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  Subjects -> WATER RESOURCES (Total: 160 journals)
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Frontiers in Water
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2624-9375
Published by Frontiers Media Homepage  [96 journals]
  • Flood damage and shutdown times for industrial process facilities: a
           vulnerability assessment process framework

    • Authors: Carol J. Friedland, Fatemeh Orooji, Ayat Al Assi, Matthew L. Flynn, Rubayet Bin Mostafiz
      Abstract: Much of the U.S. petrochemical infrastructure is heavily concentrated along the western coast of the Gulf of Mexico within the impact zone of major tropical cyclone events. Flood impacts of recent tropical disturbances have been exacerbated by an overall lack of recognition of the vulnerabilities to process systems from water intrusion, as well as insufficient disaster mitigation planning. Vulnerability assessment methods currently call for the aggregation of qualitative data to survey the susceptibility of industrial systems to floodwater damage. A means to quantify these consequences is less often employed, resulting in a poor translation of the threat of flood hazards to a crucial element of the economy. This paper reviews flood damage assessment for industrial facilities and presents a component-level conceptual methodology to assess the consequences of flood events. To more effectively communicate loss potential from flood events, the proposed methodology utilizes synthetic estimation to calculate repair requirements, shutdown time, and direct cost.
      PubDate: 2023-11-29T00:00:00Z
  • Harmful algal blooms in agricultural irrigation: risks, benefits, and

    • Authors: Amanda Rose Newton, Rajesh Melaram
      Abstract: Harmful algal blooms (HABs) have garnered increasing attention due to their adverse effects on water quality, aquatic ecosystems, and animal and human health. Prior research suggests that HAB-contaminated water containing toxins can significantly affect the development of plant structures and inhibit essential physiological processes. However, the potential benefits and risks of using HAB-contaminated water sourced from local water bodies for agricultural irrigation is not completely understood. This perspective paper delves into the origins and impacts of HABs, the environmental and agricultural repercussions of their use in irrigation, and management strategies to mitigate associated risks of HAB-contaminated water in sustainable agriculture. Future studies are needed to validate the practical benefits of HABs in agricultural irrigation for the enhancement of soil health and overall crop growth and productivity.
      PubDate: 2023-11-29T00:00:00Z
  • Sediment sources and connectivity linked to hydrologic pathways and
           geomorphic processes: a conceptual model to specify sediment sources and
           pathways through space and time

    • Authors: Se Jong Cho, Diana L. Karwan, Katherine Skalak, James Pizzuto, Max E. Huffman
      Abstract: Sediment connectivity is a conceptualization for the transfer and storage of sediment among different geomorphic compartments across upland landscapes and channel networks. Sediment connectivity and dysconnectivity are linked to the water cycle and hydrologic systems with the associated multiscale interactions with climate, soil, topography, ecology, and landuse/landcover under natural variability and human intervention. We review current sediment connectivity and modeling approaches evaluating and quantifying water and sediment transfer in catchment systems. Many studies highlight the interaction between sediment and water in defining landscape connectivity, but many efforts to quantify and/or simulate sediment connectivity rely on the topographic/structural controls on sediment erosion and delivery. More recent modeling efforts integrate functional and structural connectivity to capture hydrologic properties influencing sediment delivery. Though the recent modeling development is encouraging, a comprehensive sediment connectivity framework, which integrates geomorphic and hydrologic processes across spatiotemporal scales, has not yet been accomplished. Such an effort requires understanding the hydrologic and geomorphic processes that control sediment source, storage, and transport at different spatiotemporal scales and across various geophysical conditions. We propose a path for developing this new understanding through an integrated hydrologic and sediment connectivity conceptual model that broadly categorizes dominant processes and patterns relevant to understanding sediment flux dynamics. The conceptual model describes hydrologic–sediment connectivity regimes through spatial-temporal feedback between hydrologic processes and geomorphic drivers. We propose that in combining hydrologic and sediment connectivity into a single conceptual model, patterns emerge such that catchments will exist in a single characteristic behavior at a particular instance, which would shift with space and time, and with landscape disturbances. Using the conceptual model as a “thinking” tool, we extract case studies from a multidisciplinary literature review—from hydrology, geomorphology, biogeochemistry, and watershed modeling to remote-sensing technology—that correspond to each of the dominant hydrologic–sediment connectivity regimes. Sediment and water interactions in real-world examples through various observational and modeling techniques illustrate the advancements in the spatial and temporal scales of landscape connectivity observations and simulations. The conceptual model and case studies provide a foundation for advancing the understanding and predictive capability of watershed sediment processes at multiple spatiotemporal scales. Plain language summary: Soil erosion and movement across the landscape are closely linked to rain events and flow pathways. Landscape connectivity is a way to consider how soil erosion from different parts of the landscape is connected to the streams. We explore where soil erosion occurs and how eroded soil moves across the landscape through the interaction with rainfall and drainage. The comprehensive understanding of sediment connectivity and its dependence on rainfall characteristics and watershed hydrology may help to inform the effective distribution of conservation funds and management actions to address water pollution from excess sediment.
      PubDate: 2023-11-23T00:00:00Z
  • Quantifying thermal variation around gray infrastructure in urban
           India|Introduction|Methodology|Results and discussion

