Statistical and qualitative evaluation of multi-sources for hydrological suitability inflood-prone areas of Pakistan

Abro, Mohammad Ilyas; Zhu, Dehua; Khaskheli, Murad Ali; Elahi, Ehsan; Ramay, Muhammad Aleem ul Hassan

doi:10.1016/j.jhydrol.2020.125117

Cited by 27 publications

(9 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Peak discharge, peak lag time, runoff depth, and the entire flood process are the four key indices for flood events that are used extensively (Abro et al., 2020; Dawson et al., 2007). For a comprehensive study, the following five performance metrics were selected to evaluate the correction models. Relative error of the peak discharge

\delta {Q}_{\mathrm{m}}=\left[\left({Q}_{\text{m,obs}}-{Q}_{\text{m,c}}\right)/{Q}_{\text{m,obs}}\right]\times 100\%

…”

Section: Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

A Coupled Dynamic System Inversion Model for Higher Accuracy in Flood Forecasting

YiXin

Liang

Singh

et al. 2022

Water Resources Research

View full text Add to dashboard Cite

Flood routing is a critical component of real‐time flood forecasting and plays a pivotal role in reducing economic and life losses due to flooding. However, flood routing always possesses errors because it is affected by various sources of uncertainty, which are transmitted and accumulated in the routing process from upstream to downstream, particularly for long or complex river systems. Thus, a real‐time error correction is rapidly becoming a key measure for reducing errors and improving flood forecasting accuracy. In this study, a dynamic system inversion (DSI) model was developed as a new correction method to update forecasting errors for long river systems. The new method utilizes all observed, forecasted, and corrected information at upstream stations and couples the matrix‐based Muskingum routing method with an error‐inversed updating technique to form the DSI equation. Multisource spatiotemporal errors of the river system are simultaneously combined into the DSI equation, and the discharges at the downstream station are reforecasted, thereby providing error correction in flood routing. Based on a case study on the Le'an River (China), the DSI model performed better than the traditional autoregressive (AR) model based on five metrics for both small and large flood events. Moreover, with an increase in lead time, the metrics of AR deteriorated rapidly, whereas that of DSI deteriorated slightly. In conclusion, the proposed DSI model is a sophisticated and robust method for flood routing and provides a new alternative for error correction in flood routing simulations of complex river systems.

show abstract

\delta {Q}_{\mathrm{m}}=\left[\left({Q}_{\text{m,obs}}-{Q}_{\text{m,c}}\right)/{Q}_{\text{m,obs}}\right]\times 100\%

…”

Section: Case Studiesmentioning

confidence: 99%

“…Peak discharge, peak lag time, runoff depth, and the entire flood process are the four key indices for flood events that are used extensively (Abro et al, 2020;Dawson et al, 2007). For a comprehensive study, the following five performance metrics were selected to evaluate the correction models.…”

Section: Model Evaluationsmentioning

confidence: 99%

A Coupled Dynamic System Inversion Model for Higher Accuracy in Flood Forecasting

YiXin

Liang

Singh

et al. 2022

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…( 10), mean absolute error (MAE), Eq. ( 11) (Abro et al, 2020), root mean square error (RMSE), Eq. ( 12), coefficient of determination (R²), Eq.…”

Section: Evaluation and Calibration Of The Ds Modelmentioning

confidence: 99%

Research paper: Water Resources Management Small reservoir water dynamics modeling

Rodrigues

Marques

Villa

2022

Preprint

View full text Add to dashboard Cite

Small reservoirs play a key role in agricultural development in the Brazilian Savannah (Cerrado) region. They contribute to diminish rural communities’ vulnerability to drought and improve the livelihood of rural populations. Thousands of small reservoirs have been built in the last few decades in the Cerrado, but efficient water management and sound planning are hindered by inadequate knowledge of their water dynamics. Studies related to small reservoir water dynamics are generally scarce in the world hydrological literature. The main objective of this study was to develop a dynamic simulation model based on the system dynamics model to evaluate the water dynamics in small reservoirs over time, and the risk of not meeting the predicted water demand. Daily data on reservoir inflows were obtained for the period from October 2009 to September 2011, and extended to June 2015 through modeling. The developed model was calibrated and validated with historical data. The main variables which have impact on the water volume were evaluated through sensitivity analysis. The results indicated that reservoir inflow was the variable which had the highest impact on water volume in the reservoir, followed by the reservoir surface area and by evaporation and infiltration, which together represented 14.4% of reservoir inflow. The risk assessment of not meeting the predicted water demand showed that the water in the reservoir was above the critical level 85.9% of the time and that 81.6% of the reservoir water was available to meet water demand. Small dams pose a risk of not meeting the community’s water demand at least 18% of the time.

show abstract

“…The spatial distribution of continuous and categorical verification statistics provides information on the accuracy of satellite precipitation products at different locations and contributes toward minimizing errors in hydrological studies where satellite products are used [53][54][55]. Based on daily scale data for each year, multi-year average values of these statistics of each station were calculated.…”

Section: Spatial Differences Between Satellite Precipitation Productsmentioning

confidence: 99%

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Ren

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

show abstract

Statistical and qualitative evaluation of multi-sources for hydrological suitability inflood-prone areas of Pakistan

Cited by 27 publications

References 39 publications

A Coupled Dynamic System Inversion Model for Higher Accuracy in Flood Forecasting

A Coupled Dynamic System Inversion Model for Higher Accuracy in Flood Forecasting

Research paper: Water Resources Management Small reservoir water dynamics modeling

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Contact Info

Product

Resources

About