摘要
在构建分布式水文模型与生物地球化学模型的耦合模型(DTVGM-CASACNP)及应用元胞自动机-马尔科夫(CA-Markov)土地利用预测模型基础上,以滦河流域为例分析气候变化、土地利用/覆被变化(LUCC)及CO2浓度升高对径流的影响。研究结果表明:DTVGM-CASACNP耦合模型以及CA-Markov模型在滦河流域均具有较好的适用性;气候变化与土地利用/植被覆盖变化对滦河流域径流的影响较CO2浓度升高的影响程度大;未来不同情景下滦河流域2020—2049年径流呈减小趋势,大部分情景下年径流较基准年减少,与非汛期相比,滦河流域未来汛期径流对不同情景更敏感,总体上在汛期径流相对基准年减少,而在非汛期径流相对基准年增加。
Investigating the effects of climate change, land use and cover change （LUCC） and increased CO2 con- centration on catchment runoff is important for acquiring an understanding of ecohydrological processes under changing environment at a basin scale. To analyze these effects, a model is developed through coupling a hydrological model （Distributed Time Variant Gain Model; DTVGM） , a global terrestrial biogeochemical model for carbon, nitrogen and phosphorus （CASACNP） and a land use Cellular Automata -Markov （CA-Markov） model. The coupled model is then applied to the Luanhe River basin of Northern China. The model performs well during both model calibration and vali- dation processes. The calibrated model is then used to study the effects of climate change, LUCC and CO2 enrichment on runoff over the basin. Results show the effects of climate change and LUCC on runoff are stronger than that of in- creased CO2 concentration. The annual runoff exhibits a decreasing trend for the period 2020--2049, and shows a re- duced runoff as compared to the base year for most of the scenarios. The intra-annual analysis shows a decrease in run- off during wetter months and an increase during drier months. The monthly runoff during flood seasons would be more sensitive in the future as compared to non-flooding seasons.
出处
《水科学进展》
EI
CAS
CSCD
北大核心
2014年第1期10-20,共11页
Advances in Water Science
基金
国家自然科学基金资助项目(51279140
51279139)~~
