Estimating the added value of GRACE total water storage and uncertainty quantification in seasonal streamflow forecasting

Abstract. This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

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Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Hydrology and Earth System Sciences ◽

10.5194/hess-21-1573-2017 ◽

2017 ◽

Vol 21 (3) ◽

pp. 1573-1591 ◽

Cited By ~ 19

Author(s):

Louise Crochemore ◽

Maria-Helena Ramos ◽

Florian Pappenberger ◽

Charles Perrin

Keyword(s):

Long Range ◽

Precipitation Forecast ◽

Low Flow ◽

Drought Risk ◽

Streamflow Forecasting ◽

Streamflow Forecasts ◽

Precipitation Indices ◽

Seasonal Streamflow ◽

Prediction Approach ◽

The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

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Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil

Journal of Arid Land ◽

10.1007/s40333-021-0097-y ◽

2021 ◽

Vol 13 (3) ◽

pp. 205-223

Author(s):

Alexandre C. Costa ◽

Alvson B. S. Estacio ◽

Francisco de A. de Souza Filho ◽

Iran E. Lima Neto

Keyword(s):

Streamflow Forecasting ◽

Seasonal Streamflow

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Stepwise improvement of hydrological models using satellite-based evaporation and total water storage estimations

10.5194/egusphere-egu21-796 ◽

2021 ◽

Author(s):

Markus Hrachowitz ◽

Petra Hulsman ◽

Hubert Savenije

Keyword(s):

Model Calibration ◽

Hydrological Model ◽

Model Development ◽

Water Storage ◽

Spatiotemporal Variability ◽

Model Structure ◽

Hydrological Models ◽

Total Water ◽

Spatiotemporal Model ◽

Multiple Variables

Hydrological models are often calibrated with respect to flow observations at the basin outlet. As a result, flow predictions may seem reliable but this is not necessarily the case for the spatiotemporal variability of system-internal processes, especially in large river basins. Satellite observations contain valuable information not only for poorly gauged basins with limited ground observations and spatiotemporal model calibration, but also for stepwise model development. This study explored the value of satellite observations to improve our understanding of hydrological processes through stepwise model structure adaption and to calibrate models both temporally and spatially. More specifically, satellite-based evaporation and total water storage anomaly observations were used to diagnose model deficiencies and to subsequently improve the hydrological model structure and the selection of feasible parameter sets. A distributed, process based hydrological model was developed for the Luangwa river basin in Zambia and calibrated with respect to discharge as benchmark. This model was modified stepwise by testing five alternative hypotheses related to the process of upwelling groundwater in wetlands, which was assumed to be negligible in the benchmark model, and the spatial discretization of the groundwater reservoir. Each model hypothesis was calibrated with respect to 1) discharge and 2) multiple variables simultaneously including discharge and the spatiotemporal variability in the evaporation and total water storage anomalies. The benchmark model calibrated with respect to discharge reproduced this variable well, as also the basin-averaged evaporation and total water storage anomalies. However, the evaporation in wetland dominated areas and the spatial variability in the evaporation and total water storage anomalies were poorly modelled. The model improved the most when introducing upwelling groundwater flow from a distributed groundwater reservoir and calibrating it with respect to multiple variables simultaneously. This study showed satellite-based evaporation and total water storage anomaly observations provide valuable information for improved understanding of hydrological processes through stepwise model development and spatiotemporal model calibration.

