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Kan, G., He, X., Ding, L., Li, J., Hong, Y., Ren, M., Lei, T., Liang, K., Zuo, D., & Huang, P. (March-April, 2017). Daily streamflow simulation based on the improved machine learning method. Water Technology and Sciences (in Spanish), 8(2), 51-60. Daily streamflow simulation has usually been implemented by conceptual or distributed hydrological models. Nowadays, hydrological data, which can be easily obtained from automatic measuring systems, are more than enough. Therefore, machine learning turns into an effective and popular tool which is highly suited for the streamflow simulation task. In this paper, we propose an improved machine learning method referred to as PKEK model based on the previously proposed NU-PEK model for the purpose of generating daily streamflow simulation results with better accuracy and stability. Comparison results between the PKEK model and the NU-PEK model indicated that the improved model has better accuracy and stability and has a bright application prospect for daily streamflow simulation tasks.

Greening of Northern China and the Tibetan Plateau (NCTP) has been observed by increases in the remotely sensed leaf area index (LAI), driven primarily by CO2 fertilization effects, anthropogenic warming, and the implementation of ecological restoration programs. Continued growth of LAI throughout the 21st century is also projected by the Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios. However, the question of whether local water resources can sustain ongoing grassland greening has not been adequately investigated. Here we assessed the sustainability of water resources under grassland greening across NCTP under various climate scenarios using water yield (WY, defined as precipitation minus actual evapotranspiration) as the key metric. Unexpectedly, we observe the coexistence of increases in LAI and WY in most of NCTP. In a warming climate with increasing precipitation and CO2, we find that grasses maintain high water use efficiency to sustain their growth, contributing to continued local water resource availability. Thus, livestock production may also continue to increase under the simultaneous growth of LAI and WY in the future. Plain Language Summary: The greening of Northern China and the Tibetan Plateau (NCTP) has been observed through increases in remotely sensed leaf area index (LAI), driven primarily by ecological restoration programs, climate change, and CO2 fertilization effects. While continued growth of LAI throughout the 21st century is projected by Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios, there is limited understanding of whether local water resources may sustain the ongoing process of grassland greening in the context of more frequent and severe drought conditions under global warming. Using three schemes considering plant physiological and hydrological feedbacks toward climate change, our study unveils changes in regional water sustainability (i.e., water yield) under grassland greening across NCTP over the past four decades and up to the end of the 21st century. Surprisingly, we observe the coexistence of future grassland greening and enhanced water resource availability in most of NCTP. Moreover, increased precipitation from atmospheric transport and evaporation recycling, coupled with a warming climate and rising CO2 levels, enables grasses to maintain high water use efficiency, sustaining their growth and contributing to the availability of local water resources. Additionally, projections suggest that the area occupied by livestock production across NCTP may continue to grow due to the simultaneous improvement in grassland and water resource availability. Key Points: Grassland greening and enhanced water resource availability may coexist in most of NCTP in a warming climateEffects of plant physiological and hydrological factors along grassland greening on water yield in the future were analyzedThe positive influence of enhanced water resource availability and ongoing grassland greening on future livestock production was quantified [ABSTRACT FROM AUTHOR]

The accuracy and reliability of satellite precipitation products (SPPs) are important for their applications. In this study, four recently presented SPPs, namely, GSMaP_Gauge, GSMaP_NRT, IMERG, and MSWEP, were evaluated against daily observations from 2344 gauges of mainland China from 2001 to 2018. Bivariate Moran's I (BMI), a method that has demonstrated high applicability in characterizing spatial correlation and dependence, was first used in research to assess their spatial correlations with gauge observations. Results from four conventional indices indicate that MSWEP exhibited the best performance, with a correlation coefficient of 0.78, an absolute deviation of 1.6, a relative bias of −5%, and a root mean square error of 5. Six precipitation indices were selected to further evaluate the spatial correlation between the SPPs and gauge observations. MSWEP demonstrated the best spatial correlation in annual total precipitation, annual precipitation days, continuous wet days, continuous dry days, and very wet day precipitation with global BMI of 0.95, 0.78, 0.78, 0.78, and 0.87, respectively. Meanwhile, IMERG showed superiority in terms of maximum daily precipitation with a global BMI value of 0.91. IMERG also exhibited superior performance in quantifying the annual count days that experience precipitation events exceeding 25 mm and 50 mm, with a global BMI of 0.96, 0.92. In four sub-regions, these products exhibited significant regional characteristics. MSWEP demonstrated the highest spatial correlation with gauge observations in terms of total and persistent indices in the four sub-regions, while IMERG had the highest global BMI for extreme indices. In general, global BMI can quantitatively compare the spatial correlation between SPPs and gauge observations. The Local Indicator of Spatial Association (LISA) cluster map provides clear visual representation of areas that are significantly overestimated or underestimated. These advantages make BMI a suitable method for SPPs assessment. [ABSTRACT FROM AUTHOR]

