[期刊跟踪]3月第2期2020年第6期

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[期刊跟踪]3月第2期2020年第6期 本期【期刊跟踪】选取了与水文相关的8篇文章,分别来自Water Resources Research、Environmental Research Letters、Journal of Hydrology、Science of the Total Environment、Remote Sensing等5个期刊。1. Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets作者:Lei Xu, Nengcheng Chen, Hamid Moradkhani, Xiang Zhang, Chuli Hu期刊:Water Resources Research摘要:Precipitation estimation at a global scale is essential for global water cycle simulation and water resources management. The precipitation estimation from gauge‐based, satellite retrieval, and reanalysis data sets has heterogeneous uncertainties for different areas at global land. Here, the 13 monthly precipitation data sets and the 11 daily precipitation data sets are analyzed to examine the relative uncertainty of individual data based on the developed generalized three‐cornered hat (TCH) method. The generalized TCH method can be used to evaluate the uncertainty of multiple (3) precipitation products in an iterative optimization process. A weighting scheme is designed to merge the individual precipitation data sets to generate a new weighted precipitation using the inverse error variance‐covariance matrix of TCH estimated uncertainty. The weighted precipitation is then validated using gauged data with the minimal uncertainty among all the individual products. The merged results indicate the superiority of the weighted precipitation with substantially reduced random errors over individual data sets and a state‐of‐the‐art multisatellite merged product, namely, the Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement at validated areas. The weighted data set can largely reproduce the interannual and seasonal variations of regional precipitation. The TCH‐based merging results outperform two other mean‐based merging methods at both monthly and daily scales. Overall, the merging scheme based on the generalized TCH method is effective to produce a new precipitation data set integrating information from multiple products for hydrometeorological applications.[期刊跟踪]3月第2期2020年第6期Figure 1. The spatial map of RMSE of the TCH weighted monthly precipitation and individual precipitation data sets (without gauged data) using gauged areas for validation in parts of Europe during 2003–2016引用格式:Xu Lei, Chen Nengcheng, Moradkhani Hamid, et al. (2020). Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets. Water Resources Research, 56(3).2. Spatial and Temporal Patterns in Baseflow Recession in the Continental United States作者:Arik Tashie, Tamlin Pavelsky, Ryan E. Emanuel期刊:Water Resources Research摘要:Baseflow is often treated according to a unique storage‐discharge relationship. However, recent innovations in baseflow recession analysis have allowed novel findings regarding the variability of both the stability of baseflow and its nonlinearity (i.e., the concavity of the hydrograph), as well as the regional clustering of these characteristics. We investigate spatial and temporal patterns in the character of baseflow recession for over 1,000 watersheds in the continental United States. We discover seasonal patterns in both the stability and nonlinearity of baseflow which vary systematically across large regions. Further, we relate these baseflow characteristics to their potential physical drivers, including estimates of evapotranspiration, watershed storage, the distribution of watershed storage, and precipitation. While coincident watershed storage is the best predictor of baseflow stability in many regions (particularly the Appalachian Mountains), evapotranspiration from 2 to 3 months previous is the best predictor of baseflow stability in other regions (particularly the Pacific Northwest). We also discuss the novel finding that baseflow nonlinearity has increased significantly in most watersheds across the United States since 1980.[期刊跟踪]3月第2期2020年第6期Figure 6. Statistically significant interdecadal (pre vs. post 1980) trends in streamflow stability (top left), recession flow volumes (top right), and nonlinearity (bottom left). Purple indicates decreased log(a) (i.e., increased streamflow stability), increasedQrecess(i.e., increased recession flow volumes), and decreasedb(i.e., decreased nonlinearity), while yellow indicates the reverse trend. The size of each dot represents the strength of the trend.