Assessments of climate change impacts on streamflow and sediment processes are essential for developing science‐based sustainable watershed management plans. We assessed climate change impacts on ...streamflow and sediment load in the upstream of the Mekong River Basin, as a case study. Future climate scenarios including an ensemble‐mean climate scenario (EnM scenario) were generated based on 20 GCMs in CMIP5, using a stochastic weather generator (LARS‐WG) coupled with a distribution‐free shuffle procedure. The SWAT model was applied to simulate changes in streamflow and sediment load for the future period 2071–2100 under RCP8.5 with respect to the baseline period 1971–2000. Results show that mean monthly maximum and minimum temperature were projected to increase by all the 20 GCMs, with an ensemble‐mean increase of 4.6–5.7°C and 4.2–5.8°C across the 12 months, respectively. An increase in mean annual precipitation (3.4–55.8%) and streamflow (1.0–72.7%) was also projected by all GCMs. However, projected changes in sediment load were not consistent. One half of the GCMs projected an increase (5.2–53.2%) in annual sediment load while the other half projected a decrease (5.1%–43.2%). In each month, at least three‐quarters of the GCMs projected an increase in monthly streamflow. For monthly sediment load, an increase in May to July was projected by over half of the GCMs, while a decrease was projected by a majority of the GCMs for other months. Our results indicate large uncertainties in streamflow and sediment projections under climate change, demonstrating the need to use multi‐model ensembles in climate change impact studies. Moreover, it was found that the streamflow and sediment loads simulated using the EnM scenario were often close to the ensemble means simulated using the 20 GCMs, which implies that the single EnM scenario has the potential of effectively and efficiently estimating the ensemble means of projections in a multi‐model ensemble.
Future temperature, precipitation and streamflow are projected to increase but uncertainties are large. Projected changes in sediment load are not consistent and more uncertain than streamflow. A single ensemble‐mean climate scenario generated using a stochastic approach has the potential of effectively estimating ensemble means of the simulated streamflow and sediment load in a multi‐GCM ensemble.
Drought can have a substantial impact on the ecosystem and agriculture of the affected region and does harm to local economy. This study aims to analyze the relation between soil moisture and drought ...and predict agricultural drought in Xiangjiang River basin. The agriculture droughts are presented with the Precipitation-Evapotranspiration Index (SPEI). The Support Vector Regression (SVR) model incorporating climate indices is developed to predict the agricultural droughts. Analysis of climate forcing including El Niño Southern Oscillation and western Pacific subtropical high (WPSH) are carried out to select climate indices. The results show that SPEI of six months time scales (SPEI-6) represents the soil moisture better than that of three and one month time scale on drought duration, severity and peaks. The key factor that influences the agriculture drought is the Ridge Point of WPSH, which mainly controls regional temperature. The SVR model incorporating climate indices, especially ridge point of WPSH, could improve the prediction accuracy compared to that solely using drought index by 4.4% in training and 5.1% in testing measured by Nash Sutcliffe efficiency coefficient (NSE) for three month lead time. The improvement is more significant for the prediction with one month lead (15.8% in training and 27.0% in testing) than that with three months lead time. However, it needs to be cautious in selection of the input parameters, since adding redundant information could have a counter effect in attaining a better prediction.
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•The SVR model is applied in agricultural drought prediction in Xiangjiang River basin.•Drought index SPEI-6 is recommended to reflect the soil moisture condition.•Ridge point of WPSH is the key factor affecting SPEI-6 mainly through temperature.•Prediction of drought could be improved by incorporating climate indices in SVR model.
Informing reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under ...uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.
Key Points
Flood scenarios characterized by forecast errors were developed using Cloud model and Copula functions based on individual AI‐based forecasts
A multi‐objective robust optimization model integrating the risk, resilience, and vulnerability was proposed to enhance model robustness
Ensemble forecasts can be more valuable than deterministic forecasts in the flood control operation
Genetic and genomic studies have advanced our knowledge of inherited Parkinson's disease (PD), however, the etiology and pathophysiology of idiopathic PD remain unclear. Herein, we perform a ...meta-analysis of 8 PD postmortem brain transcriptome studies by employing a multiscale network biology approach to delineate the gene-gene regulatory structures in the substantia nigra and determine key regulators of the PD transcriptomic networks. We identify STMN2, which encodes a stathmin family protein and is down-regulated in PD brains, as a key regulator functionally connected to known PD risk genes. Our network analysis predicts a function of human STMN2 in synaptic trafficking, which is validated in Stmn2-knockdown mouse dopaminergic neurons. Stmn2 reduction in the mouse midbrain causes dopaminergic neuron degeneration, phosphorylated α-synuclein elevation, and locomotor deficits. Our integrative analysis not only begins to elucidate the global landscape of PD transcriptomic networks but also pinpoints potential key regulators of PD pathogenic pathways.
