), virtually eliminating global median bias to less than 0.01 m in the 0 to 5 m elevation band.","ai_generated":true,"unit":"climcent-000320#insight-1","date_modified":"2026-10-23"},{"text":"In areas with moderate urban development (over 1,000 people per square kilometer) and elevations below 5 m, CoastalDEM v2.1's median vertical bias is 0.09 m, which is three times lower than CoastalDEM v1.1 and ten times lower than other comparable DEMs.","ai_generated":true,"unit":"climcent-000320#insight-2","date_modified":"2026-10-23"},{"text":"In very high population density areas (over 10,000 people per square kilometer), CoastalDEM v2.1 shows a median bias of 0.42 m, whereas other DEMs range from 2.04 to 2.84 m; this difference is approximately three times the range of sea level rise projected by the IPCC AR6 for 2100 (0.5 to 1.0 m).","ai_generated":true,"unit":"climcent-000320#insight-3","date_modified":"2026-10-23"},{"text":"The model's accuracy was validated against airborne lidar data in the U.S. and Australia, where it outperformed other global DEMs and even surpassed the performance of CoastalDEM v1.1 in the U.S., despite v1.1 being specifically trained on U.S. data.","ai_generated":true,"unit":"climcent-000320#insight-4","date_modified":"2026-10-23"},{"text":"CoastalDEM v2.1 utilizes a more advanced convolutional neural network architecture with thousands of hidden units and over a thousand input variables per pixel, trained on global ICESat-2 satellite lidar data to better represent the built environment globally.","ai_generated":true,"unit":"climcent-000320#insight-5","date_modified":"2026-10-23"},{"text":"The model expands the elevation range for predicted corrections to between -10 m and 120 m, compared to the 1 to 20 m range used in CoastalDEM v1.1, to better handle low flat regions and tall skyscrapers.","ai_generated":true,"unit":"climcent-000320#insight-6","date_modified":"2026-10-23"}],"climate_tags":["sea level rise","coastal flood"],"geography_discussed":["United States","Australia","Europe"],"frame_perspective":[],"analysis_type":["technical validation","comparative performance analysis"]},{"id":"greenca-000028","url":"https://climateinsightsdirectory.org/document/greenca-000028","title":"My Report Title","publisher":{"name":"GreenCape","organization":"greencape"},"document_type":"report","language":"en","date_published":null,"source_url":"https://greencape.co.za/assets/Uploads/Appendices.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the stakeholder engagement process and technical data used to develop a regional tool for integrated water and economic planning in the Berg WMA. It includes records of workshops and meetings with provincial government, local municipalities, and academic institutions. Technical appendices provide marginal values of water by crop, estimated irrigated agriculture water requirements, and forecasts for urban water demand for 2025 and 2040. The text also validates these bottom-up estimations against the WARMS database and Aurecon's Water Resources Simulation Model, while assessing the economic opportunity cost of potential supply deficits in terms of GVA and employment.","ai_generated":true,"unit":"greenca-000028#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Stakeholders generally agreed that the Western Cape Government’s Sustainable Water Management Plan (SWMP) forum is an appropriate mechanism for coordinating integrated water and economic development, although it requires improved relations with Local Municipalities and the Department of Water and Sanitation to translate recommendations into authorisation decisions.","ai_generated":true,"unit":"greenca-000028#insight-1","date_modified":"2026-10-23"},{"text":"There has been a shift in stakeholder perception toward accepting that water and economic development cannot be planned in isolation, with a growing consensus on the need to consider the economic value of water use in strategic decision-making.","ai_generated":true,"unit":"greenca-000028#insight-2","date_modified":"2026-10-23"},{"text":"The project's bottom-up irrigated agriculture water requirement estimates for the Berg WMA were validated as reasonably close to Aurecon's Water Resources Simulation Model, which estimated 239 Mm3/a for its specific study area, while the project's clipped estimate for that same area was 201 Mm3/a.","ai_generated":true,"unit":"greenca-000028#insight-3","date_modified":"2026-10-23"},{"text":"The marginal value of water in agriculture is heavily influenced by crop type and soil fertility; for example, the overall mean marginal value for peaches is 4.84, while for wheat it is 0.14.","ai_generated":true,"unit":"greenca-000028#insight-4","date_modified":"2026-10-23"},{"text":"Forecasts for total urban water requirements in the region show significant projected growth, with the total increasing from 392,086,039 m3 in 2011 to a mid-range forecast of 896,228,911 m3 by 2040.","ai_generated":true,"unit":"greenca-000028#insight-5","date_modified":"2026-10-23"}],"climate_tags":["water management","climate modeling"],"geography_discussed":["Saldanha Bay Local Municipality","City of Cape Town"],"frame_perspective":[],"analysis_type":["stakeholder engagement","economic modelling","technical validation","demand forecasting"]}]}