Satellite-based rainfall estimation for river flow forecasting in Africa. I: Rainfall estimates and hydrological forecasts

Abstract

Reliable, real-time river flow forecasting in Africa on a time scale of days can provide enormous humanitarian and economic benefits. This study investigates the feasibility of using daily rainfall estimates based on cold cloud duration (CCD) derived from Meteosat thermal infrared imagery as input to a simple rainfall—runoff model and also whether such estimates can be improved by the inclusion of information from numerical weather prediction (NWP) analysis models. The Bakoye catchment in Mali, West Africa has been used as a test area. The data available for the study covered the main months of the rainy season for three years. The rainfall estimates were initially validated against gauge data. Improvements in quality were observed when information relating to African Easterly Wave phase and storm type was included in a multiple linear regression (MR) algorithm. The estimates were also tested by using them as input to a rainfall—runoff model. When contemporaneous calibrations from raingauges were available for calibration, both CCD-only and MR rainfall estimates gave more accurate river flow forecasts than when using raingauge data alone. In the absence of contemporaneous calibrations, the performance was reduced but the MR did better than the CCDonly input in all years. The use of satellite-derived vegetation index did not improve the quality of the river flow forecasts.     

Development of an operational low-flow index for hydrological drought monitoring over Europe

Near real-time monitoring of hydrological drought requires the implementation of an index capable of capturing the dynamic nature of the phenomenon. Starting from a dataset of modelled daily streamflow data, a low-flow index was developed based on the total water deficit of the discharge values below a certain threshold. In order to account for a range of hydrological regimes, a daily 95th percentile threshold was adopted, which was computed by means of a 31-day moving window. The observed historical total water deficits were statistically fitted by means of the exponential distribution and the corresponding probability values were used as a measure of hydrological drought severity. This approach has the advantage that it directly exploits daily streamflow values, as well as allowing a near real-time update of the index at regular time steps (i.e. 10 days, or dekad). The proposed approach was implemented on discharge data simulated by the LISFLOOD model over Europe during the period 1995–2015; its reliability was tested on four case studies found within the European drought reference database, as well as against the most recent summer drought observed in Central Europe in 2015. These validations, even if only qualitative, highlighted the ability of the index to capture the timing (starting date and duration) of the main historical hydrological drought events, and its good performance in comparison with the commonly used standardized runoff index (SRI). Additionally, the spatial evolution of the most recent event was captured well in a simulated near real-time test case, suggesting the suitability of the index for operational implementation within the European Drought Observatory.     

Evaluation of four hydrological models for operational flood forecasting in a Canadian Prairie watershed

The complexities of the Prairie watersheds, including potholes, drainage interconnectivities, changing land-use patterns, dynamic watershed boundaries and hydro-meteorological factors, have made hydrological modelling on Prairie watersheds one of the most complex task for hydrologists and operational hydrological forecasters. In this study, four hydrological models (WATFLOOD, HBV-EC, HSPF and HEC-HMS) were developed, calibrated and tested for their efficiency and accuracy to be used as operational flood forecasting tools. The Upper Assiniboine River, which flows into the Shellmouth Reservoir, Canada, was selected for the analysis. The performance of the models was evaluated by the standard statistical methods: the Nash-Sutcliffe efficiency coefficient, correlation coefficient, root mean squared error, mean absolute relative error and deviation of runoff volumes. The models were evaluated on their accuracy in simulating the observed runoff for calibration and verification periods (2005–2015 and 1994–2004, respectively) and also their use in operational forecasting of the 2016 and 2017 runoff.     

Calibration of hydrological models in ungauged basins based on satellite radar altimetry observations of river water level

