Dynamic hydrological modeling in drylands with TRMM based rainfall

This paper introduces and evaluates DryMOD, a dynamic water balance model of the key hydrological process in drylands that is based on free, public-domain datasets. The rainfall model of DryMOD makes optimal use of spatially disaggregated Tropical Rainfall Measuring Mission (TRMM) datasets to simulate hourly rainfall intensities at a spatial resolution of 1-km. Regional-scale applications of the model in seasonal catchments in Tunisia and Senegal characterize runoff and soil moisture distribution and dynamics in response to varying rainfall data inputs and soil properties. The results highlight the need for hourly-based rainfall simulation and for correcting TRMM 3B42 rainfall intensities for the fractional cover of rainfall (FCR). Without FCR correction and disaggregation to 1 km, TRMM 3B42 based rainfall intensities are too low to generate surface runoff and to induce substantial changes to soil moisture storage. The outcomes from the sensitivity analysis show that topsoil porosity is the most important soil property for simulation of runoff and soil moisture. Thus, we demonstrate the benefit of hydrological investigations at a scale, for which reliable information on soil profile characteristics exists and which is sufficiently fine to account for the heterogeneities of these. Where such information is available, application of DryMOD can assist in the spatial and temporal planning of water harvesting according to runoff-generating areas and the runoff ratio, as well as in the optimization of agricultural activities based on realistic representation of soil moisture conditions.

Meteomap: Generation Of Meteorological Variables For Distributed Physically-Based Hydrological Modeling

Distributed energy and water balance models require time-series surfaces of the meteorological variables involved in hydrological processes. Most of the hydrological GIS-based models apply simple interpolation techniques to extrapolate the point scale values registered at weather stations at a watershed scale. In mountainous areas, where the monitoring network ineffectively covers the complex terrain heterogeneity, simple geostatistical methods for spatial interpolation are not always representative enough, and algorithms that explicitly or implicitly account for the features creating strong local gradients in the meteorological variables must be applied. Originally developed as a meteorological pre-processing tool for a complete hydrological model (WiMMed), MeteoMap has become an independent software. The individual interpolation algorithms used to approximate the spatial distribution of each meteorological variable were carefully selected taking into account both, the specific variable being mapped, and the common lack of input data from Mediterranean mountainous areas. They include corrections with height for both rainfall and temperature (Herrero et al., 2007), and topographic corrections for solar radiation (Aguilar et al., 2010). MeteoMap is a GIS-based freeware upon registration. Input data include weather station records and topographic data and the output consists of tables and maps of the meteorological variables at hourly, daily, predefined rainfall event duration or annual scales. It offers its own pre and post-processing tools, including video outlook, map printing and the possibility of exporting the maps to images or ASCII ArcGIS formats. This study presents the friendly user interface of the software and shows some case studies with applications to hydrological modeling.

Integrated sensitivity analysis, calibration, and uncertainty propagation analysis approaches for supporting hydrological modeling

