Article
Engineering, Civil
Halil Ibrahim Burgan, Hafzullah Aksoy
Summary: This study developed a daily flow duration curve model for ungauged intermittent subbasins of gauged rivers, calculating the long-term mean streamflow using a regression model of annual precipitation and physical characteristics of the river basin, and incorporating the cease-to-flow point and nondimensionalized data for modeling.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Paula C. David, Pedro L. B. Chaffe, Vinicius B. P. Chagas, Marco Dal Molin, Debora Y. Oliveira, Antonio H. F. Klein, Fabrizio Fenicia
Summary: This study explores the feasibility of relating suitable model structures to the climate and streamflow characteristics in Brazilian catchments. The results show that different types of hydrological signatures result in different patterns of model performance.
WATER RESOURCES RESEARCH
(2022)
Article
Environmental Sciences
Shailesh Kumar Singh, George A. Griffiths
Summary: The study addresses the challenging problem of predicting the time for a natural basin's outflow to decline from the average to a low flow value, offering new methods for accurate predictions. The developed models show high accuracy in predicting recession time and can be confidently applied elsewhere, with further testing recommended.
WATER RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
Linfeng Fan, Xingxing Kuang, Dani Or, Chunmiao Zheng
Summary: The Yarlung Zangbo River (YZR) is the largest river in the northern Himalayas and plays a crucial role in providing water resources downstream. This study establishes a comprehensive hydrological model to understand the streamflow dynamics and water budget in the YZR basin. The results show that groundwater contributes significantly to the annual streamflow in the YZR, while precipitation and melt surface runoff also make significant contributions. Additionally, the study reveals a water imbalance in the basin, where a large portion of precipitation and meltwater remains unaccounted for. The excess water is hypothesized to discharge into deep fractured bedrock aquifers, supported by groundwater storage estimates.
WATER RESOURCES RESEARCH
(2023)
Article
Environmental Sciences
Alexander Ley, Helge Bormann, Markus Casper
Summary: Machine learning algorithms are gradually gaining acceptance for streamflow modelling in the hydrological community. However, due to the unique characteristics of catchment areas, generally valid statements about the modelling behavior of machine learning models are still unclear. In this study, we compared the performance of two machine learning models, RNN and LSTM, with the conceptual hydrological model HBV in the low-land Ems catchment in Germany. The results show that the machine learning models outperform the HBV model for a wide range of statistical performance indices, although there is a decline in performance for low-flows in two sub-catchments. The study also finds that the machine learning models provide a good representation of the water balance, while the HBV model excels in reproducing streamflow dynamics. There is no strong evidence of increasing error from upstream to downstream when applying a routing routine in the machine learning models.
Article
Multidisciplinary Sciences
Pakorn Ditthakit, Sirimon Pinthong, Nureehan Salaeh, Fadilah Binnui, Laksanara Khwanchum, Quoc Bao Pham
Summary: This study evaluated different regionalization methods for estimating monthly runoff variation in southern basin of Thailand, finding that machine learning methods have better performance in ungauged basins, while Spatial Proximity Approach is recommended in areas lacking hydrological information.
SCIENTIFIC REPORTS
(2021)
Article
Engineering, Civil
Chul Min Song
Summary: The present study aims to overcome the limitation of data use that depends on meteorological data and reflect topographical characteristics in DNN-based runoff models by proposing a data creation methodology that reflects spatial characteristics. Two types of two-dimensional features, surface flow features (SFF) and base flow features (BFF), were generated using the Soil Conservation Service curve number and groundwater level. The applicability of these features as input data of the convolutional neural network (CNN) was evaluated by simulating daily runoff.
JOURNAL OF HYDROLOGY
(2022)
Article
Green & Sustainable Science & Technology
Fazlullah Akhtar, Usman Khalid Awan, Christian Borgemeister, Bernhard Tischbein
Summary: The study introduces a methodology that couples remote sensing and the SWAT model to simulate the impact of climate change on the Kabul River Basin's water resources. The results indicate that most tributaries will experience a decrease in streamflow, while the Nawabad tributary may see an increase.
