Drought forecasting in a semi-arid watershed using climate signals: a neuro-fuzzy modeling approach

Choubin, Bahram; Khalighi-Sigaroodi, Shahram; Malekian, Arash; Ahmad, Sajjad; Attarod, Pedram

doi:10.1007/s11629-014-3020-6

Cited by 91 publications

(31 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, PCA reduces the dimensionality of a dataset consisting of a large number of interrelated variables while retaining as much as possible of the variation present in the dataset. During PCA performance, the variables were placed in some components so that the percentage of the variance decreased from one variable to the next (Choubin et al ., ). It was found that variables in the first component were the most important series in this study, and they were used as the most influential large‐scale predictors.…”

Section: Methodsmentioning

confidence: 97%

An ensemble forecast of semi‐arid rainfall using large‐scale climate predictors

Choubin

Malekian

Phillips

et al. 2017

Meteorological Applications

View full text Add to dashboard Cite

A case study examining ensemble forecasts of semi‐arid seasonal precipitation is presented. The focus is on computing an appropriate correlation between large‐scale climate predictors and seasonal precipitation over a long‐term forecast period (1967–2009) for a semi‐arid catchment in Iran. Potential predictors of the dominant precipitation modes were identified from several large‐scale climate features using principal component analysis. Linear regression together with two nonlinear models, the adaptive neuro‐fuzzy inference system (ANFIS) and the multi‐layer perceptron, was applied to forecast seasonal ensemble precipitation time series. The analysis suggests that seasonal precipitation is statistically aligned with the predictor's variability. An ensemble forecast of spring precipitation modes showed a stronger correlation with the preceding season (winter predictors) in the ANFIS algorithm. The potential effect of climate predictors during the spring may lead to severe and longer hydrological extremes especially when they are out of phase or coincident. These results highlight that skilful prediction of semi‐arid spring precipitation may be possible using winter predictors and a nonlinear ensemble forecast model.

show abstract

Section: Methodsmentioning

confidence: 97%

An ensemble forecast of semi‐arid rainfall using large‐scale climate predictors

Choubin

Malekian

Phillips

et al. 2017

Meteorological Applications

View full text Add to dashboard Cite

show abstract

“…Climate change leads to an increase of the air temperature and more variable rainfall regimes, with severe consequences for the frequency and magnitude of droughts and flood events, and an accelerated meltdown of glaciers, which can increase the river runoff in the short term but ultimately alters the discharge regimes in the long term, for this ambitious task is the system dynamics (SD) model, which was originally developed by Forrester in 1961 [19], and is an approach for understanding the interactions among driving factors and interconnected sub-systems that drive the dynamic behavior of a system [48,49]. Over the years, a number of SD models have been developed for water balance simulation and have been used to evaluate various water-related solutions [50][51][52], such as water resource planning models [53][54][55][56], hydrologic extremes models [57], agriculture water management models [58,59], and water balance models, which have been developed to test water-related and environmental issues in developing countries where the data availability is lacking [60]. With this background, the SD model satisfies the requirements for a complex analysis of the Issyk-Kul water level fluctuations and its driving factors.…”

