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Advances in Integrating Distributed Hydrologic Models with Novel Monitoring Data

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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 (28 February 2019) | Viewed by 25532

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Special Issue Editors

Prof. Dr. Mantha S. Phanikumar

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Guest Editor

Department of Civil & Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
Interests: hydrology and water resources; environmental fluid mechanics; limnology, coastal and nearshore processes; coastal water quality; biophysical modeling; computational methods; integrated hydrologic modeling

Prof. Dr. Stefan Krause

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Guest Editor

School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
Interests: reactive transport processes across aquatic–terrestrial interfaces; nutrient and carbon cycling; fate and transport of microplastics; distributed sensor networks; in-situ high-frequency sensor technologies; hyporheic zone processes
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Special Issue Information

Dear Colleagues,

Progress in sensors and sensor networks over the last decade has positively impacted water management, with accurate and timely data becoming the key to identifying existing and emerging issues. In parallel, there has been a surge of interest in the development and application of distributed hydrologic models that integrate physical, chemical, and ecological/biological processes across different hydrologic domains and scales. Applications of these new models are opening up the possibility to gain new insights into the inner workings of complex water systems (e.g., the food-energy-water nexus) while allowing model variables and states to be evaluated using new types of data.

This Special Issue on “Advances in Integrating Distributed Hydrologic Models with Novel Monitoring Data” invites papers that report recent developments in monitoring and modelling of water quality and quantity in catchments and their sub-units (rivers, streams, wetlands and groundwater) with a focus on new types of sensors and integration between models and data. These include, but not limited to, distributed sensor networks and smart, real-time sensing of temperature, nutrients, dissolved oxygen, microbial metabolism, and species abundance. We welcome theoretical, computational and field studies that involve multiple hydrologic domains and interactions, as well as contributions that demonstrate novel applications.

Prof. Dr. Mantha S. Phanikumar
Prof. Dr. Stefan Krause
Guest Editors

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Keywords

sensors
wireless sensor networks
distributed hydrologic models
water quality
smart sensing
real-time sensing
hydrologic fluxes

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Published Papers (5 papers)

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Research

20 pages, 5351 KiB

Open AccessArticle

Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation

by Lihua Xiong and Ling Zeng

Water 2019, 11(4), 666; https://doi.org/10.3390/w11040666 - 31 Mar 2019

Cited by 22 | Viewed by 4721

Abstract

With the increased availability of remote sensing products, more hydrological variables (e.g., soil moisture and evapotranspiration) other than streamflow data are introduced into the calibration procedure of a hydrological model. However, how the incorporation of these hydrological variables influences the calibration results remains unclear. This study aims to analyze the impact of remote sensing soil moisture data in the joint calibration of a distributed hydrological model. The investigation was carried out in Qujiang and Ganjiang catchments in southern China, where the Dem-based Distributed Rainfall-runoff Model (DDRM) was calibrated under different calibration schemes where the streamflow data and the remote sensing soil moisture are assigned to different weights in the objective function. The remote sensing soil moisture data are from the SMAP L3 soil moisture product. The results show that different weights of soil moisture in the objective function can lead to very slight differences in simulation performance of soil moisture and streamflow. Besides, the joint calibration shows no apparent advantages in terms of streamflow simulation over the traditional calibration using streamflow data only. More studies including various remote sensing soil moisture products are necessary to access their effect on the joint calibration. Full article

(This article belongs to the Special Issue Advances in Integrating Distributed Hydrologic Models with Novel Monitoring Data)

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18 pages, 5627 KiB

Open AccessFeature PaperArticle

Optimal Energy Recovery from Water Distribution Systems Using Smart Operation Scheduling

by Ilker T. Telci and Mustafa M. Aral

Water 2018, 10(10), 1464; https://doi.org/10.3390/w10101464 - 17 Oct 2018

Cited by 15 | Viewed by 3962

Abstract

Micro hydropower generators (micro turbines), are used to recover excess energy from hydraulic systems and these applications have important potential in renewable energy production. One of the most viable environments for the use of micro turbines is the water distribution network where, by design, there is always excess energy since minimum pressures are to be maintained throughout the system, and the system is designed to meet future water supply needs of a planning period. Under these circumstances, maintaining the target pressures is not an easy task due to the increasing complexity of the water distribution network to supply future demands. As a result, pressures at several locations of the network tend to be higher than the required minimum pressures. In this paper, we outline a methodology to recover this excess energy using smart operation management and the best placement of micro turbines in the system. In this approach, the best micro turbine locations and their operation schedule is determined to recover as much available excess energy as possible from the water distribution network while satisfying the current demand for water supply and pressure. Genetic algorithms (GAs) are used to obtain optimal solutions and a “smart seeding” approach is developed to improve the performance of the GA. The Dover Township pump-driven water distribution system in New Jersey, United States of America (USA) was selected as the study area to test the proposed methodology. This pump-driven network was also converted into a hypothetical gravity-driven network to observe the differences between the energy recovery potential of the pump-driven and gravity-driven systems. The performance of the energy recovery system was evaluated by calculating the equivalent number of average American homes that can be fed by the energy produced and the resulting carbon-dioxide emission reductions that may be achieved. The results show that this approach is an effective tool for applications in renewable energy production in water distribution systems for small towns such as Dover Township. It is expected that, for larger water distribution systems with high energy usage, the energy recovery potential will be much higher. Full article

