On Big Data and Hydroinformatics:12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future

AbstractBig data is an increasingly hot concept in the past five years in the area of computer science, e-commence, and bioinformatics, because more and more data has been collected by the internet, remote sensing network, wearable devices and the Internet of Things. The big data technology provides techniques and analytical tools to handle large datasets, so that creative ideas and new values can be extracted from them. However, the hydroinformatics research community are not so familiar with big data. This paper provides readers who are embracing the data-rich era with a timely review on big data and its relevant technology, and then points out the relevance with hydroinformatics in three aspects

The Relevance of Soil Moisture by Remote Sensing and Hydrological Modelling:12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future

AbstractAccurate soil moisture information is critically important for hydrological modelling and natural hazards (landslide & debris flow). However, its effective utilisation in those areas is still in a state of infancy. This paper focuses on exploring the advances and potential issues in current application of satellite soil moisture observations in hydrological modelling. It has proposed that hydrological application of soil moisture data requires two inter-connected components: 1) soil moisture data relevant to hydrology, and 2) appropriate hydrological model structure compatible with such data. In order to meet these two requirements, the following three research tasks are suggested: the first is to carry out comprehensive evaluations of satellite soil moisture observations for hydrological modelling; the second is that the soil moisture representations in hydrological models may need to be modified so that they are more compatible with the real field soil moisture variations; and the third is that a soil moisture product (i.e., soil moisture deficit) directly applicable to hydrological modelling should be developed

Big data and hydroinformatics

Big data is popular in the areas of computer science, commerce and bioinformatics, but is in an early stage in hydroinformatics. Big data is originated from the extremely large datasets that cannot be processed in tolerable elapsed time with the traditional data processing methods. Using the analogy from the object-oriented programming, big data should be considered as objects encompassing the data, its characteristics and the processing methods. Hydroinformatics can benefit from the big data technology with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined. This paper provides a timely review on big data with its relevance to hydroinformatics. A further exploration on precipitation big data is discussed because estimation of precipitation is an important part of hydrology for managing floods and droughts, and understanding the global water cycle. It is promising that fusion of precipitation data from remote sensing, weather radar, rain gauge and numerical weather modelling could be achieved by parallel computing and distributed data storage, which will trigger a leap in precipitation estimation as the available data from multiple sources could be fused to generate a better product than those from single sources.</jats:p

Comparative Study On Water Resources Assessment Between Kenya And England

Water resources systems are important for various uses such as agriculture, water supply, energy, transportation, recreation, ecosystems, among others. Design of water resources systems is of increasing importance due to the ever increasing demand for efficient water management. This study explores water resources assessment in two different regions with contrasting climate, socio-economic development level and data availability. The Thames catchment in England is in the temperate zone, which is data rich and is at a high socioeconomic development level; while, The Tana catchment in Kenya is in the tropics, which is data poor and at a low socioeconomic development level. Accurate estimation of precipitation is a key process in assessing water resources. To furnish optimal input data, point rainfall at un-gauged locations from measurements at surrounding sites is used in obtaining a continuous surface of relevant information. Rainfall interpolation based on the four commonly used methods (namely Thiessen polygon, Thinplate, Inverse Distance Weighting (IDW) and Kriging) is done and validation is achieved by Leave-one out cross validation (LOOCV) method. Different rain gauge densities have been explored to search the optimum interpolation method so that appropriate schemes could be adopted for the catchments in England and Kenya. Other data sources in addition to rain gauges are collected and processed including temperature, stream flow, and solar radiation. Furthermore, hydrological models suited to different catchment characteristics are explored and used to assess different water resources utilisation options. The commonality and differences for the data and the model between the two regions are then analysed. Finally, data fusion techniques are used to integrate data from different sources to quantify data uncertainty and maximise their accuracy. Findings from the study are useful to water resources specialists when assessing water resources across different regions of contrasting climates, geographical zones, socioeconomic development levels and data availability

Exploration of sub-annual calibration schemes of hydrological models

This study compared hydrological model performances under different sub-annual calibration schemes using two conceptual models, IHACRES and HYMOD. In several publications regarding sub-annual calibration, the authors showed that such an approach generally performed better than the conventional whole period method. Hence, there are advantages in dividing the data into sub-annual periods for calibration. However, little attention has been paid to the issue of how to calibrate the non-continuous sub-annual period. Unlike the conventional calibration which assumes time-invariant parameters for the calibration period, the model parameters vary in sub-annual calibration. We have explored two sub-annual calibration schemes, serial calibration scheme (SCS) and parallel calibration scheme (PCS). We assume that the relationships between the rainfall and runoff could be different for each sub-annual period and consider intra-annual variations of the system. The models are then evaluated for a different validation period to avoid over-fitting and the optimal sub-annual calibration period is explored. Overall, we have found that PCS performed slightly better than SCS and the optimal calibration periods are seasonal and bimonthly for IHACRES and biannual for HYMOD. Since there are pros and cons in both SCS and PCS, we recommend choosing the method depending on the purpose of the model usage.</jats:p

A Hybrid Approach Combining Conceptual Hydrological Models, Support Vector Machines and Remote Sensing Data for Streamflow Simulation

Understanding catchment response to rainfall events is important for accurate runoff estimation in many water-related applications, including water resources management. This study introduced a hybrid model, the Tank-least squared support vector machine (LSSVM), that incorporated intermediate state variables from a conceptual tank model within the least squared support vector machine (LSSVM) framework in order to describe aspects of the rainfall-runoff (RR) process. The efficacy of the Tank-LSSVM model was demonstrated with hydro-meteorological data measured in the Yongdam Catchment between 2007 and 2016, South Korea. We first explored the role of satellite soil moisture (SM) data (i.e., European Space Agency (ESA) CCI) in the rainfall-runoff modeling. The results indicated that the SM states inferred from the ESA CCISWI provided an effective means of describing the temporal dynamics of SM. Further, the Tank-LSSVM model&rsquo;s ability to simulate daily runoff was assessed by using goodness of fit measures (i.e., root mean square error, Nash Sutcliffe coefficient (NSE), and coefficient of determination). The Tank-LSSVM models&rsquo; NSE were all classified as &ldquo;very good&rdquo; based on their performance during the training and testing periods. Compared to individual LSSVM and Tank models, improved daily runoff simulations were seen in the proposed Tank-LSSVM model. In particular, low flow simulations demonstrated the improvement of the Tank-LSSVM model compared to the conventional tank model