scholarly journals Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural region

2019 ◽  
Author(s):  
Navid Jadidoleslam ◽  
Ricardo Mantilla ◽  
Witold F. Krajewski ◽  
Radoslaw Goska
Keyword(s):  
Soil Moisture ◽  
Agricultural Landscapes ◽  
Rainfall Runoff ◽  
Antecedent Soil Moisture ◽  
Radar Rainfall ◽  
Event Scale ◽  
Moisture Deficit ◽  
Runoff Production

Following results by Crow et al. (2017) [Geophys. Res. Lett. 44, 5495-5503] on the impact of antecedent soil moisture on runoff production, we investigate total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vegetation cover for catchments with drainage areas ranging from 80-1000 km2 in the state of Iowa, USA. For our study, we use Enhanced SMAP soil moisture estimates, the MODIS enhanced vegetation index (EVI), gauge-corrected Stage IV radar rainfall, and USGS streamflow data. We analyze the event runoff ratio as a function of event-scale rainfall, antecedent SMAP soil moisture and soil-moisture-deficit-normalized rainfall for the events in a period from March 31, 2015 to October 31, 2018. Our goal is to confirm the relationships identified by Crow et al. (2017) in heavily managed agricultural landscapes and to refine some of their methodological steps to quantify the role of additional variables controlling runoff production. To this end, we define three different strategies to identify rainfall-runoff events and add a baseflow separation step to better insulate event scale stormflow runoff. We test the effects of antecedent soil moisture, rainfall, and vegetation on the event-scale runoff ratio. The antecedent SMAP soil moisture and event-scale rainfall are found to have significant predictive power in estimating event runoff ratio. Soil moisture deficit-normalized rainfall, introduced as the ratio of event-scale rainfall to available space in top soil before initiation of the event, exhibited a more distinct relationship with runoff ratio. The long-term analysis of runoff ratio, rainfall, and MODIS EVI indicated that, in an agricultural region, vegetation plays a significant role in determining event-scale runoff ratios. The methodology and outcome of our study have direct implications on real-time flood forecasting and long-term hydrologic assessments.

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.

Exploring potential of remotely sensed data in parameterization of hydrologic model

2020 ◽  
Author(s):  
Navid Jadidoleslam ◽  
Ricardo Mantilla ◽  
Witold Krajewski
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Rainfall Runoff ◽  
Remotely Sensed Data ◽  
Hydrologic Models ◽  
Antecedent Soil Moisture ◽  
Daily Streamflow ◽  
Runoff Production

<p>Recent observation-based studies have shown that satellite-based antecedent soil moisture can provide useful information on runoff production. The patterns uncovered can be used to benchmark the degree of coupling between antecedent soil moisture, rainfall totals and runoff production, and to determine if hydrologic models can reproduce these patterns for a particular model parameterization of their rainfall-runoff processes. The goal of our study is twofold; First, it derives the relationships between runoff ratio and its major controls, including rainfall total, antecedent soil moisture, and vegetation using remotely sensed data products. Second, it aims to determine if the model is capable to reproduce these relationships and use them to validate model parameters and streamflow predictions. For this purpose, SMAP (Soil Moisture Active Passive) satellite-based soil moisture, S-band radar rainfall, MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index, and USGS (United States Geological Survey) daily streamflow observations are used. The study domain consists of thirty-eight basins less than 1000 km<sup>2</sup> located in an agricultural region in the United States Midwest. For each basin, daily streamflow predictions, before and after adjustments to the hydrologic model are compared with observations. The comparisons are done for four years (2015-2018) using multiple performance metrics. This study could serve as a data-driven approach for parameterization of rainfall-runoff partitioning in hydrologic models using remotely sensed observations. </p>

scholarly journals Impacts of Antecedent Soil moisture on the Rainfall–Runoff Transformation Process Based on High-Resolution Observations in Soil Tank Experiments