    • Authors: Divya Subramanian
      Abstract: IntroductionDense cities in developing nations face rapid urban sprawl. This alters the local ecology and contributes significantly to the local temperature variation. Gray infrastructure (GI) includes vital processes of sewage treatment and wastewater pumping stations. GI is attributed to large greenhouse gas emissions and high energy utilization, contributing to the local urban heat island effect. A knowledge gap exists in assessing GI contribution to the local temperature variation in megacities of developing nations like India.MethodologyIn this study, the Thermal Variance Index (TVI) was derived around a buffer zone for 7 Sewage Treatment Plants (STPs) in Mumbai. Landsat 8 remote sensing imagery was used with summer and winter variation for alternate years from 2014 to 2021.Results and discussionThree STPs set within densely built surroundings showed a cooling profile. Four STPs located among wetlands displayed a heating profile. The surrounding built spaces showed significant influence on the TVI recorded. The STP Cooling Effect (CE) was further quantified by deducing its Cooling Range (CR) and Cooling Intensity (CI). STPs within densely built areas showed higher Cooling Range and Cooling Intensity. Regression analysis models indicated a high positive correlation for the Normalized Difference Built-up Index (NDBI), Landscape Shape Index (LSI), and STP capacity. Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and STP area showed a strong negative correlation.
      PubDate: 2023-11-23T00:00:00Z
  • Improving water use efficiency of surface irrigated sugarcane

    • Authors: Guia Marie M. Mortel, Chandra A. Madramootoo
      Abstract: Sugarcane (Saccharum officinarum) is a traditional major crop and export of Guyana. This study aims to assess the current irrigation scenario and propose scenarios to maximize the yield and water use efficiency of sugarcane (S. officinarum) in Guyana, using the AquaCrop model. Field-measured climate and soil data, and local crop parameters were used in the simulations. The crop simulations were calibrated with actual yields from 2005 to 2008. The calibrated parameters were then validated using the 2009 to 2012 yield dataset. The good agreement (RMSE of 7.15%) with the recorded yield during validation and the low sensitivity of calibrated parameters indicate the acceptability of AquaCrop and the parameters used for simulations. During calibration, the yield was weakly sensitive (0.6–2% ΔRMSEn) to changes in crop parameter values with the highest sensitivity observed for the maximum canopy cover (CCx) and the crop coefficient (kcmax). Several irrigation scenarios were then simulated, of which no significant reduction or increase in yield was observed between the scenarios 50% to 100% of the total available water (TAW). A threshold of 50%TAW is advised during dry periods to avoid significant yield loss. It is recommended that this scenario be validated with field experiments. The results of this study will assist in maintaining high sugarcane yields even during dry conditions.
      PubDate: 2023-11-22T00:00:00Z
  • Editorial: Solutions to water crises (related to actual interventions)

