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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 propagationHuman activities play a varying role in the propagation process of drought in different river basinsHuman activities has led to a decrease in drought propagation rates and shortened/prolonging the drought lag time in northern/southern China [ABSTRACT FROM AUTHOR]

An Extended Triple Collocation for maximized Correlation (ETCC) method was proposed with a unique correlation function, the purpose of which is to maximize the correlation between the merged product and unknown truth. The method was tested over quasi‐global land by combining three independent precipitation products. The performance of the ETCC‐merged product was then evaluated against three reference data sets and compared with the existing Triple Collocation (TC) merging. The merged product was found to be generally superior to each contributor. Moreover, the ETCC method is better able to improve the correlation of merged product compared with the TC approach. Other improvements are also shown in the absolute difference of the ETCC‐merged product, such as regional validation for central North America and mainland China. These demonstrate the effectiveness of the ETCC method, and accordingly, it can provide a promising solution for maximizing the correlation of merged product without the truth. Plain Language Summary: Precipitation plays a significant role in global atmospheric and hydrological modeling systems. Owing to uncertainties in precipitation estimates from a single satellite or reanalysis product, it is necessary and challenging to blend multi‐source precipitation information to improve the quality of precipitation estimates. The present study proposed a new merging method, called Extended Triple Collocation for maximized Correlation (ETCC), which aims at maximizing the correlation of the merged product against unknown truth. The ETCC‐merged product combined three independent precipitation information derived from the satellite and reanalysis products, and then, was evaluated using three reference data sets. Meanwhile, we compared ETCC's performance with that of the original Triple Collocation (TC) merging. Results show that ETCC method not only performs better than each contributor, but also is more effective in improving precipitation accuracy relative to the TC approach especially in central North America and mainland China. Key Points: A new method is proposed for maximizing the correlation between the merged product and unknown truthThe merged product is more accurate than each contributorThe new method performs better than the original triple collocation [ABSTRACT FROM AUTHOR]

The term "flash drought" describes a type of drought with rapid onset and strong intensity, which is co-affected by both water-limited and energy-limited conditions. It has aroused widespread attention in related research communities due to its devastating impacts on agricultural production and natural systems. Based on a global reanalysis dataset, we identify flash droughts across China during 1979–2016 by focusing on the depletion rate of weekly soil moisture percentile. The relationship between the rate of intensification (RI) and nine related climate variables is constructed using three machine learning (ML) technologies, namely, multiple linear regression (MLR), long short-term memory (LSTM), and random forest (RF) models. On this basis, the capabilities of these algorithms in estimating RI and detecting droughts (flash droughts and traditional slowly evolving droughts) were analyzed. Results showed that the RF model achieved the highest skill in terms of RI estimation and flash drought identification among the three approaches. Spatially, the RF-based RI performed best in southeastern China, with an average CC of 0.90 and average RMSE of the 2.6 percentile per week, while poor performances were found in the Xinjiang region. For drought detection, all three ML technologies presented a better performance in monitoring flash droughts than in conventional slowly evolving droughts. Particularly, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of flash drought derived from RF were 0.93, 0.15, and 0.80, respectively, indicating that RF technology is preferable in estimating the RI and monitoring flash droughts by considering multiple meteorological variable anomalies in adjacent weeks to drought onset. In terms of the meteorological driving mechanism of flash drought, the negative precipitation (P) anomalies and positive potential evapotranspiration (PET) anomalies exhibited a stronger synergistic effect on flash droughts compared to slowly developing droughts, along with asymmetrical compound influences in different regions of China. For the Xinjiang region, P deficit played a dominant role in triggering the onset of flash droughts, while in southwestern China, the lack of precipitation and enhanced evaporative demand almost contributed equally to the occurrence of flash drought. This study is valuable to enhance the understanding of flash droughts and highlight the potential of ML technologies in flash drought monitoring. [ABSTRACT FROM AUTHOR]

Intermittent records of satellite soil moisture data are major obstacles that constrain their hydrometeorological applications. Based on the European Space Agency Climate Change Initiative (ESA CCI) soil moisture combined product, two machine learning models were employed to reconstruct soil moisture in China during 1979–2019 in both temporal and spatial domains, and latent errors for reconstructed series, as well as their performances for tracing climate extremes, were analyzed. The results showed that with the homogeneity of available data over space, the spatial approach performed well in reproducing the spatial heterogeneity of soil moisture (with medians of the correlation coefficient (CC) above 0.8 and root mean square errors (RMSEs) ranging from 0.02 to 0.03 m3∙m−3). The temporal approach (CC values of 0.7 and RMSEs ranging between 0.02 and 0.03 m3∙m−3) was superior in capturing the seasonality features and the timely and accurate mapping of short-term soil moisture dynamics impacted by rainstorms. However, both approaches failed to identify the location and severity of droughts accurately. The findings highlight the benefits of combining the strengths of both temporal and spatial gap-filling approaches for improving the estimation of missing values and hydrometeorological applications. [ABSTRACT FROM AUTHOR]

The availability of the new generation Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06 products facilitates the utility of long-term higher spatial and temporal resolution precipitation data (0.1° × 0.1° and half-hourly) for monitoring and modeling extreme hydrological events in data-sparse watersheds. This study aims to evaluate the utility of IMERG Final run (IMERG-F), Late run (IMERG-L) and Early run (IMERG-E) products, in flood simulations and frequency analyses over the Mishui basin in Southern China during 2000–2017, in comparison with their predecessors, the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products (3B42RT and 3B42V7). First, the accuracy of the five satellite precipitation products (SPPs) for daily precipitation and extreme precipitation events estimation was systematically compared by using high-density gauge station observations. Once completed, the modeling capability of the SPPs in daily streamflow simulations and flood event simulations, using a grid-based Xinanjiang model, was assessed. Finally, the flood frequency analysis utility of the SPPs was evaluated. The assessment of the daily precipitation accuracy shows that IMERG-F has the optimum statistical performance, with the highest CC (0.71) and the lowest RMSE (8.7 mm), respectively. In evaluating extreme precipitation events, among the IMERG series, IMERG-E exhibits the most noticeable variation while IMERG-L and IMERG-F display a relatively low variation. The 3B42RT exhibits a severe inaccuracy and the improvement of 3B42V7 over 3B42RT is comparatively limited. Concerning the daily streamflow simulations, IMERG-F demonstrates a superior performance while 3B42V7 tends to seriously underestimate the streamflow. With regards to the simulations of flood events, IMERG-F has performed optimally, with an average DC of 0.83. Among the near-real-time SPPs, IMERG-L outperforms IMERG-E and 3B42RT over most floods, attaining a mean DC of 0.81. Furthermore, IMERG-L performs the best in the flood frequency analyses, where bias is within 15% for return periods ranging from 2–100 years. This study is expected to contribute practical guidance to the new generation of SPPs for extreme precipitation monitoring and flood simulations as well as promoting the hydro-meteorological applications. [ABSTRACT FROM AUTHOR]

Under current global change, the driving force of evolution of drought has gradually transitioned from a single natural factor to a combination of natural and anthropogenic factors. Therefore, widely used standardised drought indices based on assumption of stationarity are challenged and may not accurately assess characteristics of drought processes. In this study, a nonstationary standardised streamflow index (NSSI) that incorporates climate and reservoir indices as external covariates was developed to access nonstationary hydrological drought. The first step of the proposed approach is to apply methods of trend and change point analysis to assess the nonstationarity of streamflow series to determine type of streamflow regime, that is, the natural and altered regime. Then, different nonstationary models were constructed to calculate the NSSI by selecting climate indices as covariates for streamflow series with natural regime, and climate and reservoir indices as covariate for streamflow series with altered regime. Four stations in the upper reaches of the Huaihe River basin, China, were selected to examine the performance of the proposed NSSI. The results indicated that Dapoling (DPL), Changtaiguan (CTG), and Xixian (XX) stations had natural streamflow regimes, while the Nanwan (NW) station had an altered regime. The global deviances of the optimal nonstationary models were 17 (2.2%), 18 (2.9%), 26 (4.0%), and 22 (3.5%) less than those of stationary models for DPL, CTG, NW, and XX stations, respectively. Especially, for the NW station influenced by reservoir regulations, the frequency of slight drought and moderate drought of NSSI was 12.8% lower than and 13.1% greater than those of SSI, respectively. Overall, the NSSI that incorporates the influence of climate variability and reservoir regulations provided more reliable assessment of hydrological drought than the traditional SSI. [ABSTRACT FROM AUTHOR]

