Pub Date : 2023-08-12DOI: 10.3390/hydrology10080170
Kyungeun Kim, T. Scanlon, Sophia Bakar, V. Lakshmi
Droughts are projected to increase in intensity and frequency with the rise of global mean temperatures. However, not all drought indices equally capture the variety of influences that each hydrologic component has on the duration and magnitude of a period of water deficit. While such indices often agree with one another due to precipitation being the major input, heterogeneous responses caused by groundwater recharge, soil moisture memory, and vegetation dynamics may lead to a decoupling of identifiable drought conditions. As a semi-arid basin, the Limpopo River Basin (LRB) is a severely water-stressed region associated with unique climate patterns that regularly affect hydrological extremes. In this study, we find that vegetation indices show no significant long-term trends (S-statistic 9; p-value 0.779), opposing that of the modeled groundwater anomalies (S-statistic -57; p-value 0.05) in the growing season for a period of 18 years (2004–2022). Although the Mann-Kendall time series statistics for NDVI and drought indices are non-significant when basin-averaged, spatial heterogeneity further reveals that such a decoupling trend between vegetation and groundwater anomalies is indeed significant (p-value < 0.05) in colluvial, low-land aquifers to the southeast, while they remain more coupled in the central-west LRB, where more bedrock aquifers dominate. The conclusions of this study highlight the importance of ecological conditions with respect to water availability and suggest that water management must be informed by local vegetation species, especially in the face of depleting groundwater resources.
{"title":"Decoupling of Ecological and Hydrological Drought Conditions in the Limpopo River Basin Inferred from Groundwater Storage and NDVI Anomalies","authors":"Kyungeun Kim, T. Scanlon, Sophia Bakar, V. Lakshmi","doi":"10.3390/hydrology10080170","DOIUrl":"https://doi.org/10.3390/hydrology10080170","url":null,"abstract":"Droughts are projected to increase in intensity and frequency with the rise of global mean temperatures. However, not all drought indices equally capture the variety of influences that each hydrologic component has on the duration and magnitude of a period of water deficit. While such indices often agree with one another due to precipitation being the major input, heterogeneous responses caused by groundwater recharge, soil moisture memory, and vegetation dynamics may lead to a decoupling of identifiable drought conditions. As a semi-arid basin, the Limpopo River Basin (LRB) is a severely water-stressed region associated with unique climate patterns that regularly affect hydrological extremes. In this study, we find that vegetation indices show no significant long-term trends (S-statistic 9; p-value 0.779), opposing that of the modeled groundwater anomalies (S-statistic -57; p-value 0.05) in the growing season for a period of 18 years (2004–2022). Although the Mann-Kendall time series statistics for NDVI and drought indices are non-significant when basin-averaged, spatial heterogeneity further reveals that such a decoupling trend between vegetation and groundwater anomalies is indeed significant (p-value < 0.05) in colluvial, low-land aquifers to the southeast, while they remain more coupled in the central-west LRB, where more bedrock aquifers dominate. The conclusions of this study highlight the importance of ecological conditions with respect to water availability and suggest that water management must be informed by local vegetation species, especially in the face of depleting groundwater resources.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48006938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-11DOI: 10.3390/hydrology10080169
Hamza Salahudin, M. Shoaib, R. Albano, Muhammad Azhar Inam Baig, Muhammad Hammad, Ali Raza, Alamgir Akhtar, Muhammad Usman Ali
To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R2 Multan = 0.97, R2 Skardu = 0.99, R2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance.