    • Authors: Jenia Mukherjee, Sara Marks, Melissa Haeffner, Saket Pande, Pieter van Oel, Matthew R. Sanderson, Adriana Allen
      PubDate: 2023-11-21T00:00:00Z
  • Editorial: Sustainable urban stormwater management under a changing

    • Authors: Fang Yenn Teo, Ming Fai Chow, Chun Kiat Chang
      PubDate: 2023-11-17T00:00:00Z
  • A perspective for identifying intersections among the social, engineering,
           and geosciences to address water crises

    • Authors: Carl F. Weems, Cristina Poleacovschi, Kaoru Ikuma
      Abstract: Reliable access to safe water is essential for health, wellbeing, and the livelihoods of people. However, water security innovations benefit when engineering and geoscience decisions consider systemic human, social, and organizational realities, needs, and goals. Indeed, true innovation that leads to water security requires intensively inclusive and iterative processes to occur at multiple scales of analysis across diverse sciences—for this, expertise and knowledge across the varied sciences is essential to facilitate such convergent, transdisciplinary research. Here, we articulate our perspective for identifying points of intersection and working across disciplinary boundaries to address water crises. Our perspective takes a multidimensional view of community, organization, family, and individual resilience in the face of natural and human hazards. It builds upon previous models of cumulative water related risk by nuancing the relationships amongst levels of analysis, and expanding the idea of cumulative impacts to include interactive impacts (e.g., buffering, enhancing, effects and other moderators), mediated effects (i.e., mechanisms of impact), as well as additive and suppressive linkages amongst risk and protective factors.
      PubDate: 2023-11-08T00:00:00Z
  • Challenges faced by the municipal water works management in improving
           water supply adequacy and distribution in Bontoc, Philippines

    • Authors: Epiphania B. Magwilang, Annie Lourie Yawan Paredes, Francisco C. Armas, Helen Grace P. Bugnay, Rose D. Dagupen
      Abstract: Domestic water is indispensable for daily use, yet its effective management encounters numerous challenges that impact household consumers. This study aims to identify the challenges leading to supply inadequacy and uneven distribution, while proposing interventions to enhance water supply for households. The study employed surveys, interviews, and focus group discussions to gather comprehensive data on domestic water supply issues in rural communities in Bontoc, Philippines. The findings reveal two primary issues in these rural communities: supply inadequacy and unequal distribution. Supply inadequacy is attributed to factors such as wasteful water use, water scarcity during prolonged dry seasons, limited water sources, and population growth. Uneven distribution results from factors like landslides, illegal tapping, irregular water quality, insufficient monitoring of quantity and pressure, and inadequacies in the water distribution network layout. The study suggests several crucial actions for the local government unit (LGU) of Bontoc. These include augmenting water sources, implementing regular water supply monitoring, ensuring timely repairs, replacing old pipes, optimizing distribution pipeline layouts, enhancing water pressure, and rigorously enforcing municipal water ordinances. Furthermore, the study emphasizes the importance of household water management practices, such as responsible consumption, supply conservation, and recycling. The effective implementation of these interventions, through collaboration between the LGU and households, has the potential to ameliorate the constraints in domestic water supply and distribution. This collaborative approach is essential for improving supply management and addressing the current challenges faced by domestic water consumers.
      PubDate: 2023-11-07T00:00:00Z
  • Critical zone science in the Western US—Too much information'

