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Downscaling of local daily precipitation from large-scale climatic variables is required for assessing the impact of climate change on hydrology and water resources. This study proposes wavelet transform (WT)-based Feed-Forward Neural Network (FF-NN) and Nonlinear Auto Regressive with exogenous inputs Network (NARX-NN) models for downscaling daily precipitation. The models are applied to a large river basin, the Krishna River basin, in the Indian subcontinent. Several climatic variables, including geo-potential heights, wind direction, vorticity, humidity, air temperature, mean sea level pressure, meridional velocity at surface, and 500hpa and 850hpa levels, are considered based on their statistical correlations. The results are evaluated using different performance measures and the ability of the models to capture the extreme events at five selected grid points (in different locations) having varying climatic characteristics is assessed. The performance of the proposed wavelet-based models is also compared with that of four different traditional and recent downscaling methods: Multiple Linear Regression (MLR), Statistical Downscaling Model (SDSM), Genetic Programming (GP), and Artificial Neural Networks (ANNs). The results reveal that the wavelet-based neural network models (WT-FF-NN and WT-NARX-NN) are robust compared to the other methods in terms of their ability to capture the regional precipitation patterns and the extreme events. The improvement in the wavelet-based models can be attributed to their ability to unravel the hidden relationship between the predictors and precipitation. It is also observed that there is considerable increase in the correlation between precipitation and the decomposed climatic variables. All these results suggest that wavelets aid in unravelling the relationship between local precipitation and large-scale climatic variables and improving the overall performance of the downscaling models.
Yeditha Pavan Kumar; Rathinasamy Maheswaran; Ankit Agarwal; Bellie Sivakumar. Intercomparison of downscaling methods for daily precipitation with emphasis on wavelet-based hybrid models. Journal of Hydrology 2021, 599, 126373 .
AMA StyleYeditha Pavan Kumar, Rathinasamy Maheswaran, Ankit Agarwal, Bellie Sivakumar. Intercomparison of downscaling methods for daily precipitation with emphasis on wavelet-based hybrid models. Journal of Hydrology. 2021; 599 ():126373.
Chicago/Turabian StyleYeditha Pavan Kumar; Rathinasamy Maheswaran; Ankit Agarwal; Bellie Sivakumar. 2021. "Intercomparison of downscaling methods for daily precipitation with emphasis on wavelet-based hybrid models." Journal of Hydrology 599, no. : 126373.
This study introduces a new complex networks-based method to examine the spatio-temporal connections in streamflow. The method involves reconstruction of two (or more) time series jointly in a multi-dimensional phase space using a nonlinear phase space embedding procedure and construction of the spatio-temporal streamflow network of nodes and links based on the reconstructed vectors. After this spatio-temporal network construction, the clustering property of the network is measured using the clustering coefficient, which quantifies the tendency of a network to cluster. The approach is applied to monthly streamflow time series observed at each of 639 streamflow stations in the United States. Different distance threshold values are used to identify the presence/absence of links in the streamflow network and, hence, to calculate the clustering coefficient. The clustering coefficient results help to identify the critical distance threshold and optimal embedding dimension of each streamflow time series using different distance threshold values. The optimal embedding dimensions and the clustering coefficient values of the 639 streamflow time series are also discussed in terms of the role of catchment and flow properties (drainage area, elevation, flow mean, and flow coefficient of variation). The dimensions for the 639 streamflow time series are generally found to range from 2 to 18 (but even up to 30 for a few stations), indicating a wide range of complexity in the spatio-temporal connections in streamflow across the United States. The clustering coefficient values for the 639 stations are found to be in the range of 0.53–0.99, which suggest generally strong connections. The outcomes of this study clearly indicate the usefulness of the networks-based approach for examining the spatio-temporal connections in streamflow.
Nazly Yasmin; Bellie Sivakumar. Spatio-temporal connections in streamflow: a complex networks-based approach. Stochastic Environmental Research and Risk Assessment 2021, 1 -16.
AMA StyleNazly Yasmin, Bellie Sivakumar. Spatio-temporal connections in streamflow: a complex networks-based approach. Stochastic Environmental Research and Risk Assessment. 2021; ():1-16.
Chicago/Turabian StyleNazly Yasmin; Bellie Sivakumar. 2021. "Spatio-temporal connections in streamflow: a complex networks-based approach." Stochastic Environmental Research and Risk Assessment , no. : 1-16.
This study proposes a complex networks-based method to determine the connections among the stations in a streamflow monitoring network and assess the importance of the individual stations. For implementation, 13 streamflow stations in the Pyeongchang River basin in South Korea are studied, and daily flow (discharge) data are analyzed. Three different centrality measures are employed to identify the connections in the streamflow network: degree centrality, closeness centrality, and betweenness centrality. The links between the nodes can significantly change depending upon the centrality method used and the threshold considered. Therefore, an integrated centrality method is proposed using a Bayesian network. The integrated centrality results show that stations situated along the main stream in the middle of the basin have high centrality, while the tributary stations have low centrality. To assess the importance of stations, the integrated centrality is used with community-based clusters. This assessment on the importance of the individual streamflow stations through their centrality is useful to establish strategies for their effective and efficient maintenance.
