scholarly journals Study on the effect of rainfall spatial variability on runoff modelling

2018 ◽  
Vol 20 (3) ◽  
pp. 577-587 ◽  
Author(s):  
Jun Zhang ◽  
Dawei Han ◽  
Yang Song ◽  
Qiang Dai
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Spatial Information ◽  
Model Performance ◽  
Moran’S I ◽  
Image Size ◽  
Moran's I ◽  
Rainfall Events ◽  
Lumped Model ◽  
Distributed Models

Abstract Rainfall spatial variability was assessed to explore its influence on runoff modelling. Image size, coefficient of variation (Cv) and Moran's I were chosen to assess for rainfall spatial variability. The smaller the image size after compression, the less complex is the rainfall spatial variability. The results showed that due to the drawing procedure and varied compression methods, a large uncertainty exists for using image size to describe rainfall spatial variability. Cv quantifies the variability between different rainfall values without considering rainfall spatial distribution and Moran's I describes the spatial autocorrelation between gauges rather than the values. As both rainfall values and spatial distribution have an influence on runoff modelling, the combination of Cv and Moran's I was further explored. The results showed that the combination of Cv and Moran's I is reliable to describe rainfall spatial variability. Furthermore, with the increase of rainfall spatial variability, the hydrological model performance decreases. Moreover, it is difficult for a lumped model to cope with rainfall events assigned with complex rainfall spatial variability since spatial information is not taken into consideration (i.e. the VIC model used in this study). Therefore, it is recommended to apply distributed models that can deal with more spatial input information.

scholarly journals Rainfall spatial variability in the application of catchment morphing for ungauged catchments

2021 ◽  
Author(s):  
Jun Zhang ◽  
Dawei Han ◽  
Qiang Dai
Keyword(s):  
Spatial Analysis ◽  
Spatial Variability ◽  
Spatial Information ◽  
Model Performance ◽  
Ungauged Catchments ◽  
Distributed Information ◽  
Simulation Performance ◽  
Distributed Models ◽  
Spatially Distributed ◽  
The Uk

Abstract Catchment Morphing (CM) is a newly proposed approach to apply fully distributed models for ungauged catchments and has been experimented in several catchments in the UK. As one of the most important input datasets for hydrological models, rainfall spatial variability is influential to the stream variabilities and simulation performance. A homogenous rainfall was utilized in the previous experiments with Catchment Morphing. This study applied a spatially distributed rainfall from CEH-GEAR rainfall dataset in the morphed catchment for ungauged catchments as the follow-on study. Three catchments in the UK were used for rainfall spatial analysis and CEH-GEAR rainfall data were adopted for additional spatial analysis. The results demonstrate the influence of rainfall spatial information to the model performance with CM and illustrate the ability of morphed catchment to tackle with spatially varied information. More spatially distributed information is expected to be introduced for a wider application of CM.

scholarly journals Spatial distribution and determinant factors of anemia among adults aged 15–59 in Ethiopia; using mixed-effects ordinal logistic regression model

BMC Nutrition ◽  
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Biruk Shalmeno Tusa ◽  
Sewnet Adem Kebede ◽  
Adisu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background Anemia is a global public health problem, particularly in developing countries. Assessing the geographic distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anemia. Thus, the current study is aimed to assess the spatial distribution and determinant factors of anemia in Ethiopia among adults aged 15–59. Methods A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anemia. Result The spatial distribution of anemia in Ethiopia among adults age 15–59 was found to be clustered (Global Moran’s I = 0.81, p value <  0.0001). In the multivariable mixed-effectordinal regression analysis; Females [AOR = 1.53; 95% CI: 1.42, 1.66], Never married [AOR = 0.86; 95% CI: 0.77, 0.96], highly educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anemia among adults. Conclusions A significant clustering of anemia among adults aged 15–59 were found in Ethiopia and the significant hotspot areas with high cluster anemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, sex, marital status, educational level, place of residence, region, wealth index and BMI were significant predictors of anemia. Therefore, effective public health intervention and nutritional education should be designed for the identified hotspot areas and risk groups in order to decrease the incidence of anemia.

scholarly journals Spatial distribution and predictors of domestic violence against women: evidence from analysis of Ethiopian demographic health survey 2016

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Elias Seid ◽  
Tesfahun Melese ◽  
Kassahun Alemu
Keyword(s):  
Domestic Violence ◽  
Spatial Distribution ◽  
Regression Analysis ◽  
Violence Against Women ◽  
Health Survey ◽  
Spatial Clustering ◽  
Wife Beating ◽  
Moran’S I ◽  
Demographic Health Survey ◽  
Moran's I

