Dissertations / Theses on the topic 'Hydrology Hydrology Hydrologic models'

Thesis (Ph. D.)--Ohio State University, 2003.
Title from first page of PDF file. Document formatted into pages; contains xvi, 378 p.; also includes graphics (some col.). Includes bibliographical references (p. 229-252). Available online via OhioLINK's ETD Center

El principal objectiu d'aquesta investigació és estudiar la dinàmica hidrològica d'una conca Mediterrània
afectada per canvis d'ús del sòl, mitjançant el monitoreig d'aquest i de l'aigua superficial. Aquest
objectiu s'ha treballat a partir mesuraments de components del balanç hídric pels diferents tipus de
cobertura i sòl, amb règims d'humitat i temperatura de transició.
Aquest estudi s'ha realitzat a la conca de la Ribera Salada (Prepirineu meridional Català, al NE
d'Espanya), amb una extensió de 222.5 km2, i un interval altitudinal de 420 a 2385 m i predomini de
pendents entre 12 - 25 % i 25 - 50 %. El substrat consisteix en conglomerats calcaris massius, calcilutites
i llims. La precipitació es de 507 i 763 mm. Amb sòls poc profunds, calcaris i pedregosos, essent
majoritàriament Inceptisòls (Typic Calciusteps, Typic Haploustepts) i Entisòls (Typic Ustifluvents, Typic
Udorthortents). A les zones més elevades de la conca, els sòls són més humits, degut a l'augment de la
precipitació, on es produeixen processos de descarbonatació del sòl. L'ús del sòl és majoritàriament
forestal, amb presència d'ecosistemes de ribera, subalpins i vegetació submediterrània. Algunes àrees es
troben amb cultius de patata, cereal i pastures. Una de les característiques més importants d'aquesta
conca són els canvis d'ús del sòl que ha patit en els últims 50 anys degut a l'abandó dels masos i cultius
tradicionals. Es seleccionaren vuit llocs de mostreig considerant les següents cobertes: Quercus ilex, bosc
de ribera, Pinus sylvestris, pastures, cultius (cereal-patata) i Pinus uncinata. A partir de l'any 1997 fins el
2005, s'han anat monitorejant el contingut d'humitat del sòl, l'escolament i els cabals. Des del 2004 s'han
anat anotant dades de drenatge. Les variables meteorològiques es mesuren a l'estació de Lladurs de la
XAC (Xarxa Agrometeorològica de Catalunya).
Els resultats obtenguts durant tres anys mostren una domini del règim d'humitat ústic (SSS, 2006), o xèric
en aquells anys més secs. En la modelització de règims d'humitat i temperatura del sòl, s'utilitzaren els
models de simulació NSM "Newhall simulation model" (Newhall, 1976) i JSM "Jarauta simulation
model" (Jarauta 1989). NSM (Newhall,1976) tendeix a sobre estimar el règim d'humitat del sòl, però
JSM (Jarauta, 1989) simula correctament el règim d'humitat del sòl (SSS, 2006) de la conca, funcionant
millor en condicions intermitges d'humitat del sòl. Ambdós models simulen correctament el règim de
temperatura dels sòls. Predomina un règim de temperatura mèsic-tèrmic, amb tendència a tèrmic els anys
secs. A petita escala la profunditat del sòl, pendent, pedregositat i una alta porositat del sòl són factores
que varien el règim d'humitat del sòl. La informació de sòl i clima, complementada mitjançant SIG, va
permetre l'obtenció de mapes de règim d'humitat del sòl de la conca, a escala 1:50000, els quals
permeten establir mediante simució els règims d'humitat del sòl en diferents escenaris de canvis
meteorològics.
El model TOPLATS ha sigut utilitzat en l'estimació de l'humitat del sòl en diferents usos del sòl. Aquest
model fou calibrat amb les equacions del filtre Kalman estès (EKF), que deriven de la minimització del
quadrat de la diferència entre els valors reals i els estimats (Goegebeur & Pauwels, 2007). Aquesta
metodologia interrelaciona correctament els valors de pluja, humitat del sòl, escolament i infiltració,
essent els valors d'humitat els que més s'aproximen als reals. Els resultats mostren que aquest filtre és
una eina útil per estimar el volum d'aigua del sòl emmagatzemada en conques a escala puntual,
assegurant una aplicació correcta del model hidrològic.