Hongjun Joo; Hung Soo Kim; Soojun Kim; Bellie Sivakumar. Complex networks and integrated centrality measure to assess the importance of streamflow stations in a River basin. Journal of Hydrology 2021, 598, 126280 .
AMA StyleHongjun Joo, Hung Soo Kim, Soojun Kim, Bellie Sivakumar. Complex networks and integrated centrality measure to assess the importance of streamflow stations in a River basin. Journal of Hydrology. 2021; 598 ():126280.
Chicago/Turabian StyleHongjun Joo; Hung Soo Kim; Soojun Kim; Bellie Sivakumar. 2021. "Complex networks and integrated centrality measure to assess the importance of streamflow stations in a River basin." Journal of Hydrology 598, no. : 126280.
Wetlands in urban ecosystems provide significant environmental benefits. In the present study, the concept of urban constructed wetland development is studied from the viewpoint of urban planning with dynamic water level orifice setting controller. A two-step modelling procedure is carried out: (1) development of a hybrid model, by coupling a well-established two-dimensional hydrodynamic model (International River Interface Cooperative, iRIC) with a one-dimensional physically-based, distributed-parameter model (Storm Water Management Model, SWMM), to compute and map flood scenarios and to identify the flood-prone areas; and (2) use of SWMM to simulate the water inflow to the proposed constructed wetland, which acts as a cushion for storing excess flood water. The proposed methodology is implemented on the Jahangirpuri drain catchment located in Delhi, India. Results show that the hybrid model is effective, and the simulations are observed to be in good agreement with the recorded data, which assist in detecting the flood-prone areas. Further, an estimation of the impact of the proposed constructed wetland on catchment hydrology indicates an overall reduction of 23% in flooding adjacent to the channel with a significant reduction in backflow as well as water depth in the drain. The flapgate at the outlet of the wetland helps in maintaining the desired water depth in the wetland. The outcomes of this study will assist the hydrologists and administrators in urban stormwater management and planning to mitigate the impact of floods in urban watersheds.
Satish Kumar; Ankit Agarwal; Vasant Govind Kumar Villuri; Srinivas Pasupuleti; Dheeraj Kumar; Deo Raj Kaushal; Ashwin Kumar Gosain; Axel Bronstert; Bellie Sivakumar. Constructed wetland management in urban catchments for mitigating floods. Stochastic Environmental Research and Risk Assessment 2021, 1 -20.
AMA StyleSatish Kumar, Ankit Agarwal, Vasant Govind Kumar Villuri, Srinivas Pasupuleti, Dheeraj Kumar, Deo Raj Kaushal, Ashwin Kumar Gosain, Axel Bronstert, Bellie Sivakumar. Constructed wetland management in urban catchments for mitigating floods. Stochastic Environmental Research and Risk Assessment. 2021; ():1-20.
Chicago/Turabian StyleSatish Kumar; Ankit Agarwal; Vasant Govind Kumar Villuri; Srinivas Pasupuleti; Dheeraj Kumar; Deo Raj Kaushal; Ashwin Kumar Gosain; Axel Bronstert; Bellie Sivakumar. 2021. "Constructed wetland management in urban catchments for mitigating floods." Stochastic Environmental Research and Risk Assessment , no. : 1-20.
Describing the specific details and textures implicit in real-world hydro-climatic data sets is paramount for the proper description and simulation of variables such as precipitation, streamflow, and temperature time series. To this aim, a couple of decades ago, a deterministic geometric approach, the so-called fractal-multifractal (FM) method,1,2 was introduced. Such is a holistic approach capable of faithfully encoding (describing)3, simulating4, and downscaling5 hydrologic records in time, as the outcome of a fractal function illuminated by a multifractal measure. This study employs the FM method to generate ensembles of daily precipitation and temperature sets obtained from global circulation models (GCMs). Specifically, this study uses data obtained via ten GCM models, two sets of daily records, as implied from the past, over a year, and three sets projected for the future, as downscaled via localized constructed analogs (LOCA) for a couple of sites in California. The study demonstrates that faithful representations of all sets may be achieved via the FM approach, using encodings relying on 10 and 8 geometric (FM) parameters for rainfall and temperature, respectively. They result in close approximations of the data's histogram, entropy, and autocorrelation functions. By presenting a sensitivity study of FM parameters' for historical and projected data, this work concludes that the FM representations are useful for tracking and foreseeing the records' complexity6 in the past and the future and other applications in hydrology such as bias correction.
