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The response of blue and green water to climate and land-use change in the Ganjiang River Basin (GRB) is evaluated via the SWAT model that combines three scenarios (the land-use/land-cover (LULC), climate change, and integrated climate and LULC change scenarios) in the 2040s (2031–2050) and 2060s (2051–2070). The results indicate that, for the GRB, cropland, woodland, and grassland show a decreasing trend, while build-up and water areas show an increasing trend in terms of future land-use change. The climatic conditions projected using NORESM1-M model data under the RCP4.5 and RCP8.5 scenarios suggest, respectively, increases in precipitation (31.17 and 27.24 mm), maximum temperature (2.25 and 2.69 °C), and minimum temperature (1.96 and 2.58 °C). Under climate change conditions, blue water is estimated to decrease by up to 16.89 and 21.4 mm under RCP4.5 and RCP8.5, while green water is estimated to increase up to 19.14 and 20.22 mm, respectively. Under the LULC changes, blue water is projected to increase by up to 5.50 and 7.57 mm, while green water shows decreases of 4.05 and 7.80 mm for the LULC2035 and LULC2055 scenarios, respectively. Under the four combined LULC and climate change conditions (RCP4.5_2040s, RCP4.5_2060s, RCP8.5_2040s, and RCP8.5_2060s), blue water tends to decrease by 0.67, 7.47, 7.28, and 9.99 mm, while green water increases by 19.24, 20.8, 13.87, and 22.30 mm. The influence of climate variation on blue and green water resources is comparatively higher than that of the integrated impacts of climate and land-use changes. The results of this study offer a scientific reference for the water resources management and planning department responsible for scheduling water resource management plan in the GRB. HIGHLIGHTS This study investigates the effects of climate changes and land-use changes on blue/green water.; The NORESM1-M model data from the CMIP5 are combined with the SWAT model to analyse the influence of future climate changes on blue/green water.; The CA–Markov model is applied to generate future land-use scenarios.; Multiple climate and land-use scenarios are set to quantitatively analyse the changes of blue/green water.

The evaluation uncertainty caused by a standard reference itself is harmful to both algorithm developers and data users in substantially understanding the error features and the performance of satellite precipitation products (SPPs). In this study, the Climate Precipitation Center Unified (CPCU) data and the Merged Precipitation Analysis (MPA) data are used as the benchmark to investigate the evaluation uncertainties of satellite precipitation estimates generated by the reference itself. Two SPPs, IMERG-Late and GSMaP-MVK, are employed here. The results show that the approach using two different ground-based precipitation products as the references can effectively reveal the potential evaluation uncertainties. Interestingly, it is found that the evaluation results are prone to resulting in larger uncertainties over semihumid areas. Furthermore, evaluation uncertainty of statistical metrics is closely related to rainfall intensity in that it has a gradually decreasing tendency with increasing rainfall intensities. Additionally, we also found that the dependency of the false alarm ratio (FAR) and root-mean-square error (RMSE) scores on the spatial density of rain gauges is relatively low. Both relative bias (RBIAS) and normalized root-mean-square error (NRMSE) scores for light precipitation (1–5 mm day−1) increase with the spatial density of the rain gauges, suggesting that the evaluation of light precipitation can easily cause uncertainties relative to medium-to-high rain rates. Finally, the minimum gauge density required for different scores and different rainfall intensities is discussed. This study is expected to provide criteria to investigate the reliability of evaluation results for the satellite quantitative precipitation estimation community.

Satellite- and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs; i.e., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (TMPA 3B42V7), Climate Prediction Center (CPC) morphing technique satellite-gauge blended product (CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang (XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that: (1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient (CC) and root-mean-square error (RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias (BIAS; −22.72%); (2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation; (3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash-Sutcliffe efficiency (NSE) values (0.85 and 0.75 for calibration period and validation period, respectively); and (4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in data-scarce regions with similar climates and topography in the Global Precipitation Measurement (GPM) era.

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.

Flash droughts are extreme phenomena that have been identified using two different approaches. The first approach identifies these events based on unusually rapid intensification rates, whereas the second approach implicitly identifies short-term features. This latter approach classifies flash droughts into two types, namely, precipitation deficit and heat wave flash droughts (denoted as PDFD and HWFD). In this study, we evaluate these two approaches over the Yellow River basin (YRB) to determine which approach provides more accurate information about flash droughts and why. Based on the concept of intensification rate, a new quantitative flash drought identification method focused on soil moisture depletion during the onset–development phase is proposed. Its performance was evaluated by comparing the onset time and spatial dynamics of the identified flash droughts with PDFD and HWFD events identified using the second approach. The results show that the rapid-intensification approach is better able to capture the continuous evolution of a flash drought. Since the approach for identifying PDFD and HWFD events does not consider changes in soil moisture with time, it cannot ensure that the events exhibit rapid intensification, nor can it effectively capture flash droughts’ onset. Evaluation of the results showed that the chosen hydrometeorological variables and corresponding thresholds, particularly that of temperature, are the main reasons for the poor performance of the PDFD and HWFD identification approach. This study promotes a deeper understanding of flash droughts that is beneficial for drought monitoring, early warning, and mitigation.

