Assessment of Satellite Rainfall Estimates as a Pre-Analysis for Water Environment Analytical Tools: A Case Study for Tonle Sap Lake in Cambodia

Tonle Sap Lake is the second main source of water supply and food security in Cambodia. However, this area is in the need for rainfall information which can cover the entire area for an accurate hydro-hydraulic modeling, climate modeling and other types of water or environment related modeling. In this case, Satellite Rainfall Estimates (SREs) would play a major role by filling out missing data where gauge observation is not available. The study aims to assess the spatio-temporal performance of two high resolution satellite products such as TRMM 3B42V7 and CHIRPS V.2. One-hundred and fifty four (154) stations around the Tonle Sap Lake and some close to the Mekong River were selected for the analysis within the study period of 2000 to 2004. After this, proper bias correction method is proposed. To do this, GIS and statistical indicators were used for the comparison. Both TRMM and CHIRPS provide a good correlation with the gauge. Around 90% of stations have CC varies from 0.5 to 0.9. In addition, the median bias of SREs are about 30 mm/month. Both satellite showed very similar pattern of bias spatially and temporally. This can be said that even though TRMM has the lower spatial resolution compared to CHIRPS, the performance of it is better. Moreover, TRMM have higher correlation when each of its cells was compared with the averaging of all stations within that cell. 25% of data that have extreme bias ratio maybe due to other underlying factors such as the distance from the station to the city, the soil elevation, landuse type, age of instrument, occurring of storm or drought that need to be taken into account for the further study

Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium

Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result

Flood Mapping along the Lower Mekong River in Cambodia

Located in Southeast Asia, Cambodia is one of the most disaster prone countries, where flooding rank the top of the natural disaster. Flood affects and threatens not only humans’ and animal’s life, properties, infrastructures, but it is also an obstacle to the current development. Furthermore, without having the efficient modern technology to predict flood situation in Cambodia, the disaster in this country become more serious. The objective of this research study is to simulate flood inundation area by using software HEC-RAS. HEC-RAS is a hydraulic model software capable of calculating any hydraulic river study including flood. In this study, the Lower Mekong River with approximately 50 km length was selected to delineate flood map from 2000 until 2013 and also 10-year return period map. The available data are 11 years of the measured water level at the upstream and downstream stations, 18 surveyed cross-sections and DEM with grid cell size 30 m x 30 m were used to understand the recurrence of the floods in the study area. The output from the model was delineated into map including flood extent and flood depth from 2000 until 2013 (without 2009, 2010 and 2012). The results show that flooding varied from year to year; however, the greatest flood was during 2000 and again in 2011. The simulated flood maps were compared with observed data to figure out that the model was accurate for flood mapping. These results will be useful for river engineers, experts, and decision makers to manage river floods

Méthodes de spatialisation de données pluviométriques dédiées à l’hydrologie opérationnelle et à la modélisation hydrologique à l’échelle du bassin versant : une revue bibliographique

Watershed management and hydrological modeling require data related to the very important matter of precipitation, often measured using raingages or weather stations. Hydrological models often require a preliminary spatial interpolation as part of the modeling process. The success of spatial interpolation varies according to the type of model chosen, its mode of geographical management and the resolution used. The quality of a result is determined by the quality of the continuous spatial rainfall which ensues from the interpolation method used. The objective of this article is to review the existing methods for interpolation of rainfall data that are usually required in hydrological modeling. We review the basis for the application of certain common methods and geostatistical approaches used in interpolation of rainfall. Previous studies have highlighted the need for new research to investigate ways of improving the quality of rainfall data and ultimately, the quality of hydrological modeling

