Characterizing the effects of Dry Antecedent Soil Moisture Conditions, Channel Transmission Losses, and Variable Precipitation on Peak Flow Scaling

2021 ◽  
pp. 104061
Author(s):  
Nicholas W. Thomas ◽  
Tibebu B. Ayalew ◽  
Antonio A. Arenas ◽  
Keith E. Schilling ◽  
Larry J. Weber ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Peak Flow ◽  
Transmission Losses ◽  
Antecedent Soil Moisture ◽  
Moisture Conditions ◽  
Variable Precipitation

Multidimensional context for extreme analysis of daily streamflow, rainfall and accumulated rainfall across USA

2020 ◽  
Author(s):  
Maria Nezi ◽  
Ioannis Tsoukalas ◽  
Charalampos Ntigkakis ◽  
Andreas Efstratiadis
Keyword(s):  
Soil Moisture ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Annual Rainfall ◽  
Event Analysis ◽  
Antecedent Soil Moisture ◽  
Daily Streamflow ◽  
Accumulated Rainfall ◽  
Moisture Conditions ◽  
Parameter Values

<p>Statistical analysis of rainfall and runoff extremes plays a crucial role in hydrological design and flood risk management. Usually this analysis is performed separately for the two processes of interest, thus ignoring their dependencies, which appear at multiple temporal scales. Actually, the generation of a flood strongly depends on soil moisture conditions, which in turn depends on past rainfall. Using daily rainfall and runoff data from about 400 catchments in USA, retrieved from the MOPEX repository, we investigate the statistical behavior of the corresponding annual rainfall and streamflow maxima, also accounting for the influence of antecedent soil moisture conditions. The latter are quantified by means of accumulated daily rainfall at various aggregation scales (i.e., from 5 up to 30 days) before each extreme rainfall and streamflow event. Analysis of maxima is employed by fitting the Generalized Extreme Value (GEV) distribution, using the L-moments method for extracting the associated parameters (shape, scale, location). Significant attention is paid for ensuring statistically consistent estimations of the shape parameter, which is empirically adjusted in order to minimize the influence of sample uncertainty. Finally, we seek for the possible correlations among the derived parameter values and hydroclimatic characteristics of the studied basins, and also depict their spatial distribution across USA.</p>

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

2011 ◽  
Vol 15 (10) ◽  
pp. 3171-3179 ◽  
Author(s):  
Y. Zhang ◽  
H. Wei ◽  
M. A. Nearing
Keyword(s):  
Soil Moisture ◽  
Standard Deviation ◽  
Soil Layer ◽  
Arid Environment ◽  
Storm Events ◽  
Runoff Generation ◽  
Antecedent Soil Moisture ◽  
Small Watersheds ◽  
Runoff Modeling ◽  
Moisture Conditions

Abstract. This study presents unique data on the effects of antecedent soil moisture on runoff generation in a semi-arid environment, with implications for process-based modeling of runoff. The data were collected from four small watersheds measured continuously from 2002 through 2010 in an environment where evapo-transpiration approaches 100% of the infiltrated water on the hillslopes. Storm events were generally intense and of short duration, and antecedent volumetric moisture conditions were dry, with an average in the upper 5 cm soil layer over the nine year period of 8% and a standard deviation of 3%. Sensitivity analysis of the model showed an average of 0.05 mm change in runoff for each 1% change in soil moisture, indicating an approximate 0.15 mm average variation in runoff accounted for by the 3% standard deviation of measured antecedent soil moisture. This compared to a standard deviation of 4.7 mm in the runoff depths for the measured events. Thus the low variability of soil moisture in this environment accounts for a relative lack of importance of storm antecedent soil moisture for modeling the runoff. Runoff characteristics simulated with a nine year average of antecedent soil moisture were statistically identical to those simulated with measured antecedent soil moisture, indicating that long term average antecedent soil moisture could be used as a substitute for measured antecedent soil moisture for runoff modeling of these watersheds. We also found no significant correlations between measured runoff ratio and antecedent soil moisture in any of the four watersheds.

scholarly journals On the Relationship of Soil Moisture and Extreme Temperatures in East China

2014 ◽  
Vol 18 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Lei Meng ◽  
Yanjun Shen
Keyword(s):  
Soil Moisture ◽  
Standardized Precipitation Index ◽  
Heat Fluxes ◽  
East China ◽  
Daily Minimum Temperature ◽  
Antecedent Soil Moisture ◽  
Surface Temperatures ◽  
Quantile Regression Analysis ◽  
Temperature Indices ◽  
Moisture Conditions

