Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China

Guo, Yuxue; Fang, Guohua; Xu, Yue‐Ping; Tian, Xuemin; Xie, Jingkai

doi:10.1016/j.scitotenv.2019.136275

Cited by 72 publications

(31 citation statements)

References 64 publications

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“…The type of forest in this regency consists of primary forest and secondary forest, including mangrove, swamp, and dryland [49,50]. sago palm habitat, this study examined the natural ecosystem of sago palm habitat based on two categories, i.e., (1) based on MoEF land cover scheme [44], and (2) peatland land cover in Papua [45]. We also applied statistical analysis to investigate the significant change of sago palm habitat based on their land cover.…”

Section: Study Areamentioning

confidence: 99%

“…Twenty-one land cover categories were identified: (1) 6 classes of the natural forest included primary dryland forest, secondary dryland forest, primary mangrove forest, primary swamp forest, secondary mangroves forest, secondary swamp forest; (2) 15 classes of non-forest consisting of swamp shrub, swamp, bush/shrub, estate crop plantation, settlement area, barren land, clouds, grassland, water body, dryland agriculture, shrub-mixed dryland, paddy field, fishpond, airport, transmigration area (Figure 3). By using supervised classification [1] of remotely sensed imagery, it is possible to get the resultant area estimates that occurred in this Regency, as shown by Tables 3-5. In 1990, Merauke Regency was covered by the natural forest of around 2,440,396 ha, or approximately 50.3% (Figure 4), compared to the non-forest area of about 2,411,319 ha, or 49.70% of total area (Table 3).…”

Section: Land Cover Changes In Merauke Regencymentioning

confidence: 99%

“…In contrast, land use refers to the purpose land serves, for example, recreation and wildlife habitat. Land cover and land use are often used reciprocally, but both of them can be performed to support various purposes, for instance, identification and change detection [1,2]. Land cover changes information is useful to achieve a better perspective of landscape dynamics and is also proper for evaluating the sustainability of natural resources [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Land Cover Changes from 1990 to 2019 in Papua, Indonesia: Results of the Remote Sensing Imagery

Letsoin

Herák

Rahmawan³

et al. 2020

Sustainability

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Long-term land cover changes play a significant driver of ecosystem and function of natural biodiversity. Hence, their analysis can be used for evaluating and supporting government plans, especially conservation and management of natural habitats such as sago palm. In Papua Province of Indonesia, sago palm has been stated as one of the priority plants in the Medium-Term Development Plan (R.P.J.M.). However, limited studies have examined this palm in one of the Regencies of Papua Province, namely, Merauke Regency. In this study, we performed remotely sensed data imagery and supervised classification to produce land cover maps from 1990 to 2019. During the study period, twenty-one land cover classes were identified. The six classes of the natural forest consist of primary dryland forest, secondary dryland forest, primary mangrove forest, secondary mangrove forest, primary swamp forest, and secondary swamp forest; thus, fifteen classes of non-forested area. Concerning the sago palm habitat, our study evaluated two different categories (1) based on the land cover scheme from the Ministry of Environment and Forestry and (2) according to the peatland land cover ecosystem in Papua. Based on paired samples t-test, the result indicated statistically significant changes specifically at primary dryland (p-value = 0.015), grassland (p-value = 0.002) and swamp (p-value = 0.007). Twelve from 20 districts of Merauke Regency tend to lose the forecasted natural habitat of the sago palm. Therefore, this study suggests the further need to recognize and estimate the yield of sago palm area in these various ecosystems.

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Section: Study Areamentioning

confidence: 99%

Section: Land Cover Changes In Merauke Regencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Land Cover Changes from 1990 to 2019 in Papua, Indonesia: Results of the Remote Sensing Imagery

Letsoin

Herák

Rahmawan³

et al. 2020

Sustainability

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“…Previous studies have shown that land-use and climate change are the main factors that affect runoff and cause floods [3][4][5][6][7]. Urbanization can lead to an increase in impervious areas and shorten the concentrated time of direct runoff in the drainage network, resulting in a rapid increase in runoff [8,9].…”