Accuracy and precision of culture parcel area measurement

There are a number of important reasons for determining areas: natural resource valorisation and protection, environmental management or others works. But what is a data quality? Accuracy is characterised by systematic errors and mistakes. When these errors are eliminated, the standard deviation qualifies the precision. It is characterized by the random errors that there is no absolute way to eliminate them and which obey the general law of normal distribution. Three formulas are selected according to instruments available to compute the culture parcel area: (1) Area by polar coordinates, (2) Area by rectangular coordinates and (2) formula of Sarron. When parameter measurements are subject to error, areas calculated from the parameters will also be in error. The statistical models associated to each formula are developed to evaluate the precision of area measurement and are checked by simulations. Three types of available instrument according to suitable methods: (1) Total Station, (2) GPS (RTK GPS and DGPS) and (3) Topofil and Compass (forest instruments). These instruments are used in the field to confirm the three variance computation formulas. For all instruments used, approximate and observed variances are equivalent. Two exceptions to this confirmation: the total station and DGPS in case of area determined by a small number of points.Que ce soit dans le cadre d’un travail de protection et valorisation des ressources naturelles, de gestion environnementale ou tout autre travail, la détermination des superficies de parcelles de cultures s’avère incontournable. Mais qu’en est-il de la qualité de ces données ? L’exactitude est caractérisée par les erreurs systématiques et les fautes. Quand ces dernières sont éliminées, l’écart-type qualifie la précision. Celle-ci est caractérisée par les erreurs aléatoires qui ne pourront pas être éliminées et sont généralement régies par la loi de distribution normale. Trois formules sont sélectionnées en fonction de la disponibilité des matériels pour déterminer la superficie de parcelle de culture : (1) superficie du polygone défini par les coordonnées polaires, (2) superficie du polygone défini par les coordonnées rectangulaires et (3) la formule polygonale (ou la formule de Sarron). Les erreurs aléatoires commises sur les paramètres de calcul de ces trois formules engendrent des erreurs sur les superficies. Les variances approchées associées à chaque formule sont développées pour évaluer la précision de la superficie et sont vérifiées par des simulations. Trois types de matériel disponibles correspondent aux méthodes appropriées : (1) la station totale, (2) les GPS topographiques et d’inventaire et (3) le topofil et la boussole (matériels forestiers). Ces matériels sont utilisés sur le terrain pour confirmer les trois formules de calcul de la variance. Pour tous les instruments utilisés, les variances approchées et observées sont identiques. Deux exceptions à cette confirmation : la station totale et le GPS d’inventaire dans le cas d’une superficie déterminée par un moins grand nombre de bornes

Modélisation hydrologique en bassins versants imbriqués et analyse de sensibilité de méthodes de spatialisation de la pluie journalière

Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The objective of this dissertation is to confront the performances of the several spatial interpolation methods, in particular the geostatistics for daily rainfall data in the nested catchments to realize a sensitivity analysis on discharges modelled at the outlets to the daily rainfall input in the modelling. The previous studies in the literature emphasized a requirement of novel investigation on the essential means to recover the rainfall data and eventually, the quality of the hydrological modelling. This study leaned on 30-year daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km²). Two common deterministic methods are employed here. The Thiessen Polygon (THI) assigns the value from the nearest observation to a certain grid cell. The Inverse Distance Weighting (IDW) is an advanced nearest neighbour approach that allows including more observations than only the nearest points. The value at a certain grid cell is obtained from a linear combination of the surrounding locations. In the geostatistical algorithms, the spatial variation may be better described by a stochastic function. Four versions of kriging are used. The Ordinary Kriging (ORK) is the basic form of Kriging that the prediction is also a linear combination of the measured values. But the spatial correlation between the data, as described by the variogram, determines the weights, assuming that the mean is constant but unknown. The Universal Kriging (UNK) is based on the hypothesis that this mean is a polynomial function of spatial coordinates. So, this type of kriging is not stationary with regard to the mean. The Kriging with External Drift (KED) supposes that the mean of the interest variable depends on auxiliary variables; the theory of this kriging is in fact the same as the theory of UNK, which also contains a non-constant mean. The drift is defined externally through some auxiliary variables. The Ordinary Cokriging (OCK) suggests estimating the variable of interest by weighted linear combination of its observations and the observations of the auxiliary variables. This technique requires the study of the spatial dependence between variables besides the study of the simple spatial dependences. All types of kriging use a variogram model to characterise spatial correlation. A variogram describes in terms of variances how spatial variability changes as a function of distance. Seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. The areal rainfalls are calculated for the catchment area and used for analysis of extreme rainfall. The effects of the interpolation methods on the extreme rainfall are analysed. The interpolated rainfalls are also used as rainfall input of the physically-based and distributed EPIC-GRID model. The long series of model results are analysed by comparison with the observed discharges at the different outlets of the catchments. Then, the extreme discharges at the outlets are computed. All of these investigations always take into account of the raingage density and the raingage position for very sparse raingage cases. The main results show that among the methods based on the only rainfall data, the geostatistics and IDW are the best ones. The performances change according to the density of the raingages. For the extreme rainfall and long term modelling results, no big difference is found for a high density of raingage but large difference are found for the case of the scattered raingage. For the latter case, UNK and KED are very sensitive to the position of the raingages. IDW, ORK and OCK are found to be the best performance. However for the extreme flow, KED and OCK is the best whereas IDW is not better for the high density of the raingages. For the case of the scattered raingages, the difference in extreme discharge between the interpolation methods is very large and larger than shown in the extreme rainfall and the long term modelling results. IDW, ORK and OCK always perform better. UNK and KED still are sensitive to the positions of raingages. The index of position is used to describe the form of polygon defined by the four raingages. This index is defined as the relation between the polygon perimeter and a circle perimeter having an area equivalent to that of the polygon. The best raingage position for all interpolation methods used in this research should be arround the catchment area, its index should be close to one.La spatialisation de données de précipitation est très importante pour la modélisation hydrologique. Les méthodes Géostatistiques (Krigeage) sont largement appliquées pour la spatialisation à partir de la mesure de points aux surfaces continues. L'objectif de cette dissertation est de confronter les performances de différentes méthodes d’interpolation spatiale de la pluie journalière dans des bassins versants imbriqués en vue de réaliser une analyse de sensibilité sur les flux modélisés aux exutoires. Les études précédentes dans la littérature mettent en lumière un besoin de nouvelles recherches sur les moyens nécessaires pour améliorer la donnée de pluie et in fine, la qualité de la modélisation hydrologique. Cette étude s’est basée sur les données de pluies journalières de 30 ans de 70 stations dans le paysage collinaire des bassins versants de l'Ourthe et de l’Amblève en Belgique (2908 km²). Deux méthodes déterministes communes sont employées ici. Le Polygone de Thiessen (THI) assigne la valeur de l'observation la plus proche à une certaine maille. La Distance Inverse Pondérée (IDW) est une approche avancée qui permet d’inclure plus d'observations que les points les plus proches seuls. La valeur à une certaine maille est obtenue à partir d'une combinaison linéaire des points environnants. Dans les algorithmes géostatistiques, la variation spatiale peut être mieux décrite par une fonction stochastique. Quatre versions de Krigeage sont utilisées. Le Krigeage Ordinaire (ORK) est la forme de base du Krigeage. La prédiction est aussi une combinaison linéaire des valeurs mesurées, mais la corrélation spatiale entre les données, décrite par le variogramme, détermine les poids en supposant que l’espérance est constante, mais inconnue. Le Krigeage Universel (UNK) est basé sur l'hypothèse que ce moyen est une fonction polynômiale de coordonnées spatiales. Ainsi, ce type de Krigeage n'est pas stationnaire en ce qui concerne l’espérance. Le Krigeage avec Dérive Externe (KED) suppose que la variable d'intérêt dépend de variables auxiliaires; la théorie de ce Krigeage est en fait la même que la théorie de UNK, qui contient aussi une moyenne non-constante. La dérive est définie extérieurement par quelques variables auxiliaires. Le Co-krigeage Ordinaire (OCK) suggère d'évaluer la variable d'intérêt par la combinaison linéaire pondérée de ses observations et les observations des variables auxiliaires. Cette technique exige l'étude de la dépendance spatiale entre des variables en plus de l'étude des dépendances spatiales simples. Tous les types de Krigeage utilisent un modèle de variogramme pour caractériser la corrélation spatiale. Un variogramme décrit en termes de variances comment la variabilité spatiale varie en fonction de distance. Sept modèles de semi-variogramme (logarithmique, puissance, exponentiel, Gaussien, quadratique rationnel, sphérique et penta-sphérique) ont été ajustés aux semi-variogrammes expérimentaux journaliers, et ce tous les jours. Ces sept modèles de variogrammes ont été aussi utilisés pour pallier les incohérences liées aux interpolations négatives de la pluie. L’altitude, extraite d'un modèle numérique de terrain, a été incorporée dans les géostatistiques multi-variées. Sept stations de validation et la validation croisée ont été utilisées pour comparer la performance des méthodes d'interpolation. Les comparaisons sont aussi appliquées aux différentes densités de station ainsi qu’à leur disposition. Les pluies moyennes sur les bassins versants sont calculées et utilisées pour l'analyse de la pluie extrême. Les effets des méthodes d'interpolation sur la pluie extrême sont analysés. Les pluies interpolées sont aussi utilisées comme données du modèle physiquement basé et distribué, EPIC-GRID. La longue série de résultats du modèle est analysée par la comparaison avec les débits observés aux exutoires de différents bassins versants. Ensuite, les débits extrêmes aux exutoires sont calculés. Tous ces travaux prennent toujours en compte de la densité et la position des stations pour la plus faible densité. Les résultats principaux montrent que parmi les méthodes se basant sur les données de pluie seules, les géostatistiques et IDW présentent les meilleures performances. Celles-ci évoluent en fonction de la densité de stations. Pour les résultats d’analyse de la pluie extrême et de modélisation hydrologique, peu de différence ont été constatées pour une densité de station élevée mais une grande différence pour le cas de stations plus dispersées. Pour ce cas des stations dispersées, UNK et KED sont sensibles à la position de la station. IDW, ORK et OCK sont les meilleures méthodes d’interpolation. Cependant, pour le débit extrême, KED et OCK sont les meilleures tandis qu’IDW n’est pas meilleure pour la densité élevée de station. Pour le cas des stations dispersées, la différence en débits extrêmes déterminés par les méthodes d'interpolation est très importante et plus marquée que dans les résultats de l’analyse de la pluie extrême et les résultats de modélisation à long terme. IDW, ORK et OCK sont toujours meilleures. UNK et KED sont toujours sensibles aux positions des stations. L'indice de position est utilisé pour décrire la forme du polygone défini par les quatre stations. Cet indice est défini par la relation entre le périmètre du polygone et un périmètre de cercle ayant une superficie équivalente à celui du polygone. La meilleure position de la station pour toutes les méthodes d'interpolation utilisées dans cette recherche devrait être autour du bassin versant, son indice de position devrait être proche à un

