Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling
Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling
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DOI：
10.1016/j.jhydrol.2020.124878
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发表时间：
2020-07-01
影响因子：
6.4
通讯作者：
Soulsby, Chris
中科院分区：
文献类型：
--
作者：
Dimitrova-Petrova, Katya;Geris, Josie;Soulsby, Chris
充分表征流域存储动态是至关重要的水文模型，但规模代表性的存储测量是罕见的。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尺度的土壤水分数据的强大来源。然而，在整体径流模拟的潜力是未开发的。在这里，我们提出了第一个应用CRNS数据在概念上的整体径流建模，并探讨这种潜力的背景下，在苏格兰的混合农业景观。我们部署和校准的CRNS在一个异质性的土壤土地利用足迹在一个类似的3年的时间。在这种普遍潮湿的环境中，CRNS浅感测深度和相对较高的中子计数不确定性被确定为主要挑战。然而，由于空间覆盖面更大（可达14公顷），而且易于维护，CRNS被认为是长期监测受管理的混合农业地点的最简单方法。我们使用CRNS衍生，以及单点尺度估计，近地表土壤存储（S-NS），以探讨其特征的存储动态在流域尺度。使用线性回归的相互比较表明，SN相关的流域尺度的存储动态，但这种关系是更强的CRNS（R-2 = 0.91）相比，点尺度推导的估计（R-2 = 0.76）。在此基础上，我们评估的效果，使用CRNS和点尺度派生的S-NS数据来约束存储估计控制径流产生在一个共同的全径流模型（HBV-light）。包括CRNS或点尺度场S-NS数据单独在模型校准是特别有用的中期和潮湿的时期。使用流量和S-NS存储估计的组合模型校准提供了更好的表示集水区内部动态，另外减少低流量期间的不确定性。在潮湿环境中的混合农业景观的背景下，这项研究表明，使用CRNS在点尺度数据（单点数据的代表性和实用性的点传感器网络方面）的潜力，以确定流域的存储-排放关系，并告知水文建模。
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 similar to 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 (S-NS) 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 (R-2 = 0.91) compared to point-scale derived estimates (R-2 = 0.76). Based on this, we evaluated the effect of using the CRNS and point scale derived S-NS data to constrain storage estimates controlling runoff generation in a common rainfall-runoff model (HBV-light). Including CRNS or point-scale field S-NS data alone in model calibration was especially useful for intermediate and wet periods. A combined model calibration using discharge and either S-NS 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.