The successful performance of a hydrological model is usually challenged by the quality of the sensitivity analysis, calibration and uncertainty analysis carried out in the modeling exercise and subsequent simulation results. This is especially important under changing climatic conditions where there are more uncertainties associated with climate models and downscaling processes that increase the complexities of the hydrological modeling system. In response to these challenges and to improve the performance of the hydrological models under changing climatic conditions, this research proposed five new methods for supporting hydrological modeling. First, a design of experiment aided sensitivity analysis and parameterization (DOE-SAP) method was proposed to investigate the significant parameters and provide more reliable sensitivity analysis for improving parameterization during hydrological modeling. The better calibration results along with the advanced sensitivity analysis for significant parameters and their interactions were achieved in the case study. Second, a comprehensive uncertainty evaluation scheme was developed to evaluate three uncertainty analysis methods, the sequential uncertainty fitting version 2 (SUFI-2), generalized likelihood uncertainty estimation (GLUE) and Parameter solution (ParaSol) methods. The results showed that the SUFI-2 performed better than the other two methods based on calibration and uncertainty analysis results. The proposed evaluation scheme demonstrated that it is capable of selecting the most suitable uncertainty method for case studies. Third, a novel sequential multi-criteria based calibration and uncertainty analysis (SMC-CUA) method was proposed to improve the efficiency of calibration and uncertainty analysis and control the phenomenon of equifinality. The results showed that the SMC-CUA method was able to provide better uncertainty analysis results with high computational efficiency compared to the SUFI-2 and GLUE methods and control parameter uncertainty and the equifinality effect without sacrificing simulation performance. Fourth, an innovative response based statistical evaluation method (RESEM) was proposed for estimating the uncertainty propagated effects and providing long-term prediction for hydrological responses under changing climatic conditions. By using RESEM, the uncertainty propagated from statistical downscaling to hydrological modeling can be evaluated. Fifth, an integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) was proposed for investigating the effects and contributions of different uncertainty components to the total propagated uncertainty from statistical downscaling. Using ISES-UPA, the uncertainty from statistical downscaling, uncertainty from hydrological modeling, and the total uncertainty from two uncertainty sources can be compared and quantified. The feasibility of all the methods has been tested using hypothetical and real-world case studies. The proposed methods can also be integrated as a hydrological modeling system to better support hydrological studies under changing climatic conditions. The results from the proposed integrated hydrological modeling system can be used as scientific references for decision makers to reduce the potential risk of damages caused by extreme events for long-term water resource management and planning.

Areal rainfall estimation using moving cars - computer experiments including hydrological modeling

The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have been emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rainfall amounts. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e. RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance, but also for use in hydrological modeling. The results show that the RCs considering measurement errors derived from laboratory experiments provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Even assuming higher uncertainties for RCs as obtained from the laboratory up to a certain level is observed practical.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

NDVI With Artificial Neural Networks For SRTM Elevation Model Improvement – Hydrological Model Application

Digital elevation model (DEM) plays a substantial role in hydrological study, from understanding the catchment characteristics, setting up a hydrological model to mapping the flood risk in the region. Depending on the nature of study and its objectives, high resolution and reliable DEM is often desired to set up a sound hydrological model. However, such source of good DEM is not always available and it is generally high-priced. Obtained through radar based remote sensing, Shuttle Radar Topography Mission (SRTM) is a publicly available DEM with resolution of 92m outside US. It is a great source of DEM where no surveyed DEM is available. However, apart from the coarse resolution, SRTM suffers from inaccuracy especially on area with dense vegetation coverage due to the limitation of radar signals not penetrating through canopy. This will lead to the improper setup of the model as well as the erroneous mapping of flood risk. This paper attempts on improving SRTM dataset, using Normalised Difference Vegetation Index (NDVI), derived from Visible Red and Near Infra-Red band obtained from Landsat with resolution of 30m, and Artificial Neural Networks (ANN). The assessment of the improvement and the applicability of this method in hydrology would be highlighted and discussed.

Assessing severe Drought and wet events over India in a future climate using a nested bias-correction approach

General circulation models (GCMs) are routinely used to simulate future climatic conditions. However, rainfall outputs from GCMs are highly uncertain in preserving temporal correlations, frequencies, and intensity distributions, which limits their direct application for downscaling and hydrological modeling studies. To address these limitations, raw outputs of GCMs or regional climate models are often bias corrected using past observations. In this paper, a methodology is presented for using a nested bias-correction approach to predict the frequencies and occurrences of severe droughts and wet conditions across India for a 48-year period (2050-2099) centered at 2075. Specifically, monthly time series of rainfall from 17 GCMs are used to draw conclusions for extreme events. An increasing trend in the frequencies of droughts and wet events is observed. The northern part of India and coastal regions show maximum increase in the frequency of wet events. Drought events are expected to increase in the west central, peninsular, and central northeast regions of India. (C) 2013 American Society of Civil Engineers.

Relação chuva-vazão nos telhados verdes modulares sob chuva simulada induzida.