Abstract Background Violence against women particularly that is committed by an intimate partner is becoming a social and public health problem across the world. Studies show that the spatial variation in the distribution of domestic violence was commonly attributed to neighborhood-level predictors. Despite the prominent benefits of spatial techniques, research findings are limited. Therefore, the current study intends to determine the spatial distribution and predictors of domestic violence among women aged 15–49 in Ethiopia. Methods Data from the Ethiopian demographic health survey 2016 were used to determine the spatial distribution of domestic violence in Ethiopia. Spatial auto-correlation statistics (both Global and Local Moran’s I) were used to assess the spatial distribution of domestic violence cases in Ethiopia. Spatial locations of significant clusters were identified by using Kuldorff’s Sat Scan version 9.4 software. Finally, binary logistic regression and a generalized linear mixed model were fitted to identify predictors of domestic violence. Result The study found that spatial clustering of domestic violence cases in Ethiopia with Moran’s I value of 0.26, Z score of 8.26, and P value < 0.01. The Sat Scan analysis identifies the primary most likely cluster in Oromia, SNNP regions, and secondary cluster in the Amhara region. The output from regression analysis identifies low economic status, partner alcohol use, witnessing family violence, marital controlling behaviors, and community acceptance of wife-beating as significant predictors of domestic violence. Conclusion There is spatial clustering of IPV cases in Ethiopia. The output from regression analysis shows that individual, relationship, and community-level predictors were strongly associated with IPV. Based upon our findings, we give the following recommendation: The government should give prior concern for controlling factors such as high alcohol consumption, improper parenting, and community norm that encourage IPV that were responsible for IPV in the identified hot spot areas.

scholarly journals Effects of Vegetable Fields on the Spatial Distribution Patterns of Metal(loid)s in Soils Based on GIS and Moran’s I

2019 ◽  
Vol 16 (21) ◽  
pp. 4095
Author(s):  
Qiang Wang ◽  
Shanlian Yang ◽  
Menglei Zheng ◽  
Fengxiang Han ◽  
Youhua Ma
Keyword(s):  
Spatial Distribution ◽  
Organic Fertilizer ◽  
Distribution Patterns ◽  
Moran’S I ◽  
Vegetable Production ◽  
Vegetable Crops ◽  
Moran's I ◽  
Spatial Distribution Patterns ◽  
Ph Decrease

Metal(loid) pollution in vegetable field soils has become increasingly severe and affects the safety of vegetable crops. Research in China has mainly focused on greenhouse vegetables (GV), while open field vegetables (OV) and the spatial distribution patterns of metal(loid)s in the surrounding soils have rarely been assessed. In the present study, spatial analysis methods combining Geographic Information Systems (GIS) and Moran’s I were applied to analyze the effects of vegetable fields on metal(loid) accumulation in soils. Overall, vegetable fields affected the spatial distribution of metal(loid)s in soils. In long-term vegetable production, the use of large amounts of organic fertilizer led to the bioconcentration of cadmium (Cd) and mercury (Hg), and long-term fertilization resulted in a significant pH decrease and consequent transformation and migration of chromium (Cr), lead (Pb), and arsenic (As). Thus, OV fields with a long history of planting had lower average pH and Cd, and higher average As, Cr, Hg, and Pb than GV fields, reached 0.93%, 10.1%, 5.8%, 3.0%, 80.8%, and 0.43% respectively. Due to the migration and transformation of metal(loid)s in OV soils, these should be further investigated regarding their abilities to reduce the accumulation of metal(loid)s in soils and protect the quality of the cultivated land.

The case of distributed rainfall and spatially adaptive modeling

2020 ◽  
Author(s):  
Ralf Loritz ◽  
Uwe Ehret ◽  
Malte Neuper ◽  
Erwin Zehe
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Hydrological Modeling ◽  
Spatial Information ◽  
Time Variability ◽  
State Variables ◽  
Model Framework ◽  
Adaptive Modeling ◽  
Spatial Gradients ◽  
Spatially Adaptive

