请将下列英文翻译成中文： Soil moisture is critical to agricultural business, ecosystem health, and certain hydrologically driven natural disasters. Monitoring data, though, is prone to instrumental noise, wide ranging extrema, and nonstationary response to rainfall where ground conditions change. Furthermore, existing soil moisture models generally forecast poorly for time periods greater than a few hours. To improve such forecasts, we introduce two data-driven models, the Naive Accumulative Representation (NAR) and the Additive Exponential Accumulative Representation (AEAR). Both of these models are rooted in deterministic, physically based hydrology, and we study their capabilities in forecasting soil moisture over time periods longer than a few hours. Learned model parameters represent the physically based unsaturated hydrological redistribution processes of gravity and suction. We validate our models using soil moisture and rainfall time series data collected from a steep gradient, post-wildfire site in southern California. Data analysis is complicated by rapid landscape change observed in steep, burned hillslopes in response to even small to moderate rain events. The proposed NAR and AEAR models are, in forecasting experiments, shown to be competitive with several established and state-of-the-art baselines.

土壤水分对农业业务、生态系统健康和某些水文驱动的自然灾害至关重要。然而，监测数据容易受到仪器噪声、极端宽泛和非平稳的降雨反应的影响，在地面条件发生变化时。此外，现有的土壤水分模型一般在几个小时以上的时间段内预测不佳。为了改善这样的预测，我们引入了两种数据驱动模型，即简单累积表示（NAR）和加性指数累积表示（AEAR）。这两个模型都根源于确定性的、基于物理的水文学，我们研究了它们在预测几个小时以上的土壤水分方面的能力。学习的模型参数代表了基于物理的不饱和水文重新分配过程，如重力和吸力。我们使用来自南加利福尼亚激烈坡度、野火后地点的土壤水分和降雨时间序列数据验证了我们的模型。数据分析受到坡度陡峭、燃烧过的山坡在小到中等降雨事件中快速景观变化的影响。在预测实验中，提出的NAR和AEAR模型与几种已经成熟和最先进的基线相