scholarly journals Flood Inundation Mapping of the Sparsely Gauged Large-Scale Brahmaputra Basin Using Remote Sensing Products

2019 ◽  
Vol 11 (5) ◽  
pp. 501 ◽  
Author(s):  
Biswa Bhattacharya ◽  
Maurizio Mazzoleni ◽  
Reyne Ugay

Sustainable water management is one of the important priorities set out in the Sustainable Development Goals (SDGs) of the United Nations, which calls for efficient use of natural resources. Efficient water management nowadays depends a lot upon simulation models. However, the availability of limited hydro-meteorological data together with limited data sharing practices prohibits simulation modelling and consequently efficient flood risk management of sparsely gauged basins. Advances in remote sensing has significantly contributed to carrying out hydrological studies in ungauged or sparsely gauged basins. In particular, the global datasets of remote sensing observations (e.g., rainfall, evaporation, temperature, land use, terrain, etc.) allow to develop hydrological and hydraulic models of sparsely gauged catchments. In this research, we have considered large scale hydrological and hydraulic modelling, using freely available global datasets, of the sparsely gauged trans-boundary Brahmaputra basin, which has an enormous potential in terms of agriculture, hydropower, water supplies and other utilities. A semi-distributed conceptual hydrological model was developed using HEC-HMS (Hydrologic Modelling System from Hydrologic Engineering Centre). Rainfall estimates from Tropical Rainfall Measuring Mission (TRMM) was compared with limited gauge data and used in the simulation. The Nash Sutcliffe coefficient of the model with the uncorrected rainfall data in calibration and validation were 0.75 and 0.61 respectively whereas the similar values with the corrected rainfall data were 0.81 and 0.74. The output of the hydrological model was used as a boundary condition and lateral inflow to the hydraulic model. Modelling results obtained using uncorrected and corrected remotely sensed products of rainfall were compared with the discharge values at the basin outlet (Bahadurabad) and with altimetry data from Jason-2 satellite. The simulated flood inundation maps of the lower part of the Brahmaputra basin showed reasonably good match in terms of the probability of detection, success ratio and critical success index. Overall, this study demonstrated that reliable and robust results can be obtained in both hydrological and hydraulic modelling using remote sensing data as the only input to large scale and sparsely gauged basins.

2017 ◽  
Vol 49 (2) ◽  
pp. 438-449 ◽  
Author(s):  
Shaochun Huang ◽  
Fred F. Hattermann

Abstract To bridge the gap between 1D and 2D hydraulic models for regional scale assessment and global river routing models, we coupled the CaMa-Flood (Catchment-based Macro-scale Floodplain) model and the regional hydrological model SWIM (Soil and Water Integrated Model) as a tool for large-scale flood risk assessments. As a proof-of-concept study, we tested the coupled models in a meso-scale catchment in Germany. The Mulde River has a catchment area of ca. 6,171 km2 and is a sub-catchment of the Elbe River. The modified CaMa-Flood model routes the sub-basin-based daily runoff generated by SWIM along the river network and estimates the river discharge as well as flood inundation areas. The results show that the CaMa-Flood hydrodynamic algorithm can reproduce the daily discharges from 1991 to 2003 well. It outperforms the Muskingum flow routing method (the default routing method in the SWIM) for the 2002 extreme flood event. The simulated flood inundation area in August 2002 is comparable with the observations along the main river. However, problems may occur in upstream areas. The results presented here show the potential of the coupled models for flood risk assessments along large rivers.


2011 ◽  
Vol 301-303 ◽  
pp. 1115-1122
Author(s):  
Miao Liu ◽  
Jia Hong Liu ◽  
Jun E Zhang

Water management based on “regional objective ET” had been a hotspot in water shortage region. This article detailedly overviewed the major methods for monitoring ET through field measure and remote sensing retrieval, meanwhile application of distributed hydrological model based on multivariant checking to improve the result of remote sensing retrieval of ET was discussed. The region of Handan was chosen as a example to analyze the correlation of results of MODcycle model and remote sensing retrieval for ET calculation.


Author(s):  
Haoyu Niu ◽  
Tiebiao Zhao ◽  
Dong Wang ◽  
YangQuan Chen

Estimating evapotranspiration (ET) has been one of the most important research in agriculture recently because of water scarcity, growing population, and climate change. ET is the sum of evaporation from the soil and transpiration from the crops to the atmosphere. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, people use weighing lysimeters, Bowen ratio, eddy covariance and many other methods to estimate ET. However, these ET methods are points or location-specific measurements and cannot be extended to a large scale of ET estimation. With the advent of satellites technology, remote sensing images can provide spatially distributed measurements. The satellites multispectral images spatial resolution, however, is in the range of meters, which is often not enough for crops with clumped canopy structure such as trees and vines. And, the timing or frequency of satellites overpass is not always enough to meet the research or water management needs. The Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, can help solve these spatial and temporal challenges. Lightweight cameras and sensors can be mounted on drones and take high-resolution images on a large scale of field. Compared with satellites images, the spatial resolution of UAVs’ images can be as high as 1 cm per pixel. And, people can fly a drone at any time if the weather condition is good. Cloud cover is less of a concern than satellite remote sensing. Both temporal and spatial resolution is highly improved by drones. In this paper, a review of different UAVs based approaches of ET estimations are presented. Different modified models used by UAVs, such as Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), Two-source energy balance (TSEB) model, etc, are also discussed.


