导航|地址|刷新
Catchment-scale Hydrological Modelling &…

!DOCTYPE html> Jump to Content

This site uses cookies, tags, and tracking settings to store information that help give you the very best browsing experience. Dismiss this warning

JOURNALS

Artificial Intelligence for the Earth Systems Bulletin of the American Meteorological Society Earth Interactions Journal of Applied Meteorology and Climatology Journal of Atmospheric and Oceanic Technology Journal of Climate Journal of Hydrometeorology Journal of Physical Oceanography Journal of the Atmospheric Sciences Monthly Weather Review Weather and Forecasting Weather, Climate, and Society Meteorological Monographs

BROWSE PUBLISH SUBSCRIBE ABOUT

Sign in Sign up
Search

Advanced Search Help

Sign in Sign up

JOURNALS

Artificial Intelligence for the Earth Systems Bulletin of the American Meteorological Society Earth Interactions Journal of Applied Meteorology and Climatology Journal of Atmospheric and Oceanic Technology Journal of Climate Journal of Hydrometeorology Journal of Physical Oceanography Journal of the Atmospheric Sciences Monthly Weather Review Weather and Forecasting Weather, Climate, and Society Meteorological Monographs

BROWSE PUBLISH SUBSCRIBE ABOUT

Advanced Search Help

Search within results Search within results

Filter Filter

Refine by Access

All Content

Content accessible to me

Refine by Publication

Journal of Hydrometeorology(9)

Refine By Editorial Type

Article(9)

Refine by Date

From2026202520242023202220212020201920182017201620152014201320122011201020092008200720062005200420032002200120001999199819971996199519941993199219911990198919881987198619851984198319821981198019791978197719761975197419731972197119701969196819671966196519641963196219611960195919581957195619551954195319521951195019491948194719461945194419431942194119401939193819371936193519341933193219311930192919281927192619251924192319221921192019191918191719161915191419131912191119101909190819071906190519041903190219011900189918981897189618951894189318921891189018891888188718861885188418831882188118801879187818771876187518741873—To2026202520242023202220212020201920182017201620152014201320122011201020092008200720062005200420032002200120001999199819971996199519941993199219911990198919881987198619851984198319821981198019791978197719761975197419731972197119701969196819671966196519641963196219611960195919581957195619551954195319521951195019491948194719461945194419431942194119401939193819371936193519341933193219311930192919281927192619251924192319221921192019191918191719161915191419131912191119101909190819071906190519041903190219011900189918981897189618951894189318921891189018891888188718861885188418831882188118801879187818771876187518741873

Update

Print

Save

Email this link

Share Link

------

Copy this link, or click below to email it to a friend
Email this link
or copy the link directly:

https://journals.ametsoc.org/collection/CAHMDA-III
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Copy link

------

Share on facebook Share on linkedin Share on twitter

Catchment-scale Hydrological Modelling & Data Assimilation (CAHMDA) III

Description:

The Catchment-scale Hydrological Modelling & Data Assimilation (CAHMDA) - III International Workshop, held in Melbourne Australia, 9-11 January 2008, is the third in a series.The scope of this workshop was to bring together experts in hydrological modeling, remote sensing, parameter estimation, state estimation and applications, to discuss new modeling and data observation strategies, and the potential of using advanced data assimilation methods to improve hydrologic model parameterization and initialization, and hydrologic predictability in water resource applications research. Click here to view the collection editorial. The articles will be presented below as they are published.

Collection organizers:
Paul Houser and Jeffrey Walker

Catchment-scale Hydrological Modelling & Data Assimilation (CAHMDA) III

You are looking at 1–9 of 9 items for

Refine by Access: All Contentx
Clear All

Download Citations

.ris

ProCite

RefWorks

Reference Manager

.bib

BibTeX

Zotero

.enw

EndNote

Items per page 102050

Sort by Date - Old to RecentDate - Recent to OldArticle A - ZArticle Z - AAuthor A - ZAuthor Z - AJournal A - ZJournal Z - A

