[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

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communitie...

Deep Learning, Explained: Fundamentals, Explainability, and Bridgeability to Process-based Modelling

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

. Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-on (GRACE-FO) satellites provide terrestrial water storage anomaly (TWSA) estimates globally that can be used to monitor the floods in various regions at monthly intervals. However, the coarse temporal resolution of GRACE/GRACE-FO satellites data has been limiting its applications at finer temporal scales. In this study, TWSA estimates have been reconstructed and then 10 temporally downscaled into daily values based on three different learning‐based models, namely multi-layer perceptron (MLP) model, long-short term memory (LSTM) model and 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 time scales for the Yangtze River Basin (YRB), China. The results indicated that (1) the MLP model shows the best performance in reconstructing monthly TWSA with RMSE = 10.9 15 mm/month and 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 time scales; (4) the flood events can be monitored by the proposed NDFPI earlier than traditional streamflow observations with respect to the YRB and its individual basins. All these findings can provide new opportunities for applying GRACE/GRACE-FO satellites data to investigations of sub- 20 monthly signals and have important implications for flood hazard prevention and mitigation in the study region. the flood events at sub-monthly time scales occurred in summer 2020 for the entire YRB; (4) The comparison analysis indicates that the flood events can be monitored by the proposed NDFPI earlier than traditional streamflow observations with respect to the YRB and its individual basins, which is very vital for flood forecasts and warning across this region. Overall, the present study shows the great potential of temporally downscaled GRACE/GRACE-FO satellites data in a wide 555 range of hydrological applications, such as monitoring the extreme flood events. The study provides an effective means for the temporal downscaling of original TWSA estimates from GRACE/GRACE-FO satellites data and will help facilitate the sustainable management of water resources and develop monitoring and early warning systems for severe flood events over large-scale basins.

Downscaling and monitoring the extreme flood events in the Yangtze River Basin based on GRACE/GRACE-FO satellites data

Scenario-Based Multi-Objective Optimization of Reservoirs in Silt-Laden Rivers: A Case Study in the Lower Yellow Riverscenario-Based Multi-Objective Optimization of Reservoirs in Silt-Laden Rivers: A Case Study in the Lower Yellow River

Abstract Rivers originating in the Tibetan Plateau are crucial to the population in Asia. However, research about quantifying seasonal catchment memory of these rivers is still limited. Here, we propose a model able to accurately estimate terrestrial water storage change (TWSC), and characterize catchment memory processes and durations using the memory curve and the influence/domination time, respectively. By investigating eight representative basins of the region, we find that the seasonal catchment memory in precipitation-dominated basins is mainly controlled by precipitation, and that in non-precipitation-dominated basins is strongly influenced by temperature. We further uncover that in precipitation-dominated basins, longer influence time corresponds to longer domination time, with the influence/domination time of approximately six/four months during monsoon season. In addition, the long-term catchment memory is observed in non-precipitation-dominated basins. Quantifying catchment memory can identify efficient lead times for seasonal streamflow forecasts and water resource management. 

https://www.nature.com/articles/s41467-023-38966-9.pdf

Seasonal catchment memory of high mountain rivers in the Tibetan Plateau

Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-on (GRACE-FO) satellites provide terrestrial water storage anomaly (TWSA) estimates globally that can be used to monitor the floods in various regions at monthly intervals. However, the coarse temporal resolution of GRACE/GRACE-FO satellites data has been limiting its applications at finer temporal scales. In this study, TWSA estimates have been reconstructed and then temporally downscaled into daily values based on three different learningâbased models, namely multi-layer perceptron (MLP) model, long-short term memory (LSTM) model and 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 time scales for the Yangtze River Basin (YRB), China. The results indicated that (1) the MLP model shows the best performance in reconstructing monthly TWSA with RMSE = 10.9 mm/month and 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 time scales; (4) the flood events can be monitored by the proposed NDFPI earlier than traditional streamflow observations with respect to the YRB and its individual basins. All these findings can provide new opportunities for applying GRACE/GRACE-FO satellites data to investigations of sub-monthly signals and have important implications for flood hazard prevention and mitigation in the study region. 

Reply on RC1

Climate change has emerged as a vital issue for hydropower management in the future. General circulation model (GCM)-informed climate is commonly applied to assess the impact of climate change on hydropower. However, the effect of GCM credibility on hydropower robustness has been rarely explored. One objective of this study is to seek valuable and robust policies, and the other is to evaluate the effects of GCM credibility on hydropower robustness based on the pre-optimized operating policies. A multi-objective robust optimization approach is first proposed to design operating policies by coupling stochastic simulation, heuristic parameterized policy search, and robust optimization. The GCM credibility is then evaluated based on multi-criteria ranking scores, and the hydropower robustness is illustrated by stepwise culling the lowest credible GCMs. The climate-induced impacts on hydropower robustness rely on an ensemble of 18 GCMs under three representative concentration pathways in Coupled Model Intercomparison Project 5 (CMIP5) and 17 GCMs under three shared socioeconomic pathways in CMIP6. The Longyangxia and Liujiaxia reservoirs in Upper Yellow River Basin are selected as a case study. The results show that (1) the proposed stochastic approach outperforms the deterministic approach regarding hydropower generation and firm output where the mean annual hydropower generation of stochastic operation is improved by -0.50- 19.53% compared to deterministic operation, indicating that the operating rules derived using historical stochastic inflows can adapt flexibly to future inflow changes than that using deterministic inflow. (2) CMIP6 maintains a more robust performance than CMIP5 with higher satisficing measures and lower regret measures. (3) Hydropower robustness tends to be higher when considering credibility and will be lower with time due to increasing uncertainty. Ignorance of GCM credibility may lead to biased climate change adaptation decisions. 

Jingkai Xie

Papers

Downscaling and monitoring the extreme flood events in the Yangtze River Basin based on GRACE/GRACE-FO satellites data

Scenario-Based Multi-Objective Optimization of Reservoirs in Silt-Laden Rivers: A Case Study in the Lower Yellow Riverscenario-Based Multi-Objective Optimization of Reservoirs in Silt-Laden Rivers: A Case Study in the Lower Yellow River

Seasonal catchment memory of high mountain rivers in the Tibetan Plateau

Reply on RC1

Impacts of GCM credibility on hydropower production robustness under climate change: CMIP5 vs. CMIP6