{"title":"Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data","authors":"Hamza Salahudin, M. Shoaib, R. Albano, Muhammad Azhar Inam Baig, Muhammad Hammad, Ali Raza, Alamgir Akhtar, Muhammad Usman Ali","doi":"10.3390/hydrology10080169","DOIUrl":"https://doi.org/10.3390/hydrology10080169","url":null,"abstract":"To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R2 Multan = 0.97, R2 Skardu = 0.99, R2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47453427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.3390/hydrology10080165
Lara Castagnolli, Fernando Santos Boggiani, Jeferson Alberto de Lima, Marcelle Teodoro Lima, K. Tonello
This study investigated the hydrological properties of litter in different vegetation cover types, including Eucalyptus sp. plantation, Agroforestry, and Restoration Forest. The research focused on evaluating litter accumulation, composition, water holding capacity, and effective water retention. The results revealed variations in litter accumulation among the stands, and especially Eucalyptus sp., which had a higher proportion of branches compared to leaves. The water holding capacity of the litter differed among the stands. Agroforest and Restoration Forest showed higher litter water capacities than Eucalyptus sp. The composition and decomposition stage of the litter fractions influenced their water retention capabilities, with leaves exhibiting superior water retention. In contrast, branches had lower water absorption due to their hydrophobic nature. Despite these differences, the effective water retention, which indicates the ability of litter to intercept precipitation, was similar among the stands. The findings highlight the importance of considering litter composition and species-specific characteristics in understanding the hydrological functions of litter. This knowledge contributes to effective conservation and management strategies for sustainable land use practices and water resource management. Further research is recommended to expand the study’s scope to include a wider range of forest types and natural field conditions, providing a more comprehensive understanding of litter hydrological functions and their implications for ecosystem processes.
{"title":"Hydrological Properties of Litter in Different Vegetation Types: Implications for Ecosystem Functioning","authors":"Lara Castagnolli, Fernando Santos Boggiani, Jeferson Alberto de Lima, Marcelle Teodoro Lima, K. Tonello","doi":"10.3390/hydrology10080165","DOIUrl":"https://doi.org/10.3390/hydrology10080165","url":null,"abstract":"This study investigated the hydrological properties of litter in different vegetation cover types, including Eucalyptus sp. plantation, Agroforestry, and Restoration Forest. The research focused on evaluating litter accumulation, composition, water holding capacity, and effective water retention. The results revealed variations in litter accumulation among the stands, and especially Eucalyptus sp., which had a higher proportion of branches compared to leaves. The water holding capacity of the litter differed among the stands. Agroforest and Restoration Forest showed higher litter water capacities than Eucalyptus sp. The composition and decomposition stage of the litter fractions influenced their water retention capabilities, with leaves exhibiting superior water retention. In contrast, branches had lower water absorption due to their hydrophobic nature. Despite these differences, the effective water retention, which indicates the ability of litter to intercept precipitation, was similar among the stands. The findings highlight the importance of considering litter composition and species-specific characteristics in understanding the hydrological functions of litter. This knowledge contributes to effective conservation and management strategies for sustainable land use practices and water resource management. Further research is recommended to expand the study’s scope to include a wider range of forest types and natural field conditions, providing a more comprehensive understanding of litter hydrological functions and their implications for ecosystem processes.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47602033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.3390/hydrology10080167
S. Stefanidis, Dimitra Rossiou, N. Proutsos
Drought is a significant natural hazard with widespread socioeconomic and environmental impacts. This study investigated the long-term drought characteristics in a Mediterranean oak forest ecosystem using the Standardized Precipitation Evapotranspiration Index (SPEI) at various time scales and seasons. The analysis was based on a long-term time series dataset obtained from a meteorological station located at the University Forest of Taxiarchis in Greece. The dataset encompassed a substantial time span of 47 years of continuous monitoring, from 1974 to 2020. To accomplish the goals of the current research, the SPEI was calculated for 3, 6, 12, and 24-month periods, and drought events were identified. The Mann-Kendall (M-K) test was used to analyze the trends in drought severity and evaluate the trends significance. The results showed that shorter time scales (SPEI3 and SPEI6) were more efficient for identifying short-term droughts, while longer time scales (SPEI12 and SPEI24) were better for identifying less frequent but longer-lasting drought episodes. The analysis consistently revealed positive trends across all seasons and time scales, indicating an overall transition towards wetter conditions. Nearly all the data series for SPEI12 and SPEI24 exhibited statistically significant upward trends (wetter conditions) at a 95% confidence level. However, more intense events were detected during the recent decade using the seasonal analysis. Additionally, as the time scale expanded, the magnitude of these trends increased. The findings contributed to a better understanding of drought dynamics in Mediterranean oak forests and provided valuable information for forest management and climate change adaptation planning.