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 296 ◽  
Author(s):  
Shuang Song ◽  
Wen Wang
Keyword(s):  
Soil Moisture ◽  
Overland Flow ◽  
Response Times ◽  
Hysteretic Behavior ◽  
Rainfall Runoff ◽  
Moisture Index ◽  
Antecedent Soil Moisture ◽  
Non Linear ◽  
Subsurface Runoff ◽  
The Impact

An experimental soil tank (12 m long × 1.5 m wide × 1.5m deep) equipped with a spatially distributed instrument network was designed to conduct the artificial rainfall–runoff experiments. Soil moisture (SM), precipitation, surface runoff (SR) and subsurface runoff (SSR) were continuously monitored. A total of 32 rainfall–runoff events were analyzed to investigate the non-linear patterns of rainfall–runoff response and estimate the impact of antecedent soil moisture (ASM) on runoff formation. Results suggested that ASM had a significant impact on runoff at this plot scale, and a moisture threshold-like value which was close to field capacity existed in the relationship between soil water content and event-based runoff coefficient (φe), SSR and SSR/SR. A non-linear relationship between antecedent soil moisture index (ASI) that represented the initial storage capacity of the soil tank and total runoff was also observed. Response times of SR and SM to rainfall showed a marked variability under different conditions. Under wet conditions, SM at 10 cm started to increase prior to SR on average, whereas it responds slower than SR under dry conditions due to the effect of water repellency. The predominant contributor to SR generation for all events is the Hortonian overland flow (HOF). There is a hysteretic behavior between subsurface runoff flow and soil moisture with a switch in the hysteretic loop direction based on the wetness conditions prior to the event.

Dynamics of seepage mobile inventory in forest and agricultural soils - Results from a comparative multi-year lysimeter study

2020 ◽  
Author(s):  
Katharina Lehmann ◽  
Robert Lehmann ◽  
Kai Uwe Totsche
Keyword(s):  
Land Use ◽  
Agricultural Soils ◽  
Field Studies ◽  
Critical Zone ◽  
Soil Monitoring ◽  
Event Scale ◽  
Weather Events ◽  
Physico Chemical

<p>The mobile inventory in soil seepage is of fundamental importance for soil development and for functioning of subsurface ecosystem compartments. The mobile inventory may encompass inorganic, organo-mineral and organics, dissolved and colloidal, but also particulate matter and microbiota. Still unknown are the conditions and factors that trigger the release and export of seepage-contained mobile matter within soil, and its translocation through the subsurface of the critical zone. Long-term and high-resolution field studies that includes the mobile particulate inventory are essentially lacking. To overcome this knowledge gap, we established long-term soil monitoring plots in the Hainich Critical Zone Exploratory (HCZE; NW-Thuringia, central Germany). Soil seepage from 22 tension-supported lysimeters in topsoil and subsoil, covering different land use (forest, pasture, cropland) in the topographic recharge area of the HCZE, was collected and analyzed by a variety of analytical methods (physico-/chemical and spectroscopic) on a regular (biweekly) and event-scale cycle. With our study we proved that substances up to a size of 50 µm are mobile in the soils. The material spectra comprised minerals, mineral-organic particulates, diverse bioparticles and biotic detritus. Atmospheric forcing was found to be the major factor triggering the translocation of the mobile inventory. Especially episodic infiltration events during hydrological winter seasons (e.g. snow melts) with high seepage volume influences seepage hydrochemistry (e.g. pH, EC) and is important for transport of mobile matter to deeper compartments. Seasonal events cause mobilization of significant amounts of OC. On average, 21% of the total OC of the seepage was particulate (>0.45 µm). Furthermore, our results suggest that the formation environment and the geopedological setting (soil group, parent rock, land use) are controlling factors for the composition and the amount of soil-born mobile inventory. Our study provides evidence for the importance of the mobile inventory fraction >0,45 µm for soil element dynamics and budgets and highlights the role of weather events on soil and subsoil development and subsurface ecosystem functioning.</p>

scholarly journals Variability of water regime in the forested experimental catchments