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.

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 system. 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 artificial intelligence (AI) technologies, namely, multiple linear regression (MLR), long short-term memory (LSTM), and random forest (RF) models. On this basis, the capabilities of these algorithms for estimating RI and droughts (flash droughts and traditional slowly-evolving droughts) detection were analyzed. Results showed that the RF model achieved the highest skill in terms of RI estimation and flash droughts identification among the three approaches. Spatially, the RF-based RI performed best in the southeastern China, with an average CC of 0.90 and average RMSE of 2.6th percentile per week, while the poor performances were found in Xinjiang region. For drought detection, all three AI 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 to estimate the RI and monitoring flash droughts by considering multiple meteorological variable anomalies in adjacent weeks of 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 comparing to slowly-developing droughts, along with asymmetrical compound influences in different regions over China. For the Xinjiang region, P deficit played a dominant role in triggering the onset of flash droughts, while in the 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 drought and highlight the potential of AI technologies in flash droughts monitoring.

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.

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.

The multisatellite-retrieved soil moisture (SM) products released by the Europe Space Agency's Climate Change Initiative (ESA CCI) program have been widely used in numerous fields, including drought monitoring. In this article, a cumulative distribution function is applied to match the simulated SM from the Variable Infiltration Capacity (VIC) model and fill in the missing records of ESA CCI SM. The weekly standard SM index (SSI) calculated from the ESA CCI SM dataset is utilized to monitor the agricultural drought over the Yellow River Basin (YRB) during 2000–2012. The performance of the 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 anomalies of ESA CCI SM and Normalized Difference Vegetation Index (NDVI). The results show that the interpolated ESA CCI SM is significantly (p

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.

Global satellite precipitation products, such as Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with a critical precipitation data source for hydrological applications in data-sparse or ungauged basins. This study conducts a comparative analysis on the quality of the TRMM Multi-satellite Precipitation Analysis 3B42V7 and the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 05 precipitation products and their hydrological utilities in the Yellow River source region (YRSR), a mountainous Alpine region in northwestern China, from April 2014 to December 2016. Results indicate that when evaluated against the ground precipitation observations, IMERG generally improves the daily precipitation estimates relative to its predecessor 3B42V7. In addition, and the correlation coefficients (CCs) of IMERG (0.328 and 0.527) are significantly higher at grid and basin scales than those of 3B42V7 (0.287 and 0.458). However, the 3-hourly precipitation estimates from both products poorly correlate with the ground observations at grid and basin scales. By using the grid-based Xinanjiang (GXAJ) hydrological model calibrated with the gauge-based precipitation for daily streamflow simulations, the 3B42V7-driven model run shows acceptable hydrological simulation skill with regard to the Nash-Sutcliffe model efficiency coefficient (NSE = 0.729), whereas IMERG demonstrates improved performance (NSE = 0.810), which is comparable with the gauge-based simulation (NSE = 0.807). Input-specific model recalibration effectively enhances the hydrological performance of both satellite products (NSE = 0.856 for IMERG and NSE = 0.840 for 3B42V7). Additionally, the gauge-benchmarked GXAJ model with 3B42V7 has limited hydrological skill in simulating three historical flood events at 3-hourly time intervals (NSE = −0.070 to 0.702), while IMERG has a slightly better performance (NSE = 0.266–0.792). Model recalibration also significantly improves the simulations of two out of three flood events, and the NSE skill cores of IMERG (0.581–0.901) and 3B42V7 (−0.795 to 0.852) are relatively lower than that of the gauge-based simulation (0.753–0.969) but comparable. Overall, the IMERG and 3B43V7 satellite precipitation products can be adopted as reliable precipitation sources for hydrological simulations at daily and sub-daily time scales in the study area, with IMERG better suited than 3B42V7. Considering that the low CC values exist in both IMERG and 3B42V7 products, in particular, at a sub-daily temporal scale, the GPM research community should further improve the calibration algorithms and enhance the quality of IMERG products in YRSR. Performing bias-correction of satellite precipitation products is also necessary for hydrological modelers to effectively improve their hydrological utilities.

Recently, the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) products with high spatial (0.1° × 0.1°) and temporal (half-hourly) resolution have become operationally available. It is of crucial importance to comprehensively evaluate these new products before they are applied extensively. This study focuses on statistical and hydrological evaluations of the latest IMERG (Version 05) products: the near-real-time “Early” run and “Late” run IMERG products (IMERG-E and IMERG-L, respectively), and the post-real-time “Final” run IMERG product (IMERG-F) over the mid-latitude humid Mishui basin in South China during 2014–2015, in comparison with their predecessors, the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products (3B42RT and 3B42V7). The post-real-time IMERG-F presents the best performance among the five satellite precipitation products (SPPs), with the highest daily correlation coefficient (CC) of 0.85 and the lowest daily root-mean square error of 5.58 mm at the basin scale. The near-real-time IMERG-E and IMERG-L demonstrate comparable performance with 3B42V7. In addition, the Taylor diagrams visually demonstrate that the IMERG products are better than the 3B42 products. For hydrological simulations under scenario I (model calibration based on rain gauge observations), the post-real-time IMERG-F performs obviously better than the 3B42V7 does, with a relatively high CC of 0.81, a good Nash–Sutcliffe coefficient of 0.63, and an acceptable relative bias of −3.98%. Both the IMERG-E and IMERG-L demonstrate a better performance than the 3B42RT does. For scenario II (model recalibration based on each satellite dataset), the hydrological performances of both the IMERG and 3B42 products are improved. The IMERG-E, especially, demonstrates streamflow simulation performance comparable to that of the 3B42V7, both for the whole simulation period and flood season, indicating the great potential of the latest near-real-time IMERG-E product for flood simulation and prediction. Overall, this systematic evaluation highlights that the latest IMERG products have desirable hydrological utility in the study region. This study will provide useful guidelines for hydrological applications of the new generation SPPs, as well as IMERG algorithm development.