{"title":"Drought Severity and Trends in a Mediterranean Oak Forest","authors":"S. Stefanidis, Dimitra Rossiou, N. Proutsos","doi":"10.3390/hydrology10080167","DOIUrl":"https://doi.org/10.3390/hydrology10080167","url":null,"abstract":"Drought is a significant natural hazard with widespread socioeconomic and environmental impacts. This study investigated the long-term drought characteristics in a Mediterranean oak forest ecosystem using the Standardized Precipitation Evapotranspiration Index (SPEI) at various time scales and seasons. The analysis was based on a long-term time series dataset obtained from a meteorological station located at the University Forest of Taxiarchis in Greece. The dataset encompassed a substantial time span of 47 years of continuous monitoring, from 1974 to 2020. To accomplish the goals of the current research, the SPEI was calculated for 3, 6, 12, and 24-month periods, and drought events were identified. The Mann-Kendall (M-K) test was used to analyze the trends in drought severity and evaluate the trends significance. The results showed that shorter time scales (SPEI3 and SPEI6) were more efficient for identifying short-term droughts, while longer time scales (SPEI12 and SPEI24) were better for identifying less frequent but longer-lasting drought episodes. The analysis consistently revealed positive trends across all seasons and time scales, indicating an overall transition towards wetter conditions. Nearly all the data series for SPEI12 and SPEI24 exhibited statistically significant upward trends (wetter conditions) at a 95% confidence level. However, more intense events were detected during the recent decade using the seasonal analysis. Additionally, as the time scale expanded, the magnitude of these trends increased. The findings contributed to a better understanding of drought dynamics in Mediterranean oak forests and provided valuable information for forest management and climate change adaptation planning.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46560230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.3390/hydrology10080166
Denilson Alves de Melo, P. C. Silva, Adriana Rodolfo Da Costa, J. Delmond, Ana Flávia Alves Ferreira, Johnny Alves de Souza, José Francisco de Oliveira-Júnior, Jhon Lennon Bezerra da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, P. R. Giongo, Maria Beatriz Ferreira, Abelardo Antônio de Assunção Montenegro, H. F. E. de Oliveira, Thieres George Freire da Silva, Marcos Vinícius da Silva
The objective of this study was to develop and calibrate a photovoltaic-powered soil moisture sensor (SMS) for irrigation management. Soil moisture readings obtained from the sensor were compared with gravimetric measurements. An automated SMS was used in two trials: (i) okra crop (Abelmoschus esculentus) and (ii) chili pepper (Capsicum frutescens). All sensors were calibrated and automated using an Arduino Mega board with C++. The soil moisture data were subjected to descriptive statistical analysis. The data recorded by the equipment was correlated with the gravimetric method. The determination coefficient (R2), Pearson correlation (r), and root mean square error (RMSE) were adopted as criteria for equipment validation. The results show that our SMS achieved an R2 value of 0.70 and an r value of 0.84. Notably, there was a striking similarity observed between SMS and gravimetric data, with RMSE values of 3.95 and 4.01, respectively. The global model developed exhibited highly efficient outcomes with R2 (0.98) and r (0.99) values. The applicability of the developed SMS facilitates irrigation management with accuracy and real-time monitoring using digital data. The automation of the SMS emerges as a real-time and precise alternative for performing irrigation at the right moment and in the correct amount, thus avoiding water losses.
