Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world

Remotely sensed applications in monitoring the spatio-temporal dynamics of pools and flows along non-perennial rivers: A review

Non-perennial rivers (NPRs) account for more than 50% of the world’s river network and their occurrence is expanding. Some rivers that were previously classified as perennial have evolved to be NPRs in response to climate change and socio-economic uses. There is inadequate understanding of the spatio-temporal dynamics of flows and pools along these rivers due to lack of data, as a priority of river monitoring has been placed on perennial rivers. The current understanding and methods used for monitoring NPRs are mostly derived from perennial rivers perspective. This review paper examines challenges for collecting data on these hydrological attributes of NPRs using current methods. Furthermore, this paper provides an overview of the potential and limitations of using remote sensing data for monitoring NPRs

Global satellite-based measurement of river and reservoir dynamics

Knowledge of the dynamics of rivers and reservoirs is important to help manage the impacts of climate change and anthropogenic activities on water resources, which are intimately tied to human well-being, economic wealth and environmental health. However, ground-based measurements only capture a small fraction of water bodies, and in situ observed data are generally not publicly shared in most countries for a variety of reasons. Satellite remote sensing technology provides promising new opportunities to measure global water availability at different time and space scales. The objective of this study was to develop a global monitoring capacity to measure rivers, lake, and reservoir dynamics using satellite observation. In pursuit of this objective, I propose approaches to measure river discharge, river morphology, and lake (reservoir) storage based on remote sensing data. Satellite gauging reaches (SGRs) that can predict river discharge based on optical remote sensing are shown to be applicable to many rivers globally, especially in South America, Africa, and Asia. The river discharge prediction capability of SGRs in a certain river reach can be explained by its unique river morphology characteristics. Hydromorphological attributes, including spatial and temporal river width, flow regime and river gradient were produced for 1.4 million Australian river reaches, and can be used to improve river routing in models to better estimate river discharge. Finally, storage dynamics for 6,743 reservoirs worldwide for the period 1984-2015 were reconstructed based on satellite observations. The results indicate that some storages, particularly in southeastern Australia, central Chile, the USA, and eastern Brazil, have declined, accompanied by reduced reservoir resilience and increased vulnerability. Others have increased, mainly in the Nile Basin, Mediterranean basins and southern Africa. Multi-decadal changes in rainfall and hence streamflow were found to be the main reason for these changes. The techniques and data produced in this study provide components for a global monitoring capacity. The approaches developed can be used to process near real-time observations continuously. In future, the storage estimation method developed may be extended to lakes and wetlands. This study emphasizes the importance of increasing, or at least maintaining, the number of global gauging sites, which not only provide the historical context and current status of water resources under climate change, but also provide an indispensable basis to train remote sensing data in order to create a global water availability picture. Collaboration among different counties is urgently needed to share in situ river, lake, and reservoir data to tackle current and future water crisis, a challenge people worldwide face together. Considering the essential role of water resources for human well-being, new satellite missions are required that are specially designed for simultaneously measuring water extent and elevation in rivers, lakes, reservoirs, and wetlands at high spatial (e.g. 10 meters) and temporal (e.g. daily) resolution over the next decades

Flood modellling approaches for large lowland tropical catchments.

Flooding is increasing in tropical regions, where millions of people are at risk, and challenges exist in providing reliable predictions and warnings. This research responds to this challenge by identifying and applying physics-based and data-based hydrological modelling approaches for large-scale flood modelling in lowland tropical regions. First, a distributed hydrological model was developed to accurately represent catchment conditions and processes in the model. Second, empirical data from nested catchments were analysed using statistical scaling relationships to complement the accuracy of peak discharge estimates. Finally, the effects of uncertainty propagation and interactions were quantified to increase the reliability of model results. The research was conducted in the Grijalva catchment area (57 958 km²) southeast of Mexico. A large-scale model with a 2 x 2 km grid cell resolution was developed using the SHETRAN hydrological model and run enforced with 3-hour input rainfall data. Geostatistical techniques were used to quantify and reduce errors in input data, and all diverted flows were accounted for to optimise simulations. For the first time, the application of the Scaling theory of floods was applied in the study area to improve the estimation of peak discharge. A Monte Carlo technique was used to propagate and quantify rainfall and parameter uncertainties through a coupled hydrologic and hydraulic model and into model results. Although the model under-predicted the magnitude of peak discharge, calibration results showed satisfactory model performance (NSCE = 0.72, CC = 0.74, Bias = –0.44% and RMSE 139.56 mm) and validation results were good (NSCE = 0.56, CC = 0.60, Bias = –6.3% and RMSE 62.59 mm). A statistical log-log relationship between intercepts (α) and peak discharge, from the smallest nested catchment, was used to complement the simulation of peak discharge magnitudes. It was observed that given rainfall uncertainties of ±71%, ranging from 63 to 73%; the model generates discharge with uncertainties of ± 46%, ranging from 45 to 49% and errors of ±46% ranging from 45 to 46%. The propagated uncertainties resulted in flood inundation extents of ±4.34 km² varying from 1.66 to 7.02 km² Thus, flood modelling in large tropical regions can be achieved by optimally integrating several datasets with the best combination of the model parameter, input and output datasets based on uncertainty and error quantification and removal approaches.PhD in Water, including Desig

Geo-Spatial Analysis in Hydrology

Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale

Runoff, discharge and flood occurrence in a poorly gauged tropical basin : the Mahakam River, Kalimantan

