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...
Transboundary water sharing policy between Pakistan and Afghanistan along with emerging issues over the Transboundary Kabul River have been discussed incorporating long-term hydrological trend analysis, water quality issues and temporal changes in land cover/land use. The annual (1977–2015) mean river flow of 26.32 billion (109) cubic metres (BCM) with a range of 13.77 to 42.2 BCM and standard deviation of 6.026 BCM revealed no significant trend in annual inflow data of the Kabul River. Afghanistan planned...
Land use, land cover and climate change (CC) can significantly influence the hydrologic balance and biogeochemical processes of watershed systems. These changes can alter interception, evapotranspiration (ET), infiltration, soil moisture, water balance, and biogeochemical cycling of carbon, nitrogen, and other elements. The need to evaluate the combined effect of land use change and CC of watershed systems is a focus of this study. We simulated watershed processes in the SuAsCo River watershed in MA, USA, using a...
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...
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