    • Authors: Christina Tague, W. Tyler Brandt
      Abstract: Exponentially growing publication rates are increasingly problematic for interdisciplinary fields like Critical Zone (CZ) science. How does one “keep up” across different, but related fields with unique hypotheses, field techniques, and models' By surveying CZ academics in the Western US, a region with substantial CZ research, we document the challenge. While conventional knowledge synthesis products-particularly review papers clearly support knowledge transfer, they are static and limited in scope. More informal paths for knowledge transfer, including social networking at conferences and academic mentorship, are useful but are unstructured and problematic for young scientists or others who may not have access to these resources. While new machine-learning tools, including ChatGPT, offer new ways forward for knowledge synthesis, we argue that they do not necessarily solve the problem of information overload in CZ Science. Instead, we argue that what we need is a community driven, machine aided knowledge tool that evolves and connects, but preserves the richness of detail found in peer-reviewed papers. The platform would be designed by CZ scientists, machine-aided and built on the strengths of people-driven synthesis. By involving the scientist in the design of this tool, it will better reflect the practice of CZ science-including hypothesis generation, testing across different time and space scales and in different time periods and locations, and, importantly, the use and evaluation of multiple, often sophisticated methods including fieldwork, remote sensing, and modeling. We seek a platform design that increases the findability and accessibility of current working knowledge while communicating the CZ science practice.
      PubDate: 2023-11-02T00:00:00Z
  • Metabolic processes control carbon dioxide dynamics in a boreal forest
           ditch affected by clear-cut forestry

    • Authors: Alberto Zannella, Karin Eklöf, Emma Lannergård, Hjalmar Laudon, Eliza Maher Hasselquist, Marcus B. Wallin
      Abstract: Boreal watercourses are large emitters of carbon dioxide (CO2) to the atmosphere. For forestry intensive areas of the Nordic and Baltic countries, a high share of these watercourses are man-made ditches, created to improve drainage and increase forest productivity. Previous studies have suggested that terrestrial sources sustain the CO2 in these ditches and variability in hydrology is the main temporal control. However, few studies have explored ditch CO2 dynamics and its associated controls in catchments being exposed to forest harvest. An altered hydrology, increased nutrient export and light availability following forest harvest are all factors that potentially can change both levels, dynamics, and source controls of ditch CO2. Here, high-frequency (30 min) CO2 concentration dynamics together with other hydrochemical variables were studied in a forest ditch draining a fully harvested catchment in the Trollberget Experimental Area, northern Sweden. We collected data during the snow-free season from May to October. Ditch CO2 concentrations displayed a clear seasonal pattern with higher CO2 concentrations during summer than in spring and autumn. Concentrations ranged from 1.8 to 3.5 mg C L−1 (median: 2.4 mg C L−1, IQR = 0.5 mg C L−1). Strong diel cycles in CO2 developed during early summer, with daily amplitudes in CO2 reaching up to 1.1 mg C L−1. These pronounced daily cycles in CO2 were closely related to the daily sum of shortwave radiation and water temperature. Variations in hydrology had generally a low impact on the CO2 dynamics but did vary among seasons and between individual hydrological events. It was evident from our study that growing season CO2 concentrations in a forest ditch affected by clear-cut harvest were highly variable and mainly controlled by light and temperature induced metabolism. These high dynamics and the associated controls need to be considered when scaling up ditch CO2 emissions across boreal landscapes affected by intensive forestry.
      PubDate: 2023-11-02T00:00:00Z
  • Editorial: Nonequilibrium multiphase and reactive flows in porous and
           granular materials

    • Authors: Ran Holtzman, Bjornar Sandnes, Marcel Moura, Matteo Icardi, Ramon Planet
      PubDate: 2023-11-02T00:00:00Z
  • A network-based analysis of critical resource accessibility during floods