On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation

Damian J. Barrett
and
Luigi J. Renzullo

Abstract

Data assimilation applications require the development of appropriate mathematical operators to relate model states to satellite observations. Two such “observation” operators were developed and used to examine the conditions under which satellite microwave and thermal observations provide effective constraints on estimated soil moisture. The first operator uses a two-layer surface energy balance (SEB) model to relate root-zone moisture with top-of-canopy temperature. The second couples SEB and microwave radiative transfer models to yield top-of-atmosphere brightness temperature from surface layer moisture content. Tangent linear models for these operators were developed to examine the sensitivity of modeled observations to variations in soil moisture. Assuming a standard deviation in the observed surface temperature of 0.5 K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m3 m−3 and by 29% for a background error of 0.05 m3 m−3. As the observation error approached 2 K, the assimilation of individual surface temperature observations provided virtually no constraint on estimates of soil moisture. Given the range of published errors on brightness temperature, microwave satellite observations were always a strong constraint on soil moisture, except under dense forest and in relatively dry soils. Under contrasting vegetation cover and soil moisture conditions, orthogonal information contained in thermal and microwave observations can be used to improve soil moisture estimation because limited constraint afforded by one data type is compensated by strong constraint from the other data type.

Journal: Journal of Hydrometeorology Volume/Issue: Volume 10: Issue 5

DOI: https://doi.org/10.1175/2009JHM1043.1 Published Online: Oct 2009

Abstract

Abstract

Data assimilation applications require the development of appropriate mathematical operators to relate model states to satellite observations. Two such “observation” operators were developed and used to examine the conditions under which satellite microwave and thermal observations provide effective constraints on estimated soil moisture. The first operator uses a two-layer surface energy balance (SEB) model to relate root-zone moisture with top-of-canopy temperature. The second couples SEB and microwave radiative transfer models to yield top-of-atmosphere brightness temperature from surface layer moisture content. Tangent linear models for these operators were developed to examine the sensitivity of modeled observations to variations in soil moisture. Assuming a standard deviation in the observed surface temperature of 0.5 K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m3 m−3 and by 29% for a background error of 0.05 m3 m−3. As the observation error approached 2 K, the assimilation of individual surface temperature observations provided virtually no constraint on estimates of soil moisture. Given the range of published errors on brightness temperature, microwave satellite observations were always a strong constraint on soil moisture, except under dense forest and in relatively dry soils. Under contrasting vegetation cover and soil moisture conditions, orthogonal information contained in thermal and microwave observations can be used to improve soil moisture estimation because limited constraint afforded by one data type is compensated by strong constraint from the other data type.

Download PDF

Seasonal Predictability of European Discharge: NAO and Hydrological Response Time

M. F. P. Bierkens
and
L. P. H. van Beek

Abstract

In this paper the skill of seasonal prediction of river discharge and how this skill varies between the branches of European rivers across Europe is assessed. A prediction system of seasonal (winter and summer) discharge is evaluated using 1) predictions of the average North Atlantic Oscillation (NAO) index for the coming winter based on May SST anomalies of the North Atlantic; 2) a global-scale hydrological model; and 3) 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. The skill of seasonal discharge predictions is investigated with a numerical experiment. Also Europe-wide patterns of predictive skill are related to the use of NAO-based seasonal weather prediction, the hydrological properties of the river basin, and a correct assessment of initial hydrological states. These patterns, which are also corroborated by observations, show that in many parts of Europe the skill of predicting winter discharge can, in theory, be quite large. However, this achieved skill mainly comes from knowing the correct initial conditions of the hydrological system (i.e., groundwater, surface water, soil water storage of the basin) rather than from the use of NAO-based seasonal weather prediction. These factors are equally important for predicting subsequent summer discharge.

Journal: Journal of Hydrometeorology Volume/Issue: Volume 10: Issue 4

DOI: https://doi.org/10.1175/2009JHM1034.1 Published Online: Aug 2009

Abstract

Abstract

In this paper the skill of seasonal prediction of river discharge and how this skill varies between the branches of European rivers across Europe is assessed. A prediction system of seasonal (winter and summer) discharge is evaluated using 1) predictions of the average North Atlantic Oscillation (NAO) index for the coming winter based on May SST anomalies of the North Atlantic; 2) a global-scale hydrological model; and 3) 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. The skill of seasonal discharge predictions is investigated with a numerical experiment. Also Europe-wide patterns of predictive skill are related to the use of NAO-based seasonal weather prediction, the hydrological properties of the river basin, and a correct assessment of initial hydrological states. These patterns, which are also corroborated by observations, show that in many parts of Europe the skill of predicting winter discharge can, in theory, be quite large. However, this achieved skill mainly comes from knowing the correct initial conditions of the hydrological system (i.e., groundwater, surface water, soil water storage of the basin) rather than from the use of NAO-based seasonal weather prediction. These factors are equally important for predicting subsequent summer discharge.

