EVALUATING THE PERFORMANCE OF PROCESS-BASED AND MACHINE LEARNING MODELS FOR RAINFALL-RUNOFF SIMULATION WITH APPLICATION OF SATELLITE AND RADAR PRECIPITATION PRODUCTS

Hydrology Modeling using HEC-HMS (Hydrological Engineering Centre-Hydrologic Modeling System) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, Machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research\u27s high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. In addition, Point gauge observations have historically been the primary source of the necessary rainfall data for hydrologic models. However, point gauge observation does not provide accurate information on rainfall\u27s spatial and temporal variability, which is vital for hydrological models. Therefore, this study also evaluates the performance of satellite and radar precipitation products for hydrological analysis. The results revealed that integrated Machine Learning and physical-based model could provide more confidence in rainfall-runoff and flood depth prediction. Similarly, the study revealed that radar data performance was superior to the gauging station\u27s rainfall data for the hydrologic analysis in large watersheds. The discussions in this research will encourage researchers and system managers to improve current rainfall-runoff simulation models by application of Machine learning and radar rainfall data

Merging multiple precipitation sources for flash flood forecasting

We investigated the effectiveness of combining gauge observations and satellite-derived precipitation on flood forecasting. Two data merging processes were proposed: the first one assumes that the individual precipitation measurement is non-bias, while the second process assumes that each precipitation source is biased and both weighting factor and bias parameters are to be calculated. Best weighting factors as well as the bias parameters were calculated by minimizing the error of hourly runoff prediction over Wu-Tu watershed in Taiwan. To simulate the hydrologic response from various sources of rainfall sequences, in our experiment, a recurrent neural network (RNN) model was used. The results demonstrate that the merged method used in this study can efficiently combine the information from both rainfall sources to improve the accuracy of flood forecasting during typhoon periods. The contribution of satellite-based rainfall, being represented by the weighting factor, to the merging product, however, is highly related to the effectiveness of ground-based rainfall observation provided gauged. As the number of gauge observations in the basin is increased, the effectiveness of satellite-based observation to the merged rainfall is reduced. This is because the gauge measurements provide sufficient information for flood forecasting; as a result the improvements added on satellite-based rainfall are limited. This study provides a potential advantage for extending satellite-derived precipitation to those watersheds where gauge observations are limited. © 2007 Elsevier B.V. All rights reserved

Advances in Modelling of Rainfall Fields

Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well. The need to improve the modeling of rainfall fields constitutes a key aspect both for efficiently realizing early warning systems and for carrying out analyses of future scenarios related to occurrences and magnitudes for all induced phenomena. The aim of this Special Issue was hence to provide a collection of innovative contributions for rainfall modeling, focusing on hydrological scales and a context of climate changes. We believe that the contribution from the latest research outcomes presented in this Special Issue can shed novel insights on the comprehension of the hydrological cycle and all the phenomena that are a direct consequence of rainfall. Moreover, all these proposed papers can clearly constitute a valid base of knowledge for improving specific key aspects of rainfall modeling, mainly concerning climate change and how it induces modifications in properties such as magnitude, frequency, duration, and the spatial extension of different types of rainfall fields. The goal should also consider providing useful tools to practitioners for quantifying important design metrics in transient hydrological contexts (quantiles of assigned frequency, hazard functions, intensity–duration–frequency curves, etc.)

A study of the break-up characteristics of Chena River Basin using ERTS imagery

The author has identified the following significant results. The Chena River Basin was selected because of the availability of ground truth data for comparison. Very good agreement for snow distribution and rates of ablation was found between the ERTS-1 imagery, the snowmelt model, and field measurements. Monitoring snowmelt rates for relatively small basins appears to be practical. The main limitation of the ERTS-1 imagery is the interval of coverage. More frequent overflights providing coverage are needed for the study of transient hydrologic events. ERTS-1 data is most useful when used in conjunction with snowmelt prediction models and existing snow course data. These results should prove very useful in preliminary assessment of hydrologic conditions in ungaged watersheds and will provide a tool for month-to-month volume forecasting

Hydrological Modelling and Climate Change Impact Assessment on Future Floods in the Norwegian Arctic Catchments