Per la modelització del comportament de l'humitat del sòl i diferents components del balanç hídric
s'utilitzà el modelo TOPLATS (Famiglietti & Wood, 1994). El model de simulació TOPLATS permite
simulà acceptablement el comportament de l'humitat del sòl. Els resultats de infiltració, escolament,
intercepció, evapotranspiració de referència i temperatura del sòl són correctes. Les diferències existents
entre valors simulats i observats són: l'humitat del sòl no sobrepassa el 5%, la infiltració fluctua entre 4%
i 15%, la diferència entre els valors reals i simulats d'evapotranspiració, depèn de l'estació de l'any,
essent 1mm a l'hivern i 2.7 mm a l'estiu. La temperatura varia entre 0.01ºC i 3.5ºC. El model calibrat
prediu amb precisió el comportament de les diferents components del balanç hídric. Respecte als valors
mesurats d'aigua de drenatge correspon al 11-41 % de la pluja total.
Respecte al balanç d'aigua en el sòl (ΔSW), els valors són negatius durant cert període de l'any, arribant a
valors crítics els mesos secs. La recuperació de humitat del sòl durant la resta de mesos succeeix de
manera parcial. A la part mitja de la conca, alguns mesos els valors d'humitat del sòl s'acosten a
condicions de punt de marchites (ecosistema submediterrani). A la part alta de la conca el sòl conserva
humitat (ecosistema subalpí). Els valors de cabal trobats corresponen a aportacions per escolament el
cuals són molt baixos. La majoria de les sortides es deuen a evapotranspiració, intercepció, infiltració i
drenatge (en ordre de importància).
El principal objetivo de esta investigación es estudiar la dinámica hidrológica de una cuenca Mediterránea
afectada por los cambios de uso del suelo, mediante el monitoreo del suelo y el agua superficial. Dicho objetivo
se ha abordado a partir de la medición de componentes del balance hídrico para diferentes tipos de cobertura y
suelo, considerando regimenes de humedad y temperatura de transición.
Este estudio se ha realizado en la cuenca de la Ribera Salada (Prepirineo meridional Catalán, NE España) de
222.5 km2, con un intervalo altitudinal de 420 a 2385 m y predominio de pendientes entre 12 - 25 % y 25 - 50
%. El sustrato consiste en conglomerados calcáreos masivos, calcilutitas y limos. La precipitación anual es de
507 y 763 mm. Los suelos són poco profundos, calcáreos y pedregosos, siendo en su mayoría Inceptisols
(Typic Calciusteps, Typic Haploustepts) y Entisols (Typic Ustifluvents, Typic Udorthortents). En las partes
altas de la cuenca los suelos son más húmedos, debido al aumento de la precipitación, allí ocurren procesos de
descarbonatación del suelo. Predomina el uso forestal, con ecosistemas de ribera, subalpinos y vegetación
submediterránea. Algunas áreas se dedican al cultivo de patatas, cereal y pastos. Una de las características más
importantes de esta cuenca es los importantes cambios de uso del suelo sufridos en los últimos 50 años, debido
al abandono de las masías y cultivos tradicionales.
Se seleccionaron ocho sitios de muestreo, considerando las siguientes coberturas: Quercus ilex, bosque de
ribera, Pinus sylvestris, pastos, cultivo (cereal-patata) y Pinus uncinata. A partir del año 1997 hasta 2005, se
han venido monitoreando el contenido de humedad del suelo, escorrentía y caudales. Desde 2004 se vienen
tomando datos drenaje. Las variables meteorológicas se miden la estación Lladurs perteneciente a la XAC
(Xarxa Agrometeorológica de Cataluña).
Los resultados obtenidos par un period de tres años muestran una predominancia del regimen de humedad
ústico (SSS, 2006), o xérico en los años más secos. Se utilizaron los modelos de simulación NSM "Newhall
simulation model" (Newhall, 1976) y JSM "Jarauta simulation model" (Jarauta 1989) en la modelización de
regimenes de humedad y temperatura del suelo. NSM (Newhall,1976) tiende a sobre estimar el régimen de
humedad del suelo. Por contra, JSM (Jarauta, 1989) simula de forma correcta el régimen de humedad del suelo
(SSS, 2006) presente en la cuenca, funcionando mejor bajo condiciones medias de humedad del suelo. Ambos
modelos simulan de forma correcta el régimen de temperatura de los suelos. Predomina un régimen de
temperatura mésico-térmico, con tendencia a térmico para los años secos. A pequeña escala la profundidad del
suelo, pendiente, pedregosidad y alta porosidad del suelo son factores que hacen variar el régimen de humedad
del suelo. La información de suelo y clima, complementada mediante SIG, permitió obtener mapas de régimen
de humedad del suelo para la cuenca, a una escala 1:50000, los cuales permiten establecer mediante simulación
los regimenes de humedad en el suelo bajo diferentes escenarios de cambios meteorológicos.
El modelo TOPLATS ha sido utilizado en la estimación de la humedad en el suelo para diferentes usos del
suelo. Este modelo fue calibrado con las ecuaciones del filtro Kalman extendido (EKF), que se derivan de la
minimización del cuadrado de la diferencia entre los valores reales y los estimados (Goegebeur & Pauwels,
2007). Esta metodología interrelaciona correctamente los valores de lluvia, humedad en el suelo, escorrentía y
infiltración, siendo los valores de humedad los mas ajustados a los valores reales. Los resultados muestran que
este filtro es una herramienta para estimar el volumen de agua en el suelo almacenada en las cuencas a escala
puntual, asegurando una aplicación correcta del modelo hidrológico.
