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Hydrological model preselection with a filter sequence for the national flood forecasting system in Kenya

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Abstract

The choice of model for operational flood forecasting is not simple because of different process representations, data scarcity issues, and propagation of errors and uncertainty down the modeling chain. An objective decision needs to be made for the choice of the modeling tools. However, this decision is complex because all parts of the process have inherent uncertainty. This paper provides a model selection with a filter sequence for flood forecasting applications in data scarce regions, using Kenya as an example building on the existing literature, concentrating on six aspects: (i) process representation, (ii) model applicability to different climatic and physiographic settings, (iii) data requirements and model resolution, (iv) ability to be downscaled to smaller scales, (v) availability of model code, and (vi) possibility of adoption of the model into an operation flood forecasting system. In addition, we review potential models based on the proposed criteria and apply a decision tree as a filter sequence to provide insights on the possibility of model applicability. We summarize and tabulate an evaluation of the reviewed models based on the proposed criteria and propose the potential model candidates for flood applications in Kenya. This evaluation serves as an objective model preselection criterion to propose a modeling tool that can be adopted in development and operational flood forecasting to the end‐users of an early warning system that can help mitigate the effects of floods in data scarce regions such as Kenya.

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This document describes the algorithms within the latest version of the variable infiltration capacity (VIC) model. As a semi-distributed macroscale hydrological model, VIC balances both the water and surface energy within the grid cell; and its sub-grid variations are captured statistically. Distinguishing characteristics of the VIC model include: subgrid variability in land surface vegetation classes; subgrid variability in the soil moisture storage capacity; drainage from the lower soil moisture zone (base flow) as a nonlinear recession; and the inclusion of topography that allows for orographic precipitation and temperature lapse rates resulting in more realistic hydrology in mountainous regions. VIC uses a separate routing model based on a linear transfer function to simulate the streamflow. Adaptations to the routing model are implemented in VIC to allow representation of water management effects including reservoir operation and irrigation diversions and return flows. Since its existence, VIC has been well calibrated and validated in a number of large river basins over the continental US and the globe. Applications using the VIC model cover a variety of research areas.
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Predicting water runoff in ungauged water catchment areas is vital to practical applications such as the design of drainage infrastructure and flooding defences, runoff forecasting, and for catchment management tasks such as water allocation and climate impact analysis. This full colour book offers an impressive synthesis of decades of international research, forming a holistic approach to catchment hydrology and providing a one-stop resource for hydrologists in both developed and developing countries. Topics include data for runoff regionalisation, the prediction of runoff hydrographs, flow duration curves, flow paths and residence times, annual and seasonal runoff, and floods. Illustrated with many case studies and including a final chapter on recommendations for researchers and practitioners, this book is written by expert authors involved in the prestigious IAHS PUB initiative. It is a key resource for academic researchers and professionals in the fields of hydrology, hydrogeology, ecology, geography, soil science, and environmental and civil engineering.
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This paper investigates the impacts of land use changes on river peak discharges during floods and its contribution to flood occurrence in the Sosiani River basin in the Rift Valley province of Kenya. The land use maps used in the study were derived from the Landsat imagery employing remote sensing and GIS techniques. Two dimensional Rainfall-Runoff-Inundation (RRI) model was utilized in simulating the flash flood events. The results indicated an increase in river peak discharge due to extensive deforestation in the past decades and increase in farmlands which covers up to 75% of the total watershed area. According to the observed discharge data and generated land use maps, expansion of farmlands from 15.3% (in early 70’s) to 75.2% (2013) and urban areas from 0.4% (in early 70’s) to 10% (2013) have triggered the observed river peak discharge to increase from 167 m³/s (1970) to 233 m³/s (2013). Two land use change scenarios were tested for urbanization and reforestation using the calibrated RRI model and modified land use maps accordingly. The obtained results indicated a high sensitivity for the variation in land use to the river peak discharges. The results of the study will be beneficial for future developments in the basin and its flood management activities.
