Human-induced land use land cover change and its impact on hydrology

Garg, Vaibhav; Nikam, Bhaskar R.; Thakur, Praveen K.; Aggarwal, S. P.; Gupta, P. K.; Srivastav, S. K.

doi:10.1016/j.hydres.2019.06.001

Cited by 117 publications

(62 citation statements)

References 35 publications

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“…This is consistent with previous reports that have found similar results reporting a dominance in previous decades of negative soil moisture trends across the world that were detected using field and satellite soil moisture measurements (Albergel et al, 2013). It has been shown how soil moisture decline can be intensified by land warming (Samaniego et al, 2018) or by land use change (Chen, et al, 2016, Garg et al, 2019) and agricultural practices (Bradford et al, 2017) or transformations to vegetation cover that directly affect primary productivity, evapotranspiration rates and drought (Stocker et al, 2019, Martens et al, 2018. Areas with high rates of primary productivity and the evapotranspiration rates such as the tropical rain forest of the Amazon or the Congo regions, are examples https://doi.org/10.5194/essd-2019-191 of areas where is challenging to accurately assess soil moisture trends due to limitations of historical soil moisture records (such as in the ESA-CCI).…”

Section: Discussionsupporting

confidence: 92%

Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Guevara

Taufer

Vargas

2019

Preprint

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Abstract. Soil moisture is key for quantifying soil-atmosphere interactions and the ESA-CCI (European Space Agency Climate Change Initiative) provides historical (> 30 years) satellite soil moisture gridded data at the global scale. We evaluate an alternative approach to increase the spatial resolution of the original ESA-CCI soil moisture measurements from 27 km to 15 km grids by coupling machine learning (ML) algorithms with information from digital terrain analysis at the global scale. We modeled mean annual ESA-CCI soil moisture values across 26 years of available data (1991–2016) using a ML based kernel method and multiple terrain parameters (e.g., slope, wetness index) as prediction factors. We used ground information from the International Soil Moisture Network (ISMN, n = 13376) for evaluating soil moisture predictions. We provide gap-free mean annual soil moisture predictions, which increase by nearly 50 % the spatial resolution of ESA-CCI soil moisture product. Our predictions showed a statistical accuracy varying 0.69–0.87 % and 0.04 m3/m3 of cross-validated explained variance and root mean squared error (RMSE). We found no significant differences between the ESA-CCI and our predictions, but we found discrepancy between multiple evaluation metrics (e.g., bias vs efficiency) comparing the ESA-CCI with the ISMN. We found a negative bias (−0.01 to −0.08 m3/m3) between the values of ISMN when comparing with the ESA-CCI and our predictions across the analyzed years. A temporal analysis, using a robust trend detection strategy (i.e., Theil-Sen estimator), suggests a decline of soil moisture at the global scale that is consistent in both gridded datasets and field measurements of soil moisture varying from −0.7[−0.77, −0.62] % in the ESA-CCI product, −0.9[−1.01, −0.8] % in the downscaled predictions, and −1.6 [−1.7, −1.5] % in the ISMN. The soil moisture predictions provided here (http://www.hydroshare.org/resource/b940b704429244a99f902ff7cb30a31f) could be useful for quantifying soil moisture spatial and temporal dynamics across areas with low availability of soil moisture information in the original ESA-CCI database.

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Section: Discussionsupporting

confidence: 92%

Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Guevara

Taufer

Vargas

2019

Preprint

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“…The Variable Infiltration Capacity (VIC) model is a large 85 scale physically semi-distributed land surface model developed by Liang et al, (1994). The ability of the model to simulate the impacts of LULC changes on hydrology are well documented in various research articles (Garg et al, 2017(Garg et al, , 2019Hurkmans et al, 2009;Mao and Cherkauer, 2009;Patidar and Behera, 2019;Zhang et al, 2014).…”