&lt;p&gt;&lt;em&gt;How important is information about distributed precipitation when we do rainfall-runoff modeling on the catchments scale?&lt;/em&gt;&lt;/p&gt;&lt;p&gt;The latter is surely one of the more frequently asked research questions in hydrological modeling. Most studies tackling the issue seem thereby to agree that distributed precipitation becomes more important if the ratio of catchment size against storm size decreases or if the spatial gradients of the rainfall field increase. Furthermore, is it often highlighted that catchments are surprisingly effective in smoothing out the spatial variability of the meteorological forcing, at least, if the focus is simulation integral fluxes and average states.&lt;/p&gt;&lt;p&gt;However, despite these agreements there is no straightforward guidance in the hydrological literature when these thresholds have been reached and when the spatial distribution of the precipitation starts dominating. This is because the answer to the above drawn question depends on the spatial variability of system characteristics, on the system state variables as well as on the strength of the rainfall forcing and its space-time variability. As all three controls vary greatly in space and time it is challenging to identify generally valid rules when information about the distribution of rainfall becomes important for predictive modelling.&lt;/p&gt;&lt;p&gt;The present study aims to overcome this limitation by developing a model framework to identify periods where the spatial gradients in rainfall intensity are larger than the ability of the landscape to internally dissipate those. This newly developed spatially adaptive modeling approach, uses the spatial information content of the precipitation to control the spatial distribution of our model. The main underlying idea of this approach is to use distributed models only when they are actually needed resulting in 1) a drastic decrease in computational times as well as 2) in a more appropriate representation of a hydrological system. Our results highlight that only during a few periods throughout a hydrological year do distributed precipitation data actually matter. However, they also show that these periods are often highly relevant with respect to certain extremes and that the successful simulation of these extremes require distributed information about the forcing and state of a given system.&lt;/p&gt;

scholarly journals Spatial Distribution of BTEX and Inorganic Pollutants During Summer Season in Yalova, Turkey

2017 ◽  
Vol 24 (4) ◽  
pp. 565-581
Author(s):  
Lokman Hakan Tecer ◽  
Sermin Tagil ◽  
Osman Ulukaya ◽  
Merve Ficici
Keyword(s):  
Spatial Distribution ◽  
Rural Areas ◽  
Air Pollutants ◽  
Passive Sampling ◽  
Temporal Variations ◽  
Average Concentration ◽  
Geographical Information ◽  
Moran’S I ◽  
Moran's I ◽  
Industrial Sites

Abstract The objective of this research is to determine the atmospheric concentrations and spatial distribution of benzene (B), toluene (T), ethylbenzene (E) and xylenes (X) (BTEX) and inorganic air pollutants (O3, NO2 and SO2) in the Yalova atmosphere during summer 2015. In this study, a combination of passive sampling and Geographical Information System-based geo-statistics are used with spatial statistics of autocorrelation to characterise the spatial pattern of the quality of air based on concentrations of these pollutants in Yalova. The spatial temporal variations of pollutants in the air with five types of land-use, residence, rural, highway, side road and industrial areas were investigated at 40 stations in Yalova between 7th August 2015 and 26th August 2015 using passive sampling. An inverse distance weighting interpolation technique was used to estimate variables at an unmeasured location from observed values at nearby locations. The spatial autocorrelation of air pollutants in the city was investigated using the statistical methods of Moran’s I in addition to the Getis Ord Gi. During the summer, highway and industrial sites had higher levels of BTEX then rural areas. The average concentration of toluene was measured to be 5.83 μg/m3 and this is the highest pollutant concentration. Average concentrations of NO2, O3 and SO2 are 35.64, 84.23 and 3.95 μg/m3, respectively. According to the global results of Moran’s I; NO2 and BTEX had positive correlations on a global space at a significant rate. Moreover, the autocorrelation analysis on the local space demonstrated significant hot spots on industrial sites and along the main roads.

scholarly journals Comparing model performance of two rainfall-runoff models in the Rhine basin using different atmospheric forcing data sets

2007 ◽  
Vol 4 (6) ◽  
pp. 4325-4360 ◽  
Author(s):  
A. H. te Linde ◽  
J. C. J. H. Aerts ◽  
R. T. W. L. Hurkmans ◽  
M. Eberle
Keyword(s):  
Climate Change ◽  
Model Performance ◽  
Change Scenario ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Meteorological Forcing ◽  
Lumped Model ◽  
Distributed Models ◽  
Rhine Basin ◽  
Physically Based

Abstract. Due to the growing wish and necessity to simulate the possible effects of climate change on the discharge regime on large rivers such as the Rhine in Europe, there is a need for well performing hydrological models that can be applied in climate change scenario studies. There exists large variety in available models and there is an ongoing debate in research on rainfall-runoff modelling on whether or not physically based distributed models better represent observed discharges than conceptual lumped model approaches do. In this paper, the hydrological models HBV and VIC were compared for the Rhine basin by testing their performance in simulating discharge. Overall, the semi-distributed conceptual HBV model performed much better than the distributed physically based VIC model (E=0.62, r2=0.65 vs. E=0.31, r2=0.54 at Lobith). It is argued here that even for a well-documented river basin such as the Rhine, more complex modelling does not automatically lead to better results. Moreover, it is concluded that meteorological forcing data has a considerable influence on model performance, irrespectively to the type of model structure and the need for ground-based meteorological measurements is emphasized.

