scholarly journals Modeling streamflow variability at the regional scale: (1) Perceptual model development through signature analysis

2021 ◽  
pp. 127287
Author(s):  
Fabrizio Fenicia ◽  
Jeffrey J. McDonnell
Keyword(s):  
Regional Scale ◽  
Model Development ◽  
Signature Analysis ◽  
Perceptual Model ◽  
Streamflow Variability

Communality from equal weight for GIS-based landslide susceptibility mapping

2020 ◽  
Author(s):  
Sandip Som ◽  
Saibal Ghosh ◽  
Soumitra Dasgupta ◽  
Thrideep Kumar ◽  
J. N. Hindayar ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Roc Curves ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Equal Weight ◽  
Landslide Susceptibility Index

Abstract Modeling landslide susceptibility is one of the important aspects of land use planning and risk management. Several modeling methods are available based either on highly specialized knowledge on causative attributes or on good landslide inventory data to use as training and testing attribute on model development. Understandably, these two criteria are rarely available for local land regulators. This paper presents a new model methodology, which requires minimum knowledge of causative attributes and does not depend on landslide inventory. As landslide causes due to the combined effect of causative attributes, this model utilizes communality (common variance) of the attributes, extracted by exploratory factor analysis and used for calculation of landslide susceptibility index. The model can understand the inter-relationship of different geo-environmental attributes responsible for landslide along with identification and prioritization of attributes on model performance to delineate non-performing attributes. Finally, the model performance is compared with the well established AHP method (knowledge driven) and FRM method (data driven) by cut-off independent ROC curves along with cost-effectiveness. The model shows it’s performance almost at par with the established models, involving minimum modeling expertise. The findings and results of the present work will be helpful for the town planners and engineers on a regional scale for generalized planning and assessment.

scholarly journals Systematic Climate Model Rainfall Biases over Southern Africa: Links to Moisture Circulation and Topography

2018 ◽  
Vol 31 (18) ◽  
pp. 7533-7548 ◽  
Author(s):  
C. Munday ◽  
R. Washington
Keyword(s):  
Southern Africa ◽  
Climate Models ◽  
Climate Model ◽  
Regional Scale ◽  
Model Development ◽  
Coupled Model ◽  
Climate Science ◽  
Summer Rainfall ◽  
Southwest Indian Ocean ◽  
Low Level

An important challenge for climate science is to understand the regional circulation and rainfall response to global warming. Unfortunately, the climate models used to project future changes struggle to represent present-day rainfall and circulation, especially at a regional scale. This is the case in southern Africa, where models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) overestimate summer rainfall by as much as 300% compared to observations and tend to underestimate rainfall in Madagascar and the southwest Indian Ocean. In this paper, we explore the climate processes associated with the rainfall bias, with the aim of assessing the reliability of the CMIP5 ensemble and highlighting important areas for model development. We find that the high precipitation rates in models that are wet over southern Africa are associated with an anomalous northeasterly moisture transport (~10–30 g kg−1 s−1) that penetrates across the high topography of Tanzania and Malawi and into subtropical southern Africa. This transport occurs in preference to a southeasterly recurvature toward Madagascar that is seen in drier models and reanalysis data. We demonstrate that topographically related model biases in low-level flow are important for explaining the intermodel spread in rainfall; wetter models have a reduced tendency to block the oncoming northeasterly flow compared to dry models. The differences in low-level flow among models are related to upstream wind speed and model representation of topography, both of which should be foci for model development.

scholarly journals Evaluation and Error Apportionment of an Ensemble of Atmospheric Chemistry Transport Modelling Systems: Multi-variable Temporal and Spatial Breakdown

2016 ◽  
Author(s):  
Efisio Solazzo ◽  
Roberto Bianconi ◽  
Christian Hogrefe ◽  
Gabriele Curci ◽  
Ummugulsum Alyuz ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Model Comparison ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Strong Impact ◽  
Air Quality Model ◽  
Quality Model ◽  
Transport Modelling

Abstract. Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, the uniformity of the synoptic error being region and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

A regional scale modeling study of atmospheric transport and transformation of mercury. I. Model development and evaluation

2000 ◽  
Vol 34 (28) ◽  
pp. 4933-4944 ◽  
Author(s):  
Xiaohong Xu ◽  
Xiusheng Yang ◽  
David R. Miller ◽  
Joseph J Helble ◽  
Robert J Carley
Keyword(s):  
Atmospheric Transport ◽  
Regional Scale ◽  
Model Development ◽  
Transport And Transformation ◽  
Scale Modeling ◽  
Modeling Study

scholarly journals A meta-analysis of groundwater contamination by nitrates at the African scale