The accurate estimation of urban extreme precipitation is essential for urban design and risk management, which is hard for developing countries, due to the fast urbanization and sparse rain gauges. Satellite precipitation products (SPPs) have emerged as a promising solution. Not only near real-time SPPs can provide critical information for decision making, but post-processed SPPs can also offer essential information for climate change adaption, risk management strategy development, and related fields. However, their ability in urban extreme precipitation estimation has not been examined in detail. This study presents a comprehensive evaluation of four recent SPPs that are post-processed, including IMERG, GSMaP_Gauge, MSWEP, and CMFD, for their ability to capture urban extreme precipitation in mainland China at the national, city, and inner-city scales. The performance of the four SPPs was assessed using daily observations from the 821 urban gauges from 2001 to 2018. The assessment includes: (1) the extreme precipitation estimates from the four SPPs in the total urbanized areas of mainland China were evaluated using correlation coefficients (CC), absolute deviation (AD), relative deviation (RB), and five extreme precipitation indices; (2) The extreme precipitation estimates over 21 Chinese major cities were assessed with the two most important extreme indices, namely the 99th percentile of daily precipitation on wet days (R99) and total precipitation when daily precipitation exceeding R99 (R99TOT); and (3) Bivariate Moran's I (BMI) was adopted to assess the inner-city spatial correlation of R99 and R99TOT between SPPs and gauge observations in four major cities with most gauges. The results indicate that MSWEP has the highest CC of 0.79 and the lowest AD of 1.61 mm at the national scale. However, it tends to underestimate urban precipitation, with an RB of −8.5%. GSMaP_Gauge and IMERG performed better in estimating extreme values, with close extreme indices with gauge observations. According to the 21 major cities, GSMaP_Gauge also shows high accuracy in estimating R99 and R99TOT values, with the best RB and AD in these cities, while CMFD and MSWEP exhibit the highest CC values for R99 and R99TOT, respectively, indicating a strong correlation between their estimates and those obtained from gauge observations. At the inner-city scale, MSWEP shows advantages in monitoring the spatial distribution of urban extreme precipitation in most of cities. The study firstly provided the multiscale assessment of urban extreme precipitation by SPPs over mainland China, which is useful for their applications. [ABSTRACT FROM AUTHOR]

Terrestrial water storage (TWS) is of great importance to the global water and energy budget, which modulates the hydrological cycle and then determines the spatiotemporal distributions of water resources availability. The Tibetan Plateau is the birthplace of the Yangtze, Yellow, and Lancang–Mekong River, where the water resources are directly related to the life of the Eastern and Southeastern Asian people. Based on multi-source datasets during the period 1981–2015, the long-term spatiotemporal variabilities of the TWS over the Tibetan Plateau were investigated by the Sen's slope and Mann–Kendall test trend analysis methods; the changing mechanisms were explored from two perspectives of components analysis and the hydrological cycle. The water conservation capacity of vegetation in the alpine mountainous areas was also discussed by geostatistical methods such as correlation analysis, extracted by attributes and zonal statistics. The results show that the TWS of the Tibetan Plateau increased with the speed of 0.7 mm/yr as the precipitation accumulated and the glaciers melted during the period 1981–2015. The TWS values were low and generally present a trend of obvious accumulation over the northern Tibetan Plateau, while the high and decreasing values were distributed in the south of Tibetan Plateau. The results of the components analysis indicate that the TWS mainly consisted of soil moisture at one-fourth layers, which are 0–200 cm underground in most areas of the Tibetan Plateau. The precipitation is mainly lost through evapotranspiration over the northern Tibetan Plateau, while in the northwestern corner of the Tibetan Plateau, the Himalayas, and northeastern Yarlung Zangbo River basin, the runoff coefficients were larger than 1.0 due to the influence of snow melting. In the alpine mountains, different climate and vegetation conditions have complex effects on water resources. The results are helpful for understanding the changing mechanism of water storage over the Tibetan Plateau and have scientific meaning for the development, utilization, and protection of regional water resources. [ABSTRACT FROM AUTHOR]

