scholarly journals Quantitative Estimation of the Impact of Precipitation and Land Surface Change on Hydrological Processes through Statistical Modeling

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Muhammad Saifullah ◽  
Zhijia Li ◽  
Qiaoling Li ◽  
Muhammad Zaman ◽  
Sarfraz Hashim
Keyword(s):  
Linear Regression ◽  
Land Surface ◽  
Quantitative Estimation ◽  
Precipitation Variability ◽  
Annual Runoff ◽  
Catchment Scale ◽  
Surface Change ◽  
Regression Methods ◽  
The Impact

Precipitation variability and land surface changes are the two primary factors that affect basin hydrology, and thus estimation of their impact is of great importance for sustainable development at a catchment scale. In this study, we investigated the long-term changes in precipitation and runoff, from 1961 to 2011, in the Yihe River basin by Mann-Kendall test. A new method of trend pattern was put forward and used to identify the trends of precipitation and runoff, which indicated that the basin had a decreasing trend in annual runoff. The change point occurred in the year 1985 dividing the long-term series into two periods. Precipitation elasticity and linear regression methods were used to quantify the impact of precipitation and land surface change on runoff and provided consistent results of the percentage change in an annual runoff for the postchange period. Use of these methods reveals that the reduction in annual runoff is mainly due to precipitation variability of 56.38–67.68% and land surface change of 43.62–32.32%, as estimated by precipitation elasticity and linear regression methods, respectively. Due to the rapid growth of urbanization, the land surface change increased from 1990 to 2010. The result of this study can provide a reference for the management of regional water resources.

scholarly journals Responses of Streamflow to Climate Change and Human Activities in a River Basin, Northeast China

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hanwen Zhang ◽  
Wei Xu ◽  
Xintong Xu ◽  
Baohong Lu
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
River Basin ◽  
Human Activities ◽  
Annual Runoff ◽  
Regression Method ◽  
Linear Regression Method ◽  
Climate Elasticity ◽  
The Impact

It is now common knowledge that many water resources stresses relate to access to water within a basin. Yi River Basin, a typical river basin characterized by intensive agricultural processes, significant population growth, and water management, has been undergoing grave water problems. In this paper, the long-term trend of precipitation and streamflow in Yi River Basin, from 1964 to 2010, was investigated via Mann-Kendall test. The change point occurred in the year 1965 dividing the long-term series into two periods. Climate elasticity method and linear regression method were implemented to quantify the impact of precipitation and human activities on runoff and presented basically consistent results of the percentage change in an annual runoff for the postchange period. The results reveal that the decline of annual runoff in postchange period is mainly attributed to precipitation variability of 53.66–58.25% and human activities of 46.34–41.74%, as estimated by climate elasticity method and linear regression method, respectively. This study detected the changes in the precipitation-streamflow relationship and investigated the possible causes in the Yi River, which will be helpful for providing a reference for the management of regional water resources.

scholarly journals Dominant climatic factors driving annual runoff changes at the catchment scale across China

2016 ◽  
Vol 20 (7) ◽  
pp. 2573-2587 ◽  
Author(s):  
Zhongwei Huang ◽  
Hanbo Yang ◽  
Dawen Yang
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Climate Changes ◽  
Climatic Factors ◽  
Southern China ◽  
Resource Planning ◽  
Annual Runoff ◽  
Net Radiation ◽  
Catchment Scale ◽  
The Impact

Abstract. With global climate changes intensifying, the hydrological response to climate changes has attracted more attention. It is beneficial not only for hydrology and ecology but also for water resource planning and management to understand the impact of climate change on runoff. In addition, there are large spatial variations in climate type and geographic characteristics across China. To gain a better understanding of the spatial variation of the response of runoff to changes in climatic factors and to detect the dominant climatic factors driving changes in annual runoff, we chose the climate elasticity method proposed by Yang and Yang (2011). It is shown that, in most catchments of China, increasing air temperature and relative humidity have negative impacts on runoff, while declining net radiation and wind speed have positive impacts on runoff, which slow the overall decline in runoff. The dominant climatic factors driving annual runoff are precipitation in most parts of China, net radiation mainly in some catchments of southern China, air temperature and wind speed mainly in some catchments in northern China.

scholarly journals The Impact of Export Volume and GDP on USA’s Civil Aviation in Between 1980-2012

2015 ◽  
Vol 8 (1) ◽  
pp. 229 ◽  
Author(s):  
Salih Kalayci ◽  
Sabire Yazici
Keyword(s):  
Linear Regression ◽  
Cointegration Test ◽  
Civil Aviation ◽  
Johansen Cointegration ◽  
Export Volume ◽  
Johansen Cointegration Test ◽  
The Impact