Download PDF

Use of Remotely Sensed Actual Evapotranspiration to Improve Rainfall–Runoff Modeling in Southeast Australia

Yongqiang Zhang
,
Francis H. S. Chiew
,
Lu Zhang
, and
Hongxia Li

Abstract

This paper explores the use of the Moderate Resolution Imaging Spectroradiometer (MODIS), mounted on the polar-orbiting Terra satellite, to determine leaf area index (LAI), and use actual evapotranspiration estimated using MODIS LAI data combined with the Penman–Monteith equation [remote sensing evapotranspiration (ERS)] in a lumped conceptual daily rainfall–runoff model. The model is a simplified version of the HYDROLOG (SIMHYD) model, which is used to estimate runoff in ungauged catchments. Two applications were explored: (i) the calibration of SIMHYD against both the observed streamflow and ERS, and (ii) the modification of SIMHYD to use MODIS LAI data directly. Data from 2001 to 2005 from 120 catchments in southeast Australia were used for the study. To assess the modeling results for ungauged catchments, optimized parameter values from the geographically nearest gauged catchment were used to model runoff in the ungauged catchment. The results indicate that the SIMHYD calibration against both the observed streamflow and ERS produced better simulations of daily and monthly runoff in ungauged catchments compared to the SIMHYD calibration against only the observed streamflow data, despite the modeling results being assessed solely against the observed streamflow data. The runoff simulations were even better for the modified SIMHYD model that used the MODIS LAI directly. It is likely that the use of other remotely sensed data (such as soil moisture) and smarter modification of rainfall–runoff models to use remotely sensed data directly can further improve the prediction of runoff in ungauged catchments.

Journal: Journal of Hydrometeorology Volume/Issue: Volume 10: Issue 4

DOI: https://doi.org/10.1175/2009JHM1061.1 Published Online: Aug 2009

Abstract

Abstract

This paper explores the use of the Moderate Resolution Imaging Spectroradiometer (MODIS), mounted on the polar-orbiting Terra satellite, to determine leaf area index (LAI), and use actual evapotranspiration estimated using MODIS LAI data combined with the Penman–Monteith equation [remote sensing evapotranspiration (ERS)] in a lumped conceptual daily rainfall–runoff model. The model is a simplified version of the HYDROLOG (SIMHYD) model, which is used to estimate runoff in ungauged catchments. Two applications were explored: (i) the calibration of SIMHYD against both the observed streamflow and ERS, and (ii) the modification of SIMHYD to use MODIS LAI data directly. Data from 2001 to 2005 from 120 catchments in southeast Australia were used for the study. To assess the modeling results for ungauged catchments, optimized parameter values from the geographically nearest gauged catchment were used to model runoff in the ungauged catchment. The results indicate that the SIMHYD calibration against both the observed streamflow and ERS produced better simulations of daily and monthly runoff in ungauged catchments compared to the SIMHYD calibration against only the observed streamflow data, despite the modeling results being assessed solely against the observed streamflow data. The runoff simulations were even better for the modified SIMHYD model that used the MODIS LAI directly. It is likely that the use of other remotely sensed data (such as soil moisture) and smarter modification of rainfall–runoff models to use remotely sensed data directly can further improve the prediction of runoff in ungauged catchments.

Download PDF

Adaptive Soil Moisture Profile Filtering for Horizontal Information Propagation in the Independent Column-Based CLM2.0

Gabriëlle J. M. De Lannoy
,
Paul R. Houser
,
Niko E. C. Verhoest
, and
Valentijn R. N. Pauwels

Abstract

Data assimilation aims to provide an optimal estimate of the overall system state, not only for an observed state variable or location. However, large-scale land surface models are typically column-based and purely random ensemble perturbation of states will lead to block-diagonal a priori (or background) error covariance. This facilitates the filtering calculations but compromises the potential of data assimilation to influence (unobserved) vertical and horizontal neighboring state variables. Here, a combination of an ensemble Kalman filter and an adaptive covariance correction method is explored to optimize the variances and retrieve the off-block-diagonal correlations in the a priori error covariance matrix. In a first time period, all available soil moisture profile observations in a small agricultural field are assimilated into the Community Land Model, version 2.0 (CLM2.0) to find the adaptive second-order a priori error information. After that period, only observations from single individual soil profiles are assimilated with inclusion of this adaptive information. It is shown that assimilation of a single profile can partially rectify the incorrectly simulated soil moisture spatial mean and variability. The largest reduction in the root-mean-square error in the soil moisture field varies between 7% and 22%, depending on the soil depth, when assimilating a single complete profile every two days during three months with a single time-invariant covariance correction.
…(内容过长已截断)

------
导航页打开网址