Johannes Riegger

Large-Scale Runoff from Landmasses: A Global Assessment of the Closure of the Hydrological and Atmospheric Water Balances*

AbstractThe performance of hydrological and hydrometeorological water-balance-based methods to estimate monthly runoff is analyzed. Such an analysis also allows for the examination of the closure of water budgets at different spatial (continental and catchment) and temporal (monthly, seasonal, and annual) scales. For this analysis, different combinations of gridded observations [Global Precipitation Climatology Centre (GPCC), Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC), Climatic Research Unit (CRU), and University of Delaware (DEL)], atmospheric reanalysis models [Interim ECMWF Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)], partially model-based datasets [Global Land Surface Evaporation: The Amsterdam Methodology (GLEAM), Moderate Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16), and FLUXNET Multi-Tree Ensemble (FLUXNET MTE)], and G...

Estimating Runoff Using Hydro-Geodetic Approaches

Given the continuous decline in global runoff data availability over the past decades, alternative approaches for runoff determination are gaining importance. When aiming for global scale runoff at a sufficient temporal resolution and with homogeneous accuracy, the choice to use spaceborne sensors is only a logical step. In this respect, we take water storage changes from Gravity Recovery And Climate Explorer (grace) results and water level measurements from satellite altimetry, and present a comprehensive assessment of five different approaches for river runoff estimation: hydrological balance equation, hydro-meteorological balance equation, satellite altimetry with quantile function-based stage–discharge relationships, a rudimentary instantaneous runoff–precipitation relationship, and a runoff–storage relationship that takes time lag into account. As a common property, these approaches do not rely on hydrological modeling; they are either purely data driven or make additional use of atmospheric reanalyses. Further, these methods, except runoff–precipitation ratio, use geodetic observables as one of their inputs and, therefore, they are termed hydro-geodetic approaches. The runoff prediction skill of these approaches is validated against in situ runoff and compared to hydrological model predictions. Our results show that catchment-specific methods (altimetry and runoff–storage relationship) clearly outperform the global methods (hydrological and hydro-meteorological approaches) in the six study regions we considered. The global methods have the potential to provide runoff over all landmasses, which implies gauged and ungauged basins alike, but are still limited due to inconsistencies in the global hydrological and hydro-meteorological datasets that they use.

A new method to derive river discharge from satellite altimetry (ENVISAT)

The publicly available global discharge database has been on the decline, lately. In the recent past, satellite altimetry has been investigated as an alternative for monitoring inland water level. In the present study, altimetry footprints in the vicinity of river gauging stations are analyzed for a functional relationship between the water level measurements from altimetry and discharge from the gauging stations. The functional relationship is generally established via a rating curve computed using simultaneous data. This study proposes a new method to infer this functional relation without the need for having synchronous datasets. RMS errors of 0.9-4.3 mm/month for estimated runoff of the rivers under study have been verified by the validation of estimated discharge against the in situ measurements. Further, in order to reduce the noise and cope with the problem of gaps in the estimation, a Kalman filter is used to achieve an unbiased discharge with minimum variance.

Estimating GRACE Monthly Water Storage Change Consistent with Hydrology by Assimilating Hydrological Information

A sequential estimation approach is used for constraining GRACE monthly estimates of mass changes with observed hydrological data, which is available for 20% of the land area, in order to improve the overall quality of the GRACE dataset. It is expected that the hydrological data constrains GRACE by utilising the correlations within the spherical harmonic coefficients, which is described by a simulated covariance matrix. Due to the dependancy of the approach on the stochastic information of GRACE, the influence of different structures of the GRACE covariance matrix were also tested. Initial results show that the hydrology constraints replace GRACE completely in the constrained areas, and contribute only meagrely outside the constrained areas. This hints at better parametrization of the model. The tests with different structures of the GRACE covariance matrix indicate that the block-diagonal structure approximates the full covariance matrix very well.