The complexity of climate system dynamics calls for more elaborate drought evaluation and trend projection under different future climate scenarios. In this study, the meteorological drought stress over the Yellow River basin (YRB) of China for the next forty years (2021–2060) is comprehensively evaluated based on meteorological simulations of eight climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) with three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5). The standardized precipitation evapotranspiration index (SPEI) is used as a metric of drought magnitude at multiple temporal scales, while the multi‐threshold run analysis is conducted for the definition of drought events and derivation of drought characteristics (e.g., frequency, duration and severity). The Mann–Kendall test is adopted as a universal tool to analyse the trends in climate variables (precipitation and temperature), multi‐temporal SPEI drought indices and drought characteristics (duration and severity). Our preliminary conclusions include that partially increasing precipitation and generally rising temperature are likely to be seen in the YRB from 2021 to 2060. More importantly, a warmer YRB may undergo either a nearly regular atmospheric regime (e.g., comparable to the baseline period of 1961–2000) with RCP2.6 scenario or significantly drying processes with RCP4.5 and RCP8.5 scenarios. Meanwhile, the enhanced spatiotemporal variability/heterogeneity of meteorological forcing (e.g., precipitation and temperature) and drought characteristics (e.g., magnitude, frequency, duration and severity) would also increase future uncertainty of meteorological drought stress over the YRB, such as the encounter of drought with long duration and/or high severity and relevant increasing trends. Understanding the spatiotemporal influences of specific climate and emission scenarios on projected drought vulnerability might help improving our knowledge of climate‐human interactions and selecting rational development strategy (like the low emission scenario of RCP2.6 or so) for regional adaptation to future climate change. [ABSTRACT FROM AUTHOR]

Three newly released, gauge-based precipitation products (GPPs), i.e., the Global Precipitation Climatology Center full data monthly version 2018 (GPCC 8.0), the Climate Research Unit Time Series version 4.03 (CRU TS 4.03), and the Center for Climatic Research-University of Delaware Terrestrial Precipitation: 1900−2017 Gridded Monthly Time Series version 5.01 (UDel 5.01), were evaluated and compared with the China Gauge-based Daily Precipitation Analysis (CGDPA) dataset. The evaluations involved drought monitoring over mainland China by using the Standardized Precipitation Index (SPI). The results indicated that the GPPs performed well in capturing precipitation spatial patterns (correlation coefficients > 0.9) and interannual variations (correlation coefficients > 0.7) for most regions, with the exceptions of the Xinjiang and Qinghai–Tibet plateau (TP) regions. It was also noted that the accuracy of SPIs calculated using GPPs (SPIg) showed no apparent variations at various timescales, and a 3-month SPI metric was selected for further analyzing. When examining the spatiotemporal accuracies of SPIg, we found that they exhibited high levels of consistency, small errors, and high degrees of detectivity in Eastern China, and comparatively high spatial heterogeneity in Western China, particularly in the TP region. The regional mean SPIg performances improved at the time series, as their individual spatial heterogeneities were offset by averaging. The GPPs were able to satisfactorily capture the characteristics of typical drought events, such as drought area, centroids, and severity, for the four selected specific regions. Overall, the long-term GPP records showed great potential in quantifying drought over mainland China, especially in Eastern China and Southwest. Among the three GPPs, it was found that GPCC 8.0 performed the best, in both precipitation estimation and drought monitoring, while no distinct distinctions were apparent between the CRU TS 4.03 and UDel 5.01 products. These results showed that the long-term GPPs could be applied to large-scale drought monitoring, and the findings could provide useful references for selecting gauge-based precipitation data for various hydrometeorological applications. [ABSTRACT FROM AUTHOR]

How human activities have altered hydrological droughts (streamflow deficits) in China during the past five decades (1961–2016) is investigated using the latest version (v2.0) of PCR‐GLOBWB model at high spatial resolution (~10 km). Although both human activities and climate variability have significant effects on river flows over China, there are large regional north‐south contrasts. Over northern China, human activities generally intensify hydrological droughts. We find that human activities exacerbated drought deficit by about 70–200% from 2004 to 2015. In contrast, droughts over southern China are generally alleviated by human activities. For instance, irrigation and water management (such as reservoir operation and water abstraction) increase drought StDef (standardized drought deficit volume) by about 80% in the Yellow River (north) but reduce it by about 20% in the Yangtze River (south). Human activities slightly reduce drought deficit in the Yangtze River due to the combination of large reservoir storage and low ratio of agriculture consumption to abstracted irrigation water. In contrast, hydrological drought is aggravated in the semiarid Yellow River basin because of high water consumption from agricultural sectors. This study suggests that human activities have contrasting influences on hydrological drought characteristics in the northern (intensification) and southern (mitigation) parts of China. Therefore, it is critical to consider the variable roles of human activities on hydrological drought in China when developing mitigation and adaptation strategies. Plain Language Summary: China faces unprecedented challenges for water resources management under a changing climate, which is expected to lead to more frequent and severe droughts in the future. Of particular importance is streamflow drought, which jeopardizes regional water supply and local ecosystem services. On one hand, human activities through reservoir operation can effectively alleviate drought by releasing water during the low flow period. But on the other hand, water abstraction to meet sectoral water demand (such as irrigation) could exacerbate the streamflow deficit. To what extent such human activities differ across regions is not clear. In this study, we use a physically based hydrological and water resources model to investigate how human activities have altered streamflow droughts in China during the past five decades (1961–2016). We find that human activities generally alleviate streamflow droughts in the southern region (e.g., Yangtze River) but intensify them in the northern part of China (e.g., Yellow River). Our research highlights the contrasting geographical differences of human influences on hydrological drought across China, which can be useful for making more effective drought adaptation strategies. Key Points: We used the PCR‐GLOBWB model at high spatial resolution to investigate the effects of human activities on hydrological drought over ChinaInfluences of human activities on hydrological drought characteristics have a strong and contrasting north‐south gradientReservoir operation, water abstraction, and irrigation increase drought deficit in the Yellow River but reduce it in the Yangtze River [ABSTRACT FROM AUTHOR]

A multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies. [ABSTRACT FROM AUTHOR]

Watershed management, disaster warning, and hydrological modeling require accurate spatiotemporal precipitation data sets. This paper presents a comprehensive assessment of a gauge-satellite-based precipitation product that merges the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) satellite precipitation product (SPP) and ground precipitation data at 134 rain gauges in the Xijiang River basin, South China. Two regression-based schemes, principal component regression (PCR) and multiple linear regression (MLR), were used to combine the gauge-based precipitation data and PERSIANN-CDR SPP and were compared at daily and annual scales. Furthermore, a hydrological model Variable Infiltration Capacity was used to calculate streamflow and to evaluate the impact of four different precipitation interpolation methods on the results of the hydrological model at the daily scale. The result shows that the PCR method performs better than MLR and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. On the whole, the combined scheme consistently exhibits good performance and thus serves as a suitable tool for producing high-resolution gauge-and satellite-based precipitation datasets. [ABSTRACT FROM AUTHOR]

Climate change and human activities are two major driving forces affecting the hydrologic cycle, which further influence the stationarity of the hydrologic regime. Hydrological drought is a substantial negative deviation from the normal hydrologic conditions affected by these two phenomena. In this study, we propose a framework for quantifying the effects of climate change and human activities on hydrological drought. First, trend analysis and change‐point test are performed to determine variations of hydrological variables. After that, the fixed runoff threshold level method (TLM) and the standardized runoff index (SRI) are used to verify whether the traditional assessment methods for hydrological drought are applicable in a changing environment. Finally, two improved drought assessment methods, the variable TLM and the SRI based on parameter transplantation are employed to quantify the impacts of climate change and human activities on hydrological drought based on the reconstructed natural runoff series obtained using the variable infiltration capacity hydrologic model. The results of a case study on the typical semiarid Laohahe basin in North China show that the stationarity of the hydrological processes in the basin is destroyed by human activities (an obvious change‐point for runoff series is identified in 1979). The traditional hydrological drought assessment methods can no longer be applied to the period of 1980–2015. In contrast, the proposed separation framework is able to quantify the contributions of climate change and human activities to hydrological drought during the above period. Their ranges of contributions to hydrological drought calculated by the variable TLM method are 20.6–41.2% and 58.8–79.4%, and the results determined by the SRI based on parameter transplantation method are 15.3–45.3% and 54.7–84.7%, respectively. It is concluded that human activities have a dominant effect on hydrological drought in the study region. The novelty of the study is twofold. First, the proposed method is demonstrated to be efficient in quantifying the effects of climate change and human activities on hydrological drought. Second, the findings of this study can be used for hydrological drought assessment and water resource management in water‐stressed regions under nonstationary conditions. [ABSTRACT FROM AUTHOR]

