The computational complexity of distributed groundwater models poses a significant challenge in the conjunctive management of surface and groundwater resources. Here, we propose a multi-model framework to predict groundwater recharge using a simple water balance model. We compare and contrast two lumped parsimonious conceptual models of groundwater recharge, one based on P only and another, additionally including PE as a predictor. Both model variants also include an option to specify annually fixed or seasonally varying recharge factors, as well as a number of pumping scenarios. We incorporate these models within a python based open-source toolbox, REGSim (Recharge Estimation and Groundwater Simulation). The toolbox features a multi-objective evolutionary algorithm (NSGA-II), the Generalized Likelihood Uncertainty Estimation (GLUE) method, and regional sensitivity analysis (RSA). These functionalities allow the users to perform multi-objective calibration of the models, obtain confidence intervals on predicted groundwater heads, and understand the relative importance of different model parameters. REGSim is used to simulate groundwater heads for the urban agglomeration of Hyderabad, India. Using REGSim, we tested alternative conceptualizations of groundwater recharge and pumping processes in the city. We found that the intra-annual dynamics of groundwater levels are better explained by seasonally varying the recharge factors than annually fixed recharge factors. The model achieved an NSE of 0.64 and 0.70 during the validation for the formulations based on P only and using both P and PE, respectively. Furthermore, sensitivity analysis revealed that specific yield is the most influential parameter affecting the simulated groundwater head in the region.

分布式地下水模型的计算复杂性对地表和地下水资源的联合管理提出了重大挑战。在这里，我们提出了一个多模型框架，使用简单的水平衡模型来预测地下水补给。我们比较和对比了地下水补给的两个集中简约概念模型，一个仅基于 P，另一个基于 P，另外还包括 PE 作为预测因子。两种模型变体还包括一个选项，用于指定每年固定或季节性变化的补给因子，以及许多抽水方案。我们将这些模型整合到基于 Python 的开源工具箱 REGSim（补给估算和地下水模拟）中。该工具箱具有多目标进化算法 (NSGA-II)、广义似然不确定性估计 (GLUE) 方法、和区域敏感性分析（RSA）。这些功能允许用户对模型进行多目标校准，获得预测地下水水头的置信区间，并了解不同模型参数的相对重要性。REGSim 用于模拟印度海得拉巴城市群的地下水水头。我们使用 REGSim 测试了城市地下水补给和抽水过程的替代概念。我们发现，与每年固定的补给因子相比，季节性变化的补给因子更好地解释了地下水位的年内动态。在仅基于 P 和同时使用 P 和 PE 的配方验证期间，该模型实现了 0.64 和 0.70 的 NSE。此外，