References
Mahesh Lal Maskey; David Joseph Serrano Suarez; Joshua H. Viers; Josue Medellin-Azuara; Bellie Sivakumar; Laura Elisa Garza Diaz. Fractal-multifractal ensembles of downscaled precipitation and temperature sets as implied by climate models. 2021, 1 .
AMA StyleMahesh Lal Maskey, David Joseph Serrano Suarez, Joshua H. Viers, Josue Medellin-Azuara, Bellie Sivakumar, Laura Elisa Garza Diaz. Fractal-multifractal ensembles of downscaled precipitation and temperature sets as implied by climate models. . 2021; ():1.
Chicago/Turabian StyleMahesh Lal Maskey; David Joseph Serrano Suarez; Joshua H. Viers; Josue Medellin-Azuara; Bellie Sivakumar; Laura Elisa Garza Diaz. 2021. "Fractal-multifractal ensembles of downscaled precipitation and temperature sets as implied by climate models." , no. : 1.
The South Asian summer monsoon (SASM) system is one of the most energetic regional monsoon systems. Its onset and demise timings determine the propagation, duration, and magnitude of precipitation through thermodynamic and dynamic processes in the SASM-prevailing areas. Particularly, anomalous onsets and demises of the SASM could generate a large anomaly in precipitation and serious water-related disasters over the SASM-prevailing areas.
The South-Central Tibetan Plateau (SCTP), known as the “Asian water tower”, is the origin of several major Asian rivers, including the Yellow River, Yangtze River, Brahmaputra River, Mekong River, and the Indus River, providing a huge amount of freshwater for ecosystems and billions of people in Asia. It is widely known that the SCTP is controlled by the SASM system in summer, accounting for approximately 60% of annual precipitation, but with significant spatiotemporal heterogeneity due to the complex topographic and geographic conditions. Presently, most studies have focused on the effects and physical causes of the linear trend of SASM onset over the SCTP. However, little attention has been paid to the question as to how both anomalous onset and anomalous demise of the SASM influence the interannual precipitation variation in this region. In particular, the spatial manifestation of thermodynamic and dynamic mechanisms for the interannual precipitation variation is largely unknown. Adequate knowledge about these mechanisms is critical for sustainable freshwater management and water disasters control in this region and surrounding areas.
These call a detailed study to investigate the influences of the early and late onset (demise) of the SASM system on the interannual variations in precipitation and their underlying mechanisms over the SCTP. In this study, we mainly clarify the following key questions: (1) How do the onset and demise of the SASM control the interannual variations in precipitation over the SCTP? (2) Is there an asymmetric effect of the SASM on SCTP precipitation between its onset and demise, and between its early and late onset (demise)? and (3) What are the underlying mechanisms that are responsible for the variations in interannual precipitation? The results would help improve our understanding of the SASM-precipitation relationship over the SCTP and alleviation of water-related disasters in the region.
Yanxin Zhu; Yan-Fang Sang; Deliang Chen; Bellie Sivakumar; Donghuan Li. How does the South Asian summer monsoon anomaly influence the interannual variations in precipitation over the South-Central Tibetan Plateau. 2021, 1 .
AMA StyleYanxin Zhu, Yan-Fang Sang, Deliang Chen, Bellie Sivakumar, Donghuan Li. How does the South Asian summer monsoon anomaly influence the interannual variations in precipitation over the South-Central Tibetan Plateau. . 2021; ():1.
Chicago/Turabian StyleYanxin Zhu; Yan-Fang Sang; Deliang Chen; Bellie Sivakumar; Donghuan Li. 2021. "How does the South Asian summer monsoon anomaly influence the interannual variations in precipitation over the South-Central Tibetan Plateau." , no. : 1.
In rainfall forecasting, selection of an appropriate gauging station to forecast and identification of the appropriate explanatory variables are nontrivial tasks due to the complexity of the physical processes involved and spatiotemporal variability. In this study, for the first time, the concept of transfer entropy is coupled with the complex network analysis for rainfall forecasting by determining the nonlinear directional relationship between the stations. The proposed methodology involves determining the directional relationship between the stations in a given basin, and defining the most influenced station by using the node strength and directed clustering coefficient that are specifically developed for directed-weighted networks. Further, the obtained information flow within the basin is utilized to produce current monthly forecasts of the most influenced station. The methodology is implemented for rainfall forecasting in the Western Mediterranean Basin, Turkey, considering monthly total rainfall data from seven stations. The results indicate that the proposed methodology is useful and effective in the identification of the appropriate station to forecast and the relevant explanatory variables to serve as inputs for the artificial neural networks (ANNs) model. The results from the proposed methodology are compared with those from two widely employed input determination approaches (i.e. using rainfall from the most-correlated station in the basin and using rainfall from all the other stations). It is found that the proposed methodology significantly improves the forecasting performance of the ANN model. The results obtained in this study have broad implications for designing optimal rain gauge density, identification of the complexity of the rain gauge network structure, and interpolation (or extrapolation) of hydrological data for ungauged locations.
