[1] Changes in regional groundwater storage in North China were estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites data and ground-based measurements collected from 2003 to 2010. The study area (∼370,000 km2) included the Beijing and Tianjin municipality, the Hebei and Shanxi province, which is one of the largest irrigation areas in the world and is subjected to intensive groundwater-based irrigation. Groundwater depletion in North China was estimated by removing the simulated soil moisture changes from the GRACE-derived terrestrial water storage changes. The rate of groundwater depletion in North China based on GRACE was 2.2 ± 0.3 cm/yr from 2003 to 2010, which is equivalent to a volume of 8.3 ± 1.1 km3/yr. The groundwater depletion rate estimated from monitoring well stations during the same time period was between 2.0 and 2.8 cm/yr, which is consistent with the GRACE-based result. However, the estimated groundwater depletion rate in shallow plain aquifers according to the Groundwater Bulletin of China Northern Plains (GBCNP) for the same time period was only approximately 2.5 km3/yr. The difference in groundwater depletion rates estimated from GRACE and GBCNP data indicates the important contribution of groundwater depletion from deep aquifers in the plain and piedmont regions of North China.

Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground-based measurements (EI)

[1] Long-term climate is the first-order control on mean annual water balance, and vegetation and the interactions between climate seasonality and soil water storage change have also been found to play important roles. The purpose of this paper is to extend the Budyko hypothesis to the seasonal scale and to develop a model for interannual variability of seasonal evaporation and storage change. A seasonal aridity index is defined as the ratio of potential evaporation to effective precipitation, where effective precipitation is the difference between rainfall and storage change. Correspondingly, evaporation ratio is defined as the ratio of evaporation to effective precipitation. A modified Turc-Pike equation with a horizontal shift is proposed to model interannual variability of seasonal evaporation ratio as a function of seasonal aridity index, which includes rainfall seasonality and soil water change. The performance of the seasonal water balance model is evaluated for 277 watersheds in the United States. The 99% of wet seasons and 90% of dry seasons have Nash-Sutcliffe efficiency coefficients larger than 0.5. The developed seasonal model can be applied for constructing long-term evaporation and storage change data when rainfall, potential evaporation, and runoff observations are available. On the other hand, vegetation affects seasonal water balance by controlling both evaporation and soil moisture dynamics. The correlation between NDVI and evaporation is strong particularly in wet seasons. However, the correlation between NDVI and the seasonal model parameters is only strong in dry seasons.

Modeling Inter-annual Variability of Seasonal Evaporation and Storage Change Based on the Extended Budyko Framework

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Influences of climatic variability and human activities on terrestrial water storage variations across the Yellow River basin in the recent decade

Rapidly declining surface and terrestrial water resources in Central Asia driven by socio-economic and climatic changes

Scenario-based multi-objective optimization of reservoirs in silt-laden rivers: A case study in the Lower Yellow River.

A new multi-site multi-variable stochastic model with inter-site and inter-variable correlations, low frequency attributes and stochasticity: A case study in the lower Yellow River basin

Abstract. Gravity Recovery and Climate Experiment (GRACE) and its
successor GRACE Follow-on (GRACE-FO) satellite provide terrestrial water
storage anomaly (TWSA) estimates globally that can be used to monitor flood
in various regions at monthly intervals. However, the coarse temporal
resolution of GRACE and GRACE-FO satellite data has been limiting their
applications at finer temporal scales. In this study, TWSA estimates have
been reconstructed and then temporally downscaled into daily values based on
three different learning-based models, namely a multi-layer perceptron (MLP)
model, a long-short term memory (LSTM) model and a multiple linear regression
(MLR) model. Furthermore, a new index incorporating temporally downscaled
TWSA estimates combined with daily average precipitation anomalies is
proposed to monitor the severe flood events at sub-monthly timescales for
the Yangtze River basin (YRB), China. The results indicated that (1) the MLP
model shows the best performance in reconstructing the monthly TWSA with root mean square
error (RMSE) = 10.9 mm per month and Nash–Sutcliffe efficiency (NSE) = 0.89 during the validation period; (2) the MLP
model can be useful in temporally downscaling monthly TWSA estimates into
daily values; (3) the proposed normalized daily flood potential index
(NDFPI) facilitates robust and reliable characterization of severe flood
events at sub-monthly timescales; (4) the flood events can be monitored by
the proposed NDFPI earlier than traditional streamflow observations with
respect to the YRB and its individual subbasins. All these findings can
provide new opportunities for applying GRACE and GRACE-FO satellite data to
investigations of sub-monthly signals and have important implications for
flood hazard prevention and mitigation in the study region.


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Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

ABSTRACT Monitoring terrestrial water storage anomalies (TWSA) is essential for better understanding the influences of climatic variability on hydrological cycles. Here we use Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on satellite data and meteorological observations to analyse the inter-annual trends of TWSA across the Qinghai-Tibet Plateau (QTP) during 2003–2020 and investigate the relationships between climatic variability and annual changes in TWSA. Results indicate that TWSA across the QTP generally decreased, at a rate of −0.5 ± 1.4 mm/year during 2003–2020, which mainly arises from the coupled effects of Potential evapotranspiration (PET) and precipitation. In terms of the effects of climatic variability, annual changes in TWSA show a negative correlation coefficient (r = −0.76) with PET, which is greater than that with precipitation (r = 0.67). The cross-wavelet transformation analysis revealed that the evolution of TWSA across the QTP is closely associated with the Atlantic Multidecadal Oscillation. Our conclusions can help decision makers to formulate appropriate policies for the assessment and management of water resources over the QTP.

Jingkai Xie

Papers

Scenario-based multi-objective optimization of reservoirs in silt-laden rivers: A case study in the Lower Yellow River.

A new multi-site multi-variable stochastic model with inter-site and inter-variable correlations, low frequency attributes and stochasticity: A case study in the lower Yellow River basin

Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Understanding the impact of climatic variability on terrestrial water storage in the Qinghai-Tibet Plateau of China

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