引用格式:Arik Tashie, Tamlin Pavelsky, Ryan E. Emanuel. 2020. Spatial and Temporal Patterns in Baseflow Recession in the Continental United States. Water Resources Research, 56(3).3. Ephemeral Ponds: Are They the Dominant Source of Depression‐Focused Groundwater Recharge?作者:Edward K. P. Bam, Andrew M. Ireson, Garth van der Kamp, Jim M. Hendry期刊:Water Resources Research摘要:Depression‐focused recharge is a concept proposed to explain groundwater recharge in the prairie regions of North America. Topographic depressions in this hummocky landscape collect blowing snow and snowmelt, and occasional runoff during rainfall events. Wetland ponds that form in these depressions lose water to evaporation and infiltration. Some of this infiltration contributes to groundwater recharge, both to shallow aquifers in the weathered near‐surface, and to underlying confined intertill aquifers. Here we focus on understanding recharge to the confined aquifers, which supply water for farms and rural communities. The isotopic composition of water in these aquifers shows little or no evaporative enrichment and is inconsistent with the average isotopic composition of the ponds. This observation appears to contradict the depression‐focused recharge model. In this field study, we examine the isotopic composition of diverse types of wetland ponds and groundwater at the St. Denis National Wildlife Area, Saskatchewan, Canada. We use hydraulic head data to identify potential recharge and discharge ponds. Water in permanent recharge ponds that do not dry out every year have distinctly different isotopic signatures from the aquifers, suggesting that they cannot be the dominant source of recharge. Water in ephemeral recharge ponds, which are small and dry out quickly, have isotopic signatures identical to those of aquifers. We propose that ephemeral recharge ponds are the dominant source of depression‐focused groundwater recharge in the prairies. We discuss why permanent recharge ponds may not be the main source of groundwater recharge and summarize our findings in a revised conceptual model.[期刊跟踪]3月第2期2020年第6期Figure 1. Pond water heads (height above sea level) from 1968 to 2016 showing the elevation of the base of the pond (the x axis location) and the spill elevation. Blue shading represents water level observations during the ice‐free period. Gray shading represents inferred water levels during the winter, indicating when the ponds do and do not dry out.引用格式:Edward K. P. Bam, Andrew M. Ireson, Garth van der Kamp, et al. 2020. Ephemeral Ponds: Are They the Dominant Source of Depression‐Focused Groundwater Recharge. Water Resources Research, 56(3).4. Increasing agricultural risk to hydro-climatic extremes in India作者:Tarul Sharma, HVittal, Subhankar Karmakar and Subimal Ghosh期刊:Environmental Research Letters摘要:Indian agriculture is globally well-documented to reflect the impacts of changing climate significantly. However, climate adaptation efforts are often hindered due to the inadequate assessment of coupled human-environment interactions. In this study, we propose a novel unified country-level framework to quantify the decadal agricultural risks derived from multiple hydro-meteorological exposures and adaptive consequences. We identify, for the first time, that rice and wheat risks have increased in the recent decade, with wheat at a twofold higher magnitude than rice. Increasing crops risk is found to be predominantly driven by the decreasing number of cultivators; in particular, the wheat risk is also attributed to increasing minimum temperatures during the crop growing season. We provide convincing evidence indicating that the hydro-climatic hazards related to precipitation extremes and droughts are specifically alarming the crops risk as compared to temperature extremes. These observation-based results highlight the sensitivity of India’s agriculture and the risk associated with multiple agro-ecological and climatic components. We recommend these findings to facilitate the informed planning of adaptive measures and ensure sustainable food security of the nation.