As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG &ldquo, Early,&rdquo, &ldquo, Late,&rdquo, and &ldquo, Final&rdquo, run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.

The evaporation of rainfall intercepted by canopies back into the atmosphere – often referred to as rainfall interception loss – is a significant component of terrestrial evaporation in many ecosystems. The physical process of rainfall interception loss can usually be broken down into three phases: (1) wetting up of the canopy, (2) saturated canopy conditions, and (3) drying out after rainfall has ceased. During each of these phases, the process is affected by many factors, including rainfall characteristics, such as gross rainfall, rainfall intensity and rainfall duration, vegetation characteristics such as canopy height, leaf area and the orientation of branches and leaves, and meteorological conditions such as temperature, wind speed and relative humidity. The Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al. 2011) estimates terrestrial evaporation, including forest rainfall interception loss, at the global scale mostly from satellite data. However, the model estimation of interception loss has not been updated since its release almost 10 years ago (Miralles et al. 2010).In this regard, improving the estimation of interception loss in the model remains a priority. In GLEAM, rainfall interception is estimated using the revised Gash analytical model by Valente et al. (1997), in which the canopy storage and mean wet canopy evaporation rate are both considered constants in both space and time. In addition, only tall-canopy interception is considered. Here we explore the potential of the modified Gash's model by Van Dijk and Bruijnzeel (2001), which uses time variant canopy storage and evaporation functions dependent on leaf area index, for its application at global scales. In addition, due to its dependency on leaf area index, the model is applied to the estimation of rainfall interception loss of low vegetation types such as shrubs and grasses. An extensive meta-analysis of previous interception loss field campaigns provides an extensive archive of data to parameterize and/or validate model estimates over multiple ecosystem types. This presentation provides a general overview of the challenges in rainfall interception loss modelling at global scales and the first results of the global benchmarking of the Valente et al. (1997) and the Van Dijk and Bruijnzeel (2001) formulations against in situ data.References Miralles D G, Gash J H, Holmes T R H, et al. Global canopy interception from satellite observations[J]. Journal of Geophysical Research: Atmospheres, 2010, 115(D16).Miralles D G, Holmes T R H, De Jeu R A M, et al. Global land-surface evaporation estimated from satellite-based observations[J]. Earth Syst. Sci., 2011, 15(2): 453–469.Valente F, David J S, Gash J H C. Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models[J]. Journal of Hydrology, 1997, 190(1-2): 141-162.Van Dijk A, Bruijnzeel L A. Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description[J]. Journal of Hydrology, 2001, 247(3-4): 230-238.

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 (TLMv), consists of the following steps: (1) reconstruction of natural streamflow using a hydrological model, (2) identification of hydrological drought termination characteristics using TLMv 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 (DTdur), deficit (DTdef), and rate (DTrate) 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 (DTstart) and ends (DTend) for the three catchments exhibited obvious shifts due to human influence. The preferred seasons for DTstart and DTend 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).

The Palmer drought severity index (PDSI) is physically based with multivariate concepts, but requires complicated calibration and cannot easily be used for multiscale comparison. Standardized drought indices (SDIs), such as the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), are multiscalar and convenient for spatiotemporal comparison, but they are still challenged by their lack of physical basis. In this study, a hybrid multiscalar indicator, the standardized Palmer drought index (SPDI), was used to examine drought properties of two meteorological stations (the Beijing and Guangzhou stations) in China, which have completely different drought climatologies. The results of our case study show that the SPDI is correlated with the well-established drought indices (SPI, SPEI, and PDSI) and presents generally consistent drought/wetness conditions against multiple indicators and literature records. Relative to the PDSI, the SPDI demonstrates invariable statistical characteristics and better comparable drought/wetness frequencies over time and space. Moreover, characteristics of major drought events (drought class, and onset and end times) indicated by the SPDI are generally comparable to those detected by the PDSI. As a physically-based standardized multiscalar drought indicator, the SPDI can be regarded as an effective development of the Palmer drought indices, providing additional choices and tools for practical drought monitoring and assessment. Keywords: Drought, PDSI, Multiscalar index, SPDI, Multifaceted comparison