Article
Water Resources
Yongen Lin, Dagang Wang, Yue Meng, Wei Sun, Jianxiu Qiu, Wei Shangguan, Jingheng Cai, Yeonjoo Kim, Yongjiu Dai
Summary: This study investigates the incorporation of bias learning components into data driven models for streamflow prediction. Experiments are conducted in the Andun river basin of China and 273 watersheds in the United States to validate the effectiveness of the mapping-bias-learning models. The results show that these models outperform mapping-learning-alone models and machine learning methods are superior to traditional statistical methods in terms of bias learning ability.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2023)
Article
Green & Sustainable Science & Technology
Hamid Darabi, Ehsan Moradi, Ali Akbar Davudirad, Mohammad Ehteram, Artemi Cerda, Ali Torabi Haghighi
Summary: This study developed a model using socio-environmental variables and artificial intelligence algorithms to map suitable locations for RWH structures in the Maharloo-Bakhtegan basin in Iran. Results indicated that land use, precipitation, soil type, CN value, and slope were the most important variables for RWH sites, with the lowest correlation and autocorrelation; the suitability map showed that 9.7% of the Maharloo-Bakhtegan basin was highly suitable for RWH systems.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Water Resources
Anandharuban Panchanathan, Ali Torabi Haghighi, Mourad Oussalah
Summary: This study uses a multi-criteria approach to improve streamflow predictions in a data-scarce catchment of Chennai metropolitan city, India. The use of remotely sensed evapotranspiration data, groundwater recharge estimation, and parameter regionalization leads to improved model prediction. The study also accounts for changes in land use and land cover, and applies multi-parameter calibration to reduce parameter uncertainty. The findings have shown a 10% improvement in streamflow prediction accuracy, particularly for medium and high flows, with a Nash-Sutcliffe efficiency of 0.60. The results can be applied to enhance hydrological modeling and predictions in data-scarce regions.
INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT
(2023)
Article
Water Resources
Mohamed H. Mowafy, Charles N. Kroll, Richard M. Vogel
Summary: By utilizing annual flow duration curves (FDCs) and the modified Mood's median test (MMMT), this study investigates the significance of hydrologic alteration and its impact on hydrologic regimes. Dams are found to be the main cause of hydrologic disturbance, affecting low flows more than high flows.
HYDROLOGICAL SCIENCES JOURNAL
(2023)
Article
Environmental Sciences
Aung Kyaw Kyaw, Shamsuddin Shahid, Xiaojun Wang
Summary: This study aims to develop IDF curves for Yangon, using satellite precipitation datasets and different probability distribution functions. The results show that the Generalized Extreme Value Distribution best fits the hourly rainfall distribution of satellite data. The bias-corrected IDF curve generated at four locations in Yangon indicates higher rainfall intensity at coastal stations than inland stations. The methodology presented in this study can be applied to derive IDF curves for any location in Myanmar.
Article
Computer Science, Artificial Intelligence
Priyank J. Sharma, P. L. Patel, V. Jothiprakash
Summary: The study examines the performance of model tree data-driven technique in predicting streamflows for rivers in a physio-climatically heterogeneous basin, emphasizing the importance of selecting input variables. It proposes a model evaluation procedure considering multiple criteria for better decision making.
Article
Engineering, Civil
Akshay Kadu, Basudev Biswal
Summary: Water-related issues are becoming more widespread due to extreme climatic events. A model combination approach utilizing two different models showed improved streamflow prediction, especially in low flow scenarios. The study suggests that combining models may be more effective than using a single model.