Snow and glacier are important components in the hydrological cycle of the Tibetan Plateau (TP). Air temperature, as the main driver in freezing and thawing processes, becomes vital for hydrological ...modelling and prediction in this region. Due to a sparse ground gauging network, spatial density of air temperature measurement is insufficient for hydro‐meteorological studies. Therefore, the aim of this study is to identify the best representative temperature data for hydrological applications from four widely used reanalysis products, including ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2, with reference to in situ measurements and gridded snow depth from the year 2008–2017 over the entire TP. To reduce errors, Bayesian Joint Probability (BJP) approach based on K‐Nearest Neighbour (KNN) classification algorithm (KNN‐BJP) is proposed to post‐process gridded reanalyses. The results indicate that all the reanalysis datasets provide highly correlated but cold biased air temperature. The correlation ecoefficiency is greater than 0.85. The cold biases are near −3 ° C and mainly distributed in the southeastern TP. Bias in daily maximum temperature during Spring is greater than −8 ° C for most stations. ERA‐Interim is found to have the closest agreement with in situ measurements, closely followed by GLDAS‐2.1. KNN‐BJP is found to be effective within a distance smaller than 5°. After post‐processing, the prominent underestimation is efficiently corrected with Bias near 0. RMSE is markedly reduced to be smaller than 2.5 ° C. The post‐processed ERA‐5 and GLDAS‐2.1 are as accurate as ERA‐Interim, but able to provide more detailed information for extreme events due to their finer spatial resolution. Thus, ERA‐5 and GLDAS‐2.1 are more recommended to represent air temperature in the TP. Snow depth as complementary reference data is able to present spatial variance of air temperature. Our study can help alleviate the problem of sparse air temperature data over the TP.
Reanalysis datasets present high correlated but cold biased air temperature in the TP.
KNN‐BJP is found to be effective in correcting biases and the extrapolation distance to apply this post‐processing method is up to 5 ° .
ERA‐5 and GLDAS‐2.1reanalyses are more recommended to represent air temperature, and the coarse resolution for ERA‐Interim and NCEP‐R2 usually overlooks the detailed information, especially the regional extreme temperatures.
Climate change significantly influences characteristics of rainfall events including rainfall depth, rainfall duration, inter‐event time and temporal patterns that directly affect water resources ...management, flood defence and hydraulic structure design. In this study, a framework is proposed to analyse daily‐scale rainfall event characteristics based on global climate model (GCM) simulations. This framework includes bias correction of raw GCM‐simulated rainfall series, selection of good‐performing bias‐corrected GCMs based on the mean absolute percentage error (MAPE) and evaluation of selected GCMs' skills in simulating rainfall event characteristics and finally assessment of changes in rainfall event characteristics in the future. In this study, 17 GCMs, four representative concentration pathways (i.e., RCP2.6, RCP4.5, RCP6.0 and RCP8.5) and two future periods (i.e., 2041–2070 and 2071–2100) are considered. After bias correction of the GCMs using the monthly‐scale double gamma distribution, 9 out of 17 GCMs with MAPE values smaller than 20% in the historical period 1971–2000 are selected. In general, these selected GCMs well capture the rainfall characteristics of different rainfall event classes. The multi‐model ensembles suggest that compared to the historical period, the frequency of rainfall events with an extreme depth, short duration and long inter‐event time will increase in the two future periods and the change in 2071–2100 is generally larger than that in 2041–2070, indicating that more extreme climate conditions may occur in Qu River basin in the future. Moreover, the temporal patterns of heavy rainfall events will become more non‐uniform with more concentrated peak rainfall. The frequency of the delayed rainfall type (i.e., peaks occurring at the end of the rainfall event) will increase in the future, which can probably cause more severe floods and is very detrimental to flood defence in this study area.
With global warming, the frequency of rainfall events with an extreme depth, short duration and long inter‐event time will probably increase in the future. The rainfall patterns of the delayed rainfall type (i.e., peaks occurring at the end of a rainfall event) are becoming more non‐uniform with more rainfall concentrating in the peak parts, particularly for heavy rainfall events. The future changes of these rainfall event characteristics pose a great challenge to flood defence in the study area.
Climate and land use/cover changes are the main factors altering hydrological regimes. To understand the impacts of climate and land use/cover changes on streamflow within a specific catchment, it is ...essential to accurately quantify their changes given many possibilities. We propose an integrated framework to assess how individual and combined climate and land use/cover changes impact the streamflow of Xinanjiang Basin, in East China, in the future. Five bias-corrected and downscaled General Circulation Model (GCM) projections are used to indicate the inter-model uncertainties under three Representative Concentration Pathways (RCPs). Additionally, three land use/cover change scenarios representing a range of tradeoffs between ecological protection (EP) and urban development (UD) are projected by Cellular Automata - Markov (CA-Markov). The streamflow in 2021–2050 is then assessed using the calibrated Soil and Water Assessment Tool (SWAT) with 15 scenarios and 75 possibilities. Finally, the uncertainty and attribution of streamflow changes to climate and land use/cover changes at monthly and annual scale are analyzed. Results show that while both land use/cover change alone and combined changes project an increase in streamflow, there is a disagreement on the direction of streamflow change under climate change alone. Future streamflow may undergo a more blurred boundary between the flood and non-flood seasons, potentially easing the operation stress of Xinanjiang Reservoir for water supply or hydropower generation. We find that the impacts of climate and land use/cover changes on monthly mean streamflow are sensitive to the impermeable area (IA). The impacts of climate change are stronger than those induced by land use/cover change under EP (i.e., lower IA); and land use/cover change has a greater impact in case of UD (i.e., higher IA). However, changes in annual mean streamflow are mainly driven by land use/cover change, and climate change may decrease the influence attributed to land use/cover change.