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The main focus of this paper is the time series analysis of the precipitation-runoff process with transfer functions. Starting from there, a horizontal routing model is constructed to be coupled to the existing land surface parametrization (LSP) schemes which provide the lower boundary conditions in numerical weather prediction and atmospheric general circulation models. As these models currently have a resolution of 10 km−300 km (what we some kind of arbitrary define as the “large scale”), it will be assumed that the horizontal routing process can be lumped as a linear time invariant system. While the main physical properties of the soil (temperature, moisture) and all physical processes (partition of the energy and water fluxes) have to be represented by an LSP scheme, the coupling with a simple routing scheme allows the direct comparison of predicted and measured streamflow data as an integrated quantity and validation tool for both, the atmospheric and the LSP model. The main task of the routing scheme is to preserve the horizontal travel time of water within each grid box as well as from grid box to grid box in the coupled model to first order, while the correct amount of runoff must be given by the LSP scheme. Inverse calculation also allows the direct estimation of runoff which should have been produced by an LSP scheme. As we don't want to deal with snow processes the scheme is applied from February to November. DOI: 10.1034/j.1600-0870.1996.t01-3-00009.x
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Hydro-climatic data at short time steps are considered essential to model the rainfall-runoff relationship, especially for short-duration hydrological events, typically flash floods. Also, using fine time step information may be beneficial when using or analysing model outputs at larger aggregated time scales. However, the actual gain in prediction efficiency using short time-step data is not well understood or quantified. In this paper, we investigate the extent to which the performance of hydrological modelling is improved by short time-step data, using a large set of 240 French catchments, for which 2400 flood events were selected. Six-minute rain gauge data were available and the GR4 rainfall-runoff model was run with precipitation inputs at eight different time steps ranging from 6 min to 1 day. Then model outputs were aggregated at seven different reference time scales ranging from sub-hourly to daily for a comparative evaluation of simulations at different target time steps. Three classes of model performance behaviour were found for the 240 test catchments: (i) significant improvement of performance with shorter time steps; (ii) performance insensitivity to the modelling time step; (iii) performance degradation as the time step becomes shorter. The differences between these groups were analysed based on a number of catchment and event characteristics. A statistical test highlighted the most influential explanatory variables for model performance evolution at different time steps, including flow auto-correlation, flood and storm duration, flood hydrograph peakedness, rainfall-runoff lag time and precipitation temporal variability.
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MIKE SHE is one of the most extensively used physically based distributed hydrological models that are capable of credibly interpreting the response mechanism of ecohydrological processes to ecosystem components and organization. However, implementing MIKE SHE in integrated catchment modeling is a complex task requiring an experienced modeling team with skills in a variety of disciplines, making it difficult for scientists without a background in hydrology or computer science to utilize this useful model. This review, from the perspective of ecology and environmental science, illustrates the important role of a physically based distributed model as a solution to issues related to ecohydrological processes, offering a major discussion of issues that received little attention from the previous reviewers and that would interest researchers without a hydrology background. The inevitable and possible difficulties confronting these potential users at the beginning and early stages of utilizing the model are also brought to light, with suggestions for solutions. This paper offers a brief review of MIKE SHE development and elaborates its main merits, key problems, and research on the model's theories and practices implemented by scholars. Lingering problems in current solutions are analyzed, and potential approaches to solutions are proposed. We hope that this paper provides some practical advice to researchers who want to initiate study of water issues related to modeling ecohydrological processes by employing MIKE SHE.
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Climate change is altering temperature, precipitation, and other climatic parameters, affecting sectors dependent on water resources, e.g. energy production. The purpose of this study is to analyze the possible influences of climate change on hydropower potential in North-Estonia. In Estonian run-of-river hydropower plants, energy comes mainly from water volume. Thus, changes in hydropower production are related to changes in river runoff. The Soil and Water Assessment Tool (SWAT) model is used to study runoff responses to climate change in Kunda, Keila and Valgejõe river basins. A sequential uncertainty fitting algorithm is used for calibration and validation of hydrological models. Two modeling studies from EURO-CORDEX high-resolution simulations are used: RACMO regional climate model (RCM) from the Netherlands (KNMI) and HIRHAM5 RCM from Denmark (DMI). Hydrological model efficiency is evaluated with coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE) and percent bias (PBIAS). The NSE values range from 0.71 to 0.77 during calibration and validation. The PBIAS reveals no significant bias. Daily discharge data of the baseline period (1971–2000) and the future period (2071–2100) for KNMI and DMI scenarios reveal an overall increase in hydropower potential. Larger changes are predicted by the DMI model, while KNMI prediction is lower, 25% and 45% respectively.
