Impact of climate change on net primary production (NPP) in south Iran

Azhdari, Zahra; Sardooi, Elham Rafeie; Bazrafshan, Ommolbanin; Zamani, Hossein; Singh, Vijay P.; Saravi, M Mohseni; Ramezani, Mohamadreza

doi:10.1007/s10661-020-08389-w

Cited by 22 publications

(12 citation statements)

References 34 publications

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“…The HadGEM2‐ES model predicted the climatic data more appropriately compared with the other models, so it was selected to evaluate drought in the basin. This model has already been tested for predicting various meteorological data and different purposes in other semi‐arid regions of Iran with a high performance (Ahmadi et al, 2019; Azhdari et al, 2020; Fallah‐Ghalhari & Shakeri, 2021; Khalili et al, 2012). The lower efficiency of the model and scenarios in estimating daily precipitation is because of the complex and discrete nature of the precipitation variable.…”

Section: Discussionmentioning

confidence: 99%

Past and future drought trends, duration, and frequency in the semi‐arid Urmia Lake Basin under a changing climate

Mirgol

Nazari

Etedali

et al. 2021

Meteorological Applications

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Although the Urmia Lake has undergone remarkable drought conditions within the past decades mainly due to climate change, drought studies covering the entire Urmia Lake Basin and all drought aspects are lacking. The present study investigated the spatial and temporal drought conditions in the Urmia Lake Basin for the past and future (2021-2050 and 2051-2080) periods using five general circulation models (GCMs) under the IPCC (Intergovernmental Panel on Climate Change) scenarios RCP 2.6, RCP 4.5, and RCP 8.5. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were compared. The SPEI predicted more drought events than the SPI, and it seemed to be a more suitable drought index than the SPI for the basin. In general, the future periods would encounter less drought conditions in terms of significant drought trends and duration than the observed period under all scenarios, but the frequency of quarterly severe drought events in the future periods would be higher than in the observed period. Furthermore, the stations Urmia (western bank) and Tabriz and Maragheh (eastern banks) would face the highest frequency of different types of quarterly drought events in the future periods compared with the observed period. The predicted high frequencies of drought events for the future periods can intensify the current low water level situation of the Urmia Lake, which seriously threatens all types of ecosystems in the basin. Therefore, serious actions need to be taken into account for efficient ecosystem and water resources management in the basin.

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Section: Discussionmentioning

confidence: 99%

Past and future drought trends, duration, and frequency in the semi‐arid Urmia Lake Basin under a changing climate

Mirgol

Nazari

Etedali

et al. 2021

Meteorological Applications

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“…The Kondoran watershed (KW) is located along 54 [28]. The annual precipitation average in the study region is about 83.62 mm and the climate is hot and humid [29].…”

Section: Study Areamentioning

confidence: 99%

Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

et al. 2021

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Gully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.

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“…The remaining land is covered by croplands (Crop) and closed shrubland (CShrub), with percentages of 5.61 % and 1.09 %, respectively. The forests of the HKH cover about 24 % of the region, supporting the 12 % of the population of the world by provision of diverse ecosystem goods and ecosystem services including energy, timber and freshwater (Behera et al, 2018).…”

Section: Study Areamentioning

confidence: 99%

“…The HKH region has been experiencing a temperature rise of 0.2 • C decade −1 since 1960 (Chen et al, 2013). The forests of HKH are undergoing changes of varied intensity as a result of climatic and human disturbances, alongside the various forest management policies practised in the different countries (Behera et al, 2018;Pulakesh et al, 2017). The rate of deforestation along the HKH has been reported to be 0.5 % yr −1 in Bhutan and 1.7 % yr −1 in Myanmar from 2000 to 2014 (Brandt et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Climate change projections of terrestrial primary productivity over the Hindu Kush Himalayan forests

et al. 2021

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Abstract. Increasing atmospheric carbon dioxide concentration [CO2] caused by anthropogenic activities has triggered a requirement to predict the future impact of [CO2] on forests. The Hindu Kush Himalayan (HKH) region comprises a vast territory including forests, grasslands, farmlands and wetland ecosystems. In this study, the impacts of climate change and land-use change on forest carbon fluxes and vegetation productivity are assessed for HKH using the Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS). LPJ-GUESS simulations were driven by an ensemble of three climate models participating in the CMIP5 (Coupled Model Intercomparison Project phase 5) database. The modelled estimates of vegetation carbon (VegC) and terrestrial primary productivity were compared with observation-based estimates. Furthermore, we also explored the net biome productivity (NBP) and its components over HKH for the period 1851–2100 under the future climate scenarios RCP2.6 and RCP8.5. A reduced modelled NBP (reduced C sink) is observed from 1986–2015 primarily due to land-use change. However, an increase in NBP is predicted under RCP2.6 and RCP8.5. The findings of the study have important implications for the management of the HKH region, in addition to informing strategic decision making and land-use planning, and clarifying policy concerns.

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Impact of climate change on net primary production (NPP) in south Iran

Cited by 22 publications

References 34 publications

Past and future drought trends, duration, and frequency in the semi‐arid Urmia Lake Basin under a changing climate

Past and future drought trends, duration, and frequency in the semi‐arid Urmia Lake Basin under a changing climate

Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

Climate change projections of terrestrial primary productivity over the Hindu Kush Himalayan forests

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