{"title":"Development and Automation of a Photovoltaic-Powered Soil Moisture Sensor for Water Management","authors":"Denilson Alves de Melo, P. C. Silva, Adriana Rodolfo Da Costa, J. Delmond, Ana Flávia Alves Ferreira, Johnny Alves de Souza, José Francisco de Oliveira-Júnior, Jhon Lennon Bezerra da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, P. R. Giongo, Maria Beatriz Ferreira, Abelardo Antônio de Assunção Montenegro, H. F. E. de Oliveira, Thieres George Freire da Silva, Marcos Vinícius da Silva","doi":"10.3390/hydrology10080166","DOIUrl":"https://doi.org/10.3390/hydrology10080166","url":null,"abstract":"The objective of this study was to develop and calibrate a photovoltaic-powered soil moisture sensor (SMS) for irrigation management. Soil moisture readings obtained from the sensor were compared with gravimetric measurements. An automated SMS was used in two trials: (i) okra crop (Abelmoschus esculentus) and (ii) chili pepper (Capsicum frutescens). All sensors were calibrated and automated using an Arduino Mega board with C++. The soil moisture data were subjected to descriptive statistical analysis. The data recorded by the equipment was correlated with the gravimetric method. The determination coefficient (R2), Pearson correlation (r), and root mean square error (RMSE) were adopted as criteria for equipment validation. The results show that our SMS achieved an R2 value of 0.70 and an r value of 0.84. Notably, there was a striking similarity observed between SMS and gravimetric data, with RMSE values of 3.95 and 4.01, respectively. The global model developed exhibited highly efficient outcomes with R2 (0.98) and r (0.99) values. The applicability of the developed SMS facilitates irrigation management with accuracy and real-time monitoring using digital data. The automation of the SMS emerges as a real-time and precise alternative for performing irrigation at the right moment and in the correct amount, thus avoiding water losses.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42877703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.3390/hydrology10080168
G. Senay, S. Kagone, Gabriel E. L. Parrish, K. Khand, O. Boiko, N. Velpuri
We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of an evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources.
{"title":"Improvements and Evaluation of the Agro-Hydrologic VegET Model for Large-Area Water Budget Analysis and Drought Monitoring","authors":"G. Senay, S. Kagone, Gabriel E. L. Parrish, K. Khand, O. Boiko, N. Velpuri","doi":"10.3390/hydrology10080168","DOIUrl":"https://doi.org/10.3390/hydrology10080168","url":null,"abstract":"We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of an evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45578909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.3390/hydrology10080164
Clara Letessier, Jean-Louis Cardi, A. Dussel, Isa Ebtehaj, H. Bonakdari
Given that the primary cause of flooding in Ontario, Canada, is attributed to spring floods, it is crucial to incorporate temperature as an input variable in flood prediction models with machine learning algorithms. This inclusion enables a comprehensive understanding of the intricate dynamics involved, particularly the impact of heatwaves on snowmelt, allowing for more accurate flood prediction. This paper presents a novel machine learning approach called the Adaptive Structure of the Group Method of Data Handling (ASGMDH) for predicting daily river flow rates, incorporating measured discharge from the previous day as a historical record summarizing watershed characteristics, along with real-time data on air temperature and precipitation. To propose a comprehensive machine learning model, four different scenarios with various input combinations were examined. The simplest model with three parameters (maximum temperature, precipitation, historical daily river flow discharge) achieves high accuracy, with an R2 value of 0.985 during training and 0.992 during testing, demonstrating its reliability and potential for practical application. The developed ASGMDH model demonstrates high accuracy for the study area, with a significant number of samples having a relative error of less than 15%. The final ASGMDH-based model has only a second-order polynomial (AICc = 19,648.71), while it is seven for the classical GMDH-based model (AICc = 19,701.56). The sensitivity analysis reveals that maximum temperature significantly impacts the prediction of daily river flow discharge.
