scholarly journals Characterization of post-fire streamflow response across western US watersheds

2016 ◽  
Author(s):  
Samuel Saxe ◽  
Terri S. Hogue ◽  
Lauren Hay
Keyword(s):  
Regression Models ◽  
Flow Regimes ◽  
Peak Flows ◽  
Daily Streamflow ◽  
Area Burned ◽  
Low Flows ◽  
Relative Response ◽  
Streamflow Response ◽  
High Flows ◽  
The Impact

Abstract. This research investigates the impact of wildfires on watershed flow regimes, specifically focusing on evaluation of fire events within specified hydroclimatic regions in the western United States. Information on fire events and watershed characteristics were collected through federal and state-level databases and streamflow data were collected from U.S. Geological Survey stream gages. Eighty two watersheds were identified with at least ten years of continuous pre-fire daily streamflow records and five years of continuous post-fire daily flow records. For each watershed, percent change in annual runoff ratio, low-flows, high-flows, peak flows, number of zero flow days, baseflow index, and Richards-Baker flashiness index were calculated using pre- and post-fire periods. The gathered watersheds were divided into nine regions or clusters through k-means clustering and regression models were produced for watersheds grouped by total area burned. The coefficient of determination (R2) was used to determine the accuracy of the resulting models. Results show that low flows, high flows, and peak flows increase significantly in the first two years following a wildfire and decrease over time. Relative response was utilized to scale response variables with respective percent area of watershed burned in order to compare regional differences in watershed response. Watersheds in Cluster 9 (eastern CA, western NV, OR) typically demonstrate a negative relative post-fire response, in that when scaling response to area burned, a slight negative response is observed in flow regimes. Most other watersheds show a positive mean relative response. In addition, regression models show limited correlation between percent watershed burned and streamflow response, implying that other watershed factors strongly influence response.

scholarly journals Characterization and evaluation of controls on post-fire streamflow response across western US watersheds

2018 ◽  
Vol 22 (2) ◽  
pp. 1221-1237 ◽  
Author(s):  
Samuel Saxe ◽  
Terri S. Hogue ◽  
Lauren Hay
Keyword(s):  
Regional Differences ◽  
Regression Models ◽  
Flow Regimes ◽  
Burn Severity ◽  
Area Burned ◽  
Relative Response ◽  
Streamflow Response ◽  
High Flows ◽  
Percent Area ◽  
The Impact

Abstract. This research investigates the impact of wildfires on watershed flow regimes, specifically focusing on evaluation of fire events within specified hydroclimatic regions in the western United States, and evaluating the impact of climate and geophysical variables on response. Eighty-two watersheds were identified with at least 10 years of continuous pre-fire daily streamflow records and 5 years of continuous post-fire daily flow records. Percent change in annual runoff ratio, low flows, high flows, peak flows, number of zero flow days, baseflow index, and Richards–Baker flashiness index were calculated for each watershed using pre- and post-fire periods. Independent variables were identified for each watershed and fire event, including topographic, vegetation, climate, burn severity, percent area burned, and soils data. Results show that low flows, high flows, and peak flows increase in the first 2 years following a wildfire and decrease over time. Relative response was used to scale response variables with the respective percent area of watershed burned in order to compare regional differences in watershed response. To account for variability in precipitation events, runoff ratio was used to compare runoff directly to PRISM precipitation estimates. To account for regional differences in climate patterns, watersheds were divided into nine regions, or clusters, through k-means clustering using climate data, and regression models were produced for watersheds grouped by total area burned. Watersheds in Cluster 9 (eastern California, western Nevada, Oregon) demonstrate a small negative response to observed flow regimes after fire. Cluster 8 watersheds (coastal California) display the greatest flow responses, typically within the first year following wildfire. Most other watersheds show a positive mean relative response. In addition, simple regression models show low correlation between percent watershed burned and streamflow response, implying that other watershed factors strongly influence response. Spearman correlation identified NDVI, aridity index, percent of a watershed's precipitation that falls as rain, and slope as being positively correlated with post-fire streamflow response. This metric also suggested a negative correlation between response and the soil erodibility factor, watershed area, and percent low burn severity. Regression models identified only moderate burn severity and watershed area as being consistently positively/negatively correlated, respectively, with response. The random forest model identified only slope and percent area burned as significant watershed parameters controlling response. Results will help inform post-fire runoff management decisions by helping to identify expected changes to flow regimes, as well as facilitate parameterization for model application in burned watersheds.

