Predictability of Seasonal Streamflow and Soil Moisture in National Water Model and a Humid Alabama–Coosa–Tallapoosa River Basin

2020 ◽  
Vol 21 (7) ◽  
pp. 1447-1467 ◽  
Author(s):  
Yanan Duan ◽  
Sanjiv Kumar

AbstractThis study investigates the potential predictability of streamflow and soil moisture in the Alabama–Coosa–Tallapoosa (ACT) river basin in the southeastern United States. The study employs the state-of-the-art National Water Model (NWM) and compares the effects of initial soil moisture condition with those of seasonal climate anomalies on streamflow and soil moisture forecast skills. We have designed and implemented seasonal streamflow forecast ensemble experiments following the methodology suggested by Dirmeyer et al. The study also compares the soil moisture variability in the NWM with in situ measurements and remote sensing data from the Soil Moisture Active and Passive (SMAP) satellite. The NWM skillfully simulates the observed streamflow in the ACT basin. The soil moisture variability is 46% smaller in the NWM compared with the SMAP data, mainly due to a weaker amplitude of the seasonal cycle. This study finds that initial soil moisture condition is a major source of predictability for the seasonal streamflow forecast. The contribution of the initial soil moisture condition is comparable or even higher than that of seasonal climate anomaly effects in dry seasons. In the boreal summer season, the initial soil moisture condition contributes to 65% and 48% improvements in the seasonal streamflow and soil moisture forecast skills, respectively. This study attributes a greater improvement in the streamflow forecast skill to the lag effects between the soil moisture and streamflow anomalies. The results of this study can inform the development and improvement of the operational streamflow forecasting system.

2021 ◽  
Vol 9 ◽  
Author(s):  
Miaoling Liang ◽  
Xing Yuan

The unprecedented 2012 summer drought over the central United States was characterized by rapid intensification and severe impact and was known as a flash drought. Since then, flash drought has raised a wide concern, with considerable progresses on the definition, detection of anthropogenic footprints, and assessment of ecological impact. However, physical mechanisms related to the flash drought predictability remain unclear. Here, we show that the severity of the 2012 flash drought will be heavily underestimated without realistic initial soil moisture condition. The global Weather Research and Forecasting (GWRF) model was employed during the summers of 1979–2012, driven by observed sea surface temperature but without lateral boundary controls, which is similar to two-tier global seasonal prediction. The 2012 United States drought pattern was roughly captured by the GWRF ensemble global simulations, although with obvious underestimation of the severity. To further diagnose the role of soil moisture memory, dry and wet simulations that decrease and increase initial soil moisture by 10% were conducted. While the dry case does not significantly differ from the control case, the wet case totally missed the drought over the Central and Southern Great Plains by changing the anticyclonic circulation anomaly to a cyclonic anomaly and simulating a northward anomaly of meridional wind that brought anomalous moisture from the Gulf of Mexico and finally resulted in a failure to predict the drought. This study highlights the importance of soil moisture memory in predicting flash drought that often occurred without strong oceanic signal.


2006 ◽  
Vol 10 (20) ◽  
pp. 1-24 ◽  
Author(s):  
Diandong Ren ◽  
Ann Henderson-Sellers

Abstract Besides the atmospheric forcing such as solar radiation input and precipitation, the heterogeneity of the surface cover also plays an important role, especially in the distribution characteristics of the latent heat flux (LE). In this study, scaling issues are discussed based on an analytical hydrological model that describes the transpiration and diffusion processes of soil water. The solution of this analytical model is composed of a transient part that depends primarily on initial conditions and a steady part that depends on the boundary conditions. To know how sensitive the different averaging approaches are to the initial conditions, three initial profiles are chosen that cover the prevailing soil moisture regimes. After analyzing its solution, the study shows that 1) upon reaching the steady state, directly taking an average of soil properties will cause systematic overestimation in the calculation of area-averaged LE. For an initially very dry condition, averaging of a sandy soil and a clay soil can cause a percentage error as large as 40%. 2) For vegetation growing on sandy soils, a direct averaging of the transpiration rate results in persistent overestimation of LE. For vegetation growing on clay soil, however, even after reaching the steady state, averaging of two water extraction weights can be either an overestimation or an underestimation, depending on which two vegetation types are involved. 3) During the interim stage of drying down, averaging of the soil/vegetation properties can lead to either an overestimation or an underestimation, depending on the evolving stage of the soil moisture profile. 4) The initial soil moisture condition matters during the transient stage of drying down. Different initial soil moisture conditions yield different scenarios of underestimation and overestimation patterns and a differing severity of errors. The simplicity of the analytical model and the heuristic initial soil profiles make the generalization easier than using sophisticated numerical models and make the causality mechanism clearer for physical interpretations.