A perda de vegetação natural e o aumento das superfícies impermeáveis decorrentes da expansão urbana têm mostrado que os tradicionais sistemas de drenagem urbana são insuficientes e pouco adaptáveis às alterações de uso do solo. Uma das consequências disso é o aumento da velocidade do escoamento superficial (runoff) que favorece as inundações, com enormes prejuízos materiais e ambientais. As inundações ocorrem geralmente quando ha ocorrência de chuvas de alta intensidade. O objetivo deste trabalho foi estudar a contribuição dos telhados verdes modulares submetidos a chuvas de alta intensidade, 155mm/h com duração de 7,0 minutos para retenção e retardo do escoamento superficial. Além disso, foram determinados valores para parâmetros de modelos clássicos chuva-vazão: Método Racional (C) e CN (SCS), que poderá, futuramente, servir de modelagem hidrológica dos impactos da adoção de telhados verdes no controle das enchentes urbanas. A metodologia adotada foi de natureza experimental e envolveu a construção de bancadas com inclinação regulável para suporte dos módulos experimentais e um sistema para indução de chuvas com intensidade controlada. Foram estudados três modelos de sistema modular para telhado verde que permitem o armazenamento de água no fundo da bandeja que compõe os módulos, sendo 2 de 17,0 L (M-17 e F-17) e 1 de 4,0 L (M-4), nas condições de solo seco e solo úmido. Em cada módulo vegetado foram utilizadas 3 espécies de vegetação: Portulaca oleracea (Onze horas), Callisia repens (Dinheiro em penca) e Apnia cordfolia (Rosinha do sol). Os resultados demonstraram que os volumes retidos, calculados a partir da observação do runoff, nas diferentes situações, foram coerentes entre si e com dados relatados na literatura. Os módulos vegetados produziram os melhores resultados com solo seco e os piores resultados com solo úmido. O percentual médio de retenção, considerando todos os tipos de módulos, foi de 58% do volume total de água induzida, com retardo médio de 12 minutos no runoff. Os valores médios de C (Método Racional) foram 0,4, 0,48, 0,36, para os módulos M-17, M-4 e F-17, respectivamente e os de CN (SCS) foram 93, 95, 93, para os mesmos módulos. Conforme esperado, os maiores valores de CN foram para solos úmidos, mantendo a relação que quanto menor o volume retido, maior o runoff e o CN. O módulo F-17 foi o que apresentou melhor desempenho em todos os aspectos (redução do escoamento, retenção hídrica e retardo do runoff). Este estudo demonstra a boa contribuição que esse tipo de sistema pode proporcionar na retenção e retardo do escoamento superficial, mesmo para chuvas intensas de curta duração, principalmente após período de curta estiagem, situação comum em locais de clima tropical. Futuros estudos deverão avaliar o desempenho dos sistemas modulares de telhados verdes com outras características e intensidades de chuvas. A adoção de telhados verdes deve ser cautelosa, sobretudo pela carga extra que esse tipo de sistema representa.

ANUDEM和TIN两种建立DEM方法的对比研究

针对流域水文和土壤侵蚀定量模拟分析需要,对基于TIN和Hutchinson方法建立的DEM表现地形形态和起伏的能力进行了对比研究。结果表明,基于TIN建立的DEM始终存在一些平顶现象,一些较小的侵蚀沟被忽略,其上提取的河流不完全连续,多处出现多重线条河流,因而不能如实地反映地形起伏的细部特征。而基于ANUDEM建立的DEM,其派生等高线的形状与输入等高线吻合较好,较好地表现了地形的形态和起伏,对地形和坡度的反映更加连续和光滑,其上提取的河流信息基本与地形图上的河流一致。

Utilisation du concept de connectivité en hydrologie : définitions, approches expérimentales et éléments de modélisation

