Research

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25 pages, 5581 KiB

Open AccessArticle

Evaluation of Green and Grey Flood Mitigation Measures in Rural Watersheds

by Ranko Pudar, Jasna Plavšić and Andrijana Todorović

Appl. Sci. 2020, 10(19), 6913; https://doi.org/10.3390/app10196913 - 02 Oct 2020

Cited by 8 | Viewed by 2825

Floods cause considerable damages worldwide and mitigation of their adverse effects through effective protection measures is needed. Along with the commonly applied “grey” infrastructure, “green” measures that can offer additional benefits, such as ecosystem services, are increasingly being considered lately. While the recent [...] Read more.

Floods cause considerable damages worldwide and mitigation of their adverse effects through effective protection measures is needed. Along with the commonly applied “grey” infrastructure, “green” measures that can offer additional benefits, such as ecosystem services, are increasingly being considered lately. While the recent research tendencies are focused on the effectiveness and the value of green measures in urban areas, this paper presents a comprehensive financial evaluation of green and grey flood mitigation scenarios for a smaller rural watershed. A micro-scale damage model that builds on the hydrodynamic modeling of hazard, detailed asset identification, and damage assessment is presented and applied for evaluation of benefits from various flood mitigation measures in the Tamnava watershed in Serbia. Four scenarios are considered: (1) existing flood protection system; (2) green scenario involving new detention basins; (3) grey infrastructure enhancement by rising of the existing levees and diverting flood discharges; and (4) green-grey scenario that combines scenarios (2) and (3). The benefits (loss reduction) are the greatest with the green scenario and marginally higher with the combined green-grey scenario. The results suggest that for small rural watersheds, a holistic, integrative approach that includes both types of infrastructure can provide the most effective flood risk mitigation. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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23 pages, 1999 KiB

Open AccessArticle

Comparison of Entropy Methods for an Optimal Rain Gauge Network: A Case Study of Daegu and Gyeongbuk Area in South Korea

by Taeyong Kwon, Junghyun Lim, Seongsim Yoon and Sanghoo Yoon

Appl. Sci. 2020, 10(16), 5620; https://doi.org/10.3390/app10165620 - 13 Aug 2020

Cited by 3 | Viewed by 1727

Abstract

To reduce hydrological disasters, it is necessary to operate rain gauge stations at locations where the spatio-temporal characteristics of rainfall can be reflected. Entropy has been widely used to evaluate the designs and uncertainties associated with rain gauge networks. In this study, the [...] Read more.

To reduce hydrological disasters, it is necessary to operate rain gauge stations at locations where the spatio-temporal characteristics of rainfall can be reflected. Entropy has been widely used to evaluate the designs and uncertainties associated with rain gauge networks. In this study, the optimal rain gauge network in the Daegu and Gyeongbuk area, which requires the efficient use of water resources due to low annual precipitation and severe drought damage, was determined using conditional and joint entropy, and the selected network was quantitatively evaluated using the root mean square error (RMSE). To consider spatial distribution, prediction errors were generated using kriging. Four estimators used in entropy calculations were compared, and weighted entropy was calculated by weighting the precipitation. The optimal number of rain gauge stations was determined by calculating the RMSE reduction and the reduction ratio according to the number of selected rain gauge stations. Our findings show that the results of conditional entropy were better than those of joint entropy. The optimal rain gauge stations showed a tendency wherein peripheral rain gauge stations were selected first, with central stations being added afterward. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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15 pages, 3782 KiB

Open AccessArticle

Simulation of Evapotranspiration at a 3-Minute Time Interval Based on Remote Sensing Data and SEBAL Model

by Guoqing Li, Alona Armstrong and Xueli Chang

Appl. Sci. 2020, 10(14), 4919; https://doi.org/10.3390/app10144919 - 17 Jul 2020

Viewed by 1830

Abstract

Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses [...] Read more.

Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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26 pages, 6422 KiB

Open AccessArticle

Modeling Future Streamflow for Adaptive Water Allocation under Climate Change for the Tanjung Karang Rice Irrigation Scheme Malaysia

by Habibu Ismail, Md Rowshon Kamal, Ahmad Fikri b. Abdullah, Deepak Tirumishi Jada and Lai Sai Hin

Appl. Sci. 2020, 10(14), 4885; https://doi.org/10.3390/app10144885 - 16 Jul 2020

Cited by 5 | Viewed by 2547

Abstract

Spatial and temporal climatic variability influence on the productivity of agricultural watershed and irrigation systems. In a large irrigation system, the quantification and regulation of the flow at different locations of the channel is quite difficult manually, leading to a poor delivery of [...] Read more.