Satellite precipitation products (SPPs) are critical data sources for hydrological prediction and extreme event monitoring, especially for ungauged basins. This study conducted a comprehensive hydrological evaluation of six mainstream SPPs (i.e., TMPA 3B42RT, CMORPH-RT, PERSIANN-RT, TMPA 3B42V7, CMORPH-CRT, and PERSIANN-CDR) over humid Xixian basin in central eastern China for a period of 14 years (2000–2013). The evaluation specifically focused on the performance of the six SSPs in capturing precipitation and streamflow extremes. Results show that the two post-real-time research products of TMPA 3B42V7 and CMORPH-CRT exhibit much better performance than that of their corresponding real-time SPPs for precipitation estimation at daily and monthly time scales. By contrast, the newly released post-real-time research product PERSIANN-CDR insignificantly improves precipitation estimates compared with the real-time PERSIANN-RT does at daily time scale. The daily streamflow simulation of TMPA 3B42V7 fits best with the observed streamflow series among those of the six SPPs. The three month-to-month gauge-adjusted post-real-time research products can simulate acceptable monthly runoff series. TMPA 3B42V7 and CMORPH-CRT present good performance in capturing precipitation and streamflow extremes, although they still exhibit non-ignorable deviation and occurrence time inconsistency problems compared with gauge-based results. Caution should be observed when using the current TMPA, CMORPH, and PERSIANN products for monitoring and predicting extreme precipitation and flood at such medium-sized basin. This work will be valuable for the utilization of SPPs in extreme precipitation monitoring, streamflow forecasting, and water resource management in other regions with similar climate and topography characteristics. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH) are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products (i.e., Version-7 real-time TMPA (T-rt) and gauge-adjusted TMPA (T-adj), and Version-1.0 real-time CMORPH (C-rt) and Version-1.0 gauge-adjusted CMORPH (C-adj)) are evaluated and intercompared by using independent rain gauge observations for a 12-year (2000-2011) period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite precipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era. [ABSTRACT FROM AUTHOR]

The Palmer indices (PIs) that have been most widely used for drought monitoring and assessment are criticized for two main drawbacks: coarse hydrological accounting processes with a simplified two-stage bucket soil water balance model and arbitrary rules for defining drought properties and standardizing index values through limited calibration and comparison. In this study, we introduce a new proposal of the VIC hydrologic model-based Palmer drought scheme, where traditional PIs (e.g. PDSI) can readily be calculated on the basis of distributed finescale hydrologic simulations. Moreover, recent variants of PI (i.e., SPDI and SPDI-JDI) also provide a preferable standardization strategy that allows probabilistic invariability and better spatio-temporal comparability of computed drought indices. Using gridded meteorological forcing, soil and vegetation data to drive the three-layer VIC model, both non-VIC and VIC-based PIs are investigated to examine their performances for drought characterization and detection. Results indicate that VIC hydrologic model would allow for adjustments in statistical properties of computed PDSI and VIC-based SPDI is also preferable to PDSI for better statistical robustness and spatio-temporal consistency/comparability. Moreover, the joint SPDI-JDI has the strength of integrating multi-scale probabilistic properties and drought information released by individual SPDI, providing overall drought conditions that take into account the onset, persistence and termination of droughts. At proposed 0.25° grid scale, the VIC-based SPDI-JDI indicates high frequency and long total time of drought condition in the Yellow River basin (YRB), China. Although no significant temporal trends are found in identified drought duration and severity, both the seasonal and annual drought index values demonstrate a downward trend (higher drought intensity) for considerable proportions of the YRB. These findings imply high drought risk and potential drying stress for this region. The new framework of hydrologic model-based PIs can help to strengthen our knowledge and/or practices in regional drought monitoring and assessment. [ABSTRACT FROM AUTHOR]

The Diaoyu Islands are a group of uninhabited islets located in the East China Sea between Japan, China, and Taiwan. Here, four mainstream gauge-adjusted multisatellite precipitation estimates [TRMM Multisatellite Precipitation Analysis, version 7 (TMPA-V7); CPC morphing technique-bias-corrected product (CMORPH-CRT); Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR); and Global Satellite Mapping of Precipitation-gauge adjusted (GSMaP_Gauge)] are adopted to detect the rainfall characteristics of the Diaoyu Islands area with a particular focus on typhoon contribution. Out of the four products, CMORPH-CRT and GSMaP_Gauge show much more similarity both in terms of the spatial patterns and error structures because of their use of the same morphing technique. Overall, GSMaP_Gauge performs better than the other three products, likely because of denser in situ observations integrated in its retrieval algorithms over East Asia. All rainfall products indicate that an apparent rain belt exists along the northeastern 45° direction of Taiwan extending to Kyushu of Japan, which is physically associated with the Kuroshio. The Diaoyu Islands are located on the central axis of this rain belt. During the period 2001-09, typhoon-induced rainfall accounted for 530 mm yr−1, and typhoons contributed on average approximately 30% of the annual precipitation budget over the Diaoyu Islands. Higher typhoon contribution was found over the southern warmer water of the Diaoyu Islands, while the northern cooler water presented less contribution ratio. Supertyphoon Chaba, the largest typhoon of 2004, recorded 53 h of rainfall accumulation totaling 235 mm on the Diaoyu Islands, and this event caused severe property damage and human casualties for Japan. Hence, the Diaoyu Islands play an important role in weather monitoring and forecasting for the neighboring countries and regions. [ABSTRACT FROM AUTHOR]

Quantitative evaluation of future climate change impacts on hydrological drought characteristics is one of important measures for implementing sustainable water resources management and effective disaster mitigation in drought-prone regions under the changing environment. In this study, a modeling system for projecting the potential future climate change impacts on hydrological droughts in the Weihe River basin (WRB) in North China is presented. This system consists of a large-scale hydrological model driven by climate outputs from three climate models (CMs) for future streamflow projections, a probabilistic model for univariate drought assessment, and a copula-based bivariate model for joint drought frequency analysis under historical and future climates. With the observed historical climate data as the inputs, the Variable Infiltration Capacity hydrological model projects an overall runoff reduction in the WRB under the Intergovernmental Panel on Climate Change A1B scenario. The univariate drought assessment found that although fewer hydrological drought events would occur under A1B scenario, drought duration and severity tend to increase remarkably. Moreover, the bivariate drought assessment reveals that future droughts in the same return period as the baseline droughts would become more serious. With these trends in the future, the hydrological drought situation in the WRB would be further deteriorated. [ABSTRACT FROM AUTHOR]

In the Huaibei Plain basin, China, soybean is a main crop. During the soybean growth period, rainfall can vary largely and depth to watertable can also vary largely. The amount of water supplied to the soybean rootzone by groundwater affects soybean growth and yield. Accurate simulation of groundwater contributions to soybean rootzone soil moisture (groundwater contribution) can be important for determining irrigation to and drainage from soybean fields. Based on field observations and local weather data of 2005, HYDRUS-1D was validated by comparing simulated and measured rootzone soil water contents. The validated model was used to estimate the daily groundwater contributions for three different soybean hydrological growing seasons, i.e., an average year (1997), a wet year (2005), and a dry year (2004) with soybean growth at its optimal state. The main results were: (1) seasonal groundwater contribution was 157 mm in the experimental field, and the estimated groundwater contributions were 158, 222, and 387 mm in the wet, average, and dry seasons, respectively; (2) the groundwater contribution was about 63% of the total seasonal transpiration in the experimental field, and those were about 142, 80, and 66% of the total seasonal transpiration in dry, average, and wet seasons, respectively. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is an important, but unmeasurable, component of the hydrological cycle in semiarid regions. Traditionally, actual ET is computed as residual in water balance equations. It is derived from estimates of potential ET or, indirectly, from field measurements at meteorological stations. Recently, researchers have begun using scintillometers, remote sensing data, and hydrological models to estimate areal actual ET. In this study, the surface energy balance algorithm for land (SEBAL) was used to derive ET maps from moderate resolution imaging spectroradiometer (MODIS) images over the Laohahe basin and Shalamulun River basin. The effect of ground parameters on ET of the study area was quantified using the spatial analysis techniques of ArcGIS. At the end, ET estimated from Landsat 7 enhanced thematic mapper plus (ETM+) was compared with that from MODIS data over the Shalamulun River basin. SEBAL is a suitable algorithm for mapping evaporation over semiarid areas, using MODIS and Landsat images with few or no ground measurements. The ET of the study changes from 0 to . The land use types, elevation, land surface temperature (LST), and terrain all have a direct effect on the spatial distribution of ET. ET simulated from both MODIS and Landsat data give reasonable values; however, results from Landsat ETM+ are better compared to those of MODIS because Landsat data have higher spatial resolutions than MODIS data. [ABSTRACT FROM AUTHOR]

