Abstract
Abstract. There are various methods available for annual groundwater recharge estimation with in situ observations (i.e., observations obtained at the site/location of interest), but a great number of watersheds around the world still remain ungauged, i.e., without in situ observations of hydrologic responses. One approach for making estimates at ungauged watersheds is regionalization, namely, transferring information obtained at gauged watersheds to ungauged ones. The reliability of regionalization depends on (1) the underlying system of hydrologic similarity, i.e., the similarity in how watersheds respond to precipitation input, as well as (2) the approach by which information is transferred. In this paper, we present a nested tree-based modeling approach for conditioning estimates of hydrologic responses at ungauged watersheds on ex situ data (i.e., data obtained at sites/locations other than the site/location of interest) while accounting for the uncertainties of the model parameters as well as the model structure. The approach is then integrated with a hypothesis of two-leveled hierarchical hydrologic similarity, where the higher level determines the relative importance of various watershed characteristics under different conditions and the lower level performs the regionalization and estimation of the hydrologic response of interest. We apply the nested tree-based modeling approach to investigate the complicated relationship between mean annual groundwater recharge and watershed characteristics in a case study, and apply the hypothesis of hierarchical hydrologic similarity to explain the behavior of a dynamic hydrologic similarity system. Our findings reveal the decisive roles of soil available water content and aridity in hydrologic similarity at the regional and annual scales, as well as certain conditions under which it is risky to resort to climate variables for determining hydrologic similarity. These findings contribute to the understanding of the physical principles governing robust information transfer.
Highlights
Groundwater resources supply approximately 50 % of the drinking water and roughly 40 % of the irrigation water worldwide (National Ground Water Association, 2016)
With the metrics of accuracy and uncertainty defined, we are able to quantify the predictive performance of the Bayesian Additive Regression Tree (BART) models, and classify them based on either the root mean squared error (RMSE)-based labels or the log predictive probability density (LPD)-based labels with the nested tree-based modeling approach
One may argue how a modeler would make an informed proposal of plausible BART models in the first place. This is where physical knowledge comes into play, and the proposal is case specific. This is why we proposed the hypothesis of hierarchical similarity, which can be integrated with the nested tree-based modeling approach to study the behavior of a dynamic hydrologic similarity system, like what was demonstrated with the case study
Summary
Groundwater resources supply approximately 50 % of the drinking water and roughly 40 % of the irrigation water worldwide (National Ground Water Association, 2016). Groundwater recharge, here broadly defined as the replenishing of water to a groundwater reservoir, plays a critical role in sustainable water resource management (de Vries and Simmers, 2002). The aforementioned methods rely on in situ data, while many watersheds worldwide still remain effectively ungauged (i.e., ungauged, poorly gauged, or previously gauged) (Loukas and Vasiliades, 2014). This fact leads us to a critical question: how can one estimate hydrologic responses without in situ data? Facing the lack of in situ data, studies have attempted
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