[期刊跟踪]3月第2期2020年第6期引用格式:Sharma T, Vittal H, Karmakar S, Ghosh S. Increasing agricultural risk to hydro-climatic extremes in India[J]. Environmental Research Letters, IOP Publishing, 2020, 15(3): 034010.5. GRACE: Gravity Recovery and Climate Experiment long-term trend investigation over the Nile River Basin: Spatial variability drivers作者:Emad Hasan, Aondover Tarhule期刊:Journal of Hydrology摘要:GRACE (Gravity Recovery and Climate Experiment) long-term terrestrial water storage anomaly (TWSA) is attributed to the complex interaction of climatic, physical and anthropogenic drivers. This paper, therefore, explores how different hydroclimatic and anthropogenic processes interact and combine over “space” to produce the mass variations that GRACE-TWSA detects. Using the Nile River Basin (NRB) as a case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to the TWSA in different climatic zones. The analytic method employed the long-term trends derived for both the dependent (TWSA) and independent (explanatory) variables via applying two geographically multiple regression (GMR) approaches: (i) an ordinary least square regression (OLS) model in which the contributions of all variables to TWSA variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on clustering occurrences. The models’ efficacy was investigated using standard goodness of fit diagnostics. The OLS explains that the basin at large TWSA spatial variability significantly attributed to five variables, i.e., precipitation, runoff, surface water soil moisture, and population density, (p0.0001). The OLS model, however, produced an R2 value of 0.14 with skewed standardized residuals. In contrast, the GWR model retained varying explanatory variables by different climate zone. For instance, the results showed that all nine variables contribute significantly, with varying ranking, to the trend in TWSA in the tropical zone. The evapotranspiration (ET) and population density are the only significant variables in the semiarid zone; population density contributes significantly to TWSA variability in all zones. The GWR model yielded R2 values with a median of 0.71 and normally distributed standard residuals. To evaluate the robustness of the GWR approach, the basin-wide TWSA pattern was simulated using the GWR model outputs. Herein, the GWR highlights the importance of the spatial locations to attribute the spatial variability in GRACE TSWA long-term trends. This spatial information, therefore, is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE and for gap-filling between GRACE and GRACE Follow-On (GRACE-FO) mission.[期刊跟踪]3月第2期2020年第6期Figure 1. Research Idea Map引用格式:Hasan, E., Tarhule, A., 2020. GRACE: Gravity Recovery and Climate Experiment long-term trend investigation over the Nile River Basin: Spatial variability drivers. Journal of Hydrology 124870. https://doi.org/10.1016/j.jhydrol.2020.124870.6. Sequence-based statistical downscaling and its application to hydrologic simulations based on machine learning and big data作者:Qingrui Wang, Jing Huang, Ruimin Liu, Cong Men, Lijia Guo, Yuexi Miao, Lijun Jiao, Yifan Wang, Muhammad Shoaib, Xinghui Xia期刊:Journal of Hydrology摘要:In this study, a recurrent neural network (RNN) was used to perform statistical downscaling, and its advantages were showed compared to the traditional artificial neural network (ANN). The hydrological response to the downscaled meteorological data was evaluated using the Soil and Water Assessment Tool (SWAT) model. The results indicated that the temperature downscaled in southeastern China was better than that in northwestern China, while precipitation was the opposite. Although RNN and ANN model had different feasibility in different regions of China, the performance of RNN model for maximum and minimum temperature downscaling was about 6% and 10% better than that of ANN model overall, respectively. And RNN model was better for extreme temperature conditions simulation. Regarding precipitation, the performance of RNN and ANN model was similar when simulating precipitation amount. However, the use of RNN model improved the prediction accuracy of dry and wet days. In order to improve the accuracy of extreme precipitation downscaling, a new model, RNN-RandExtreme, was proposed. Compared with the ANN and single RNN model, RNN-RandExtreme model improved the prediction accuracy of extreme precipitation by 28.32% and 16.56%, respectively. The hydrological simulation results of SWAT model showed that the RNN and RNN-RandExtreme model significantly improved the accuracy of hydrological simulations of flow and evapotranspiration compared to the ANN model. However, as the time scale became rougher (from daily to annual scale), the improvement effect of RNN and RNN-RandExtreme model would weaken. The results of this study may help improving the accuracy of statistical downscaling, and support choosing downscaling models in different areas.