Climate change is expected to alter the hydrological cycle in the Arctic, which would result in the increase in intensity and frequency of hydrological extreme events such as flooding. Noticeably, the changes in flooding due to climate change would severely affect human life, infrastructures, the environment, ecosystem, and socio-economic development in the impacted areas. Hydrological models are state-of-the-art tools for assessing the impact of climate change on hydrological processes. However, performing hydrological simulation/projection in the Arctic is challenging because of the complex hydrological processes and data-sparse features in the region. In consideration of those issues, this PhD research aims: (1) to assess the performances of hydrological models in the Arctic, (2) to investigate the alternative weather inputs for running the hydrological models in the Arctic region with scattered monitoring data, (3) to evaluate the effects of the models’ structure and parameterization and the spatial resolution of weather inputs on the results of hydrological simulations, and (4) to project future hydrological events under climate change impacts using the current hydrological model, and analyse the reliability/uncertainty of the projection. To fulfil the research’s objectives, several methodologies were applied. Firstly, a comprehensive review was conducted to address the current capacities and challenges of twelve well-known hydrological models, including surface hydrological models and subsurface hydrological models/groundwater models/cryo-hydrogeological models. These models have previously been applied or have the potential for application in the Arctic. Next, the physically based, semi-distributed model, SWAT (soil and water assessment tool), was selected as a suitable model, among other potential models, to assess its performance for hydrological simulations and to verify the potential application of reanalysis weather data. Moreover, the SWAT was coupled with multiple ensemble global and regional climate models’ simulations to project the future hydrological impacts under climate change (in 2041-2070). The study areas were mainly focused in the Norwegian Arctic catchments

Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August, 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses

Development of tools for water management in the Hatra watershed (Northwestern Iraq) using satellite technologies

“All around the world the demand for water is increasing, especially in arid and semi-arid regions, including Iraq which subject to continuous desertification that is worsening, more importantly the Jezira region in northwestern Iraq. Thus, it’s crucial to have a better strategy for water management. One of these strategies is to promote groundwater recharge for restoring the aquifer depletion. The successful groundwater recharge is limited by some potential data such as the annual water budge and precipitation measurements. The atomospheric and hydrological observations are limited by sparse population which tends to be less in arid and semi-arid regions. Therefore, an alternative to the ground measurement of rainfall is needed. Satellite-based measurements limit the restriction of ground station. However, the satellite products have significant uncertainty. Therefore, seven precipitation estimates have tested against rain gauges in Orange County and Los Angeles County, California. In order to establish a water management strategy in Jezira region, annual water budget should be known, which could be measure through observational discharge station. Unfortunately, only few months of discharge was measured manually in the north Jezira, which Hatra subwatershed. Computer model was used to recover the streamflow measurement. The Soil and Water Assessment Tool (SWAT) was great candidate to overcome the problem. The observational data of stream discharge was used to calibrate the model. In conclusion, water management is possible in ungauged arid and semi-arid regions by using remote sensing data and computer modeling”--Abstract, page iv

Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh

Predicting the occurrence and spatial patterns of rainfall induced flash floods is still a challenge. Instant genesis and typically smaller areal coverage of the flash floods are the major impediments to their forecasting. Analysis of the morphometric parameters provides useful insight on hydrological response of the drainage basins to high intensity rainfall events. This information is valuable for understanding the flash flood potential of the drainage basins and for evading the destructions caused by the hazard. Here, we use eighteen morphometric parameters that influence the runoff volume, flow velocity, and inundation depth scenario of a flash flood. The analysis has been carried out for simulating the relative flash flood susceptibility of thirteen watersheds (B1 to B13) of variable sizes in southeastern Bangladesh. The morphometric parameters were derived from Digital Elevation Model (DEM) using Geographic Information System (GIS). The evaluated basin parameters include: area (A), perimeter (P), length (Lb), stream order (Su), stream number (Nu), stream length (Lu), stream frequency (Fs), drainage density (Dd), texture ratio (Rt), bifurcation ratio (Rb), basin relief (Hr), relief ratio (Rr), ruggedness number (Rn), time of concentration (Tc), infiltration number (If), and form factor (F). Two relative flash flood susceptibility scenarios were generated: (i) general watershed level, and (ii) more precise pixel level status. The watershed level comparison reveals that B4 and B6 watersheds constituting 72.61% of the total area are ‘very high’ susceptible, whereas the susceptibility of the other watersheds has been found as ‘high’ [B5 (6.95%)], ‘moderate’ [B8 and B13 (8.63%)], ‘low’ [B2, B10, B11 (4.64%)], and ‘very low’ [B1, B3, B7, B9, and B12 (7.18%)]. The derived watershed susceptibility map was subsequently integrated with two spatial analysis algorithms i.e., topographic wetness index (TWI) and topographic position index (TPI) through overlay analysis. The integration helped to understand the combined role of the general watershed morphometry and the in situ topography for determining flash flood susceptibility of each spot (30  m × 30  m) within all the selected watersheds. The quantitative analysis and characterization of the watersheds from the perspective of flash flood hazard in this investigation is expected to be useful for implementing the site-specific mitigation measures and alleviating the effects of the hydrological hazard in the study area

