Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins

The calibration of macroscale hydrological models is often challenged by the lack of adequate observations of river discharge and infrastructure operations. This modeling backdrop creates a number of potential pitfalls for model calibration, potentially affecting the reliability of hydrological models. Here, we introduce a novel numerical framework conceived to explore and overcome these pitfalls. Our framework consists of VIC-Res (a macroscale model setup for the Upper Mekong Basin), which is a novel variant of the Variable Infiltration Capacity (VIC) model that includes a module for representing reservoir operations, and a hydraulic model used to infer discharge time series from satellite data. Using these two models and global sensitivity analysis, we show the existence of a strong relationship between the parameterization of the hydraulic model and the performance of VIC-Res – a codependence that emerges for a variety of performance metrics that we considered. Using the results provided by the sensitivity analysis, we propose an approach for breaking this codependence and informing the hydrological model calibration, which we finally carry out with the aid of a multi-objective optimization algorithm. The approach used in this study could integrate multiple remotely sensed observations and is transferable to other poorly gauged and heavily regulated river basins.</p

Estimation of Stage-Area-Storage Relationships in Reservoirs and Stage-Discharge Relationships in Rivers Using Remotely Sensed Data

This thesis estimates the relationships between the water surface levels and quantities of water in reservoirs and rivers using remotely sensed data without any field measurements. However, the accuracies of the estimates were validated using the field measurements from ground stations. The relationships between the water surface levels and quantities are fundamental for monitoring water quantities for the operation of hydraulic structures, as well as analyzing variability and changes in hydrology. Accessibility and transparency are big issues in establishing a monitoring system that can provide field validation of efforts to model global climate systems. Remote sensing has a capability of global spatial coverage and stable temporal frequency in data acquisition, and hence can be helpful. Two types of remotely sensed data are used: satellite images, and satellite altimeter elevations. The thesis has two parts. First, the relationships among water surface levels, areas and volumes were estimated for reservoirs. A strategic procedure, which is missing in the current literature, was formulated to estimate the water surface area, and then the water volume. Water levels were derived from Hydroweb, a satellite altimetry database. Areas were estimated from Landsat surface reflectance images by classifying the Modified Normalized Difference Water Index (MDNWI) into binary images using an internally calibrated threshold. Internal calibration of the threshold was performed by computing the overall accuracies of classification from confusion matrices created for selected regions in the classified image. Finally, water surface heights from the lowest levels and areas were used to estimate volumes assuming an inverted pyramidal shape; then, second-order polynomials were fitted to compute relationships. The fits were tested to be statistically significant by performing t-tests for coefficients and F-test for overall significance at α = 0.05. Stage-area-storage relationships were developed for Lake Mead (LM) and Lake Powell (LP) that are reservoirs formed in the Colorado River. The study estimated the areas of LM with a Root Mean Square Difference (RMSD) of 17.8 km2 and LP with an RMSD of 53.7 km2 compared with in-situ measurements. The RMSD in volumes were 699 Million Cubic Meters (MCM) for LM and 1330 MCM for LP. The second-order polynomial fits between water surface heights and volumes were established with R2 = 0.999 for both LM and LP. The coefficients of the fit and the overall fit were tested to be statistically significant at α = 0.05. The RMSD is higher in LP than LM and were explained by comparatively more shadows and a higher number of mixed pixels in the LP Landsat images than LM. Secondly, the relationships between the water surface levels and discharges for rivers were estimated. Two major rivers, the Mississippi River and the Colorado River, representing an alluvial and rocky terrain, were selected to highlight the differences in estimates between varied terrain and size of the river. A variant of Manning’s equation was used that required a channel cross-section, water surface slope, and roughness coefficient as input parameters. A parabolic cross-section was fitted for each river using the width of river estimated from the Landsat images at several water levels. Water surface slopes were estimated from water elevations at different locations on each river using two sources. For the Mississippi River, water surface elevations were obtained at virtual stations from the DAHITI database. For the Colorado River, elevations were extracted using the MAPS at river crossings. Roughness coefficients were estimated using empirical models that utilized meander length. Results showed that discharges were estimated to within 31.4% of the average discharge with root mean square error of 5700 cu.m/sec for the Mississippi River. Colorado River discharges were estimated within 30.5% of the average discharge with RMSE of 50 cu.m/sec. A linear relationship was fitted between the water surface elevation and discharges in the Mississippi River with R2 = 0.62. For the Colorado River, second-order polynomial was fitted for a relationship between water surface elevations and discharges with R2 = 0.99. The coefficients of the fits and the overall significance of the fit were statistically significant at α = 0.05 tested by performing t-tests and F-test respectively. It was difficult to estimate a cross-section for rivers with smaller channel widths or smaller changes in width with water level as in the case of the Colorado River. However, estimated accuracies were similar in both the cases in terms of percentage of error

