Rationale and Efficacy of Assimilating Remotely Sensed Potential Evapotranspiration for Reduced Uncertainty of Hydrologic Models

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.

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Modelling Wetland Growing Season Rainfall Interception Losses Based on Maximum Canopy Storage Measurements

10.20944/preprints201712.0095.v1 ◽

2017 ◽

Author(s):

Wojciech Ciężkowski ◽

Tomasz Berezowski ◽

Małgorzata Kleniewska ◽

Sylwia Szporak-Wasilewska ◽

Jarosław Chormański

Keyword(s):

Water Balance ◽

Potential Evapotranspiration ◽

Growing Season ◽

Meteorological Conditions ◽

Natural Wetland ◽

Hydrologic Models ◽

Rainfall Interception ◽

Wetland Ecosystems ◽

Weight Method

This study estimates rainfall interception losses from natural wetland ecosystems based on maximum canopy storage measurements. Rainfall interception losses play an important role in water balance, which is crucial in wetlands, and has not yet been thoroughly studied in relation to this type of ecosystem. Maximum canopy storage was measured using the weight method. Based on these measurements, daily values of interception losses were estimated and then used to calculate long term interception losses based on precipitation and potential evapotranspiration data for the 1971–2015 period. Depending mainly on the number of days with precipitation, the results show that total interception losses for the growing season as well as monthly interception losses are around 13% of gross rainfall. This value is similar to the values observed for some forests. Hence, interception losses should not be disregarded in hydrologic models of wetlands, especially because data trends in meteorological conditions (mainly number of days with precipitation) show that interception losses will increase in the future if those trends stay the same.

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Estimating Evapotranspiration in a Post-Fire Environment Using Remote Sensing and Machine Learning

Remote Sensing ◽

10.3390/rs10111728 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1728 ◽

Cited By ~ 7

Author(s):

Patrick Poon ◽

Alicia Kinoshita

Keyword(s):

Machine Learning ◽

Land Surface ◽

Vegetation Index ◽

Hydrological Cycle ◽

Potential Evapotranspiration ◽

Remotely Sensed ◽

Support Vector ◽

Moisture Index ◽

Input Variables ◽

Normalized Burn Ratio

In the hydrological cycle, evapotranspiration (ET) transfers moisture from the land surface to the atmosphere and is sensitive to disturbances such as wildfires. Ground-based pre- and post-fire measurements of ET are often unavailable, limiting the potential to understand the extent of wildfire impacts on the hydrological cycle. This research estimated both pre- and post-fire ET using remotely sensed variables and support vector machine (SVM) methods. Input variables (land surface temperature, modified soil-adjusted vegetation index, normalized difference moisture index, normalized burn ratio, precipitation, potential evapotranspiration, albedo and vegetation types) were used to train and develop 56 combinations that yielded 33 unique SVM models to predict actual ET. The models were trained to predict a spatial ET, the Operational Simplified Surface Energy Balance (SSEBop), for the 2003 Coyote Fire in San Diego, California (USA). The optimal SVM model, SVM-ET6, required six input variables and predicted ET for fifteen years with a root-mean-square error (RMSE) of 8.43 mm/month and a R2 of 0.89. The developed model was transferred and applied to the 2003 Old Fire in San Bernardino, California (USA), where a watershed balance approach was used to validate SVM-ET6 predictions. The annual water balance for ten out of fifteen years was within ±20% of the predicted values. This work demonstrated machine learning as a viable method to create a remotely-sensed estimate with wide applicability for regions with sparse data observations and information. This innovative work demonstrated the potential benefit for land and forest managers to understand and analyze the hydrological cycle of watersheds that experience acute disturbances based on this developed predictive ET model.

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Exploring potential of remotely sensed data in parameterization of hydrologic model

10.5194/egusphere-egu2020-12675 ◽

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.&#160;</p>

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On the Hydrologic Adjustment of Climate-Model Projections: The Potential Pitfall of Potential Evapotranspiration

Earth Interactions ◽

10.1175/2010ei363.1 ◽

2011 ◽

Vol 15 (1) ◽

pp. 1-14 ◽

Cited By ~ 74

Author(s):

P. C. D. Milly ◽

Krista A. Dunne

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Potential Evapotranspiration ◽

Climate Change Impacts ◽

Hydrologic Model ◽

Energy Budgets ◽

Drought Indices ◽

Hydrologic Models ◽

Runoff Change

Abstract Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement (“downscaling”), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median −11%) caused by the hydrologic model’s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen–Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors’ findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water.

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Assessment of remotely sensed drought features in vulnerable agriculture

Natural Hazards and Earth System Science ◽

10.5194/nhess-12-3139-2012 ◽

2012 ◽

Vol 12 (10) ◽

pp. 3139-3150 ◽

Cited By ~ 11

Author(s):

N. R. Dalezios ◽

A. Blanta ◽

N. V. Spyropoulos

Keyword(s):

Remote Sensing ◽

Potential Evapotranspiration ◽

Meteorological Drought ◽

Remotely Sensed ◽

Drought Indices ◽

Areal Extent ◽

Drought Assessment ◽

Noaa Avhrr ◽

Drought Episode ◽

Wide Range

Abstract. The growing number and effectiveness of Earth observation satellite systems, along with the increasing reliability of remote sensing methodologies and techniques, present a wide range of new capabilities in monitoring and assessing droughts. A number of drought indices have been developed based on NOAA-AVHRR data exploiting the remote sensing potential at different temporal scales. In this paper, the remotely sensed Reconnaissance Drought Index (RDI) is employed for the quantification of drought. RDI enables the assessment of hydro-meteorological drought, since it uses hydrometeorological parameters, such as precipitation and potential evapotranspiration. The study area is Thessaly, central Greece, which is a drought-prone agricultural region characterized by vulnerable agriculture. Several drought features are analyzed and assessed by using monthly RDI images over the period 1981–2001: severity, areal extent, duration, periodicity, onset and end time. The results show an increase in the areal extent during each drought episode and that droughts are classified into two classes, namely small areal extent drought and large areal extent drought, respectively, lasting 12 or 13 months coinciding closely with the hydrological year. The onset of large droughts coincides with the beginning of the hydrological year, whereas the onset of small droughts is in spring. During each drought episode, the maximum occurs usually in the summer and they all last until the end of the hydrological year. This finding could justify an empirical prognostic potential of drought assessment.

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