This paper proposes a new orientation to address the problem of hydrological model calibration in ungauged basin. Satellite radar altimetric observations of river water level at basin outlet are used to calibrate the model, as a surrogate of streamflow data. To shift the calibration objective, the hydrological model is coupled with a hydraulic model describing the relation between streamflow and water stage. The methodology is illustrated by a case study in the Upper Mississippi Basin using TOPEX/Poseidon (T/P) satellite data. The generalized likelihood uncertainty estimation (GLUE) is employed for model calibration and uncertainty analysis. We found that even without any streamflow information for regulating model behavior, the calibrated hydrological model can make fairly reasonable streamflow estimation. In order to illustrate the degree of additional uncertainty associated with shifting calibration objective and identifying its sources, the posterior distributions of hydrological parameters derived from calibration based on T/P data, streamflow data and T/P data with fixed hydraulic parameters are compared. The results show that the main source is the model parameter uncertainty. And the contribution of remote sensing data uncertainty is minor. Furthermore, the influence of removing high error satellite observations on streamflow estimation is also examined. Under the precondition of sufficient temporal coverage of calibration data, such data screening can eliminate some unrealistic parameter sets from the behavioral group. The study contributes to improve streamflow estimation in ungauged basin and evaluate the value of remote sensing in hydrological modeling. Copyright © 2011 John Wiley & Sons, Ltd.     

A new measure for assessing the efficiency of hydrological data-driven forecasting models

Abstract

There is a lack of consistency and generality in assessing the performance of hydrological data-driven forecasting models, and this paper presents a new measure for evaluating that performance. Despite the fact that the objectives of hydrological data-driven forecasting models differ from those of the conventional hydrological simulation models, criteria designed to evaluate the latter models have been used until now to assess the performance of the former. Thus, the objectives of this paper are, firstly, to examine the limitations in applying conventional methods for evaluating the data-driven forecasting model performance, and, secondly, to present new performance evaluation methods that can be used to evaluate hydrological data-driven forecasting models with consistency and objectivity. The relative correlation coefficient (RCC) is used to estimate the forecasting efficiency relative to the naïve model (unchanged situation) in data-driven forecasting. A case study with 12 artificial data sets was performed to assess the evaluation measures of Persistence Index (PI), Nash-Sutcliffe coefficient of efficiency (NSC) and RCC. In particular, for six of the data sets with strong persistence and autocorrelation coefficients of 0.966–0.713 at correlation coefficients of 0.977–0.989, the PIs varied markedly from 0.368 to 0.930 and the NSCs were almost constant in the range 0.943–0.972, irrespective of the autocorrelation coefficients and correlation coefficients. However, the RCCs represented an increase of forecasting efficiency from 2.1% to 37.8% according to the persistence. The study results show that RCC is more useful than conventional evaluation methods as the latter do not provide a metric rating of model improvement relative to naïve models in data-driven forecasting.

Editor D. Koutsoyiannis, Associate editor D. Yang

Citation Hwang, S.H., Ham, D.H., and Kim, J.H., 2012. A new measure for assessing the efficiency of hydrological data-driven forecasting models. Hydrological Sciences Journal, 57 (7), 1257–1274.     

Estimation of regional meteorological and hydrological drought characteristics: a case study for Denmark

Information on regional drought characteristics provides critical information for adequate water resource management. This study introduces a method to calculate the probability of a specific area to be affected by a drought of a given severity and demonstrates its potential for calculating both meteorological and hydrological drought characteristics. The method is demonstrated using Denmark as a case study. The calculation procedure was applied to monthly precipitation and streamflow series separately, which were linearly transformed by the Empirical Orthogonal Functions (EOF) method. Denmark was divided into 260 grid-cells of 14×17 km, and the monthly mean and the EOF-weight coefficients were interpolated by kriging. The frequency distributions of the first two (streamflow) or three (precipitation) amplitude functions were then derived. By performing Monte Carlo simulations, amplitude functions corresponding to 1000 years of data were generated. Based on these simulated functions as well as interpolated mean and weight coefficients, long time series of precipitation and streamflow were simulated for each grid-cell. The probability distribution functions of the area covered by a drought and the drought deficit volumes were then derived and combined to produce drought severity-area-frequency curves. These curves allowed an estimation of the probability of an area of a certain extent to have a drought of a given severity, and thereby return periods could be assigned to historical drought events. A comparison of drought characteristics showed that streamflow droughts are less homogeneous over the region, less frequent and last for longer time periods than precipitation droughts.     

Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa

Abstract

The increasing demand for water in southern Africa necessitates adequate quantification of current freshwater resources. Watershed models are the standard tool used to generate continuous estimates of streamflow and other hydrological variables. However, the accuracy of the results is often not quantified, and model assessment is hindered by a scarcity of historical observations. Quantifying the uncertainty in hydrological estimates would increase the value and credibility of predictions. A model-independent framework aimed at achieving consistency in incorporating and analysing uncertainty within water resources estimation tools in gauged and ungauged basins is presented. Uncertainty estimation in ungauged basins is achieved via two strategies: a local approach for a priori model parameter estimation from physical catchment characteristics, and a regional approach to regionalize signatures of catchment behaviour that can be used to constrain model outputs. We compare these two sources of information in the data-scarce region of South Africa. The results show that both approaches are capable of uncertainty reduction, but that their relative values vary.

Editor D. Koutsoyiannis

Citation Kapangaziwiri, E., Hughes, D.A., and Wagener, T., 2012. Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa. Hydrological Sciences Journal, 57 (5), 1000–1019.     

Methodology for construction, calibration and validation of a national hydrological model for Denmark

An integrated groundwater/surface water hydrological model with a 1 km² grid has been constructed for Denmark covering 43,000 km². The model is composed of a relatively simple root zone component for estimating the net precipitation, a comprehensive three-dimensional groundwater component for estimating recharge to and hydraulic heads in different geological layers, and a river component for streamflow routing and calculating stream–aquifer interaction. The model was constructed on the basis of the MIKE SHE code and by utilising comprehensive national databases on geology, soil, topography, river systems, climate and hydrology. The present paper describes the modelling process for the 7330 km² island of Sjælland with emphasis on the problems experienced in combining the classical paradigms of groundwater modelling, such as inverse modelling of steady-state conditions, and catchment modelling, focussing on dynamic conditions and discharge simulation. Three model versions with different assumptions on input data and parameter values were required until the performance of the final, according to pre-defined accuracy criteria, model was evaluated as being satisfactory. The paper highlights the methodological issues related to establishment of performance criteria, parameterisation and assessment of parameter values from field data, calibration and validation test schemes. Most of the parameter values were assessed directly from field data, while about 10 ‘free’ parameters were subject to calibration using a combination of inverse steady-state groundwater modelling and manual trial-and-error dynamic groundwater/surface water modelling. Emphasising the importance of tests against independent data, the validation schemes included combinations of split-sample tests (another period) and proxy-basin tests (another area).     

A hybrid optimization approach for efficient calibration of computationally intensive hydrological models

ABSTRACT

The calibration of hydrological models is formulated as a blackbox optimization problem where the only information available is the objective function value. Distributed hydrological models are generally computationally intensive, and their calibration may require several hours or days which can be an issue for many operational contexts. Different optimization algorithms have been developed over the years and exhibit different strengths when applied to the calibration of computationally intensive hydrological models. This paper shows how the dynamically dimensioned search (DDS) and the mesh adaptive direct search (MADS) algorithms can be combined to significantly reduce the computational time of calibrating distributed hydrological models while ensuring robustness and stability regarding the final objective function values. Five transitional features are described to adequately merge both algorithms. The hybrid approach is applied to the distributed and computationally intensive HYDROTEL model on three different river basins located in Québec (Canada).     

Defining a chronological framework for the Middle Stone Age in West Africa: Comparison of methods and models for OSL ages at Ounjougou (Mali)

Ounjougou is the name of a large complex of archaeological sites, that were extensively studied between 1997 and 2008 by the international team of the “Human settlement and palaeoenvironment in West Africa” project. This complex is important because well stratified Palaeolithic sites in West Africa are rare, and because it covers a long period of time (most of the Upper Pleistocene and Holocene) and exhibits a wide set of technical cultures. Therefore, for the first time it is possible to propose a chronological framework for the human settlement in this region that can be related to other technical cultures in Africa and to palaeoenvironmental data.The purpose of this paper is to present the OSL dating results obtained from 57 sediment samples that led to this framework. Measurements were first performed between 2004 and 2006 in Oxford (School of Geography), using micro-aliquots (2–10 grains) OSL and ICP-MS for determination of equivalent doses and beta-dose rates, respectively (gamma dose rates being deduced from field gamma spectrometry). More recently, new measurements were done on 28 samples of this former set at the IRAMAT-CRP2A laboratory in Bordeaux, where “true” single quartz grain OSL and high resolution gamma spectrometry measurements were performed. Both sets of results are, for all but two samples, statistically consistent with each other. A consistent chrono-stratigraphic framework can thus be deduced, covering the Upper Pleistocene. It suggests that the region was regularly visited during this time interval and more particularly during Marine Isotope Stage 3, when groups with different technical cultures followed each other relatively rapidly.     