Satellite Altimetry and Hydrologic Modeling of Poorly-Gauged Tropical Watershed

This report was prepared for and submitted to the Graduate School of the Ohio State University as a dissertation for partial fulfillment of the requirements for the Doctor of Philosophy (PhD) degree.This research was carried out under supervision of Professor C.K. Shum, Division of Geodetic Science, School of Earth Sciences, The Ohio State University. Hidayat at Hydrology and Quantitative Water Management Department of Wageningen University and Limnology Research Agency of Indonesian Institute of Sciences (LIPI) are especially acknowledged for providing in-situ discharge, rating curve and precipitation data for the Upper Mahakam Sub-watershed study region.This research is primarily supported by the Fulbright PhD Presidential Scholarship administered by American Indonesian Exchange Foundation (AMINEF) and the Institute for International Education (IIE). In addition, this study is partially funded by grants from NASA's Ocean Surface Topography Science Team project (Univ. of Colorado, 154-5322), NASA's Geodetic Imaging project (NNX12AQ07G), NASA's Application Science Program under the SERVIR project (NNX12AM85G), and The Ohio State University's Climate, Water, and Carbon (http://cwc.osu.edu/) program.Fresh water resources are critical for daily human consumption. Therefore, a continuous monitoring effort over their quantity and quality is instrumental. One important model for water quantity monitoring is the rainfall-runoff model, which represents the response of a watershed to the variability of precipitation, thus estimating the discharge of a channel (Bedient and Huber, 2002, Beven, 2012). Remote sensing and satellite geodetic observations are capable to provide critical hydrological parameters, which can be used to support hydrologic modeling. For the case of satellite radar altimetry, limited temporal resolutions (e.g., satellite revisit period) prohibit the use of this method for a short (<weekly) interval monitoring of water level or discharge. On the other hand, the current satellite radar altimeter footprints limit the water level measurement for rivers wider than 1 km (Birkett, 1998, Birkett et al., 2002). Some studies indeed reported successful retrieval of water level for small-size rivers as narrow as 80 m (Kuo and Kao, 2011, Michailovsky et al., 2012); however, the processing of current satellite altimetry signals for small water bodies to retrieve accurate water levels, remains challenging. To address this scientific challenge, this study poses two main objectives: (1) to monitor small (40–200 m width) and medium-sized (200–800 m width) rivers and lakes using satellite altimetry through identification and choice of the over-water radar waveforms corresponding to the appropriately waveform-retracked water level; and (2) to develop a rainfall-runoff hydrological model to represent the response of mesoscale watershed to the variability of precipitation. Both studies address the humid tropics of Southeast Asia, specifically in Indonesia, where similar studies do not yet exist. This study uses the Level 2 radar altimeter measurements generated by European Space Agency’s (ESA’s) Envisat (Environmental Satellite) mission. The first study proves that satellite altimetry provides a good alternative or the only means in some regions to measure the water level of medium-sized river (200–800 m width) and small lake (extent <1000 km2) in Southeast Asia humid tropic with reasonable accuracy. In addition, the procedure to choose retracked Envisat altimetry water level heights via identification or selection of over water waveform shapes is reliable; therefore this study concluded that the use of waveform shape selection procedure should be a standard measure in determining qualified range measurements especially over small rivers and lakes. This study also found that Ice-1 is not necessarily the best retracker as reported by previous studies, among the four standard waveform retracking algorithms for Envisat altimetry observing hydrologic bodies. The second study modeled the response of the poorly-gauged watershed in the Southeast Asia’s humid tropic through the application of Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS). The performance evaluation of HEC-HMS discharge estimation confirms a good match between the simulated discharges with the observed ones. As the result of precipitation data analysis, this study found that Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is the preferred input forcing for the model, given the thorough evaluation of its relationship with field-measured precipitation data prior to its use as primary climatic forcing. This iii research also proposes a novel approach to process the TRMM precipitation estimation spatially through Thiessen polygon and area average hybrid method, which model the spatial distribution of TRMM data to match the spatial location of field meteorological stations. Through a simultaneous validation that compares the water level anomaly transformed from HEC-HMS simulated discharge and satellite altimetry measurement, this study found that satellite altimetry measures water level anomaly closer to the true water level anomaly than the water level anomaly converted from HEC-HMS simulated discharge. Some critical recommendations for future studies include the use of waveform shape selection procedure in the satellite altimetry based water level measurement of small and medium-sized rivers and small lakes, as well as the exploration to implement data assimilation between satellite altimetry and the hydrologic model for better discharge and water level estimations

