Risk and contributing factors of ecosystem shifts over naturally vegetated land under climate change in China.

Identifying the areas at risk of ecosystem transformation and the main contributing factors to the risk is essential to assist ecological adaptation to climate change. We assessed the risk of ecosystem shifts in China using the projections of four global gridded vegetation models (GGVMs) and an aggregate metric. The results show that half of naturally vegetated land surface could be under moderate or severe risk at the end of the 21st century under the middle and high emission scenarios. The areas with high risk are the Tibetan Plateau region and an area extended northeastward from the Tibetan Plateau to northeast China. With the three major factors considered, the change in carbon stocks is the main contributing factor to the high risk of ecosystem shifts. The change in carbon fluxes is another important contributing factor under the high emission scenario. The change in water fluxes is a less dominant factor except for the Tibetan Plateau region under the high emission scenario. Although there is considerable uncertainty in the risk assessment, the geographic patterns of the risk are generally consistent across different scenarios. The results could help develop regional strategies for ecosystem conservation to cope with climate change

Understanding the Water–Food–Energy Nexus for Supporting Sustainable Food Production and Conserving Hydropower Potential in China

Optimizing water–food–energy (WFE) relations has been widely discussed in recent years as an effective approach for formulating pathways toward sustainable agricultural production and energy supply. However, knowledge regarding the WFE nexus is still largely lacking, particularly beyond the conceptual description. In this study, we combined a grid-based crop model (Python-based Environmental Policy Integrated Climate—PEPIC) with a hydropower scheme based on the Distributed Biosphere Hydrological (DBH) model to investigate the WFE interplays in China concerning irrigated agricultural production and hydropower potential. The PEPIC model was used to estimate crop yields and irrigation water requirements under various irrigated cropland scenarios, while the DBH model was applied to simulate hydrological processes and associated hydropower potential. Four major crops, i.e., maize, rice, soybean, and wheat, were included for the analyses. Results show that irrigation water requirements present high values (average about 400 mm yr−1) in many regions of northern China, where crop yields are much higher on irrigated land than on rainfed land. However, agricultural irrigation has largely reduced hydropower potential up to 50% in some regions due to the substantial withdrawal of water from streams. The Yellow River basin, the Hai River basin, and the Liao River basin were identified as the hotspot regions concerning the WFE interactions and tradeoffs. Further expansion the irrigated cropland would increase the tradeoffs between supporting sustainable food production and conserving hydropower potential in many parts of China. The results provide some insights into the WFE nexus and the information derived is useful for supporting sustainable water management, food production while conserving the potential for hydropower generation in China

Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained

GRACE satellites enable long-lead forecasts of mountain contributions to streamflow in the low-flow season

Terrestrial water storage (TWS) in high mountain areas contributes large runoff volumes to nearby lowlands during the low-flow season when streamflow is critical to downstream water supplies. The potential for TWS from GRACE (Gravity Recovery and Climate Experiment) satellites to provide long-lead streamflow forecasting in adjacent lowlands during the low-flow season was assessed using the upper Yellow River as a case study. Two linear models were trained for forecasting monthly streamflow with and without TWS anomaly (TWSA) from 2002 to 2016. Results show that the model based on streamflow and TWSA is superior to the model based on streamflow alone at up to a five-month lead-time. The inclusion of TWSA reduced errors in streamflow forecasts by 25% to 50%, with 3–5-month lead-times, which represents the role of terrestrial hydrologic memory in streamflow changes during the low-flow season. This study underscores the high potential of streamflow forecasting using GRACE data with long lead-times that should improve water management in mountainous water towers and downstream areas

The critical role of the routing scheme in simulating peak river discharge in global hydrological models

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies

Understanding each other's models: a standard representation of global water models to support improvement, intercomparison, and communication

Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data

Understanding the Water–Food–Energy Nexus for Supporting Sustainable Food Production and Conserving Hydropower Potential in China

International audienceOptimizing water–food–energy (WFE) relations has been widely discussed in recent years as an effective approach for formulating pathways toward sustainable agricultural production and energy supply. However, knowledge regarding the WFE nexus is still largely lacking, particularly beyond the conceptual description. In this study, we combined a grid-based crop model (Python-based Environmental Policy Integrated Climate—PEPIC) with a hydropower scheme based on the Distributed Biosphere Hydrological (DBH) model to investigate the WFE interplays in China concerning irrigated agricultural production and hydropower potential. The PEPIC model was used to estimate crop yields and irrigation water requirements under various irrigated cropland scenarios, while the DBH model was applied to simulate hydrological processes and associated hydropower potential. Four major crops, i.e., maize, rice, soybean, and wheat, were included for the analyses. Results show that irrigation water requirements present high values (average about 400 mm yr−1) in many regions of northern China, where crop yields are much higher on irrigated land than on rainfed land. However, agricultural irrigation has largely reduced hydropower potential up to 50% in some regions due to the substantial withdrawal of water from streams. The Yellow River basin, the Hai River basin, and the Liao River basin were identified as the hotspot regions concerning the WFE interactions and tradeoffs. Further expansion the irrigated cropland would increase the tradeoffs between supporting sustainable food production and conserving hydropower potential in many parts of China. The results provide some insights into the WFE nexus and the information derived is useful for supporting sustainable water management, food production while conserving the potential for hydropower generation in China

Environmental flow requirements largely reshape global surface water scarcity assessment

The inclusion of environmental flow requirements (EFRs) in global water scarcity assessments is essential to obtain a reasonable representation of the water scarcity status. However, at a global scale, the quantification of EFRs is subject to large uncertainties resulting from various methods. So far, it is unclear to what extent the uncertainties in EFRs affect global water scarcity assessments. In this study, we examined the differences between EFR estimation methods and quantified their effects on spatially explicit water scarcity assessments, based on reconstructed global water withdrawal data and naturalized streamflow simulations. The global mean EFRs estimated by different methods ranged from 129 m ^3 s ^−1 to 572 m ^3 s ^−1 . Consequently, with the fulfillment of the EFRs, the area under water scarcity ranged between 8% and 52% of the total global land area, and the affected population ranged between 28% and 60% of the total population. In India and Northern China, 44%–66% and 22%–58% of the country’s land area, respectively, is affected by water scarcity; this percentage is higher than that found in other countries. The effects of different EFRs on water scarcity assessment are large in many regions, but relatively small in regions that experience intensive water use due to anthropological activities (such as Northern China and India). Through this study, we have put forth the need for the reconciliation of the estimates of EFRs to produce more reasonable and consistent water scarcity assessments