A case for a reassessment of the risks of extreme hydrological hazards in the Caribbean

There is an urgent need for the development and implementation of modern statistical methodology for long-term risk assessment of extreme hydrological hazards in the Caribbean. Notwithstanding the inevitable scarcity of data relating to extreme events, recent results and approaches call into question standard methods of estimation of the risks of environmental catastrophes that are currently adopted. Estimation of extreme hazards is often based on the Gumbel model and on crude methods for estimating predictive probabilities. In both cases the result is often a remarkable underestimation of the predicted probabilities for disasters of large magnitude. Simplifications do not stop here: assumptions of data homogeneity and temporal independence are usually made regardless of potential inconsistencies with genuine process behaviour and the fact that results may be sensitive to such mis-specifications. These issues are of particular relevance for the Caribbean, given its exposure to diverse meteorological climate conditions.In this article we present an examination of predictive methodologies for the assessment of long-term risks of hydrological hazards, with particular focus on applications to rainfall and flooding, motivated by three data sets from the Caribbean region. Consideration is given to classical and Bayesian methods of inference for annual maxima and daily peaks-over-threshold models. We also examine situations where data non-homogeneity is compromised by an unknown seasonal structure, and the situation in which the process under examination has a physical upper limit. We highlight the fact that standard Gumbel analyses routinely assign near-zero probability to subsequently observed disasters, and that for San Juan, Puerto Rico, standard 100-year predicted rainfall estimates may be routinely underestimated by a factor of two.     

Thermal history of the Rio Muni (West Africa)–NE Brazil margins during continental breakup

We document the thermal record of breakup of the conjugate Rio Muni (West Africa) and NE Brazil margins using apatite fission track analysis, vitrinite reflectance data and stratigraphic observations from both margins. These results permit determination of the timing of four cooling episodes, and the temperature of samples at the onset of each episode. All samples are interpreted to have experienced higher temperatures in the geological past due to i) elevated basal heatflow (palaeogeothermal gradient in Rio Muni-1 well decaying from 58 °C/km during the Mid Cretaceous to 21.5 °C/km in the Late Cenozoic) and ii) progressive exhumation from formerly greater burial depth. A well constrained history of changing palaeogeothermal gradient allows for much more precise quantification of the thickness of eroded section (exhumation) than if a constant heatflow is assumed. Cooling episodes identified from the palaeotemperature data at 110–95 Ma (both margins) and 85–70 Ma (Rio Muni only) coincide with major unconformities signifying, respectively, the cessation of rifting (breakup) and compressional shortening that affected the African continent following the establishment of post-rift sedimentation (drift). The interval between these separate unconformities is occupied by allochthonous rafts of shallow-water carbonates recording gravitational collapse of a marginal platform. The rift shoulder uplift that triggered this collapse was enhanced by local transpression associated with the obliquely divergent Ascension Fracture Zone, and thermal doming due to the coeval St Helena and Ascension Plumes. The data also reveal a c.45–35 Ma cooling episode, attributed to deep sea erosion at the onset of Eo-Oligocene ice growth, and a c.15–10 Ma episode interpreted as the record of Miocene exhumation of the West African continental margin related to continent-wide plume development. Integration of thermal history methods with traditional seismic- and stratigraphy-based observations yields a dynamic picture of kilometre-scale fluctuations in base level through the breakup and early drift phases of development of these margins. Major unconformities at ocean margins are likely to represent composite surfaces recording not only eustasy, but also regional plate margin-generated deformation, local ‘intra-basinal’ reorganization, and the amplifying effect of negative feedbacks between these processes.     