Hakan Tongal; Bellie Sivakumar. Forecasting rainfall using transfer entropy coupled directed–weighted complex networks. Atmospheric Research 2021, 255, 105531 .
AMA StyleHakan Tongal, Bellie Sivakumar. Forecasting rainfall using transfer entropy coupled directed–weighted complex networks. Atmospheric Research. 2021; 255 ():105531.
Chicago/Turabian StyleHakan Tongal; Bellie Sivakumar. 2021. "Forecasting rainfall using transfer entropy coupled directed–weighted complex networks." Atmospheric Research 255, no. : 105531.
Catchment classification is useful for a variety of purposes in hydrologic, environmental, and ecosystem studies. In the context of classification, the concept of community structure, within the realm of complex networks, is particularly attractive and gaining attention in catchment classification studies. Among the many community structure methods, the edge betweenness (EB) method, which applies a hierarchical clustering concept, is one of the most widely used. The EB method, however, is susceptible to the issue of scale (or size) of the network, essentially due to the modularity function that is used to measure the strength of the community structure. To overcome this limitation, the present study proposes an improvement to the EB method. The proposed method, termed as the Modularity Density-based Edge Betweenness (MDEB) method, uses a modularity density function (or D value) by maximization, to obtain the best split of the network. The effectiveness of the MDEB method is evaluated through its application for catchment classification using streamflow data from two large networks: 218 stations from Australia and 639 stations from the United States (US). For each network, three different scenarios in network sizes are studied: (1) the entire network; (2) smaller network sizes based on 100 random realizations, with each realization having 100 and 300 stations for Australia and the US, respectively; and (3) smaller network sizes based on nine different drainage division regions in Australia and 18 different hydrologic units in the US. The classification outcomes from the MDEB method for these three scenarios are compared with those from the EB method. The results indicate that the MDEB method generally performs better than the EB method, for both Australia and the US. The superiority of the MDEB method is evaluated in terms of the number of communities identified and the number of stations that change communities when different network sizes are considered. The catchment communities are also interpreted in terms of the distance-correlation relationship. The results from the present study offer further evidence as to the usefulness and effectiveness of the community structure concept for catchment classification, especially the proposed MDEB method.
Siti Aisyah Tumiran; Bellie Sivakumar. Community structure concept for catchment classification: A modularity density-based edge betweenness (MDEB) method. Ecological Indicators 2021, 124, 107346 .
AMA StyleSiti Aisyah Tumiran, Bellie Sivakumar. Community structure concept for catchment classification: A modularity density-based edge betweenness (MDEB) method. Ecological Indicators. 2021; 124 ():107346.
Chicago/Turabian StyleSiti Aisyah Tumiran; Bellie Sivakumar. 2021. "Community structure concept for catchment classification: A modularity density-based edge betweenness (MDEB) method." Ecological Indicators 124, no. : 107346.
Hydrological prediction in ungauged catchments remains a challenge despite numerous attempts in the past. The well-known solution to this challenge is transfer of information from gauged catchments to ‘hydrologically-similar’ ungauged catchments, an approach known as ‘regionalization.’ The basis of regionalization is, thus, classification of catchments into hydrologically-similar groups. A major limitation of the traditional classification methods, such as the K-means clustering algorithm, is that they are not very suitable when the classes are not well separated from each other. Additionally, they cannot determine the number of classes in a dataset automatically. To overcome these limitations, some recent studies have used complex networks-based classification algorithms, widely known as community structure algorithms, for catchment classification. However, such studies have applied the community structure algorithms only to time series of hydrological variables (e.g. streamflow) and have not so far used lumped information (e.g. mean rainfall and mean slope). In this short communication, we propose a Canberra distance-based metric that can enable a community structure algorithm to exploit lumped information. For demonstration, the proposed metric is used to compute link weights for the multilevel modularity optimization algorithm. The proposed classification method is applied to lumped data from 494 basins situated in the CONtiguous United States (CONUS) for their classification, and its performance is compared with that of the K-means clustering algorithm. By and large, the proposed classification framework opens up an alternative avenue towards prediction in ungauged catchments.
Prashant Istalkar; S. L. Kesav Unnithan; Basudev Biswal; Bellie Sivakumar. A Canberra distance-based complex network classification framework using lumped catchment characteristics. Stochastic Environmental Research and Risk Assessment 2021, 35, 1293 -1300.
AMA StylePrashant Istalkar, S. L. Kesav Unnithan, Basudev Biswal, Bellie Sivakumar. A Canberra distance-based complex network classification framework using lumped catchment characteristics. Stochastic Environmental Research and Risk Assessment. 2021; 35 (6):1293-1300.