2010 ◽  
Vol 4 (Special Issue 2) ◽  
pp. S93-S101 ◽  
Author(s):  
J. Buchtele ◽  
M. Tesař ◽  
P. Krám
Keyword(s):  
Soil Moisture ◽  
Annual Cycle ◽  
Vegetation Cover ◽  
Water Regime ◽  
Time Span ◽  
Rainfall Runoff ◽  
Regime Changes ◽  
Significant Phenomenon ◽  
Demand Variability

The water regime variability in most catchments is frequently influenced not only by the changes of the vegetation cover in the annual cycle but also by its development in the time span of decades. That means that the resulting evapotranspiration depends not only on the actual climatic situation but also on the soil moisture. The simulations of the rainfall-runoff process have been used with the intention to follow the possible role of the developing land cover. The differences between the observed and simulated flows in relatively long periods can be considered as an appropriate tool for the assessment of the water regime changes, in which the evapotranspiration demand variability is a significant phenomenon.

scholarly journals The Role of Hydrological Signatures in Calibration of Conceptual Hydrological Model

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3401
Author(s):  
Eva Melišová ◽  
Adam Vizina ◽  
Linda R. Staponites ◽  
Martin Hanel
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Goodness Of Fit ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Balance Assessment ◽  
The Czech Republic ◽  
Hydrological Signatures

Determining an optimal calibration strategy for hydrological models is essential for a robust and accurate water balance assessment, in particular, for catchments with limited observed data. In the present study, the hydrological model Bilan was used to simulate hydrological balance for 20 catchments throughout the Czech Republic during the period 1981–2016. Calibration strategies utilizing observed runoff and estimated soil moisture time series were compared with those using only long-term statistics (signatures) of runoff and soil moisture as well as a combination of signatures and time series. Calibration strategies were evaluated considering the goodness-of-fit, the bias in flow duration curve and runoff signatures and uncertainty of the Bilan model. Results indicate that the expert calibration and calibration with observed runoff time series are, in general, preferred. On the other hand, we show that, in many cases, the extension of the calibration criteria to also include runoff or soil moisture signatures is beneficial, particularly for decreasing the uncertainty in parameters of the hydrological model. Moreover, in many cases, fitting the model with hydrological signatures only provides a comparable fit to that of the calibration strategies employing runoff time series.

Copula-based interpretation of continuous rainfall–runoff simulations of a watershed in northern Iran

2012 ◽  
Vol 49 (5) ◽  
pp. 681-691 ◽  
Author(s):  
Saeed Golian ◽  
Bahram Saghafian ◽  
Ashkan Farokhnia
Keyword(s):  
Soil Moisture ◽  
Peak Discharge ◽  
Rainfall Runoff ◽  
Antecedent Soil Moisture ◽  
Rainfall Duration ◽  
Initial Soil Moisture ◽  
Hourly Rainfall ◽  
Copula Method ◽  
Initial Soil ◽  
Rainfall Depth

In the present work, the joint response of key hydrologic variables, including total precipitation depths and the corresponding simulated peak discharges, are investigated for different antecedent soil moisture conditions using the copula method. The procedure started with the calibration and validation of the soil moisture accounting (SMA) loss rate algorithm incorporated in the Hydrologic Engineering Center – hydrologic modeling system (HEC–HMS) model for the study watershed. A 1000 year long time series of hourly rainfall was then generated by the Neyman–Scott rectangular pulses (NSRP) rainfall generator, which was then transformed into the runoff rate by the HEC–HMS model. This long-term continuous hydrological simulation resulted in characterizing the response of the watershed for various input conditions such as initial soil moisture content (AMC), total rainfall depth, and rainfall duration. For each initial soil moisture class, the copula method was employed to determine the joint probability distribution of rainfall depth and peak discharge. For instance, for dry AMC condition and 1 h rainfall duration, the Joe family fitted best to the data, compared with six other one-parameter families of copulas. Results showed that the bivariate analysis of rainfall–runoff using the copula method can well characterize the watershed hydrological behavior. The derived offline curves could provide a probabilistic real-time peak discharge forecast.

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