Outlier identification and correction for GRACE aggregated data

Gravity measurements within the Gravity Recovery and Climate Experiment (GRACE) provide a direct measure of monthly changes in mass over the Earth’s land masses. As such changes in mass mainly correspond to water storage changes, these measurements allow to close the continental water balance on large spatial scales and on a monthly time scale within the respective error bounds. When quantifying uncertainties, positive and negative peaks are detected in GRACE aggregated monthly time series (from different data providers) that do not correspond to hydrological or hydro-meteorological signals. These peaks must be interpreted as outliers, which carry the danger of signal degradation. In this paper an algorithm is developed to identify outliers and replace them with hydrologically plausible values. The algorithm is based on a statistical approach in which hydrological and hydro-meteorological signals are used to control the algorithm. The procedure of outlier detection is verified by evaluating catchment based aggregated GRACE monthly signals with ground truth from hydrology and hydro-meteorological signals. The results show improvement in the correlation of GRACE versus hydrometeorological and hydrological signals in most catchments. Also, the noise level is significantly reduced over 255 largest catchments.     

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.     

Compounding effects of human activities and climatic changes on surface water availability in Iran

Climatic Change - By combining long-term ground-based data on water withdrawal with climate model projections, this study quantifies the compounding effects of human activities and climate change...     

The Inflection-Point Retracking Algorithm: Improved Jason-2 Sea Surface Heights in the Strait of Hormuz

In this study, a waveform retracking algorithm based on finding the inflection-point of the waveform is proposed. After two-steps pre-processing procedure, we employ this method for 145 cycles of Jason-2 data for two tracks 81 and 16 over the Strait of Hormuz. Moreover, we obtain the corrected SSH by the common empirical methods namely Offset Centre of Gravity, Beta and Threshold as well as the ALES. We compare the SSH time series from proposed algorithm with those from common empirical methods. Results are validated against three nearby tide-gauges in the case study. The correlation coefficient and RMSE between the corrected SSH and tide-gages data were computed for three distance classes from the coastline: 0～5, 5～10 and 10～15 kilometer. Our method improves the averaged RMSE of raw SSH up to 41%, 41% and 24%, for these classes over track 81 and 51%, 38% and 41% over track 16, respectively. The averaged correlation values of the proposed method indicate 33%, 11% and 2% improvement over track 81 and are 29%, 14% and 3% over track 16 for three distance groups, respectively. Our method leads to slightly better results than the successful ALES method, especially within the range of 0～5 km.