{"title":"Enhancing Flood Prediction Accuracy through Integration of Meteorological Parameters in River Flow Observations: A Case Study Ottawa River","authors":"Clara Letessier, Jean-Louis Cardi, A. Dussel, Isa Ebtehaj, H. Bonakdari","doi":"10.3390/hydrology10080164","DOIUrl":"https://doi.org/10.3390/hydrology10080164","url":null,"abstract":"Given that the primary cause of flooding in Ontario, Canada, is attributed to spring floods, it is crucial to incorporate temperature as an input variable in flood prediction models with machine learning algorithms. This inclusion enables a comprehensive understanding of the intricate dynamics involved, particularly the impact of heatwaves on snowmelt, allowing for more accurate flood prediction. This paper presents a novel machine learning approach called the Adaptive Structure of the Group Method of Data Handling (ASGMDH) for predicting daily river flow rates, incorporating measured discharge from the previous day as a historical record summarizing watershed characteristics, along with real-time data on air temperature and precipitation. To propose a comprehensive machine learning model, four different scenarios with various input combinations were examined. The simplest model with three parameters (maximum temperature, precipitation, historical daily river flow discharge) achieves high accuracy, with an R2 value of 0.985 during training and 0.992 during testing, demonstrating its reliability and potential for practical application. The developed ASGMDH model demonstrates high accuracy for the study area, with a significant number of samples having a relative error of less than 15%. The final ASGMDH-based model has only a second-order polynomial (AICc = 19,648.71), while it is seven for the classical GMDH-based model (AICc = 19,701.56). The sensitivity analysis reveals that maximum temperature significantly impacts the prediction of daily river flow discharge.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42611556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-09DOI: 10.3390/hydrology10080163
F. Costa, António Vieira
The identification and characterization of barriers to river continuity are essential for the preparation of an inventory of hydraulic infrastructure. To this end, it is necessary to define the main identifying and characterizing elements of hydraulic infrastructures and descriptors of ecological continuity, with information that can characterize them from the point of view of their impact on the watercourse. Several authors have defined decision criteria for the removal of existing hydraulic structures in watercourses and their application, reinforcing the environmental benefits of the elimination of these hydraulic structures. In the present work, we proposed to develop a methodology for the evaluation of barriers in the Selho River (Guimarães Municipality, Northwest Portugal), elaborating an Environmental Condition Index (ECI) based on hydromorphological, socioeconomical, and ecological criteria, which allowed the identification of 43 weirs, of which 95% revealed quality inferior to Good. Following the application of a decision support methodology for the removal of hydraulic structures, it was possible to determine that 16 of the 43 weirs evaluated could be subject to removal, 26 would be under conditioned removal, and only 1 would be able to remain unchanged.
{"title":"Stream Barrier Removal: Are New Approaches Possible in Small Rivers? The Case of the Selho River (Northwestern Portugal)","authors":"F. Costa, António Vieira","doi":"10.3390/hydrology10080163","DOIUrl":"https://doi.org/10.3390/hydrology10080163","url":null,"abstract":"The identification and characterization of barriers to river continuity are essential for the preparation of an inventory of hydraulic infrastructure. To this end, it is necessary to define the main identifying and characterizing elements of hydraulic infrastructures and descriptors of ecological continuity, with information that can characterize them from the point of view of their impact on the watercourse. Several authors have defined decision criteria for the removal of existing hydraulic structures in watercourses and their application, reinforcing the environmental benefits of the elimination of these hydraulic structures. In the present work, we proposed to develop a methodology for the evaluation of barriers in the Selho River (Guimarães Municipality, Northwest Portugal), elaborating an Environmental Condition Index (ECI) based on hydromorphological, socioeconomical, and ecological criteria, which allowed the identification of 43 weirs, of which 95% revealed quality inferior to Good. Following the application of a decision support methodology for the removal of hydraulic structures, it was possible to determine that 16 of the 43 weirs evaluated could be subject to removal, 26 would be under conditioned removal, and only 1 would be able to remain unchanged.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47649056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.3390/hydrology10080162
N. Malamos, Dimitrios Koulouris, I. Tsirogiannis, Demetris Koutsoyiannis
The evaluation of weather forecast accuracy is of major interest in decision making in almost every sector of the economy and in civil protection. To this, a detailed assessment of Bologna Limited-Area Model (BOLAM) seven days fine grid 3 h predictions is made for precipitation, air temperature, relative humidity, and wind speed over a large lowland agricultural area of a Mediterranean-type climate, characterized by hot summers and rainy moderate winters (plain of Arta, NW Greece). Timeseries that cover a four-year period (2016–2019) from seven agro-meteorological stations located at the study area are used to run a range of contingency and accuracy measures as well as Taylor diagrams, and the results are thoroughly discussed. The overall results showed that the model failed to comply with the precipitation regime throughout the study area, while the results were mediocre for wind speed. Considering relative humidity, the results revealed acceptable performance and good correlation between the model output and the observed values, for the early days of forecast. Only in air temperature, the forecasts exhibited very good performance. Discussion is made on the ability of the model to predict major rainfall events and to estimate water budget components as rainfall and reference evapotranspiration. The need for skilled weather forecasts from improved versions of the examined model that may incorporate post-processing techniques to improve predictions or from other forecasting services is underlined.