This paper assays how the effect of USA’s both export volume and GDP have on civil aviation by implementing econometrical models such as linear regression and Johansen Co-integration tests in order to realize the dimension of its influence. The impact of both export volume and GDP on civil aviation have analyzed between the years 1980 and 2012 in order to make it a parametrical test by using E-Views Programme. According to Johansen cointegration test there is a long term relationship between the variables in between 1980-2012.Furthermore, It has been founded that USA’s export volume and GDP have crucial influence on civil aviation according to the E-Views programme results within the periods of 1980-2012. influence. The impact of both export volume and GDP on civil aviation have analyzed between the years 1980 and 2012 in order to make it a parametrical test by using E-Views Programme. According to Johansen cointegration test there is a long term relationship between the variables in between 1980-2012. Furthermore, It has been founded that USA’s export volume and GDP have crucial influence on civil aviation according to the E-Views programme results within the periods of 1980-2012.

Examination of Long-Term Growth of Cryptobranchus alleganiensis Predicted by Linear Regression Methods

Copeia ◽  
1985 ◽  
Vol 1985 (2) ◽  
pp. 492 ◽  
Author(s):  
Chris L. Peterson ◽  
Milton S. Topping ◽  
Robert F. Wilkinson ◽  
Charles A. Taber
Keyword(s):  
Linear Regression ◽  
Regression Methods ◽  
Cryptobranchus Alleganiensis

Modeling the impact of oceanic circulation and marine productivity on Cretaceous seafloor anoxia

2020 ◽  
Author(s):  
Yannick Donnadieu ◽  
Marie Laugie ◽  
Jean-Baptiste Ladant ◽  
François Raisson ◽  
Laurent Bopp
Keyword(s):  
Land Surface ◽  
Oceanic Circulation ◽  
Equatorial Pacific Ocean ◽  
Oxygen Minimum Zones ◽  
Tethys Sea ◽  
Marine Biogeochemistry ◽  
Anoxic Events ◽  
Marine Productivity ◽  
The Impact

<p>Oceanic anoxic events (OAEs) are abrupt events of widespread deposition of organic-rich sediments and extensive seafloor anoxia. Mechanisms usually invoked as drivers of oceanic anoxia are various and still debated today. They include a rise of the CO2 atmospheric level due to increased volcanic activity, a control by the paleogeography, changes in oceanic circulation or enhanced marine productivity. In order to assess the role of these mechanisms, we use an IPCC-class model, the IPSL-CM5A2 Earth System Model, which couples the atmosphere, land surface, and ocean components, this last one including sea ice, physical oceanography and marine biogeochemistry which allows to simulate oceanic oxygen.</p><p>We focus here on OAE2, which occurs during the Cretaceous at the Cenomanian-Turonian boundary (93.5 Ma), and is identified as a global event with evidence for seafloor anoxia in the Atlantic and Indian Oceans, the Southwest Tethys Sea and the Equatorial Pacific Ocean. Using a set of simulations from 115 to 70 Ma, we analyze the long-term paleogeographic control on oceanic circulation and consequences on oceanic oxygen concentration and anoxia spreading. Short-term controls such as an increase of pCO<sub>2</sub>, nutrients, or orbital configurations are also studied with a second set of simulations with a Cenomano-Turonian (90 Ma) paleogeographic configuration. The different simulated maps of oxygen are used to study the evolution of marine productivity and oxygen minimum zones as well as the spreading of seafloor anoxia, in order to unravel the interlocking of the different mechanisms and their specific impact on anoxia through space and time.</p>

scholarly journals Extreme return, extreme volatility and investor sentiment

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 3949-3961 ◽  
Author(s):  
Xu Gong ◽  
Fenghua Wen ◽  
Zhifang He ◽  
Jia Yang ◽  
Xiaoguang Yang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Investor Sentiment ◽  
Generalized Least Squares ◽  
Regression Methods ◽  
Leading Role ◽  
The Impact

The extreme return and extreme volatility have great influences on the investor sentiment in stock market. However, few researchers have taken the phenomenon into consideration. In this paper, we first distinguish the extreme situations from non-extreme situations. Then we use the ordinary generalized least squares and quantile regression methods to estimate a linear regression model by applying the standardized AAII, the return and volatility of SP 500. The results indicate that, except for extremely negative return, other return sequences can cause great changes in investor sentiment, and non-extreme return plays a leading role in affecting the overall American investor sentiment. Extremely positive (negative) return can rapidly improve (further reduce) the level of investor sentiment when investors encounter extremely pessimistic situations. The impact gradually decreases with improvement of the sentiment until the situation turns optimistic. In addition, we find that extreme and non-extreme volatility cannot a_ect the overall investor sentiment.

