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Catchment-Scale Solutions in the Context of Climate Change

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

Deadline for manuscript submissions: closed (12 November 2021) | Viewed by 13086

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Special Issue Editor

Dr. Tom Ball

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Guest Editor

University of Winchester, Winchester, UK
Interests: climate change; flood risk; functional ecosystem restoration and catchment management

Special Issue Information

Dear Colleagues,

You are invited to submit to this special edition investigating the use of catchment scale solutions to sustainable mangement in the context of climate change. Papers are invited on topics across a broad spectrum of hydrological interest from monitoring and data collection, including use of remote sensing and GIS, through to modelling for resource management, peak flow estimation and flood risk management. Key areas of interest are, at the catchment scale:

quantifying future uncertainty including downscaling
managing uncertainty in future risk
better understanding natural processes
using understanding gained to inform management from low to high extremes of water availability and/ or flow

Papers are particulalry encouraged on the interaction between short and long term trends, for example, surface water-groundwater interactions and the effect of sudden shifts from water scarcity to abundance. Papers are also encouraged on the issue of how management approaches to cope with uncertainty in both low and high flows can be scaled from small to larger catchments.

Dr. Tom Ball
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

monitoring
natural processes
hydrological modelling
scaling
remote sensing
Geographical Information Systems

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Published Papers (4 papers)

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Research

21 pages, 4994 KiB

Open AccessArticle

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

by Helen Weierbach, Aranildo R. Lima, Jared D. Willard, Valerie C. Hendrix, Danielle S. Christianson, Michaelle Lubich and Charuleka Varadharajan

Water 2022, 14(7), 1032; https://doi.org/10.3390/w14071032 - 24 Mar 2022

Cited by 12 | Viewed by 3442

Abstract

Stream temperature (

T_{s}

) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate

T_{s}

predictions at different spatial and temporal scales can inform water management decisions that account for the effects [...] Read more.

Stream temperature (

T_{s}

) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate

T_{s}

predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of

T_{s}

in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly

T_{s}

predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted

T_{s}

with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly

T_{s}

estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly

T_{s}

for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly

T_{s}

in pristine and managed basins with reasonable (<1 °C) accuracy for most locations. Full article

(This article belongs to the Special Issue Catchment-Scale Solutions in the Context of Climate Change)

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20 pages, 12347 KiB

Open AccessArticle

Data- and Model-Based Discharge Hindcasting over a Subtropical River Basin

by Khondoker Billah, Tuan B. Le and Hatim O. Sharif

Water 2021, 13(18), 2560; https://doi.org/10.3390/w13182560 - 17 Sep 2021

Cited by 2 | Viewed by 2498

Abstract

This study aims to evaluate the performance of the Soil and Water Assessment Tool (SWAT), a simple Auto-Regressive with eXogenous input (ARX) model, and a gene expression programming (GEP)-based model in one-day-ahead discharge prediction for the upper Kentucky River Basin. Calibration of the models were carried out for the period of 2002–2005 using daily flow at a stream gauging station unaffected by the flow regulation. Validation of the calibrated models were executed for the period of 2008–2010 at the same gauging station along with another station 88 km downstream. GEP provided the best calibration (coefficient of determination (R) value 0.94 and Nash-Sutcliffe Efficiency (NSE) value of 0.88) and validation (R values of 0.93 and 0.93, NSE values of 0.87 and 0.87, respectively) results at the two gauging stations. While SWAT performed reasonably well in calibration (R value 0.85 and NSE value 0.72), its performance somewhat degraded in validation (R values of 0.85 and 0.82, NSE values of 0.65 and 0.65, for the two stations). ARX performed very well in calibration (R value 0.92, NSE value 0.82) and reasonably well in validation (R values of 0.88 and 0.92, NSE values of 0.76 and 0.85) at the two stations. Research results suggest that sophisticated hydrological models could be outperformed by simple data-driven models and GEP has the advantage to generate functional relationships that allows investigation of the complex nonlinear interrelationships among the input variables. Full article

(This article belongs to the Special Issue Catchment-Scale Solutions in the Context of Climate Change)