{"title":"Evaluation of BOLAM Fine Grid Weather Forecasts with Emphasis on Hydrological Applications","authors":"N. Malamos, Dimitrios Koulouris, I. Tsirogiannis, Demetris Koutsoyiannis","doi":"10.3390/hydrology10080162","DOIUrl":"https://doi.org/10.3390/hydrology10080162","url":null,"abstract":"The evaluation of weather forecast accuracy is of major interest in decision making in almost every sector of the economy and in civil protection. To this, a detailed assessment of Bologna Limited-Area Model (BOLAM) seven days fine grid 3 h predictions is made for precipitation, air temperature, relative humidity, and wind speed over a large lowland agricultural area of a Mediterranean-type climate, characterized by hot summers and rainy moderate winters (plain of Arta, NW Greece). Timeseries that cover a four-year period (2016–2019) from seven agro-meteorological stations located at the study area are used to run a range of contingency and accuracy measures as well as Taylor diagrams, and the results are thoroughly discussed. The overall results showed that the model failed to comply with the precipitation regime throughout the study area, while the results were mediocre for wind speed. Considering relative humidity, the results revealed acceptable performance and good correlation between the model output and the observed values, for the early days of forecast. Only in air temperature, the forecasts exhibited very good performance. Discussion is made on the ability of the model to predict major rainfall events and to estimate water budget components as rainfall and reference evapotranspiration. The need for skilled weather forecasts from improved versions of the examined model that may incorporate post-processing techniques to improve predictions or from other forecasting services is underlined.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42885825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-02DOI: 10.3390/hydrology10080161
Ouyang Ying, John A. Stanturf, Marcus D. Williams, Evgeniy Botmann, Palle Madsen
Estimation of hydrological processes is critical to water resource management, water supply planning, ecological protection, and climate change impact assessment. Mountains in Central Asia are the major source of water for rivers and agricultural practices. The disturbance of mountain forests in the region has altered the hydrological processes and accelerated soil erosion, mudflow, landslides, and flooding. We used the SWAT (Soil and Water Assessment Tool) model calibrated and validated with remote sensing data to quantify the mountainous hydrological processes in the Aktash River watershed (ARW) of Uzbekistan, Central Asia. Simulations showed that the daily surface runoff and streamflow closely responded to daily precipitation. Groundwater discharge reached its maximum in winter because of snowmelt. The wet months were from July to December, and the dry months were from January to June. The magnitudes of the seasonal hydrological processes were in the following order: fall > summer > winter > spring for precipitation and surface runoff; summer > spring > fall > winter for evapotranspiration (ET); winter > spring > fall > summer for snowmelt; fall > winter > summer > spring for water yield and streamflow; and winter > fall > spring > summer for groundwater discharge. The Mann–Kendall statistical test revealed a significant increasing trend for the annual precipitation (τ = 0.45, p < 0.01) and surface runoff (τ = 0.41, p < 0.02) over the past 17 years from 2003 to 2019. Compared to rangeland, forested land decreased monthly and annual average surface runoff by 20%, and increased monthly and annual average groundwater recharge by about 5%. Agricultural land had much higher unit-area values (mm/km2/y) of ET, groundwater recharge, and water yield than those of urban, forest, and range lands. Our research findings provide useful information to farmers, foresters, and decision makers for better water resource management in the ARW, Central Asia, and other mountain watersheds with similar conditions.
