scholarly journals Similarity of climate control on base flow and perennial stream density in the Budyko framework

2013 ◽  
Vol 17 (1) ◽  
pp. 315-324 ◽  
Author(s):  
D. Wang ◽  
L. Wu
Keyword(s):  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Climate Control ◽  
Stream Network ◽  
Monotonic Trend ◽  
Perennial Stream ◽  
Stream Density ◽  
The Mean ◽  
Annual Scale

Abstract. Connection between perennial stream and base flow at the mean annual scale exists since one of the hydrologic functions of perennial stream is to deliver runoff even in low flow seasons. The partitioning of precipitation into runoff and evaporation at the mean annual scale, on the first order, is captured by the ratio of potential evaporation to precipitation (EP/P called climate aridity index) based on Budyko hypothesis. Perennial stream density (DP), which is obtained from the high resolution National Hydrography Dataset, for 185 watersheds declines monotonically with climate aridity index, and an inversely proportional function is proposed to model the relationship between DP and EP/P. The monotonic trend of perennial stream density reconciles with the Abrahams curve since perennial stream density is only a small portion of the total drainage density. The correlation coefficient between the ratio of base flow to precipitation (Qb/P), which follows a complementary Budyko type curve and perennial stream density is found to be 0.74. The similarity between Qb/P and DP reveals the co-evolution between water balance and perennial stream network.

scholarly journals Similarity between runoff coefficient and perennial stream density in the Budyko framework

2012 ◽  
Vol 9 (6) ◽  
pp. 7571-7589 ◽  
Author(s):  
D. Wang ◽  
L. Wu
Keyword(s):  
Hydrological Modeling ◽  
Aridity Index ◽  
The United States ◽  
Annual Runoff ◽  
Runoff Coefficient ◽  
Runoff Generation ◽  
Perennial Stream ◽  
Stream Density ◽  
The Mean ◽  
Annual Scale

Abstract. Streams are categorized into perennial and temporal streams based on flow durations. Perennial stream is the basic network, and temporal stream (ephemeral or intermittent) is the expanded network. Connection between perennial stream and runoff generation at the mean annual scale exists since one of the hydrologic functions of perennial stream is to deliver runoff. The partitioning of precipitation into runoff and evaporation at the mean annual scale, on the first order, is represented by the Budyko hypothesis which quantifies the ratio of evaporation to precipitation (E/P) as a function of climate aridity index (Ep/P, ratio of potential evaporation to precipitation). In this paper, it is hypothesized that similarity exists between perennial stream density (Dp) and runoff coefficient (Q/P) as a function of climate aridity index, i.e.,   DpDp* (EpP) and   QP   (EpP) where Dp* is a scaling factor and Q is mean annual runoff. To test the hypothesis, perennial stream densities for 185 watersheds in the United States are computed based on the high resolution national hydrography dataset (NHD). The similarity between perennial stream density and runoff coefficient is promising based on the case study watersheds. As a potential application for macroscale hydrological modeling, perennial stream density in ungauged basin can be predicted based on climate aridity index using the complementary Budyko curve.

scholarly journals Multiple causes of nonstationarity in the Weihe annual low-flow series

2018 ◽  
Vol 22 (2) ◽  
pp. 1525-1542 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Lingqi Li
Keyword(s):  
Frequency Analysis ◽  
Driving Forces ◽  
Potential Evapotranspiration ◽  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Distribution Model ◽  
Flow Index ◽  
Explanatory Variables ◽  
Distribution Parameters

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

scholarly journals Multiple Causes of Nonstationarity in the Weihe Annual Low Flow Series

2017 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Lingqi Li
Keyword(s):  
Frequency Analysis ◽  
Driving Forces ◽  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Flow Index ◽  
Time Varying ◽  
Explanatory Variables ◽  
Flow Generation ◽  
Flow Frequency