This study focuses on tracing the error sources of the latest global satellite mapping of precipitation for global precipitation measurement (GPM-GSMaP) over the Tibetan Plateau by the corresponding satellite information flag provided by the retrieval system. We investigated the characteristics of GPM-GSMaP estimates from 11 passive microwave sensors (including 7 imagers and 4 sounders) and 1 geo-infrared data source integrating the morphing technique (i.e., morph). Assessment results show that imagers are generally superior to sounders and morph. Among the seven types of imagers, the Tropical Rainfall Measuring Mission Microwave Imager, which rarely underestimates heavy rain events, exhibits the best performance with total bias of −8.8%. In contrast, the worst performance is found in the special sensor microwave imager/sounder on defense meteorological satellite program (DMSP)-F19 with the largest bias of approximately 39.8%. The GPM Microwave Imager shows acceptable accuracy in detecting heavy rain, but it tends to overestimate light and moderate rain. Compared to the imagers, all the sounders produced larger biases (>120%), although they show lower probability of missing rainfall events. Particularly, the AMSU-A/MHS (i.e., Advanced Microwave Sounding Unit-A; Microwave Humidity Sounder) on national oceanic and atmospheric administration (NOAA) satellites (i.e., NOAA-18 and −19) display higher biases than those on meteorological operational satellite (MetOp) satellites (i.e., MetOp-A and −B), with dramatic overestimation up to 168.4%. Additionally, the error components of morph show a similar pattern to those of sounders, except for substantial underestimation of heavy rain events. The approach of tracing error reported here can enable both GPM-GSMaP users and developers to better understand the error sources of precipitation retrievals.

In this study, the equidistant cumulative distribution function (EDCDF) quantile-based mapping method was used to develop bias-corrected and downscaled monthly precipitation and temperature for China at 0.5° × 0.5° spatial resolution for the period 1961–2099 for eight CMIP5 GCM simulations. The downscaled dataset was constructed by combining observations from 756 meteorological stations across China with the monthly GCM outputs for the historical (1961–2005) and future (2006–99) periods for the lower (RCP2.6), medium (RCP4.5), and high (RCP8.5) representative concentration pathway emission scenarios. The jackknife method was used to cross validate the performance of the EDCDF method and was compared with the traditional quantile-based matching method (CDF method). This indicated that the performance of the two methods was generally comparable over the historic period, but the EDCDF was more efficient at reducing biases than the CDF method across China. The two methods had similar mean absolute error (MAE) for temperature in January and July. The EDCDF method had a slight advantage over the CDF method for precipitation, reducing the MAE by about 0.83% and 1.2% at a significance level of 95% in January and July, respectively. For future projections, both methods exhibited similar spatial patterns for longer periods (2061–90) under the RCP8.5 scenario. However, the EDCDF was more sensitive to a reduction in variability.

Monitoring vegetation dynamics at global scale is equally important in the context of terrestrial ecosystem carbon exchange and climate-biosphere interactions. The Breaks For Additive Seasonal and Trend (BFAST) method and Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day Normalized Difference Vegetation Index (NDVI) at a spatial resolution of 250 m were used to detect vegetation dynamics in Quebec during 2000–2011. The overall agreement between BFAST detected breaks and observed disturbances was about 64% with the highest agreement up to 80% for “Fire” disturbance. The results presented in this study indicated that 25.7% of the total study area experienced NDVI trend changes with one or more breaks during 2000–2011, most of which were detected in the Boreal Shield eco-zone along the coastline of the Gulf of St. Lawrence. Abrupt vegetation changes barely varied under different eco-zones while considerably varied with different land cover types. The abrupt changes areas in 2002 and 2009 were the two greatest, with area percentages of 17.4% and 29.1% of the whole area, respectively. The area percentages of years with abrupt trend changes indicated that abrupt vegetation greening occurred in 2008 and 2009, especially in 2009, with 58.3% of the overall abrupt greening. Abrupt vegetation browning occurred in 2002, 2003, 2005 and 2007, especially in 2002, with 28.2% of the overall abrupt browning. Moreover, our results indicated that the detected vegetation trends varied temporally and spatially. Disturbances from existing field observations or remotely sensed images could only interpret

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.

With a copula-based probabilistic model, this study presents a global view on the propagation of meteorological to soil moisture drought. Meta-Gaussian and three-dimensional vine copulas were employed to construct the bivariate and trivariate conditional joint drought distributions, respectively, using the time series of standardized precipitation evapotranspiration index (SPEI) and standardized soil moisture index (SMI) derived from the Global Land Data Assimilation System (GLDAS) simulations. Three different cases, including the probability and magnitude of soil moisture drought (SMI) conditioned on 1-month meteorological drought (SPEI1), seasonal meteorological drought (SPEI3), and the joint effects of SPEI1 and antecedent soil moisture conditions (SMIlag-1), were analyzed. Then, the duration of soil moisture drought in response to meteorological drought, and potentially affected cropland area were analyzed to assess potential agricultural impacts. Results showed that the likelihood of soil moisture drought conditioned on SPEI1 in the Amazon rainforest, southern and eastern China, Southeast Asia, southeastern United States, and west Africa was generally higher than in other regions. Under the impact of seasonal meteorological drought, except for the Qinghai-Tibet Plateau in China, northern Russia, and North Africa, the probability of soil moisture drought occurrence overall increased with significant increments in Europe and North America. The antecedent soil moisture condition also influenced the probability of drought propagation, and this effect was particularly significant for Northern China, Russia, the U.S. Midwest, Canada, and Australia. Spatially, the western U.S., northern Canada, South Africa, eastern Europe, North China, northern Russia, and most parts of Australia present longer duration of soil moisture drought in response to meteorological drought. Comprehensive considerations of the conditional probability, duration, and potentially affected area of soil moisture drought for croplands revealed that among five continents, South America was more vulnerable to agricultural drought under the condition of meteorological 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.

