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Concept# Hydrological model

Résumé

A hydrologic model is a simplification of a real-world system (e.g., surface water, soil water, wetland, groundwater, estuary) that aids in understanding, predicting, and managing water resources. Both the flow and quality of water are commonly studied using hydrologic models.
Analog models
Prior to the advent of computer models, hydrologic modeling used analog models to simulate flow and transport systems. Unlike mathematical models that use equations to describe, predict, and manage hydrologic systems, analog models use non-mathematical approaches to simulate hydrology.
Two general categories of analog models are common; scale analogs that use miniaturized versions of the physical system and process analogs that use comparable physics (e.g., electricity, heat, diffusion) to mimic the system of interest.
Scale analogs
Scale models offer a useful approximation of physical or chemical processes at a size that allows for greater ease of visualization. The mod

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MODFLOW

MODFLOW is the U.S. Geological Survey modular finite-difference flow model, which is a computer code that solves the groundwater flow equation. The program is used by hydrogeologists to simulate the

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"Hydrology for Engineers" is an introduction to the study of floods, droughts and a fair distribution of water. The course will introduce basic hydrologic concepts and methods: probability and statistics, surface and subsurface hydrological processes

Hydroelectric power (HP) represents the main source of electricity in Africa, including the Democratic Republic of Congo. The demand for new dam construction is high, and major projects are currently progressing through planning and implementation stages. New HP dams should comply with both past and emerging environmental requirements. River systems need water to maintain hydraulic and ecological functions. Flow regime disturbance can prevent rivers from providing their ecosystem services and disrupt riparian communities. Most dammed rivers in Africa are understudied, however, in terms of their environmental flow requirements. This study analysed the hydrological regime and water quality of the Ruzizi River. The research investigated conditions of minimum water flow and hydropeaking at the Ruzizi I HP dam in terms of land management constraints and ecological impacts. According to Gumbel's hydrological model, a discharge of similar to 130 m(3)/s showed the longest return period (12 years) among the most recurrent flows. By contrast, the maximum recorded discharge of 143 m(3)/s showed a return time of 76 years. Any discharge between 46 and 120 m(3)/s could occur at any time within three years. The discharge-hydropower production relationship for the power plant provided a possible minimum environmental flow of 28 m(3)/s (i.e., 25%). Drinking water quality was assessed according to WHO water quality index (WQI) standards. Turbidity (i.e., total suspended solids) upstream and downstream of dams correlated strongly with rainfall (r = 0.8; n = 12) and land use. WQI values observed in excess of WHO drinking water standards indicate that the Ruzizi River is currently unsuitable for drinking water purposes.

Geophysical surveys can provide useful, albeit indirect, information on vadose zone processes. However, the ability to provide a quantitative description of the subsurface hydrological phenomena requires to fully integrate geophysical data into hydrological modeling. Here, we describe a controlled infiltration experiment that was monitored using both electrical resistivity tomography (ERT) and ground-penetrating radar (GPR). The experimental site has a simple, well-characterized subsoil structure: the vadose zone is composed of aeolic sand with largely homogeneous and isotropic properties. In order to estimate the unknown soil hydraulic conductivity, we apply a data assimilation technique based on a sequential importance resampling (SIR) approach. The SIR approach allows a simple assimilation of either or both geophysical datasets taking into account the associated measurement uncertainties. We demonstrate that, compared to a simpler, uncoupled hydro-geophysical approach, the coupled data assimilation process provides a more reliable parameter estimation and better reproduces the evolution of the infiltrating water plume. The coupled procedure is indeed much superior to the uncoupled approach that suffers from the artifacts of the geophysical inversion step and produces severe mass balance errors. The combined assimilation of GPR and ERT data is then investigated, highlighting strengths and weaknesses of the two datasets. In the case at hand GPR energy propagates in form of a guided wave that, over time, shows different energy distribution between propagation modes as a consequence of the evolving thickness of the wet layer. We found that the GPR inversion procedure may produce estimates on the depth of the infiltrating front that are not as informative as the ERT dataset.

2015The modeling of unsaturated groundwater flow is affected by a high degree of uncertainty related to both measurement and model errors. Geophysical methods such as Electrical Resistivity Tomography (ERT) can provide useful indirect information on the hydrological processes occurring in the vadose zone. In this paper, we propose and test an iterated particle filter method to solve the coupled hydrogeophysical inverse problem. We focus on an infiltration test monitored by time-lapse ERT and modeled using Richards equation. The goal is to identify hydrological model parameters from ERT electrical potential measurements. Traditional uncoupled inversion relies on the solution of two sequential inverse problems, the first one applied to the ERT measurements, the second one to Richards equation. This approach does not ensure an accurate quantitative description of the physical state, typically violating mass balance. To avoid one of these two inversions and incorporate in the process more physical simulation constraints, we cast the problem within the framework of a SIR (Sequential Importance Resampling) data assimilation approach that uses a Richards equation solver to model the hydrological dynamics and a forward ERT simulator combined with Archie's law to serve as measurement model. ERT observations are then used to update the state of the system as well as to estimate the model parameters and their posterior distribution. The limitations of the traditional sequential Bayesian approach are investigated and an innovative iterative approach is proposed to estimate the model parameters with high accuracy. The numerical properties of the developed algorithm are verified on both homogeneous and heterogeneous synthetic test cases based on a real-world field experiment.

2015