中国大陆强震的早期余震概率预测效能评估与制约因素

为系统地考察中国大陆地区强震之后的早期余震概率预测效能及制约因素,以期开展适合板内地震活跃地区的"可操作"强余震概率预测,服务震后抗震救灾、应急救援等工作,本文采用充分利用震后早期小震信息的Omi-R-J模型对中国大陆的86个强震序列进行系统性的预测效能评估.通过连续分段滑动、拟合和余震发生率预测,对地震序列的早期模型参数、余震预测限度及其制约因素进行了讨论,并利用N-test检验方法开展了分级分段分档预测结果的效能评估.通过对比1天和30天数据拟合参数的相关性发现,b、k、c具有较强的相关性,差别较小,而p值的相关性较弱,这或许和p值表示余震活动在长期内的衰减特性有关,Omi-R-J模型可在震后早期记录相对不完整的阶段更早的获得较为稳定的序列参数;利用Omi-R-J模型对中国大陆强震序列的预测结果显示,Omi-R-J模型对中国大陆早期的强余震具有较好的预测能力,总的预测有效率为81.16%,平均预测有效率为83.82%,预测过少的比例大于预测过多的比例,早期序列发育较差或监测能力有限的区域,其预测效果受到很大制约;地震序列参数k是影响地震数目的关键因素,而地震序列发育程度可能是影响震后0.05天地震预测效率的重要因素之一.     

Application of spatial EOF and multivariate time series model for evaluating agricultural drought vulnerability in Korea

This study proposed a methodology using the empirical orthogonal function (EOF) and multivariate time series model for the analysis of drought both in time and space. The methodology proposed was then applied to evaluate the vulnerability of agricultural drought of major river basins in Korea. First, the three-month SPI data from 59 rain gauge stations over the Korean Peninsula were analyzed by deriving and spatially characterizing the EOFs. The shapes of major estimated EOFs were found to well reflect the observed spatial pattern of droughts. Second, the coefficient time series of estimated EOFs were then fitted by a multivariate time series model to generate the SPI data for 10,000 years, which were used to derive the annual maxima series of areal average drought severity over the Korean Peninsula. These annual maxima series were then analyzed to determine the mean drought severity for given return periods. Four typical spatial patterns of drought severity could also be selected for those return periods considered. This result shows that the southern part of the Korean Peninsula is most vulnerable to drought than the other parts. Finally, the agricultural drought vulnerability was evaluated by considering the potential water supply from dams. In an ideal case, when all the maximum dam storage was assumed to be assigned to agriculture, all river basins in Korea were found to have the potential to overcome a 30-year drought. However, under more realistic conditions considering average dam storage and water allocation priorities, most of the river basins could not overcome a 30-year drought.     

A novel soil moisture‐based drought severity index (DSI) combining water deficit magnitude and frequency

A correct identification of drought events over vegetated lands can be achieved by detecting those soil moisture conditions that are both unusually dry compared with the ‘normal’ state and causing severe water stress to the vegetation. In this paper, we propose a novel drought index that accounts for the mutual occurrence of these two conditions by means of a multiplicative approach of a water deficit factor and a dryness probability factor. The former quantifies the actual level of plant water stress, whereas the latter verifies that the current water deficit condition is unusual for the specific site and period. The methodology was tested over Europe between 1995 and 2012 using soil moisture maps simulated by Lisflood, a distributed hydrological precipitation–runoff model. The proposed drought severity index (DSI) demonstrates to be able to detect the main drought events observed over Europe in the last two decades, as well as to provide a reasonable estimation of both extension and magnitude of these events. It also displays an improved adaptability to the range of possible conditions encountered in the experiment as compared with currently available indices based on the sole magnitude or frequency. The results show that, for the analyzed period, the most extended drought events observed over Europe were the ones in Central Europe in 2003 and in southern Europe in 2011/2012, while the events affecting the Iberian Peninsula in 1995 and 2005 and Eastern Europe in 2000 were among the most severe ones. © 2015 European Commission ‐ Joint Research Centre. Hydrological Processes published by John Wiley & Sons Ltd.     

Evaluating simulated daily discharge for operational hydrological drought monitoring in the Global Drought Observatory (GDO)

ABSTRACT

Hydrological drought is currently underrepresented in global monitoring systems, mainly due the shortage of near real-time estimates of river discharge at the global scale. In this study, the outputs of the Lisflood model are used to define a low-flow drought index, which shows a good correspondence with long-term records of the Global Runoff Data Centre in the period 1980–2014, as well as with verified information from the literature on six major drought events (covering different regions and watershed sizes). In contrast, the near real-time simulation (from 2015 onward) provides temporally inconsistent estimates over about 20% of the modelled cells (mostly over South America and Central Africa), even if reasonable results are obtained over other regions, as confirmed by intercomparison with the operational outcomes of the European Drought Observatory for the 2018 drought. In spite of the highlighted limitations, valuable information for operational drought monitoring can be retrieved from these simulations.     

