Combining static and portable Cosmic Ray Neutron Sensor (CRNS) data to assess catchment scale heterogeneity in soil water content and implications for runoff generation

2021 ◽  
Author(s):  
Katya Dimitrova Petrova ◽  
Rafael Rosolem ◽  
Chris Soulsby ◽  
Mark Wilkinson ◽  
Allan Lilly ◽  
...  
Keyword(s):  
Soil Water ◽  
Model Calibration ◽  
Cosmic Ray ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Catchment Scale ◽  
Near Surface ◽  
Mineral Soils ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>Soil water content (SWC) dynamics can strongly influence catchment runoff generation processes. Knowledge about the amount and spatiotemporal distribution of SWC at the catchment scale can be useful for constraining and evaluating rainfall-runoff models.  While it is still challenging to obtain catchment scale-representative measurements of SWC, recent advances in cosmic ray neutron sensor (CRNS) technology have provided opportunities to obtain hectare scale data on SWC. Here we present a new method for obtaining spatially variable near-surface SWC by combining a high temporal resolution static CRNS sensor with ‘snapshot’ surveys using a portable CRNS. We also explored the role of these soil water storage data for catchment in rainfall-runoff generation models. We used ~4-years of near-surface SWC data from a static CRNS located in a humid mixed-agricultural catchment (~10km<sup>2</sup>) in Scotland. These data were complemented with at least three ‘snapshot’ portable CRNS surveys in each of the four main soil-land use (SLU) units in the catchment to produce SWC timeseries for each of these units. Two SLU units involved rotational crops under poorly or imperfectly draining mineral soils; one SLU unit typically supports livestock farming on freely draining mineral soils and the fourth, moorland on organic-rich soils.  While the moorland SLU unit on organic soils had the greatest difference in SWC dynamics under the static CRNS and other SLUs, we also found subtle SWC differences between mineral soil SLU units under different agricultural management. We then evaluated the additional information generated by the combined CRNS method in a rainfall-runoff model (HBV-light) calibration of dynamic catchment storage. For the purpose, we used areal weighted SLU SWC timeseries and compared the model calibration to that using the static CRNS alone. In this case, differences were marginal and model efficiencies similar, suggesting that static CRNS data from a landscape-representative location may be sufficient to inform rainfall-runoff model calibration at the catchment scale.  However, this may depend on model structure and the degree to which SWC dynamics vary within the landscape. This study demonstrated the potential of expanding the information value of permanently installed CRNS sensors using portable CRNS surveys in the context of humid mixed-agricultural environment, although testing in different environments would be required to evaluate wider applicability.</p>

Integrating water isotopes and cosmic ray sensor data with modelling to understand near-surface storage-discharge relationships in managed landscapes

2020 ◽  
Author(s):  
Katya Dimitrova Petrova ◽  
Josie Geris ◽  
Mark Wilkinson ◽  
Allan Lilly ◽  
Lucile Verrot ◽  
...  
Keyword(s):  
Land Use ◽  
Model Calibration ◽  
Cosmic Ray ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Catchment Scale ◽  
Stream Network ◽  
Near Surface ◽  
Transit Times ◽  
Water Isotope