Integrated modelling of water security in data-sparse regions under uncertainty

Freshwater scarcity and sustainability is one of the most complicated and difficult issues the world is currently facing, and it has been identified as a global concern. According to expert studies, 80% of the world’s population is projected to live in freshwater threats due to a plethora of factors viz., rapid population growth, urbanization, global climate change resulting from spatial and temporal changes in magnitude, frequencies and intensity of precipitation and temperature which leads to the transformation of the hydrologic cycle. Recent initiatives, including sustainable development goals, have been made to address these problems and offer solutions. However, the quantity and quality of freshwater systems and resources must be objectively and comprehensively understood and assessed at the scale of river basins to provide sufficient mitigation and resilience planning. Hydrologic modelling has been one the most suitable and efficient strategies for basin-scale assessment of freshwater dynamics to current and projected climate change and the focus has been on the application of traditional modelling framework which is tenable where data requirements are sufficient to couple hydrologic models with atmospheric data to account for climate change. The aforementioned strategy is a challenge in regions with inadequate ground-based observations necessary for climate and hydrologic modelling. The rarely available data in such regions may have repetitive gaps of missing data points with negative consequences including biased statistical representation of basin climatic features, ineffective model calibration and unreliable timing of peak flows which may amplify the uncertainties of the hydrologic dynamics leading to flawed depictions of watershed responses. Recently, integrated strategies are evolving that couple hydrologic models with climate data in water resource studies to account for uncertainties through the use of alternative data sources of many spatial climate data products from climate research centres to overcome the identified challenges. This research developed and applied a multi-criteria approach to examine the efficacy of gridded climate products using different performance metrics, a machine learning-based approach, Boruta random forest (BRF) to assess multiple GCM datasets required for hydro climatic studies and an integrated BRF-SWAT technique to define the relationship between the hydrologic variables and improve rainfall-runoff modelling in a data-sparse and climate sensitive watersheds. The developed model was applied to assess the projected green and blue water dynamics and sustainability in the Yobe-Komadugu basin of the greater Lake Chad, a watershed that is prone to extreme events (SPEI of flood and drought hazards). The results demonstrate that though the performance of the gridded data varies in space and time, multi-criteria assessment enhances the choice of a product with reduced uncertainty for climate modelling. The incorporation of the BRF approach in GCM evaluation indicates a consistent spatial and temporal representation of the climatological features with suitable mean correlation (R2 = 0.95), reduced mean annual precipitation bias of 0.69 mm/year and enhanced statistical trend and magnitude of the SPEI drought and flood hazards relative to identified and tested approaches from the literature. The integrated framework of the rainfall-runoff modelling strategy indicated that the hydrologic fluxes can be simulated fairly accurately with varying degrees of acceptability, irrespective of the watershed morphological properties, although there are significant trade-offs in model parameter sensitivity. The availability of satellite-based measurements of hydrologic fluxes and states, coupled with a machine learning feature selection and data refinement process has made integrated water balance modelling widely seen as a viable alternative for improving watershed hydrologic processes in data-sparse regions within acceptable uncertainty limits. Furthermore, the sub-watershed assessment of the projected changes in spatial and temporal green and blue water sustainability status has shown that the sub-basins will be ecologically fragile, and the identified freshwater geographic hotspots may be beyond restoration without adequate long-term river basin water resources plans. The modelling framework developed is, however, independent of the model and data type and can be applied to watersheds with similar modelling challenges. This study has provided a pathway or methods for managing and securing water resources information as a decision support tool to guarantee ongoing watershed monitoring and assessment of water security even in the face of increasingly unpredictable future circumstances in data-sparse watersheds that take into account uncertainty and chat a course for prospective risk assessment or the possibility and understanding that a certain effect brought on by climate-induced hazards would prevail in watershed freshwater sustainability. Therefore, it is essential to comprehend the constraints associated with forecasting changes in the water cycle to improve the climate and hydrologic modelling process, which is required to create effective strategies for adapting to climate change-related water resource hazards. Even in the face of severe uncertainty about the future, this will be essential in addressing concerns related to water security and management and promoting the climatic