Assimilation of Observed Soil Moisture Data in Storm Rainfall-Runoff Modeling

2009 ◽  
Vol 14 (2) ◽  
pp. 153-165 ◽  
Author(s):  
L. Brocca ◽  
F. Melone ◽  
T. Moramarco ◽  
V. P. Singh
Keyword(s):  
Soil Moisture ◽  
Rainfall Runoff ◽  
Soil Moisture Data ◽  
Runoff Modeling ◽  
Storm Rainfall

scholarly journals Contrasting seasonality of storm rainfall and flood runoff in the UK and some implications for rainfall-runoff methods of flood estimation

2019 ◽  
Vol 50 (5) ◽  
pp. 1309-1323 ◽  
Author(s):  
Jamie Ledingham ◽  
David Archer ◽  
Elizabeth Lewis ◽  
Hayley Fowler ◽  
Chris Kilsby
Keyword(s):  
Soil Moisture ◽  
Risk Estimation ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Rainfall Runoff ◽  
Frequency Model ◽  
Soil Moisture Storage ◽  
Flood Estimation ◽  
Storm Rainfall ◽  
The Uk

Abstract Using data from 520 gauging stations in Britain and gridded rainfall datasets, the seasonality of storm rainfall and flood runoff is compared and mapped. Annual maximum (AMAX) daily rainfall occurs predominantly in summer, but AMAX floods occur most frequently in winter. Seasonal occurrences of annual daily rainfall and flood maxima differ by more than 50% in dry lowland catchments. The differences diminish with increasing catchment wetness, increase with rainfalls shorter than daily duration and are shown to depend primarily on catchment wetness, as illustrated by variations in mean annual rainfall. Over the whole dataset, only 34% of AMAX daily flood events are matched to daily rainfall annual maxima (and only 20% for 6-hour rainfall maxima). The discontinuity between rainfall maxima and flooding is explained by the consideration of coincident soil moisture storage. The results have serious implications for rainfall-runoff methods of flood risk estimation in the UK where estimation is based on a depth–duration–frequency model of rainfall highly biased to summer. It is concluded that inadequate treatment of the seasonality of rainfall and soil moisture seriously reduces the reliability of event-based flood estimation in Britain.

Rainfall-runoff modeling considering soil moisture accounting algorithm, case study: Karoon III River basin

2016 ◽  
Vol 43 (4) ◽  
pp. 699-710 ◽  
Author(s):  
Homa Razmkhah ◽  
Bahram Saghafian ◽  
Ali-Mohammad Akhound Ali ◽  
Fereydoun Radmanesh
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Rainfall Runoff ◽  
Soil Moisture Accounting ◽  
Runoff Modeling

scholarly journals Effects of antecedent soil moisture on runoff modeling in small semiarid watersheds of southeastern Arizona

2011 ◽  
Vol 8 (3) ◽  
pp. 6227-6256 ◽  
Author(s):  
Y. Zhang ◽  
H. Wei ◽  
M. A. Nearing
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Rain Event ◽  
Rainfall Runoff ◽  
Antecedent Soil Moisture ◽  
Runoff Modeling ◽  
Semiarid Watersheds ◽  
Walnut Gulch ◽  
Semiarid Rangeland

Abstract. Antecedent soil moisture prior to a rain event influences the rainfall-runoff relationship. Very few studies have looked at the effects of antecedent soil moisture on runoff modeling sensitivities in arid and semi-arid areas. This study examines the influence of initial soil moisture on model runoff prediction capability in small semiarid watersheds using model sensitivity and by comparing the use of antecedent vs. average long term soil water content for defining the model initial conditions for the modified Green-Ampt Mein-Larson model within the Rangeland Hydrology and Erosion Model (RHEM). Measured rainfall, runoff, and soil moisture data from four semiarid rangeland watersheds ranging in size from 0.34 to 4.53 ha on the Walnut Gulch Experimental Watershed in southeastern Arizona, USA, were used. Results showed that: (a) there were no significant correlations between measured runoff ratio and antecedent soil moisture in any of the four watersheds; (b) average sensitivities of simulated runoff amounts and peaks to antecedent soil moisture were 0.05 mm and 0.18 mm h−1, respectively, with each 1 % change in antecedent soil moisture; (c) runoff amounts and peaks simulated with long term average soil moisture were statistically equivalent to those simulated with measured antecedent soil moisture. The relative lack of sensitivity of modeled runoff to antecedent soil moisture in this case is contrary to results reported in other studies, and is largely due to the fact that the surface soil is nearly always very dry in this environment.

scholarly journals Design soil moisture estimation by comparing continuous and storm-based rainfall-runoff modeling

2011 ◽  
Vol 47 (5) ◽  
Author(s):  
S. Camici ◽  
A. Tarpanelli ◽  
L. Brocca ◽  
F. Melone ◽  
T. Moramarco
Keyword(s):  
Soil Moisture ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Moisture Estimation

scholarly journals Can soil moisture be mapped onto the terrain?

2004 ◽  
Vol 8 (5) ◽  
pp. 923-930 ◽  
Author(s):  
E. M. Blyth ◽  
J. Finch ◽  
M. Robinson ◽  
P. Rosier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Mapping Method ◽  
Rainfall Runoff ◽  
Topographic Index ◽  
Soil Moisture Data ◽  
Aggregate Effect ◽  
Large Scale Land ◽  
Soil Moisture Heterogeneity

Abstract. Soil moisture heterogeneity has an effect on the rainfall–runoff characteristics of a landscape. The aggregate effect on the mean water balance over an area can be quantified successfully using models such as the PDM (Moore, 1986) and TOPMODEL (Beven and Kirkby, 1979). These rainfall–runoff models have been embedded in the large-scale land surface schemes used in meteorological models. However, there is also a requirement (e.g. model validation) to identify the spatial structure of the fine-scale soil moisture heterogeneity that makes up these aggregate models. In some types of landscape, this will be dictated by topography, in others by soil characteristics, or by a combination of both. A method to distribute area-average soil moisture according to the likely effect of local topography is presented and tested. The heterogeneity of the soil moisture is described by the Xinanxiang distribution (Zhao et al., 1980), commonly used to describe the natural spatial heterogeneity of the landscape. This distribution is then mapped onto the terrain using a topographic index to locate the wettest and driest areas. Soil moisture data from the Wye catchment in Wales and from the Pang catchment in Berkshire, England, are used to test the method. It is found that soil moisture data from the Wye catchment follow the topographic index reasonably well, whereas data from the quick-draining, chalky Pang catchment do not. The conclusion that topographic index is a useful indicator only in some landscapes applies equally to using this mapping method and those models that use topographic index directly. Keywords: soil moisture, heterogeneity, topographic index, data

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Intra-Storm Pattern Recognition through Fuzzy Clustering

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 57
Author(s):  
Konstantinos Vantas ◽  
Epaminondas Sidiropoulos
Keyword(s):  
Fuzzy Clustering ◽  
A Priori ◽  
Temporal Distribution ◽  
Distribution Patterns ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Domain Specific ◽  
Runoff Modeling ◽  
National Bank ◽  
Rainfall Patterns

The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former.

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