The Laohahe River basin, located in northeastern China, was selected as a case study to quantify the magnitude of changes in land use and land cover (LULC) during the period from the 1970s to the 2010s and its quantitative effects on surface hydrology, based on hydrologic modeling of a distributed Soil and Water Assessment Tool ( SWAT) model and catchment-scale spatial information analyses from remotely sensed data. Land cover maps with 30-m resolution from 1979, 1989, 1999, and 2007, interpreted from Landsat images, were used to analyze LULC changes during the last decades. The observed daily hydro-meteorological data from 1970 to 2006 were divided into four periods: 1970-1979, 1980-1989, 1990-1999, and 2000-2006. The SWAT model was utilized for each period with four LULC scenarios, which were developed by using the four LULC maps. Annual and monthly surface runoff and actual evapotranspiration (AET) were selected as important hydrologic elements to indicate the hydrologic response to LULC changes. The results revealed that distinct land cover changes occurred in the basin; the most important change was the conversion among vegetation cover classes of cropland, forest land, and grassland. Surface runoff always decreased as the LULC scenarios changed from 1976 to 2007 during all periods, but AET did not change regularly following the present LULC changes. Multiple regression equations between quantitative changes of LULC and hydrologic elements were developed. The equations indicated that the changes in three vegetation cover classes of grassland, cropland, and forest areas significantly affected hydrological elements and the increases of vegetation cover class areas all led to decreases in surface runoff and increases in AET. Moreover, given the same quantitative area change, the effects of cropland on hydrologic elements were the strongest, the effects of forest land were the second strongest, and the effects of grassland were the third. The effects of LULC changes on the seasonal distribution of hydrologic elements were also investigated. The results demonstrated that LULC changes have less influence on surface runoff and AET in nonflood seasons, but more influence in flood seasons, especially in July, August, and September, when the crops grow best. The results of this study improved the understanding of hydrologic responses to LULC changes and provided needed knowledge for agricultural decisions and the management of land use and integrated water resources. [ABSTRACT FROM AUTHOR]

This study aims to investigate the changing properties of drought events in Weihe River basin, China, by modeling the multivariate joint distribution of drought duration, severity and peak using trivariate Gaussian and Student t copulas. Monthly precipitations of Xi'an gauge are used to illustrate the meta-elliptical copula-based methodology for a single-station application. Gaussian and Student t copulas are found to produce a better fit comparing with other six symmetrical and asymmetrical Archimedean copulas, and, checked by the goodness-of-fit tests based on a modified bootstrap version of Rosenblatt's transformation, both of them are acceptable to model the multivariate joint distribution of drought variables. Gaussian copula, the best fitting, is employed to construct the dependence structures of positively associated drought variables so as to obtain the multivariate joint and conditional probabilities of droughts. A Kendall's return period (KRP) introduced by Salvadori and De Michele (2010) is then adopted to assess the multivariate recurrent properties of drought events, and its spatial distributions indicate that prolonged droughts are likely to break out with rather short recurrence intervals in the whole region, while drought status in the southeast seems to be severer than the northwest. The study is of some merits in terms of multivariate drought modeling using a preferable copula-based method, the results of which could serve as a reference for regional drought defense and water resources management. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: As a consequence of wildlife habitat is in constant evolution, periodic monitoring is essential to assess habitat quality. In this study the change to the red-crowned crane habitat in the Yellow River Delta Nature Reserve (YRDNR) was detected from multi-temporal remote sensing data from 1992 to 2008 in a geographic information system (GIS). The changing quality of the habitat was evaluated from both physical constraint and human disturbance. The results indicate that potential habitat shank 37.8% during 1992-2001, but recovered 96.8% by 2008. Human disturbance in the forms of roads, oil wells and residential areas caused a high degree of behavioral fragmentation. Suitable habitat shrank by 1,569ha to a level below that of 1992 despite an increase of 4,450ha in potential habitat due to an increase of 6,019ha in fragmented areas. Therefore, efforts should also be directed at improving habitat quality by minimizing human activities in the Reserve [Copyright &y& Elsevier]

The following basins in the northern area of China are selected as the case study areas: the Yellow River, the Haihe River, the Liaohe River, and the Songhuajiang River. Qualitative and quantitative analyses of the impact of human activity on river runoff in different spatio-temporal scales, including daily, monthly, annual, inter-decade, inter-century variations, according to long-term observation data, are carried out. The driving forces of annual runoff change in those rivers are also analyzed. It can be seen from observed data that the quantity of downstream flow in the northern part of China depends on precipitation over a catchment and withdrawal from the upstream. River runoff has a tendency towards decrease in terms of a natural basin as well as in terms of an administrative region. Besides the climatic changes, the increase in the amount of water taken from river course is the direct cause why observed runoff decreases in the northern part of China. The frequency, at which minor daily discharge appears, increases rapidly. Annual mean runoff generated from the same order of precipitation in 1980s and 1990s decreases by 20–50% as compared in 1950s and 1960s. The impact of human activity on river discharge is more in arid or semi-arid areas than in humid areas. [Copyright &y& Elsevier]

The Laohahe basin is one of the typical semi-arid regions in Northeast China. Quantitatively estimating the contributions of human intervention and climatic variability on runoff changes in this region will help enhance the understanding of local hydrological mechanisms and provide an effective reference for water resources planning and management in other semi-arid regions of the world. The non-parametric Mann–Kendall test was used to analyze the temporal trends of annual precipitation, potential evapotranspiration (PET), and runoff in the whole Laohahe basin and its three sub-basins from 1964 to 2015. The annual runoff showed a decreasing trend in each sub-basin. The change-points of annual runoff detected by Pettitt's test and residual analysis based on double mass curves (RA-DMC) are 1979 and 1998, and the baseline period and change period of each basin are 1964–1979 and 1980–2015. The RA-DMC method and the VIC model were used to quantitatively evaluate the contributions of human intervention and climatic variability to runoff change, which vary in time and space over the past 52 years. The contributions of human intervention to runoff reduction during 1980–2015 was more than 80%. On a multi-decadal temporal scale, human intervention had a stronger impact on runoff during 1980–1989, 2000–2009, and 2010–2015. The influence of human intervention on runoff reduction is gradually increasing in the 21st century. Besides, human intervention has a greater impact in dry years than in wet years. The increase of cropland area leads to a significant increase in irrigation area, which further leads to an increase in the demand for agricultural water, which is also the main reason for the sharp reduction of runoff in the Laohahe basin. [ABSTRACT FROM AUTHOR]

In the context of global climate change, it is important to monitor abnormal changes in extreme precipitation events that lead to frequent floods. This research used precipitation indices to describe variations in extreme precipitation and analyzed the characteristics of extreme precipitation in four climatic (arid, semi-arid, semi-humid and humid) regions across China. The equidistant cumulative distribution function (EDCDF) method was used to downscale and bias-correct daily precipitation in eight Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). From 1961 to 2005, the humid region had stronger and longer extreme precipitation compared with the other regions. In the future, the projected extreme precipitation is mainly concentrated in summer, and there will be large areas with substantial changes in maximum consecutive 5-day precipitation (Rx5) and precipitation intensity (SDII). The greatest differences between two scenarios (RCP4.5 and RCP8.5) are in semi-arid and semi-humid areas for summer precipitation anomalies. However, the area of the four regions with an increasing trend of extreme precipitation is larger under the RCP8.5 scenario than that under the RCP4.5 scenario. The increasing trend of extreme precipitation in the future is relatively pronounced, especially in humid areas, implying a potential heightened flood risk in these areas. [ABSTRACT FROM AUTHOR]

Comprehensively evaluating satellite precipitation products (SPPs) for hydrological simulations on watershed scales is necessary given that the quality of different SPPs varies remarkably in different regions. The Yellow River source region (YRSR) of China was chosen as the study area. Four SPPs were statistically evaluated, namely, the Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Integrated Multisatellite Retrievals for Global Precipitation Measurement final run (IMERG-F), and gauge-corrected Global Satellite Mapping of Precipitation (GSMaP-Gauge) products. Subsequently, the hydrological utility of these SPPs was assessed via the variable infiltration capacity hydrological model on a daily temporal scale. Results show that the four SPPs generally demonstrate similar spatial distribution pattern of precipitation to that of the ground observations. In the period of January 1998 to December 2016, 3B42V7 outperforms PERSIANN-CDR on basin scale. In the period of April 2014 to December 2016, GSMaP-Gauge demonstrates the highest precipitation monitoring capability and hydrological utility among all SPPs on grid and basin scales. In general, 3B42V7, IMERG-F, and GSMaP-Gauge show a satisfactory hydrological performance in streamflow simulations in YRSR. IMERG-F has an improved hydrological utility than 3B42V7 in YRSR. [ABSTRACT FROM AUTHOR]

This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Precipitation Estimation from the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China. [ABSTRACT FROM AUTHOR]

Global climate change not only affects the processes within the water cycle but also leads to the frequent occurrences of local and regional extreme drought events. In China, spatial and temporal characterizations of drought events and their future changing trends are of great importance in water resources planning and management. In this study, we employed self-calibrating Palmer drought severity index (SC-PDSI), cluster algorithm, and severity-area-duration (SAD) methods to identify drought events and analyze the spatial and temporal distributions of various drought characteristics in China using observed data and CMIP5 model outputs. Results showed that during the historical period (1961–2000), the drought event of September 1965 was the most severe, affecting 47.07% of the entire land area of China, and shorter duration drought centers (lasting less than 6 months) were distributed all over the country. In the future (2021–2060), under both representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios, drought is projected to occur less frequently, but the duration of the most severe drought event is expected to be longer than that in the historical period. Furthermore, drought centers with shorter duration are expected to occur throughout China, but the long-duration drought centers (lasting more than 24 months) are expected to mostly occur in the west of the arid region and in the northeast of the semi-arid region. [ABSTRACT FROM AUTHOR]

