Explore the vast range of titles available.

The economic and human losses caused by drought are increasing, driven by climate change, human activities, and increased exposure of livelihood activities in water‐dependent sectors. Mitigation of these impacts for socio‐ecological securit is necessary to gain a better understanding of how human activities contribute to the propagation of drought as water management further develops. The previous studies investigated the impact of human activities on a macro level, but they overlooked the specific effects caused by human water management measures. In addition, most studies focus on the propagation time (PT, the number of months from meteorological drought propagation to hydrological drought), while other drought propagation characteristics, such as duration, magnitude, and recovery time, are not yet sufficiently understood. To tackle these issues, the PCR‐GLOBWB v2.0 hydrological model simulated hydrological processes in China under natural and human‐influenced scenarios. The study assessed how human activities impact hydrological drought and its propagation. Result shows that human activities have exacerbated hydrological drought in northern China, while it is mitigated in the south. The propagation rate (PR, proportion of meteorological drought propagation to hydrological drought) ranges from 45% to 75%, and the PT is 6–23 months. The PR does not differ substantially between the north and south, while the PT is longer in the north. The PR decreases by 1%–60% due to human activities, and the PT decreases (1–13 months) in the north and increases (1–10 months) in the south. Human activities display significant variations in how they influence the propagation process of drought across different basins. The primary factors driving the spatial pattern of drought disparities are regional variations in irrigation methods and the storage capacity of reservoirs. Plain Language Summary Under the combined impact of climate change and human activities, economic and human losses caused by drought in China have been increasing year by year. To mitigate the impact of disasters, we conducted research using PCR‐GLOBWB v2.0 model to investigate how human activities have altered hydrological drought in China. And the role of human activities in the propagation process of drought was explored. The results indicate that human activities have intensified hydrological drought in northern China, while providing some alleviation in the southern regions. Human activities disrupt the natural processes of drought propagation, resulting in a decrease in propagation rates. Furthermore, human activities have shortened the propagation lag time of drought in the north, while increasing it in the south. Additionally, smaller basins are more sensitive to human activities compared to larger basins. Our study reveals the impact of human activities on hydrological drought and drought propagation, providing valuable insights for the development of more effective drought adaptation strategies. Key Points We used the PCR‐GLOBWB v2.0 model to study the impact of human activities on the process of drought propagation Human activities play a varying role in the propagation process of drought in different river basins Human activities has led to a decrease in drought propagation rates and shortened/prolonging the drought lag time in northern/southern China

Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ > Na+ > Mg2+ > K+ and alkalinity > SO42− > Cl− > NO3− > F−. The trace elements concentrations were reported in the following order: Fe > Mn > B > Cr > Pb > Ni > Cu > Zn > Cd. The surface water belongs to the Ca2+-Mg2+-HCO3− and Ca2+-Mg2+-Cl−-SO42− water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.

When many cities need quantitative forecasts of air quality to adjust industrial production plans and urbanization development, how to build an efficient forecast model remain a challenge. Methodology for quantitative prediction of air quality can no longer rely on single‐site observations, and thus approaches that require fewer input data and are more efficient and more reliable need to be explored. This paper proposes the principles and steps of a new model using the empirical orthogonal function (EOF) and principal component regression (PCR), which is a hybrid EOF‐PCR approach that decomposes the panel data of PM2.5 and predictors into spatial structures (EOFs) and time expansion coefficients (ECs) by EOF analysis, establishes the PCR of the ECs of PM2.5, and simulates the PM2.5 concentrations in each city by projecting the fitted ECs through EOFs. The very heart of the new model is the PCR modeling and projection. The results are presented for PM2.5 concentrations over Jiangsu Province in eastern China. The results show that this EOF‐PCR model, which is based on EC1s with a cumulative variance contribution rate above 90%, has an average prediction accuracy of 65%. The model performs best in spring and autumn, better in summer and worst in winter. Most predictors have a maximum lag of a week, and they are quite different among seasons. Considering the influence of the spatial distributions of predictors, rather than covariates at a single site, this model can reflect regional influences and effectively improve the simulation effect. Plain Language Summary This study aims to develop a hybrid statistical model for regional forecasting of PM2.5 concentrations using EOF analysis and principal component regression, based on daily observations of PM2.5 concentrations and meteorological predictors. In the study area, the emission sources are relatively fixed, and meteorological conditions represent the main factor affecting short‐term air quality. The panel data of PM2.5 concentration and predictors are decomposed into spatial structures (EOFs) and temporal expansion coefficients (ECs) by EOF analysis. A principal component regression model was built to simulate the ECs of the PM2.5 concentrations, with the ECs of the predictors. Finally, through the inverse operation of the EOF analysis, the fitting values of the PM2.5 ECs are projected as PM2.5 concentrations at each site. The proposed model can reduce input requirements, improve computational efficiency, and emphasize the synergistic effect of meteorological factor distributional modes, rather than a point influence. Key Points Proposed new regional forecast approach has less demand for input data, especially emissions data Forecasting accuracy was improved and reached nearly 70%, and computational efficiency and speed also increased PM2.5 concentrations are sensitive to regional distributions of different meteorological predictors, that is, EOFs

The polymerase chain reaction (PCR) has been used to amplify specific gene regions for many taxonomic studies and there have been substantial efforts to develop protocols that efficiently amplify target regions from a majority of mixed-template populations. Most protocols include the use of degenerate oligonucleotide primer pools, which contain mixed nucleotide sequences to improve priming from templates containing non-consensus sequence variations in their primer-binding sites. In this work, computational modeling and experimental measurements revealed that degenerate primers reduce efficiency well before a substantial product pool has been generated. It was also discovered that non-degenerate primers produced amplicons significantly better than their degenerate counterparts when amplifying either a consensus or a non-consensus target. Using quantitative, real-time PCR (qPCR) and data fitting as a guide, a new PCR protocol was developed that avoids the use of degenerate primers and allows for the stable amplification of targets containing mismatches to the targeting primers. This protocol involves the use of only two non-degenerate primers with no intermediate processing steps and it allows for the reproducible production of amplicon sequencing libraries that maintain the fractional representations of rare members.