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced a variety of non-stationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, a nonstationary framework of low-flow frequency analysis has been developed on basis of the Generalized Linear Model (GLM) to consider time-varying distribution parameters. In GLMs, the candidate explanatory variables to explain the time-varying parameters are comprised of the eight measuring indices of the climate and catchment conditions in low flow generation, i.e., total precipitation (P), mean frequency of precipitation events (λ), temperature (T), potential evapotranspiration (ET), climate aridity index (AIET), base-flow index (BFI), recession constant (K) and the recession-related aridity index (AIK). This framework was applied to the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China. Stepwise regression analysis was performed to obtain the best subset of those candidate explanatory variables for the final optimum model. The results show that the inter-annual variability in the variables of those selected best subsets plays an important role in modeling annual low flow series. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that AIK is of the highest relative importance among the best subset of eight candidates, followed by BFI and AIET. The incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to predict future occurrences of low-flow extremes in similar areas.

scholarly journals Forensic Investigation of Four Monitored Green Infrastructure Inlets

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1787
Author(s):  
Leena J. Shevade ◽  
Franco A. Montalto
Keyword(s):  
Measurement Uncertainty ◽  
Green Infrastructure ◽  
Stormwater Management ◽  
Catchment Area ◽  
Low Flow ◽  
Inflow Rate ◽  
Forensic Investigation ◽  
Flow Conditions ◽  
The Mean

Green infrastructure (GI) is viewed as a sustainable approach to stormwater management that is being rapidly implemented, outpacing the ability of researchers to compare the effectiveness of alternate design configurations. This paper investigated inflow data collected at four GI inlets. The performance of these four GI inlets, all of which were engineered with the same inlet lengths and shapes, was evaluated through field monitoring. A forensic interpretation of the observed inlet performance was conducted using conclusions regarding the role of inlet clogging and inflow rate as described in the previously published work. The mean inlet efficiency (meanPE), which represents the percentage of tributary area runoff that enters the inlet was 65% for the Nashville inlet, while at Happyland the NW inlet averaged 30%, the SW inlet 25%, and the SE inlet 10%, considering all recorded events during the monitoring periods. The analysis suggests that inlet clogging was the main reason for lower inlet efficiency at the SW and NW inlets, while for the SE inlet, performance was compromised by a reverse cross slope of the street. Spatial variability of rainfall, measurement uncertainty, uncertain tributary catchment area, and inlet depression characteristics are also correlated with inlet PE. The research suggests that placement of monitoring sensors should consider low flow conditions and a strategy to measure them. Additional research on the role of various maintenance protocols in inlet hydraulics is recommended.

scholarly journals Estimation of recharge in mountain hard-rock aquifers based on discrete spring discharge monitoring during base-flow recession

2021 ◽  
Author(s):  
Stefano Segadelli ◽  
Maria Filippini ◽  
Anna Monti ◽  
Fulvio Celico ◽  
Alessandro Gargini
Keyword(s):  
Hard Rock ◽  
Monitoring Program ◽  
Base Flow ◽  
Low Flow ◽  
Reliable Estimate ◽  
Spring Discharge ◽  
Aquifer Recharge ◽  
Clear Cut ◽  
Groundwater Circulation ◽  
Hard Rock Aquifers

AbstractEstimation of aquifer recharge is key to effective groundwater management and protection. In mountain hard-rock aquifers, the average annual discharge of a spring generally reflects the vertical aquifer recharge over the spring catchment. However, the determination of average annual spring discharge requires expensive and challenging field monitoring. A power-law correlation was previously reported in the literature that would allow quantification of the average annual spring discharge starting from only a few discharge measurements in the low-flow season, in a dry summer climate. The correlation is based upon the Maillet model and was previously derived by a 10-year monitoring program of discharge from springs and streams in hard-rock aquifers composed of siliciclastic and calcareous turbidites that did not have well defined hydrogeologic boundaries. In this research, the same correlation was applied to two ophiolitic (peridotitic) hard-rock aquifers in the Northern Apennines (Northern Italy) with well-defined hydrogeologic boundaries and base-outflow springs. The correlation provided a reliable estimate of the average annual spring discharge thus confirming its effectiveness regardless of bedrock lithology. In the two aquifers studied, the measurable annual outputs (i.e. sum of average annual spring discharges) could be assumed equal to the annual inputs (i.e. vertical recharge) based on the clear-cut aquifer boundaries and a quick groundwater circulation inferable from spring water parameters. Thus, in such setting, the aforementioned correlation also provided an estimate of the annual aquifer recharge allowing the assessment of coefficients of infiltration (i.e. ratio between aquifer recharge and total precipitation) ranging between 10 and 20%.

