Future Opportunities and Challenges in Remote Sensing of Drought

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health

Future Opportunities and Challenges in Remote Sensing of Drought

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation, as summarized by Anyamba and Tucker (2012, Chapter 2). Other indices such as the Vegetation Health Index (VHI) (Kogan, 1995) were also developed during this time period and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers like AVHRR could provide for operational drought monitoring through near-daily, synoptic observations of earth’s land surface. However, the advancement of satellite remote sensing for drought monitoring was limited by the relatively few spectral bands on operational global sensors such as AVHRR, along with a relatively short observational record

SWOT data assimilation for operational reservoir management on the upper Niger River Basin

International audienceThe future Surface Water and Ocean Topography (SWOT) satellite mission will provide two-dimensional maps of water elevation for rivers with width greater than 100 m globally. We describe a mod-eling framework and an automatic control algorithm that prescribe optimal releases from the Selingue dam in the Upper Niger River Basin, with the objective of understanding how SWOT data might be used to the benefit of operational water management. The modeling framework was used in a twin experiment to simulate the ''true'' system state and an ensemble of corrupted model states. Virtual SWOT observations of reservoir and river levels were assimilated into the model with a repeat cycle of 21 days. The updated state was used to initialize a Model Predictive Control (MPC) algorithm that computed the optimal reservoir release that meets a minimum flow requirement 300 km downstream of the dam. The data assimilation results indicate that the model updates had a positive effect on estimates of both water level and discharge. The ''per-sistence,'' which describes the duration of the assimilation effect, was clearly improved (greater than 21 days) by integrating a smoother into the assimilation procedure. We compared performances of the MPC with SWOT data assimilation to an open-loop MPC simulation. Results show that the data assimilation resulted in substantial improvements in the performances of the Selingue dam management with a greater ability to meet environmental requirements (the number of days the target is missed falls to zero) and a minimum volume of water released from the dam

Evaluation of Regional-Scale River Depth Simulations Using Various Routing Schemes within a Hydrometeorological Modeling Framework for the Preparation of the SWOT Mission

The Surface Water and Ocean Topography (SWOT) mission will provide free water surface elevations, slopes, and river widths for rivers wider than 50 m. Models must be prepared to use this new finescale information by explicitly simulating the link between runoff and the river channel hydraulics. This study assesses one regional hydrometeorological model’s ability to simulate river depths. The Garonne catchment in southwestern France (56 000 km2) has been chosen for the availability of operational gauges in the river network and finescale hydraulic models over two reaches of the river. Several routing schemes, ranging from the simple Muskingum method to time-variable parameter kinematic and diffusive waves schemes, are tested. The results show that the variable flow velocity schemes are advantageous for discharge computations when compared to the original Muskingum routing method. Additionally, comparisons between river depth computations and in situ observations in the downstream Garonne River led to root-mean-square errors of 50–60 cm in the improved Muskingum method and 40–50 cm in the kinematic–diffusive wave method. The results also highlight SWOT’s potential to improve the characterization of hydrological processes for subbasins larger than 10 000 km2, the importance of an accurate digital elevation model, and the need for spatially varying hydraulic parameters

Toward impact-based monitoring of drought and its cascading hazards

Growth in satellite observations and modelling capabilities has transformed drought monitoring, offering near-real-time information. However, current monitoring efforts focus on hazards rather than impacts, and are further disconnected from drought-related compound or cascading hazards such as heatwaves, wildfires, floods and debris flows. In this Perspective, we advocate for impact-based drought monitoring and integration with broader drought-related hazards. Impact-based monitoring will go beyond top-down hazard information, linking drought to physical or societal impacts such as crop yield, food availability, energy generation or unemployment. This approach, specifically forecasts of drought event impacts, would accordingly benefit multiple stakeholders involved in drought planning, and risk and response management, with clear benefits for food and water security. Yet adoption and implementation is hindered by the absence of consistent drought impact data, limited information on local factors affecting water availability (including water demand, transfer and withdrawal), and impact assessment models being disconnected from drought monitoring tools. Implementation of impact-based drought monitoring thus requires the use of newly available remote sensors, the availability of large volumes of standardized data across drought-related fields, and the adoption of artificial intelligence to extract and synthesize physical and societal drought impacts.</p

