1,721,016 research outputs found
Analysis of the Water-Food-Energy Nexus and Water Competition Based on a Bayesian Network
No full text© 2022, The Author(s), under exclusive licence to Springer Nature B.V.Water competition is a key issue in the study of the water-food-energy nexus (WFEN), which can affect water, food, and energy security and can generate notable challenges in water resource management. Since Bayesian network can express parameter uncertainty with a certain probability distribution while reflecting the dependencies of each variable, this study used a Bayesian network to model the WFEN in the Pearl River Region (PRR). The network structure can intuitively represent complex causal relationships, and the form of the probability distribution can effectively reflect the variable uncertainty. The responses of the Bayesian network model under different scenarios were used to analyse the major influencing factors, and water competition relationships in various sectors were explored. The results indicated that water competition between the different sectors was very complex and could dynamically change under the different scenarios. For example, an increase in hydropower and flow to sea could lead to a decrease in irrigation water, but an increase in irrigation water did not necessarily reduce hydropower and flow to sea. Water for hydropower generation and salt tide alleviation were obviously affected by the total offstream water use, but there existed no obvious water competition between these aspects in general. However, when offstream water use remained stable, a competitive relationship was observed between hydropower and flow to sea. Overall, the outcomes of this study could be of great significance to further analyse the WFEN in other regions.11Nsciescopu
Comparison of climate change impacts on the growth of C3 and C4 crops in China
No full textGlobal agricultural production has been significantly affected by climate change. As a large but also weak agricultural country, China must take corresponding adaptation measures in regard to climate change. As C3 and C4 crops have different carbon sequestration pathways, the responses of their growth to climate change are different. This study comprehensively compared the impacts of climate change on the growth of C3 and C4 crops in China by considering several key variables, such as solar radiation, temperature, precipitation, CO2 concentration, and agro–climatic constraints. The WOFOST (WOrld FOod STudies) model was used to quantitatively simulate and analyze the impacts of these variables on crop yield under four different scenarios. The results show that 1) during the growth period, solar radiation had the most significant change, followed by temperature difference between day and night, daily minimum temperature, daily maximum temperature, and precipitation; 2) the growth indicators of both C3 and C4 crops were more strongly correlated with solar radiation and temperature; and 3) under the four scenarios, changes in temperature and solar radiation had negative effects on both C3 and C4 crops in most regions, and changes in CO2 concentration had greater impacts on crop yields than other factors. This study revealed the temporal and spatial patterns of crop growth indicators under different climate change scenarios in the past 30 years, which provides a scientific basis for exploring how to adapt to climate change and provide higher levels of crop productivity in China. © 2022 Elsevier B.V.11Nsciescopu
A global perspective on propagation from meteorological drought to hydrological drought during 1902-2014
No full textMeteorological drought is generally regarded as the cause of other types of droughts. This study firstly analyzed the characteristics of meteorological drought and hydrological drought in different climate regions all over the world during a long time period (1902-2014); then, the maximum Pearson correlation coefficients (MPCC) of meteorological drought and hydrological drought at different time scales were calculated to determine the drought response time (DRT) in each climate region. The results revealed that: 1) meteorological drought in most climate regions intensified during 1902-1958 but showed a wetting trend during 1959-2014. Compared with the characteristics of meteorological drought, the change of hydrological drought was slightly different. Hydrolog-ical drought weakened during 1902-1958 but intensified slightly during 1959-2014; however, the magnitude of the changing rate was relatively small. 2) The drought response relationship in the Cf (i.e., continental wet warm) climate region was the strongest, and that in the E (i.e., polar) climate region was the weakest. 3) Globally, the DRTs in various climate regions were mainly 5-10 months, which were mainly related to the climate type. The outcomes of this study can provide a reference for further revealing the propagation mecha-nism from meteorological drought to hydrological drought in different climate regions.11Nsciescopu
Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis
No full text© 2022 Elsevier LtdThe increase in drought frequency in recent years is considered as an important factor affecting vegetation diversity. Understanding the responses of vegetation dynamics to drought is helpful to reveal the behavioral mechanisms of terrestrial ecosystems and propose effective drought control measures. In this study, long time series of Normalized Difference Vegetation Index (NDVI) and Solar-induced chlorophyll fluorescence (SIF) were used to analyze the vegetation dynamics in the Pearl River Basin (PRB). The relationship between vegetation and meteorological drought was evaluated, and the corresponding differences among different vegetation types were revealed. Based on an improved partial wavelet coherence (PWC) analysis, the influences of teleconnection factors (i.e., large-scale climate patterns and solar activity) on the response relationship between meteorological drought and vegetation were quantitatively analyzed to determine the roles of factors. The results indicate that (a) vegetation in the PRB showed an increasing trend from 2001 to 2019, and the SIF increased more than that of NDVI; (b) the vegetation response time (VRT) based on NDVI (VRTN) was typically 4–6 months, while the VRT based on SIF (VRTS) was typically 2–4 months. The VRT was shortest in the woody savannas and longest in the evergreen broadleaf forests. (c) The relationship between the SIF and meteorological drought was more significant than that between the NDVI and meteorological drought. (d) There was a significant positive correlation between meteorological drought and vegetation in the period of 8–20 years. The El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and sunspots were important driving factors affecting the response relationship between drought and vegetation. Specifically, the PDO had the greatest impacts among these factors.11Nsciescopu
Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method
No full text© 2022 Elsevier B.V.Model development in groundwater simulation and physics informed deep learning (DL) has been advancing separately with limited integration. This study develops a general hybrid model for groundwater level (GWL) simulations, wherein water balance-based groundwater processes are embedded as physics constrained recurrent neural layers into prevalent DL architectures. Because of the automatic parameterizing process, physics-informed deep learning algorithm (DLA) equips the hybrid model with enhanced abilities of inferring geological structures of catchment and unobserved groundwater-related processes implicitly. The main purposes of this study are: 1) to explore an optimized data-driven method as alternative to complicated groundwater models; 2) to improve the awareness of hydrological knowledge of DL model for lumped GWL simulation; and 3) to explore the lumped data-driven groundwater models for cross-region applications. The 91 illustrative cases of GWL modeling across the middle eastern continental United States (CONUS) demonstrate that the hybrid model outperforms the pure DL models in terms of prediction accuracy, generality, and robustness. More specifically, the hybrid model outperforms the pure DL models in 78 % of catchments with the improved Δ NSE = 0.129. Meanwhile, the hybrid model simulates more stably with different input strategies. This study reveals the superiority and powerful simulation ability of the DL model with physical constraints, which increases trust in data-driven approaches on groundwater modellings.11Nsciescopu
Impacts of Climate Change and Land Use/Cover Change on Regional Hydrological Processes: Case of the Guangdong-Hong Kong-Macao Greater Bay Area
No full textCopyright © 2022 Tan, Liu, Tian, Zhou, Wang, Jiang and Shi.Climate change and land use/cover change (LUCC) have been widely recognized as the main driving forces that can affect regional hydrological processes, and quantitative assessment of their impacts is of great importance for the sustainable development of regional ecosystems, land use planning and water resources management. This study investigates the impacts of climate change and LUCC on variables such as streamflow (SF), soil moisture (SM) and evapotranspiration (ET) in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) by using Soil and Water Assessment Tools (SWAT) model under different scenarios during 1979–2018. The results show that the simulation performances were overall good, with Nash-Sutcliffe Efficiency Coefficient (NSE) and coefficient of determination (R2) greater than 0.80 for the monthly-scale SF calibration and validation. According to the results of trend and change point tests of meteorological series, the baseline period (1979–1997) and the interference period (1998–2018) were determined. Interestingly, other land use types were basically converted to urban land, leading to a rapid urbanization in the GBA. Compared with the SF values of the eight estuaries of the Pearl River Basin in the baseline period, both climate change and LUCC has led to the decrease in the SF values in the interference period, and the combined effect of climate change and LUCC was slightly greater than their individual effect. Overall, climate change and LUCC both have important impacts on regional hydrological processes in the GBA.11Nsciescopu
Building a large dam: identifying the relationship between catchment area and slope using the confidence ellipse approach
