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 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 Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

2021 ◽  
Vol 13 (4) ◽  
pp. 2375
Author(s):  
Sangchul Lee ◽  
Junyu Qi ◽  
Hyunglok Kim ◽  
Gregory W. McCarty ◽  
Glenn E. Moglen ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Assessment Tool ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Hydrologic Models ◽  
Daily Streamflow ◽  
Model Predictions ◽  
Improved Model ◽  
Water Assessment

There is a certain level of predictive uncertainty when hydrologic models are applied for operational purposes. Whether structural improvements address uncertainty has not well been evaluated due to the lack of observational data. This study investigated the utility of remotely sensed evapotranspiration (RS-ET) products to quantitatively represent improvements in model predictions owing to structural improvements. Two versions of the Soil and Water Assessment Tool (SWAT), representative of original and improved versions, were calibrated against streamflow and RS-ET. The latter version contains a new soil moisture module, referred to as RSWAT. We compared outputs from these two versions with the best performance metrics (Kling–Gupta Efficiency [KGE], Nash-Sutcliffe Efficiency [NSE] and Percent-bias [P-bias]). Comparisons were conducted at two spatial scales by partitioning the RS-ET into two scales, while streamflow comparisons were only conducted at one scale. At the watershed level, SWAT and RSWAT produced similar metrics for daily streamflow (NSE of 0.29 and 0.37, P-bias of 1.7 and 15.9, and KGE of 0.47 and 0.49, respectively) and ET (KGE of 0.48 and 0.52, respectively). At the subwatershed level, the KGE of RSWAT (0.53) for daily ET was greater than that of SWAT (0.47). These findings demonstrated that RS-ET has the potential to increase prediction accuracy from model structural improvements and highlighted the utility of remotely sensed data in hydrologic modeling.

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 A new data assimilation approach for improving hydrologic prediction using remotely-sensed soil moisture retrievals

2008 ◽  
Vol 5 (4) ◽  
pp. 2005-2044 ◽  
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 ◽  
Hydrologic Prediction ◽  
Moisture Conditions

Abstract. A number of recent studies have focused on enhancing hydrologic 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 exclusively on the constraint of antecedent soil moisture conditions.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals On the scaling characteristics of observed and simulated spatial soil moisture fields

2009 ◽  
Vol 16 (1) ◽  
pp. 141-150 ◽  
Author(s):  
M. Gebremichael ◽  
R. Rigon ◽  
G. Bertoldi ◽  
T. M. Over
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Spatial Correlation ◽  
Hydrologic Model ◽  
Invariance Property ◽  
Spatial Average ◽  
Hydrologic Models ◽  
Model Simulations

Abstract. By providing continuous high-resolution simulations of soil moisture fields, distributed hydrologic models could be powerful tools to advance the scientific community's understanding of the space-time variability and scaling characteristics of soil moisture fields. However, in order to use the soil moisture simulations from hydrologic models with confidence, it is important to understand whether the models are able to represent in a reliable way the processes regulating soil moisture variability. In this study, a comparison of the scaling characteristics of spatial soil moisture fields derived from a set of microwave radiometer observations from the Southern Great Plains 1997 experiment and corresponding simulations using the distributed hydrologic model GEOtop is performed through the use of generalized variograms. Microwave observations and model simulations are in agreement with respect to suggesting the existence of a scale-invariance property in the variograms of spatial soil moisture fields, and indicating that the scaling characteristics vary with changes in the spatial average soil water content. However, observations and simulations give contradictory results regarding the relationship between the scaling parameters (i.e. spatial organization) and average soil water content. The drying process increased the spatial correlation of the microwave observations at both short and long separation distances while increasing the rate of decay of correlation with distance. The effect of drying on the spatial correlation of the model simulations was more complex, depending on the storm and the simulation examined, but for the largest storm in the simulation most similar to the observations, drying increased the long-range correlation but decreased the short-range. This is an indication that model simulations, while reproducing correctly the total streamflow at the outlet of the watershed, may not accurately reproduce the runoff production mechanisms. Consideration of the scaling characteristics of spatial soil moisture fields can therefore serve as a more intensive means for validating distributed hydrologic models, compared to the traditional approach of only comparing the streamflow hydrographs.

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.

scholarly journals Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

2020 ◽  
Vol 12 (13) ◽  
pp. 2148 ◽  
Author(s):  
Adnan Rajib ◽  
I Luk Kim ◽  
Heather E. Golden ◽  
Charles R. Lane ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Dynamics ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Area Index ◽  
Daily Streamflow ◽  
Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

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