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Enhancing Hydrological Prediction through Modelling with Large Datasets

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 22348

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Dr. Francis Chiew

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Guest Editor

CSIRO Land and Water, Black Mountain, Canberra, ACT 2601, Australia
Interests: water resources assessment; forecasting and prediction; climate change adaptation; integrated basin management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robust prediction of hydrological characteristics (long-term averages, high flow extremes, low flow characteristics, river and floodplain connectivity) are essential for assessments, planning and adaptation in the water and environmental and related sectors. Research through targeted modelling experiments and comparative assessment and characterisation using datasets (streamflow and climate, and physical characteristics) from a very large number of catchments can provide valuable insight and significantly improve hydrological prediction, particularly for ungauged regions. There are increasingly more studies learning from exploring large hydrological datasets, accelerated by faster computing, enhanced digital technology and stronger global collaborative networks. This Special Issue will publish seminal papers on enhancing hydrological prediction through modelling with large data sets. Key areas include predicting hydrological characteristics or signatures, modelling runoff in ungagued catchments and over large regions, hydrological prediction in data sparse regions, predicting impact of development and land use change, and extrapolating hydrological models to predict a future under a different climate and hydrologic regime.

Dr. Francis Chiew
Guest Editor

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Keywords

Hydrological modelling
Hydrological prediction
Large data set
Large sample hydrology
Streamflow
Water
Hydroclimate

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Published Papers (5 papers)

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Research

26 pages, 5515 KiB

Open AccessArticle

Research on the Data-Driven Quality Control Method of Hydrological Time Series Data

by Qun Zhao, Yuelong Zhu, Dingsheng Wan, Yufeng Yu and Xifeng Cheng

Water 2018, 10(12), 1712; https://doi.org/10.3390/w10121712 - 23 Nov 2018

Cited by 19 | Viewed by 4162

Abstract

Ensuring the quality of hydrological data has become a key issue in the field of hydrology. Based on the characteristics of hydrological data, this paper proposes a data-driven quality control method for hydrological data. For continuous hydrological time series data, two combined forecasting models and one statistical control model are constructed from horizontal, vertical, and statistical perspectives and the three models provide three confidence intervals. Set the suspicious level based on the number of confidence intervals for data violations, control the data, and provide suggested values for suspicious and missing data. For the discrete hydrological data with large time-space difference, the similar weight topological map between the neighboring stations is established centering on the hydrological station under the test and it is adjusted continuously with the seasonal changes. Lastly, a spatial interpolation model is established to detect the data. The experimental results show that the quality control method proposed in this paper can effectively detect and control the data, find suspicious and erroneous data, and provide suggested values. Full article

(This article belongs to the Special Issue Enhancing Hydrological Prediction through Modelling with Large Datasets)

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26 pages, 8621 KiB

Open AccessArticle

Computationally Efficient Multivariate Calibration and Validation of a Grid-Based Hydrologic Model in Sparsely Gauged West African River Basins

by Thomas Poméon, Bernd Diekkrüger and Rohini Kumar

Water 2018, 10(10), 1418; https://doi.org/10.3390/w10101418 - 10 Oct 2018

Cited by 23 | Viewed by 4794

Abstract

The prediction of freshwater resources remains a challenging task in West Africa, where the decline of in situ measurements has a detrimental effect on the quality of estimates. In this study, we establish a series of modeling routines for the grid-based mesoscale Hydrologic Model (mHM) using Multiscale Parameter Regionalization (MPR). We provide a computationally efficient application of mHM-MPR across a diverse range of data-scarce basins using in situ observations, remote sensing, and reanalysis inputs. Model performance was first screened for four precipitation datasets and three evapotranspiration calculation methods. Subsequently, we developed a modeling framework in which the pre-screened model is first calibrated using discharge as the observed variable (mHM Q), and next calibrated using a combination of discharge and actual evapotranspiration data (mHM Q/ET). Both model setups were validated in a multi-variable evaluation framework using discharge, actual evapotranspiration, soil moisture and total water storage data. The model performed reasonably well, with mean discharge KGE values of 0.53 (mHM Q) and 0.49 (mHM Q/ET) for the calibration; and 0.23 (mHM Q) and 0.13 (mHM Q/ET) for the validation. Other tested variables were also within a good predictive range. This further confirmed the robustness and well-represented spatial distribution of the hydrologic predictions. Using MPR, the calibrated model can then be scaled to produce outputs at much smaller resolutions. Overall, our analysis highlights the worth of utilizing additional hydrologic variables (together with discharge) for the reliable application of a distributed hydrologic model in sparsely gauged West African river basins. Full article