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•Streamflow uncertainty was analyzed by fuzzy extension principle.•Future streamflow may undergo a blurred boundary between flood and non-flood seasons.•Climate change decreased the impacts on streamflow of land use/cover change.•Impermeable area is an important factor affecting changes in streamflow.
•Terrestrial water storage across the Yellow River basin is evaluated by GRACE data.•Terrestrial water storage change is highly correlated with precipitation and PET.•Human activities are critical ...factors that affect terrestrial water storage change.•A new index is proposed to assess the contributions of different factors.
Terrestrial water storage (TWS) variations can be influenced by both climatic variability and human activities. In this study, we made an integrated use of GRACE data and meteorological data to characterize the TWS variations in the Yellow River basin (YRB) during 2003–2015 and investigated the relationships between terrestrial water storage change (TWSC) and human activities and climatic variability respectively. Additionally, a novel, simple but effective index was also proposed to quantify the contributions of different factors from climatic variability and human activities to TWSC in the YRB and its different subregions. The results indicated that there existed a significant TWS decrease at a rate of −4.6 ± 1.4 mm/yr (approximately 3.7 ± 1.1 Gt/yr) across the entire YRB during the past decade. The variables including precipitation, PET and NDVI had a correlation coefficient (r) of 0.76, −0.63 and −0.38 respectively with TWSC at annual scale. Apart from these, the correlations between reservoir operation and annual TWSC also have been estimated for the first time, which showed a better performance (r = 0.85) than that between TWSC and water withdrawals (r = 0.40). The results mainly indicated that TWSC was attributed to the climatic variability while the intense human activities especially reservoir operation also generated a significant effect on TWSC in the YRB. These conclusions can provide beneficial guidance for the management and assessments of local water resources.
Baseflow plays a vital role in protecting the environment and ensuring a stable water supply for farming. There are still gaps in the current understanding of baseflow convergence rates in the humid ...region due to the abundance of rainfall and the high‐water table. Therefore, this study focused on the evolution and hysteresis characteristics of baseflow in humid basins of southeastern China. The baseflow ensemble simulation (BES) method was established to improve the reliability and applicability of baseflow simulation. We suggest a way of differentiating the wet and dry seasons based on the multi‐year average monthly baseflow index (BFI) to determine the intra‐annual distribution of water effectively and simply. The hydrological hysteresis effect of baseflow on precipitation is revealed by characterizing baseflow response to precipitation under precipitation events during wet and dry seasons. A methodology for assessing the performance of baseflow simulation was proposed from observations of streamflow and precipitation. We found that the BES method performed better in baseflow simulation than other single separation methods. Using the BES method, the lag time of baseflow to precipitation during the wet and dry seasons was found to be 3.09 and 4.04 days after utilizing the BFI to divide the hydrological situation into wet and dry seasons. Additionally, precipitation had nearly twice as much intensity influence on baseflow during the dry season compared to the wet season. These findings have significant ramifications for the use, management, and planning of water resources in humid areas of China.
Plain Language Summary
The importance of researching baseflow in humid places is expanding as drought conditions occur more frequently. The lag time effect of baseflow on precipitation varies spatially and temporally, while the applicability of each baseflow simulation method varies in different regions. In this study, we validated the performance of a baseflow ensemble simulation method in the humid region of southeastern China. Humid regions had a shorter lag between baseflow and precipitation than desert, semiarid, and semi‐humid zones. The lag time of baseflow for rainfall simulated by the BES method was in the middle of the four methods. Additionally, compared to the dry season, the baseflow lag time was noticeably shorter during the wet season. This is because the humid region basin receives most of its yearly precipitation during the rainy season, primarily in the form of intense rainfall that lasts just a brief time. In addition, baseflow variations coincided with variations in precipitation during the rainy season, while there was a delay between variations in baseflow and changes in precipitation during the dry season. Understanding the effects of climate change and water use on groundwater‐surface water interactions in humid regions of China is significantly impacted by these findings.
Key Points
An ensemble‐based baseflow simulation method is proposed to characterize the uncertainty of each baseflow separation method
The hydrologic hysteresis between baseflow and rainfall was found to be within 1 week in the humid basins of Southeastern China
The influence of precipitation on baseflow in the humid basins is significantly stronger in the dry season than in the wet season
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