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’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’ NSE were all classified as “very good” 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

Hydrological modelling under climate change considering nonstationarity and seasonal effects

Traditional hydrological modelling assumes that the catchment does not change with time. However, due to changes of climate and catchment conditions, this stationarity assumption may not be valid in the future. It is a challenge to make the hydrological model adaptive to the future climate and catchment conditions. In this study IHACRES, a conceptual rainfall–runoff model, is applied to a catchment in southwest England. Long observation data (1961–2008) are used and seasonal calibration (only the summer) has been done since there are significant seasonal rainfall patterns. Initially, the calibration is based on changing the model parameters with time by adapting the parameters using the step forward and backward selection schemes. However, in the validation, both models do not work well. The problem is that the regression with time is not reliable since the trend may not be in a monotonic linear relationship with time. Therefore, a new scheme is explored. Only one parameter is selected for adjustment while the other parameters are set as the fixed and the regression of one optimised parameter is made not only against time but climate condition. The result shows that this nonstationary model works well both in the calibration and validation periods.</jats:p

A Development of Multi-Site Rainfall Simulation Model Using Piecewise Generalize Pareto Distribution

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Editorial: Current water challenges require holistic and global solutions

The world population is exploding and is estimated to reach 9.8 billion within the next 10 years (Gerland et al. 2014). Desire for more convenient lifestyles is not likely to be satisfied (United Nations 2009). Such lifestyles entail the unsustainable exploitation of water resources and the environment (Vitousek et al. 1997). Advanced technology and transportation systems have enabled the transfer of goods across the world and, eventually, also the water that is used to produce them. This means that luxurious lifestyles on one side of the planet can cause water and food scarcity on the other side (Hoekstra & Mekonnen 2012). We are also witnessing drastic global climate change: sea levels are rising, and droughts and floods have become more intense. These have exacerbated the global water and food crises (Vorosmarty et al. 2000; Hanjra & Qureshi 2010). Our generation's water challenge is no longer a local or isolated issue. It must be recognized, understood, and analyzed from a holistic and global perspective (Wagener et al. 2010). As such, the growing complexity of global water challenges requires better collection and analysis of ever increasing data with equipping