Tidal rivers and lowland wetlands present a transition region where the interests of hydrologists and physical oceanographers overlap. Physical oceanographers tend to simplify river hydrology, by often assuming a constant river discharge when studying estuarine dynamics. Hydrologists, in turn, generally ignore the direct or indirect effects of tides in water level and discharge records. This thesis aims to improve methods to monitor, model and predict discharge dynamics in tidal rivers and lowland wetlands, by focussing on the central and lower reaches of the River Mahakam (East Kalimantan, Indonesia), and the surrounding lakes area. The 980-km long river drains an area of about 77100 km2 between 2°N - 1°S and 113°E - 118°E. Due to its very mild bottom slope, a significant tidal influence occurs in this river. The middle reach of the river is located in a subsiding basin, parts of which are below mean sealevel, featuring peat swamps and about thirty lakes connected to the river via tie channels. Upstream of the lakes area, at about 300 km from the river mouth, an acoustic Doppler current profiler (H-ADCP) has been horizontally deployed at a station near the city of Melak (Chapter 2). The H-ADCP profiles of velocity are converted to discharge adopting a new calibration methodology. The obtained time-series of discharge show the tidal signal is clearly visible during low flow conditions. Besides tidal signatures, the discharge series show influences by variable backwater effects from the lakes, tributaries and floodplain ponds. The discharge rate at the station exceeds 3250 m3s-1 with a hysteretic behaviour. For a specific river stage, the discharge range can be as high as 2000 m3s-1. Analysis of alternative types of rating curves shows this is far beyond what can be explained from kinematic wave dynamics. Apart from backwater effects, the large variation of discharge for a specified river stage can be explained by river-tide interaction, impacting discharge variation especially in the fortnightly frequency band. A second H-ADCP station has been setup in the lower reach of the Mahakam, near the city of Samarinda, where the tidal discharge amplitude generally exceeds the discharge related to runoff (Chapter 3). Conventional rating curve techniques are inappropriate to model river discharge at this tidally influenced station. As an alternative, an artificial neural network (ANN) model is developed to investigate the degree to which tidal river discharge at Samarinda station can be predicted from an array of level gauge measurements along the tidal river, and from tidal level predictions at sea. The ANN-based model produces a good discharge estimation, as established from a consistent performance during both the training and the validation periods, showing the discharges can be predicted from water levels only, once that a trained model is available. The ANN models perform well in predicting discharges up to two days in advance. Chapter 4 addresses the role of backwater effects and tidal influences on discharge time-series used to calibrate a rainfall-runoff model. The HBV rainfall-runoff model is implemented for the Mahakam sub-catchment upstream of Melak (25700 km2). In a first approach, the model is calibrated using a discharge series derived from the H-ADCP measurements from Melak station. In a second approach, discharge estimates derived from a rating curve are used to calibrate the model. Adopting the first approach, a comparatively low model efficiency is obtained, which is attributed to the backwater and tidal effects that are not captured in the model. The second approach produces a relatively higher model efficiency, since the rating curve filters the backwater effects out of the discharge series. Seasonal variation of terms in the water balance is not affected by the choice for one of the two calibration strategies, which shows that backwaters do not have a systematic seasonal effect on the river discharge. To allow for investigation of the causes of backwater effects, satellite radar remote sensing is employed to monitor water levels in wetlands (Chapter 5). A series of Phased Array L-band Synthetic Aperture Radar (PALSAR) images is used to observe the dynamics of the Mahakam River floodplain. To analyze radar backscatter behavior for different land cover types, several regions of interest are selected, based on land cover classes. Medium shrub, high shrub, fern/grass, and degraded forest are found to be sensitive to flooding, whereas peat forest, riverine forest and tree plantation backscatter signatures only slightly change with flood inundation. An analysis of the relationship between radar backscatter and water levels is carried out. For lakes and shrub covered peatland, for which the range of water level variation is high, a good water level-backscatter correlation is obtained. In peat forest covered peatland, subject to a small range of water level variation, water level-backscatter correlations are poor, limiting the ability to obtain a floodplain-wide water surface topography from the radar images. Chapter 6 continues to investigate the degree in which satellite radar remote sensing can serve to distinguish between dry areas and wetlands, which is a difficult task in densely vegetated areas such as peat domes. Flood extent and flood occurrence information are successfully extracted from a series of radar images of the middle Mahakam lowland area. A fully inundated region is easily recognized from a dark signature on radar images. Open water flood occurrence is mapped using a threshold value taken from radar backscatter of the permanently inundated areas. Radar backscatter intensity analysis of the vegetated floodplain area reveals consistently higher backscatter values, indicating flood inundation under forest canopy. Those observations are used to establish thresholds for flood occurrence mapping in the vegetated area. An all-encompassing flood occurrence map is obtained by combining the flood occurrence maps for areas with and without vegetation. Chapter 7 synthesizes the findings from the previous chapters. It is concluded that the backwater effects and subtle tidal influences may prevent the option to predict river discharge using rating curves, which can best be interpreted as a stage-runoff relationship. H-ADCPs offer a promising alternative to monitor river discharge. For a tidal river, an ANN model can be used as a tool for data gap filling in an H-ADCP based discharge series, or even to derive discharge estimates solely from water levels and water level predictions. Discharge can be predicted several time-steps ahead, allowing water managers to take measures based on forecasts. The stage-runoff relationship derived from a continuous series of H-ADCP based discharge estimates may be expected to be much more accurate than a similar rating curve derived from a small number of boat surveys. The flood occurrence map derived from PALSAR images can offer a detailed insight into the hydroperiod, the period in which a soil area is waterlogged, and flood extent of the lowland area, illustrating the added value of radar remote sensing to wetland hydrological studies. In future work, radar-based floodplain observations may serve to calibrate hydrodynamic models simulating the processes of flooding and emptying of the lakes area.</p