Chicago/Turabian StylePrashant Istalkar; S. L. Kesav Unnithan; Basudev Biswal; Bellie Sivakumar. 2021. "A Canberra distance-based complex network classification framework using lumped catchment characteristics." Stochastic Environmental Research and Risk Assessment 35, no. 6: 1293-1300.
The present study applies the concept of community structure to classify catchments in two large regions: Australia and the United States. Specifically, the edge betweenness method is applied to monthly streamflow data from a network of 218 stations across Australia and from a network of 639 stations across the United States. The influence of streamflow correlation threshold (i.e. spatial correlation in streamflow between streamflow stations) on catchment classification is examined, through use of different thresholds, suitable for each region, as appropriate. The results reveal that, for both regions, a very small number of communities have a large number of catchments within them (for instance, considering both regions as small as 16–18% of the largest communities combine to represent as much as 70–75% of the catchments), and a significantly large number of communities have only a very few catchments within them (for instance, almost 70% of the communities have only one or two stations within them, and thus represent only about 20% and 10% of the catchments in Australia and the US, respectively). An interpretation of the identified catchment communities in terms of catchment characteristics (station drainage area, station stream length, and station elevation) and flow properties (mean and coefficient of variation) is also made. The catchment classification is also explained using the correlation–distance relationship between the stations.
Siti Aisyah Tumiran; Bellie Sivakumar. Catchment classification using community structure concept: application to two large regions. Stochastic Environmental Research and Risk Assessment 2021, 35, 561 -578.
AMA StyleSiti Aisyah Tumiran, Bellie Sivakumar. Catchment classification using community structure concept: application to two large regions. Stochastic Environmental Research and Risk Assessment. 2021; 35 (3):561-578.
Chicago/Turabian StyleSiti Aisyah Tumiran; Bellie Sivakumar. 2021. "Catchment classification using community structure concept: application to two large regions." Stochastic Environmental Research and Risk Assessment 35, no. 3: 561-578.
Senlin Zhu; Bahrudin Hrnjica; Jiangyu Dai; Bellie Sivakumar. Retraction notice to “Machine learning approaches for estimation of sediment settling velocity” [J. Hydrol. (2020) 124911]. Journal of Hydrology 2020, 592, 125678 .
AMA StyleSenlin Zhu, Bahrudin Hrnjica, Jiangyu Dai, Bellie Sivakumar. Retraction notice to “Machine learning approaches for estimation of sediment settling velocity” [J. Hydrol. (2020) 124911]. Journal of Hydrology. 2020; 592 ():125678.
Chicago/Turabian StyleSenlin Zhu; Bahrudin Hrnjica; Jiangyu Dai; Bellie Sivakumar. 2020. "Retraction notice to “Machine learning approaches for estimation of sediment settling velocity” [J. Hydrol. (2020) 124911]." Journal of Hydrology 592, no. : 125678.
Applications of the concepts of complex networks for studying streamflow dynamics are gaining momentum at the current time. The present study applies a coupled phase space reconstruction–network construction method to examine the clustering property of the temporal dynamics of streamflow. The clustering of the temporal streamflow network is determined using clustering coefficient, which quantifies the tendency of a network to cluster (a measure of local density). Monthly streamflow time series observed from each of 639 stations (i.e. 639 networks) in the United States are studied. The presence of links between nodes (i.e. phase space reconstructed vectors) in each streamflow network (i.e. station) is identified using the Euclidean distance. Different distance thresholds are used to examine the influence of threshold on the clustering coefficient results and to identify the critical threshold. The results indicate that the distance threshold has significant influence on the clustering coefficient values of the temporal streamflow networks. With the critical distance threshold values, the clustering coefficients for the 639 stations are found to be between 0.15 and 0.81, suggesting very different types of network connections and dynamics. The clustering coefficient values are found to provide useful information on the influence of a given month (i.e. timestep) of the year on the temporal dynamics. Reliable interpretations of the clustering coefficient values in terms of catchment characteristics and flow properties are also possible.
Nazly Yasmin; Bellie Sivakumar. Study of temporal streamflow dynamics with complex networks: network construction and clustering. Stochastic Environmental Research and Risk Assessment 2020, 35, 579 -595.
AMA StyleNazly Yasmin, Bellie Sivakumar. Study of temporal streamflow dynamics with complex networks: network construction and clustering. Stochastic Environmental Research and Risk Assessment. 2020; 35 (3):579-595.
Chicago/Turabian StyleNazly Yasmin; Bellie Sivakumar. 2020. "Study of temporal streamflow dynamics with complex networks: network construction and clustering." Stochastic Environmental Research and Risk Assessment 35, no. 3: 579-595.