Section: Context and Backgroundmentioning

confidence: 99%

Quantifying the impact of land cover changes on hydrological extremes in India

Naha

Rico‐Ramirez

Rosolem

2020

Preprint

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Abstract. Several research studies have addressed the effects of future climate changes on the hydrological regime of Mahanadi river basin located in eastern part of India. However, studies investigating the effects of future land cover changes on hydrology are limited owing to the lack of availability of projected land cover scenarios. Our study investigates how the hydrology of Mahanadi river basin would respond to the current and future land cover scenarios under a large-scale hydrological modelling framework. Both historical and future land cover scenarios from the recently released, Land use Harmonisation (LUH2) project for CMIP6, indicates cropland and forest are the major land cover types in the basin with a noticeable increase in the cropland (23.3 %) at the expense of forest (22.65 %) by the end of year 2100 compared to the baseline year, 2005. A physically semi-distributed model, the Variable Infiltration Capacity has been set up and implemented over the Mahanadi river basin system for the time period 1990–2010. The uncertain model parameters were subjected to Sensitivity Analysis and calibrated within a Monte Carlo framework. The best set of calibrated models obtained is used in conjunction with the harmonized set of present and future land use scenarios from LUH2 at 25 km by 25 km resolution to generate an ensemble of model simulations that captures a range of plausible impacts of land cover changes on discharge and other hydrological components of the basin. Overall, model simulation results indicate an increase in the extreme flows (i.e., 95th percentile or higher) in the range of 0.12 to 21 % at multiple subcatchments within the basin. This increase can be attributed to the direct conversion of forested areas to agriculture (on the order of 30,000 km2) that has reduced the Leaf Area Index and subsequently reduces the Evapotranspiration (ET). These changes ultimately affect other water balance components at the land surface, resulting in an increase in surface runoff and baseflow, respectively.

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“…Among them, they were either limited to one province or one region and were not extended to the whole country, or the research period was relatively short and only studied the land-use efficiency for several years [19,[24][25][26][27]. In addition, among Sustainability 2019, 11,4756 3 of 22 studies on the influence factors of land-use efficiency, there is still a lack of researches that focuses on the perspective of demographic transition. To sum up, this paper takes the land-use efficiency in China from 1991 to 2016 as the research object, and undertakes an intensive discussion on its current situation, temporal and spatial evolution characteristics, regional disparity, and the response to demographic transition.…”

Section: An Analytical Frameworkmentioning

confidence: 99%

“…Sustainability 2019, 11, 4756 Model type FE FE FE FE Note: The symbols *, ** and *** denote that p < 0.10, p < 0.05 and p < 0.01, respectively.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…At present, there is already a considerable body of literature on land-use efficiency and its influencing factors. Many studies mention the effect of population quantity on land-use efficiency, and suggest that the former has a significant influence on the latter [8][9][10][11][12]. This means that population is one of the main factors of land-use efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Temporal-Spatial Variations and Regional Disparities in Land-Use Efficiency, and the Response to Demographic Transition

Wang

Yang

et al. 2019

Sustainability

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China has undergone rapid industrialization and urbanization over the past 40 years. In this process, as a large country with a vast territory and a large population, China's population development and land utilization have been greatly affected and undergone dramatic changes. In this paper, we mainly discuss the temporal and spatial variation characteristics of land-use efficiency in China from 1991 to 2016 and the regional disparities and explore the impacts of demographic transition on land-use efficiency by employing a STIRPAT model. In terms of space, China's land-use efficiency has significant agglomeration distribution characteristics and regional inequality, and the degrees of agglomeration and differentiation have gradually become enhanced over time. Our study on the influences of demographic transition on land-use efficiency found a Kuznets curve relationship between the transition of population size and land-use efficiency, as well as between the income level transition and land-use efficiency. Especially, land-use efficiency first increases up to the population threshold of 10,611.877 × 10 4 , then efficiency decreases as the population grows. The overall average population in the whole country is 4117.753 × 10 4 , which is smaller than the identified threshold. Interestingly, the factors influencing land-use efficiency also showed very significant regional disparities. In the eastern region, there is a U-curve relationship between the population employed in secondary industries (ES2) and land-use efficiency. Land-use efficiency decreases down to the ES2 threshold of 343.674 × 10 4 for the eastern region, whereas the overall average ES2 is 874.976 × 10 4 , indicating that this region has reached the turning point where land-use efficiency will improve as the population employed in secondary industries increases. Meanwhile, the increase in the human capital level was significantly positively correlated with land-use efficiency in the eastern region. For the central region, the transition of the urban-rural population structure (measured by the urbanization rate) significantly increased land-use efficiency. In addition, the results of panel estimation showed a Kuznets relationship between the population employed in tertiary industries (ES3) and land-use efficiency in the western region. Land-use efficiency increases up to the ES3 threshold of 455.545 × 10 4 , and then decreases with an increasing population employed in tertiary industries, whereas the overall average ES3 in the western region is 415.97 × 10 4 , which is smaller than the identified threshold. Policymakers could use these findings to inform rational suggestions with a sound scientific basis regarding the promotion of land-use transition.

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Human-induced land use land cover change and its impact on hydrology

Cited by 117 publications

References 35 publications

Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Quantifying the impact of land cover changes on hydrological extremes in India

Temporal-Spatial Variations and Regional Disparities in Land-Use Efficiency, and the Response to Demographic Transition

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