WATER RESOURCES MANAGEMENT
(2022)
Article
Geosciences, Multidisciplinary
Sanjib Sharma, Rocky Talchabhadel, Santosh Nepal, Ganesh R. Ghimire, Biplob Rakhal, Jeeban Panthi, Basanta R. Adhikari, Soni M. Pradhanang, Shreedhar Maskey, Saurav Kumar
Summary: Cascading hazards are increasing in prevalence in the central Himalayas, posing risks to human settlements, infrastructures, and ecosystems. Current risk management strategies are inadequate in addressing cascading hazards. A scientifically sound understanding of these hazards and the tools to design risk management strategies are crucial. An integrated modeling framework, reliable prediction and early warning system, and sustainable disaster mitigation and adaptation strategies are needed.
Article
Geosciences, Multidisciplinary
Dipendra Gautam, Rabindra Adhikari, Suraj Gautam, Vishnu Prasad Pandey, Bhesh Raj Thapa, Suraj Lamichhane, Rocky Talchabhadel, Saraswati Thapa, Sunil Niraula, Komal Raj Aryal, Pravin Lamsal, Subash Bastola, Sanjay Kumar Sah, Shanti Kala Subedi, Bijaya Puri, Bidur Kandel, Pratap Sapkota, Rajesh Rupakhety
Summary: This study investigates the vulnerability of riparian-reinforced concrete buildings to floods using forensic damage interpretations and empirical/analytical vulnerability analyses. The findings suggest that flow velocity and sediment load are the main factors influencing the damages in steep terrains.
Editorial Material
Geosciences, Multidisciplinary
Jeeban Panthi, Rachel Autumn Spinti
Editorial Material
Geosciences, Multidisciplinary
Jeeban Panthi, Rachel A. Spinti
Article
Computer Science, Interdisciplinary Applications
Sanjib Sharma, Ganesh Raj Ghimire, Ridwan Siddique
Summary: Skillful streamflow forecasts using integrated numerical weather prediction ensembles, distributed hydrological models, and machine learning have been shown to improve forecast skill and reliability in various water policy and management areas. A case study in the Upper Susquehanna River basin demonstrated that the machine learning postprocessor can outperform low-complexity forecasts and standalone modeling approaches in improving streamflow forecasts, particularly at medium-range lead times, for high flows, and during the warm season. Overall, this study highlights the benefits of incorporating machine learning techniques in streamflow forecasting.
JOURNAL OF HYDROINFORMATICS
(2023)
Article
Environmental Sciences
Ganesh R. Ghimire, Carly Hansen, Sudershan Gangrade, Shih-Chieh Kao, Peter E. Thornton, Debjani Singh
Summary: This study proposes a scalable modeling framework integrating the VIC and RAPID models with high-performance computing to assimilate streamflow data at USGS monitoring sites. The result is a reconstructed 36-year (1980-2015) daily and monthly streamflow dataset (Dayflow) at 2.7 million NHDPlusV2 stream reaches in the US. The evaluation shows improved accuracy in naturalized streamflow, particularly in arid regions, and comparisons with other national and global streamflow datasets indicate better performance and directions for further improvement.
WATER RESOURCES RESEARCH
(2023)
Article
Astronomy & Astrophysics
Manisha Maharjan, Minoru Yoneda, Rocky Talchabhadel, Bhesh Raj Thapa, Anil Aryal
Summary: This paper provides a comprehensive analysis of extreme precipitation in Nepal from 1976 to 2015. Based on daily precipitation data from 28 stations across the country, 11 extreme precipitation indices were computed to assess the impact of heavy rainfall on agricultural production and drought. The results show an increasing trend of extreme precipitation indices in the latter half of the study period, along with elongated dry spells that may negatively affect crop growth. The study recommends effective management strategies for both drier and wetter extremes, such as irrigation facilities and flood mitigation measures.