scholarly journals Topography significantly influencing low flows in snow-dominated watersheds

2017 ◽  
Author(s):  
Qiang Li ◽  
Xiaohua Wei ◽  
Xin Yang ◽  
Krysta Giles-Hansen ◽  
Mingfang Zhang ◽  
...  
Keyword(s):  
Regression Models ◽  
Low Flow ◽  
Mean Flow ◽  
Slope Length ◽  
Low Flows ◽  
Slope Length Factor ◽  
High Flows ◽  
Flow Variables ◽  
Characterization Index

Abstract. Watershed topography plays an important role in determining the spatial heterogeneity of ecological, geomorphological, and hydrological processes. Few studies have quantified the role of topography on various flow variables. In this study, 28 watersheds with snow-dominated hydrological regimes were selected with daily flow records from 1989 to 1996. The watersheds are located in the Southern Interior of British Columbia, Canada and range in size from 2.6 to 1,780 km2. For each watershed, 22 topographic indices (TIs) were derived, including those commonly used in hydrology and other environmental fields. Flow variables include annual mean flow (Qmean), Q10%, Q25%, Q50%, Q75%, Q90%, and annual minimum flow (Qmin), where Qx% is defined as flows that at the percentage (x) occurred in any given year. Factor analysis (FA) was first adopted to exclude some redundant or repetitive TIs. Then, stepwise regression models were employed to quantify the relative contributions of TIs to each flow variable in each year. Our results show that topography plays a more important role in low flows than high flows. However, the effects of TIs on flow variables are not consistent. Our analysis also determines five significant TIs including perimeter, surface area, openness, terrain characterization index, and slope length factor, which can be used to compare watersheds when low flow assessments are conducted, especially in snow-dominated regions.

Improvement of the simulation of high and low flows in the LSM based hydrological modeling chain SASER applied to the Ebro river basin

2021 ◽  
Author(s):  
Omar Cenobio-Cruz ◽  
Anaïs Barella-Ortiz ◽  
Pere Quintana-Seguí ◽  
Luis Garrote
Keyword(s):  
Spatial Resolution ◽  
Hydrological Modeling ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Hydrogeological Model ◽  
Peak Flows ◽  
Hourly Precipitation ◽  
Daily Streamflow ◽  
Low Flows ◽  
Hourly Distribution

<p>The SASER (Safran-Surfex-Eaudysee-Rapid) hydrological modeling chain is a physically-based and distributed hydrological model that has been implemented over two domains: Iberia and the Pyrenees. Currently, it is used for drought studies (HUMID project) and water resources analysis (PIRAGUA project).</p><p>In this modeling chain, SAFRAN provides the meteorological forcing, SURFEX is the LSM that performs the water and energy balances and Eaudyssée-RAPID simulates daily streamflow. SAFRAN and SURFEX are run at a spatial resolution of 5 km for the Iberian implementation and 2.5 km for the Pyrenean one. Daily streamflow is calculated by the RAPID river routing scheme at a spatial resolution of 1 km in both cases. SAFRAN analyzes daily observed precipitation, which is then interpolated to the hourly scale. For precipitation, relative humidity is currently used to hourly distribute the daily precipitation.</p><p>SASER is able to simulate adequate streamflow on the Ebro basin (KGE>0.5 on 62% of near-natural gauging stations when the LSM is run at 2.5 km of spatial resolution). However, due to the lack of a hydrogeological model, low flows are often poorly reproduced by this scheme. Furthermore, peak flows could also be improved.</p><p>This work aims at improving high and lows by correcting the distribution of hourly precipitation and adding linear reservoirs to improve low flows.</p><p>The increase of the spatial resolution from 5 to 2.5 km has caused a relevant improvement of peak flows. However, most of the peak flows are still underestimated. One way of improving simulated streamflow is improving the hourly distribution of the precipitation, as SAFRAN distributes precipitation through the day with unrealistic low hourly intensities. This will impact runoff generation and, thus, peak flow. We have used two ERA-Interim driven RCM simulations from the CORDEX project to improve the hourly distribution of precipitation. As a result, we now produce more realistic temporal patterns of hourly precipitation.</p><p>The current SASER implementation is not able to sustain low flows. A physical-based solution (hydrogeological model) would be desirable, but as it is difficult to implement we chose to introduce a linear reservoir, following the steps of Artinyan et al (2008) and Getinara et al. (2014). The reservoir is able to improve low flows in most near-natural subbasins. The challenge now is how to set its parameters in non-natural basins.</p>