scholarly journals Use and success evaluation of percutaneous aortic balloon valvuloplasty in different hemodynamic entities of severe aortic stenosis in the TAVR era

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
K Piayda ◽  
A Wimmer ◽  
H Sievert ◽  
K Hellhammer ◽  
S Afzal ◽  
...  
Keyword(s):  
Heart Failure ◽  
Aortic Valve ◽  
Aortic Stenosis ◽  
Aortic Valve Replacement ◽  
Pressure Gradient ◽  
Severe Aortic Stenosis ◽  
Primary Treatment ◽  
Low Flow ◽  
Mean Pressure ◽  
The Mean

Abstract Background In the era of transcatheter aortic valve replacement (TAVR), there is renewed interest in percutaneous balloon aortic valvuloplasty (BAV), which may qualify as the primary treatment option of choice in special clinical situations. Success of BAV is commonly defined as a significant mean pressure gradient reduction after the procedure. Purpose To evaluate the correlation of the mean pressure gradient reduction and increase in the aortic valve area (AVA) in different flow and gradient patterns of severe aortic stenosis (AS). Methods Consecutive patients from 01/2010 to 03/2018 undergoing BAV were divided into normal-flow high-gradient (NFHG), low-flow low-gradient (LFLG) and paradoxical low-flow low-gradient (pLFLG) AS. Baseline characteristics, hemodynamic and clinical information were collected and compared. Additionally, the clinical pathway of patients (BAV as a stand-alone procedure or BAV as a bridge to aortic valve replacement) was followed-up. Results One-hundred-fifty-six patients were grouped into NFHG (n=68, 43.5%), LFLG (n=68, 43.5%) and pLFLG (n=20, 12.8%) AS. Underlying reasons for BAV and not TAVR/SAVR as the primary treatment option are displayed in Figure 1. Spearman correlation revealed that the mean pressure gradient reduction had a moderate correlation with the increase in the AVA in patients with NFHG AS (r: 0.529, p<0.001) but showed no association in patients with LFLG (r: 0.145, p=0.239) and pLFLG (r: 0.030, p=0.889) AS. Underlying reasons for patients to undergo BAV and not TAVR/SAVR varied between groups, however cardiogenic shock or refractory heart failure (overall 46.8%) were the most common ones. After the procedure, independent of the hemodynamic AS entity, patients showed a functional improvement, represented by substantially lower NYHA class levels (p<0.001), lower NT-pro BNP levels (p=0.003) and a numerical but non-significant improvement in other echocardiographic parameters like the left ventricular ejection fraction (p=0.163) and tricuspid annular plane systolic excursion (TAPSE, p=0.066). An unplanned cardiac re-admission due to heart failure was necessary in 23.7% patients. Less than half of the patients (44.2%) received BAV as a bridge to TAVR/SAVR (median time to bridge 64 days). Survival was significantly increased in patients having BAV as a staged procedure (log-rank p<0.001). Conclusion In daily clinical practice, the mean pressure gradient reduction might be an adequate surrogate of BAV success in patients with NFHG AS but is not suitable for patients with other hemodynamic entities of AS. In those patients, TTE should be directly performed in the catheter laboratory to correctly assess the increase of the AVA. BAV as a staged procedure in selected clinical scenarios increases survival and is a considerable option in all flow states of severe AS. (NCT04053192) Figure 1 Funding Acknowledgement Type of funding source: None

scholarly journals Responses of runoff to historical and future climate variability over China

2017 ◽  
Author(s):  
Chuanhao Wu ◽  
Bill X. Hu ◽  
Guoru Huang ◽  
Peng Wang ◽  
Kai Xu
Keyword(s):  
Climate Variability ◽  
Arid Regions ◽  
Southern China ◽  
Aridity Index ◽  
Global Climate Models ◽  
Future Climate ◽  
Western China ◽  
Climate Projections ◽  
Positive Contribution ◽  
The Mean