Alors que certains mécanismes pourtant jugés cruciaux pour la transformation de la pluie en débit restent peu ou mal compris, le concept de connectivité hydrologique a récemment été proposé pour expliquer pourquoi certains processus sont déclenchés de manière épisodique en fonction des caractéristiques des événements de pluie et de la teneur en eau des sols avant l’événement. L’adoption de ce nouveau concept en hydrologie reste cependant difficile puisqu’il n’y a pas de consensus sur la définition de la connectivité, sa mesure, son intégration dans les modèles hydrologiques et son comportement lors des transferts d’échelles spatiales et temporelles. Le but de ce travail doctoral est donc de préciser la définition, la mesure, l’agrégation et la prédiction des processus liés à la connectivité hydrologique en s’attardant aux questions suivantes : 1) Quel cadre méthodologique adopter pour une étude sur la connectivité hydrologique ?, 2) Comment évaluer le degré de connectivité hydrologique des bassins versants à partir de données de terrain ?, et 3) Dans quelle mesure nos connaissances sur la connectivité hydrologique doivent-elles conduire à la modification des postulats de modélisation hydrologique ? Trois approches d’étude sont différenciées, soit i) une approche de type « boite noire », basée uniquement sur l’exploitation des données de pluie et de débits sans examiner le fonctionnement interne du bassin versant ; ii) une approche de type « boite grise » reposant sur l’étude de données géochimiques ponctuelles illustrant la dynamique interne du bassin versant ; et iii) une approche de type « boite blanche » axée sur l’analyse de patrons spatiaux exhaustifs de la topographie de surface, la topographie de subsurface et l’humidité du sol. Ces trois approches sont ensuite validées expérimentalement dans le bassin versant de l’Hermine (Basses Laurentides, Québec). Quatre types de réponses hydrologiques sont distingués en fonction de leur magnitude et de leur synchronisme, sachant que leur présence relative dépend des conditions antécédentes. Les forts débits enregistrés à l’exutoire du bassin versant sont associés à une contribution accrue de certaines sources de ruissellement, ce qui témoigne d’un lien hydraulique accru et donc d’un fort degré de connectivité hydrologique entre les sources concernées et le cours d’eau. Les aires saturées couvrant des superficies supérieures à 0,85 ha sont jugées critiques pour la genèse de forts débits de crue. La preuve est aussi faite que les propriétés statistiques des patrons d’humidité du sol en milieu forestier tempéré humide sont nettement différentes de celles observées en milieu de prairie tempéré sec, d’où la nécessité d’utiliser des méthodes de calcul différentes pour dériver des métriques spatiales de connectivité dans les deux types de milieux. Enfin, la double existence de sources contributives « linéaires » et « non linéaires » est mise en évidence à l’Hermine. Ces résultats suggèrent la révision de concepts qui sous-tendent l’élaboration et l’exécution des modèles hydrologiques. L’originalité de cette thèse est le fait même de son sujet. En effet, les objectifs de recherche poursuivis sont conformes à la théorie hydrologique renouvelée qui prône l’arrêt des études de particularismes de petite échelle au profit de l’examen des propriétés émergentes des bassins versants telles que la connectivité hydrologique. La contribution majeure de cette thèse consiste ainsi en la proposition d’une définition unifiée de la connectivité, d’un cadre méthodologique, d’approches de mesure sur le terrain, d’outils techniques et de pistes de solution pour la modélisation des systèmes hydrologiques.

HidroSIG: un sistema de información geográfica para la gestión de recursos naturales y modelación del medio ambiente

This paper describes HidroGIS, a GIS platform developed by Water Resources Program at Universidad Nacional de Colombia at Medellín. HidroSIG is a tool for hydrological variables visualization and analysis, using a set of modules that make this software a powerful tool for hydrological modeling. HidroSIG has tools for digital terrain models processing, water supply estimation using long term water balance in watersheds, a rainfall-runoff model, a model for landslide susceptibility estimation, an one-dimensional pollutant transport model, tools for homogeneity analysis in time series and tools for satellite images classification. The tools in development status are also described

Combining Satellite And Gauge Precipitation Data With Co-Kriging Method For Jakarta Region