scholarly journals Both Forest Harvesting and Hydropower Dams Yielded Negative Impact on Low Flow Regimes in the Zagunao River Watershed, Southwest China

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 787
Author(s):  
Zhiwei Jiang ◽  
Mingfang Zhang ◽  
Yiping Hou
Keyword(s):  
Return Period ◽  
Negative Impact ◽  
Flow Regimes ◽  
Forest Harvesting ◽  
Forest Recovery ◽  
Low Flow ◽  
River Watershed ◽  
Low Flows ◽  
Hydropower Dams ◽  
The Impact

Forest harvesting and hydropower dams can significantly affect flow regimes (magnitude, timing, duration, frequency, and variability), resulting in changes in degraded aquatic ecosystems and unstable water supply. Despite numerous studies on the effects of forest harvesting on mean flows, the impact of forest harvesting on flow regimes has been less investigated. A great difficulty lies in separating the hydrological effect of forest harvesting from that of climate variability and other watershed disturbances such hydropower dams. In this study, the Upper Zagunao River watershed (2242 km2) was selected as an example to provide a quantitative assessment of the effects of forest harvesting and hydropower dams on low flow regimes. The key findings include: (1) Forest harvesting led to a significant reduction in the magnitude and return period of low flows, and a significant increment in the variability and duration of low flows; (2) the recovery of low flow regimes occurred 40 years after forest harvesting as forest recovery processed; and (3) hydropower dams caused significant impact on all components of low flow regimes, e.g., a reduction in the magnitude, return period, and timing of low flows, and an increment in the variability and duration of low flows. Our findings highlight the negative impact of both forest harvesting and hydropower dams on low flow regimes in the Upper Zagunao River watershed. A watershed management strategy for offsetting the negative effect of hydropower dams on low flow regimes by restoring hydrological functions of subalpine forests is highly recommended in subalpine watersheds of the Upper Yangtze River.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals Impact of Preharvest and Postharvest on Color Changes during Convective Drying of Mangoes

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 490
Author(s):  
Alioune Diop ◽  
Jean–Michel Méot ◽  
Mathieu Léchaudel ◽  
Frédéric Chiroleu ◽  
Nafissatou Diop Ndiaye ◽  
...  
Keyword(s):  
Regression Models ◽  
Convective Drying ◽  
Color Changes ◽  
Ripening Time ◽  
Physico Chemical ◽  
C 30 ◽  
The Impact

The purpose of this study was to evaluate the impact of the harvest stage, ripening conditions and maturity on color changes of cv. ‘Cogshall’ and cv. ‘Kent’ variety mangoes during drying. A total of four harvests were undertaken, and the fruits were ripened at 20 and 35 °C for five different ripening times at each temperature. At each ripening time, mangoes were dried at 60 °C/30% RH/1.5 m/s for 5 h. A wide physico-chemical and color variability of fresh and dry pulp was created. The relationships according to the L*, H* and C* coordinates were established using mixed covariance regression models in relation to the above pre- and postharvest (preprocess) parameters. According to the L* coordinate results, browning during drying was not affected by the preprocess parameters. However, dried slices from mangoes ripened at 35 °C exhibited better retention of the initial chroma, and had a greater decrease in hue than dried slices from mangoes ripened at 20 °C. However, fresh mango color, successfully managed by the pre- and postharvest conditions, had more impact on dried mango color than the studied parameters. The preprocess parameters were effective levers for improving fresh mango color, and consequently dried mango color.