(This article belongs to the Special Issue Advances in Integrating Distributed Hydrologic Models with Novel Monitoring Data)

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23 pages, 6819 KiB

Open AccessArticle

Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling over the Luanhe River Basin, China

by Peizhen Ren, Jianzhu Li, Ping Feng, Yuangang Guo and Qiushuang Ma

Water 2018, 10(6), 677; https://doi.org/10.3390/w10060677 - 24 May 2018

Cited by 46 | Viewed by 5072

Abstract

Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. The 3B42 product shows the best hydrological performance, while PERSIANN shows unsatisfactory hydrological performance. Therefore, 3B42 performs better than the other two satellite precipitation products over the study area. Full article

(This article belongs to the Special Issue Advances in Integrating Distributed Hydrologic Models with Novel Monitoring Data)

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26 pages, 2120 KiB

Open AccessFeature PaperArticle

Ensemble Kalman Filter Assimilation of ERT Data for Numerical Modeling of Seawater Intrusion in a Laboratory Experiment

by Véronique Bouzaglou, Elena Crestani, Paolo Salandin, Erwan Gloaguen and Matteo Camporese

Water 2018, 10(4), 397; https://doi.org/10.3390/w10040397 - 28 Mar 2018

Cited by 18 | Viewed by 6762

Abstract

Seawater intrusion in coastal aquifers is a worldwide problem exacerbated by aquifer overexploitation and climate changes. To limit the deterioration of water quality caused by saline intrusion, research studies are needed to identify and assess the performance of possible countermeasures, e.g., underground barriers. Within this context, numerical models are fundamental to fully understand the process and for evaluating the effectiveness of the proposed solutions to contain the saltwater wedge; on the other hand, they are typically affected by uncertainty on hydrogeological parameters, as well as initial and boundary conditions. Data assimilation methods such as the ensemble Kalman filter (EnKF) represent promising tools that can reduce such uncertainties. Here, we present an application of the EnKF to the numerical modeling of a laboratory experiment where seawater intrusion was reproduced in a specifically designed sandbox and continuously monitored with electrical resistivity tomography (ERT). Combining EnKF and the SUTRA model for the simulation of density-dependent flow and transport in porous media, we assimilated the collected ERT data by means of joint and sequential assimilation approaches. In the joint approach, raw ERT data (electrical resistances) are assimilated to update both salt concentration and soil parameters, without the need for an electrical inversion. In the sequential approach, we assimilated electrical conductivities computed from a previously performed electrical inversion. Within both approaches, we suggest dual-step update strategies to minimize the effects of spurious correlations in parameter estimation. The results show that, in both cases, ERT data assimilation can reduce the uncertainty not only on the system state in terms of salt concentration, but also on the most relevant soil parameters, i.e., saturated hydraulic conductivity and longitudinal dispersivity. However, the sequential approach is more prone to filter inbreeding due to the large number of observations assimilated compared to the ensemble size. Full article

(This article belongs to the Special Issue Advances in Integrating Distributed Hydrologic Models with Novel Monitoring Data)

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16 pages, 20103 KiB

Open AccessArticle

Identification of a Contaminant Source Location in a River System Using Random Forest Models

by Yoo Jin Lee, Chuljin Park and Mi Lim Lee

Water 2018, 10(4), 391; https://doi.org/10.3390/w10040391 - 27 Mar 2018

Cited by 24 | Viewed by 4239

Abstract

We consider the problem of identifying the source location of a contaminant via analyzing changes in concentration levels observed by a sensor network in a river system. To address this problem, we propose a framework including two main steps: (i) pre-processing data; and (ii) training and testing a classification model. Specifically, we first obtain a data set presenting concentration levels of a contaminant from a simulation model, and extract numerical characteristics from the data set. Then, random forest models are generated and assessed to identify the source location of a contaminant. By using the numerical characteristics from the prior step as their inputs, the models provide outputs representing the possibility, i.e., a value between 0 and 1, of a spill event at each candidate location. The performance of the framework is tested on a part of the Altamaha river system in the state of Georgia, United States of America. Full article

(This article belongs to the Special Issue Advances in Integrating Distributed Hydrologic Models with Novel Monitoring Data)

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