The Ayeyarwady River (Myanmar): Washload transport and its global role among rivers in the Anthropocene

The Ayeyarwady (Irrawaddy) is the second largest river of Southeast Asia and one of the rivers with the highest load of suspended sediment delivered to the sea in the world. The Ayeyarwady is the lifeline of Myanmar which concentrates the majority of the population and GDP of the country. It is the main way of transport, a source of fluvial aggregates for development projects, hydropower, and the basin plays a major role in food supply and irrigation. Despite the Ayeyarwady ranking amongst the world’s largest rivers and its vital importance to Myanmar, scarce research has been undertaken to understand its morphodynamics and sediment transport regime. Current load estimates still heavily rely on the only systematic study of sediment transport dating back to the 19th century. Here, we provide a novel estimate for the recent washload sediment transport based on a field calibrated remote sensing model of surface suspended sediments concentrations. We show that the Ayeyarwady has likely become the river with the second or third largest delivery of washload to the sea in the world since it has so far been much less affected by damming compared to the vast majority of other rivers.

Development of a Three-Source Remote Sensing Model for Estimation of Urban Evapotranspiration (TRU)

<p>Evapotranspiration (ET) from an urban area and its components are important  when estimating the urban ‘heat island’ effect and the urban hydrological cycle. Multi-source satellite-based ET models for ecosystems (e.g. farmland, forest, and wetland) have been developed and applied, but satellite-based ET model dimensions for urban areas are lacking, especially since all currently available models are designed for single-source schemes. This paper proposes the first Three-source Remote sensing model for Urban areas (TRU) to discriminate between soil evaporation, vegetation transpiration, and impervious surface evaporation. TRU uses a new parameterization scheme, based on the use of a complementary relationship integrating soil surface temperature to estimate soil evaporation. An iterative procedure was developed for decomposing land surface temperature (LST) into component temperatures. Also, the ET for impervious areas was independently delineated using the “patch” approach. The model was tested for 45 cloudless days in Tianjin for 2017-2020 based on 30 m Operational Land Imager (OLI)/Enhanced Thematic Mapper Plus (ETM<sup>+</sup>) images. Results indicated the root mean square error (RMSE) of 38.8 W/m<sup>2</sup> and Bias of 9.9 W/m<sup>2</sup> compared with two Eddy Correlation (EC) observations for instantaneous latent heat (LE) simulation and RMSE was 0.087 and Bias was -0.012, compared with stable water isotope measurements for the estimation of the ratio of vegetation latent heat flux to latent heat flux (LE<sub>v</sub>/LE).Comparison with urban single-source models and two-source models for ecosystem suggest TRU provide best accuracy for ET and its components simulation. The spatial pattern suggested impervious surface evaporation exhibited minimal seasonal variation and maintained a very lower level due to limited availability of water. The results emphasized the importance of using land use and land cover (LULC) in urban ET modeling and the necessity to calculate ET as independent of impervious areas. TRU represents a groundbreaking development of multi-source urban satellite-based ET models and facilitates the mapping of urban ET components.</p>

Flood monitoring using passive microwave remote sensing in the Senegal River, Western Mali

<div>Water supports life, however it does come with hazards. Floods area amongst the most impactful environmental disasters. Accurate flood forecasting and early warning are critical for disaster risk management. Detecting and forecasting floods at an early stage is certainly relevant for Mali, hence crucial in order to prevent a hazard from turning into a disaster. Remotely sensed river monitoring can be an effective, systematic and time-efficient technique to detect and forecast extreme floods. Conventional flood forecasting systems require extensive data inputs and software to model floods. Moreover, most models rely on discharge data, which is not always available and is less accurate in a overbank flow situations. There is a need for an alternative method which detects riverine inundation, while making use of the available state-of-the-art.</div><div>This research investigates the use of passive microwave remote sensing with different spatial resolutions for the detection and forecasting of flooding. Brightness temperatures from two different downscaled spatial resolutions  (1 x 1 km and 10 x 10 km) are extracted from passive microwave remote sensing sensors and are converted into discharge estimators: a dry CM ratio and a wet CMc ratio. Surface water has a low emission, thus let the CM ratio increase as the surface water percentage in the pixel increases. Sharp increases are observed for over-bank flow conditions.<br><p>Overall, we compared the passive microwave remote sensing model results of the different spatial resolutions to the results of a conventional global runoff model GloFAS. The passive microwave remote sensing model does not require extensive input data when used as an Early Warning System (EWS),<span> as many smaller-scale EWS do, we suggest that when improved, the passive microwave remote sensing method is implemented as part of an integrative EWS solution, including a passive microwave remote sensing model and various other models. This would allow for early warnings in data-scarce regions and at a variety of lead times. In order for this to be effective, we suggest that more research be done on correctly setting the trigger threshold, and into the potential spatial interpretation of CMc.</span></p> </div>

Comparative Study on Variable Selection Approaches in Establishment of Remote Sensing Model for Forest Biomass Estimation

In the field of quantitative remote sensing of forest biomass, a prominent phenomenon is the increasing number of explanatory variables. Then how to effectively select explanatory variables has become an important issue. Linear regression model is one of the commonly used remote sensing models. In the process of establishing the linear regression model, a vital step is to select explanatory variables. Focusing on variable selection and model stability, this paper conducts a comparative study on the performance of eight linear regression parameter estimation methods (Stepwise Regression Method (SR), Criterions Based on The Bayes Method (BIC), Criterions Based on The Bayes Method (AIC), Criterions Based on Prediction Error (Cp), Least Absolute Shrinkage and Selection Operator (Lasso), Adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Non-negative garrote (NNG)) in the subtropical forest biomass remote sensing model development. For the purpose of comparison, OLS and RR, are commonly used as methods with no variable selection ability, and are also compared and discussed. The performance of five aspects are evaluated in this paper: (i) Determination coefficient, prediction error, model error, etc., (ii) significance test about the difference between determination coefficients, (iii) parameter stability, (iv) variable selection stability and (v) variable selection ability of the methods. All the results are obtained through a five ten-fold CV. Some evaluation indexes are calculated with or without degrees of freedom. The results show that BIC performs best in comprehensive evaluation, while NNG, Cp and AIC perform poorly as a whole. Other methods show a great difference in the performance on each index. SR has a strong capability in variable selection, although it is poor in commonly used indexes. The short-wave infrared band and the texture features derived from it are selected most frequently by various methods, indicating that these variables play an important role in forest biomass estimation. Some of the conclusions in this paper are likely to change as the study object changes. The ultimate goal of this paper is to introduce various model establishment methods with variable selection capability, so that we can have more choices when establishing similar models, and we can know how to select the most appropriate and effective method for specific problems.