[Display omitted] • A cascade model chain suitable for the risk of future DWAA events is proposed. • Evolution characteristics of DWAA in the future period of YRB are revealed. • Severity of DWAA events in far-future is greater than that in the near-future. • Extreme precipitation events have strong synchronization with DWAA events. At present, many studies have investigated the evolution characteristics, frequency estimation techniques, and prediction of droughts and floods, but a comprehensive understanding of the spatiotemporal evolution law and joint risk of these two composite extreme events is still lacking. The synergistic effect of dry and wet abrupt alternation (DWAA) events has more significant consequences than a single drought and flood event, and these events have a considerable impact on agriculture, ecology, and economy. Furthermore, owing to the intensification of global warming and climate change, the potential synchronisation between DWAA events and extreme precipitation in the future requires special attention. Therefore, it is necessary to establish a reliable framework to analyse the evolution characteristics and risks of composite extreme events (DWAA). In this study, a cascade modelling chain comprising climate model downscaling, non-uniform bias-correction technique, and model integration was developed to study the spatiotemporal evolution characteristics and combined risk impact of DWAA events. The proposed methodology was applied to the Yellow River Basin (YRB) in the historical period (1960–2014, Hist) and the future period (2021–2060, FUT1; and 2061–2100, FUT2) under the Shared Socioeconomic Pathway (SSP) 3–7.0 and 5–8.5 scenarios. The results show that: (1) The constructed cascade modelling chain has high simulation accuracy for precipitation, and can be used to analyse future DWAA events. (2) Considering the precipitation characteristics under SSP3-7.0 and SSP5-8.5 scenarios, the precipitation increases slightly in the near future FUT1 (about 5–10%), and increases significantly in the far future FUT2 (about 10–70%). (3) Following either scenario, the frequency of DWAA events is expected to decrease in the future compared to that in the Hist, but the average intensity is expected to increase compared to that in the Hist. (4) The joint risk of the impact area and intensity of future DWAA events of FUT2 is greater than that of FUT1. [ABSTRACT FROM AUTHOR]

This study focused on improving the performance of the near real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Early Run (IMERG-E) product based on a newly developed bias-correction scheme, LSCDF. The LSCDF was established by integrating the mean-based Linear Scaling (LS) and quantile-mapping Cumulative Distribution Function (CDF) matching approaches. The daily updating gauge-based precipitation data from the Climate Prediction Center (CPC) unified analysis were used as the benchmark for bias correction. The IMERG-E bias-corrected precipitation data were evaluated against the daily ground measurements from 807 meteorological stations across mainland China and compared with the raw IMERG-E and IMERG Final Run (IMERG-F; research-level with gauge calibration) retrievals. Evaluation results for the period 2015 to 2017 showed that the LSCDF method effectively improves near real-time IMERG-E precipitation estimates at the daily scale. The bias-corrected IMERG-E precipitation estimates were in significantly better agreement with ground measurements than the original IMERG-E at the point-daily resolutions, with the correlation coefficient increasing from 0.66 to 0.77, relative bias decreasing from 11.1% to −3.1%, and root-mean-square error dropping from 6 mm/day to 4.39 mm/day. In addition, the bias-corrected IMERG-E was apparently superior to IMERG-E in detecting precipitation events at various intensity levels including small, light, moderate, and heavy precipitation. Although IMERG-E tended to significantly underestimate or overestimate precipitation during three typical typhoons, the bias-corrected IMERG-E can match the rainfall spatial distributions well, demonstrating a considerable capability in capturing the extreme precipitation. Generally, the bias-corrected IMERG-E featured a substantial improvement over the original IMERG-E, and it even exhibited a more satisfactory overall performance than the research-level gauge-calibrated IMERG-F product. Our study demonstrates that the bias-correction method LSCDF can improve the accuracy of satellite precipitation products to benefit the near real-time application of satellite products for natural hazards. • The LSCDF method was developed by combining linear scaling (LS) and cumulative distribution function (CDF) matching. • LSCDF was used for bias correction of daily satellite precipitation data (IMERG-E). • The LSCDF significantly improved the accuracy of satellite precipitation estimates. • The bias-correction method, LSCDF, can be used in near real-time applications. [ABSTRACT FROM AUTHOR]

Groundwater-dependent ecosystems (GDEs) in the lower reaches of arid inland river basins of Northwest China have degraded because of a decrease in water supply caused by excessive socio-economic development in the middle reaches of the river. The degraded GDEs have resulted in serious eco-environmental problems, such as drying of terminal lakes, river channel shifts, declines in riparian forest, desert activation, intensification of desert dust storms, and salinization of secondary soils. These problems have further hindered local and regional efforts at sustainable development. An ecosystem conservation and restoration project was initiated in 2000 in order to deal with the eco-environmental problems. The project has been underway for 15 years, but problems still exist. Using the Heihe River and Tarim River basins as examples, this paper explains the ecological–environmental problems and their causes, describes with the status of ecosystem restoration and conservation, analyzes the problems in the ongoing ecosystem conservation and restoration, and suggests strategies for achieving sustainable ecosystem restoration in the lower reaches of the arid inland river basins of Northwest China. [ABSTRACT FROM AUTHOR]

• Nonstationary models that incorporate climatic variables and reservoir index are constructed. • The nonstationary models provide a better fitting of nonstationary flood and low flow series. • Nonstationary frequency analysis of flood and low flow in upper of Huaihe River Basin are conducted. • Climatic and reservoir impacts are related to nonstationarity of flood and low flow respectively. Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q 95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design. [ABSTRACT FROM AUTHOR]

• Most areas of the Yellow River Basin have experienced abrupt vegetation changes over the past four decades. • Abrupt greening events were concentrated in the middle and upper reaches of the Yellow River Basin after 2000. • The vegetation in the middle reaches was more prone to gradually greening after disturbances compared to the vegetation in the upper reaches. • Human activities predominated over climate in influencing vegetation dynamics in most areas of the basin after 2000. A comprehensive understanding of vegetation dynamics over long term periods is crucial for protecting regional ecosystems. The Yellow River Basin in China has experienced vegetation greening over recent decades, but detailed documentation of abrupt vegetation changes during this process is limited. Here, based on the Global Inventory Modeling and Mapping Studies (GIMMS) dataset, we applied the Breaks for Additive Seasonal and Trend (BFAST) to the normalized difference vegetation index (NDVI) from 1982 to 2020, in order to investigate the spatiotemporal patterns of abrupt and gradual vegetation changes in the Yellow River Basin. During the target period, 67 % of the basin areas experienced at least one abrupt change. Abrupt changes occurred frequently in most areas of the upper reaches, but were relatively rare in the source region. Abrupt greening events were widespread in the middle and upper reaches, with over 90 % following the large-scale implementation of ecological restoration projects. Abrupt browning events were mainly concentrated in areas with intensive agricultural and urban lands. 74 % of the areas that experienced abrupt changes maintained their previous gradual trends after the abrupt changes. Compared to vegetation in the upper reaches, vegetation in the middle reaches was more prone to gradually greening after disturbances. Through residual analysis, we identified both climate change and human activities as drivers of greening in most parts of the basin, with human activities dominating over climate change in driving greening after 2000. Our findings suggest that non-linear analysis methods that consider abrupt changes can reveal more details of long-term vegetation dynamics and provide scientific guidance for regional ecological restoration. [ABSTRACT FROM AUTHOR]

In this study, a comprehensive evaluation of the reliability of seventeen satellite-, reanalysis-, and gauge-based precipitation products (i.e., IMERG-F, GSMaP-G, TMPA 3B42, CMORPH-CRT, PERSIANN-CDR, CHIRPS, IMERG-E, IMERG-L, GSMaP-RT, TMPA-RT, PERSIANN-RT, ERA5, ERA-Interim, MERRA2, GPCC, CPC, and CRU) was conducted for drought monitoring from June 2000 to December 2019 over mainland China. The girded China monthly Precipitation Analysis Product (CPAP) with a dense station network was used as the benchmark observed precipitation data. The Standardized Precipitation Index (SPI) was used as a drought monitoring tool. The comprehensive evaluation procedures were implemented by a monthly precipitation evaluation, various timescales SPI verification, and typical drought event analysis. The results indicated that the performance of per precipitation product is in accordance with the generally higher monthly accuracy in eastern China than that in western China. The post-process satellite precipitation products, particularly bias-adjusted GPM-based products, i.e., the IMERG-F and GSMaP-G, are significantly better at detecting the monthly precipitation than the reanalysis (ERA5 and ERA-Interim), and near real-time satellite precipitation datasets. The best performances are observed for MERRA2 from reanalysis-based precipitation datasets, and GPCC from gauge-based precipitation products, for both precipitation estimation and drought monitoring. Regarding drought monitoring, the performance ranking of most precipitation products is basically consistent with monthly precipitation estimation. The SPI behaviors based on the bias-adjusted GPM-based products have inferior performance than those in MERRA2 and GPCC at the continental scale. Nevertheless, some discrepancies exist in the reliability level of drought detection in sub-regions and multi-timescales. Integrally, the IMERG-F is evaluated to be the most ideal precipitation data among the seventeen precipitation products for drought monitoring in eastern and southwest China. However, in comparison to the GPCC performance, the accuracy of satellite precipitation products should be further improved in the Xinjiang and Tibet Plateau regions. The outcomes of this study will provide valuable insights into the drought monitoring by using multi-sources open access precipitation products across mainland China. • 17 precipitation products reliability were analyzed over mainland China. • Reanalysis- and gauge-based datasets were used to reveal the performance level of satellite-estimated drought. • IMERG-F is the most suitable dataset for drought research in eastern and southwest China [ABSTRACT FROM AUTHOR]