scholarly journals Spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia; using mixed-effects ordinal logistic regression model

2021 ◽  
Author(s):  
biruk shalmeno tusa ◽  
Sewnet Adem kebede ◽  
Adisu Birhanu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background: Anaemia is a global public health problem particularly in developing countries. Assessing the geographical distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anaemia. Thus, the current study aimed to assess the spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia.Methods: A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of Anaemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of Anaemia.Result: The spatial distribution of anemia among adults age 15-59 was found to be clustered in Ethiopia (Global Moran’s I = 0.81, p value < 0.0001). In the multivariable mixed-effect ordinal regression analysis; Females [AOR = 1.53; 95% CI: 1.42, 1.66], Never married [AOR = 0.86; 95% CI: 0.77, 0.96], higher educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anemia among adults.Conclusions: A significant clustering of anemia among adults aged 15-59 were found in Ethiopia and the significant hotspot areas with high cluster anemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, gender, marital status, educational level, place of residence, region, wealth index and BMI were significant predictors of anemia. Therefore, effective public health intervention and nutritional education should be designed in the identified hotspot areas and risk groups to decrease the incidence of anaemia.

scholarly journals Understanding Spatial Distribution of Retirement Villages: An Analysis of the Greater Brisbane Region

Urban Science ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 89
Author(s):  
Bo Xia ◽  
Jiaxuan E ◽  
Qing Chen ◽  
Laurie Buys ◽  
Tan Yigitcanlar ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Urban Policy ◽  
Distribution Patterns ◽  
Developed Countries ◽  
Moran’S I ◽  
Market Demand ◽  
Moran's I ◽  
Study Region ◽  
For Profit ◽  
Retirement Village

The nature of the increasingly ageing populations of developed countries places residential issues of these populations at the heart of urban policy. Retirement villages as housing options for older adults in Australia has been growing steadily in recent years; however, there have been a dearth of geographical studies looking into the distribution of existing retirement villages at the regional level. This study aims to reveal the geographical distributions and cluster patterns of retirement villages in the Greater Brisbane Region of Australia to better understand and serve the living requirements of current and potential retirement village residents. The geovisualization method was adopted to visually explore the distribution patterns of retirement villages. The Global Moran’s I and Local Moran’s I measures were employed to analyze the spatial correlation and the clusters of retirement villages in the study region. The study revealed that distribution of retirement villages was not random (z-score = 7.11; p < 0.001), but clustered in nature and included hotspot patterns, especially along the coastline and Brisbane River areas. Moreover, for-profit and not-for-profit retirement villages have different distribution patterns and adopted significantly different tenure agreements. In the study region, the spatial distribution of retirement villages aligns with the aggregation trend of older residents. The findings of this study disclosed the spatial distribution patterns of retirement villages and will provide developers and policymakers with geographically referenced data for the choice of new development sites to meet the market demand of potential customers, forming aged-friendly development strategies, and eventually leading to improved quality of life for older Australians.

scholarly journals Spatial Distribution and Determinant Factors of Anaemia Among Adults Aged 15-59 in Ethiopia; Using Mixed-Effects Ordinal Logistic Regression Model

2020 ◽  
Author(s):  
Biruk Shalmeno Tusa ◽  
Sewnet Adem kebede ◽  
Adisu Birhanu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background: Anaemia is a global public health problem particularly in developing countries. Assessing the geographical distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anaemia. Thus, the current study aimed to assess the spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia.Methods: A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anaemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anaemia.Result: The spatial distribution of anaemia among adults age 15-59 was found to be clustered in Ethiopia (Global Moran’s I = 0.81, p value < 0.0001). In the multivariable mixed-effect ordinal regression analysis; being females [AOR = 1.53; 95% CI: 1.42, 1.66], never married [AOR = 0.86; 95% CI: 0.77, 0.96], higher educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anaemia among adults.Conclusions: A significant clustering of anaemia among adults aged 15-59 were found in Ethiopia and the significant hotspot areas with high clusters of anaemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, gender, marital status, educational level, place of residence, region, wealth index and body mass index (BMI) were significant predictors of anaemia. Therefore, effective public health intervention and nutritional education should be designed in the identified hotspot areas and risk groups to decrease the incidence of anaemia.

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