Section: Introductionmentioning

confidence: 99%

Future Flood Risk Assessment under the Effects of Land Use and Climate Change in the Tiaoxi Basin

Yang

2020

Sensors

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Global warming and land-use change affects runoff in the regional basin. Affected by different factors, such as abundant rainfall and increased impervious surface, the Taihu basin becomes more vulnerable to floods. As a result, a future flood risk analysis is of great significance. This paper simulated the land-use expansion and analyzed the surface change from 2020 to 2050 using the neural network Cellular Automata Markov (CA-Markov) model. Moreover, the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) dataset was corrected for deviation and used to analyze the climate trend. Second, the verified SWAT model was applied to simulate future runoff and to analyze the future flood risk. The results show that (1) land use is dominated by cultivated land and forests. In the future, the area of cultivated land will decrease and construction land will expand to 1.5 times its present size. (2) The average annual precipitation and temperature will increase by 1.2% and 1.5 degrees from 2020 to 2050, respectively. During the verified period, the NSE and r-square values of the SWAT model are greater than 0.7. (3) Compared with the historical extreme runoff, the extreme runoff in the return period will increase 10%~25% under the eight climate models in 2050. In general, the flood risk will increase further under the climate scenarios.

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“…Guo et al 11 evaluated the impact of land-use changes and climate changes on the river flow velocity in Xinjiang and presented the results that the mean monthly flow is affected by climate change and the average yearly flow is affected by both land-use and climate factors, but the impact of climate change is more than the impact of land-use change.…”

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confidence: 99%

Evaluating climate changes and land use changes on water resources using hybrid Soil and Water Assessment Tool‐DEEP optimized by metaheuristics

Liu

Mizzi

2020

Concurrency and Computation

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The objective of this research is to investigate the effect of land-use and climate change on the volume of runoff produced and its impact on water resources. It is clear that the more accurate the assessment of runoff in the future, the better the evaluation of the impact of land-use and climate changes on water resources. Thus, to more accurately predict water sources in the future, runoff is estimated using two Soil and Water Assessment Tool (SWAT) and SWAT-DEEP-IFSO models. The results showed that the SWAT-DEEP-IFSO model has a better estimation than the SWAT method. Because Deep Belief Network and an improved optimization algorithm called Improved Fluid Search Optimization (IFSO) algorithm have been utilized in optimizing the hybrid SWAT model that can reduce the error of runoff simulation. After selecting the best model for estimating runoff, the effect of land-use and climate change is evaluated using different scenarios. Results show that the simulated runoff under the influence of climate and land-use changes can impact on the average yearly and monthly runoff. These factors have a significant effect on the average yearly runoff changes and are predicted an increasing trend for average yearly runoff. But climate and land-use changes have a different impact on the different months and for some months of the year the runoff has an increasing trend and for some months the runoff has a decreasing trend. Based on land-use scenarios, it is found that due to the decrease in rangelands and increase in agricultural lands, there is a possibility of severe floods and drought periods in the region, which reduces the water resources of the study area. K E Y W O R D S deep belief network, fluid search optimization algorithm, runoff, soil and water assessment tool, water resource 1 INTRODUCTION Today, all climate scientists agree that global warming causes important climatic phenomena and due to climate changes, the probability of heavy rainfall and flood risk has been increased. 1 Global warming dramatically affects rainfall characteristics, soil moisture, water levels of the seas and oceans, and airflow patterns. Also, it can cause severe floods and storms and reduce glaciers. Reducing snowfall and rain and increasing drought, thereby increasing soil salinity and reducing freshwater resources in coastal areas can be another result of global warming. 2 One of the effects of climate change can be seen in renewables resources such as water resources which have different impacts from point-to-point. This effect of

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Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China

Cited by 72 publications

References 64 publications

Land Cover Changes from 1990 to 2019 in Papua, Indonesia: Results of the Remote Sensing Imagery

Land Cover Changes from 1990 to 2019 in Papua, Indonesia: Results of the Remote Sensing Imagery

Future Flood Risk Assessment under the Effects of Land Use and Climate Change in the Tiaoxi Basin

Evaluating climate changes and land use changes on water resources using hybrid Soil and Water Assessment Tool‐DEEP optimized by metaheuristics

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