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.

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.

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.

Soil moisture (SM) is a vital variable for controlling water and heat exchange between the atmosphere and land surface. Spatiotemporally continuous satellite-derived SM information is urgently needed for large-scale meteorological and hydrological applications. This study proposes the application of a spatial gap-filling method using an artificial neural network (ANN) to reconstruct missing records of daily surface SM from the Climate Change Initiative program of the European Space Agency (ESA CCI) in the growing seasons of 1982–2015 across China. Ten environmental variables were taken into consideration, including meteorological forcing, geographic and topographic features, vegetation conditions, and soil texture. The ANN-reconstructed SM and RF-reconstructed SM were validated using the ground-based observations and three global reanalysis SM datasets. The gap-filling results by the ANN model were compared to that by the original kriging (OK) model under three simulation scenarios: SM images with removed swaths, varying vegetation densities, and different percentages of data gaps. The results showed that the ANN-reconstructed SM was in good agreement with in-situ SM observations, with the CC more than 0.607 and the RMSE less than 0.074 m3/m3 over the northwestern and eastern parts of China. Compared with the original ESA CCI SM, the ANN model performed better than the OK model in reconstructing SM with absent swaths and in densely and sparsely vegetated regions. Specifically, the ANN-reconstructed SM provided a higher estimation accuracy in regions with low-density vegetation than in those with high-density vegetation. The weaker performance may due to the complex interactions between surface SM and various environmental variables underneath the dense vegetation. In addition, the ANN model significantly outperformed the OK model by accurately estimating SM when the percentage of data gaps succeeded 40%. Our study is valuable for providing an alternative reference to reconstruct satellite-derived SM and highlights the potential of using the ANN model.

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.

Land evapotranspiration ET is a key component of terrestrial ecosystems, as it is the nexus of hydrological, energy, and carbon cycles. Satellite-based observations are commonly utilized to provide high-resolution, large-scale ET estimates. The ground-based validation of such large-scale estimates is necessary to ensure that remotely sensed ET characteristics are accurate, and to extend their various applications. The Global Land-surface Evaporation Amsterdam Methodology GLEAM combines a wide range of multi-satellite observations to estimate daily actual evaporation through a process-based methodology. In this study, we focused on evaluating a daily GLEAM 0.25° ET product using in-situ eddy covariance EC ET data 2003–2005 as a benchmark at eight sites from the Chinese Flux Observation and Research Network ChinaFLUX, which contains several biome types croplands, grasslands, shrublands, savannas, and forests across China at a range of temporal scales from daily, to monthly, to annual. The results indicated that the ET products of the Global Land-surface Evaporation Amsterdam Methodology GLEAM ET over different time scales can estimate actual ET with reasonable accuracy. GLEAM showed high skill scores for most of the land-cover types except at the Xishuangbanna forest site XSBN, where significantly systematic bias was detected at each individual temporal scale. Overall, GLEAM ET products were closer to the EC observations at the three grassland sites than at the four forest sites or the cropland site. GLEAM significantly overestimated the EC measurements at the four forest sites and one cropland site, while a slight underestimation occurred at the three grassland sites; there was a year-long systematic overestimation for GLEAM at the four forest sites. The daily GLEAM ET aggregated by monthly and annual data agreed more closely with EC measurements than those taken at the daily timescale. The results also showed a high average correlation coefficient r with in-situ EC observations at all sites, at daily r = 0.71, monthly r = 0.86, and annual r = 0.79 time scales in addition to ET season-dependent characteristics for satellite estimation errors. The results presented here contribute to further assessment of the quality and uncertainty of GLEAM ET products, which may benefit future advancements in the ET algorithm and its product quality.

As one of the current mainstream satellite precipitation estimates, the Global Satellite Mapping of Precipitation (GSMaP) system of Japan has been developed to produce high-precision and high-resolution global rainfall products by integrating almost all of the available precipitation-related satellite sensors. To quantify the error features of GSMaP estimates and understand their hydrological potentials at short temporal scale, three widely used GSMaP products (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) were comprehensively investigated at 1 hourly and 0.1° × 0.1° resolutions over nine major basins of China. Assessment results show that GSMaP_NRT apparently underestimates the rainfall amounts, while GSMaP_MVK with both forward and backward propagation processes algorithm can effectively capture the most rainfall events and has the lower error and bias. GSMaP_Gauge displays the best error stability and error structure over most basins of China and also exhibits stronger rain-rate dependencies. However, its unexpected positive biases in southeastern basins, which mainly come from the overestimation at lower rain rates, still need to improve further in future developments. We expected the results documented here can both provide the retrieval developers with some valuable references and offer hydrologic users of GSMaP data a better understanding of their error features and potential utilizations for various hydrological applications.

