Research

We welcome students interested in hydrogeology and biogeoscience.

Research projects range from fundamental hydrological-process studies to practical applications in subsurface characterization, contaminant remediation, and climate-resilient hydrological environments. Students may have opportunities to work with hydrological and geophysical datasets, numerical models, and computational tools.

人類在關鍵區的活動影響自然環境以及生態系統的永續性，監測與追蹤自然環境如何因人們行為而改變，分析地表—地下水系統的水分遷移轉化及其生態效應，有助於減輕人類活動對自然的負面影響，是有效率管理區域地下水資源的可行方法之一。本實驗室結合野外調查及數值模擬，廣泛發展高精度資料融合演算法，透過分析不同類型不同尺度觀測資料隱含的水文地質與生態水文資訊，探討入滲、土壤水、地下水動態分佈變化，分析影響補給的主控因子，解析植物對土壤水與地下水循環的影響。研究內容分成三大主題：植物-土壤水-地下水遷移轉化規律、開發序率多源資料融合演算法、多尺度數據的篩選和其反演功能分析。

Research Interests

Groundwater Hydrology

Groundwater flow, recharge, and surface water–groundwater interactions.

Plant–Soilwater–Groundwater Interactions

Water movement among vegetation, soil, the unsaturated zone, and groundwater, including infiltration, evapotranspiration, and groundwater recharge.

Hydrogeology

Using hydraulic and geophysical observations to better understand subsurface structures and hydraulic properties.

Data Fusion and Inverse Modeling

Integrating multiple datasets to estimate difficult-to-observe subsurface parameters and quantify uncertainty.

Machine Learning and Deep Learning for Hydrology

Applying data-driven methods to prediction, parameter estimation, pattern recognition, and hydrological modeling.

The dynamic feedbacks between groundwater and land surface processes 植物-土壤水-地下水遷移轉化規律

透過研究場域觀測實驗，對大氣—地表—未飽和帶—地下水系統中的狀態變量進行即時監測，研究裸土與植被條件下未飽和帶水分遷移轉化的規律，解析蒸發散與凝結水的動力學機制，量化植被的耗水來源(Ran et al. 2024)。研究結果為風沙灘地區水資源的合理開發利用和生態環境的保護提供了理論依據。

Stochastic data fusion approach 序率多源資料融合演算法

Simulations of groundwater dynamic can be highly uncertain in groundwater modeling due to poor constraints on belowground properties and processes, including soil hydraulic properties and moisture and root distributions, over the entire aquifer and unsaturated zones. Because the groundwater flow model is a complex nonlinear, spatially distributed model that uses a large number of parameters to simulate hydrological processes, model calibration is critical but also challenging.

We adapt a novel deep learning architecture to reduce the dimension of geometric/topological features in highly parameterized model. After developing a deep learning architecture to reduce the model complexity, this algorithm will be implemented into an information fusion approach based on a stochastic successive linear estimator. The information fusion approach yields the most likely hydrological parameters and their uncertainty.

We expect that the data fusion algorithm will leverage the computational burden of model calibration and help us delineate the subsurface heterogeneity efficiently. This outcome will be employed to answer the water resources issues tailored to the study sites.

地層的滲透性和比儲量等特性會影響地下水的流動速度和方向，以及含水層中有害物質的遷移。準確繪製這些屬性的分佈圖有助於制定有效的地下水使用策略，包括地下水可使用量、預防地層下陷、海水入侵、確定潛在的污染源、優化修復井的位置等。我們延續多年來的研究工作，依據野外場域的不同特性，開發適用於各場域的序率資料融合演算法。在河川流過的區域，使用豐枯水期地表-地下水交互影響的差異性，刻劃流域尺度含水層水力參數空間分布(Wang et al., 2019; Lin et al., 2023)，此方法目前已應用於不同氣候區的不同流域。而在缺乏水文觀測網的區域，我們則開發利用人工引雷的地球物理方法，對地層電磁特徵進行初步探勘(Wang et al., 2022)，此方法尚在起步階段，目前正在進行數值試驗與規劃小尺度試驗，冀望能在這專業領域有更進一步的表現。

Characterize subsurface hydraulic heterogeneity with multiscale and multisource datasets 多尺度數據的篩選和其反演功能分析

Mapping aquifer heterogeneity is a pressing need for managing regional groundwater resources. Properties like permeability and specific storage of the subsurface affect the movement of water, which impacts the rate and direction of groundwater flow and migration of hazardous and valuable chemicals in the aquifer. Accurate mapping the distributions of these properties can assist in developing effective groundwater management strategies, including identifying potential contamination sources or optimizing the remediation wells' placement.

We plan to integrating multi-source and multi-scale datasets to identify subsurface features from site-scale to a tens of kilometers scale aquifer system. We expect to answer how these datasets help us see into the subsurface.

不同空間與時間尺度觀測數據隱含相似但不完全相同水文地質訊息，有效融合這些資訊有助於提高描繪含水層特徵的空間解析度。我們利用時頻分析、時間空間相關性分析、聚類等方法，自觀測數據中擷取具有時間與空間自相關性的特徵，並使用資料融合演算法提取數據中隱含的地層水文地質、地球物理特徵，深入探討不同空間與時間尺度觀測數據在反演中所扮演的功能。在這些不同空間尺度的資料中，自岩心判讀的點尺度地質資訊，因地層形成過程具有一定空間上的相關性，可使用適當的空間聚類，將點尺度資訊upscale至相似於地下水流場的區域尺度，再進行數據融合(Lin et al, 2023)。而不同時間尺度的資料中，我們藉由時間域與頻率域分析，指出不同水位波動形態的地下水位觀測數據，因獨特的相關性時間空間分布特徵，可在不同程度上提高反演空間解析度(Wang et al., 2019; Wang et al., 2021)。