• An observation-based natural and human-impacted catchment comparison method is proposed. • Human influence are quantified based on gridded socio-economic and land use data. • Climatic and anthropogenic influences on drought propagation are investigated. • Climate change accelerates the response of hydrological drought to meteorological drought. • Human activities delay the propagation from meteorological to hydrological drought. It is crucial to investigate how a precipitation deficit is transformed into hydrological drought and how climate change and human activities affect this transformation process, which is helpful to gain a deep understanding of drought propagation process in this changing environment. This study proposed an observation-based natural and human-impacted catchment comparison method to assess the impacts of climate change and human activities on propagation from meteorological drought to hydrological drought. The method mainly consists of the following three steps: (1) selection of natural catchments through analysis of trends and change points of hydro-meteorological data, as well as statistics analysis of human influence based on land use and socio-economic indicators data sets; (2) calculation of drought propagation characteristics (e.g., drought severity, duration, and propagation time) based on run theory and the Pearson correlation coefficient; and (3) comparison of drought propagation characteristics of natural catchments between undisturbed and disturbed periods to identify the impacts of climate change on drought propagation, and comparison of the propagation characteristics between natural and human-impacted catchments during the disturbed period to investigate human influence on drought propagation. The Laohahe basin (with eleven sub-catchments), located in northern China, was evaluated via the proposed procedure, and standardized precipitation index (SPI) and standardized runoff index (SRI) were used to characterize meteorological and hydrological droughts, respectively. The results demonstrate that the proposed method is suitable tool for distinguishing natural and human-impacted catchments, and separating the impacts of climate change and human activities on drought propagation. Furthermore, the comparison results of different schemes show that climate change accelerates the propagation from meteorological drought to hydrological drought in the Laohahe basin, shortening it by approximately 3 months. Human activities, however, disturb and then delay the natural propagation from meteorological drought to hydrological drought, retarding it by 11–12 months. Although the Laohahe basin was selected as a case study in this paper, the proposed method can be applied in other regions as well to improve drought prediction and water resources management. [ABSTRACT FROM AUTHOR]

• A comprehensive framework for quantifying the climatic and anthropogenic influences on river hydrological health is proposed. • Human activities significantly aggravate the degradation of river hydrological health for the studied catchment. • Widespread artificial water withdrawal and reservoir operation are the two crucial human activities. • Adequate rainfall maintains the river hydrological health at a good status in the 1990 s. Climate change and human activities have together altered the river hydrological regime and consequently threatened the health of river ecosystems. Quantifying the impact of climate change and human activities on river hydrological health regimes is essential for water resource management and river ecology protection. Although previous studies have analysed the hydrologic alterations using some indicators, separating effects of climate change and human activities on river hydrological health is needed for developing adaptive measures to protect the ecosystem of river basins. In this study, a comprehensive assessment framework for quantifying climatic and anthropogenic influences on river hydrological health variation was proposed. The framework consists of the following steps: (1) the reconstruction of natural river streamflow using the variable infiltration capacity (VIC) hydrological model, (2) calculation of river hydrological health through the ecological flow threshold method, and (3) quantification of the impacts of climate change and human activities on river hydrological health using the 'observed–simulated' comparison approach. The semi-arid Laohahe Basin in northern China, which consists of three human-influenced catchments (Taipingzhuang, Chifeng, and Xinglongpo) and one natural catchment (Xiquan), was selected as the case study area. The case study demonstrated that the proposed procedure is efficient in quantifying climatic and anthropogenic influences on river hydrological health. The results revealed that the hydrological health level has significantly declined in the three human-influenced catchments for the human-influenced period (1980–2016), particularly in the 2000 s and 2010 s, where it degraded much more severely. Whereas, the relatively adequate rainfall in the 1990 s maintained the river hydrological health at a good status. The quantitative evaluation showed that human activities were the main driving factors for the hydrological health degradation during the whole human-influenced period, with contributions of 80.8%, 91.9%, and 86.0% for the Taipingzhuang, Chifeng, and Xinglongpo catchments, respectively. Widespread artificial water withdrawal and reservoir operation were the two crucial human activities that caused the degradation of river hydrological health for the studied catchment. The proposed procedure and findings of this study not only help in deeper understanding of the evolutionary characteristics and driving mechanisms of river hydrological health in a changing environment in general, but also provide scientific basis for local water resources management and river ecosystems protection. [ABSTRACT FROM AUTHOR]

• The LSTM network was used to fill data gap between GRACE and GRACE-FO. • The spatiotemporal variations of TWS in the Qaidam Basin were analyzed. • The TWS demonstrated significant increase trend during 2002.04 to 2020.03. • The accumulated precipitation had the strongest impact on the TWS changes. Terrestrial water storage (TWS) is a crucial indicator of regional water balance and water resources changes. Due to limited hydrological observations, we combined the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) products using the Long Short-Term Memory (LSTM) neural network to monitor the TWS changes from April 2002 to March 2020 over the closed Qaidam Basin in northwest China and examined the impacts of climate and meteorological changes on TWS variations. The results indicated that the LSTM model, driven by the cumulative precipitation, temperature, and Global Land Data Assimilation System datasets, was reliable for use in reconstruction of the GRACE products in the closed basin. The TWS variations featured seasonal variation characteristics and a significant upward trend at internal-annual scales, which were tested via linear statistics and a modified Mann–Kendall method. The increasing trend is likely to remain strongly sustainable in the near future with a Hurst index over 0.75 in most regions. Moreover, the TWS oscillation has a periodicity and nonlinearity increase trend of 0.43 mm/month as observed using ensemble empirical mode decomposition analysis, and the TWS components (including snow water equivalent, soil moisture, and groundwater) demonstrate discordant increasing trends in the basin. Under climate change conditions, teleconnection factors have strong impacts on TWS variability, particularly for the Pacific Decadal Oscillation index with a significant negative correlation by cross wavelet transform technology. Nonetheless, the increase in TWS is primarily influenced by precipitation increases and is more sensitive to the accumulated precipitation in this region. In this study, the GRACE products in combination with GRACE-FO data may help us to better understand the spatiotemporal characterization of TWS in Qaidam Basin, which will provide an important support for the water resource management and ecological environment protection in such data-scarce regions. [ABSTRACT FROM AUTHOR]

In this paper, we comprehensively evaluated the utility of integrated long-term satellite-based precipitation and evapotranspiration products for drought monitoring over mainland China. The latest Integrated Multi-satelliteE Retrievals for Global Precipitation Measurement V06 three Runs precipitation products, i.e., the near real-time Early Run (IMERG-E) and Late Run (IMERG-L) and the post-real time Final Run (IMERG-F), and the Global Land Evaporation Amsterdam Model V3.3a (GLEAM) potential evapotranspiration (PET) products from 2001 to 2017 were considered. The accuracy of IMERG precipitation and GLEAM PET products was first evaluated against observed precipitation and Penman-Monteith method estimated PET, respectively, based on dense meteorological station network. The Standard Precipitation Evapotranspiration Index (SPEI) calculated based on IMERG precipitation and GLEAM PET products (SPEIs, including SPEI E , SPEI L and SPEI F corresponding to IMERG-E, IMERG-L and IMERG-F, respectively) were then validated by using SPEI calculated based on meteorological data (SPEIm) at multiple temporal-spatial scales. Finally, four typical drought events were selected to analyse the ability of SPEIs to characterize the temporal-spatial evolution of drought situations. The results showed that the IMERG-F presents much better performance than IMERG-E and IMERG-L in terms of higher CC and smaller BIAS and RMSE values over mainland China. The GLEAM PET well simulated the change trend of reference PET, but generally underestimated reference PET in Northwest China (NW), Xinjiang (XJ) and Qinghai–Tibet plateau (TP). In general, the performances of SPEIs over eastern China and Southwest China (SW) were significantly superior to their performances in the NW, XJ, and TP regions. Even though the SPEI F performed the best, the SPEI E and SPEI L also performed reasonably well in some specific regions. SPEIs can well capture the temporal process and reasonably reflect the spatial characteristics for four typical drought events. It is thus highlighted that the latest IMERG precipitation (especially for IMERG-F) and GLEAM PET products could be used as alternative data sources for comprehensive drought monitoring, on account of the water balance principle over mainland China, particularly in eastern China and SW China. The outcomes of this study will provide valuable references for drought monitoring by integration of multi-source remote-sensing datasets in the GPM era. • The utility of integrated IMERG precipitation and GLEAM PET for drought monitoring was evaluated over mainland China. • SPEIs based on IMERG precipitation and GLEAM PET were validated at multiple spatial and temporal scales over mainland China. • IMERG precipitation and GLEAM PET show great potential for drought monitoring over eastern China and Southwest China. • SPEIs can well capture the temporal process and reasonably reflect the spatial characteristics for typical drought events. [ABSTRACT FROM AUTHOR]

