Comparison of IDW and Physically Based IDEW Method in Hydrological Modelling for a Large Mountainous Watershed, Northwest China

Zhang, L.; He, Chunbo; Li, J.; Wang, Y.; Wang, Z.

doi:10.1002/rra.3147

Cited by 14 publications

(13 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The upper reach of the Heihe River Watershed is located in the Qilian Mountain ranges (Figure 1), with a length of 313 km and a drainage area of 10,009 km 2 , producing the majority of runoff for the entire Heihe River Watershed [25]. The local climate is influenced by both the continental climate and the Qinghai-Tibet plateau climate [26], the mean annual temperature ranges from −3.1 to 3.6 • C with the minimum temperature of −28 • C. Mean annual precipitation ranges from 250 mm below 1900 m asl (meters above sea level) to 700 mm above 3600 m asl, and most precipitation (over 60%) falls between June and September.…”

Section: Study Areamentioning

confidence: 99%

“…The local climate is influenced by both the continental climate and the Qinghai-Tibet plateau climate [26], the mean annual temperature ranges from −3.1 to 3.6 • C with the minimum temperature of −28 • C. Mean annual precipitation ranges from 250 mm below 1900 m asl (meters above sea level) to 700 mm above 3600 m asl, and most precipitation (over 60%) falls between June and September. The elevation in the region ranges from 1674 to 5584 m asl [25,27]. In the upper reach of the Heihe River Watershed, the major vegetation types include coniferous forest (Picea crassifolia), shrub (Potentilla fruticosa), steppe (Stipa purpurea Griseb), alpine meadow (Kobresia pygmaea Clarke), alpine sparse vegetation (Saussurea medusa Maxim), and desert (Sympegma regelii Bunge) [28].…”

Section: Study Areamentioning

confidence: 99%

See 1 more Smart Citation

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Zhang

2017

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Abstract:As the latest L-band mission to date, evaluation of the Soil Moisture Active Passive (SMAP) products is one of its post-launch objectives. However, almost all previous studies have been conducted at the core validation sites (CVS) of the SMAP mission. This paper presents an evaluation of the SMAP soil moisture Level 3 (L3) and Level 4 (L4) products under different vegetation types at multiple tempo-spatial scales over the upper reach of the Heihe River Watershed, a topographically complex mountainous area in Northwest China. This was done through comparisons of the L3 and L4 products with ground-based observations from a sparse in situ network of permanent and temporary stations from 1 April 2015 to 22 June 2017. Results show that, compared with in situ observations at point scale, both the L3 and L4 products represent the temporal trends of the in situ observations in the study area well, with R values of 0.601 and 0.538 for the L3 ascending and descending products, respectively, and ranging from 0.353 to 0.410 for the L4 product at eight overpassing moments. However, because of the uncertainties of brightness temperature T Bp and effective temperature T eff as well as their propagations in the inversion algorithm, both products did not achieve the accuracy of 0.04 m 3 /m 3 in mountainous area. These uncertainties also result in the "dry bias" of the SMAP products in almost all the evaluations to date. Compared with areal average values at the watershed scale, the L3 product is far beyond the accuracy of 0.04 m 3 /m 3 and the L4 product basically achieves the accuracy. In vegetation-covered land, the suitability and the variability of the coefficient b p result in both products performing best in cropland, then coniferous forest, sparse grassland, dense grassland, and alpine meadow, and worst in shrub. In barren land, the errors in estimating surface roughness h caused by the complex topography lead to poor performance of the SMAP products. With the relative errors of the SMAP brightness temperature observations and the corresponding land model forecast in the assimilation; the L3 and L4 products show different performance at both temporal and spatial scales; and the L3 product provides more reliable soil moisture estimates in the study area. Based on the results of this study, we propose: quantifying the uncertainties in estimating brightness temperature T Bp and effective temperature T eff ; determine coefficient b p and surface roughness h factor under various conditions; improving Goddard Earth Observing Model System Version 5 (GEOS-5) model; and deriving the SMAP-only climatology to improve the SMAP soil moisture estimates in the future.

