Precipitation and evapotranspiration, and in particular the precipitation minus evapotranspiration deficit ( P − E ), are climate variables that may be better represented in reanalyses based on numerical weather prediction (NWP) models than in other datasets. P − E provides essential information on the interaction of the atmosphere with the land surface, which is of fundamental importance for understanding climate change in response to anthropogenic impacts. However, the skill of models in closing the atmospheric-terrestrial water budget is limited. Here, total water storage estimates from the Gravity Recovery and Climate Experiment (GRACE) mission are used in combination with discharge data for assessing the closure of the water budget in the recent high-resolution Consortium for Small-Scale Modelling 6-km Reanalysis (COSMO-REA6) while comparing to global reanalyses (Interim ECMWF Reanalysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)) and observation-based datasets (Global Precipitation Climatology Centre (GPCC), Global Land Evaporation Amsterdam Model (GLEAM)). All 26 major European river basins are included in this study and aggregated to 17 catchments. Discharge data are obtained from the Global Runoff Data Centre (GRDC), and insufficiently long time series are extended by calibrating the monthly Génie Rural rainfall-runoff model (GR2M) against the existing discharge observations, subsequently generating consistent model discharge time series for the GRACE period. We find that for most catchments, COSMO-REA6 closes the water budget within the error estimates. In contrast, the global reanalyses underestimate P − E with up to 20 mm/month. For all models and catchments, short-term (below the seasonal timescale) variability of atmospheric terrestrial flux agrees well with GRACE and discharge data with correlations of about 0.6. Our large study area allows identifying regional patterns like negative trends of P − E in eastern Europe and positive trends in northwestern Europe.
Predicting freshwater resources is a major concern in West Africa, where large parts of the population depend on rain-fed subsistence agriculture. However, a steady decline in the availability of in-situ measurements of climatic and hydrologic variables makes it difficult to simulate water resource availability with hydrological models. In this study, a modeling framework was set up for sparsely-gauged catchments in West Africa using the Soil and Water Assessment Tool (SWAT), whilst largely relying on remote sensing and reanalysis inputs. The model was calibrated using two different strategies and validated using discharge measurements. New in this study is the use of a multi-objective validation conducted to further investigate the performance of the model, where simulated actual evapotranspiration, soil moisture, and total water storage were evaluated using remote sensing data. Results show that the model performs well (R2 calibration: 0.52 and 0.51; R2 validation: 0.63 and 0.61) and the multi-objective validation reveals good agreement between predictions and observations. The study reveals the potential of using remote sensing data in sparsely-gauged catchments, resulting in good performance and providing data for evaluating water balance components that are not usually validated. The modeling framework presented in this study is the basis for future studies, which will address model response to extreme drought and flood events and further examine the coincidence with Gravity Recovery and Climate Experiment (GRACE) total water storage retrievals.
Abstract. The Niger River represents a challenging target for deriving discharge from spaceborne radar altimeter measurements, particularly since most terrestrial gauges ceased to provide data during the 2000s. Here, we propose deriving altimetric rating curves by “bridging” gaps between time series from gauge and altimeter measurements using hydrological model simulations. We show that classical pulse-limited altimetry (Jason-1 and Jason-2, Envisat, and SARAL/Altika) subsequently reproduces discharge well and enables continuing the gauge time series, albeit at a lower temporal resolution. Also, synthetic aperture radar (SAR) altimetry picks up the signal measured by earlier altimeters quite well and allows the building of extended time series of higher quality. However, radar retracking is necessary for pulse-limited altimetry and needs to be further investigated for SAR. Moreover, forcing data for calibrating and running the hydrological models must be chosen carefully. Furthermore, stage–discharge relations must be fitted empirically and may need to allow for break points.
