Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale

Tayfur, Gökmen; Zucco, Graziano; Brocca, Luca; Moramarco, Tommaso

doi:10.1016/j.jhydrol.2013.12.045

Cited by 36 publications

(13 citation statements)

References 41 publications

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“…observed and modelled water level, forecasted precipitation, and state variables (GW, SMD, SWE, SD). Although the benefits of using the soil moisture (observed or modelled) and groundwater level information for modelling rainfall-runoff processes and predicting runoff are well known in the literature (Aubert et al, 2003;Lee and Seo, 2011;Tayfur et al, 2014), we cannot cite any studies exploring the possible advantages of using such information for improving predictive capabilities of uncertainty analysis methods. Therefore, the dependence of model residuals on variables expressing the internal state of the catchments is also analysed.…”

Section: Upper Severn Catchments: Yeaton Llanyblodwel and Llanerfylmentioning

confidence: 99%

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dogulu

López

Solomatine

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.

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Section: Upper Severn Catchments: Yeaton Llanyblodwel and Llanerfylmentioning

confidence: 99%

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dogulu

López

Solomatine

et al. 2015

Hydrol. Earth Syst. Sci.

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“…These results were similar to that found for the watershed. The surface runoff is predominantly Hortonian processes in the semiarid regions; thus, the higher magnitudes (CAMMERAAT, 2002;BAKKER et al, 2005;TAYFUR et al, 2014;WEI et al, 2014), which evidences the dependence of SY on transport energy. 1 PPT -precipitation (mm); I5 -maximum intensity in 5 min (mm h -1 ); I10 -maximum intensity in 10 min (mm h -1 ); I15 -maximum intensity in 15 min (mm h -1 ); I20 -maximum intensity in 20 min (mm h -1 ); I30 -maximum intensity in 30 min (mm h -1 ); I45 -maximum intensity in 45 min (mm h -1 ); I60 -maximum intensity in 60 min (mm h -1 ); EI30 -rainfall erosivity index (MJ mm ha -1 h -1 ); AMS -antecedent soil moisture based on mass (%); PPT5 -precipitation of the antecedent five days (mm); CDD -consecutive dry days (days with PPT < 1 mm); RD -runoff depth (mm); SY -sediment yield (kg ha -1 ).…”

Section: Resultsmentioning

confidence: 94%

Erosive Process Control in the Brazilian Semiarid Region

et al. 2018

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The adoption of measures to prevent and control erosive processes requires information about the factors affecting the erosion and the sediment transport conditions. However, the sediment yield of a basin depends on the availability of eroded material and the sediment transport capacity. Thus, the objective of this study was to identify the factors that affect the sediment transport capacity at different spatial scales in an area with caatinga vegetation. The study was carried out in the Iguatu Experimental Basin, in the state of Ceará, Brazil, from 2009 to 2014, by monitoring two scale levels: watershed with 2.06 ha, and erosion plot with 20 m². The variables evaluated for the rainfall events were precipitation, intensity of rainfall, antecedent soil moisture, precipitation of the antecedent five days, consecutive dry days, peak flow, runoff depth, and sediment yield. During the study period, 263 rainfall events (>2 mm) and 86 events generating runoff were recorded. Three Principal Components (PC) were developed using the Principal Component Analysis, which explained more than 79% of the total variance. Variables connected to the kinetic energy capacity of the rainfall to disaggregate soil particles, the energy for sediment transport, and the soil water content were framed in the CP1, CP2, and CP3, respectively. In the evaluated scales, the sediment yield presented a high correlation with the runoff depth, which indicates limiting conditions for sediment yield by the mass flow energy.

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“…Soil moisture (SM) is an important terrestrial water cycle component that not only influences evaporation, infiltration, and runoff formation (Sorman & Abdulrazzak, 1995;Tayfur, Zucco, Brocca, & Moramarco, 2014) but also affects climate system by redistributing sensible and latent heat (Deng et al, 2018;LeMone et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

Deng

Wang

Bai

et al. 2019

Hydrological Processes

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High-quality soil moisture (SM) datasets are in great demand for climate, hydrology, and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) (C-and X-bands) and European Space Agency's Climate Change Initiative (ESA CCI)], land surface model [Global Land Data Assimilation System (GLDAS)], and reanalysis data [ECMWF Re-Analysis-Interim (ERA-Interim) and National Centers for Environmental Prediction (NCEP)] under different time scales and various climates and land covers. We find that: (a) ESA CCI and GLDAS have the closest values to the in situ SM on the annual scale, whereas others overestimate the SM; ERA-Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in situ data, while GLDAS performs worst. (b) Overall, the deviations of each product vary in seasons. ESA CCI and ERA-Interim products are closer to the in situ SM at seasonal scales, and AMSR-E and NCEP perform worst in December-February and June-August, respectively. (c) Except for NCEP and ERA-Interim, others can well reflect the intermonthly variation of the in situ SM. (d) Under various climates and land covers, AMSR-E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the Bias and R of each SM product differobviously under different forest types, especially the AMSR-E products. In summary, SM from ESA CCI is the best, followed by ERA-Interim product, and precipitation is an important auxiliary data for selecting high-quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products. K E Y W O R D S

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Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale

Cited by 36 publications

References 41 publications

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Erosive Process Control in the Brazilian Semiarid Region

Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

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