    • Authors: Matthew Preisser, Paola Passalacqua, R. Patrick Bixler, Stephen Boyles
      Abstract: Numerous government and non-governmental agencies are increasing their efforts to better quantify the disproportionate effects of climate risk on vulnerable populations with the goal of creating more resilient communities. Sociodemographic based indices have been the primary source of vulnerability information the past few decades. However, using these indices fails to capture other facets of vulnerability, such as the ability to access critical resources (e.g., grocery stores, hospitals, pharmacies, etc.). Furthermore, methods to estimate resource accessibility as storms occur (i.e., in near-real time) are not readily available to local stakeholders. We address this gap by creating a model built on strictly open-source data to solve the user equilibrium traffic assignment problem to calculate how an individual's access to critical resources changes during and immediately after a flood event. Redundancy, reliability, and recoverability metrics at the household and network scales reveal the inequitable distribution of the flood's impact. In our case-study for Austin, Texas we found that the most vulnerable households are the least resilient to the impacts of floods and experience the most volatile shifts in metric values. Concurrently, the least vulnerable quarter of the population often carries the smallest burdens. We show that small and moderate inequalities become large inequities when accounting for more vulnerable communities' lower ability to cope with the loss of accessibility, with the most vulnerable quarter of the population carrying four times as much of the burden as the least vulnerable quarter. The near-real time and open-source model we developed can benefit emergency planning stakeholders by helping identify households that require specific resources during and immediately after hazard events.
      PubDate: 2023-10-31T00:00:00Z
  • NeuralFlood: an AI-driven flood susceptibility index

    • Authors: Justice Lin, Chhayly Sreng, Emma Oare, Feras A. Batarseh
      Abstract: Flood events have the potential to impact every aspect of life, economic loss and casualties can quickly be coupled with damages to agricultural land, infrastructure, and water quality. Creating flood susceptibility maps is an effective manner that equips communities with valuable information to help them prepare for and cope with the impacts of potential floods. Flood indexing and forecasting are nonetheless complex because multiple external parameters influence flooding. Accordingly, this study explores the potential of utilizing artificial intelligence (AI) techniques, including clustering and neural networks, to develop a flooding susceptibility index (namely, NeuralFlood) that considers multiple factors that are not generally considered otherwise. By comparing four different sub-indices, we aim to create a comprehensive index that captures unique characteristics not found in existing methods. The use of clustering algorithms, model tuning, and multiple neural layers produced insightful outcomes for county-level data. Overall, the four sub-indices' models yielded accurate results for lower classes (accuracy of 0.87), but higher classes had reduced true positive rates (overall average accuracy of 0.68 for all classes). Our findings aid decision-makers in effectively allocating resources and identifying high-risk areas for mitigation.
      PubDate: 2023-10-27T00:00:00Z
  • Study of the effect of the compaction level on the hydrodynamic properties
           of loamy sand soil in an agricultural context

    • Authors: Yasmin Mbarki, Silvio José Gumiere, Paul Celicourt, Jhemson Brédy
      Abstract: Agricultural soil compaction adversely affects crop water use and yield performance and should be avoided or remediated through appropriate soil management strategies. The investigation of the impact of different levels of soil compaction on its hydrodynamic properties remains a crucial step in improving water use and crop yields. We examined five compaction levels of silty sand soil sampled from a potato field in the agricultural regions of northern Quebec (Canada). Soil hydraulic characteristics (saturated and unsaturated hydraulic conductivity, soil water retention capacity) were measured using the constant head method, the HYPROP device, and a WP4C dew point potentiometer. The sixteen hydraulic models integrated into the HYPROP software were fitted to the soil water retention curve (SWRC) data for the studied compaction levels. Statistical parameters such as the mean bias error, mean absolute error, correlation coefficient, and root mean square error were used to measure the performance of the models. The results show that saturated and unsaturated conductivity decreases with increasing soil compaction. The lowest saturated hydraulic conductivity (Ks) value is observed for the highest level of soil compaction, reflecting a solid medium with less pore space and connectivity. Among the hydraulic models, the Peters-Durner-Iden (PDI) variant of van Genuchten's unconstrained bimodal model (VGm-b-PDI) outperformed all other models for SWRC simulation of different soil compaction levels and was, accordingly, selected as the optimal model. This model was implemented in HYDRUS-1D to estimate the amount of irrigation for different compaction levels. We simulated irrigation scenarios with the dual-porosity model. The results indicated that soil compaction can strongly influence soil hydraulic properties and water differently. However, the amount of irrigation for the potato crop was optimal at a moderate level of soil compaction. Overall, combined HYPROP and HYDRUS 1D can provide helpful information on the soil hydraulics properties dynamics and a rigorous simulation for irrigation planning and management in potato fields.
      PubDate: 2023-10-25T00:00:00Z
  • Evapotranspiration and groundwater inputs control the timing of diel
           cycling of stream drying during low-flow periods

