Daily hidrometric and stable isotope database at Quebrada Grande rainforest catchment, Costa Rica

There is still limited understanding of how waters mix, where waters come from and for how long they reside in tropical catchments. In this study, we used a tracer-aided model (TAM) and a gamma convolution integral model (GM) to assess runoff generation, mixing processes, water ages and transit times (TT) in the pristine humid tropical rainforest Quebrada Grande catchment in central Costa Rica. Models are based on a four-year data record (2016 to 2019) of continuous hydrometric and stable isotope observations. Both models agreed on a young water component of fewer than 95 days in age for 75% of the study period. The streamflow water ages ranged from around two months for wetter years (2017) and up to 9.5 months for drier (2019) years with a better agreement between the GM estimated TTs and TAM water ages for younger waters. Such short TTs and water ages result from high annual rainfall volumes even during drier years with 4,300 mm of annual precipitation (2019) indicating consistent quick near-surface runoff generation with limited mixing of waters and a supra-regional groundwater flow of likely unmeasured older waters. The TAM in addition to the GM allowed simulating streamflow (KGE > 0.78), suggesting an average groundwater contribution of less than 40% to streamflow. The model parameter uncertainty was constrained in calibration using stable water isotopes (δ2H), justifying the higher TAM model parameterization. We conclude that the multi-model analysis provided consistent water age estimates of a young water dominated catchment. This study represents an outlier compared to the globally predominant old water paradox, exhibiting a tropical rainforest catchment with higher new water fractions than older water.

目前学界对热带流域内水体的混合机制、补给来源以及驻留时长的认知仍较为有限。本研究采用示踪辅助模型（tracer-aided model, TAM）与伽马卷积积分模型（gamma convolution integral model, GM），对哥斯达黎加中部原始湿润热带雨林流域Quebrada Grande的产流过程、水体混合机制、水体年龄及滞留时间（transit times, TT）开展评估。上述模型基于2016至2019年共四年的连续水文观测与稳定同位素监测数据构建。两种模型均得出：在研究周期的75%时段内，流域水体以年龄小于95天的年轻水体为主。径流水体年龄的范围在2个月左右（湿润年份2017年）至9.5个月（干旱年份2019年）之间；其中，针对年轻水体，伽马卷积积分模型估算的滞留时间与示踪辅助模型估算的水体年龄吻合度更高。即便在2019年这类年降水量达4300毫米的干旱年份，流域年降雨量仍处于较高水平，这也是导致水体滞留时间与年龄普遍偏短的原因：这表明流域始终以快速近地表产流为主，水体混合程度有限，且存在未被观测的超区域地下径流（携带年龄更老的水体）。示踪辅助模型与伽马卷积积分模型均能较好模拟径流过程（克林-古普塔效率系数Kling-Gupta Efficiency, KGE>0.78），其结果显示地下水对径流的平均贡献率不足40%。本次校准过程中，研究通过氢稳定同位素δ²H数据约束了模型参数的不确定性，这也佐证了示踪辅助模型更高复杂度的参数化方案的合理性。本研究认为，本次多模型分析针对以年轻水体为主的流域得到了一致的水体年龄估算结果。与全球范围内普遍存在的"老水悖论"研究结论相比，本研究的案例属于特例：该热带雨林流域的新水占比高于老水占比。