MAIN EXISTING IRRIGATION SYSTEMS IN THE SUB-BASINS OF THE TONLE SAP LAKE OF CAMBODIA

The Tonle Sap Lake and its complex tributaries are the main water body and water courses of Cambodia. The catchment area of the lake is generally divided into twelve major sub-basins. The tributaries of the lake have great potential of water resources for agricultural and industrial development. The river networks and existing irrigation projects are not well surveyed. A survey trip was conducted around the lake. The aim of this trip is to understand the river networks and identify main existing irrigation systems in the sub-basins of the Tonle Sap Lake. In this poster, we are mainly focused on the existing irrigation schemes. The schemes are classified into five categories. (1) “Surface runoff collector”: it is formed by a small dike to collect surface runoff from upstream part. The water collected is used as a supplementary water source for rainy season cultivation and for irrigating a small area of dry season cultivation. (2) “Dam reservoir”: a dam is put in place on a river to store the river runoff during the rainy season and the water stored is supplied to a command area in the dry season. (3) “Indirect diversion”: it is characterized by a diversion structure and a storage reservoir. Water is diverted and stored in the reservoir during the rainy season and used for dry season cultivation. (4) “Direct diversion”: characteristic of this type is similar to that of the indirect diversion, except the river water is diverted directly to irrigated area without storing in a reservoir. (5) “Flood storage”: it is generally developed in the floodplain of the Tonle Sap Lake. A ring dike prevents floodwater from entering the reservoir and rainy season rice is grown inside the dike. After harvesting, the gates on the ring dike are opened to receive floodwater. The water is stored inside the dike for cultivating dry season rice outside the dike. This study found that there are only small-scale irrigation schemes developed around/in the Tonle Sap Lake. The water resources of this lake have not been effectively used

Effect of raingage density, position and interpolation on rainfall-discharge modelling

Precipitation traditionally observed using raingages or weather stations, is one of the main parameters that has direct impact on runoff production. This pPrecipitation data requires a preliminary spatial interpolation prior to hydrological modeling. The accuracy of modelling result is determined bydepends on the accuracy of the interpolated spatial rainfall which differs according to different interpolation methods. The accuracy of the interpolated spatial rainfall is usually determined by cross-validation method. The objective of this study is to assess the different interpolation methods of daily rainfall at the watershed scale through hydrological modelling and to explore the best methods that provides a good long term simulation. Four versions of geostatistics: Ordinary Kriging (ORK), Universal Kriging (UNK), Kriging with External Dridft (KED) and Ordinary Cokriging (OCK) and two types of deterministic methods: Thiessen polygon (THI) and Inverse Distance Weighting (IDW) are used to produce 30-year daily rainfall inputs for a distributed physically-based hydrological model (EPIC-GRID). This work is conducted in the Ourthe and Ambleve nested catchments, located in the Ardennes hilly landscape in the Walloon region, Belgium. The total catchment area is 2908 km², lies between 67 and 693 m in elevation. The multivariate geostatistics (KED and OCK) are also used by incorporating elevation as external data to improve the rainfall prediction. This work also aims at analysing the effect of different raingage densities and position used for interpolation, on the stream flow modelled to get insight in terms of the capability and limitation of the geostatistical methods. The number of raingage varies from 70, 60, 50, 40, 30, 20, 8 to 4 stations located in and surrounding the catchment area. In the latter case, we try to use different positions: around the catchment and only a part of the catchment. The result shows that the simple method like THI fails to capture the rainfall and to produce good flow simulation when using 4 raingages. The KED and UNK are comparable to other methods for a raingage case that in which stations are located around the catchment area, especially in the high elevation catchment but the worst methods for other raingage position cases where the rainfall stations are located only at a part and mostly outside of the catchment area. However, three methods (IDW, ORK and OCK) can overcome this problem since they are more robust and can provide good performance of simulation in all raingage densities. When using 70, 60, 50, 40, 30, 20, 8 raingages in the catchment area (2908 km²), no substantial differences in model performance are observed

Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium

peer reviewedSpatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result