scholarly journals Contribution Analysis of the Spatial-Temporal Changes in Streamflow in a Typical Elevation Transitional Watershed of Southwest China over the Past Six Decades

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 495 ◽  
Author(s):  
Chengcheng Meng ◽  
Huilan Zhang ◽  
Yujie Wang ◽  
Yunqi Wang ◽  
Jian Li ◽  
...  
Keyword(s):  
Land Surface ◽  
Southwest China ◽  
Potential Evapotranspiration ◽  
Influencing Factor ◽  
Climate Impacts ◽  
Spatial And Temporal Scales ◽  
Attribution Analysis ◽  
Land Surface Parameter ◽  
The Impact

Attribution analyses on streamflow variation to changing climate and land surface characteristics are critical in studies of watershed hydrology. However, attribution results may differ greatly on different spatial and temporal scales, which has not been extensively studied previously. This study aims to investigate the spatial-temporal contributions of climate change and underlying surface variation to streamflow alteration using Budyko framework. Jiangling River Watershed (JRW), a typical landform transitional watershed in Southwest China, was chosen as the study area. The watershed was firstly divided into eight sub-basins by hydrologic stations, and hydrometeorological series (1954–2015) were divided into sub-intervals to discriminate spatial-temporal features. The results showed that long-term tendencies of hydrometeorological variables, i.e., precipitation (P), potential evapotranspiration (E0), and runoff depth (R), exhibited clear spatial patterns, which were highly related to topographic characteristics. Additionally, sensitivity analysis, which interpreted the effect of one driving factor by unit change, showed that climate factors P and E0, and catchment characteristics (land surface parameter n) played positive, negative, and negative roles in R, according to elastic coefficients (ε), respectively. The spatial distribution of ε illustrated a greater sensitivity and heterogeneity in the plateau and semi-humid regions (upstream). Moreover, the results from attribution analysis showed that the contribution of the land surface factor accounted for approximately 80% of the R change for the entire JRW, with an obvious spatial variation. Furthermore, tendencies of the contribution rates demonstrated regulations across different sub-regions: a decreasing trend of land surface impacts in trunk stream regions and increasing tendencies in tributary regions, and vice versa for climate impacts. Overall, both hydrometeorological variables and contributions of influencing factors presented regularities in long-term tendencies across different sub-regions. More particularly, the impact of the primary influencing factor on all sub-basins exhibited a decreasing trend over time. The evidence that climate and land surface change act on streamflow in a synergistic way, would complicate the attribution analysis and bring a new challenge to attribution analysis.

scholarly journals Development and calibration of a global hydrological model for integrated assessment modeling

2017 ◽  
Author(s):  
Tingju Zhu ◽  
Petra Döll ◽  
Hannes Müller Schmied ◽  
Claudia Ringler ◽  
Mark W. Rosegrant
Keyword(s):  
Land Surface ◽  
Crop Production ◽  
Hydrological Model ◽  
Computational Cost ◽  
Base Flow ◽  
Grid Cells ◽  
Runoff Generation ◽  
Runoff Series ◽  
The Impact

Abstract. This paper describes the IMPACT Global Hydrological Model (IGHM), a component of the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) integrated modeling system. IMPACT has been developed in the early 1990s to identify and analyze long-term challenges and opportunities for food, agriculture, and natural resources at global and regional scales and builds on a series of previous food demand and supply projections models developed at the International Food Policy Research Institute since the early 1980s. The IGHM has been developed to assess water availability and variability as drivers of water use and irrigated crop production in IMPACT. It adopts a saturation runoff generation scheme and uses a linear groundwater reservoir to simulate base flow in 0.5º latitude by 0.5º longitude grid cells over the global land surface excluding Antarctica. The IGHM has four cell-specific calibration parameters, which are determined through maximizing the Kling–Gupta efficiency (KGE) with a genetic algorithm at the grid cell level, using gridded natural runoff series generated by the WaterGAP Global Hydrological Model (WGHM). During the calibration and validation periods, globally, the majority of grid cells attain KGE values greater than 0.50. As a meta-model of the more computationally expensive WGHM, IGHM transfers the climate-hydrology dynamics provided by WGHM into the integrated IMPACT model at a lower computational cost and enables coupling hydrology and other related processes considered in IMPACT which are important for analyzing long-term water and food security under a range of environmental and socioeconomic changes.

scholarly journals Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1540
Author(s):  
Zhengwu Cai ◽  
Chao Fan ◽  
Falin Chen ◽  
Xiaoma Li
Keyword(s):  
Linear Regression ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Changes ◽  
Invariant Feature ◽  
Multi Temporal ◽  
Feature Based ◽  
Impact Of Urbanization ◽  
The Impact

The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view.

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