Simulating hydrological response of a monsoon dominated reservoir catchment and command with heterogeneous cropping pattern using VIC model

Maza, Minotshing; Srivastava, Ankur; Bisht, Deepak Singh; Raghuwanshi, N. S.; Bandyopadhyay, Arnab; Chatterjee, Chandranath; Bhadra, A.

doi:10.1007/s12040-020-01468-z

Cited by 22 publications

(8 citation statements)

References 64 publications

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“…Step (2). Set the range of layers needed to divide the experimental area; for example, from 2 to h, where h represents the maximum number of the layer will be divided.…”

Section: Constructing the Integrative Stratified Sampling Approachmentioning

confidence: 99%

“…Vegetation can have a significant effect on hydrological fluxes due to variations in the physical characteristics of the land surface, soil, and vegetation [1,2]. The spectral vegetation indices (VIs), based on remotely sensed reflectance in the near-infrared and visible bands, are deemed as the fundamental and most widely used indicators for many regional and global ecological and environmental applications [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution

Zhou

Tao

et al. 2021

Remote Sensing

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Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.

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“…Step (2). Set the range of layers needed to divide the experimental area; for example, from 2 to h, where h represents the maximum number of the layer will be divided.…”

Section: Constructing the Integrative Stratified Sampling Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution

Zhou

Tao

et al. 2021

Remote Sensing

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“…On the other hand, urbanization causes changes in land-use distribution regarding impermeable urban materials and land-surface processes which could promote societal well-being [6] but can also impact the local climate [7]. Furthermore, in terms of hydrologically-related aspects, land cover changes also have a significant effect on hydrological streamflow due to the variations in the physical-based characteristics of the land surface, soil, and vegetation coverage such as in the surface roughness, infiltration capacity, root depth, soil resistivity, leaf area index (LAI), and stomatal conductance [8,9]. In Vietnam, the economic policy reforms from 1987 have resulted in rapid urbanization and social growth.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Urbanization-Induced Land-Use Change and Its Impact on Temperature, Evaporation, and Humidity in Central Vietnam

Nguyen

Tran

Grodzka-Łukaszewska

et al. 2022

Water

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In the present day, the acceleration of urban surface heat impacts resulting from urbanization and industrialization is critical for citizens and municipal governments in developing-country cities. The previous key findings have indicated the association between urban surface heat and the following areas: forests, mixed agricultural land, built-up area, and water bodies. This study was motivated by a lack of knowledge regarding the variation of temperature, evaporation, and humidity in Central Vietnam’s major region. The non-parametric Mann–Kendall test, Sen’s slope estimator, and Landsat image analysis were employed to determine the trend and statistical significance of the variables across the 42-year study period for Da Nang city and Quang Nam province. Our results show that Da Nang city has a consistent trend with a high correlation between temperature, evaporation, and relative humidity, whereas Quang Nam province showed an inverse relationship between temperature and relative humidity since the beginning of the regional urbanization. The maximum, minimum, and mean temperatures have increased by at least 0.29 °C in Quang Nam province and 0.71 °C in Da Nang city since 2000. Between 1979 and 2021, the frequency of days with temperatures exceeding 35°C has increased by two and seven days during the past decade at the meteorological stations in Da Nang and Tam Ky, respectively. The temperature in Da Nang city varied from 31.80°C to 32.82°C with high temperatures concentrated in urbanized regions with less coverage of small trees, plants, and water bodies. Thus, the results of this study will serve as a scientific basis for decision-makers and regional officials for land-use management and to increase community awareness of sustainable planning, particularly in Da Nang city and Quang Nam province in Central Vietnam.

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“…Traditional streamflow prediction models are mostly physically based models including multiple physical processes establishing the physical relationship between rainfall and runoff [10][11][12][13]. Examples of physically based hydrological models are the SWAT [10], VIC [11], MIKE-SHE [12], and Xinanjiang models [13]. These models describe the physical mechanism in explaining rainfall-runoff processes.…”

Section: Introductionmentioning

confidence: 99%

Improving Daily Streamflow Forecasting Using Deep Belief Net-Work Based on Flow Regime Recognition

Shen

Zou

Dong

et al. 2022

Water

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Streamflow forecasting is of great significance for water resources planning and management. In recent years, numerous data-driven models have been widely used for streamflow forecasting. However, the traditional single data-driven model ignores the utilization of different streamflow regimes. This study proposed an integrated framework for daily streamflow forecasting based on the regime recognition of flow sequences. The framework integrates self-organizing maps (SOM) for identifying streamflow sub-sequences, the random forests (RF) algorithm to select input variables for different streamflow sub-sequences, and a deep belief network (DBN) for establishing complex relationships between the selected input variables and streamflows for different sub-sequences. Specifically, the integrated framework was applied to forecast daily streamflow at the Xiantao hydrological station in the Hanjiang River Basin, China. The results show that the developed integrated framework has higher streamflow prediction accuracy than the single data-driven model (i.e., the DBN model in this study), with Nash efficiency coefficient (NSE) of 0.91/0.81 and coefficient of determination (R2) of 0.93/0.89 for the integrated framework/DBN model during the validation period, respectively. Additionally, the prediction accuracy of the peak flood was also improved. The relative error of the peak flood derived from the integrated framework was reduced by 4.6%, compared with the single DBN model. Overall, the constructed integration framework, considering the complex characteristic of different flow regimes, could improve the accuracy for daily streamflow forecasting.

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Simulating hydrological response of a monsoon dominated reservoir catchment and command with heterogeneous cropping pattern using VIC model

Cited by 22 publications

References 64 publications

Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution

Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution

Assessment of Urbanization-Induced Land-Use Change and Its Impact on Temperature, Evaporation, and Humidity in Central Vietnam

Improving Daily Streamflow Forecasting Using Deep Belief Net-Work Based on Flow Regime Recognition

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