2019 ◽  
Author(s):  
Ali Ajaz

Asia holds 70% of global irrigated areas which accounts for 62% of world food demand. Reliable information regarding irrigated areas are of crucial importance for effective future planning. National datasets for irrigated areas, collected by different agencies, e.g. statistical agency, agriculture department, irrigation authorities often vary from each other, while global datasets such as FAO’s show a huge divergence with remote sensing estimates of irrigated areas. Confusions about the accuracy and reliability of data could jeopardize the effectiveness of future policies aiming at securing food production for rapidly growing population of Asia. Without having consistent and dependable data of such a basic input, food and water security of Asian nations would be at stake. In addition, global commitments such as SDGs, climate change, increasing domestic and industrial water demand and ecological concerns would also put more pressure on irrigated agriculture. In this study, a detailed analysis has been conducted for the growth track of irrigated areas in Asia, with the purpose to understand the development of different types of irrigation, investments, trends and resilience of irrigated agriculture against major climate events over the time. Secondly, comprehensive comparison has been made within national statistics, FAO’s data and high resolution irrigated area maps (up to 250m) from IWMI (International Water Management Institute) together with other available raster datasets. Variations in data have been estimated using different statistical tools while distribution and dispersion analysis has also been made to look into the extent of irrigated areas in different climatic regions and to find country wise clusters/patterns of large, medium and small scale irrigation schemes. Furthermore, country’s reporting methods have been investigated thoroughly for the limitations and strengths of existing data collection mechanisms to find the possible loop holes, which might induce uncertainty in data. Results of the study showed a 15% average decline per decade in irrigated areas growth in Asia for last 50 years, while focus on rehabilitation of old infrastructure and implementation of climate smart irrigation has been relatively increased. Uncertainty analysis indicated significant difference in irrigated areas information collected from different sources. Remote sensing estimates were found 96% higher than country estimates on an average, while dispersion analysis showed 300 M ha of non-reported irrigated areas in large scale irrigated schemes for Asia. Qualitative analysis of irrigated areas’ reporting mechanisms showed that mostly traditional statistical methods are used by data collection agencies, e.g. sample surveys based on farmer interviews and global datasets also receive their information from same agencies. Reliability of these methods have been scaled by developing a scoring mechanism by using a quantitative analysis approach. On the other hand, implications of uncertainty came up with some critical questions, i.e. what is the actual annual land productivity? what about per capita irrigated areas? What is the actually utilized irrigation potential? Consequently this study has been concluded by putting forward some genuine facts and recommendations to improve the existing reporting systems of irrigated areas information and to look for imminent role of remote sensing to compare the national statistics with ground facts.


Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.


2021 ◽  
Vol 10 (6) ◽  
pp. 384
Author(s):  
Javier Martínez-López ◽  
Bastian Bertzky ◽  
Simon Willcock ◽  
Marine Robuchon ◽  
María Almagro ◽  
...  

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.


Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 20
Author(s):  
Kleoniki Demertzi ◽  
Vassilios Pisinaras ◽  
Emanuel Lekakis ◽  
Evangelos Tziritis ◽  
Konstantinos Babakos ◽  
...  

Simple formulas for estimating annual actual evapotranspiration (AET) based on annual climate data are widely used in large scale applications. Such formulas do not have distinct compartments related to topography, soil and irrigation, and for this reason may be limited in basins with high slopes, where runoff is the dominant water balance component, and in basins where irrigated agriculture is dominant. Thus, a simplistic method for assessing AET in both natural ecosystems and agricultural systems considering the aforementioned elements is proposed in this study. The method solves AET through water balance based on a set of formulas that estimate runoff and percolation. These formulas are calibrated by the results of the deterministic hydrological model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) for a reference surface. The proposed methodology is applied to the country of Greece and compared with the widely used climate-based methods of Oldekop, Coutagne and Turk. The results show that the proposed methodology agrees very well with the method of Turk for the lowland regions but presents significant differences in places where runoff is expected to be very high (sloppy areas and areas of high rainfall, especially during December–February), suggesting that the proposed method performs better due to its runoff compartment. The method can also be applied in a single application considering irrigation only for the irrigated lands to more accurately estimate AET in basins with a high percentage of irrigated agriculture.


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