Strategies to enhance the reliability of flow quantile prediction in the gauged and ungauged basins

Abstract

The precise reproduction of different flow regimes in both gauged and ungauged watersheds is crucial for managing environmental flow and water quality requirements. However, the ability of hydrological models to reproduce flow quantiles (FQs) is often influenced by the process of calibrating the most dominant parameters through traditional parameter estimation methods. This research proposes a systematic parameter estimation approach to improve the credibility of the hydrologic model in reproducing FQs in gauged and ungauged watersheds through the following steps: (a) implementation of parameter sensitivity analysis to identify the dominant parameters, (b) initial estimation of the sensitive parameter values, (c) an iterative search for the optimal value of the dominant parameter in reproducing FQs and (d) regionalization of parameters to estimate FQs in the ungauged watersheds. The analysis shows the highest sensitivity of the runoff curve number (CN2) in simulating the hydrologic process in all test watersheds. Moreover, the best value of CN2 was found to be different for each flow quantile. Therefore, CN2 was updated for reproduction of FQs, which resulted in an overall average improvement of the regionalized model accuracy (across all test watersheds and flow quantiles) by 37 and 46% during the calibration and validation periods, respectively. The spatiotemporal dynamics of the water balance components that often control the behaviours of FQs in both the gauged and ungauged watersheds were also quantified. The results show a wide range of spatiotemporal variations for the majority of the water balance components.

A Cost-Efficient Method to Remotely Monitor Streamflow in Small-Scale Watersheds

“…Continuous discharge measurements are imperative for water resources management, but currently these data are limited or nonexistent for many small-scale streams. Over the last two decades, efforts to predict flow in ungauged watersheds have been an active area of research (Hauet et al, 2008;Royem et al, 2012;Atieh et al, 2017;Tegegne and Kim, 2020), with methods often involving hydrologic models (Gitau and Chaubey, 2010;Tegegne and Kim, 2020), instantaneous flow measurements and rating curves (Harmel et al, 2006), or regression relationships between watershed/stream characteristics and flow (Chen and Chiu, 2004;Gianfagna et al, 2015). However, these efforts can be data intensive (Razavi and Coulibaly, 2013), unreliable (e.g., with indirect measurements), or logistically unfeasible across numerous, remote sites.…”

Using Extreme Value Theory to Estimate Available Water in the Upper Awach-Kibuon Catchment in Nyamira County, Kenya

“…In hydrology, the quantification of design peak discharges on data-scarce catchments has been a continuing problem [8,18]. Precise estimates of flood quantiles are needed for efficient design of hydraulic structures [19,20]; however, historical data that are required to quantify the flood statistics are usually unavailable at the site of interest or the available information may not be representative of the catchment studied because of the changes in the watershed characteristics, such as urbanization [8,21].…”

Case study of continental‐scale hydrologic modeling's ability to predict daily streamflow percentiles for regulatory application

“…Daily streamflow percentile estimates from continental-scale hydrologic models can help inform regulatory practitioners by providing hydroclimatic context to observations. curves (Atieh et al, 2017;Tegegne & Kim, 2020;Worland et al, 2019), and streamflow intermittency (Yu et al, 2020). The researchers are unaware of a daily comparison of observed and estimated streamflow percentiles using a current-generation large spatial scale hydrologic model.…”

“…Estimating whether a streamflow is below normal, normal, or above normal can help the regulatory practitioner to determine if streamflow conditions observed during a site visit or within aerial imagery are potentially normal or abnormal conditions. While there are instances where the literature evaluates a hydrologic model's ability to capture annual streamflow percentiles based on daily streamflow estimates (Safeeq et al., 2014), the existing literature tends to focus on estimating flow regimes (Merritt et al., 2021), flow duration curves (Atieh et al., 2017; Tegegne & Kim, 2020; Worland et al., 2019), and streamflow intermittency (Yu et al., 2020). The researchers are unaware of a daily comparison of observed and estimated streamflow percentiles using a current‐generation large spatial scale hydrologic model.…”