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Published in Jeremiah Kipkulei Kiptala, Managing Basin Interdependencies in a Heterogeneous, Highly Utilized and Data Scarce River Basin in Semi-Arid Africa, 2020
In most cases those variables are derived from models using (limited) river discharge data which increases equifinality problems (Savenije, 2001; Uhlenbrook et al., 2004; McDonnell et al., 2007; Immerzeel and Droogers, 2008). On the other hand, grid based distributed models at fine spatial scales do not explicitly account for additional blue water use (Qb), i.e. transpiration from supplementary irrigation or withdrawals from open water evaporation. In fact in tropical arid regions, Qb can be a large percentage of the river discharge during low flow. Calibrating models using modified stream flow data may lead to incorrect parameterization, and may lead to high predictive uncertainty in the hydrological model outputs especially when dealing with scenarios for water use planning.
Literature review
Published in Isnaeni Murdi Hartanto, Integrating Multiple Sources of Information for Improving Hydrological Modelling: An Ensemble Approach, 2019
The most obvious use of in-situ meteorological data (top-right corner of Figure 2-2), in-situ water level time series (top-left corner of Figure 2-2) and water management data (bottom right corner of Figure 2-2) with the hydrological model, is through calibration and validation. Calibration of hydrological models is based on comparing model result with in-situ data at catchment outlet such as water level and discharge (Andersen et al. 2001; Sahoo et al. 2006; Jian et al. 2017) or combining it with other in-situ measurements like soil moisture and infiltration (Loaiza Usuga and Pauwels 2008). In-situ measurements of output variables, e.g. streamflow, have also been integrated through data assimilation (Aubert et al. 2003; Liu et al. 2012a; Mazzoleni et al. 2015). The catchment characteristics and water system data, such as soil maps, land-use maps, channel dimensions, and slopes, are mostly used to build and parameterise the hydrological model.
Introduction
Published in Maurizio Mazzoleni, Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models, 2017
Hydrological models are tools used to represent a catchment’s response to climate and/or land use variability in order to assess the flow hydrograph at the concentration point of the basin for purposes such as real-time flood forecasting (Solomatine and Wagener, 2011). Hydrological models can be divided into three distinct classes using the classification proposed by Wheater et al. (1993), namely mechanistic (i.e. physically-based models), parametric (known as grey box or conceptual models) and metric (called empirical, black box, or data-driven models). In Figure 1.1, a representation of these classes is given. Hydrological models can also be classified according to the spatial discretization of the model itself as distributed, semi-distributed or lumped (Solomatine and Wagener, 2011).
Estimation of Surface-Subsurface Water Balance in Lower Tapi River Basin Using Gridded Data and Station-Based Observed Data
Published in ISH Journal of Hydraulic Engineering, 2023
Bhumika Mistry, Narendra Shrimali, Hiteshri Shastri
The four major categories for hydrological models are: (1) Event and Continuous Simulation Models, (2) Conceptual and Hydrodynamic Models, (3) Lumped and Distributed Parameter Models, and (4) Models with Fitted, Physically Determined, or Empirically Derived Parameters. (Pérez-Sánchez et al. 2019). These models formulate physical or statistical relationships between hydro-meteorological, geographical and hydrological data inputs. Therefore the availability of quality data is essential to obtain realistic hydrological simulations. As primitively studied with the hydrological cycle, these hydro-meteorological data mainly includes atmospheric variables e.g. precipitation, temperature, air pressure, wind speed, relative humidity, sun shine hours etc. The regional water management and hydrological studies simultaneously requires a good understanding of the geographical and related spatial data such as water sources, terrain, watershed, land cover, land use, soil condition etc. The hydrological data e.g. stream flow is essential for model calibration and validation. Traditionally, all these observations are collected by calibrated instruments set in a ground observatory. However, this system of ground based observations of hydro meteorological data is however largely enhanced through the satellite and remote sensor based observations. Preparing of different gridded climate reanalysis datasets that compile information from all the traditional and modern means of observations provides reliable and consistent data for a longer time period. The long-time observations are essential to draw inferences on climate change.
Subway travel risk evaluation during flood events based on smart card data
Published in Geomatics, Natural Hazards and Risk, 2022
Dianchen Sun, Huimin Wang, Upmanu Lall, Jing Huang, Gaofeng Liu
The USDA-developed Soil Conservation Service Curve Number (SCS-CN) technique is commonly used as an empirical hydrological model that is widely applied for predicting direct runoff or infiltration for a particular rainfall event (Soulis and Valiantzas 2012). This model has been widely employed in recent years for runoff estimation at various spatial scales (Du et al. 2015; Hu et al. 2020). A direct relationship was developed between hydrological model parameters and remote sensing data based on rainfall and runoff, considering underlying surface features, and the influence of land use was shown. Several studies also suggest that the model can be used to evaluate runoff risk in densely populated locations where actual hydrological data are difficult to collect (Li et al. 2018; Yao et al. 2018).
Different calibration procedures for flows estimation using SWAT model
Published in Journal of Applied Water Engineering and Research, 2020
Luana Lavagnoli Moreira, Dimaghi Schwamback, Daniel Rigo
Hydrological models are important tools in decision-making processes helping actual situations and ensuring water availability for future uses in areas such as public water supply, power generation, irrigation, and water use conflict. (Fatichi et al. 2016; Mello et al. 2016). As a result of its applicability, a large number of hydrology simulation models were developed and Devia et al. (2015) give a brief discussion comparing different existing hydrological models, while Singh (2018) presents some progress and future directions for hydrology modeling. Recently, in Gupta and Sorooshian (2017), it is discussed the calibration and evaluation of watershed models. Given the number of models available, in Brazil, the most used ones are: HECHMS – Hydrologic Modeling Simulation (Feldman 2000); SWMM – Storm Water Management Model (Metcalf and Eddy et al. 1971); MGB – Modelo de Grandes Bacias, a Portuguese acronym meaning Large Basins Model (Collischonn et al. 2007); and SWAT – Soil and Water Assessment Tool (Arnold, Moriasi, et al. 2012).