1959 ◽  
Vol 31 (1) ◽  
pp. 233-239
Author(s):  
Mikko Sillanpää

The effect of the soil moisture content (varying from the field-moist to air-dry before re-wetting the muddy clay soil samples for aggregate analysis) on aggregation was studied. Two wetting procedures were used and compared: They were spraying samples with a fine mist and wetting them by immersion; aggregate analyses were made by wet sieving method. The results of the aggregate analyses proved to be practically independent of the initial moisture condition of the soil samples when the samples were wetted slowly with a spray. When wetting the samples by direct immersion the mean weight diameters of aggregates decrease with decreasing initial soil moisture content to values of less than half of those obtained from samples in their original field-moist condition (34.6—36.7 % dry wt.) or of those wetted with a spray. Air-drying seems to be a minor factor affecting the destruction of aggregates but the destruction effect of the sample pre-treatment may be very harmful if immersion wetting is used. This, however, can be eliminated almost completely if wetting with a fine mist is used.


2012 ◽  
Vol 13 (1) ◽  
pp. 404-412 ◽  
Author(s):  
Sonia I. Seneviratne ◽  
Randal D. Koster

Abstract A revised framework for the analysis of soil moisture memory characteristics of climate models and observational data is derived from the approach proposed by Koster and Suarez. The resulting equation allows the expression of the month-to-month soil moisture autocorrelation as a function of 1) the initial soil moisture variability, 2) the (atmospheric) forcing variability over the considered time period, 3) the correlation between initial soil moisture and subsequent forcing, 4) the sensitivity of evaporation to soil moisture, and 5) the sensitivity of runoff to soil moisture. A specific new feature is the disentangling of the roles of initial soil moisture variability and forcing variability, which were both (for the latter indirectly) contributing to the seasonality term of the original formulation. In addition, a version of the framework entirely based on explicit equations for the underlying relationships (i.e., independent of soil moisture statistics at the following time step) is proposed. The validity of the derived equation is exemplified with atmospheric general circulation model (AGCM) simulations from the Global Land–Atmosphere Coupling Experiment (GLACE).


2007 ◽  
Vol 34 (20) ◽  
Author(s):  
Zoltan Bartalis ◽  
Wolfgang Wagner ◽  
Vahid Naeimi ◽  
Stefan Hasenauer ◽  
Klaus Scipal ◽  
...  

2021 ◽  
Author(s):  
Nunziarita Palazzolo ◽  
David J. Peres ◽  
Enrico Creaco ◽  
Antonino Cancelliere

<p>Landslide triggering thresholds provide the rainfall conditions that are likely to trigger landslides, therefore their derivation is key for prediction purposes. Different variables can be considered for the identification of thresholds, which commonly are in the form of a power-law relationship linking rainfall event duration and intensity or cumulated event rainfall. The assessment of such rainfall thresholds generally neglects initial soil moisture conditions at each rainfall event, which are indeed a predisposing factor that can be crucial for the proper definition of the triggering scenario. Thus, more studies are needed to understand whether and the extent to which the integration of the initial soil moisture conditions with rainfall thresholds could improve the conventional precipitation-based approach. Although soil moisture data availability has hindered such type of studies, yet now this information is increasingly becoming available at the large scale, for instance as an output of meteorological reanalysis initiatives. In particular, in this study, we focus on the use of the ERA5-Land reanalysis soil moisture dataset. Climate reanalysis combines past observations with models in order to generate consistent time series and the ERA5-Land data actually provides the volume of water in soil layer at different depths and at global scale. Era5-Land project is, indeed, a global dataset at 9 km horizontal resolution in which atmospheric data are at an hourly scale from 1981 to present. Volumetric soil water data are available at four depths ranging from the surface level to 289 cm, namely 0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm. After collecting the rainfall and soil moisture data at the desired spatio-temporal resolution, together with the target data discriminating landslide and no-landslide events, we develop automatic triggering/non-triggering classifiers and test their performances via confusion matrix statistics. In particular, we compare the performances associated with the following set of precursors: a) event rainfall duration and depth (traditional approach), b) initial soil moisture at several soil depths, and c) event rainfall duration and depth and initial soil moisture at different depths. The approach is applied to the Oltrepò Pavese region (northern Italy), for which the historical observed landslides have been provided by the IFFI project (Italian landslides inventory). Results show that soil moisture may allow an improvement in the performances of the classifier, but that the quality of the landslide inventory is crucial.</p>


2012 ◽  
Author(s):  
Raheleh Malekian ◽  
Robert Gordon ◽  
Ali Madani ASABE Member ◽  
Seyyed Ebrahim Hashemi

1979 ◽  
Vol 27 (3) ◽  
pp. 191-198
Author(s):  
J.H. Smelt ◽  
A. Dekker ◽  
M. Leistra

The decomposition of oxamyl in four soils under moist conditions was measured in incubation experiments at 15 deg C. Half-lives of oxamyl in soils with moisture tensions of approx. -9.8 X 103 Pa were 13 days in a clay loam, 14 days in a loamy sand, 34 days in a peaty sand and 39 days in a humic loamy sand. The rate of oxamyl decomposition in the clay loam decreased with decreasing soil moisture content down to values for below wilting point. Oxamyl decomposition in the humic loamy sand decreased with decreasing soil moisture content, but increased sharply in the very dry range. (Abstract retrieved from CAB Abstracts by CABI’s permission)


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