scholarly journals The Impact of Weather and Seasons on Falls and Physical Activity among Older Adults with Glaucoma: A Longitudinal Prospective Cohort Study

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3415
Author(s):  
Hursuong Vongsachang ◽  
Aleksandra Mihailovic ◽  
Jian-Yu E ◽  
David S. Friedman ◽  
Sheila K. West ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Fall Prevention ◽  
Regression Models ◽  
Activity Promotion ◽  
Injurious Fall ◽  
Multivariable Regression ◽  
Multivariable Regression Models ◽  
The Impact ◽  
The Relationship

Understanding periods of the year associated with higher risk for falling and less physical activity may guide fall prevention and activity promotion for older adults. We examined the relationship between weather and seasons on falls and physical activity in a three-year cohort of older adults with glaucoma. Participants recorded falls information via monthly calendars and participated in four one-week accelerometer trials (baseline and per study year). Across 240 participants, there were 406 falls recorded over 7569 person-months, of which 163 were injurious (40%). In separate multivariable regression models incorporating generalized estimating equations, temperature, precipitation, and seasons were not significantly associated with the odds of falling, average daily steps, or average daily active minutes. However, every 10 °C increase in average daily temperature was associated with 24% higher odds of a fall being injurious, as opposed to non-injurious (p = 0.04). The odds of an injurious fall occurring outdoors, as opposed to indoors, were greater with higher average temperatures (OR per 10 °C = 1.46, p = 0.03) and with the summer season (OR = 2.69 vs. winter, p = 0.03). Falls and physical activity should be understood as year-round issues for older adults, although the likelihood of injury and the location of fall-related injuries may change with warmer season and temperatures.

scholarly journals Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Trend ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Nonlinear Trends ◽  
The Impact ◽  
Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

scholarly journals Assessing the Energy Resilience of Office Buildings: Development and Testing of a Simplified Metric for Real Estate Stakeholders

Buildings ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 96
Author(s):  
Paul Mathew ◽  
Lino Sanchez ◽  
Sang Hoon Lee ◽  
Travis Walter
Keyword(s):  
Regression Models ◽  
Health And Safety ◽  
Office Buildings ◽  
Extreme Weather Events ◽  
Economic Losses ◽  
Power Outage ◽  
Continuity Of Operations ◽  
Quasi Monte Carlo ◽  
Building Characteristics ◽  
The Impact

Increasing concern over higher frequency extreme weather events is driving a push towards a more resilient built environment. In recent years there has been growing interest in understanding how to evaluate, measure, and improve building energy resilience, i.e., the ability of a building to provide energy-related services in the event of a local or regional power outage. In addition to human health and safety, many stakeholders are keenly interested in the ability of a building to allow continuity of operations and minimize business disruption. Office buildings are subject to significant economic losses when building operations are disrupted due to a power outage. We propose “occupant hours lost” (OHL) as a means to measure the business productivity lost as the result of a power outage in office buildings. OHL is determined based on indoor conditions in each space for each hour during a power outage, and then aggregated spatially and temporally to determine the whole building OHL. We used quasi-Monte Carlo parametric energy simulations to demonstrate how the OHL metric varies due to different building characteristics across different climate zones and seasons. The simulation dataset was then used to develop simple regression models for assessing the impact of ten key building characteristics on OHL. The most impactful were window-to-wall ratio and window characteristics. The regression models show promise as a simple means to assess and screen for resilience using basic building characteristics, especially for non-critical facilities where it may not be viable to conduct detailed engineering analysis.

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