{"query_echo":{"q":"","filters":{"analysis_type":["empirical modeling"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'empirical modeling' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":1,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=empirical+modeling","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":"clipolin-000012","url":"https://climateinsightsdirectory.org/document/clipolin-000012","title":"Mapping the Effect of Deforestation on Rainfall: A Case Study from the State of Mato Grosso","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"case study","language":"en","date_published":"2021-10-04","source_url":"https://www.climatepolicyinitiative.org/publication/mapping-the-effect-of-deforestation-on-rainfall-a-case-study-from-the-state-of-mato-grosso/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report demonstrates that deforestation in the Amazon, specifically within the Xingu River Basin, significantly reduces rainfall in Mato Grosso. A counterfactual scenario where all Xingu Indigenous Territories are deforested predicts an average annual precipitation decrease of 7%, with more severe reductions during the dry season. These changes threaten agricultural productivity, urban water supplies, and hydroelectric power generation, with the center and north of the state being most vulnerable.","ai_generated":true,"unit":"clipolin-000012#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Deforestation of the Xingu River region's Indigenous territories, which cover 140,000 km2, is estimated to cause an average decrease of 7% in the annual historical average precipitation for the state of Mato Grosso.","ai_generated":true,"unit":"clipolin-000012#insight-1","date_modified":"2026-10-23"},{"text":"The impact of deforestation on rainfall varies by season, with the dry season (May to August) experiencing a more pronounced decrease of up to 15% of the historical seasonal average, compared to up to 8% during the wet season (September to April).","ai_generated":true,"unit":"clipolin-000012#insight-2","date_modified":"2026-10-23"},{"text":"Geographic vulnerability to rainfall reduction is uneven across Mato Grosso: the center and north are most affected during the wet season, while the center and northwest are most affected during the dry season; the eastern part remains largely unaffected due to wind patterns.","ai_generated":true,"unit":"clipolin-000012#insight-3","date_modified":"2026-10-23"},{"text":"Rainfall reductions from deforestation pose significant risks to critical economic infrastructure in Mato Grosso, specifically affecting agricultural productivity, urban water supplies, and the reservoirs of eight hydroelectric power plants, including the Teles Pires plant.","ai_generated":true,"unit":"clipolin-000012#insight-4","date_modified":"2026-10-23"},{"text":"The study utilizes an atmospheric transport model based on ERA5 wind data and MapBiomas land use data (1985-2018) to establish that air parcels traveling over forest areas maintain more humidity and deliver more rainfall than those over deforested terrain.","ai_generated":true,"unit":"clipolin-000012#insight-5","date_modified":"2026-10-23"},{"text":"The findings represent a lower bound of the actual effect of deforestation because the model does not account for the synergies of climate change, deforestation in other regions, or the hydrological effects of deforestation on the Xingu River itself.","ai_generated":true,"unit":"clipolin-000012#insight-6","date_modified":"2026-10-23"}],"climate_tags":["deforestation","rain","hydroelectricity","agricultural productivity","water-supply management","climate change"],"geography_discussed":["Brazil","Argentina","South America"],"frame_perspective":[],"analysis_type":["case study","counterfactual analysis","empirical modeling"]}]}