scholarly journals Assessment of Hydrologic Alteration Metrics for Detecting Urbanization Impacts

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1017 ◽  
Author(s):  
Reid D. McDaniel ◽  
Frances C. O’Donnell
Keyword(s):  
Water Management ◽  
Large Scale ◽  
Hydrologic Model ◽  
Hydrologic Cycle ◽  
Network Data ◽  
Hydrologic Alteration ◽  
Dynamic Nature ◽  
Watershed Characteristic ◽  
Maximum Flows

Urbanization is increasing rapidly and has the potential to alter the hydrologic cycle. It is uncertain if hydrologic alteration metrics developed for large-scale analyses detect the impacts of urbanization. This study tests the ability of two such methods, Indicators of Hydrologic Alteration (IHA) and streamflow signatures, to detect the effects of urbanization in two watersheds in the southeastern U.S.A. A hydrologic model (HEC-HMS) was used to simulate flows in ungauged upstream tributaries to determine if analysis of flow from a large gauged watershed detects urbanization effects on upstream tributaries. IHA analysis detected trends in time in the watersheds, but the results were the opposite of what would be expected as urbanization increased minimum flows, decreased maximum flows, and decreased flashiness based on the trend in time and comparison with an undeveloped watershed. IHA parameters were more sensitive to urbanization than streamflow signatures. Subcatchments that transitioned from low to moderate or high levels of urbanization had greater levels of hydrologic alteration than was detected at the watershed outlet. Analyses of stream gauge network data may underestimate the importance of urbanization as a watershed characteristic due to scale issues, the variable effects of water management, and the dynamic nature of urbanization.

scholarly journals Assessing Freshwater Provision and Consumption in the Alpine Space Applying the Ecosystem Service Concept

2019 ◽  
Vol 11 (4) ◽  
pp. 1131 ◽  
Author(s):  
Claude Meisch ◽  
Uta Schirpke ◽  
Lisa Huber ◽  
Johannes Rüdisser ◽  
Ulrike Tappeiner
Keyword(s):  
Water Management ◽  
Water Supply ◽  
Ecosystem Service ◽  
Large Scale ◽  
Management Strategies ◽  
Hydrologic Model ◽  
Small Scale ◽  
European Alps ◽  
Mountain Areas ◽  
High Dependency

A key challenge in the sustainable management of freshwater is related to non-stationary processes and transboundary requirements. The assessment of freshwater is often hampered due to small-scale analyses, lacking data and with the focus on only its provision. Based on the ecosystem service (ES) concept, this study aims at quantitatively comparing potential water supply with the demand for freshwater in the European Alps and their surrounding lowlands. We propose an easy-to-use combination of different mapping approaches, including a large-scale hydrologic model to estimate water supply and the downscaling of regional data to the local scale to map demand. Our results demonstrate spatial mismatches between supply and demand and a high dependency of the densely populated lowlands from water providing mountain areas. Under expected climate variations and future demographic changes, our results suggest increasing pressures on freshwater in the south of the Alps. Hence, sustainable water management strategies need to assure the supply of freshwater under changing environmental conditions to meet the increasing water demand of urbanized areas in the lowlands. Moreover, national water management strategies need to be optimally concerted at the international level, as transboundary policies and frameworks can strengthen future water provision.

Computer algorithms

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Data Structures ◽  
Analysis Of Algorithms ◽  
Shortest Paths ◽  
Minimum Cut ◽  
Network Data ◽  
Computer Algorithms ◽  
Breadth First Search ◽  
Augmenting Path ◽  
Basic Concepts ◽  
Maximum Flows

This chapter introduces some of the fundamental concepts of numerical network calculations. The chapter starts with a discussion of basic concepts of computational complexity and data structures for storing network data, then progresses to the description and analysis of algorithms for a range of network calculations: breadth-first search and its use for calculating shortest paths, shortest distances, components, closeness, and betweenness; Dijkstra's algorithm for shortest paths and distances on weighted networks; and the augmenting path algorithm for calculating maximum flows, minimum cut sets, and independent paths in networks.

scholarly journals Greenhouse Gas Balance of Sphagnum Farming on Highly Decomposed Peat at Former Peat Extraction Sites

Ecosystems ◽  
2021 ◽  
Author(s):  
Jan Oestmann ◽  
Bärbel Tiemeyer ◽  
Dominik Düvel ◽  
Amanda Grobe ◽  
Ullrich Dettmann
Keyword(s):  
Water Management ◽  
Drip Irrigation ◽  
Large Scale ◽  
Ghg Emissions ◽  
The Other ◽  
Ghg Mitigation ◽  
Greenhouse Gas Balance ◽  
Peat Extraction ◽  
Water Tables ◽  
Mitigation Potentials

