A soil moisture monitoring network to assess controls on runoff generation during atmospheric river events

Sumargo, Edwin; McMillan, Hilary; Weihs, R. R.; Ellis, C. J.; Wilson, Anna M.; Ralph, F. Martin

doi:10.1002/hyp.13998

Cited by 7 publications

(6 citation statements)

References 47 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This runoff generation process is referred to as the saturation-excess runoff. Q/P (%) of a watershed will become larger when effective soil water-storage is reducing [19][20][21][22]. Even a medium-sized storm event over a saturated watershed with a high runoff coefficient may cause severe flooding; therefore, it is critical to assess the performance of different runoff models under different runoff coefficient conditions in order to select the best performing parsimonious urban rainfall-runoff model for urban runoff estimation.…”

Section: Methodology and Study Sitementioning

confidence: 99%

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

et al. 2022

View full text Add to dashboard Cite

The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

show abstract

Section: Methodology and Study Sitementioning

confidence: 99%

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Contrarily, some reported that the usage of CN in representing a watershed is often contradictory in describing related land cover areas [13]. Some researchers still reported difficulty to calibrate the existing model [14,15] while other studies started to incorporate soil moisture and saturation-excess concepts in their modelling approach [16][17][18][19]. US researchers [2,20] were first to conduct large scale studies on the SCS CN model by analyzing more than half a million rainfall events across 24 states in the USA and reported an optimum λ = 0.05 to achieve better runoff modelling results than Equation (2) in USA.…”

Section: Introductionmentioning

confidence: 99%

Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia

2021

View full text Add to dashboard Cite

Flood related disasters continue to threaten mankind despite preventative efforts in technological advancement. Since 1954, the Soil Conservation Services (SCS) Curve Number (CN0.2) rainfall-runoff model has been widely used but reportedly produced inconsistent results in field studies worldwide. As such, this article presents methodology to reassess the validity of the model and perform model calibration with inferential statistics. A closed form equation was solved to narrow previous research gap with a derived 3D runoff difference model for type II error assessment. Under this study, the SCS runoff model is statistically insignificant (alpha = 0.01) without calibration. Curve Number CN0.2 = 72.58 for Peninsula Malaysia with a 99% confidence interval range of 67 to 76. Within these CN0.2 areas, SCS model underpredicts runoff amounts when the rainfall depth of a storm is < 70 mm. Its overprediction tendency worsens in cases involving larger storm events. For areas of 1 km2, it underpredicted runoff amount the most (2.4 million liters) at CN0.2 = 67 and the rainfall depth of 55 mm while it nearly overpredicted runoff amount by 25 million liters when the storm depth reached 430 mm in Peninsula Malaysia. The SCS model must be validated with rainfall-runoff datasets prior to its adoption for runoff prediction in any part of the world. SCS practitioners are encouraged to adopt the general formulae from this article to derive assessment models and equations for their studies.

show abstract

“…For each event, event rising time was calculated as the time‐lag from the start of an event to the soil moisture peak. Event amplitude was calculated as the difference between the soil moisture values at their maximum and at the start of the event, normalized using estimated field capacity and wilting point at the station (defined in Section 3.1.6) as practiced by Sumargo et al (2021). Soil moisture was judged as not responding if there was no soil moisture peak detected.…”

Section: Methodsmentioning

confidence: 99%

A signature‐based approach to quantify soil moisture dynamics under contrasting land‐uses

Araki

Branger

Wiekenkamp

et al. 2022

Hydrological Processes

Self Cite

View full text Add to dashboard Cite

Soil moisture signatures provide a promising solution to overcome the difficulty of evaluating soil moisture dynamics in hydrologic models. Soil moisture signatures are metrics that quantify the dynamic aspects of soil moisture timeseries and enable process‐based model evaluations. To date, soil moisture signatures have been tested only under limited land‐use types. In this study, we explore soil moisture signatures' ability to discriminate different dynamics among contrasting land‐uses. We applied a set of nine soil moisture signatures to datasets from six in‐situ soil moisture networks worldwide. The dataset covered a range of land‐use types, including forested and deforested areas, shallow groundwater areas, wetlands, urban areas, grazed areas, and cropland areas. Our set of signatures characterized soil moisture dynamics at three temporal scales: event, season, and a complete timeseries. Statistical assessment of extracted signatures showed that (1) event‐based signatures can distinguish different dynamics for all the land‐uses, (2) season‐based signatures can distinguish different dynamics for some types of land‐uses (deforested vs. forested, urban vs. greenspace, and cropped vs. grazed vs. grassland contrasts), (3) timeseries‐based signatures can distinguish different dynamics for some types of land‐uses (deforested vs. forested, urban vs. greenspace, shallow vs. deep groundwater, wetland vs. non‐wetland, and cropped vs. grazed vs. grassland contrasts). Further, we compared signature‐based process interpretations against literature knowledge; event‐based and timeseries‐based signatures generally matched well with previous process understandings from literature, but season‐based signatures did not. This study will be a useful guideline for understanding how catchment‐scale soil moisture dynamics in various land‐uses can be described using a standardized set of hydrologically relevant metrics.

show abstract

A soil moisture monitoring network to assess controls on runoff generation during atmospheric river events

Cited by 7 publications

References 47 publications

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia

A signature‐based approach to quantify soil moisture dynamics under contrasting land‐uses

Contact Info

Product

Resources

About