EARTH AND SPACE SCIENCE
(2023)
Article
Environmental Sciences
Kaushal Gnyawali, Kshitij Dahal, Rocky Talchabhadel, Sadhana Nirandjan
Summary: Rainfall-induced landslides pose a threat to critical infrastructure in mountainous countries, and climate change intensifies this risk by altering rainfall patterns. To plan climate-resilient infrastructure in landslide-prone regions, it is crucial to understand the changing susceptibility of landslides in relation to rainfall extremes and overlay them with critical infrastructure to identify risk zones. In this study, a framework linking changing rainfall extremes to landslide susceptibility and critical infrastructure intensity was developed using Nepal as a case study. By analyzing a set of rainfall indices and utilizing satellite imagery, a landslide susceptibility map and a gridded critical infrastructure spatial density map were created. The framework successfully identified critical infrastructure hotspots in Nepal prone to landslides, highlighting the need for improved land management practices and targeted infrastructure financing strategies.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Meteorology & Atmospheric Sciences
Mahendra B. Baniya, Takeshi Fujino, Rocky Talchabhadel, Arjun Baniya, Saroj Karki, Shivaram K.C., Biswo Jha
Summary: This study analyzes rainfall variability and its impact on streamflow in a mountain river basin in the Himalayas. The results reveal a hysteresis loop between streamflow and rainfall within the basin.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Article
Geosciences, Multidisciplinary
Rocky Talchabhadel, Shreedhar Maskey, Manish R. Gouli, Kshitij Dahal, Amrit Thapa, Sanjib Sharma, Amod M. Dixit, Saurav Kumar
Summary: Extreme precipitation in the Himalayan region triggers water and sediment hazards, interacting with the environment and human systems. The 2021 Melamchi disaster resulted from a series of non-extreme events, including landslide damming and heavy rainfall, leading to flooding and extensive damage downstream. A holistic approach, integrating satellite data, modeling, and machine learning techniques, is used to diagnose the causal effects and understand the characteristics of the disaster.
GEOMATICS NATURAL HAZARDS & RISK
(2023)
Article
Water Resources
Albert Larson, Abdeltawab Hendawi, Thomas Boving, Soni M. Pradhanang, Ali S. Akanda
Summary: The impact of climate change on extreme events and conditions like droughts, floods, heatwaves, and storms is evident. To respond to these hazards and adapt to the changing environment, better forecasting tools are necessary. This study introduces a deep convolutional residual regressive neural network (dcrrnn) platform called Flux to Flow (F2F) that analyzes the response of watersheds to water-cycle fluxes and extremes.
Article
Engineering, Civil
Sudershan Gangrade, Ganesh R. Ghimire, Shih-Chieh Kao, Mario Morales-Hernandez, Ahmad A. Tavakoly, Joseph L. Gutenson, Kent H. Sparrow, George K. Darkwah, Alfred J. Kalyanapu, Michael L. Follum
Summary: This study analyzes the impact of various drivers on flood inundation estimates by using multiple precipitation inputs to drive hydrologic and hydrodynamic models. The evaluation shows that the models are most sensitive to rainfall estimates and that precipitation forecasts significantly underestimate flood magnitudes and extents, leading to unanticipated severe flooding.
JOURNAL OF HYDROLOGY
(2023)
Article
Water Resources
Sushma Tiwari, Sanot Adhikari, Udhab Raj Khadka, Motilal Ghimire, Rocky Talchabhadel
Summary: Water poverty is a growing problem in Nepal, influenced by various factors such as population growth, climate change, land-use transitions, and poorly planned road construction. This study examines water poverty in Alital Rural Municipality in Rangun Watershed, using elements of the water poverty index (WPI). The WPI score of 57 indicates a medium-low level of water poverty, with the environment component scoring the highest and the use component scoring the lowest. Effective water management plans are crucial for increasing household water use and consumption in the watershed.
WATER PRACTICE AND TECHNOLOGY
(2023)
Article
Environmental Sciences
Shiva Gopal Shrestha, Soni M. Pradhanang
Summary: A comparative analysis of SWAT and LSTM models in the Cork Brook watershed shows that LSTM's flow prediction results are competitive with SWAT, given limited data availability. LSTM models do not overestimate high flows like SWAT, but both models struggle with low value estimation.