resilience of ecosystems and society.Freshwater scarcity and sustainability is one of the most complicated and difficult issues the world is currently facing, and it has been identified as a global concern. According to expert studies, 80% of the world’s population is projected to live in freshwater threats due to a plethora of factors viz., rapid population growth, urbanization, global climate change resulting from spatial and temporal changes in magnitude, frequencies and intensity of precipitation and temperature which leads to the transformation of the hydrologic cycle. Recent initiatives, including sustainable development goals, have been made to address these problems and offer solutions. However, the quantity and quality of freshwater systems and resources must be objectively and comprehensively understood and assessed at the scale of river basins to provide sufficient mitigation and resilience planning. Hydrologic modelling has been one the most suitable and efficient strategies for basin-scale assessment of freshwater dynamics to current and projected climate change and the focus has been on the application of traditional modelling framework which is tenable where data requirements are sufficient to couple hydrologic models with atmospheric data to account for climate change. The aforementioned strategy is a challenge in regions with inadequate ground-based observations necessary for climate and hydrologic modelling. The rarely available data in such regions may have repetitive gaps of missing data points with negative consequences including biased statistical representation of basin climatic features, ineffective model calibration and unreliable timing of peak flows which may amplify the uncertainties of the hydrologic dynamics leading to flawed depictions of watershed responses. Recently, integrated strategies are evolving that couple hydrologic models with climate data in water resource studies to account for uncertainties through the use of alternative data sources of many spatial climate data products from climate research centres to overcome the identified challenges. This research developed and applied a multi-criteria approach to examine the efficacy of gridded climate products using different performance metrics, a machine learning-based approach, Boruta random forest (BRF) to assess multiple GCM datasets required for hydro climatic studies and an integrated BRF-SWAT technique to define the relationship between the hydrologic variables and improve rainfall-runoff modelling in a data-sparse and climate sensitive watersheds. The developed model was applied to assess the projected green and blue water dynamics and sustainability in the Yobe-Komadugu basin of the greater Lake Chad, a watershed that is prone to extreme events (SPEI of flood and drought hazards). The results demonstrate that though the performance of the gridded data varies in space and time, multi-criteria assessment enhances the choice of a product with reduced uncertainty for climate modelling. The incorporation of the BRF approach in GCM evaluation indicates a consistent spatial and temporal representation of the climatological features with suitable mean correlation (R2 = 0.95), reduced mean annual precipitation bias of 0.69 mm/year and enhanced statistical trend and magnitude of the SPEI drought and flood hazards relative to identified and tested approaches from the literature. The integrated framework of the rainfall-runoff modelling strategy indicated that the hydrologic fluxes can be simulated fairly accurately with varying degrees of acceptability, irrespective of the watershed morphological properties, although there are significant trade-offs in model parameter sensitivity. The availability of satellite-based measurements of hydrologic fluxes and states, coupled with a machine learning feature selection and data refinement process has made integrated water balance modelling widely seen as a viable alternative for improving watershed hydrologic processes in data-sparse regions within acceptable uncertainty limits. Furthermore, the sub-watershed assessment of the projected changes in spatial and temporal green and blue water sustainability status has shown that the sub-basins will be ecologically fragile, and the identified freshwater geographic hotspots may be beyond restoration without adequate long-term river basin water resources plans. The modelling framework developed is, however, independent of the model and data type and can be applied to watersheds with similar modelling challenges. This study has provided a pathway or methods for managing and securing water resources information as a decision support tool to guarantee ongoing watershed monitoring and assessment of water security even in the face of increasingly unpredictable future circumstances in data-sparse watersheds that take into account uncertainty and chat a course for prospective risk assessment or the possibility and understanding that a certain effect brought on by climate-induced hazards would prevail in watershed freshwater sustainability. Therefore, it is essential to comprehend the constraints associated with forecasting changes in the water cycle to improve the climate and hydrologic modelling process, which is required to create effective strategies for adapting to climate change-related water resource hazards. Even in the face of severe uncertainty about the future, this will be essential in addressing concerns related to water security and management and promoting the climatic resilience of ecosystems and society