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Long‐Term (1979‐Present) Total Water Storage Anomalies Over the Global Land Derived by Reconstructing GRACE Data

Geophysical Research Letters ◽

10.1029/2021gl093492 ◽

2021 ◽

Vol 48 (8) ◽

Author(s):

Fupeng Li ◽

Jürgen Kusche ◽

Nengfang Chao ◽

Zhengtao Wang ◽

Anno Löcher

Keyword(s):

Water Storage ◽

Total Water ◽

Grace Data ◽

Global Land

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Estimating the water balance and uncertainty bounds in a highly groundwater-dependent and data-scarce areas: An example for the upper Citarum basin

10.5194/egusphere-egu21-9943 ◽

2021 ◽

Author(s):

Steven Reinaldo Rusli ◽

Albrecht Weerts ◽

Victor Bense

Keyword(s):

Water Balance ◽

Water Storage ◽

Total Water ◽

Groundwater Abstraction ◽

Water Balance Components ◽

Groundwater Storage ◽

Actual Evaporation ◽

Basin Scale ◽

Basin Water ◽

Storage Change

In this study, we estimate the water balance components of a highly groundwater-dependent and hydrological data-scarce basin of the upper reaches of the Citarum river in West Java, Indonesia. Firstly, we estimate the groundwater abstraction volumes based on population size and a review of literature (0.57mm/day). Estimates of other components like rainfall, actual evaporation, discharge, and total water storage changes are derived from global datasets and are simulated using a distributed hydrological wflow_sbm model which yields additional estimates of discharge, actual evaporation, and total water storage change. We compare each basin water balance estimate as well as quantify the uncertainty of some of the components using the Extended Triple Collocation (ETC) method.The ETC application on four different rainfall estimates suggests a preference of using the CHIRPS product as the input to the water balance components estimates as it delivers the highest r2&#160; and the lowest RMSE compared to three other sources. From the different data sources and results of the distributed hydrological modeling using CHIRPS as rainfall forcing, we estimate a positive groundwater storage change between 0.12 mm/day - 0.60 mm/day. These results are in agreement with groundwater storage change estimates based upon GRACE gravimetric satellite data, averaged at 0.25 mm/day. The positive groundwater storage change suggests sufficient groundwater recharge occurs compensating for groundwater abstraction. This conclusion seems in agreement with the observation since 2005, although measured in different magnitudes. To validate and narrow the estimated ranges of the basin water storage changes, a devoted groundwater model is necessary to be developed. The result shall also aid in assessing the current and future basin-scale groundwater level changes to support operational water management and policy in the Upper Citarum basin.

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Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3083-2012 ◽

2012 ◽

Vol 16 (9) ◽

pp. 3083-3099 ◽

Cited By ~ 40

Author(s):

H. Xie ◽

L. Longuevergne ◽

C. Ringler ◽

B. R. Scanlon

Keyword(s):

Regional Scale ◽

Water Storage ◽

Hydrologic Model ◽

Sub Saharan Africa ◽

Model Parameters ◽

Total Water ◽

Discharge Data ◽

Model Based ◽

Grace Data ◽

Sub Saharan

Abstract. Irrigation development is rapidly expanding in mostly rainfed Sub-Saharan Africa. This expansion underscores the need for a more comprehensive understanding of water resources beyond surface water. Gravity Recovery and Climate Experiment (GRACE) satellites provide valuable information on spatio-temporal variability in water storage. The objective of this study was to calibrate and evaluate a semi-distributed regional-scale hydrologic model based on the Soil and Water Assessment Tool (SWAT) code for basins in Sub-Saharan Africa using seven-year (July 2002–April 2009) 10-day GRACE data and multi-site river discharge data. The analysis was conducted in a multi-criteria framework. In spite of the uncertainty arising from the tradeoff in optimising model parameters with respect to two non-commensurable criteria defined for two fluxes, SWAT was found to perform well in simulating total water storage variability in most areas of Sub-Saharan Africa, which have semi-arid and sub-humid climates, and that among various water storages represented in SWAT, water storage variations in soil, vadose zone and groundwater are dominant. The study also showed that the simulated total water storage variations tend to have less agreement with GRACE data in arid and equatorial humid regions, and model-based partitioning of total water storage variations into different water storage compartments may be highly uncertain. Thus, future work will be needed for model enhancement in these areas with inferior model fit and for uncertainty reduction in component-wise estimation of water storage variations.

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