Application of open-access and 3rd party geospatial technology for integrated flood risk management in data sparse regions of developing countries

Floods are one of the most devastating disasters known to man, caused by both natural and anthropogenic factors. The trend of flood events is continuously rising, increasing the exposure of the vulnerable populace in both developed and especially developing regions. Floods occur unexpectedly in some circumstances with little or no warning, and in other cases, aggravate rapidly, thereby leaving little time to plan, respond and recover. As such, hydrological data is needed before, during and after the flooding to ensure effective and integrated flood management. Though hydrological data collection in developed countries has been somewhat well established over long periods, the situation is different in the developing world. Developing regions are plagued with challenges that include inadequate ground monitoring networks attributed to deteriorating infrastructure, organizational deficiencies, lack of technical capacity, location inaccessibility and the huge financial implication of data collection at local and transboundary scales. These limitations, therefore, result in flawed flood management decisions and aggravate exposure of the most vulnerable people. Nigeria, the case study for this thesis, experienced unprecedented flooding in 2012 that led to the displacement of 3,871,53 persons, destruction of infrastructure, disruption of socio-economic activities valued at 16.9 billion US Dollars (1.4% GDP) and sadly the loss of 363 lives. This flood event revealed the weakness in the nation’s flood management system, which has been linked to poor data availability. This flood event motivated this study, which aims to assess these data gaps and explore alternative data sources and approaches, with the hope of improving flood management and decision making upon recurrence. This study adopts an integrated approach that applies open-access geospatial technology to curb data and financial limitations that hinder effective flood management in developing regions, to enhance disaster preparedness, response and recovery where resources are limited. To estimate flood magnitudes and return periods needed for planning purposes, the gaps in hydrological data that contribute to poor estimates and consequently ineffective flood management decisions for the Niger-South River Basin of Nigeria were filled using Radar Altimetry (RA) and Multiple Imputation (MI) approaches. This reduced uncertainty associated with missing data, especially at locations where virtual altimetry stations exist. This study revealed that the size and consistency of the gap within hydrological time series significantly influences the imputation approach to be adopted. Flood estimates derived from data filled using both RA and MI approaches were similar for consecutive gaps (1-3 years) in the time series, while wide (inconsecutive) gaps (> 3 years) caused by gauging station discontinuity and damage benefited the most from the RA infilling approach. The 2012 flood event was also quantified as a 1-in-100year flood, suggesting that if flood management measures had been implemented based on this information, the impact of that event would have been considerably mitigated. Other than gaps within hydrological time series, in other cases hydrological data could be totally unavailable or limited in duration to enable satisfactory estimation of flood magnitudes and return periods, due to finance and logistical limitations in several developing and remote regions. In such cases, Regional Flood Frequency Analysis (RFFA) is recommended, to collate and leverage data from gauging stations in proximity to the area of interest. In this study, RFFA was implemented using the open-access International Centre for Integrated Water Resources Management–Regional Analysis of Frequency Tool (ICI-RAFT), which enables the inclusion of climate variability effect into flood