A general Bayesian framework for calibrating and evaluating stochastic models of annual multi-site hydrological data

Multi-site simulation of hydrological data are required for drought risk assessment of large multi-reservoir water supply systems. In this paper, a general Bayesian framework is presented for the calibration and evaluation of multi-site hydrological data at annual timescales. Models included within this framework are the hidden Markov model (HMM) and the widely used lag-1 autoregressive (AR(1)) model. These models are extended by the inclusion of a Box–Cox transformation and a spatial correlation function in a multi-site setting. Parameter uncertainty is evaluated using Markov chain Monte Carlo techniques. Models are evaluated by their ability to reproduce a range of important extreme statistics and compared using Bayesian model selection techniques which evaluate model probabilities. The case study, using multi-site annual rainfall data situated within catchments which contribute to Sydney’s main water supply, provided the following results: Firstly, in terms of model probabilities and diagnostics, the inclusion of the Box–Cox transformation was preferred. Secondly the AR(1) and HMM performed similarly, while some other proposed AR(1)/HMM models with regionally pooled parameters had greater posterior probability than these two models. The practical significance of parameter and model uncertainty was illustrated using a case study involving drought security analysis for urban water supply. It was shown that ignoring parameter uncertainty resulted in a significant overestimate of reservoir yield and an underestimation of system vulnerability to severe drought.     

A simplified approach for vulnerability assessment in moderate-to-low seismic hazard regions: application to Grenoble (France)

Due to the moderate seismic risks in France, the building vulnerability assessment methods developed for high seismic risk countries could not easily be used here because of their cost and the low-risk perception among the public and officials. A light vulnerability assessment method is proposed and tested in Grenoble (France), based on classes and scores provided in the GNDT method but simplified in terms of visual screening and number of structural parameters used. Compared to the RiskUE method, the damage obtained by our approach shows that 90% of buildings have residuals smaller than 0.2, i.e. one grade of the EMS98 damage scale. A large scale survey is devised and conducted among the inhabitants of Grenoble in order to collect the main structural parameters. By comparing the results from the survey to the historical urbanization of Grenoble and to expert surveys performed in two urban districts, the information useful for the light method of vulnerability assessment can be rapidly collected by non-experts reducing substantially the estimate cost. The average damage is then computed using the GNDT formula considering the probable intensities which could be observed in Grenoble (VII and VIII). The average damage reaches 0.4 in the oldest part of Grenoble mainly made of masonry buildings and 0.2 in reinforced concrete suburbs where reinforced concrete predominates. The results are a relative vulnerability assessment that provides useful initial information for the urban zones of Grenoble where the vulnerability is higher. This method can be used to classify the seismic vulnerability in wide seismic-prone regions to a fair degree of accuracy and at low cost.     

Hydraulic characteristics and habitat suitability in presence of woody debris: A flume experiment

Data from a flume experiment were used to explore the modified hydraulic conditions and habitat suitability in streams where feeding of large woody debris (LWD) is present. Feeding of LWD was simulated by insertion of wood dowels with varying diameter and length. Two processes were mimicked, namely (i) lumped LWD load, and (ii) distributed LWD load. Lumped load may occur for wood coming either from upstream or from a tributary, and entering the stream of interest in one only section. Distributed load occurs for wood entering along the considered stream, in several sections. Distributed wood income resulted in homogeneously increased bed roughness, leading to increased flow depth and decreased velocity, whereas lumped input of wood from upstream resulted in larger local clustering and change of the flow properties, but with less influence on the distributed hydraulic properties. A method is proposed to predict bulk flow properties in presence of LWD. Then, a simple approach is used based upon the concept of wetter usable area WUA to investigate modified habitat conditions for fish species in presence of woody debris. An application to a real world case study from the literature is then shown, where increasing density of wood increases habitat availability for colonization by fish guilds.