ESTIMATION OF LONG-TERM SOIL MOISTURE USING A DISTRIBUTED PARAMETER HYDROLOGIC MODEL AND VERIFICATION USING REMOTELY SENSED DATA

2005 ◽  
Vol 48 (3) ◽  
pp. 1101-1113 ◽  
Author(s):  
B. Narasimhan ◽  
R. Srinivasan ◽  
J. G. Arnold ◽  
M. Di Luzio
Keyword(s):  
Soil Moisture ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Distributed Parameter ◽  
Remotely Sensed Data

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 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.

A model for retrieving soil moisture saturation with Landsat remotely sensed data

2012 ◽  
Vol 33 (14) ◽  
pp. 4553-4566 ◽  
Author(s):  
Ji Jian ◽  
Wunian Yang ◽  
Hong Jiang ◽  
Xinnan Wan ◽  
Yuxia Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Moisture Saturation

scholarly journals The Influence of Atlantic Tropical Cyclones on Drought over the Eastern United States (1980–2007)

2013 ◽  
Vol 26 (10) ◽  
pp. 3067-3086 ◽  
Author(s):  
Jonghun Kam ◽  
Justin Sheffield ◽  
Xing Yuan ◽  
Eric F. Wood
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Tropical Cyclones ◽  
Land Surface ◽  
Hydrologic Model ◽  
Eastern United States ◽  
Short Term ◽  
Drought Duration ◽  
Study Region

Abstract To assess the influence of Atlantic tropical cyclones (TCs) on the eastern U.S. drought regime, the Variable Infiltration Capacity (VIC) land surface hydrologic model was run over the eastern United States forced by the North American Land Data Assimilation System phase 2 (NLDAS-2) analysis with and without TC-related precipitation for the period 1980–2007. A drought was defined in terms of soil moisture as a prolonged period below a percentile threshold. Different duration droughts were analyzed—short term (longer than 30 days) and long term (longer than 90 days)—as well as different drought severities corresponding to the 10th, 15th, and 20th percentiles of soil moisture depth. With TCs, droughts are shorter in duration and of a lesser spatial extent. Tropical cyclones variously impact soil moisture droughts via late drought initiation, weakened drought intensity, and early drought recovery. At regional scales, TCs decreased the average duration of moderately severe short-term and long-term droughts by less than 4 (10% of average drought duration per year) and more than 5 (15%) days yr−1, respectively. Also, they removed at least two short-term and one long-term drought events over 50% of the study region. Despite the damage inflicted directly by TCs, they play a crucial role in the alleviation and removal of drought for some years and seasons, with important implications for water resources and agriculture.

Predictions in a data-sparse region using a regionalized grid-based hydrologic model driven by remotely sensed data

2011 ◽  
Vol 42 (5) ◽  
pp. 338-355 ◽  
Author(s):  
Luis Samaniego ◽  
Rohini Kumar ◽  
Conrad Jackisch
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Shuttle Radar Topography Mission ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Distributed Model ◽  
Remotely Sensed Data ◽  
Model Driven ◽  
Moderate Resolution ◽  
Daily Discharge ◽  
Moderate Resolution Imaging Spectroradiometer

The goal of this study was to assess the feasibility of using Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) products to drive a mesoscale hydrologic model (mHM) in a poorly gauged basin. Other remotely sensed products such as LandSat and Shuttle Radar Topography Mission (SRTM) were also used to complement the local geoinformation. For this purpose, three data blending techniques that combine satellite with in situ observations were implemented and evaluated in the Mod basin (512 km2) in India. The climate of the basin is semi-arid and monsoon-dominated. The rainfall gauging network comprised six stations with daily records spanning 9 years. Daily discharge time series was only 4 years long and incomplete. Lumped and distributed versions of mHM were evaluated. Parameters of the lumped version were obtained through calibration. A multiscale regionalization technique was used to parameterize the distributed version using global parameters from other gauged basins. Both mHM versions were evaluated during six monsoon seasons. Results of numerical experiments indicated that driving mHM with satellite-based products is possible and promising. The distributed model with regionalized parameters was at least 20% more efficient than that of its lumped version. Initialization conditions must be carefully considered when the model is only driven by remotely sensed inputs.

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