• A comprehensive approach for identifying drought termination characteristics is proposed. • Natural and human-influenced hydrological drought termination characteristics are identified. • Human activities significantly aggravate the drought termination duration, deficit and rate. • Human activities lead to shifts in drought termination seasonality. Although many previous studies have analysed the impacts of human activities on hydrological drought, studies that analysed these impacts from the perspective of drought termination, a critical re-wetting phase of hydrological drought, are limited. A deeper understanding on how human alter hydrological drought termination phase is essential for improving drought recovery prediction and performance of the drought early warning system. In this study, a comprehensive approach for identifying hydrological drought termination characteristics and quantifying the impact of human activities on drought termination was proposed. This approach, which combines the concept of drought termination (DT), an 'observed–simulated' comparison approach, and the variable threshold level method (TLM v), consists of the following steps: (1) reconstruction of natural streamflow using a hydrological model, (2) identification of hydrological drought termination characteristics using TLM v method and the concept of drought termination, and (3) quantification of human influence by comparison of the hydrological drought termination characteristics of human-influenced (observed) series and those of natural (simulated) series. The Laohahe basin, consists of four catchments (Xiquan, Xiaochengzi, Dianzi, and Taipingzhuang) in northern China, was evaluated using the proposed procedure. The study demonstrated that the proposed approach is efficient in quantifying human influence on hydrological drought termination phase. The results revealed that human activities have significant impacts on the hydrological drought termination phase in the Xiaochengzi, Dianzi, and Taipingzhuang catchments. All the average drought termination duration (DT dur), deficit (DT def), and rate (DT rate) in the human-influenced series of the three catchments (Xiaochengzi, Dianzi, and Taipingzhuang) increased in comparison to those in the natural series, with maximum increases of 230%, 865%, and 35%, respectively. The seasonality of the drought termination phase starts (DT start) and ends (DT end) for the three catchments exhibited obvious shifts due to human influence. The preferred seasons for DT start and DT end were shifted to summer and autumn, respectively. The proposed approach and findings of this study may help to gain a deeper understanding of how human activities alter hydrological drought termination severity (drought termination duration, deficit, and rate) and time (drought termination starts or ends). [ABSTRACT FROM AUTHOR]

The multi-satellite retrieved soil moisture products released from Europe Space Agency's Climate Change Initiative (ESA CCI) program have been widely used in plenty of fields including drought monitoring. In this study, Cumulative Distribution Function (CDF) is applied to match the simulated soil moisture from the Variable Infiltration Capacity (VIC) model for filling the missing records of ESA CCI soil moisture (ESA CCI SM). The weekly Standard Soil Moisture Index (SSI) calculated from ESA CCI SM dataset (ESA CCI SSI) is conducted to monitor the agricultural drought over the Yellow River Basin (YRB) during 2000~2012. The performance of ESA CCI SSI is compared with the Standard Precipitation Index (SPI), the Standard Precipitation Evapotranspiration Index (SPEI), the self-calibrating Palmer Drought Severity Index based on VIC model (VIC_scPDSI) and the anomaly of Normalized Difference Vegetation Index (NDVI). The results show that the interpolated ESA CCI SM is significant (p<0.05) correlated with the observed soil moisture and more than 84% of the girds in the YRB display good correlations with the global reanalysis soil moisture. In terms of drought monitoring (i.e. drought area and drought intensity), ESA CCI SSI presents a similar spatiotemporal pattern with VIC_scPDSI, while exhibits a delayed response to SPI and SPEI significantly. Analysis of the typical drought event in 2002 indicates that ESA CCI SSI could effectively capture the emerge and development process of this event and the drought severity detected by ESA CCI SSI is higher than that of SPI and SPEI. Besides, it is found that spatial characteristics of ESA CCI SSI and the anomaly of NDVI are consistent in some areas of the basin. [ABSTRACT FROM AUTHOR]

In this study, a new Grid-Xinanjiang model, which considering the spatial variability of grid cell topographical information and soil moisture storage capacity, was developed to simulate the flash flood for small mountain watersheds. The soil moisture storage capacity of each grids was calculated by using the Curve Number of SCS-CN (Soil Conservation Service) model. The model was performed at 90m grid cells, which highlights the well representing of the sub-grid variability. The developed model was applied to six typical small mountain watersheds in different provinces of China for flash flood simulations. The performance of the Grid-Xinanjiang model was compared with that of the commonly used HEC-HMS model. The results show that the Grid-Xinanjiang model performs a little better than the HEC-HMS model in terms of the estimates of streamflow and the model efficiency coefficient. Most of the qualified ratios relative to runoff depth, peak flow, and peak time for the Grid-Xinanjiang model in the applied watersheds are more than 95%. Combining the critical discharge and the antecedent soil moisture condition, the newly developed Grid-Xinanjiang model can be used to calculate the flash flood warning critical rainfall for Chinese small mountain watersheds. [ABSTRACT FROM AUTHOR]

• We investigated the error features of the latest GPM-GSMaP estimates by separating the input sources. • We found that the input sources exhibit larger biases in the semi-arid and arid regions. • The retrievals of the input sources are affected by topography in different extents. • The performance of imagers generally outperforms that of sounders. • GMI exhibits inadequacies in detection capability and has large biases in the winter and spring months. The impact of crucial geographic and climatic factors on the input source errors of integrated multi-satellite precipitation estimates is an important but still unclear issue for both algorithm developers and data users. This study primarily focused on the impacts of the twelve input sources used in the latest Global Satellite Mapping of Precipitation for Global Precipitation Measurement (GPM-GSMaP) for different climatic regions, elevations, and seasons over mainland China. Our evaluation results show that the error features of the input sources from several passive microwave and infrared sensors are related to the accuracy of GPM-GSMaP precipitation estimates. The input sources show larger hits, misses and false biases in the semi-arid and arid regions, for which the false bias was particularly significant. As for the seasonality, the input data sources exhibit a better performance in summer and have relatively lower hits and higher biases in winter. We also found that precipitation retrievals of the input sources are affected by topography in different extents. In terms of passive microwave sensors, the conical-scanning imagers generally outperform the cross-track-scanning sounders, but the sounders-based precipitation estimates have relatively better detection capability. As one of the core sensors for GPM, the microwave imager GMI reveals inadequacies in representing areal rainfall patterns and has relatively large biases especially in the winter and spring seasons, suggesting that the current GMI algorithm might need to be further improved. [ABSTRACT FROM AUTHOR]

Projections of hydrological changes are associated with large uncertainties from different sources, which should be quantified for an effective implementation of water management policies adaptive to future climate change. In this study, a modeling chain framework to project future hydrological changes and the associated uncertainties in the Xijiang River basin, South China, was established. The framework consists of three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distributions (PDs) for extreme flow frequency analyses. Direct variance method was adopted to analyze the manner by which uncertainty sources such as ES, CM, SD, and HM affect the estimates of future evapotranspiration (ET) and streamflow, and to quantify the uncertainties of PDs in future flood and drought risk assessment. Results show that ES is one of the least important uncertainty sources in most situations. CM, in general, is the dominant uncertainty source for the projections of monthly ET and monthly streamflow during most of the annual cycle, daily streamflow below the 99.6% quantile level, and extreme low flow. SD is the most predominant uncertainty source in the projections of extreme high flow, and has a considerable percentage of uncertainty contribution in monthly streamflow projections in July–September. The effects of SD in other cases are negligible. HM is a non-ignorable uncertainty source that has the potential to produce much larger uncertainties for the projections of low flow and ET in warm and wet seasons than for the projections of high flow. PD contributes a larger percentage of uncertainty in extreme flood projections than it does in extreme low flow estimates. Despite the large uncertainties in hydrological projections, this work found that future extreme low flow would undergo a considerable reduction, and a noticeable increase in drought risk in the Xijiang River basin would be expected. Thus, the necessity of employing effective water-saving techniques and adaptive water resources management strategies for drought disaster mitigation should be addressed. [ABSTRACT FROM AUTHOR]