The high resolution satellite precipitation products bear great potential for large-scale drought monitoring, especially for those regions with sparsely or even without gauge coverage. This study focuses on utilizing the latest Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA 3B42V7) data for drought condition monitoring in the Weihe River Basin (0.135×106 km2). The accuracy of the monthly TMPA 3B42V7 satellite precipitation data was firstly evaluated against the ground rain gauge observations. The statistical characteristics between a short period data series (1998–2013) and a long period data series (1961–2013) were then compared. The TMPA 3B42V7-based SPI (Standardized Precipitation Index) sequences were finally validated and analyzed at various temporal scales for assessing the drought conditions. The results indicate that the monthly TMPA 3B42V7 precipitation is in a high agreement with the rain gauge observations and can accurately capture the temporal and spatial characteristics of rainfall within the Weihe River Basin. The short period data can present the characteristics of long period record, and it is thus acceptable to use the short period data series to estimate the cumulative probability function in the SPI calculation. The TMPA 3B42V7-based SPI matches well with that based on the rain gauge observations at multiple time scales (i.e., 1-, 3-, 6-, 9-, and 12-month) and can give an acceptable temporal distribution of drought conditions. It suggests that the TMPA 3B42V7 precipitation data can be used for monitoring the occurrence of drought in the Weihe River Basin.

This study investigated the accuracy and drought monitoring application of two newly-released long-term satellite precipitation products (i.e., the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR and the Climate Hazards Group Infrared Precipitation with Station data version 2.0 CHIRPS) and the latest reanalysis precipitation product (i.e., the Global Precipitation Climatology Centre full data monthly version 2018, GPCC 8.0). Satellite- and reanalysis-based precipitation sequences and standardized precipitation indices (SPIs) were compared comprehensively with background estimates of the China Gauge-based Daily Precipitation Analysis (CGDPA) dataset at spatial and multiple temporal scales over the Yellow River Basin (YRB) in China during 1983&ndash, 2016. Results indicated the PERSIANN-CDR, CHIRPS and GPCC 8.0 precipitation products generally had good consistency with CGDPA (correlation coefficient, CC &gt, 0.78). At spatial, monthly and seasonal scales, the consistency between GPCC 8.0 and CGDPA precipitation was found to be better than that of the two satellite products. Due to their good performance at the spatiotemporal scale, the satellite with long-time record and GPCC 8.0 products were evaluated and compared with CGDPA to derive SPI-1 (1-month SPI), SPI-3 (3-month SPI), and SPI-12 (12-month SPI) for drought monitoring in the YRB. The results showed that they had good application in monitoring droughts (CC &gt, 0.65 at spatial scale, CC &gt, 0.84 at temporal scale). The historical drought years (i.e., 1997, 1999, and 2006) and the spatial distribution of drought area in August 1997 were captured successfully, but the performance of GPCC 8.0 was found to be the best. Overall, GPCC 8.0 is considered best suited to complement precipitation datasets for long-term hydrometeorological research in the YRB.

Understanding the patterns and mechanisms of precipitation variations is important for assessing flood and drought risks and for ensuring sustainable water use. Here, we analyzed the characteristics of annual precipitation changes in eight subregions of China using the Mann&ndash, Kendall test based on daily precipitation data from 774 rain gauge stations during 1961&ndash, 2017. Then, we used the monthly precipitation concentration index (PCI) and daily concentration index (CI) to analyze precipitation concentrations. The results are as follows: (1) PCI and CI in northern China generally decreased with time, indicating a uniform precipitation distribution. Notably, the annual precipitation increased significantly in Xinjiang and the Qinghai-Tibet Plateau, which could alleviate future drought. (2) PCI increased and CI decreased in the plain regions of the Yangtze River and Southeast China, exhibiting high CI values with marked increases of annual precipitation. Such heavy rainfall events combined with high rainfall concentrations could increase the flood risk. (3) A significant PCI increase and CI decrease occurred in Southwest China, where annual precipitation decreased significantly. Regular rainfall decreased notably, which could increase the likelihood of drought hazards. (4) Overall, both indices showed negative trends at most stations, precipitation distribution was generally more uniform over China. These findings improve our understanding of extreme rainfall evolution and water resource distribution over China. Furthermore, PCI and CI can serve as warning tools for disaster control and water resource management.