Spatial and temporal climatic variability influence on the productivity of agricultural watershed and irrigation systems. In a large irrigation system, the quantification and regulation of the flow at different locations of the channel is quite difficult manually, leading to a poor delivery of supply and demand. Water shortage is a crucial issue due to mismatch between available water and demand at intake point of Tanjung-Karang Irrigation Scheme. This study assessed the potential impacts of climate change on basin outflow for 2010–2039, 2040–2069, and 2070–2099 to the baseline period (1976–2005) and used it as input hydrograph to simulate river discharge. A Hydrologic Engineering Corps Hydrologic Modeling System (HEC-HMS) model driven by projections from ten global climate models (GCMs) with three scenarios (Representative Concentration Pathways (RCPs) 4.5, 6.0, and 8.5) used to simulate the outflow and the Hydrologic Engineering Centers River Analysis System (HEC-RAS) model applied for hydraulic modeling. The projected seasonal streamflow showed a decreasing trend for future periods. The average available irrigation supply for historical period is 15.97 m³/s, which would decrease by 12%, 18%, and 21% under RCPs 4.5, 6.0, and 8.5, respectively. Projected irrigation supply showed oversupply and undersupply to the required supply during the growing season. Simulated discharge could therefore be incorporated into cropping practices to boost the sustainable distribution of water under the new realities of climate change in the future. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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13 pages, 2729 KiB

Open AccessArticle

Climate Change Effects on Hydropower in Mozambique

by Miguel Meque Uamusse, Kamshat Tussupova and Kenneth M Persson

Appl. Sci. 2020, 10(14), 4842; https://doi.org/10.3390/app10144842 - 14 Jul 2020

Cited by 12 | Viewed by 4424

Abstract

The impact of climate change on the production of hydropower in Mozambique is reviewed and regression analysis is applied to evaluate future climate scenarios. The results show that climate change will cause increased variability of precipitation and create flooding that can damage infrastructure [...] Read more.

The impact of climate change on the production of hydropower in Mozambique is reviewed and regression analysis is applied to evaluate future climate scenarios. The results show that climate change will cause increased variability of precipitation and create flooding that can damage infrastructure such as hydropower dams. Climate change can also cause drought that will decrease surface water and reduce hydroelectric generation in Mozambique. Electricity generation is to a major extent performed through large-scale hydropower in Mozambique. To fulfill the sustainable development goals (SDGs) and an increased demand for electricity, several large and many small hydropower projects are planned and were built in the country. The economic lifetime of a hydropower plant is typically 100 years, meaning that the hydrologic regimes for the plants should be evaluated for at least this period. Climate change effects are rarely included in present feasibility studies. Economic implications associated with climate change phenomena are higher in Mozambique than in neighboring countries as its future electricity demand to a large extent is forecasted to be met by hydropower. The large hydropower potential in Mozambique should as well be considered when investing in new power plants in southern Africa. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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30 pages, 585 KiB

Open AccessArticle

A Numerical Model for Enzymatically Induced Calcium Carbonate Precipitation

by Johannes Hommel, Arda Akyel, Zachary Frieling, Adrienne J. Phillips, Robin Gerlach, Alfred B. Cunningham and Holger Class

Appl. Sci. 2020, 10(13), 4538; https://doi.org/10.3390/app10134538 - 30 Jun 2020

Cited by 21 | Viewed by 4857

Abstract

Enzymatically induced calcium carbonate precipitation (EICP) is an emerging engineered mineralization method similar to others such as microbially induced calcium carbonate precipitation (MICP). EICP is advantageous compared to MICP as the enzyme is still active at conditions where microbes, e.g., Sporosarcina pasteurii, [...] Read more.

Enzymatically induced calcium carbonate precipitation (EICP) is an emerging engineered mineralization method similar to others such as microbially induced calcium carbonate precipitation (MICP). EICP is advantageous compared to MICP as the enzyme is still active at conditions where microbes, e.g., Sporosarcina pasteurii, commonly used for MICP, cannot grow. Especially, EICP expands the applicability of ureolysis-induced calcium carbonate mineral precipitation to higher temperatures, enabling its use in leakage mitigation deeper in the subsurface than previously thought to be possible with MICP. A new conceptual and numerical model for EICP is presented. The model was calibrated and validated using quasi-1D column experiments designed to provide the necessary data for model calibration and can now be used to assess the potential of EICP applications for leakage mitigation and other subsurface modifications. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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20 pages, 2401 KiB

Open AccessArticle

Efficient Calibration of a Conceptual Hydrological Model Based on the Enhanced Gauss–Levenberg–Marquardt Procedure

by Andrej Vidmar, Mitja Brilly, Klaudija Sapač and Andrej Kryžanowski

Appl. Sci. 2020, 10(11), 3841; https://doi.org/10.3390/app10113841 - 31 May 2020

Cited by 5 | Viewed by 2528

Abstract

Various models were developed in the past to simulate different hydrological processes. However, discrepancies between simulated and observed values are still significant and pose a challenge to many researchers. Models contain many parameters that cannot be directly measured. The values of most of [...] Read more.