Section: Introductionmentioning

confidence: 99%

System Dynamics Modeling of Water Level Variations of Lake Issyk-Kul, Kyrgyzstan

Alifujiang

Abuduwaili

et al. 2017

Water

View full text Add to dashboard Cite

Lake Issyk-Kul is an important endorheic lake in arid Central Asia. Climate change, anthropogenic water consumption and a complex basin hydrology with interlocked driving forces have led to a high variability of the water balance and an overall trend of decreasing lake water levels. The main objective of this study was to investigate these main driving forces and their interactions with the lake's water level. Hydro-meteorological and socioeconomic data from 1980 to 2012 were used for a dynamic simulation model, based on the system dynamics (SD) method. After the model calibration and validation with historical data, the model provides accurate simulation results of the water level of Lake Issyk-Kul. The main factors impacting the lake's water level were evaluated via sensitivity analysis and water resource scenarios. Results based on the sensitivity analysis indicated that socio-hydrologic factors had different influences on the lake water level change, with the main influence coming from the water inflow dynamic, namely, the increasing and decreasing water withdrawal from lake tributaries. Land use changes, population increase, and water demand decrease were also important factors for the lake water level variations. Results of four scenario analyses demonstrated that changes in the water cycle components as evaporation and precipitation and the variability of river runoff into the lake are essential parameters for the dynamic of the lake water level. In the future, this SD model can help to better manage basins with water availability uncertainties and can guide policymakers to take necessary measures to restore lake basin ecosystems.

show abstract

“…Furthermore, water managers need to diligently balance increasing water demands while dealing with the effects of climate variability and change (Forsee and Ahmad, ; Dawadi and Ahmad, , ; Kalra et al , , ). These growing requirements have led scientists and engineers to focus on understanding the relationship between climate variability and its hydrologic consequences (Hamlet and Lettenmaier, ; Choubin et al , ; Sagarika et al , ). The relationship between climate variability in oceans i.e.…”

Section: Introductionmentioning

confidence: 99%

Pacific Ocean SST and Z₅₀₀ climate variability and western U.S. seasonal streamflow

Sagarika

Kalra

Ahmad

2015

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

The current study focuses on evaluating the relationship between the Pacific Ocean climate variability and western US streamflow for six hydrologic regions of the western United States, as defined by United States Geological Survey: Rio Grande, Upper Colorado, Lower Colorado, Great Basin, Pacific Northwest, and California. The singular‐valued decomposition (SVD) technique was applied on data for 50 years (1960–2010) of sea‐surface temperatures (SST), geopotential height index of 500 mbar (Z500), and 90 unimpaired western US streamflows; the results established a spatio‐temporal association for each major hydrologic region in the western United States with Pacific Oceanic variability. An approach using a 3‐ to 9‐month lead time was utilized, i.e. the previous year's July to August SST/Z500, the previous year's October to December SST/Z500 to predict streamflow for current year spring–summer (April to September), spring (April to June), and summer (July to September) seasons. Significant regions in the Pacific were identified that influence hydrology of the western United States. The traditional El Niño/Southern Oscillation (ENSO) and Pacific Decadal Oscillation regions were identified along with regions over eastern Russia, Canadian British Columbia, and the ‘Hondo’ region along the east coast of Japan. Z500 showed pronounced association with 3‐month lead time streamflow, whereas SST had better association with 6‐month lead time streamflow. The SVD results showed improvement in correlation values of smaller spatial regions over larger regions, and a lagged response of adjacent hydrologic regions to the same physical indicators. The results obtained in this study could be helpful in improving the current forecasting models for water management.

show abstract

Drought forecasting in a semi-arid watershed using climate signals: a neuro-fuzzy modeling approach

Cited by 91 publications

References 56 publications

An ensemble forecast of semi‐arid rainfall using large‐scale climate predictors

An ensemble forecast of semi‐arid rainfall using large‐scale climate predictors

System Dynamics Modeling of Water Level Variations of Lake Issyk-Kul, Kyrgyzstan

Pacific Ocean SST and Z₅₀₀ climate variability and western U.S. seasonal streamflow

Contact Info

Product

Resources

About

Drought forecasting in a semi-arid watershed using climate signals: a neuro-fuzzy modeling approach

Cited by 91 publications

References 56 publications

An ensemble forecast of semi‐arid rainfall using large‐scale climate predictors

An ensemble forecast of semi‐arid rainfall using large‐scale climate predictors

System Dynamics Modeling of Water Level Variations of Lake Issyk-Kul, Kyrgyzstan

Pacific Ocean SST and Z500 climate variability and western U.S. seasonal streamflow

Contact Info

Product

Resources

About

Pacific Ocean SST and Z₅₀₀ climate variability and western U.S. seasonal streamflow