Accurate and reliable forecasts of key hydrological variables such as stream flow are of importance due to their profound impacts on real world water resources applications. Data-driven methods have proven their applicability to modeling complex and non-linear hydrological processes. This paper presents a novel visual modeling system that has been developed to overcome the problems involved in implementation of data-driven models for hydrological forecasts using conventional programming languages: problems such as the effort and skill needed to program the models, the lack of reusability of existing models, and the lack of shared tools to perform tedious tasks such as preprocessing data. The system provides an integrated visual modeling environment within which users are able to graphically design and verify specific forecasting models for particular problems without writing code. A set of popular data-driven models are offered by the system. Plug-in models created by wrapping existing code are also allowed to run within the system due to the system's open architecture. The system's feasibility and capability is demonstrated through a case study of forecasting 1-day ahead flow in a river basin located in China. The encouraging simulation results show that the system can simplify the process of implementing hydrological forecast.
In this study, the mode of groundwater level fluctuations is analyzed by statistical approaches for 51 monitoring wells located in a semi-arid basin in Beijing from 1998 to 2013. Firstly, a geostatistical method was performed to characterize the spatial and temporal behaviors of data sets and analyze the responding mechanism of groundwater level to hydrologic features using the Geostatistics Module of ArcGIS software. Secondly, multiple statistical methods were applied to classify groundwater hydrographs with...
Given that the common medium in existing green roofs is a single layer composed of organic and inorganic substrates, seven pilot-scale dual-substrate-layer extensive green roofs (G1–G7), which include nutrition and adsorption substrate layers, were constructed in this study. The effectiveness of porous inert substrates (activated charcoal, zeolite, pumice, lava, vermiculite and expanded perlite) used as the adsorption substrate for stormwater retention was investigated. A single-substrate-layer green roof (G8)...
CEQUEAU is a process-based hydrological model capable of simulating river flows and temperatures. Despite an active user base, no facility yet exists for the automatic assembly and input of watershed data required for flow simulations. CEQUEAU can therefore be time-consuming to implement, particularly on large (≥104 km2) watersheds. We detail a new MATLAB toolbox designed to remove this key limitation by automatically computing CEQUEAU's key drainage direction and physiographic inputs from geographic information...
AbstractAims Aquatic ecosystems are a priority for conservation as they have become rapidly degraded with land-use changes. Predicting the habitat range of an endangered species provides crucial information for biodiversity conservation in such rapidly changing environments. However, the complex network structure of aquatic ecosystems restricts spatial prediction variables and has hitherto limited the use of habitat models to predict species occurrence in aquatic ecosystems. We used the maximum entropy model to...
November 2015 to early February 2016 was an incredibly challenging time for home and business owners in the UK. Throughout much of the country, particularly the North of England and West of Scotland, flooding caused millions of pounds of damage – and caused around 16,000 properties to be flooded in England in December alone. A new report by the Centre for Ecology & Hydrology (CEH) in affiliation with the British Hydrological Society shines new light on the totality of this damage, and offers insights...
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