Jakarta is vulnerable to flooding mainly caused by prolonged and heavy rainfall and thus a robust hydrological modeling is called for. A good quality of spatial precipitation data is therefore desired so that a good hydrological model could be achieved. Two types of rainfall sources are available: satellite and gauge station observations. At-site rainfall is considered to be a reliable and accurate source of rainfall. However, the limited number of stations makes the spatial interpolation not very much appealing. On the other hand, the gridded rainfall nowadays has high spatial resolution and improved accuracy, but still, relatively less accurate than its counterpart. To achieve a better precipitation data set, the study proposes cokriging method, a blending algorithm, to yield the blended satellite-gauge gridded rainfall at approximately 10-km resolution. The Global Satellite Mapping of Precipitation (GSMaP, 0.1⁰×0.1⁰) and daily rainfall observations from gauge stations are used. The blended product is compared with satellite data by cross-validation method. The newly-yield blended product is then utilized to re-calibrate the hydrological model. Several scenarios are simulated by the hydrological models calibrated by gauge observations alone and blended product. The performance of two calibrated hydrological models is then assessed and compared based on simulated and observed runoff.

Avaliação da disponibilidade hídrica e da demanda hídrica no trecho do Rio Piranhas-Açu entre os açudes Coremas-mãe D´água e Armando Ribeiro Gonçalves

Piranhas-Açu basin is a Federal watershed with a drainage area of 43.681,5 km2, sited at Brazilian northeast semi arid, with 60% of your area in Paraiba State and 40% in Rio Grande do Norte State. The main river, Piranhas-Açu, has strategic importance for development of these states, because it s an essential source for many socio-economics activities developed along watercourse. The river s reach between Coremas-Mãe D`água Dam and Armando Ribeiro Gonçalves Dam has many irrigation projects, and supply many riverside cities. All this activities practiced in this river s reach consumes high water volumes. Due the importance of this stream and the necessity of an adequate management, this work aims for an impartial and detailed evaluation of real water supply conditions in this river s reach, by the application of hydrological modeling, including the arrangement of main dams in tributaries, and storage reservoir water balance. The rainfall-discharge model s applied in each sub-basins it was selected the model MODHISA- Hydrological Model of Semi Arid, that is a concentrated model with easy application. The simulation produced 50 years of inflows into the reservoirs, for which, were constructed the guaranties curves; and produced 50 years of synthetic discharge data in relevant points on the river and on its affluents; so it was constructed the permanence curves. Confronting the available discharge with the current and futures volumes of raw water captured in this river s reach, it was verified that de demands have high guaranties. This work concluded that the MODHISA Model is suitable to reproduce the hydrologic characteristics of Piranhas-Açu sub-basins, and showing good results

Uso de indicadores hidrológicos para classificação de trechos de estradas florestais quanto ao escoamento superficial

Forest roads are frequently pointed as source of environmental problems related to erosion and they also influence harvest cost due to maintenance operations. Roads not well designed are sources of hydrological problems on catchments and the current attention to sustainability of forest exploration projects point out to the need of diagnostics tools for guiding the redesign of the road system. At this study, runoff hydrological indicators for forest road segments were assessed in order to identify critical points of erosion and water concentration on soils. A road network of a forest production area was divided into 252 road segments that were used as observations of four variables: mean terrain slope, main segment slope, LS factor and topographic index. The data analysis was based on descriptive statistics for outliers' identification, principal component analysis and for variability study between variables and between observations, and cluster analysis for similar segments groups' identification. The results allowed classifying roads segments into five mains road types: road on the ridge, on the valley, on the slopes, on the slopes but in a contour line and on the steepest slope. The indicators were able to highlight the most critical segments that differ of others and are potential sources of erosion and water accumulation problems on forest roads. The principal component analysis showed two main variability sources related to terrain topographic characteristics and also road design, showing that indicators represent well those elements. The methodology seems to be appropriated for identification of critical road segments that need to be redesigned and also for road network planning at new forest exploration projects.