Abstract. China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the alteration of the temporal-spatial patterns of water resources. Based on the long-term (1960–2012) water budget data and climate projections from 28 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5), this study investigated the responses of runoff (R) to historical and future climate variability in China at both grid and catchment scales using the Budyko-based elasticity method. Results show that there is a large spatial variation in precipitation (P) elasticity (from 1.2 to 3.3) and potential evaporation (PET) elasticity (from −2.3 to −0.2) across China. The P elasticity is larger in northeast and western China than in southern China, while the opposite occurs for PET elasticity. The catchment properties elasticity of R appears to have a strong non-linear relationship with the mean annual aridity index and tends to be more significant in more arid regions. For the period 1960–2012, the climate contribution to R ranges from −2.4 % a−1 to 3.3 % a−1 across China, with the negative contribution in the North China plain and the positive contribution in western China and some parts of the southwest. The results of climate projections indicate that although there is large uncertainty involved in the 28 GCMs, most project a consistent change in P (or PET) in China at the annual scale. For the period 2071–2100, the mean annual P will likely increase in most parts of China, especially the western regions, while the mean annual PET will likely increase in all of China, particularly the southern regions. Furthermore, greater increases are projected for higher emission scenarios. Overall, due to climate change, the arid regions and humid regions of China will likely become wetter and drier in the period 2071–2100, respectively (relative to the baseline 1971–2000).

Combining statistical learning and geostatistical approaches in a spatiotemporal framework for low flow estimation

2021 ◽  
Author(s):  
Johannes Laimighofer ◽  
Michael Melcher ◽  
Juraj Parajka ◽  
Gregor Laaha
Keyword(s):  
Statistical Learning ◽  
Mean Squared Error ◽  
Temporal Structure ◽  
Empirical Orthogonal Functions ◽  
Regression Coefficients ◽  
Low Flow ◽  
Support Vector ◽  
Spatiotemporal Model ◽  
Residual Field ◽  
The Mean

<p><span>This paper aims to develop a spatiotemporal model to estimate monthly low flow quantiles Q95 [P(Q<Q95=0.05)] standardized by catchment area in Austria. Our dataset consists of 325 gauging stations that where consistently monitored between 1976 to 2015, and it covers about 60% of the national territory of Austria. </span></p><p><span>In a first step we are adapting a spatiotemporal model initially designed for modeling air pollution data. This approach is based on empirical orthogonal functions (EOF), that should capture the temporal structure of the spatiotemporal model. The EOFs are weighted by regression coefficients estimated by universal kriging. We extend the model by using GLM-boosting, LASSO, Principal Component Regression (PCR) and Random Forest (RF) for selecting the regression coefficients of the EOFs. Furthermore, we do not limit the kriging structure of the residual field to geographical coordinates but use a broader approach of physiographic kriging. In a second step we implement separate models for the mean parts of the model and the residual parts of the model. The mean field is defined by statistical learning methods as RF, GAM-boosting, LASSO and Support Vector Machines (SVM). For the residual field we define two different approaches, either the </span><span>method developed in the first step</span><span> or spatiotemporal kriging.</span></p><p><span>Model performance is evaluated by cross validation and the best model is selected by the mean squared error (MSE). </span></p><p> </p>

Estimation of base flow recession constant and regression of low flow indices in eastern Japan

2021 ◽  
Author(s):  
Andrew C. Whitaker ◽  
Stanley N. Chapasa ◽  
Cristencio Sagras ◽  
Uwitonze Theogene ◽  
Ronald Veremu ◽  
...  
Keyword(s):  
Base Flow ◽  
Low Flow ◽  
Recession Constant ◽  
Eastern Japan

scholarly journals Forecasting Low Stream Flow Rate Using Monte—Carlo Simulation of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 580 ◽  
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Flow Rate ◽  
Stream Flow ◽  
Simulation Method ◽  
Base Flow ◽  
Low Flow ◽  
Rate Data ◽  
Storm Flow ◽  
Management Plans

A small number of scientific research studies with reference to extremely low flow conditions, have been conducted in Greece, so far. Predicting future low stream flow rate values is an essential and of paramount importance task when compiling watershed and drought management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow values, separating groundwater base flow and storm flow of storm hydrographs etc. The Monte-Carlo simulation method generates multiple attempts to define the anticipated value of a random (hydrological in this specific case) variable. The present study compiles, correspondingly, artificial low stream flow time series of both the same part of the year (2016) as well as a part of the calendar year (2017), based on the stream flow data observed during the same two different interval periods of the years 2016 and 2017, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The recorded data were plotted against the fitted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the simulation procedure performance. Finally, we plot the observed against the calculated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metric and calculate statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

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