2016 ◽  
Author(s):  
Issoufou Ouedraogo ◽  
Marnik Vanclooster
Keyword(s):  
Statistical Model ◽  
Nitrate Concentration ◽  
Regional Scale ◽  
Model Development ◽  
Shallow Groundwater ◽  
Nitrate Pollution ◽  
Nitrate Contamination ◽  
Pan African ◽  
Nitrate Concentrations ◽  
Groundwater Systems

Abstract. Contamination of groundwater with nitrate poses a major health risk to millions of people around Africa. Assessing the space-time distribution of this contamination, as well as understanding the factors that explain this contamination is important to manage sustainable drinking water at the regional scale. This study aims to assess the variables that contribute to nitrate pollution in groundwater at the pan-African scale by statistical modeling. We compiled a literature database of nitrate concentration in groundwater (around 250 studies) and combined it with digital maps of physical attributes such as soil, geology, climate, hydrogeology and anthropogenic data for statistical model development. The maximum, medium and minimum observed nitrate concentrations were analysed. In total, 13 explanatory variables were screened to explain observed nitrate pollution in groundwater. For the mean nitrate concentration, 4 variables are retained in the statistical explanatory model: (1) Depth to groundwater (shallow groundwater, typically < 50 m); (2) Recharge rate; (3) Aquifer type; and (4) Population density. The former three variables represent intrinsic vulnerability of groundwater systems towards pollution, while the latter variable is a proxy for anthropogenic pollution pressure. The model explains 65 % of the variation of mean nitrate contamination in groundwater at the pan-Africa scale. Using the same proxy information, we could develop a statistical model for the maximum nitrate concentrations that explains 42 % of the nitrate variation. For the maximum concentrations, other environmental attributes such as soil type, slope, rainfall, climate class and region type improve the prediction of maximum nitrate concentrations at the pan-African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the dataset, we do not develop a statistical model for these data. The data based statistical model presented here represents an important step toward developing tools that will allow us to accurately predict nitrate distribution at the African scale and thus may support groundwater monitoring and water management that aims to protect groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data becomes available and/or combined with more conceptual descriptions of the fate of nutrients in the hydro system.

Understanding what to account for and what to ignore in the design of distributed hydrological models

2020 ◽  
Author(s):  
Fabrizio Fenicia ◽  
Marco Dal Molin
Keyword(s):  
Large Scale ◽  
Model Development ◽  
Sensitivity Analyses ◽  
Hydrological Models ◽  
Large Variability ◽  
Landscape Characteristics ◽  
Geological Features ◽  
Streamflow Variability ◽  
Rainfall Runoff Model ◽  
Distributed Hydrological Models

&lt;p&gt;Designing a distributed rainfall-runoff model requires many not obvious decisions, such as whether to include regional groundwater flow, whether to account for the spatial variability of topography, geology, soils and vegetation, and at which spatial resolution to resolve model inputs. Typically, the effect of such decisions is determined a posteriori, for example based on sensitivity analyses, with the disadvantage that if a decision is poorly made, it is necessary to restart the model development from the conceptualization stage, which is a time consuming process. We here show that a more effective strategy is to base such decisions on a preliminary analysis of the available data, hence by &amp;#8220;looking at data first&amp;#8221;. In particular, similarly to what done in catchment classification studies, we start by identifying potential climatic and landscape controls on streamflow signatures. These insights are subsequently used to inform model decisions such as the ones above described. This approach is illustrated in the Thur catchment in Switzerland (1702 km&lt;sup&gt;2&lt;/sup&gt;), with 10 sub-catchments. The catchment shows a large variability in streamflow, climatic, and landscape characteristics. Results demonstrate that precipitation (quantity and type) is the main control of the water balance and of streamflow seasonality; geological features control the partition of the fluxes between baseflow and quick flow; other catchment characteristics are not of primary importance in determining streamflow variability. The present study, that conjugates some aspects of catchment classification with hydrological modelling, represents a step forward in understanding catchment dominant processes at the large scale and in designing a procedure for constructing distributed hydrological models with limited complexity.&lt;/p&gt;

Sinkhole susceptibility evaluation in Apulia, southern Italy

2021 ◽  
Author(s):  
Carmela Vennari ◽  
Mauro Rossi ◽  
Luca Pisano ◽  
Veronica Zumpano ◽  
Mario Parise
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Model Development ◽  
Temporal Distribution ◽  
Statistical Modelling ◽  
Mitigation Strategies ◽  
Susceptibility Map ◽  
Thematic Layers ◽  
Landslide Susceptibility Zonation ◽  
Validation Set

