Distributed Hydrological Modeling: Determination of Theoretical Hydraulic Potential & Streamflow Simulation of Extreme Hydrometeorological Events

Abstract

Abstract:The progressive change in climatic conditions worldwide has increased frequency and severity of extreme hydrometeorological events (EHEs). México is an example that has been affected by the occurrence of EHE leading to economic, social, and environmental losses. The objective of this research was to apply a Canadian distributed hydrological model (DHM) to tropical conditions and to evaluate its capacity to simulate flows in a basin in the central Gulf of Mexico. In addition, the DHM (once calibrated and validated) was used to calculate the theoretical hydraulic power (THP) and the performance to predict streamflow before the presence of an EHE. The results of the DHM show that the goodness of fit indicators between the observed and simulated flows in the calibration process Nash-Sutcliffe efficiency (NSE) = 0.83, ratio of the root mean square error to the standard deviation of measured data (RSR) = 0.41, and percent bias (PBIAS) = −4.3) and validation (NSE = 0.775, RSR = 0.4735, and PBIAS = 2.45) are satisfactory. The DHM showed its applicability: determination of THP showed that the mean flows are in synchrony with the order of the river reaches and streamflow simulation of 13 EHEs (NSE = 0.78 ± 0.13, RSR = 0.46 ± 0.14 and PBIAS = −0.48 ± 7.5) confirmed a reliable efficiency. This work can serve as a tool for identifying vulnerabilities before floods and for the rational and sustainable management of water resources.

Feasibility of heart girth models in estimating live weight of fat‐long‐tailed sheep

“…The lower the RSR value, the better the predictability (Moriasi et al., 2007). Root mean square of the error to standard deviation ratio with a value of less than 0.7 indicates satisfactory accuracy of a model (Ibarra‐Zavaleta et al., 2017).…”

Eye-balling and heart girth models for live weight estimation of highly admixed Sudani Shorthorn Zebu Cattle for Precise Production and Veterinary Services

“…Calibration and validation sets were used to calculate RSRC and RSRV, respectively. The root means square of the error to the standard deviation ratio with a value of less than 0.7 indicates a satisfactory accuracy of a model (Ibarra-Zavaleta et al, 2017). The Nash-Sutcliffe efficiency (NSE) is a normalized parameter that identifies the relative magnitude of residual variance compared to measured data variance (Nash & Sutcliffe, 1970).…”

Hydrological Modeling in Tropical Regions via TopModel. Study Case: Central Sector of the Middle Magdalena Valley - Colombia

“…This processes must be evaluated for different environmental conditions (Jenson, 1991;Loosvelt et al, 2014;Hollanda et al, 2015). However, they are generally focused on fulfilling two main objectives: i) to improve the understanding of the hydrological phenomena in the basins and how the changes generated in them affect the hydrological phenomena and, ii) the generation of 40 synthetic sequences of hydrological data for the design of infrastructure or for its use in forecasting (Refsgaard, 1997;Kauffeldt et al, 2016;Ibarra-Zavaleta et al, 2017).…”

“…TopModel us usually applied to assess the management of water resources at the regional scale, 50 using conceptual models for detailed assessment of surface flow (Beven and Freer, 2001a;Mockler, O'Loughlin and Bruen, 2016;Teng et al, 2017). Likewise, distributed and semi-distributed models (which do not simulate the basin as a group, but as a set of divided parts) are necessary for the simulation of spatial patterns of hydrological response within a basin (Mazzoleni et al, 2015;Ibarra-Zavaleta et al, 2017) . Moreover, hydrological models also provide valuable information to study changes in land use or climate (Karlsson et al, 2016).…”

“…In addition, it is necessary to generate tools for calibration and validation of synthetic data to the hydrological modeling process (Blanco-Gutiérrez, Varela-Ortega and Purkey, 2013;da Silva et al, 2015). The calibration and validation of data through modeling, has enabled the verification of the assumptions that underlie the hydrological model used, which has contributed significantly to the generation of new knowledge (Ibarra-Zavaleta et al, 2017).…”