The Generalized Likelihood Uncertainty Estimation (GLUE) method has been thrived for decades, huge number of applications in the field of hydrological model have proved its effectiveness in uncertainty and parameter estimation. However, for many years, the poor computational efficiency of GLUE hampers its further applications. A feasible way to solve this problem is the integration of modern CPU-GPU hybrid high performance computer cluster technology to accelerate the traditional GLUE method. In this study, we developed a CPU-GPU hybrid computer cluster-based highly parallel large-scale GLUE method to improve its computational efficiency. The Intel Xeon multi-core CPU and NVIDIA Tesla many-core GPU were adopted in this study. The source code was developed by using the MPICH2, C++ with OpenMP 2.0, and CUDA 6.5. The parallel GLUE method was tested by a widely-used hydrological model (the Xinanjiang model) to conduct performance and scalability investigation. Comparison results indicated that the parallel GLUE method outperformed the traditional serial method and have good application prospect on super computer clusters such as the ORNL Summit and Sierra of the TOP500 super computers around the world. [ABSTRACT FROM AUTHOR]

Landscape heterogeneity is a comprehensive reflection of spatial patch and gradient, which is an important feature of landscape pattern. The China land use/cover dataset (CLUD) with 1 km spatial resolution was used in this study to investigate the dynamic changes of land use/cover in the Yarlung Tsangpo River basin (YTRB) of China from 1980 to 2015 through the land use transition matrix and dynamic degrees. The semi‐variogram model of the study area was constructed at different scales to determine the landscape characteristic scale of the YTRB. The traditional landscape index method and moving window method were used to investigate the watershed landscape pattern at the class and landscape levels, respectively. The results showed that the overall change in land use over the YTRB was not significant during the period 1980–2015, whereas the relative change in each type of land use was significant. Permanent glacier‐snow significantly degraded by 22.72%, while built‐up areas increased by 106.38%. The degree of the landscape fragmentation and diversity gradually increased from upstream to downstream. The unused land has the highest degree of fragmentation, and the agglomeration degree of urban land patches increased. The result of land use dynamic degree indicated that natural factors were the main causes of changes in land use during the first sub‐period 1980–2000, while human activities were the major drivers of changes in land use during the second sub‐period 2000–2015. The results can provide important information on the impact of regional development and environmental governance policies on the changes in land use/cover and landscape in Tibet. [ABSTRACT FROM AUTHOR]

Seasonal snow cover in mountainous areas is a key source of water for downstream regions. The snow cover area (SCA) in the Northern Hemisphere has significantly decreased during the past decades in response to global warming. Therefore, it is necessary to investigate the primary factors controlling the spatiotemporal variabilities in the SCA in high altitude regions. In this study, based on remote sensing data of SCA, temperature, and precipitation in the Yarlung Zangbo River basin, Pearson's correlation analysis was used to identify the relative importance of temperature and precipitation on the SCA variability with altitude. The results showed that temperature played a more dominant role for the SCA than precipitation at all elevation during the period 2001–2017; three threshold altitudes at high, moderate, and low elevation were identified, and these threshold altitudes are dynamic and vary with time; the threshold altitude at high elevation was 5,925 ± 125 m, below which temperature (precipitation) was negatively (positively) correlated and above which temperature (precipitation) was positively (negatively) correlated with the SCA; the threshold altitude at moderate elevation was 3,200 ± 300 m, below which temperature (precipitation) was positively (negatively) correlated and above which temperature (precipitation) was negatively (positively) correlated with the SCA; the threshold altitude in low elevation regions was 2,100 ± 200 m. This study sheds light on the dominant climatic control factors on the variability in SCA in high altitude regions. These results have major implications for SCA and water resources availability under future climate change. [ABSTRACT FROM AUTHOR]