Abstract Soil moisture conditions affect energy partitioning between sensible and latent heat fluxes, resulting in a change in surface temperatures. In this study, the relationships between antecedent soil moisture conditions [as indicated by the 6-month standardized precipitation index (SPI)] and several temperature indices are statistically quantified using the quantile regression analysis across East China to investigate the influence of soil moisture on summer surface temperatures. These temperature indices include percentage of hot days (%HD), heat-wave duration (HWD), daily temperature range (DTR), and daily minimum temperature (Tmin). It was demonstrated that soil moisture had a significant impact on %HD and HWD at higher quantiles in all regions but the east, suggesting that drier soil moisture conditions tend to intensity summer hot extremes. It was also found that hot extremes (%HD and HWD at higher quantiles) had increased substantially from 1958 to 2010. Soil moisture also significantly affected the DTR in all regions but tended to have more impacts on the DTR in soil moisture-limited regimes than in energy-limited regimes. This study provides observational evidence of soil moisture influences on hot extremes in East China.

scholarly journals Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides Occurrence

2020 ◽  
Author(s):  
Maurizio Lazzari ◽  
Marco Piccarreta ◽  
Ram L. Ray ◽  
Salvatore Manfreda
Keyword(s):  
Soil Moisture ◽  
Rainfall Intensity ◽  
Shallow Landslide ◽  
Rainfall Thresholds ◽  
Antecedent Soil Moisture ◽  
Correct Definition ◽  
Triggering Conditions ◽  
Definition Of ◽  
Moisture Conditions ◽  
Traditional Approaches

Rainfall-triggered shallow landslide events have caused losses of human lives and millions of euros in damage to property in all parts of the world. The need to prevent such hazards combined with the difficulty of describing the geomorphological processes over regional scales led to the adoption of empirical rainfall thresholds derived from records of rainfall events triggering landslides. These rainfall intensity thresholds are generally computed, assuming that all events are not influenced by antecedent soil moisture conditions. Nevertheless, it is expected that antecedent soil moisture conditions may provide critical support for the correct definition of the triggering conditions. Therefore, we explored the role of antecedent soil moisture on critical rainfall intensity-duration thresholds to evaluate the possibility of modifying or improving traditional approaches. The study was carried out using 326 landslide events that occurred in the last 18 years in the Basilicata region (southern Italy). Besides the ordinary data (i.e., rainstorm intensity and duration), we also derived the antecedent soil moisture conditions using a parsimonious hydrological model. These data have been used to derive the rainfall intensity thresholds conditional on the antecedent saturation of soil quantifying the impact of such parameters on rainfall thresholds.

scholarly journals A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals

2009 ◽  
Vol 13 (1) ◽  
pp. 1-16 ◽  
Author(s):  
W. T. Crow ◽  
D. Ryu
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Runoff ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Surface Soil Moisture ◽  
Antecedent Soil Moisture ◽  
Runoff Prediction ◽  
Moisture Conditions

Abstract. A number of recent studies have focused on enhancing runoff prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies) and storm-scale rainfall totals (required to estimate the total surface runoff volume). In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing solely on the constraint of antecedent soil moisture conditions.

scholarly journals Effect of GPR-derived within-field soil moisture variability on the runoff response using a distributed hydrologic model

2010 ◽  
Vol 7 (6) ◽  
pp. 8947-8986 ◽  
Author(s):  
J. Minet ◽  
E. Laloy ◽  
S. Lambot ◽  
M. Vanclooster
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Variability ◽  
Hydrologic Model ◽  
Topographic Wetness Index ◽  
Antecedent Soil Moisture ◽  
Distributed Hydrologic Model ◽  
Ground Penetrating ◽  
Moisture Conditions ◽  
Soil Moisture Variability

Abstract. The importance of the spatial variability of the antecedent soil moisture conditions on the runoff response is widely acknowledged in hillslope hydrology. Using a distributed hydrologic model, this paper aims at investigating the effects of soil moisture spatial variability on the runoff in various field conditions and at finding the soil moisture scenario that behaves the most closely as the measured soil moisture pattern in term of runoff hydrograph. Soil moisture was surveyed in ten different field campaigns using a proximal ground penetrating radar (GPR) that allowed to perform high-resolution (~m) mapping at the field scale (several ha). Based on these soil moisture measurements, seven scenarios of antecedent soil moisture were used to feed hydrological simulations and the resulting hydrographs were compared. The novelty of this work is to benefit from high-resolution soil moisture measurements using an advanced GPR in various soil moisture conditions. Accounting for the spatial variability of soil moisture resulted in a larger discharge than using a spatially constant soil moisture. The ranges of possible hydrographs were delineated by the extreme scenarios where soil moisture was directly and inversely arranged according to the topographic wetness index (TWI). These behaviours could be explained in terms of runoff contributing areas, with respect to their sizes and their relative locations within the field. The most efficient scenario for soil moisture appeared to be when soil moisture is directly arranged according to the TWI. This was related to the correlation of the measured soil moisture and the TWI. These observations generalised some of the statements pointed out in previous studies. Similar findings are thus expected under similar soil and rainfall forcing conditions.

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