The impacts of land cover change have traditionally been assessed in hydrological modeling with a priori knowledge, e.g., using methods based on the curve number, or by calibrating hydrological models over different time periods. However, how hydrological processes respond to such changes is extremely context-dependent. Thus, there is an opportunity for the development of hydrological models that can learn from large hydrological data sets under the context of severe environmental changes. In this study, a single regional hydrological model is developed based on long short-term memory (LSTM) neural networks using different input configurations. One model considers only meteorological forcings as inputs (I1), another model considers meteorological forcings and static catchment attributes (I2), and a third model also considers meteorological forcings and catchment attributes but where the land cover characteristics are dynamic (I3). The models are trained using information from 411 catchments in the Brazilian Cerrado biome. The data set includes, for each catchment, the daily streamflow observations (target), daily precipitation and reference evapotranspiration (meteorological forcings), and 21 catchment attributes including topography, climate indices, soil characteristics, and land cover characteristics. Considering catchment attributes increases the performance of the LSTM model (I2 and I3 median KGE: 0.69). Considering the land use cover characteristics as dynamic improves the predictions under low-flow conditions (I3 median rNSE: 0.62) when compared to the model considering such characteristics as static (I2 median rNSE: 0.53). This study also uses the deep network with the integrated gradients technique to explore the contribution of the catchment characteristics to streamflow and the number of time steps of influence for the deep network in different regions.

土地覆盖变化的影响传统上是在具有先验知识的水文建模中进行评估的，例如，使用基于曲线数的方法，或通过校准不同时间段的水文模型。然而，水文过程如何对这种变化做出反应是极其依赖于环境的。因此，有机会开发可以在严重环境变化的背景下从大型水文数据集中学习的水文模型。在这项研究中，基于使用不同输入配置的长短期记忆 (LSTM) 神经网络开发了一个单一的区域水文模型。一个模型只考虑气象强迫作为输入（I1），另一个模型考虑气象强迫和静态流域属性（I2），第三个模型也考虑了气象强迫和流域属性，但土地覆盖特征是动态的 (I3)。这些模型使用来自巴西塞拉多生物群落 411 个集水区的信息进行训练。该数据集包括每个流域的每日流量观测（目标）、每日降水和参考蒸发量（气象强迫）以及 21 个流域属性，包括地形、气候指数、土壤特征和土地覆盖特征。考虑流域属性会提高 LSTM 模型的性能（I2 和 I3 中值 每日流量观测（目标）、每日降水量和参考蒸散量（气象强迫）以及 21 个流域属性，包括地形、气候指数、土壤特征和土地覆盖特征。考虑流域属性会提高 LSTM 模型的性能（I2 和 I3 中值 每日流量观测（目标）、每日降水量和参考蒸散量（气象强迫）以及 21 个流域属性，包括地形、气候指数、土壤特征和土地覆盖特征。考虑流域属性会提高 LSTM 模型的性能（I2 和 I3 中值KGE : 0.69)。与考虑静态特征（I2 中值rNSE：0.53）的模型相比，将土地利用覆盖特征视为动态可改善低流量条件下的预测（I3 中值rNSE：0.62 ）。本研究还利用深度网络和积分梯度技术，探讨了流域特征对河流流量的贡献以及不同区域对深度网络影响的时间步长数。