Bellie Sivakumar. Water-energy-food nexus: challenges and opportunities. Stochastic Environmental Research and Risk Assessment 2020, 35, 1 -2.
AMA StyleBellie Sivakumar. Water-energy-food nexus: challenges and opportunities. Stochastic Environmental Research and Risk Assessment. 2020; 35 (1):1-2.
Chicago/Turabian StyleBellie Sivakumar. 2020. "Water-energy-food nexus: challenges and opportunities." Stochastic Environmental Research and Risk Assessment 35, no. 1: 1-2.
This study examined the spatial and temporal patterns of the paleobiome types in the Three-River Headwaters Region (TRHR, Sanjiangyuan) in China from the middle Holocene (i.e. 6000 BP). Existing studies explored the patterns of paleobiome types by identifying the type of biomes in pollen sites, based on the taxonomical assemblage of pollen samples. This site-wise identification, however, is not suited for the analysis of the TRHR because pollen sites in the region-level are discrete and low-resolution. In this study – to solve the lack of the pollen sites – climate data, which are easy to interpolate, were extracted from the pollen data by pollen transfer functions. Next, the extracted climate data were calibrated and interpolated over the TRHR and study period. Then, a physiological biome model based on climate was used to produce the chronology of the distribution of the biome types. Consequently, the chronology was reconstructed with a time interval of 50 years and a spatial cell size of 0.5 ° × 0.5 °. From the results, the variations in the distribution of the paleobiome types were mainly dominated by 36 patterns over 10 biome types. In detail, tundra and semidesert were the main types, and the alternation of these two types was the main pattern. Further, the long-term evolution was from tundra to semi-desert, and the precipitation was the main driving force for changes in biomes, while temperature also had some influence.
Xin Mao; Tiejian Li; Chen Chen; Jiaye Li; Bellie Sivakumar; Jiahua Wei. Reconstructing the distribution of the paleobiome types in the Three-River Headwaters Region in China from the middle Holocene. The Holocene 2020, 30, 1706 -1715.
AMA StyleXin Mao, Tiejian Li, Chen Chen, Jiaye Li, Bellie Sivakumar, Jiahua Wei. Reconstructing the distribution of the paleobiome types in the Three-River Headwaters Region in China from the middle Holocene. The Holocene. 2020; 30 (12):1706-1715.
Chicago/Turabian StyleXin Mao; Tiejian Li; Chen Chen; Jiaye Li; Bellie Sivakumar; Jiahua Wei. 2020. "Reconstructing the distribution of the paleobiome types in the Three-River Headwaters Region in China from the middle Holocene." The Holocene 30, no. 12: 1706-1715.
For managing the worsening urban water disasters in China, the Government of China proposed the concept of “Sponge City” in 2013 and initiated the strategy in 30 pilot cities from 2015. Despite the promise of the concept, there have been many challenges in implementing the “Sponge City” program (SCP). In this manuscript, we discuss the hydrology-related challenges in implementing the SCP. In particular, we consider two key challenges: (1) Determination of the “Volume Capture Ratio of Annual Rainfall” (VCRAR), as controlling urban stormwater runoff is one of the core targets of the SCP; and (2) Estimation of a proper rainfall threshold, which influences the layout of green-infrastructures in the SCP to achieve the core VCRAR target. To discuss these challenges, we consider the city of Beijing, the capital of China, as a case study. Our analysis shows that the trade-offs between the investment for the SCP and its potential economic benefits should be considered by undertaking a proper determination of VCRAR. The VCRAR estimated for Beijing from the present analysis is 0.73. This value is more reasonable than the empirical value of 0.80 that is presently used, as it can guarantee the positive rate of return on the investment. We also find that the nonstationary characteristics of rainfall data and their spatiotemporal differences are important for the estimation of the rainfall threshold in SCP. For instance, even using the daily rainfall data over a period of 30 years (1983–2012) in Beijing, as required by the National Assessment Standard, the estimated rainfall threshold of 27.3 mm underestimates the reasonable rainfall threshold that should at least be larger than 30.0 mm. Thus, the former cannot ensure the VCRAR target of 0.80. Based on these results, we offer proper approaches and key suggestions towards useful guidelines for delivering better SCP in the Chinese cities.
Moyuan Yang; Yan-Fang Sang; Bellie Sivakumar; Faith Ka Shun Chan; Xingyao Pan. Challenges in urban stormwater management in Chinese cities: A hydrologic perspective. Journal of Hydrology 2020, 591, 125314 .
AMA StyleMoyuan Yang, Yan-Fang Sang, Bellie Sivakumar, Faith Ka Shun Chan, Xingyao Pan. Challenges in urban stormwater management in Chinese cities: A hydrologic perspective. Journal of Hydrology. 2020; 591 ():125314.