<p>Subsurface water storage strongly influences runoff generation processes, regulates agricultural production and defines catchment buffering capacities to hydrometeorological extremes. Knowledge about the amount and spatio-temporal distribution of catchment storage can also be important for constraining and evaluating hydrological models. While it is still challenging to measure this directly, characterisation of catchment-scale storage is more likely to be achieved via a combination of estimation methods at appropriate scales. While stable water isotopes can provide insights into (timescales of) dominant stores and flow paths, novel cosmic ray sensors (CRS) offer insights into large scale water storage dynamics.</p><p>Here, we combined stable water isotope analyses with CRS data and rainfall runoff modelling to better understand subsurface storage dynamics and how these relate to catchment runoff generation. We focussed specifically on humid managed environments, such as in NE Scotland, where short-term changes in both storage and management activities occur predominantly at or near the surface. To understand spatial patterns in flow pathways and the evolution of water ages (as mean transit times), we conducted long-term (~5y) stable water isotope monitoring of a nested stream network in a 10km<sup>2</sup> mixed-agricultural catchment. Monitoring also involved artificial drains of agricultural fields and country roads. This was complemented with a short-term study (~14 months) of mobile soil water in key soil-land use units. Additionally, we characterised field scale near-surface storage dynamics in these same key soil-land use units using CRS technology. Finally, we explored the storage-discharge relationships based on these CRS storage estimates and the information content of these novel data for rainfall-runoff model calibration to better characterise catchment-scale storage dynamics.</p><p>The outcomes of both transit time and rainfall-runoff modelling highlighted the importance of near-surface storage dynamics for catchment functioning and streamflow generation. Predominantly young waters (<1 y) across the stream network were associated mainly with shallow soils and the extensive artificial field drainage, which short-circuits water delivery to the streams, especially during wet periods. Water ages in soil mobile water were also short (1 – 6 months) and subtle differences between the key soil-land use units were associated with land management practices, which either enhanced (artificial drainage, ploughing) or delayed (compaction) transit times in the soil. As CRS near-surface storage estimates related well to catchment scale storage dynamics (R<sup>2</sup>=0.91) and stream discharge (R<sup>2</sup>=0.71), we evaluated the effect of using CRS data in model calibration. Including it in the model calibration was especially useful during intermediate and wet periods. Overall, our results showed that a combined model calibration using discharge and CRS estimates provided a better representation of catchment internal dynamics, additionally reducing uncertainty during low flows.</p><p>In the context of a humid managed catchment, our results showed that the integration of water isotope analyses and CRS-derived storage estimates can provide unique insights into catchment scale sub-surface storage dynamics, runoff generation and the evolution of water ages in soils and streams. They also demonstrated the potential of these data for informing rainfall-runoff modelling frameworks, but further work is needed across a range of different environments to explore wider applications.</p>

scholarly journals Modelling subsurface storm flow with the Representative Elementary Watershed (REW) approach: application to the Alzette River Basin

2006 ◽  
Vol 10 (6) ◽  
pp. 937-955 ◽  
Author(s):  
G. P. Zhang ◽  
H. H. G. Savenije ◽  
F. Fenicia ◽  
L. Pfister
Keyword(s):  
River Basin ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Catchment Scale ◽  
Model Code ◽  
Main River ◽  
Storm Flow ◽  
Rainfall Runoff Model ◽  
Table Data ◽  
Runoff Model

Abstract. A new domain, the macropore domain describing subsurface storm flow, has been introduced to the Representative Elementary Watershed (REW) approach. The mass balance equations have been reformulated and the closure relations associated with subsurface storm flow have been developed. The model code, REWASH, has been revised accordingly. With the revised REWASH, a rainfall-runoff model has been built for the Hesperange catchment, a sub-catchment of the Alzette River Basin. This meso-scale catchment is characterised by fast catchment response to precipitation, and subsurface storm flow is one of the dominant runoff generation processes. The model has been evaluated by a multi-criteria approach using both discharge and groundwater table data measured at various locations in the study site. It is demonstrated that subsurface storm flow contributes considerably to stream flow in the study area. Simulation results show that discharges measured along the main river course are well simulated and groundwater dynamics is well captured, suggesting that the model is a useful tool for catchment-scale hydrological analysis.

scholarly journals Modelling subsurface storm flow with the Representative Elementary Watershed (REW) approach: application to the Alzette River Basin

2006 ◽  
Vol 3 (1) ◽  
pp. 229-270 ◽  
Author(s):  
G. P. Zhang ◽  
H. H. G. Savenije ◽  
F. Fenicia ◽  
L. Pfister
Keyword(s):  
River Basin ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Catchment Scale ◽  
Model Code ◽  
Main River ◽  
Storm Flow ◽  
Rainfall Runoff Model ◽  
Table Data ◽  
Runoff Model