show abstract

Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Zhang

2017

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to reduce the excessive segmentation of watershed technology, several methods have been proposed in relevant foreign literatures. We can refer to labeling-based technology, region merging method [18], scale space method [19], image denoising or edge enhancement based on partial differential equation [20], wavelet technology combined with watershed transform [21], etc. In addition, other scholars have also put forward some suggestions to improve the over-segmented watershed, using space constraint technology to apply to the watershed (SCOW) [22].…”

Section: Related Workmentioning

confidence: 99%

“…then the quantum superposition state structure be defined as: indicate the probability that l  takes 1 and takes 0, that is, the probability that the structural element of the 1st point whose coordinate position is X is "1" and "0". Equation (28) can be expanded according to formula (19) as:…”

mentioning

confidence: 99%

Road Identification Algorithm for Remote Sensing Images Based on Wavelet Transform and Recursive Operator

2020

View full text Add to dashboard Cite

Road edge detection from remote sensing images, as an important ground object type, plays an important role in people's life and travel and urban planning and development, and extracting road information from remote sensing images has practical scientific value and practical significance. However, with the development of remote sensing technology, while the resolution of remote sensing images is improved, the information describing ground objects becomes more and more abundant, and the difficulty of identifying and extracting road information is also increased. In the process of acquiring remote sensing images, the actual system is subjected to various kinds of noise interference. Different environmental interference and system defects will introduce noises with completely different distribution and statistical characteristics to remote sensing images. Aiming at the problem that the detection effect of traditional algorithms becomes worse due to the influence of noise on remote sensing images, a wavelet transform denoising method and morphological gradient operator are proposed. By selecting appropriate structural elements of remote sensing images, noise pixels cannot participate in morphological calculation, and the noise intensity changes with the size of quantum superposition state structural elements. Therefore, a morphological gradient operator is established and applied to edge detection of remote sensing images. Finally, the experimental results show that the method proposed in this paper is better than other directions in terms of effect through road edge detection and matching. This method can effectively reduce noise.

show abstract

“…TS analysis plays an important role in hydrological research studies, which are handled by mathematical models to predict new records and identify trends. In recent times, mathematical models have taken over most of the important tasks in problem solving in hydrology (Borgonovo, Lu, Plischke, Rakovec, & Hill, 2017;Zhang, He, Li, Wang, & Wang, 2017;Fonesca, Santos, & Santos, 2018;Martin &McCutcheon, 2018 andKavetski, 2018). In TS analysis, it is assumed that the data consist of a systematic pattern with a set of identifiable components and random noise (error).…”

Section: Introductionmentioning

confidence: 99%

Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020

Fashae

Olusola

Ndubuisi

et al. 2018

River Research & Apps

View full text Add to dashboard Cite

Many attempts have been made in the recent past to model and forecast streamflow using various techniques with the use of time series techniques proving to be the most common. Time series analysis plays an important role in hydrological research. Traditionally, the class of autoregressive moving average techniques models has been the statistical method most widely used for modelling water discharge, but it has been shown to be deficient in representing nonlinear dynamics inherent in the transformation of runoff data. In contrast, the relatively newly improved and efficient soft computing technique artificial neural networks has the capability to approximate virtually any continuous function up to an arbitrary degree of accuracy, which is not otherwise true of other conventional hydrological techniques. This technique corresponds to human neurological system, which consists of a series of basic computing elements called neurons, which are interconnected together to form networks. The aim of the study is to compare the artificial neural network and autoregressive integrated moving average to model River Opeki discharge (1982–2010) and to use the best predictor to forecast the discharge of the river from 2010 to 2020. The performance of the two models was subjected to statistical test based on correlation coefficient (r) and the root‐mean‐square error. The result showed that autoregressive integrated moving average performs better considering the level of root‐mean‐square error and higher correlation coefficient.

show abstract

Comparison of IDW and Physically Based IDEW Method in Hydrological Modelling for a Large Mountainous Watershed, Northwest China

Cited by 14 publications

References 63 publications

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

Road Identification Algorithm for Remote Sensing Images Based on Wavelet Transform and Recursive Operator

Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020

Contact Info

Product

Resources

About