To assess the impacts of climate change on runoff in Golestan Province, Iran

Silakhori, Esmaeil; Ghaleno, Mohammad Reza Dahmardeh; Meshram, Sarita Gajbhiye; Alvandi, Ehsan

doi:10.1007/s11069-021-05181-y

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…The LarsWG weather generator can generate a series of meteorological data for a certain period in the future based on historical meteorological data and geographical data, such as rainfall, SR, T max , and T min . The generator has been widely applied in the projections of crop yield, soil erosion, and extreme environmental events 59,60 . In this study, we utilized LarsWG to downscale the projections from five GCMs in the CMIP6 dataset across three emission scenarios, producing daily rainfall and temperature data.…”

Section: Assessment Of Climate Change Impact On Streamflow Cmip6 (Cou...mentioning

confidence: 99%

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Zhang,

Qi,

Liu

et al. 2023

Sci Rep

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Against the backdrop of accelerated global climate change and urbanization, the frequency and severity of flood disasters have been increasing. In recent years, influenced by climate change, the Hai-River Basin (HRB) has experienced multiple large-scale flood disasters. During the widespread extraordinary flood event from July 28th to August 1st, 2023, eight rivers witnessed their largest floods on record. These events caused significant damage and impact on economic and social development. The development of hydrological models with better performance can help researchers understand the impacts of climate change, provide risk information on different disaster events within watersheds, support decision-makers in formulating adaptive measures, urban planning, and improve flood defense mechanisms to address the ever-changing climate environment. This study examines the potential for enhancing streamflow simulation accuracy in the HRB located in Northeast China by combining the physically-based hydrological model with the data-driven model. Three hybrid models, SWAT-D-BiLSTM, SWAT-C-BiLSTM and SWAT-C-BiLSTM with SinoLC-1, were constructed in this study, in which SWAT was used as a transfer function to simulate the base flow and quick flow generation process based on weather data and spatial features, and BiLSTM was used to directly predict the streamflow according to the base flow and quick flow. In the SWAT-C-BiLSTM model, SWAT parameters with P values less than 0.4 in each hydrological station-controlled watershed were calibrated, while the SWAT-D-BiLSTM model did not undergo calibration. Additionally, this study utilizes both 30 m resolution land use and land cover (LULC) map and the first 1 m resolution LULC map SinoLC-1 as input data for the models to explore the impact on streamflow simulation performance. Among five models, the NSE of SWAT-C-BiLSTM with SinoLC-1 reached 0.93 and the R2 reached 0.95 during the calibration period, and both of them stayed at 0.92 even in the validation period, while the NSE and R2 of the other four models were all below 0.90 in the validation period. The potential impact of climate change on streamflow in the HRB was evaluated by using predicted data from five global climate models from CMIP6 as input for the best-performing SWAT-C-BiLSTM with SinoLC-1. The results indicate that climate change exacerbates the uneven distribution of streamflow in the HRB, particularly during the concentrated heavy rainfall months of July and August. It is projected that the monthly streamflow in these two months will increase by 34% and 49% respectively in the middle of this century. Furthermore, it is expected that the annual streamflow will increase by 5.6% to 9.1% during the mid-century and by 6.7% to 9.3% by the end of the century. Both average streamflow and peak streamflow are likely to significantly increase, raising concerns about more frequent urban flooding in the capital economic region within the HRB.

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Section: Assessment Of Climate Change Impact On Streamflow Cmip6 (Cou...mentioning

confidence: 99%

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Zhang,

Qi,

Liu

et al. 2023

Sci Rep

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“…With the increasing trend of wildfires and the resulting damages, it is necessary to develop methods to deal with them (Parnian et al, 2021). Considering the ecological importance of forests and pastures in Golestan and the adverse climatic effects of recent years on this area (such as droughts and strong winds), the risk of wildfire is expected to be a threatening factor (Meftahhalaghi & Ghorbani, 2015;Silakhori et al, 2022). Considering the mentioned cases, to predict and adequately manage this phenomenon, it is necessary to monitor the spatial and temporal pattern of wildfire occurrence in the natural lands of this province.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal monitoring of wildfires in Golestan province using remote sensing data

Asadi Oskouei,

Shobairi,

Sadeghi

et al. 2024

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Wildfires are one of the most significant factors of ecosystem change. Knowing the wildfire regime (frequency, intensity, and distribution pattern) is essential in wildfire management. This research aims to analyze the spatiotemporal pattern of wildfires in Golestan in 2001-2021 using MODIS data, burned area product (MCD64A1). For this purpose, the annual and monthly frequency, as well as the trend of wildfires based on types of forest, pasture, and crop cover, were statistically analyzed. The local Moran pattern analysis method and kernel density function were used to analyze the spatial dynamics of wildfire. The results showed that 18,462 wildfires occurred in Golestan, the highest of which was in 2010, with 2,517 wildfires (13.8%). The lowest number of wildfires, with only 57 events (0.5%), was in 2001. Based on the local Moran model results and the kernel density function, the wildfires' extent and intensity were greater in the plains and foothills to the south and southeast of Golestan. The lowest extent and intensity of the wildfire corresponded to the eastern parts of the province. The frequency of wildfires was higher in the hot period of the year (spring and summer). However, the period of occurrence of wildfire and the peak of wildfire changes in different uses. The wildfire zones in June were wider and more intense than in other months. The frequency and spatial extent of wildfires in agricultural lands from May to July, pasture lands in July, August, and September, and forest lands in November and December were more than in other months. Weather conditions play a significant role in the occurrence of wildfire in the forest lands of Golestan. The results of this research help understand wildfire risk areas and provide a scientific basis for predicting and controlling wildfires and reducing carbon emissions related to them.

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“…In addition, Silakhori et al (2022) conducted a comprehensive study to evaluate the impact of climate change on runoff patterns in Golestan Province. Their research encompassed a wide-ranging data set spanning from 1985 to 2013, which included daily records of minimum and maximum temperatures, precipitation and sunny hours collected from a synoptic station.…”

Section: Introductionmentioning

confidence: 99%

An assessment of flash flood susceptibility in Golestan province, Iran, using multiple computational approaches

Hosseini Sabzevari,

Mehdipour,

Aslani

2023

IJDRBE

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Purpose Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours. Design/methodology/approach This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed. Findings Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk. Originality/value The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

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To assess the impacts of climate change on runoff in Golestan Province, Iran

Cited by 15 publications

References 26 publications

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Spatial and temporal monitoring of wildfires in Golestan province using remote sensing data

An assessment of flash flood susceptibility in Golestan province, Iran, using multiple computational approaches

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