Identification of trend in long term precipitation and reference evapotranspiration over Narmada river basin (India)

Pandey, Brij Kishor; Khare, Deepak

doi:10.1016/j.gloplacha.2017.12.017

Cited by 76 publications

(30 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Trend analysis of hydrological extremes provides essential information for regional water resource planning, risk assessment of hydrological hazards and adaptation and mitigation strategies to climate change [25,26]. To this end, various parametric and non-parametric methods have been developed and used over time.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Tabari

2019

Water

View full text Add to dashboard Cite

Analysis of hydrological extremes is challenging due to their rarity and small sample size and the interconnections between different types of extremes and gets further complicated by an untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The special issue “Statistical Analysis and Stochastic Modelling of Hydrological Extremes”—motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions —encompass 13 research papers. Case studies presented in the papers exploit a wide range of innovative techniques for hydrological extremes analyses. The papers focus on six topics: Historical changes in hydrological extremes, projected changes in hydrological extremes, downscaling of hydrological extremes, early warning and forecasting systems for drought and flood, interconnections of hydrological extremes and applicability of satellite data for hydrological studies. This Editorial provides an overview of the covered topics and reviews the case studies relevant for each topic.

show abstract

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Tabari

2019

Water

View full text Add to dashboard Cite

show abstract

“…It is applicable to data of non-normal distributions such as hydrology and meteorology. The MK test method has the characteristics of simple calculation, wide detection range and a high degree of quantification [25,[35][36][37][38].…”

Section: Trend Analysis Methodsmentioning

confidence: 99%

“…It was found that the Spearman rank test is more concise than MK. The Spearman rank test principle is based on the correlation coefficient test [25,34,35,37,39,40].…”

Section: Trend Analysis Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Variation Analysis of Streamflows from 1956 to 2016 Along the Yellow River, China

Wang

Engel

Yuan

et al. 2018

Water

View full text Add to dashboard Cite

With the change of climate and the impacts of human activities, the water resources crisis of the Yellow River is becoming increasingly serious. How and why did the streamflows of the Yellow River basin change? Based on observed annual runoff data (1956–2016) of 10 main hydrological stations along the Yellow River, the linear regression method, the Spearman rank correlation method and the Mann-Kendall test method are used to analyze runoff trend. The orderly clustering method, the sliding t test method and the Lee-Heghinian Method are used to identify the abrupt change point. Finally, the wavelet analysis method is used to identify runoff time series period. The results show that: (1) With the exception of the streamflow of Tangnaihai, the streamflows of all examined stations have significantly declining trends. The decrease of the streamflow from the upper to the middle to the lower reaches is becoming more and more obvious; (2) The runoff of the Yellow River has changed greatly. The abrupt change point at Tangnaihai occurred in 1989. The abrupt change points of the other stations took place in 1985; (3) The runoff along the Yellow River presents multi-time scale changes. The streamflows appear to have strongest periods of 25–40 years with a 40-year scale, which indicate the alternate oscillations of the high and the low water periods. The periods of <6 and 7–24 years are not stable and are complicated. The first main period of runoff in the Yellow River is 30 years; (4) The streamflow upstream of Tangnaihai station is mainly affected by the climate. The streamflows downstream of Tangnaihai station are influenced by human activities, especially water extraction and diversion and the operations of the large reservoirs. These research results have important practical guiding significance for hydrological forecasting, evaluation and management of water resources, construction of water conservancy projects and sustainable utilization of water resources in the region.

show abstract

“…This disturbance has negotiated itself in the form of alterations in various water balance parameters, be it altered runoff or be it altered potential evapotranspiration. Thus, the occurrences of basin wide hydro-meteorological events like extreme wetness (rainfall), extreme dryness (drought) or decrease in the revisit periods of frequent dry years intermittent with wet years have disrupted the basin hydrology worldwide (Awange et al 2014;Gebre and Getahun 2016;Oguntunde et al 2017;Pandey and Khare 2018). The association between the rain fed crop yield e.g.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Vegetation Response to Seasonal Rainfall in the Gomati River Basin (India) using Earth Observation Data Sets

Bhatt¹,

Kundu²,

Mall³

et al. 2020

JSR

View full text Add to dashboard Cite

Present study evaluates the vegetation responses towards the rainfall variability in the Gomati basin during 1901-2015. To accomplish the same, high resolution observed daily gridded rainfall data from India Meteorological Department (IMD) was analysed using non-parametric trend test. The long-term rainfall showed significant decreasing trend (α=0.05) which varied from -0.96 to -4.89 per year. Identified trends in rainfall were then evaluated in tandem with the basin-wide changes in the vegetation signatures during the recent extreme years (two pairs of dry and wet years). Each pair of extreme years was assessed for its seasonal Normalised Difference Vegetation Index (NDVI) related to the seasonal rainfall variations with special emphasis to rice and wheat yields (major crops). Contribution of spatio-temporal occurrences of rainy days with that of the heavy rainfall events was also analysed. The findings suggest that the prevalent basin-wide longterm rainfall deficiency intensified during the extreme dry years e.g. 2002 and 2009 and impacted the health of vegetation in the basin. Vegetation of the basin was found closely related with rainfall variability; regular and well distributed occurrences of rainy days benefited the basin's vegetation as well as the yields of rice and wheat crops.

show abstract

Identification of trend in long term precipitation and reference evapotranspiration over Narmada river basin (India)

Cited by 76 publications

References 42 publications

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Variation Analysis of Streamflows from 1956 to 2016 Along the Yellow River, China

Dynamics of Vegetation Response to Seasonal Rainfall in the Gomati River Basin (India) using Earth Observation Data Sets

Contact Info

Product

Resources

About