[期刊跟踪]3月第2期2020年第6期Figure 1. Research Idea Map.引用格式:Wang, Q., Huang, J., Liu, R., Men, C., Guo, L., Miao, Y., Jiao, L., Wang, Y., Shoaib, M., Xia, X., 2020. Sequence-based statistical downscaling and its application to hydrologic simulations based on machine learning and big data. Journal of Hydrology 124875. https://doi.org/10.1016/j.jhydrol.2020.124875.7. Homogenization and polarization of the seasonal water discharge of global rivers in response to climatic and anthropogenic effects作者:Yuanfang Chai, Yao Yue, Lin Zhang, Chiyuan Miao, Alistair G.L. Borthwick, Boyuan Zhu, Yitian Li, A.J. Dolman期刊:Science of the Total Environment摘要:We investigate global trends in seasonal water discharge using data from 5668 hydrological stations in catchments whose total drainage area accounts for 2/3 of the Earth's total land area. Homogenization of water discharge, which occurs when the gap in water discharge between dry and flood seasons shrinks significantly, affects catchments occupying 2/5 of the total land area, and is mainly concentrated in Eurasia and North America. By contrast, polarization of water discharge, associated with widening of the gap in water discharge between dry and flood seasons, occurs in catchments covering 1/6 of the land area, most notably in the Amazon Basin and river basins in West Africa. Considering the major climatic and anthropogenic controlling factors, i.e. precipitation (P), evaporation (E), glacial runoff (G), and dam operations (D), the world's river basins are classified as P, DEP, GEP, and EP types. Contributions from each controlling factor to either the homogenization or polarization of the seasonal water discharge for each type of river have been analyzed. We found that homogenization of discharge is dominated by dam operations in GDEP and DEP river basins (contributing 48% and 64%) and by homogenized precipitation in GEP and EP river basins. Evaporation and precipitation are primary factors behind the polarization of discharge, contributing 56% and 41%. This study provides a basis for a possible decision tool for controlling drought/flood disasters and for assessing and preventing ecological damage in endangered regions.[期刊跟踪]3月第2期2020年第6期引用格式:Chai Y, Yue Y, Zhang L, et al. (2020). Homogenization and polarization of the seasonal water discharge of global rivers in response to climatic and anthropogenic effects. Science of the Total Environment, 709, 136062.8. Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks作者:Guojie Wang, Mengjuan Wu, Xikun Wei, Huihui Song期刊:Remote Sensing摘要:The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment. However, there are significant limitations in the traditionally used index for water body identification. In this study, we have proposed a deep convolutional neural network (CNN), based on the multidimensional densely connected convolutional neural network (DenseNet), for identifying water in the Poyang Lake area. The results from DenseNet were compared with the classical convolutional neural networks (CNNs): ResNet, VGG, SegNet and DeepLab v3+, and also compared with the Normalized Difference Water Index (NDWI). Results have indicated that CNNs are superior to the water index method. Among the five CNNs, the proposed DenseNet requires the shortest training time for model convergence, besides DeepLab v3+. The identification accuracies are evaluated through several error metrics. It is shown that the DenseNet performs much better than the other CNNs and the NDWI method considering the precision of identification results; among those, the NDWI performance is by far the poorest. It is suggested that the DenseNet is much better in distinguishing water from clouds and mountain shadows than other CNNs.[期刊跟踪]3月第2期2020年第6期引用格式:Wang G, Wu M, Wei X, et al. Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks. Remote Sensing. 2020, 12(5): 795.1END1供稿 / 贾凯、邓越、彭凯锋、荔琢、王晓雅 蒋梓杰、王春林、邓雅文指导 / 蒋卫国 制作 / 荔琢跟踪和发布生态环境、生态系统、生态水文、湿地生态、水资源、洪水灾害、湿地资源、城市生态、城市湿地、海绵城市等方向的国内外学术研究进展、遥感和空间信息技术应用前沿资讯。生态水文遥感前沿微信号 : gh_f2514dbfc97d联系方式:jiangweiguo@bnu.edu.cn
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