No full textAbstract With the population projections indicating continued growth during this century, construction of large dams can be considered as one of the best available options to meet the future increases in water, food, and energy demands. While there are reports that thousands of large dams will be built in the near future, a key question is: what are the appropriate conditions for selecting the sites for these dams? The site of a large dam should be carefully evaluated based on many factors, such as socioeconomic development, water resources availability, topographic characteristics, and environmental impacts. This study aims to partly address the above question through identifying the relationship between two topographic characteristics (i.e., catchment area and slope) of a river reach to build a large dam based on the 30-m-resolution global drainage networks. The information about 2815 existing large dams from the Global Reservoir and Dam (GRanD) database is collected for analysis. The confidence ellipse approach is introduced to establish the quantitative relationship between these two variables, which is then used to evaluate the site selection of a large dam from the perspective of topographic characteristics. The results show that: (1) each large dam can well correspond to the nearest river reach in the global drainage networks and (2) the logarithmic values of catchment area and slope can be well described by a confidence ellipse, which is obtained based on the means, standard deviations, and Pearson correlation coefficients of the two variables. The outcomes of this study will be of great value for policymakers to have a more comprehensive understanding of large dam development in future
A New Perspective on Drought Propagation: Causality
No full text© 2022. American Geophysical Union. All Rights Reserved.The essence of propagation from meteorological to hydrological drought is the cause-effect relationship between precipitation and runoff. This study challenged the reliability of applying linear or non-linear correlation (i.e., closeness/similarity, a non-directional scalar) to study drought propagation (i.e., causality, a directional vector). Meanwhile, in the field of hydrometeorology, causality analysis is burgeoning in model simulations, but still rare in analyzing the observations. Therefore, this study aims to provide a new perspective on drought propagation (i.e., causality) using convergent cross mapping (CCM) based on pure observations. Compared with the results in previous studies, the effectiveness of applying causality analysis in drought propagation study was proven, indicating that causality analysis would be more powerful than correlation analysis, especially for detecting drought propagation direction.11Nsciescopu
Investigating the spatiotemporal variations of extreme rainfall and its potential driving factors with improved partial wavelet coherence
No full textExtreme rainfall can be affected by various climatic factors such as the large-scale climate patterns (LCPs). Understanding the changing LCPs can improve the accuracy of extreme rainfall prediction. This study explores the variation trend of extreme rainfall in the middle and lower reaches of the Yangtze River Basin (MLRYRB) and the telecorrelation with four LCPs, namely WPSHI (Western Pacific Subtropical High Index), EAMI (East Asia Monsoon Index), ENSO (El Nino-Southern Oscillation) and PDO (Pacific Decadal Oscillation), through modified Mann-Kendall (MMK) analysis, Pearson correlation coefficient, wavelet coherence analysis (WTC) and improved partial wavelet analysis (PWC). Previous studies have ignored the interdependence between these climate indices when analyzing their effects on precipitation. This study introduces the improved PWC, which can remove the correlations between them and reveal the influence of a single LCP. The results show that: 1) extreme rainfall in the MLRYRB has an obvious increasing trend and has a significant correlation with the LCPs; 2) the LCPs have a significant cyclical relationship with extreme rainfall, which can be significantly affected by the intergenerational variation of the LCPs; and 3) the improved PWC can accurately reveal the influence of a single LCP. EAMI is the main influencing factor in the 1-year cycle, while WPSHI is the main influencing factor in the 5-year cycle. ENSO and PDO can always influence extreme rainfall by coupling WPSHI or EAMI.11Nsciescopu
Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China
No full textVegetation, a key intermediary linking water, the atmosphere, and the ground, performs extremely important functions in nature and for our existence. Although satellite-based remote-sensing technologies have become important for monitoring vegetation dynamics, selecting the correct remote-sensing vegetation indicator has become paramount for such investigations. This study investigated the consistencies between a photosynthetic activity index (the solar-induced chlorophyll fluorescence (SIF) indicator) and the traditional vegetation index (the Normalized Difference Vegetation Index (NDVI)) among different land-cover types and in different seasons and explored the applicability of NDVI and SIF in different cases by comparing their performances in gross primary production (GPP) and grain-yield-monitoring applications. The vegetation cover and photosynthesis showed decreasing trends, which were mainly concentrated in northern Xinjiang and part of the Qinghai–Tibet Plateau; a decreasing trend was also identified in a small part of Northeast China. The correlations between NDVI and SIF were strong for all land-cover types except evergreen needleleaf forests and evergreen broadleaf forests. Compared with NDVI, SIF had some advantages when monitoring the GPP and grain yields among different land-cover types. For example, SIF could capture the effects of drought on GPP and grain yields better than NDVI. To summarize, as the temporal extent of the available SIF data is extended, SIF will certainly perform increasingly wide applications in agricultural-management research that is closely related to GPP and grain-yield monitoring
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