水文过程估算对于水资源管理、供水规划、生态保护和气候变化影响评估至关重要。中亚的山脉是河流和农业活动的主要水源。山区森林的扰动改变了水文过程，加速了水土流失、泥石流、滑坡和洪水的发生。利用遥感数据校准和验证的SWAT(土壤和水分评估工具)模型，对中亚乌兹别克斯坦阿克塔什河流域(ARW)的山地水文过程进行了量化。模拟结果表明，日地表径流量与日降水量密切相关。地下水流量在冬季因融雪而达到最大值。7月至12月为雨季，1月至6月为旱季。降水和地表径流的季节水文过程大小为:秋季>夏季>冬季>春季;蒸散发(ET)夏季>，春季>，秋季>，冬季>;冬天>春天>秋天>夏天融雪;秋天b>冬天b>夏天b>春天水量和流量;冬季b>秋季b>春季b>夏季为地下水排放。Mann-Kendall统计检验结果显示，2003 - 2019年17 a来，年降水量(τ = 0.45, p < 0.01)和地表径流量(τ = 0.41, p < 0.02)呈显著增加趋势。与牧场相比，林地的月和年平均地表径流量减少了20%，月和年平均地下水补给量增加了约5%。农业用地的单位面积值(mm/km2/y)高于城市、森林和牧场用地的ET、地下水补给和水量。我们的研究结果为农民、林农和决策者提供了有用的信息，以改善ARW、中亚和其他具有类似条件的山地流域的水资源管理。
{"title":"Quantification of Mountainous Hydrological Processes in the Aktash River Watershed of Uzbekistan, Central Asia, over the Past Two Decades","authors":"Ouyang Ying, John A. Stanturf, Marcus D. Williams, Evgeniy Botmann, Palle Madsen","doi":"10.3390/hydrology10080161","DOIUrl":"https://doi.org/10.3390/hydrology10080161","url":null,"abstract":"Estimation of hydrological processes is critical to water resource management, water supply planning, ecological protection, and climate change impact assessment. Mountains in Central Asia are the major source of water for rivers and agricultural practices. The disturbance of mountain forests in the region has altered the hydrological processes and accelerated soil erosion, mudflow, landslides, and flooding. We used the SWAT (Soil and Water Assessment Tool) model calibrated and validated with remote sensing data to quantify the mountainous hydrological processes in the Aktash River watershed (ARW) of Uzbekistan, Central Asia. Simulations showed that the daily surface runoff and streamflow closely responded to daily precipitation. Groundwater discharge reached its maximum in winter because of snowmelt. The wet months were from July to December, and the dry months were from January to June. The magnitudes of the seasonal hydrological processes were in the following order: fall > summer > winter > spring for precipitation and surface runoff; summer > spring > fall > winter for evapotranspiration (ET); winter > spring > fall > summer for snowmelt; fall > winter > summer > spring for water yield and streamflow; and winter > fall > spring > summer for groundwater discharge. The Mann–Kendall statistical test revealed a significant increasing trend for the annual precipitation (τ = 0.45, p < 0.01) and surface runoff (τ = 0.41, p < 0.02) over the past 17 years from 2003 to 2019. Compared to rangeland, forested land decreased monthly and annual average surface runoff by 20%, and increased monthly and annual average groundwater recharge by about 5%. Agricultural land had much higher unit-area values (mm/km2/y) of ET, groundwater recharge, and water yield than those of urban, forest, and range lands. Our research findings provide useful information to farmers, foresters, and decision makers for better water resource management in the ARW, Central Asia, and other mountain watersheds with similar conditions.","PeriodicalId":37372,"journal":{"name":"Hydrology","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48354652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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