Abstract. A new domain, the macropore domain, for describing subsurface storm flow has been introduced to the Representative Elementary Watershed (REW) approach. The mass balance equations have been reformulated and the closure relations associated with subsurface storm flow have been developed. The model code, REWASH, has been revised accordingly. With the revised REWASH, a rainfall-runoff model has been built for the Hesperange catchment, a sub-catchment of the Alzette River Basin. This meso-scale catchment is characterised by fast catchment response to precipitation and subsurface storm flow is one of the dominant runoff generation processes. The model has been evaluated by a multi-criteria approach using both discharge and groundwater table data measured at various locations in the study site. It is demonstrated that subsurface storm flow contributes considerably to stream flow in the study area. Simulation results show that discharges measured along the main river course are well simulated and groundwater dynamics is well captured, suggesting that the model is a useful tool for catchment-scale hydrological analysis.

Multicore Parallel Genetic Algorithm with Tabu Strategy for Rainfall-Runoff Model Calibration

2017 ◽  
Vol 22 (8) ◽  
pp. 04017024 ◽  
Author(s):  
Shengli Liao ◽  
Qianying Sun ◽  
Chuntian Cheng ◽  
Ruhong Zhong ◽  
Huaying Su
Keyword(s):  
Genetic Algorithm ◽  
Model Calibration ◽  
Rainfall Runoff ◽  
Parallel Genetic Algorithm ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Technical Note: Updating procedure for flood forecasting with conceptual HBV-type models

2006 ◽  
Vol 10 (6) ◽  
pp. 783-788 ◽  
Author(s):  
Th. Wöhling ◽  
F. Lennartz ◽  
M. Zappa
Keyword(s):  
Flood Forecasting ◽  
Technical Note ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Mountainous Catchments ◽  
Rainfall Runoff Model ◽  
Updating Procedure ◽  
Global And Local ◽  
Hbv Model ◽  
Runoff Model

Abstract. Flood forecasting is of increasing importance as it comes to an increasing variability in global and local climates. But rainfall-runoff models are far from being perfect. In order to achieve a better prediction for emerging flood events, the model outputs have to be continuously updated. This contribution introduces a rather simple, yet effective updating procedure for the conceptual semi-distributed rainfall-runoff model PREVAH, whose runoff generation module relies on similar algorithms as the HBV-Model. The current conditions of the system, i.e. the contents of the upper soil reservoirs, are updated by the proposed method. The testing of the updating procedure on data from two mountainous catchments in Switzerland reveals a significant increase in prediction accuracy with regards to peak flow.

scholarly journals A comparison between natural forests and reforested lands in terms of runoff generation potential and hydrologic response (case study: Kasilian Watershed)

2012 ◽  
Vol 7 (No. 4) ◽  
pp. 166-173 ◽  
Author(s):  
E.H. Gholzom ◽  
V. Gholami
Keyword(s):  
Slope Aspect ◽  
Vegetation Density ◽  
Rainfall Runoff ◽  
Natural Forests ◽  
Runoff Generation ◽  
Mazandaran Province ◽  
Northern Iran ◽  
Runoff Volume ◽  
Rainfall Runoff Model ◽  
Runoff Model

Afforested lands are different from natural forests in terms of hydrologic conditions, runoff generation potential, and sediment generation rate. These differences emerge due to changes in soil structure and vegetation density, litter amount, trees heights, and so on. In this study, a comparison has been made between natural forests and afforested lands in Kasilian – a watershed located in Mazandaran province, Northern Iran. To achieve this purpose, harmonious units have been defined by overlay analysis of these layers in GIS environment: slope, aspect, Digital Elevation Model (DEM) and soil. Then, the location of couple plots was defined by field studies in the harmonious units. The plot locations were selected in a way that runoff generation was a function of tree species and tree conditions, assuming that rainfall intensity is equal in all areas. Initial loss and runoff volume were measured in even plots after rainfall. Then, the initial loss parameter in a rainfall-runoff model was applied to compare runoff volume and peak discharge in the afforested lands and natural forests. The rainfall-runoff model was presented using GIS and HEC-HMS model. The results showed that reforested lands have lower infiltration, lower initial loss, and higher runoff due to lower density, canopy, litter, and soil compaction. Furthermore, the runoff generation potential of reforested lands is several times higher than that of natural forests.

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