{"query_echo":{"q":"","filters":{"analysis_type":["hydrological simulation"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'hydrological simulation' 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=hydrological+simulation","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":"sei-000195","url":"https://climateinsightsdirectory.org/document/sei-000195","title":"Assessing Climate Change Impacts on River Flows in the Tonle Sap Lake Basin, Cambodia","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2019-03-25","source_url":"https://www.sei.org/publications/assessing-climate-change-impacts-on-river-flows-in-the-tonle-sap-lake-basin-cambodia/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The study addresses the lack of hydrological monitoring in the tributaries of Cambodia's Tonle Sap Lake, which contribute 25–30% of the annual flow and are critical for maintaining dry-season lake levels. It aims to simulate baseline flows and project future climate change impacts across 11 sub-basins to support water resource management in a country where 80% of the population depends on agriculture.","ai_generated":true,"unit":"sei-000195#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The tributaries of the Tonle Sap Lake are critical for maintaining water levels during the dry season and contribute 25–30% of the annual flow, with precipitation accounting for 12.5%.","ai_generated":true,"unit":"sei-000195#insight-1","date_modified":"2026-10-23"},{"text":"Cambodia is highly vulnerable to climate change because approximately 80% of its population relies on the agriculture sector for their livelihoods, making them susceptible to food shortages and crop failures caused by flooding and water scarcity.","ai_generated":true,"unit":"sei-000195#insight-2","date_modified":"2026-10-23"},{"text":"Water resources in the Tonle Sap basin have been degraded by several factors, including hydropower demand, agricultural expansion, deforestation, urbanization, and rapid population growth.","ai_generated":true,"unit":"sei-000195#insight-3","date_modified":"2026-10-23"}],"climate_tags":["climate impact","water management","hydrological model","agriculture","food security","deforestation","hydropower"],"geography_discussed":["Southeast Asia","Mekong River"],"frame_perspective":[],"analysis_type":["hydrological simulation","impact assessment"]},{"id":"sei-001859","url":"https://climateinsightsdirectory.org/document/sei-001859","title":"Modelación del recurso hídrico en la cuenca del río La Vieja en Colombia","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"fact sheet","language":"es","date_published":"2014","source_url":"https://www.sei.org/mediamanager/documents/Publications/Water-sanitation/SEI-USAID-FS-2014-Modelacion-recurso-hidrico-rio-La-Vieja-Colombia.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document details the application of the Water Evaluation And Planning (WEAP) system to model the La Vieja river basin, covering an area of approximately 2,880 km2 across the departments of Quindío, Valle del Cauca, and Risaralda. The model incorporates 37 watercourses, 16 human consumption demands, and 4 hydroelectric demands. By utilizing a soil moisture rain-runoff method and calibrating parameters such as runoff resistance and hydraulic conductivity, the project established a framework to evaluate 1,728 scenarios involving climate variability and demographic changes. This process aims to support regional environmental authorities in formulating adaptation strategies for water resource planning through 2050.","ai_generated":true,"unit":"sei-001859#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The hydrological model of the La Vieja river basin was developed using the WEAP tool, incorporating 37 direct and indirect tributary watercourses, 16 water demands for human consumption, and 4 demands for hydroelectric generation on the Quindío river.","ai_generated":true,"unit":"sei-001859#insight-1","date_modified":"2026-10-23"},{"text":"The study area of the La Vieja river basin covers approximately 2,880 km2, distributed across the departments of Quindío (68%), Valle del Cauca (22%), and Risaralda (10%), with a general water yield of 34.34 l/s/km2 and an annual water supply of 2,975.74 Mm3.","ai_generated":true,"unit":"sei-001859#insight-2","date_modified":"2026-10-23"},{"text":"The modeling utilized a quasi-physical one-dimensional soil moisture rain-runoff method, which represents the soil matrix as two buckets to simulate hydrological phenomena.","ai_generated":true,"unit":"sei-001859#insight-3","date_modified":"2026-10-23"},{"text":"Model calibration at the Cartago station of the La Vieja River showed a good correlation between simulated and observed flows, with statistical indices of Nash=0.78 and BIAS=2.88.","ai_generated":true,"unit":"sei-001859#insight-4","date_modified":"2026-10-23"},{"text":"The most influential parameters for adjusting flow responses in the model are the runoff resistance factor (RRF), which ranged from 3.00 to 6.50, and the root zone hydraulic conductivity (Ks), which ranged from 90mm to 180mm.","ai_generated":true,"unit":"sei-001859#insight-5","date_modified":"2026-10-23"},{"text":"A hypothesis was formulated regarding a potential interaction between the river and the 'Glacis del Quindío' aquifer system, suggesting the river may be losing water to the aquifer, which could explain poor correlations at the bocatoma EPA measuring station.","ai_generated":true,"unit":"sei-001859#insight-6","date_modified":"2026-10-23"},{"text":"Using the XLRM analytical methodology, the project defined 1,728 scenarios to evaluate uncertainties—including climate, demographics, per capita consumption, water loss, and agricultural dynamics—and adaptation strategies such as wastewater treatment plants and multipurpose reservoirs.","ai_generated":true,"unit":"sei-001859#insight-7","date_modified":"2026-10-23"}],"climate_tags":["water management","climate change adaptation","water-supply management","hydroelectricity"],"geography_discussed":["Quindío Department","Valle del Cauca Department","Risaralda Department","Armenia","Pereira"],"frame_perspective":[],"analysis_type":["hydrological simulation","scenario analysis","model calibration"]}]}