Chicago/Turabian StyleMoyuan Yang; Yan-Fang Sang; Bellie Sivakumar; Faith Ka Shun Chan; Xingyao Pan. 2020. "Challenges in urban stormwater management in Chinese cities: A hydrologic perspective." Journal of Hydrology 591, no. : 125314.
The 2019 coronavirus disease, called COVID-19, is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since it was first identified in China in December 2019, COVID-19 has spread to almost all countries and territories and caused over 310,000 deaths, as on May 16, 2020. The impacts of the COVID-19 pandemic are now seen in almost every sector of our society. In this article, I discuss the impacts of COVID-19 on the water sector. I point out that our efforts to control the spread of COVID-19 will increase the water demand and worsen the water quality, leading to additional challenges in water planning and management. In view of the impacts of COVID-19 and other global-scale phenomena influencing water resources (e.g., global climate change), I highlight the urgent need for interdisciplinary collaborations among researchers studying water and new strategies to address water issues.
Bellie Sivakumar. COVID-19 and water. Stochastic Environmental Research and Risk Assessment 2020, 35, 531 -534.
AMA StyleBellie Sivakumar. COVID-19 and water. Stochastic Environmental Research and Risk Assessment. 2020; 35 (3):531-534.
Chicago/Turabian StyleBellie Sivakumar. 2020. "COVID-19 and water." Stochastic Environmental Research and Risk Assessment 35, no. 3: 531-534.
Understanding the spatiotemporal variability of rainfall is vital for water resources planning and management, flood and drought mitigation, and erosion control, among others. Despite the progress in this direction, through proposal of many different approaches and their applications to rainfall data at various regions around the world, our knowledge of the spatiotemporal variability of rainfall remains limited. Studies in this direction have largely focused on the amount of rainfall and its spatial patterns, and investigations of spatiotemporal variability at multiscale are limited. In this study, we introduce a novel measure, Standardized Variability Index (SVI), based on the concept of entropy to investigate the spatiotemporal variability of gridded rainfall in the Indian subcontinent at different timescales. The results show distinct spatial patterns in the inter-annual rainfall variability at all timescales. Also, the intra-annual variability of rainfall amount, as well as rainy days, is found to increase from east to west of India. The Mann-Kendall trend test at different timescales reveals significant increase in rainfall variability. In addition, coupling the mean annual rainfall with SVI enables a relative assessment of the water resources availability.
Ravi Kumar Guntu; Maheswaran Rathinasamy; Ankit Agarwal; Bellie Sivakumar. Spatiotemporal variability of Indian rainfall using multiscale entropy. Journal of Hydrology 2020, 587, 124916 .
AMA StyleRavi Kumar Guntu, Maheswaran Rathinasamy, Ankit Agarwal, Bellie Sivakumar. Spatiotemporal variability of Indian rainfall using multiscale entropy. Journal of Hydrology. 2020; 587 ():124916.
Chicago/Turabian StyleRavi Kumar Guntu; Maheswaran Rathinasamy; Ankit Agarwal; Bellie Sivakumar. 2020. "Spatiotemporal variability of Indian rainfall using multiscale entropy." Journal of Hydrology 587, no. : 124916.
Sediment settling velocity (SSV) is one of the most important parameters in sediment transport studies. Accurate estimation of SSV is, thus, of great significance for river basin planning and management. Many factors influence SSV in highly complex and nonlinear ways, which make accurate estimation of SSV a very challenging task. To better estimate SSV, in the present study, three machine learning models, namely Feed Forward Neural Network (FFNN), Deep Learning (DL), and Decision Tree (DT), are developed. Data from the previous literature for sand and gravel classes, including nominal diameter of the sediment, kinematic viscosity of the fluid, submerged specific gravity of the sediment, and observed SSV are used as inputs to these models. To assess the superiority of these models against traditional methods, if any, the modeling results are compared with four common SSV estimation formulas and also with the results from a genetic programming model previously developed. The results show that the DT model outperforms all the conventional formulas and the genetic programming model, as well as the FFNN and DL models, for both the sand and gravel classes. The DL method does not perform well when the data size is small, and the results are even worse than those from the FFNN model. The results from this study are certainly encouraging regarding the suitability and effectiveness of machine learning models for reliable estimation of SSV, provided the limitations about the data are properly understood.
Senlin Zhu; Bahrudin Hrnjica; Jiangyu Dai; Bellie Sivakumar. RETRACTED: Machine learning approaches for estimation of sediment settling velocity. Journal of Hydrology 2020, 586, 124911 .
AMA StyleSenlin Zhu, Bahrudin Hrnjica, Jiangyu Dai, Bellie Sivakumar. RETRACTED: Machine learning approaches for estimation of sediment settling velocity. Journal of Hydrology. 2020; 586 ():124911.