<p>We will present our atmospheric physics contribution to the New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV) research group, which is funded by the German Research Foundation (DFG). The goal of NEROGRAV is to develop new analysis methods and modeling approaches to improve the current data analysis of the GRACE and GRACE-FO missions. Our contribution to this research unit is to is to (re)analyze data fields on horizontal grid sizes below 10 km from non-hydrostatic regional atmospheric models: the 1995-2019 COSMO-REA6 regional reanalysis and the July 2021 ICON-EU/D2 analyses. From these models, the mass densities of dry air and all water phases (gaseous, liquid clouds and rain, icy snow, sleet, and hail) are available in each of the 40-50 vertical layers, so that the total local and column-by-column atmospheric mass density can be calculated without using the hydrostatic assumption.</p><p>Current non-hydrostatic, high-resolution atmospheric models run under some idealizations (e.g., assumption of a perfect sphere, spatially constant gravitational acceleration, approximation of a flat atmosphere) that are inconsistent with the real measurements of the GRACE/GRACE-FO missions. Furthermore, high-resolution atmospheric model data are currently only available within the CORDEX-EU domain, which covers the North Atlantic-European region, or an even smaller domain. The results to be presented will answer the following questions: (1) Do the model idealizations cause discrepancies with respect to the GRACE/GRACE-FO measurements that are larger than the sum of the actual measurement uncertainties and the uncertainties resulting from the remaining dealiasing models? (2) Can the idealizations be corrected to achieve better agreement with the GRACE/GRACE-FO data without sacrificing the underlying model dynamics, e.g. by introducing additional gradients in the models? (3) Do extreme weather events, such as heavy rainfall observed in the Ruhr/Ahr/Erft/Maas basin in July 2021, cause mass variations that exceeds typical GRACE/GRACE-FO uncertainties? And finally, (4) does high-resolution but regional atmospheric mass variability leave a statistically significant fingerprint in the global loading coefficients?</p>
Using data from the Gravity Recovery And Climate Experiment (GRACE) mission, we derive statistically robust “hot spot” regions of high probability of peak anomalous—i.e., with respect to the seasonal cycle—water storage (of up to 0.7 m one-in-five-year return level) and flux (up to 0.14 m/month). Analysis of, and comparison with, up to 32 years of ERA-Interim reanalysis fields reveals generally good agreement of these hot spot regions to GRACE results and that most exceptions are located in the tropics. However, a simulation experiment reveals that differences observed by GRACE are statistically significant, and further error analysis suggests that by around the year 2020, it will be possible to detect temporal changes in the frequency of extreme total fluxes (i.e., combined effects of mainly precipitation and floods) for at least 10–20% of the continental area, assuming that we have a continuation of GRACE by its follow-up GRACE Follow-On (GRACE-FO) mission.
Abstract Using data from the Gravity Recovery And Climate Experiment (GRACE) mission, we derive statistically robust “hot spot” regions of high probability of peak anomalous—i.e., with respect to the seasonal cycle—water storage (of up to 0.7 m one‐in‐five‐year return level) and flux (up to 0.14 m/month). Analysis of, and comparison with, up to 32 years of ERA‐Interim reanalysis fields reveals generally good agreement of these hot spot regions to GRACE results and that most exceptions are located in the tropics. However, a simulation experiment reveals that differences observed by GRACE are statistically significant, and further error analysis suggests that by around the year 2020, it will be possible to detect temporal changes in the frequency of extreme total fluxes (i.e., combined effects of mainly precipitation and floods) for at least 10–20% of the continental area, assuming that we have a continuation of GRACE by its follow‐up GRACE Follow‐On (GRACE‐FO) mission.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'%3e%3cpath fill='%23001489' d='M0 0v6h9V0z'/%3e%3cpath fill='%23e03c31' d='M0 0v3h9V0z'/%3e%3cg stroke='%23fff' stroke-width='2'%3e%3cpath id='d' d='m0 0 4.5 3L0 6m4.5-3H9'/%3e%3cuse xlink:href='%23b' stroke='%23ffb81c' clip-path='url(%23c)'/%3e%3c/g%3e%3cuse xlink:href='%23d' fill='none' stroke='%23007749' stroke-width='1.2'/%3e%3c/g%3e%3c/svg%3e)