Flash droughts, with rapid onset and strong intensity, have attracted much attention in recent literature due to their devastating socio-agricultural impacts. Based on the microwave remote sensing soil moisture data released by the European Space Agency Climate Change Initiative program (ESA CCI SM), flash droughts were identified by focusing on the attenuation rate of weekly soil moisture percentile. On this basis, the spatiotemporal patterns of flash drought frequency, their evolution process combined with meteorological conditions, and the relationship with seasonal droughts were analyzed. Results showed that the frequency of flash drought occurrence over China during 2000–2016 mainly varied between 6% and 20%. Spatially, such events were most likely to occur in the northern and southern region, and the Qinghai-Tibet Plateau showed the lowest frequency of occurrence. Influenced by the complex roles of negative anomalies of precipitation and relative humidity, along with positive anomalies of potential evapotranspiration (PET) and temperature, the southwest region suffered three severe drought attacks in summer 2006. In addition, a coexisting relationship was also observed between flash droughts and seasonal droughts. Initiated by the rapid reduction of soil moisture, the northern, northeastern, southern, and southwestern areas were more vulnerable to droughts with duration no more than 12 weeks. In contrast, the southern China were likely to suffer longer periods of droughts (more than 12 weeks) in spring (April to May), while for southwestern regions such type of droughts were mainly concentrated in summer (June and July).

Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with important precipitation data sources for hydrological applications in sparsely gauged or ungauged basins. This study proposes a framework for statistical and hydrological assessment of the TRMM- and GPM-era satellite-based precipitation products (SPPs) in both near- and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Myanmar. It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). Statistical assessment at grid and basin scales shows that 3B42RT generally presents higher quality, followed by IMERG-F and 3B42V7. IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE largely underestimate total precipitation, and the three GSMaP SPPs have the lowest accuracy. Given that 3B42RT demonstrates the best quality among the evaluated four near-real-time SPPs, 3B42RT obtains satisfactory hydrological performance in 3-hourly flood simulation, with a Nash–Sutcliffe model efficiency coefficient (NSE) of 0.868, and it is comparable with the rain-gauge-based precipitation data (NSE = 0.895). In terms of post-real-time SPPs, IMERG-F and 3B42V7 demonstrate acceptable hydrological utility, and IMERG-F (NSE = 0.840) slightly outperforms 3B42V7 (NSE = 0.828). This study found that IMERG-F demonstrates comparable or even slightly better accuracy in statistical and hydrological evaluations in comparison with its predecessor, 3B42V7, indicating that GPM-era IMERG-F is the reliable replacement for TRMM-era 3B42V7 in the study area. The GPM scientific community still needs to further refine precipitation retrieving algorithms and improve the accuracy of SPPs, particularly IMERG-E, IMERG-L, and GSMaP SPPs, because ungauged basins urgently require accurate and timely precipitation data for flood control and disaster mitigation.

Climate change directly impacts the hydrological cycle via increasing temperatures and seasonal precipitation shifts, which are variable at local scales. The water resources of the Upper Yangtze River Basin (UYRB) account for almost 40% and 15% of all water resources used in the Yangtze Basin and China, respectively. Future climate change and the possible responses of surface runoff in this region are urgent issues for China’s water security and sustainable socioeconomic development. This study evaluated the potential impacts of future climate change on the hydrological regimes (high flow (Q5), low flow (Q95), and mean annual runoff (MAR)) of the UYRB using global climate models (GCMs) and a variable infiltration capacity (VIC) model. We used the eight bias-corrected GCM outputs from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) to examine the effects of climate change under two future representative concentration pathways (RCP4.5 and RCP8.5). The direct variance method was adopted to analyze the contributions of precipitation and temperature to future Q5, Q95, and MAR. The results showed that the equidistant cumulative distribution function (EDCDF) can considerably reduce biases in the temperature and precipitation fields of CMIP5 models and that the EDCDF captured the extreme values and spatial pattern of the climate fields. Relative to the baseline period (1961–1990), precipitation is projected to slightly increase in the future, while temperature is projected to considerably increase. Furthermore, Q5, Q95, and MAR are projected to decrease. The projected decreases in the median value of Q95 were 21.08% to 24.88% and 16.05% to 26.70% under RCP4.5 and RCP8.5, respectively; these decreases were larger than those of MAR and Q5. Temperature increases accounted for more than 99% of the projected changes, whereas precipitation had limited projected effects on Q95 and MAR. These results indicate the drought risk over the UYRB will increase considerably in the future.

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.

Focusing on the shortages of moisture estimation and time scale in the self-calibrating Palmer drought severity index (scPDSI), this study proposed a new Palmer variant by introducing the Variable Infiltration Capacity (VIC) model in hydrologic accounting module, and modifying the standardization process to make the index capable for monitoring droughts at short time scales. The performance of the newly generated index was evaluated over the Yellow River Basin (YRB) during 1961&ndash, 2012. For time scale verification, the standardized precipitation index (SPI), and standardized precipitation evapotranspiration index (SPEI) at a 3-month time scale were employed. Results show that the new Palmer variant is highly correlated with SPI and SPEI, combined with a more stable behavior in drought frequency than original scPDSI. For drought trend detection, this new index is more inclined to reflect comprehensive moisture conditions and reveals a different spatial pattern from SPI and SPEI in winter. Besides, two remote sensing products of soil moisture and vegetation were also employed for comparison. Given their general consistent behaviors in monitoring the spatiotemporal evolution of the 2000 drought, it is suggested that the new Palmer variant is a good indicator for monitoring soil moisture variation and the dynamics of vegetation growth.