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22 pages, 54126 KiB

Open AccessArticle

Climate Change Impacts on Water Resources of the Largest Hydropower Plant Reservoir in Southeast Brazil

by Carlos R. Mello, Nayara P. A. Vieira, Jorge A. Guzman, Marcelo R. Viola, Samuel Beskow and Lívia A. Alvarenga

Water 2021, 13(11), 1560; https://doi.org/10.3390/w13111560 - 31 May 2021

Cited by 21 | Viewed by 4106

Abstract

Approximately 70% of all the electric energy produced in Brazil comes from hydropower plants. In this context, the Grande River Basin (GRB) stands out in Brazil. Some studies have been carried out to investigate the impacts of climate change in tropical regions to support water resources’ management and planning. This study aims to project the changes in the runoff that feed the Furnas Hydropower Plant (FHP) reservoir (GRB-Furnas basin), the largest and most important facility in Southeast Brazil. The lavras simulation of hydrology model (LASH) was used to project the impacts on runoff and hydrological droughts over the century in GRB-Furnas. The regional climate models (RCMs) Eta-HadGEM-ES, Eta-MIROC5, and Eta-CanESM2 forced the LASH model from 2007 to 2099, taking the representative concentration pathways (RCPs) 4.5 and 8.5. LASH simulated the runoff adequately for the baseline period (1961–2005) using the RCMs’ outputs. A noticeable reduction in precipitation was identified in the wet season, especially in the 2007–2040 period for RCP4.5 and in the 2071–2099 period for RCP8.5. As a result, a significant reduction in the runoff, mainly in the baseflow, and an increase in droughts’ severity were projected throughout the XXI Century, which may compromise the water security to the FHP reservoir. Full article

(This article belongs to the Special Issue Catchment-Scale Solutions in the Context of Climate Change)

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17 pages, 4786 KiB

Open AccessArticle

Exploring the Dominant Runoff Processes in Two Typical Basins of the Yellow River, China

by Guang Ran, Shengqi Jian, Qiang Wu, Li Zhang and Caihong Hu

Water 2020, 12(11), 3055; https://doi.org/10.3390/w12113055 - 30 Oct 2020

Cited by 6 | Viewed by 2223

Abstract

Storm runoff in basins is comprised of various runoff processes with widely disparate infiltration and storage capacities, such as Hortonian overland flow (HOF), saturated overland flow (SOF), sub-surface flow (SSF), and deep percolation (DP). Areas may be classified according to these various runoff processes based on the soil characteristics, geology, topography, and land-use. This study analyzes changes in runoff components in the Jialu River basin and the Fen River (Jingle sub-basin) during runoff generation from 1980 to 2013 using the runoff segmentation method. Based on the decision scheme, the dominant runoff process (DRP) in the basins was distinguished using geographic information system (GIS) tools. The impact of different runoff process distributions on the changes in the runoff for the basin was determined. The results show that the floods in the Jialu River basin and Jingle sub-basin were dominated by overland flow components. Compared with 1980–1999, the proportion of overland flow components for 2000–2013 in two basins showed a decreasing trend by 8.3% and 7.1%, respectively, while the interflow and underground runoff components increased. In addition, HOF was the DRP in the Jialu River basin and Jingle sub-basin from 2000 to 2013. The area of the rapid runoff processes (HOF, SOF1, and SSF1) in the Jialu River basin and Jingle sub-basin accounted for 89% and 78% of the entire basin, respectively. In contrast, the slow runoff processes (SOF2, SSF2, and DP) accounted for 11% and 22% of the entire basin, respectively. The runoff of the Jingle sub-basin was substantially lower than that of the Jialu River basin under the same rainfall conditions, because of the influence of the distribution of different runoff processes. Compared with the Jialu River Basin, the peak discharge and runoff of Jingle sub-basin were 190.4 m³/s and 2.85 mm lower on average, respectively. The results of this study provide useful information to understand land-use changes and formulate management practices to reduce flooding in the Yellow River. Full article

(This article belongs to the Special Issue Catchment-Scale Solutions in the Context of Climate Change)

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