frequency estimation at locations where the assumption of hydrological stationarity is not viable. The Madden-Julian Oscillation was identified as the dominant flood influencing climate mechanism, with its effect increasing with return period. Similar to other studies, climate variability inclusive regional flood estimates were less than those derived from direct techniques at various locations, and higher in others. Also, the maximum historical flood experienced in the region was less than the 1-in-100-year flood event recommended for flood management. The 2012 flood in the Niger-South river basin of Nigeria was recreated in the CAESAR-LISFLOOD hydrodynamic model, combining open-access and third-party Digital Elevation Model (DEM), altimetry, bathymetry, aerial photo and hydrological data. The model was calibrated/validated in three sub-domains against in situ water level, overflight photos, Synthetic Aperture Radar (SAR) (TerraSAR-X, Radarsat2, CosmoSkyMed) and optical (MODIS) satellite images where available, to access model performance for a range of geomorphological and data variability. Improved data availability within constricted river channel areas resulted in better inundation extent and water level reconstruction, with the F-statistic reducing from 0.808 to 0.187 downstream into the vegetation dominating delta where data unavailability is pronounced. Overflight photos helped improve the model to reality capture ratio in the vegetation dominated delta and highlighted the deficiencies in SAR data for delineating flooding in the delta. Furthermore, the 2012 flood was within the confine of a 1-in-100-year flood for the sub-domain with maximum data availability, suggesting that in retrospect the 2012 flood event could have been managed effectively if flood management plans were implemented based on a 1-in-100-year flood. During flooding, fast-paced response is required. However, logistical challenges can hinder access to remote areas to collect the necessary data needed to inform real-time decisions. Thus, this adopts an integrated approach that combines crowd-sourcing and MODIS flood maps for near-real-time monitoring during the peak flood season of 2015. The results highlighted the merits and demerits of both approaches, and demonstrate the need for an integrated approach that leverages the strength of both methods to enhance flood capture at macro and micro scales. Crowd-sourcing also provided an option for demographic and risk perception data collection, which was evaluated against a government risk perception map and revealed the weaknesses in the government flood models caused by sparse/coarse data application and model uncertainty. The C4.5 decision tree algorithm was applied to integrate multiple open-access geospatial data to improve SAR image flood detection efficiency and the outputs were further applied in flood model validation. This approach resulted in F-Statistic improvement from 0.187 to 0.365 and reduced the CAESAR-LISFLOOD model overall bias from 3.432 to 0.699. Coarse data resolution, vegetation density, obsolete/non-existent river bathymetry, wetlands, ponds, uncontrolled dredging and illegal sand mining, were identified as the factors that contribute to flood model and map uncertainties in the delta region, hence the low accuracy depicted, despite the improvements that were achieved. Managing floods requires the coordination of efforts before, during and after flooding to ensure optimal mitigation in the event of an occurrence. In this study, and integrated flood modelling and mapping approach is undertaken, combining multiple open-access data using freely available tools to curb the effects of data and resources deficiency on hydrological, hydrodynamic and inundation mapping processes and outcomes in developing countries. This approach if adopted and implemented on a large-scale would improve flood preparedness, response and recovery in data sparse regions and ensure floods are managed sustainably with limited resources