Our environment is heterogeneous. In hydrological sciences, the heterogeneity of subsurface properties, such as hydraulic conductivities or porosities, exerts an important control on water balance. This notably includes groundwater recharge, which is an important variable for efficient and sustainable groundwater resources management. Current large-scale hydrological models do not adequately consider this subsurface heterogeneity. Here we show that regions with strong subsurface heterogeneity have enhanced present and future recharge rates due to a different sensitivity of recharge to climate variability compared with regions with homogeneous subsurface properties. Our study domain comprises the carbonate rock regions of Europe, Northern Africa, and the Middle East, which cover ∼25% of the total land area. We compare the simulations of two large-scale hydrological models, one of them accounting for subsurface heterogeneity. Carbonate rock regions strongly exhibit “karstification,” which is known to produce particularly strong subsurface heterogeneity. Aquifers from these regions contribute up to half of the drinking water supply for some European countries. Our results suggest that water management for these regions cannot rely on most of the presently available projections of groundwater recharge because spatially variable storages and spatial concentration of recharge result in actual recharge rates that are up to four times larger for present conditions and changes up to five times larger for potential future conditions than previously estimated. These differences in recharge rates for strongly heterogeneous regions suggest a need for groundwater management strategies that are adapted to the fast transit of water from the surface to the aquifers.

We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D–2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.

For users of hydrological models, the suitability of models can depend on how well their simulated outputs align with observed discharge. This study emphasizes the crucial role of factoring in discharge observation uncertainty when assessing the performance of hydrological models. We introduce an ad hoc approach, implemented through the eWaterCycle platform, to evaluate the significance of differences in model performance while considering the uncertainty associated with discharge observations. The analysis of the results encompasses 299 catchments from the Catchment Attributes and MEteorology for Large-sample Studies Great Britain (CAMELS-GB) large-sample catchment dataset, addressing three practical use cases for model users. These use cases involve assessing the impact of additional calibration on model performance using discharge observations, conducting conventional model comparisons, and examining how the variations in discharge simulations resulting from model structural differences compare with the uncertainties inherent in discharge observations. Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations. Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users.

An accurate estimation of water resources dynamics is crucial for proper management of both agriculture and the local ecology, particularly in semi-arid regions. Imperfections in model physics, uncertainties in model land parameters and meteorological data, as well as the human impact on land changes often limit the accuracy of hydrological models in estimating water storages. To mitigate this problem, this study investigated the assimilation of terrestrial water storage variation (TWSV) estimates derived from the Gravity Recovery And Climate Experiment (GRACE) data using an ensemble Kalman filter (EnKF) approach. The region considered was the Hexi Corridor in northern China. The hydrological model used for the analysis was PCR-GLOBWB, driven by satellite-based forcing data from April 2002 to December 2010. The impact of the GRACE data assimilation (DA) scheme was evaluated in terms of the TWSV, as well as the variation of individual hydrological storage estimates. The capability of GRACE DA to adjust the storage level was apparent not only for the entire TWSV but also for the groundwater component. In this study, spatially correlated errors in GRACE data were taken into account, utilizing the full error variance–covariance matrices provided as a part of the GRACE data product. The benefits of this approach were demonstrated by comparing the EnKF results obtained with and without taking into account error correlations. The results were validated against in situ groundwater data from five well sites. On average, the experiments showed that GRACE DA improved the accuracy of groundwater storage estimates by as much as 25 %. The inclusion of error correlations provided an equal or greater improvement in the estimates. In contrast, a validation against in situ streamflow data from two river gauges showed no significant benefits of GRACE DA. This is likely due to the limited spatial and temporal resolution of GRACE observations. Finally, results of the GRACE DA study were used to assess the status of water resources over the Hexi Corridor over the considered 9-year time interval. Areally averaged values revealed that TWS, soil moisture, and groundwater storages over the region decreased with an average rate of approximately 0.2, 0.1, and 0.1 cm yr−1 in terms of equivalent water heights, respectively. A particularly rapid decline in TWS (approximately −0.4 cm yr−1) was seen over the Shiyang River basin located in the southeastern part of Hexi Corridor. The reduction mostly occurred in the groundwater layer. An investigation of the relationship between water resources and agricultural activities suggested that groundwater consumption required to maintain crop yield in the growing season for this specific basin was likely the cause of the groundwater depletion.

Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (>50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins.

As an extremely important region for the socioeconomic development of China, the Yangtze River Basin (YRB) is vulnerable to climate change and natural disasters. In recent decades, hydrological droughts have jeopardized regional water supply, local ecosystem services, and economic development in this region. This study simulates the characteristics of hydrological droughts in the YRB using the PCR-GLOBWB v2.0 model and the variable threshold method. High-spatial-resolution (about 10 km) PCR-GLOBWB v2.0 simulations considering the effects of human activities closely match the observed hydrological data for the YRB. Thus, the results indicate that human activities directly influence the tempo-spatial characteristics of hydrological droughts in the YRB. Reservoir operation decreases the multi-year monthly discharge but increases low flow in the severe drought years in the middle and lower subbasins of the YRB. These findings highlight the uneven effects of human activities on hydrological droughts in the YRB. In conclusion, anthropogenic activities must be considered when developing future mitigation and adaptation strategies for the YRB.