Various models were developed in the past to simulate different hydrological processes. However, discrepancies between simulated and observed values are still significant and pose a challenge to many researchers. Models contain many parameters that cannot be directly measured. The values of most of these parameters are determined in the calibration process conditioning the efficiency of such models. This paper introduces the use of the enhanced Gauss–Levenberg–Marquardt (GLM) procedure in combination with the singular value decomposition (SVD) and Tikhonov regularization to improve the process of hydrological model calibration. The procedure is tested on a freely available hydrological model using a synthetic dataset. Based on several efficiency measures, the GLM procedure, in combination with SVD and Tikhonov regularization, was found to provide efficient model history matching and almost perfect parameter calibration. Moreover, by comparing the results of the proposed procedure with the results of global evolutionary calibration procedures, it was found that the only calibration using the combined GLM procedure gave a perfect fit in low flows. Last but not least, the noise in the calculation results with the combined GLM method was practically the same in either the calibration or validation procedure, suggesting that only computational noise remained in the results. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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17 pages, 8022 KiB

Open AccessArticle

Optimizing Height and Spacing of Check Dam Systems for Better Grassed Channel Infiltration Capacity

by Ahmed Mohammed Sami Al-Janabi, Abdul Halim Ghazali, Badronnisa Yusuf, Saad Sh. Sammen, Haitham Abdulmohsin Afan, Nadhir Al-Ansari, Shamsuddin Shahid and Zaher Mundher Yaseen

Appl. Sci. 2020, 10(11), 3725; https://doi.org/10.3390/app10113725 - 28 May 2020

Cited by 8 | Viewed by 2908

Abstract

The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel [...] Read more.

The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel infiltration capacity. In this study, a mass-balance method was applied to a grassed channel model to investigate the effects of height and spacing of check dams on channel infiltration capacity. Moreover, an empirical infiltration model was derived by improving the modified Kostiakov model for reliable estimation of infiltration capacity of a grassed stormwater channel due to check dams from four hydraulic parameters of channels, namely, the water level, channel base width, channel side slope, and flow velocity. The result revealed that channel infiltration was increased from 12% to 20% with the increase of check dam height from 10 to 20 cm. However, the infiltration was found to decrease from 20% to 19% when a 20 cm height check dam spacing was increased from 10 to 30 m. These results indicate the effectiveness of increasing height of check dams for maximizing the infiltration capacity of grassed stormwater channels and reduction of runoff volume. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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24 pages, 4641 KiB

Open AccessArticle

Assessment of the Future Climate Change Projections on Streamflow Hydrology and Water Availability over Upper Xijiang River Basin, China

by Muhammad Touseef, Lihua Chen, Tabinda Masud, Aziz Khan, Kaipeng Yang, Aamir Shahzad, Muhammad Wajid Ijaz and Yan Wang

Appl. Sci. 2020, 10(11), 3671; https://doi.org/10.3390/app10113671 - 26 May 2020

Cited by 16 | Viewed by 3401

Abstract

Hydrological models are widely applied for simulating complex watershed processes and directly linking meteorological, topographical, land-use, and geological conditions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at two monitoring stations, which improved model performance and increased the reliability [...] Read more.

Hydrological models are widely applied for simulating complex watershed processes and directly linking meteorological, topographical, land-use, and geological conditions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at two monitoring stations, which improved model performance and increased the reliability of flow predictions in the Upper Xijiang River Basin. This study evaluated the potential impacts of climate change on the streamflow and water yield of the Upper Xijiang River Basin using Arc-SWAT. The model was calibrated (1991–1997) and validated (1998–2001) using the Sequential Uncertainty Fitting Algorithm (SUFI-2). Model calibration and validation suggest a good match between the measured and simulated monthly streamflow, indicating the applicability of the model for future daily streamflow predictions. Large negative changes of low flows are projected under future climate scenarios, exhibiting a 10% and 30% decrease in water yield over the watershed on a monthly scale. Overall, findings generally indicated that winter flows are expected to be affected the most, with a maximum impact during the January–April period, followed by the wet monsoon season in the May–September period. Water balance components of the Upper Xijiang River Basin are expected to change significantly due to the projected climate change that, in turn, will seriously affect the water resources and streamflow patterns in the future. Thus, critical problems, such as ground water shortages, drops in agricultural crop yield, and increases in domestic water demand are expected at the Xijiang River Basin. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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15 pages, 3661 KiB