&lt;p&gt;In some regions in Italy sinkholes are frequent and pose a serious threat to structures and infrastructures. Apulia region is largely affected by sinkholes of both natural and anthropogenic origin, due to the karst nature of large portions of the regional territory and to high diffusion of artificial cavities.&lt;/p&gt;&lt;p&gt;For this reasons, susceptibility, hazard and risk posed by sinkholes must be estimated in order to gain more insights into their spatial and temporal distribution, and to apply appropriate risk management and to take proper mitigation strategies.&lt;/p&gt;&lt;p&gt;In order to estimate the susceptibility to sinkholes in Apulia, the ensemble statistical modelling proposed by Rossi et al. (2010) and later refined by Rossi &amp; Reichenbach (2016) is used. This allows assessing susceptibility using differentiated statistical approaches, quantifying accurately the modelling performances, and evaluating the associated uncertainty. In order to obtain accurate and reliable results thematic layers related to the sinkholes occurrence were carefully evauated and selected. This contribution shows the preliminary results of the analyses to evaluate the susceptibility to natural sinkholes, which used &amp;#160;as training dependent (i.e. grouping) set, data extracted from the regional inventory of natural caves, edited by the Apulian Speleological Federation (www.catasto.fspuglia.it), and as validation set the natural sinkholes occurred in Apulia, collected in the chronological catalogue of sinkholes in Italy (Parise &amp; Vennari, 2013, 2017). Appropriate thematic layers, were selected heuristically on the base of the knowledge on the triggering mechanisms and the nature of the phenomenon gained previously in the study area.&lt;/p&gt;&lt;p&gt;Resulting regional-scale susceptibility map will be appropriately validated. The methodological procedure will be applied to the evaluation of susceptibility for anthropogenic sinkholes as well.&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Parise M. &amp; Vennari C. (2017) Distribution and features of natural and anthropogenic sinkholes in Apulia. In: Renard P. &amp; Bertrand C. (Eds.), EuroKarst 2016, Neuchatel. Advances in the hydrogeology of karst and carbonate reservoirs. Springer, ISBN 978-3-319-45464-1, p. 27-34.&lt;/p&gt;&lt;p&gt;Parise M. &amp; Vennari C. (2013) A chronological catalogue of sinkholes in Italy: the first step toward a real evaluation of the sinkhole hazard. Proceedings 8th Multidisciplinary Conference on Sinkholes &amp; the Engineering and Environmental Impacts of Karst, Carlsbad, USA.&lt;/p&gt;&lt;p&gt;Rossi, M. &amp; Reichenbach P. (2016) LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0.&amp;#160;Geoscientific Model Development,&amp;#160;9(10).&lt;/p&gt;&lt;p&gt;Rossi M., Guzzetti F., Reichenbach P., Mondini A. C., Peruccacci S. (2010) Optimal landslide susceptibility zonation based on multiple forecasts, Geomorphology, 114, 129&amp;#8211;142.&lt;/p&gt;

Towards more systematic perceptual model development: a case study using 3 Luxembourgish catchments

2014 ◽  
Vol 29 (12) ◽  
pp. 2731-2750 ◽  
Author(s):  
Sebastian Wrede ◽  
Fabrizio Fenicia ◽  
Núria Martínez-Carreras ◽  
Jérôme Juilleret ◽  
Christophe Hissler ◽  
...  
Keyword(s):  
Model Development ◽  
Perceptual Model

scholarly journals Conceptual model development using a generic Features, Events, and Processes (FEP) database for assessing the potential impact of hydraulic fracturing on groundwater aquifers

2018 ◽  
Vol 45 ◽  
pp. 185-192 ◽  
Author(s):  
Alexandru Tatomir ◽  
Christopher McDermott ◽  
Jacob Bensabat ◽  
Holger Class ◽  
Katriona Edlmann ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Conceptual Model ◽  
Safety Assessment ◽  
Regional Scale ◽  
Model Development ◽  
Unconventional Reservoirs ◽  
Gas Migration ◽  
Groundwater Aquifers ◽  
Potential Impact ◽  
Relevant Factors

Abstract. Hydraulic fracturing for natural gas extraction from unconventional reservoirs has not only impacted the global energy landscape but has also raised concerns over its potential environmental impacts. The concept of “features, events and processes” (FEP) refers to identifying and selecting the most relevant factors for safety assessment studies. In the context of hydraulic fracturing we constructed a comprehensive FEP database and applied it to six key focused scenarios defined under the scope of FracRisk project (http://www.fracrisk.eu, last access: 17 August 2018). The FEP database is ranked to show the relevance of each item in the FEP list per scenario. The main goal of the work is to illustrate the FEP database applicability to develop a conceptual model for regional-scale stray gas migration.

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