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

The Climate Hazards group Infrared Precipitation (CHIRP) and with Stations (CHIRPS) datasets are two new quasi-global (50oS–50oN), high-resolution (0.05° × 0.05°), long-term (1981-present) precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. This study investigates, for the first time, the global performance of CHIRP and CHIRPS against the gauge-based GPCC (Global Precipitation Climatology Centre) data at monthly scale using 36 complete years of data record (1981–2016). Global assessment results indicate that both CHIRP and CHIRPS have negative biases (−5.93% for CHIRP and −2.01% for CHIRPS) before 2000, while this systematic underestimation was effectively removed after 2000. Global analyses also show that the gauge-adjusted CHIRPS estimates generally represent a substantial improvement over CHIRP due to gauge-based bias correction. With respect to regional statistics, temporal analysis and intensity distribution, the gauge-adjusted CHIRPS estimates agree well with GPCC and outperforms CHIRP over most regions, such as the United States, Europe, Africa, Australia and South America. However, southeast China is an exception. Over this region, CHIRPS has a systematic overestimation of 5.55% against GPCC during 2000–2016, especially for spring and summer months, while such positive biases were not found for the pure satellite-derived CHIRP. Possible causes for the discrepancy between these two satellite products over the global and regional scales were further discussed and analyzed. The results reported here will both provide the algorithm developers of CHIRP and CHIRPS with some valuable information and offer the hydrometeorological users a better understanding of their error characteristics and potential limits for various hydrological applications from the global perspective.

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.

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.

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.

The main objective of this study was to evaluate four latest global high-resolution satellite precipitation products (TMPA 3B42RT, CMORPH, TMPA 3B42V7, and CMORPH_adj) against gauge observations of the Yellow River Basin from March 2000 to December 2012. The assessment was conducted with several commonly used statistical indices at daily and monthly scales. Results indicate that 3B42V7 and CMORPH_adj perform better than the near real-time products (3B42RT and CMORPH), particularly the 3B42V7 product. The adjustment by gauge data significantly reduces the systematic biases in the research products. Regarding the near real-time datasets, 3B42RT overestimates rainfall over the whole basin, while CMORPH presents a mixed pattern with negative and positive values of relative bias in low- and high-latitude regions, respectively, and CMORPH performs better than 3B42RT on the whole. According to the spatial distribution of statistical indices, these values are optimized in the southeast and decrease toward the northwest, and the trend is similar for the spatial distribution of the mean annual precipitation during the period from 2000 to 2012. This study also reveals that all the four products can effectively detect rainfall events. This study provides useful information about four mainstream satellite products in the Yellow River Basin, and the findings can facilitate the use of global precipitation measurement (GPM) data in the future.

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.

The upper region of the Yangtze River, as the freshwater source for the middle and lower Yangtze River, is highly vulnerable to climate change. Understanding and projecting the spatiotemporal variability of extreme flows in this region are key to creating appropriate adaptation policies and strategies. In this study, climate change signals were scaled from seven bias-corrected CMIP5 models and were used to force a variable infiltration capacity (VIC) model to quantify and assess the impact of climate change on the hydrology of the region. The respective contribution of precipitation (P) and temperature (T) to evapotranspiration (ET) and hydrological regimes (annual mean runoff, high flow, and low flow) were also quantified using the analysis of variance method (ANOVA). For the scenario period (2031–2060), the projected runoff is projected to increase in vary between 11.33% and 64.08% in winter, while increases in summer range from 8.64% to 40.97%. The changes in ET are different depending on the climate mode used; these increase during winter and decrease in summer under the influence of temperature. In addition, we found that low flows are projected to decrease while the mean annual runoff will increase in the future period. These projected changes demonstrated that temperature effects dominate in the study area for the future period under the representative concentration pathway 8.5 scenario.

For winter wheat ( L.) that grows during the rainless season, the contribution of groundwater to the root zone (CGWR) is an important water source for growth. Accurately estimating the CGWR is important for making decisions on irrigation and discharge for winter wheat fields and preventing water pollution. Because winter wheat slows and even stops root growth over winter, so the fixed root density distribution function that is suitable for soybean [ (L.) Merr.] may not suit winter wheat calculations. Therefore, when estimating the CGWR of winter wheat with the numerical model HYDRUS-1D, the root density distribution function should first be determined from two types: fixed or piecewise root density distribution functions. Based on field observations and local weather data for 2004–2005 and 2005–2006, HYDRUS-1D was evaluated with different root density distribution functions by comparing simulated and measured root zone soil water contents. The evaluated model with the most suitable distribution function was used to estimate the daily CGWR for six winter wheat hydrological growth seasons. For all seasons, winter wheat growth was assumed to be at its optimal state. The main results were: (i) a piecewise root density distribution function was the most suitable for winter wheat; (ii) simulated seasonal CGWRs were 154, 128, and 136 mm in the dry, normal, and wet seasons, respectively; and (iii) the CGWR for winter wheat transpiration was about 58, 47, and 69% of the total in dry, normal, and wet seasons, respectively. Overall, we concluded that accurate description of the root density distribution was helpful to estimate the CGWR.