On the assimilation of altimetric data in 1D Saint-Venant river flow models

Given altimetry measurements, the identification capability of time varying inflow discharge Qin(t) and the Strickler coefficient K (defined as a power-law in h the water depth) of the 1D river Saint-Venant model is investigated. Various altimetry satellite missions provide water level elevation measurements of wide rivers, in particular the 21 future Surface Water and Ocean Topography (SWOT) mission. An original and synthetic reading of all the available information (data, wave propagation and the Manning-Strickler’s law residual) are represented on the so-called identifiability map. The latter provides in the space-time plane a comprehensive overview of the inverse problem features. Inferences based on Variational Data Assimilation (VDA) are investigated at the limit of the data-model inversion capability : relatively short river portions, relatively infrequent observations, that is inverse problems presenting a low identifiability index . The inflow discharge Qin(t) is infered simultaneously with the varying coefficient K(h). The bed level is either given or infered from a lower complexity model. The experiments and analysis are conducted for different scenarios (SWOT-like or multi-sensors-like). The scenarios differ by the observation frequency and by the identifiability index. Sensitivity analyses with respect to the observation errors and to the first guess values demonstrate the robustness of the VDA inferences. Finally this study aiming at fusing relatively sparse altimetric data and the 1D Saint-Venant river flow model highlights the spatiotemporal resolution lower limit, also the great potential in terms of discharge inference including for a single river reach

An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results

Optical remote sensing technique for the generation of meandering river channel topography and sediment size

Traditional techniques for river cross-section surveys are costly, time-consuming, and difficult to implement. They are also limited by logistical constraints. The practical limitation in the spacing and frequency of survey points restricts ground-based surveys to those of reach scale. This research has demonstrated the potential of generating river topography and sizing bed materials within complex shallow channels, using high-resolution multispectral and stereo images. The demonstration uses a 13-km long reach of meandering, alluvial river (the Goulais River in Ontario). Fluvial remote sensing provides a complimentary alternative to field surveys, in which a detailed synoptic view of the river may be acquired. The presented technique generates a river topography model (RTM) by combining the bathymetry map (the channel-bed to the free surface) and digital elevation map (the free surface and above). The former is generated from the depth-to-brightness ratio that is empirically estimated by correlating available field survey points to the digital numbers of the image. The latter is generated through a photogrammetric analysis of stereo images. A challenge arises in combining the maps when the images used to derive the bathymetry map and the digital elevation map are captured at different times (or at different stages). The difficulty is overcome by applying gradually-varied flow type technique (one-dimensional conservation of mass, energy and momentum) to resolve the discrepancy in the stages per cross section. The resulting RTM is a continuous digital terrain that encompasses the channel-bed, floodplains, and the dry terrain features. Qualitative observations of the RTM indicate a correct placement of geomorphic features, including pool-riffle zones, and point bars. The RTM is used as a model domain for simulations of depth-averaged two-dimensional hydrodynamics in order to estimate the boundary shear stress corresponding to the formative discharge. The median sediment diameter in the riffle zones derived from the simulated boundary shear stress compares well with field observations. The method presented offers a promising complimentary tool for river dynamics analysis and river management

Generating water level time series from satellite altimetry measurements for inland applications

Inland surface water bodies, e.g. lakes and rivers, play vital roles in the nature and in the society. To understand the impact of the human activities and climate change on these vulnerable water resources, monitoring the water level variations with a finer spatial and temporal resolutions is a primary issue. On the other hand, the global available and free accessible in-situ gauge databases are unsatisfactory and insufficient. The spatial distribution of gauge stations is severely uneven and the data accuracy is highly dependent on processing method. Therefore, it is an essential requisite to have a constantly and reliable data stream. Over the past two decades, satellite altimetry has shown the capability to provide repeatable monitoring results for hydrologic cycle and inland water bodies. Several researches and studies are carried out with respect to the improvements on multi-mission data fusion, retracking methods, error estimation and outliers rejection. In this thesis, we take advantage of this state-of-art inland surface water level monitoring technique to generate the water level time series over Amazon River, Benue River and Tsimlyansk Reservoir. Initially, we investigate the measurement principle, corrections and retracking algorithms of radar altimeter throughly. Afterwards, the processing scheme for water level time series is divided into three steps: data selection, correction and result generation. In this thesis, we have chosen Jason-2 measurement data and the on-board Ice retracker. A validation has been performed between our results and the time series from other databases, e.g. DAHITI and Hydroweb. The comparisons showed a feasible and acceptable outcomes regarding to correlation coefficient and root-mean-square error (RMSE). The best result was given by Benue River case with 0:98 and 0:96 of correlation coefficient against DAHITI and Hydroweb, respectively. Also, the minimum RMSE difference, 17.1 cm, was achieved between our time series and the one from DAHITI. We also examined the potential error sources when encountering disagreements with others. Furthermore, possible solutions for error elimination and further improvements were also discussed in experiments and outlook