Geospatial approach for assessing spatiotemporal dynamics of urban green space distribution among neighbourhoods: A demonstration in Mumbai

Green spaces are an integral part of urban landscape and offer numerous benefits related to quality of urban life. However, due to various factors, the distribution of green spaces among city neighbourhoods is often skewed. Hence, urban planners require effective tools to routinely map and monitor the greening/ un-greening phenomena among the neighbourhoods. This study caters to this need by adopting a geospatial green space distribution assessment approach that encompasses green space quantity, quality and accessibility aspects. The green space distribution indicators were derived from remote sensing data, which facilitates cost-effective green space assessments at desired time scales. In this study, the approach is demonstrated in Mumbai, a typical city in a developing country undergoing urbanization transition accompanied by stark environmental challenges. The study results revealed that the green spaces in Mumbai had generally diminished, fragmented and disaggregated between 2001 and 2011. However, the level of degeneration of green spaces was found to vary significantly among the neighbourhoods. The results also unveiled that the verdant spaces in the city's western suburbs had experienced the worst degradation during the study period. These results would aid Mumbai's planners in formulating local greening strategies in a cost-effective manner. Further, the study has wider implications for green space planning, especially in the understudied, fast-urbanizing developing countries

A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coeffcient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates

Assimilation of SMAP products for improving streamflow simulations over tropical climate region — is spatial information more important than temporal information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology

Modified-INSAT Multi-Spectral Rainfall Algorithm (M-IMSRA) at climate region scale: Development and validation

The present article reports an improvement in the INSAT Multispectral Rainfall Algorithm which is currently operational in the Indian Meteorological Department (IMD). The proposed Modified-IMSRA (M-IMSRA) algorithm deviates from original IMSRA in two ways: first is by improvement in rain/no-rain area detection scheme using a Multi Index Rain Detection (MIRD) index; second is based on the climate region-wise correction through Least Absolute Shrinkage and Selection Operator (LASSO) models developed for each climate regions using rainfall (obtained based on first improvement) and static topographic variables extracted from Digital Elevation Model (DEM). The overall results indicate that the M-IMSRA is performing better than the IMSRA in all climatic regions when compared with the IMD gridded gauge data. However, the improvement is not uniform in all the regions. The inclusion of the MIRD index led to considerable improvement in M-IMSRA-based rainfall estimates mainly in the arid regions. Likewise, the results obtained after the LASSO regression corrections indicate that they are necessary only for the orographic regions where significant improvements are observed in the rainfall estimates. Finally, the inter-comparison of the simple hybrid M-IMSRA estimates with Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and TRMM 3B42-RT V7 illustrates that the M-IMSRA performs nearly as well as even better (except in terms of Correlation Coefficient) than the complex multi-satellite-based rainfall estimates in all the climate regions of India. Considering the above results, it can be said that the performance of simple hybrid algorithms such as IMSRA can be improved to match the quality or even outperform complex multi-satellite rainfall estimates by incorporating appropriate corrections. (C) 2016 Elsevier Inc. All rights reserved

Modelling evolution of a large, glacier-fed lake in the Western Indian Himalaya

Abstract In this study, we simulated the evolution of a large glacier-fed lake called the Gepan Gath lake located in Western Himalayas by numerically modelling the evolution of the Gepan Gath glacier that feeds the lake. Due to the extremely large volume and steep lake sidewalls, the lake has been classified as ‘critical’ or prone to hazards such as lake outburst floods in the future, by various scientific investigations. This modelling was carried out by a 1D model that is based on the principle of mass conservation. The 1D model was forced with the glacier surface mass balance (SMB). Due to non-availability of published in-situ estimates, the SMB was estimated using an energy balance-based model on station derived and reanalysis derived meteorological data. Modelled glacier length fluctuations for over 134 years matched reasonably well with that of observed within the RMSE error ~ 320 m. In addition to that, between 2004 and 2019, the modelled and observed lake lengths were in agreement with each other with the RMSE ~ 110 m. Modelled glacier lake lengths also match well with published, satellite imagery derived lengths within 15% uncertainty. The uncertainty in future lake length fluctuations is within 100–200 m. Our ultimate aim is to show that numerical ice-flow modelling can be an asset in modelling glacier-fed lake evolution even in the case of highly data-sparse regions of the IHR

Error modelling for modified-INSAT multi-spectral rainfall algorithm

Considering the importance of the error estimates for satellite rainfall products in various applications, the present article deals with the development of an Error Model for Modified-INSAT Multi-Spectral Rainfall Algorithm (M-IMSRA) Estimates (EMME), a recently developed climate region scale rainfall algorithm across India. A non-parametric framework has been adopted to model all the four error components: Correct No-Rain Detection, Miss Rain, False Rain, and Hit Rain in M-IMSRA estimates at climate region scale. The developed error model generated convincing realization of reference rainfall for the estimated rainfall from M-IMSRA algorithm across all the climate regions of India. Exceptions are the high intensity hit rain events across arid Thar Desert and arid Himalayan regions and miss rain events across arid Himalayan region. Overall, the developed error model showed promising results in modelling hit, miss, and false error components of daily M-IMSRA estimates and thus can be associated with the M-IMSRA estimates

Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions

Monitoring snowpack depth is essential in many applications at regional and global scales. Space-borne passive microwave (PMW) remote sensing observations have been widely used to estimate snow depth (SD) information for over four decades due to their responsiveness to snowpack characteristics. Many approaches comprised of static and dynamic empirical models, non-linear, machine-learning-based models, and assimilation approaches have been developed using spaceborne PMW observations. These models cannot be applied uniformly over all regions due to inherent limitations in the modelling approaches. Further, the global PMW SD products have masked out in their coverage critical regions such as the Himalayas, as well as very high SD regions, due to constraints triggered by prevailing topographical and snow conditions. Therefore, the current review article discusses different models for SD estimation, along with their merits and limitations. Here in the review, various SD models are grouped into four types, i.e., static, dynamic, assimilation-based, and machine-learning-based models. To demonstrate the rationale behind these drawbacks, this review also details various causes of uncertainty, and the challenges present in the estimation of PMW SD. Finally, based on the status of the available PMW SD datasets, and SD estimation techniques, recommendations for future research are included in this article