Abstract Adequate characterization of catchment storage dynamics is crucial in hydrological models, yet scale-representative storage measurements are rare. Recent developments in Cosmic Ray Neutron Sensor (CRNS) technology and monitoring networks provide a powerful source of more scale-appropriate soil moisture data for many modelling applications. However, the potential in rainfall-runoff modelling is undeveloped. Here we present the first application of CRNS data in conceptual rainfall-runoff modelling and explore this potential in the context of a mixed-agricultural landscape in Scotland. We deployed and calibrated a CRNS in a heterogeneous soil-land use footprint over a ∼3-year period. In this generally wet environment, the CRNS shallow sensing depth and relatively high neutron count uncertainty were identified as major challenges. However, given the better spatial coverage (up to 14 ha) and ease for maintenance, CRNS was thought to represent the simplest approach for long-term monitoring of managed mixed-agricultural sites. We used CRNS-derived, as well as single point-scale estimates, of near-surface soil storage (SNS) to explore their characterisation of storage dynamics at the catchment-scale. Inter-comparison using linear regression showed that SNS related well to catchment-scale storage dynamics, however this relationship was stronger for CRNS (R2 = 0.91) compared to point-scale derived estimates (R2 = 0.76). Based on this, we evaluated the effect of using the CRNS and point scale derived SNS data to constrain storage estimates controlling runoff generation in a common rainfall-runoff model (HBV-light). Including CRNS or point-scale field SNS data alone in model calibration was especially useful for intermediate and wet periods. A combined model calibration using discharge and either SNS storage estimates provided a better representation of catchment internal dynamics, additionally reducing uncertainty during low flows. In the context of mixed-agricultural landscapes in humid environments, this study showed the potential of using CRNS over point scale data (in terms of representativeness for single point data and practicality for point sensor networks) to characterise the catchment storage-discharge relationship and inform hydrological modelling.

中文翻译：

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使用来自宇宙射线中子传感器的集水区规模存储估计进行降雨径流建模的机遇和挑战

摘要 流域蓄水动态的充分表征在水文模型中至关重要，但具有规模代表性的蓄水测量很少见。宇宙射线中子传感器 (CRNS) 技术和监测网络的最新发展为许多建模应用提供了更适合规模的土壤水分数据的强大来源。然而，降雨径流建模的潜力尚未开发。在这里，我们展示了 CRNS 数据在概念性降雨径流建模中的首次应用，并在苏格兰混合农业景观的背景下探索了这种潜力。我们在大约 3 年的时间内在异质土壤-土地利用足迹中部署和校准了 CRNS。在这种普遍潮湿的环境中，CRNS 浅层传感深度和相对较高的中子数不确定性被确定为主要挑战。然而，鉴于更好的空间覆盖（高达 14 公顷）和易于维护，CRNS 被认为是对管理的混合农业场地进行长期监测的最简单方法。我们使用近地表土壤储存 (SNS) 的 CRNS 衍生以及单点尺度估计来探索它们在流域尺度上的储存动态特征。使用线性回归的相互比较表明，SNS 与流域尺度的蓄水动态相关性很好，但是与点尺度推导的估计值 (R2 = 0.76) 相比，CRNS 的这种关系 (R2 = 0.91) 更强。在此基础上，我们评估了使用 CRNS 和点尺度派生的 SNS 数据来约束存储估计的效果，这些估计控制普通降雨径流模型（HBV-light）中的径流生成。在模型校准中单独包括 CRNS 或点尺度场 SNS 数据对于中间和潮湿时期特别有用。使用排放和 SNS 存储估计的组合模型校准提供了更好的流域内部动态表示，另外减少了低流量期间的不确定性。在潮湿环境中混合农业景观的背景下，本研究显示了在点尺度数据上使用 CRNS 的潜力（就单点数据的代表性和点传感器网络的实用性而言）来表征集水区的储存-排放关系并提供信息水文建模。另外减少低流量期间的不确定性。在潮湿环境中混合农业景观的背景下，本研究显示了在点尺度数据上使用 CRNS 的潜力（就单点数据的代表性和点传感器网络的实用性而言）来表征集水区的储存-排放关系并提供信息水文建模。另外减少低流量期间的不确定性。在潮湿环境中混合农业景观的背景下，本研究显示了在点尺度数据上使用 CRNS 的潜力（就单点数据的代表性和点传感器网络的实用性而言）来表征集水区的储存-排放关系并提供信息水文建模。