(This article belongs to the Special Issue Enhancing Hydrological Prediction through Modelling with Large Datasets)

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19 pages, 3633 KiB

Open AccessFeature PaperArticle

Rainfall-Runoff Modelling Considerations to Predict Streamflow Characteristics in Ungauged Catchments and under Climate Change

by Francis H.S. Chiew, Hongxing Zheng and Nicholas J. Potter

Water 2018, 10(10), 1319; https://doi.org/10.3390/w10101319 - 24 Sep 2018

Cited by 15 | Viewed by 5074

Abstract

This paper investigates the prediction of different streamflow characteristics in ungauged catchments and under climate change, with three rainfall-runoff models calibrated against three different objective criteria, using a large data set from 780 catchments across Australia. The results indicate that medium and high flows are relatively easier to predict, suggesting that using a single unique set of parameter values from model calibration against an objective criterion like the Nash–Sutcliffe efficiency is generally adequate and desirable to provide a consistent simulation and interpretation of daily streamflow series and the different medium and high flow characteristics. However, the low flow characteristics are considerably more difficult to predict and will require careful modelling consideration to specifically target the low flow characteristic of interest. The modelling results also show that different rainfall-runoff models and different calibration approaches can give significantly different predictions of climate change impact on streamflow characteristics, particularly for characteristics beyond the long-term averages. Predicting the hydrological impact from climate change, therefore, requires careful modelling consideration and calibration against appropriate objective criteria that specifically target the streamflow characteristic that is being assessed. Full article

(This article belongs to the Special Issue Enhancing Hydrological Prediction through Modelling with Large Datasets)

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11 pages, 2121 KiB

Open AccessArticle

Correction of Precipitation Records through Inverse Modeling in Watersheds of South-Central Chile

by Enrique Muñoz, Mauricio Acuña, Juan Lucero and Ignacio Rojas

Water 2018, 10(8), 1092; https://doi.org/10.3390/w10081092 - 17 Aug 2018

Cited by 5 | Viewed by 3216

Abstract

Precipitation is the main input in the water balance of watersheds; therefore, correct estimates are necessary for water resources management and decision making. In south-central Chile there is a low density of precipitation gauges (~1 station/675 km²), most of which are located in low-altitude areas. The spatial distribution of precipitation is, therefore, not properly recorded. In this study an inverse modeling approach is used to estimate the extent to which precipitation amounts must be corrected. Using a lumped water balance model, a factor for correcting precipitation data is calculated for 41 watersheds located in south-central Chile. Then, based on a geo-statistical interpolation, a map for correcting the precipitation amounts is proposed and a validation of these corrections is achieved. The results show that in gently sloping areas, the precipitation records are more representative than in steep mountain areas. In addition, the higher the mountains, the less representative the precipitation records become. Full article

(This article belongs to the Special Issue Enhancing Hydrological Prediction through Modelling with Large Datasets)

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14 pages, 2053 KiB

Open AccessArticle

Potential Threats from Variations of Hydrological Parameters to the Yellow River and Pearl River Basins in China over the Next 30 Years

by Lüliu Liu, Tong Jiang, Hongmei Xu and Yong Wang

Water 2018, 10(7), 883; https://doi.org/10.3390/w10070883 - 2 Jul 2018

Cited by 21 | Viewed by 4379

Abstract

An assessment of the impact of climate change on regional hydrological processes is vital for effective water resources management and planning. This study investigated the potential effects of climate change on water availability, seasonal runoff, flooding, and water stress in the Yellow River and Pearl River basins in China over the next 30 years, using a semi-distributed hydrological model based on a combination of five general circulation models with four Representative Concentration Pathway scenarios and five Shared Socioeconomic Pathways. The results indicated annual mean temperature could rise higher in the Yellow River Basin than the Xijiang River Basin during 2021–2050. Higher risks of small floods and some big floods, but lower risks of rare big floods are projected for both basins. Water scarcity will continually threaten the Yellow River Basin, especially during the dry season and around 2025. In comparison with the effects of climate change, population variation was expected to have a greater impact on water scarcity. A longer and drier dry season is projected for the Pearl River Basin, which could aggravate water stress and saltwater intrusion into the Pearl River delta. Although the present findings have implications for water resource management planning, caution should be observed because of the neglect of reservoir/dam operations and inherent projection uncertainty. Full article

(This article belongs to the Special Issue Enhancing Hydrological Prediction through Modelling with Large Datasets)

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