AbstractFor two years, we quantified the exchange of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) at two different large-scale Sphagnum farming sites. At both, peat extraction left a shallow layer of highly decomposed peat and low hydraulic conductivities. One site was characterized by preceding multi-annual inundation and irrigated by ditches, while the other one was inoculated directly after peat extraction and irrigated by ditches and drip irrigation. Further, GHG emissions from an irrigation polder and the effect of harvesting Sphagnum donor material at a near-natural reference site were determined. GHG mitigation potentials lag behind the results of less decomposed sites, although our results were also affected by the extraordinary hot and dry summer 2018. CO2 exchanges ranged between -0.6 and 2.2 t CO2-C ha−1 y−1 and were mainly influenced by low water table depths. CH4 emissions were low with the exception of plots with higher Eriophorum covers, while fluctuating water tables and poorly developing plant covers led to considerable N2O emissions at the ditch irrigation site. The removal of the upper vegetation at the near-natural site resulted in increased CH4 emissions and, on average, lowered CO2 emissions. Overall, best plant growth and lowest GHG emissions were measured at the previously inundated site. At the other site, drip irrigation provided more favourable conditions than ditch irrigation. The size of the area needed for water management (ditches, polders) strongly affected the areal GHG balances. We conclude that Sphagnum farming on highly decomposed peat is possible but requires elaborate water management.

Incremental Community Detection on Large Complex Attributed Network

2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Zhe Chen ◽  
Aixin Sun ◽  
Xiaokui Xiao
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Network Data ◽  
Topological Information ◽  
Community Membership ◽  
Attributed Network ◽  
Benchmark Datasets ◽  
Modularity Maximization ◽  
Large Scale Networks

Community detection on network data is a fundamental task, and has many applications in industry. Network data in industry can be very large, with incomplete and complex attributes, and more importantly, growing. This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains users with transactions. The results demonstrate inc-AGGMMR’s effectiveness and practicability.

scholarly journals Assessing the impact of hydrodynamics on large-scale flood wave propagation – a case study for the Amazon Basin

2017 ◽  
Vol 21 (1) ◽  
pp. 117-132 ◽  
Author(s):  
Jannis M. Hoch ◽  
Arjen V. Haag ◽  
Arthur van Dam ◽  
Hessel C. Winsemius ◽  
Ludovicus P. H. van Beek ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Hydrodynamic Model ◽  
Large Scale ◽  
Hydrologic Model ◽  
Flood Events ◽  
Coupled Models ◽  
Validation Parameters ◽  
Directional Coupling ◽  
Inundation Extent ◽  
The Impact

Abstract. Large-scale flood events often show spatial correlation in neighbouring basins, and thus can affect adjacent basins simultaneously, as well as result in superposition of different flood peaks. Such flood events therefore need to be addressed with large-scale modelling approaches to capture these processes. Many approaches currently in place are based on either a hydrologic or a hydrodynamic model. However, the resulting lack of interaction between hydrology and hydrodynamics, for instance, by implementing groundwater infiltration on inundated floodplains, can hamper modelled inundation and discharge results where such interactions are important. In this study, the global hydrologic model PCR-GLOBWB at 30 arcmin spatial resolution was one-directionally and spatially coupled with the hydrodynamic model Delft 3D Flexible Mesh (FM) for the Amazon River basin at a grid-by-grid basis and at a daily time step. The use of a flexible unstructured mesh allows for fine-scale representation of channels and floodplains, while preserving a coarser spatial resolution for less flood-prone areas, thus not unnecessarily increasing computational costs. In addition, we assessed the difference between a 1-D channel/2-D floodplain and a 2-D schematization in Delft 3D FM. Validating modelled discharge results shows that coupling PCR-GLOBWB to a hydrodynamic routing scheme generally increases model performance compared to using a hydrodynamic or hydrologic model only for all validation parameters applied. Closer examination shows that the 1-D/2-D schematization outperforms 2-D for r2 and root mean square error (RMSE) whilst having a lower Kling–Gupta efficiency (KGE). We also found that spatial coupling has the significant advantage of a better representation of inundation at smaller streams throughout the model domain. A validation of simulated inundation extent revealed that only those set-ups incorporating 1-D channels are capable of representing inundations for reaches below the spatial resolution of the 2-D mesh. Implementing 1-D channels is therefore particularly of advantage for large-scale inundation models, as they are often built upon remotely sensed surface elevation data which often enclose a strong vertical bias, hampering downstream connectivity. Since only a one-directional coupling approach was tested, and therefore important feedback processes are not incorporated, simulated discharge and inundation extent for both coupled set-ups is generally overpredicted. Hence, it will be the subsequent step to extend it to a two-directional coupling scheme to obtain a closed feedback loop between hydrologic and hydrodynamic processes. The current findings demonstrating the potential of one-directionally and spatially coupled models to obtain improved discharge estimates form an important step towards a large-scale inundation model with a full dynamic coupling between hydrology and hydrodynamics.

scholarly journals Are General Circulation Models Ready for Operational Streamflow Forecasting for Water Management in the Ganges and Brahmaputra River Basins?