    • Authors: Sara R. Warix, Sarah E. Godsey, Gerald Flerchinger, Scott Havens, Kathleen A. Lohse, H. Carrie Bottenberg, Xiaosheng Chu, Rebecca L. Hale, Mark Seyfried
      Abstract: Geologic, geomorphic, and climatic factors have been hypothesized to influence where streams dry, but hydrologists struggle to explain the temporal drivers of drying. Few hydrologists have isolated the role that vegetation plays in controlling the timing and location of stream drying in headwater streams. We present a distributed, fine-scale water balance through the seasonal recession and onset of stream drying by combining spatiotemporal observations and modeling of flow presence/absence, evapotranspiration, and groundwater inputs. Surface flow presence/absence was collected at fine spatial (~80 m) and temporal (15-min) scales at 25 locations in a headwater stream in southwestern Idaho, USA. Evapotranspiration losses were modeled at the same locations using the Simultaneous Heat and Water (SHAW) model. Groundwater inputs were estimated at four of the locations using a mixing model approach. In addition, we compared high-frequency, fine-resolution riparian normalized vegetation difference index (NDVI) with stream flow status. We found that the stream wetted and dried on a daily basis before seasonally drying, and daily drying occurred when evapotranspiration outputs exceeded groundwater inputs, typically during the hours of peak evapotranspiration. Riparian NDVI decreased when the stream dried, with a ~2-week lag between stream drying and response. Stream diel drying cycles reflect the groundwater and evapotranspiration balance, and riparian NDVI may improve stream drying predictions for groundwater-supported headwater streams.
      PubDate: 2023-10-25T00:00:00Z
  • An active learning convolutional neural network for predicting river flow
           in a human impacted system

    • Authors: Scott M. Reed
      Abstract: The South Platte river system contains a mixture of natural streams, reservoirs, and pipeline projects that redirect water to front range communities in Colorado. At many timepoints, a simple persistence model is the best predictor for flow from pipelines and reservoirs but at other times, flows change based on snowmelt and inputs such as reservoir fill rates, local weather, and anticipated demand. Here we find that a convolutional Long Short-Term Memory (LSTM) network is well suited to modeling flow in parts of this basin that are strongly impacted by water projects as well as ones that are relatively free from direct human modifications. Furthermore, it is found that including an active learning component in which separate Convolutional Neural Networks (CNNs) are used to classify and then select the data that is then used for training a convolutional LSTM network is advantageous. Models specific for each gauge are created by transfer of parameter from a base model and these gauge-specific models are then fine-tuned based a curated subset of training data. The result is accurate predictions for both natural flow and human influenced flow using only past river flow, reservoir capacity, and historical temperature data. In 14 of the 16 gauges modeled, the error in the prediction is reduced when using the combination of on-the-fly classification by CNN followed by analysis by either a persistence or convolutional LSTM model. The methods designed here could be applied broadly to other basins and to other situations where multiple models are needed to fit data at different times and locations.
      PubDate: 2023-10-17T00:00:00Z
  • Widespread dominance of methane ebullition over diffusion in freshwater
           aquaculture ponds