Open AccessArticle

Impacts of Upstream Structures on Downstream Discharge in the Transboundary Imjin River Basin, Korean Peninsula

by Doan Thi Thu Ha, Seon-Ho Kim and Deg-Hyo Bae

Appl. Sci. 2020, 10(9), 3333; https://doi.org/10.3390/app10093333 - 11 May 2020

Cited by 12 | Viewed by 3036

Abstract

The transboundary river basin is a great challenge for water management and disaster reduction due to its specific characteristics. In this study, upstream impacts from natural and artificial sources on the downstream discharge on the Imjin river basin, the well-known transboundary region in [...] Read more.

The transboundary river basin is a great challenge for water management and disaster reduction due to its specific characteristics. In this study, upstream impacts from natural and artificial sources on the downstream discharge on the Imjin river basin, the well-known transboundary region in the Korean peninsula, were evaluated using a hydrological model integrating a dam operation module at an hourly timescale. The module uses a concept of the AutoROM method as the operational rule to update the dam storage and decide water release. Dam storages were translated into water levels using a water level–storage curve. To quantify the impact of hydraulic structures on the Northern Imjin river basin, change in discharge was analyzed in four flood events (2009, 2010, 2011, and 2012). Dam failure scenarios were developed under conditions of the 2010 flood event, in which the releases of 100%, 80%, 50%, and 20% of water storage of Hwanggang dam were simulated. The results indicate that the amount of water released from upstream dams is the main cause of floods in the downstream region. To reduce the risk of floods in the downstream river basin, an optimal dam operation module and information on upstream dams play an important role and contribute to the effective use of water resources. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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24 pages, 16259 KiB

Open AccessArticle

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

by Phong Tung Nguyen, Duong Hai Ha, Mohammadtaghi Avand, Abolfazl Jaafari, Huu Duy Nguyen, Nadhir Al-Ansari, Tran Van Phong, Rohit Sharma, Raghvendra Kumar, Hiep Van Le, Lanh Si Ho, Indra Prakash and Binh Thai Pham

Appl. Sci. 2020, 10(7), 2469; https://doi.org/10.3390/app10072469 - 03 Apr 2020

Cited by 126 | Viewed by 5331

Abstract

Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and [...] Read more.

Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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12 pages, 1904 KiB

Open AccessFeature PaperArticle

Investigation of Rain-On-Snow Floods under Climate Change

by Cenk Sezen, Mojca Šraj, Anže Medved and Nejc Bezak

Appl. Sci. 2020, 10(4), 1242; https://doi.org/10.3390/app10041242 - 12 Feb 2020

Cited by 17 | Viewed by 3009

Abstract

Rain-on-snow (ROS) floods can cause economic damage and endanger human lives due to the compound effect of rainfall and snowmelt, especially under climate change. In this study, possible future changes of seasonality, magnitude and frequency characteristics of ROS floods were investigated for the [...] Read more.

Rain-on-snow (ROS) floods can cause economic damage and endanger human lives due to the compound effect of rainfall and snowmelt, especially under climate change. In this study, possible future changes of seasonality, magnitude and frequency characteristics of ROS floods were investigated for the selected catchments in Slovenia, Europe. For this purpose, five global/regional climate models (GCM/RCM) combinations were applied using the RCP4.5 climate scenario for the period 1981–2100. To determine ROS floods’ characteristics in the future, a lumped conceptual hydrological model Génie Rural à 6 paramètres Journalier (GR6J) with snow module CemaNeige was applied. The results indicate that the number of ROS floods could increase in the future. Moreover, also the magnitudes of extreme ROS floods could increase, while a slight decrease in the median values of ROS flood magnitudes was observed. The strength of seasonality for a high-altitude catchment could decrease in the future. A slight shift in the average ROS floods’ timing could be expected. Furthermore, a catchment located in a temperate continental climate could have a different response to the climate change impact in comparison to a catchment located in a mountain climate with alpine characteristics. Additionally, differences among investigated climate models show a large variability. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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Review

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49 pages, 3409 KiB

Open AccessReview

A Review of the Artificial Neural Network Models for Water Quality Prediction

by Yingyi Chen, Lihua Song, Yeqi Liu, Ling Yang and Daoliang Li

Appl. Sci. 2020, 10(17), 5776; https://doi.org/10.3390/app10175776 - 20 Aug 2020

Cited by 199 | Viewed by 16990

Abstract

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot [...] Read more.

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development. Full article

(This article belongs to the Special Issue Hydrologic and Water Resources Investigations and Modeling)

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