• A method of regional non-stationary flood frequency analysis is proposed. • The index incorporates climatic and reservoir factors as covariates. • MRI is more suitable for non-stationary flood frequency analysis. • The prediction period of flood frequency analysis is extended. Global climate change and reservoir regulations can alter the natural flow of rivers. Influenced by these two drivers, flood sequences may no longer satisfy the assumption of stationary, thereby making it difficult to accurately analysis flood frequency and to design water conservancy projects. Therefore, it is of great significance to analyse the non-stationary frequency of flood sequences in a changing environment. In this study, we proposed a method for conducting nonstationary flood frequency analysis caused by cascade reservoirs as well as the low-frequency climate indices. The proposed non-stationary model 2, with the explanatory variables of climate indices and modified reservoir index (MRI), was compared with the traditional stationary model and the widely used non-stationary model 1 with time as the explanatory variable. The study was conducted at six hydrological stations in the main stream and tributaries of the upper reaches of the Yangtze River in China (considered as the Three Gorges Reservoir Area). The results of the generalized additive model for location, scale and shape (GAMLSS) showed that the Akaike information criterion and Bayesian information criterion values of the proposed non-stationary model method 2 are smaller than those of the two comparison models. When the low-frequency South Oscillation Index is high or the Arctic Oscillation and North Pacific Oscillation are low, the stationary model underestimates the design value of flood quantiles compared with the non-stationary model 2. Compared with the non-stationary model 1, the MRI and low-frequency climate indices as the explanatory variables in model 2 can better describe the non-stationary characteristics of flood frequency and amplitude. In addition, the non-stationary model considering external physical factors can provide better prediction of future design flood compared with two traditional models. [ABSTRACT FROM AUTHOR]

• Four daily precipitation datasets were integrated based on the modifiable Bayesian model averaging (BMA) framework. • Seven blended precipitation products were generated by considering different inputs. • Some BMA products outperformed the representative merged product, MSWEP. • Impacts of the ensemble members on BMA predictions were quantitatively analyzed. Selection of the number and which of multisource precipitation datasets is crucially important for precipitation fusion. Considering the effects of different inputs, this study proposes a new framework based on the Bayesian model averaging (BMA) algorithm to integrate precipitation information from gauge-based analysis CPC, reanalysis-derived dataset ERA5, and satellite-retrieval products IMERG-E and GSMaP-RT. The BMA weights were optimized for the period 2001–2010 using daily measurements and then applied to the period 2011–2015 for model validation. Seven BMA-merged precipitation products (i.e., MCE, MCI, MCG, MCEI, MCEG, MCIG, and MCEIG) were thoroughly evaluated across mainland China and then compared against the state-of-the-art ensemble-based product, MSWEP. The results indicate that the BMA predictions performed substantially better than the reanalysis and satellite precipitation datasets in both daily statistics and seasonal analyses. MCE, MCI, and MCEG demonstrated better performances relative to CPC in terms of individual metrics. Moreover, MCI, MCG, and MCEI generally outperformed MSWEP over the entire study area, particularly in local regions, such as southwestern China and the eastern Tibetan Plateau. During Typhoon Rammasun in 2014, MCG and MCEG provided greater detail for heavy rainfall events than the four ensemble members and the MSWEP product. Thus, the performance of the BMA predictions exhibited evident differences because of various input sources. CPC was the major internal influencing factor with the highest weight score. Meanwhile, the increased-input CPC dataset into the BMA-based schemes exerted a significant influence on the precipitation estimates, which markedly facilitated the performance improvement of the fusion model, and its improved degree (greater than 14 %) was obtained using a 'changed-initial' comparison method. Our results demonstrate that the developed modifiable BMA framework is useful for analyzing the impacts of ensemble members on BMA predictions and suggests that it is considerate in the use of different input sources for generating ensemble-based precipitation products. [ABSTRACT FROM AUTHOR]

Extreme flow projections are of considerable necessity for flood and drought disaster prevention and sustainable water resources management to adapt to future climate change. However, these projections are usually associated with large uncertainties arising from different sources. A numerical modeling system including three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distribution (PD) functions was established in this study to project future extreme flow changes at basin scales. This modeling system was employed in the following three watersheds with different climatic and topographical characteristics in China: The Upper Ganjiang River basin (UGRB) in the humid region, the Laoha River basin (LRB) in the semi-arid region, and the Yellow River source region (YRSR) in the semi-humid region. Subsequently, the analysis of variance approach (ANOVA) was used to quantify the contribution of different uncertainty sources in extreme flow projections. Results show that extreme high flow was projected to increase in all basins. By contrast, extreme low flow would decrease in UGRB, and inconsistent change signals were found in LRB and YRSR. The ANOVA-based assessment reveals that CM is generally the dominant uncertainty source for mean monthly streamflow and extreme low flow projections. ES explains a large proportion of total uncertainty for extreme low flow projection in the 2060s in UGRB because of the large difference in non-precipitation frequency projected under different ESs. SD is the main uncertainty source in extreme high flow projection in UGRB where extreme precipitation events frequently occur. Compared with other basins, HM produces relatively high uncertainty in LRB due to its inferior performance in historical streamflow simulations. PD contributes a low percentage of uncertainty in extreme flow projections. Interactions among uncertainty sources even produce larger uncertainties than most individual sources. Although future extreme flow projections are associated with non-negligible uncertainties, the projected remarkable changes in extreme high and low flows indicate that stakeholders should take certain measures to mitigate flood risks in the three basins and alleviate drought risks in UGRB • Extreme high flow would increase in the three basins with different climate features. • Extreme low flow would decrease in the watershed in humid regions. • Uncertainty of extreme flow projection is generally dominated by climate models. • Uncertainty sources have different impacts associated with watershed characteristics. [ABSTRACT FROM AUTHOR]

The new version of PRC-GLOBWB model was applied to evaluate the hydrological drought under natural conditions and human influences at a high spatial resolution (10 km by 10 km) for the period of 1961-2016. The new version of PCR-GLOBWB model performance well in simulating river discharge over China, considering the water management in the modeling framework. Results show that human activities reduce the drought frequency and standardized drought deficit volume (StDef) up to 80% in parts regions of southeast of China during the period of 1961-2016. Human water management reduce the drought frequency in south east regions about 20%-100% compared to the natural variability. Return period of the severest drought year in each province or city demonstrates that human activities release the StDef of southeast region, while natural variability play a major role in the north parts of China. Human water use (irrigation and reservoir) has influences on the changes of hydrological drought characteristic of China during 1961-2016. [ABSTRACT FROM AUTHOR]

Ecological drought is a complex process in terrestrial ecosystems where vegetation's eco-physiological functions are impaired due to water stress. However, there is currently a lack of long-term assessment of ecological drought from an eco-physiological perspective. In this study, the standardized ecological drought index (SESNDI) was developed using actual evaporation, root soil moisture, and kernel normalized difference vegetation index via the Euclidean distance method, reflecting ecosystem physiology, water supply capacity, and vegetation status. Solar-induced chlorophyll fluorescence validated SESNDI by reflecting vegetation photosynthesis. Using China as an example, severely impacted by climate change and ecological restoration, ecological drought's spatio-temporal variation and propagation characteristics was evaluated using clustering algorithms. The results demonstrated that (1) SESNDI showed superior performance over several other drought indices. (2) During 1982-2020, ecological drought was prevalent from 1990 to 2010, especially in the central and northeastern regions. (3) Compared to 1982-2000, the median duration and affected area of ecological drought events during 2001-2020 reduced by four months and 1.51 × 10 5  km 2 , respectively, while the median intensity increased by 0.06. (4) Decreased precipitation and increased temperature were the primary factors contributing to the frequent occurrence of ecological drought in China from 1990 to 2010. This study offers a crucial methodology for evaluating ecological drought, serving as a reference for developing effective terrestrial restoration strategies., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Under the influence of climate change and human activities, water scarcity and uneven spatial distribution have become critical factors constraining societal development and threatening ecological security. Accurately assessing changes in blue and green water resources (BW and GW) caused by human activities can reveal the actual situation of water scarcity. However, previous research often overlooked the calibration of GW and human water usage, and it rarely delved into the primary human factors leading to water scarcity and potential impact mechanisms. Therefore, based on the PCR-GLOBWB model that considers human impacts, and with reasonable calibration of B/GW and human water usage, hydrological processes were simulated under both human-influenced and natural conditions. A comprehensive assessment of the impact of human activities on BW and GW was conducted. The results show that: (1) BW and GW exhibit a spatial pattern of increasing from northwest to southeast in the basin. From 1961 to 2020, the proportion of BW showed an upward trend, while GW was decreasing; (2) The impact of human activities on changes in water resources is mainly concentrated in the midstream and dowmstream of the basin. Due to human influences, the green water flow (GWF) increased by 3-24.4 mm, and the BW volume increased by 67.2-146.4 mm. However, the green water storage (GWS) decreased by 5.6-75.4 mm; (3) The impact of human activities on blue water scarcity (BW scarcity ) is significantly greater than green water scarcity (GW scarcity ). The worsening of GW scarcity does not exceed 0.2, while areas where BW reaches significant deterioration (BW scarcity  > 1.5) account for 1.3 %, 9.8 %, and 17 % of the upstream, midstream and downstream, respectively. (4) Irrigation activities are the main factor causing water resource scarcity. In the future, it is important to reasonably develop the potential for GW utilization and optimize BW management measures to address water resource crises., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)