Chicago/Turabian StyleSenlin Zhu; Bahrudin Hrnjica; Jiangyu Dai; Bellie Sivakumar. 2020. "RETRACTED: Machine learning approaches for estimation of sediment settling velocity." Journal of Hydrology 586, no. : 124911.
Information entropy theory has been largely applied in hydrological modeling and engineering optimization. Recently the entropy description and explanation of reactive solute mixing and transport process has received increasing attentions. Literatures mainly focus theoretical analysis on hypothetical cases, however, the direct observation and calculation with field datasets are hardly reported.
This work studied the change of information entropy in surface water solute transport system with field data. A comprehensive information entropy based analysis framework were proposed, which works like a combined optical system with Optical Sources-Filters-Prisms-Images. We established four basic probability space, leading to four basic information entropy indexes: Dilution index (E), Flux index (F), Spatial entropy index (Gx) , and Temporal entropy index (Gt).
The evolution characteristic of information entropy in one-component solute diffusion system is studied by using the method of discrete information entropy analysis. In the system boundary definition of fixed observation, the information entropy appears a peak in time and space dimension, and the peak value of information entropy appears in the first 20%-30% of the fixed observation interval, while in the system boundary definition of dynamic observation, information entropy decreases continuously with the increase of time and space distance. Through the local sensitivity analysis of the hydrodynamic parameters of the above analytical solutions, it is found that the sensitivity of information entropy H to diffusion coefficient Dx is relatively constant, and the greater the degradation coefficient k is, the more sensitive the monitoring time t is to k, the more sensitive the spatial change of information entropy is to the change of flow velocity ux with the increase of distance, while the change of time is insensitive to ux.
Furthermore, the evolution characteristic of information entropy in complex water quality process of rivers is studied. The Guangming section of Maozhou River in Shenzhen is taken as the research area. BOD-DO and nitrogen elements (NH3-N, NO3-N, Org-N) water quality process were selected, and one-dimensional S-P model and WASP_EUTRO water quality model were constructed respectively. After model calibration and verification, the changing characteristics of information entropy, mutual information and information transfer index are analyzed under the system definition of fixed observation. It was found that the transformation reaction process gradually replaced the diffusion process in the complex water quality process as the main factor affecting the change of information entropy, and the information entropy change law in the single component diffusion process no longer exists in the complex water quality process.
Tianrui Pang; Jiping Jiang; Bellie Sivakuamr; Yi Zheng; Tong Zheng. The Information Entropy Prisms on Riverine Water Quality Evolution. 2020, 1 .
AMA StyleTianrui Pang, Jiping Jiang, Bellie Sivakuamr, Yi Zheng, Tong Zheng. The Information Entropy Prisms on Riverine Water Quality Evolution. . 2020; ():1.
Chicago/Turabian StyleTianrui Pang; Jiping Jiang; Bellie Sivakuamr; Yi Zheng; Tong Zheng. 2020. "The Information Entropy Prisms on Riverine Water Quality Evolution." , no. : 1.
Due to global climate change and growing population, fresh water resources are becoming more vulnerable to pollution. Protecting fresh water resources, especially lakes and the associated environment, is one of the key challenges faced by policy makers and water managers. Lake water level is an important physical indicator of lakes, and its fluctuation may significantly impact lake ecosystems. Therefore, reliable forecasting of lake water level is vital for a proper assessment of the health of lake ecosystems and their management. In this study, two machine learning models, including feed forward neural network (FFNN) and Deep Learning (DL) technique, were used to predict monthly lake water level. The two models were employed for one month ahead forecasting of lake water level in 69 temperate lakes in Poland. The results show that both the FFNN and the DL models performed generally well for forecasting of lake water level of the 69 lakes, with only marginal differences. The results also indicate that the DL model did not show significant superiority over the traditional FFNN model; indeed, the FFNN model slightly outperformed the DL model for 33 of the 69 lakes. These results seem to suggest that traditional machine learning models may just be sufficient for forecasting of lake water level when they are properly trained. The outcomes of the present study have important implications for water level forecasting and water resources management of lakes, especially from the perspective of machine learning models and their complexities.
Senlin Zhu; Bahrudin Hrnjica; Mariusz Ptak; Adam Choiński; Bellie Sivakumar. Forecasting of water level in multiple temperate lakes using machine learning models. Journal of Hydrology 2020, 585, 124819 .
AMA StyleSenlin Zhu, Bahrudin Hrnjica, Mariusz Ptak, Adam Choiński, Bellie Sivakumar. Forecasting of water level in multiple temperate lakes using machine learning models. Journal of Hydrology. 2020; 585 ():124819.
Chicago/Turabian StyleSenlin Zhu; Bahrudin Hrnjica; Mariusz Ptak; Adam Choiński; Bellie Sivakumar. 2020. "Forecasting of water level in multiple temperate lakes using machine learning models." Journal of Hydrology 585, no. : 124819.