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A digital model has been developed for the simulation of the rainfall-runoff process of rural watersheds. Input data are daily values of precipitation and temperature together with mean monthly potential evapotranspiration. The model produces daily values of streamflow as well as information on the time variation of the soil moisture content. In all, ten model parameters have to be identified, seven of which have a major influence on the performance of the model. The model operates by accounting continuously for the moisture content in four different and mutually interrelated storages representing physical elements in the watershed. It has been applied to three different Danish watersheds. Several statistical measures of accuracy have been utilized for a quantitative evaluation of the simulation results. The simulations demonstrate that the main shortcomings of the model are due to the lack of a procedure accounting for frozen ground during extended periods of frost, which could improve some of the simulation results during winter and spring.
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The Canadian Precipitation Analysis (CaPA) is a data analysis system used operationally at the Canadian Meteorological Center (CMC) since April 2011 to produce gridded 6-h and 24-h precipitation accumulations in near real-time on a regular grid covering all of North America. The current resolution of the product is 10-km. Due to the low density of the observational network in most of Canada, the system relies on a background field provided by the Regional Deterministic Prediction System (RDPS) of Environment Canada, which is a short-term weather forecasting system for North America. For this reason, the North American configuration of CaPA is known as the Regional Deterministic Precipitation Analysis (RDPA). Early in the development of the CaPA system, weather radar reflectivity was identified as a very promising additional data source for the precipitation analysis, but necessary quality control procedures and bias-correction algorithms were lacking for the radar data. After three years of development and testing, a new version of CaPA–RDPA system was implemented in November 2014 at CMC. This version is able to assimilate radar quantitative precipitation estimates (QPEs) from all 31 operational Canadian weather radars. The radar QPE is used as an observation source and not as a background field, and is subject to a strict quality control procedure, like any other observation source. The November 2014 upgrade to CaPA–RDPA was implemented at the same time as an upgrade to the RDPS system, which brought minor changes to the skill and bias of CaPA–RDPA. This paper uses the frequency bias indicator ( ), the equitable threat score ( ) and the departure from the partial mean ( ) in order to assess the improvements to CaPA–RDPA brought by the assimilation of radar QPE. Verification focuses on the 6-h accumulations, and is done against a network of 65 synoptic stations (approximately two stations per radar) that were withheld from the station data assimilated by CaPA–RDPA. It is shown that the and the scores are both improved for precipitation events between 0.2 mm and 25 mm per 6-h, and that the is unchanged.
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In recent years, the conceptual detail of hydrological models has dramatically increased as a result of improved computational techniques and the availability of spatially-distributed digital data. Nevertheless modelling spatially-distributed hydrological processes can be challenging, particularly in strongly heterogeneous urbanized areas. Multiple interactions occur between urban structures and the water system at various temporal and spatial scales. So far, no universal methodology exists for simulating the urban water system at catchment scale. This paper reviews the state of the art on the scientific knowledge and practice of modelling the urban hydrological system at the catchment scale, with the purpose of identifying current limitations and defining a blueprint for future modelling advances.
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Global flood risk models were developed to identify risk hotspots in a world with increasing flood occurrence. Here we assess the ability and limitations of the current models and suggest what is needed moving forward.
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The characterisation of flood behaviour in data poor regions has been receiving considerable attention in recent years. In this context, we present the results of regional flood frequency analyses (RFFA) conducted using a global database of discharge data. A hybrid-clustering approach is used in conjunction with a flood-index methodology to provide a regionalised discharge estimates with global coverage. The procedures are implemented with varying complexity, with results indicating that catchment area and average annual rainfall explain the bulk of variability in flood frequency; a split-sample validation procedure revealed median errors in the estimation of the 100 year flood to be around 56%. However, far larger errors were also found, with performance varying between climate regions and estimation of the index-flood found to be the dominant source of uncertainty. Moreover, the RFFA procedure is utilised to provide insights on the statistical characteristics of floods across different climates and catchments. This article is protected by copyright. All rights reserved.