    • Authors: Renske J. E. Vroom, Sarian Kosten, Rafael M. Almeida, Raquel Mendonça, Ive S. Muzitano, Icaro Barbosa, Jonas Nasário, Ernandes S. Oliveira Junior, Alexander S. Flecker, Nathan Barros
      Abstract: An ever-increasing demand for protein-rich food sources combined with dwindling wild fish stocks has caused the aquaculture sector to boom in the last two decades. Although fishponds are potentially strong emitters of the greenhouse gas methane (CH4), little is known about the magnitude, pathways, and drivers of these emissions. We measured diffusive CH4 emissions at the margin and in the center of 52 freshwater fishponds in Brazil. In a subset of ponds (n = 31) we additionally quantified ebullitive CH4 fluxes and sampled water and sediment for biogeochemical analyses. Sediments (n = 20) were incubated to quantify potential CH4 production. Ebullitive CH4 emissions ranged between 0 and 477 mg m−2 d−1 and contributed substantially (median 85%) to total CH4 emissions, surpassing diffusive emissions in 81% of ponds. Diffusive CH4 emissions were higher in the center (median 11.4 mg CH4 m−2 d−1) than at the margin (median 6.1 mg CH4 m−2 d−1) in 90% of ponds. Sediment CH4 production ranged between 0 and 3.17 mg CH4 g C−1 d−1. We found no relation between sediment CH4 production and in situ emissions. Our findings suggest that dominance of CH4 ebullition over diffusion is widespread across aquaculture ponds. Management practices to minimize the carbon footprint of aquaculture production should focus on reducing sediment accumulation and CH4 ebullition.
      PubDate: 2023-10-13T00:00:00Z
  • Editorial: Resiliency of urban systems to water-related disasters

    • Authors: Sohom Mandal, Abhishek Gaur, Hamidreza Shirkhani
      PubDate: 2023-10-09T00:00:00Z
  • SWAT model calibration for hydrological modeling using concurrent methods,
           a case of the Nile Nyabarongo River basin in Rwanda

    • Authors: Aboubakar Gasirabo, Chen Xi, Alishir Kurban, Tie Liu, Hamad R. Baligira, Jeanine Umuhoza, Adeline Umugwaneza, Umwali Dufatanye Edovia
      Abstract: The Nile Nyabarongo, which is Rwanda's largest river, is facing stress from both human activities and climate change. These factors have a substantial contribution to the water processes, making it difficult to effectively manage water resources. To address this issue, it is important to find out the most accurate techniques for simulating hydrological processes. This study aimed to calibrate the SWAT model employing various algorithms such as GLUE, ParaSol, and SUFI-2 for the simulation of hydrology in the basin of the Nile Nyabarongo River. Different data sources, such as DEM, Landsat images, soil data, and daily meteorological data, were utilized to input information into the SWAT modeling process. To divide the basin area effectively, 25 sub-basins were created, with due consideration of soil characteristics and the diverse land cover. The outcomes point out that SUFI-2 outperformed the other algorithms for SWAT calibration, requiring fewer computing model runs and producing the best results. ParaSol established residing the least effective algorithm. After calibration with SUFI-2, the most sensitive parameters for modeling were revealed to be (1) the Effective Channel Hydraulic Conductivity (CH K2) measuring how well water can flow through a channel, with higher values indicating better conductivity, (2) Manning's n value (CH N2) representing the roughness or resistance to flow within the channel, with smaller values suggesting a smoother channel, (3) Surface Runoff Lag Time (SURLAG) quantifying the delay between rainfall and the occurrence of surface runoff, with shorter values indicating faster runoff response, (4) the Universal Soil-Loss Equation (USLE P) estimating the amount of soil loss. The average evapotranspiration within the basin was calculated to be 559.5 mma-1. These calibration results are important for decision-making and updating policies related to water balance management in the basin.
      PubDate: 2023-10-06T00:00:00Z
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