2015 ◽  
Vol 17 (1) ◽  
pp. 195-210 ◽  
Author(s):  
Safat Sikder ◽  
Xiaodong Chen ◽  
Faisal Hossain ◽  
Jason B. Roberts ◽  
Franklin Robertson ◽  
...  
Keyword(s):  
Water Management ◽  
River Basins ◽  
Hydrologic Model ◽  
Streamflow Forecasting ◽  
Management Decisions ◽  
Brahmaputra River ◽  
Ganges Basin ◽  
Monthly Scale ◽  
Brahmaputra Basin ◽  
The Ganges

Abstract This study asks the question of whether GCMs are ready to be operationalized for streamflow forecasting in South Asian river basins, and if so, at what temporal scales and for which water management decisions are they likely to be relevant? The authors focused on the Ganges, Brahmaputra, and Meghna basins for which there is a gridded hydrologic model calibrated for the 2002–10 period. The North American Multimodel Ensemble (NMME) suite of eight GCM hindcasts was applied to generate precipitation forecasts for each month of the 1982–2012 (30 year) period at up to 6 months of lead time, which were then downscaled according to the bias-corrected statistical downscaling (BCSD) procedure to daily time steps. A global retrospective forcing dataset was used for this downscaling procedure. The study clearly revealed that a regionally consistent forcing for BCSD, which is currently unavailable for the region, is one of the primary conditions to realize reasonable skill in streamflow forecasting. In terms of relative RMSE (normalized by reference flow obtained from the global retrospective forcings used in downscaling), streamflow forecast uncertainty (RMSE) was found to be 38%–50% at monthly scale and 22%–35% at seasonal (3 monthly) scale. The Ganges River (regulated) experienced higher uncertainty than the Brahmaputra River (unregulated). In terms of anomaly correlation coefficient (ACC), the streamflow forecasting at seasonal (3 monthly) scale was found to have less uncertainty (>0.3) than at monthly scale (<0.25). The forecast skill in the Brahmaputra basin showed more improvement when the time horizon was aggregated from monthly to seasonal than the Ganges basin. Finally, the skill assessment for the individual seasons revealed that the flow forecasting using NMME data had less uncertainty during monsoon season (July–September) in the Brahmaputra basin and in postmonsoon season (October–December) in the Ganges basin. Overall, the study indicated that GCMs can have value for management decisions only at seasonal or annual water balance applications at best if appropriate historical forcings are used in downscaling. The take-home message of this study is that GCMs are not yet ready for prime-time operationalization for a wide variety of multiscale water management decisions for the Ganges and Brahmaputra River basins.

Proposal for the generation of network data for pedestrians in South Korea using large-scale maps

2015 ◽  
Vol 19 (5) ◽  
pp. 1483-1491 ◽  
Author(s):  
Jiyoung Kim ◽  
Yong Huh ◽  
Jung Ok Kim ◽  
Jae Bin Lee
Keyword(s):  
South Korea ◽  
Large Scale ◽  
Network Data

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

The State of Irrigation Infrastructure in Ghana: The Way Forward

2018 ◽  
Vol 1 (1) ◽  
pp. 54-67
Author(s):  
P. Glitse ◽  
B. V. Nyamadi ◽  
K. W. Darkwah ◽  
K. A. Mintah
Keyword(s):  
Water Management ◽  
Large Scale ◽  
Irrigated Agriculture ◽  
Energy Access ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Irrigation Development ◽  
Factors Affecting ◽  
Cost Of Energy ◽  
Irrigation Infrastructure

The Ghana Irrigation Development Authority (GIDA) is a public sector organization established to promote agricultural growth through the provision of irrigation infrastructure and other agricultural water management techniques. Irrigated agriculture in Ghana is categorized into formal, informal or smallholder and large-scale commercial irrigation. Over the years, irrigation development in the country has been faced with a number of challenges, which necessitated the development of the National Irrigation Policy, Strategies and Regulatory Measures and the Ghana Agricultural Water Management Pre-Investment Reform Action Framework. A number of factors affecting irrigation development in the country include lack of capital, commitment by successive governments, cost of energy, access to land and credit, lack of technical know-how and encroachment, among others. Analysis of budget provided by government for public irrigation development was carried out using simple linear regression. Results indicate a bright prospect of irrigation development, with reforms under implementation. A minimum of GHS 633.43 million is required for release into the sub-sector by government together with investments from private sector in the next ten years to shift the balance towards positive growth. To solve the problem of inadequate funding of the sub-sector activities, it is recommended that the GIDA collaborates with Development Partners to fund projects and activities in line with their objectives. GIDA should develop effective programmes for building capacity of contractors involved in development of infrastructure. GIDA should deepen its collaboration with private investors under PPPs and convert electric and diesel/petrol powered irrigation pumps to solar powered ones.

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