Para la modelización del comportamiento de la humedad del suelo y los diferentes componentes del balance
hídrico se utilizó el modelo TOPLATS (Famiglietti & Wood, 1994). El modelo de simulación TOPLATS
permite simular aceptablemente el comportamiento de la humedad del suelo. Los resultados para infiltración,
escorrentía, intercepción, evapotranspiración de referencia y temperatura del suelo son correctos. Las
diferencias existentes entre valores simulados y observados son: la humedad del suelo no sobrepasa el 5%, la
infiltración fluctúa entre 4% y 15%, la diferencia entre los valores reales y simulados de evapotranspiración,
depende de la estación del año, siendo 1mm en invierno y 2.7 mm en verano, la temperatura varia entre 0.01 ºC
y 3.5ºC. El modelo calibrado predice con precisión el comportamiento de las diferentes componentes del
balance hídrico. Respecto a los valores medidos para agua de drenaje corresponde al 11-41 % de la lluvia total.
Respecto al balance de agua en el suelo (ΔSW), los valores son negativos para un corto periodo del año,
alcanzando valores críticos en meses secos. La recuperación de humedad del suelo para el resto de los meses
ocurre de manera parcial. En la parte media de la cuenca, para algunos meses los valores de humedad del suelo
son cercanos a condiciones de punto de marchites permanente (ecosistema submediterráneo). En la parte alta
de la cuenca el suelo conserva condiciones intermedias de humedad (ecosistema subalpino). Los valores de
caudal encontrados corresponden a los aportes por escorrentía, los cuales son muy bajos. La mayor parte de las
salidas ocurren por evapotranspiración, intercepción, infiltración y drenaje (en orden de importancia).
The main aim of this research is to study the hydrological dynamics of a Mediterranean mountain basin
affected by land use changes, by means of the monitoring of soil and surface water. This aim has been
reached by measuring and simulating hydric balance components of different soils and under different
vegetational types, considering water and temperature transition regimes.
This research was done in Ribera Salada basin (Catalan Pre Pyrenees, NE Spain), with an area of 222.5
km2, altitudes between 420 and 2385 m, with predominance slopes between 12 - 25 % and 25 - 50 %. The
substrate consists of massive calcareous conglomerates, calcilutites and limestones. Main annual
precipitation are 507 to 763 mm. Soils are shallow, calcareous and stony, being most of them Inceptisols
(Typic Calciusteps, Typic Haploustepts) and Entisols (Typic Ustifluvents, Typic Udorthortents). In the
upper and moister part of the basin soil decarbonatation takes place. Forest use is predominant, going
from brook forest environments to subalpine and submediterranean vegetation. Agricultural uses include
mainly the growing of cereals, potatoes and pastures. One of the most important characteristics in this
basin are the significant soil use changes in the last 50 years, due to the abandonment of farms and
traditional crops.
Eight sites were studied, corresponding to soils under Quercus ilex, brook forest, Pinus sylvestris, pasture,
crops (cereal-potatoes) and Pinus uncinata. From 1997 until 2005, soil moisture, run-off, water flow and
interception were monitored. From 2004 on, drainage data has been recorded. Meteorological variables
were measured by means of a complete Lladurs meteorological station, belonging to XAC (Catalan
Agrometeorological Network).
The obtained results to three years show the predominance of ustic moisture regime (SSS, 2006), or xeric
during the driest years. The simulation models NSM "Newhall simulation model" (Newhall, 1976) and
JSM "Jarauta simulation model" (Jarauta 1989) were used to represent soil moisture and temperature
regimes. NSM estimates a higher level of soil moisture regimes than observed. On the contrary, JSM
simulates correctly soil moisture regimes, working better under intermediate soil moisture conditions.
Both models simulate correctly the soil temperature regimes, being mesic-thermic to thermic during the
driest years. At detailed scale (plot observation), soil depth, slope, stone amount and high soil porosity are
factors that affect the soil moisture regimes. Soil and climate information, implemented through a GIS,
allowed us to obtain soil moisture regime maps of the basin at a 1:50000 scale, which are very useful to
simulate soil moisture regimes in different scenarios of meteorological changes.
The TOPLATS model, when used to estimate soil moisture under different cover types, was calibrated
with Extend Kalman filter (EKF) equations derived through a minimization of the square difference
between the true and estimated model state (Goegebeur & Pauwels, 2007). This methodology interrelates
correctly rainfall, soil moisture, runoff and infiltration. Among them, the obtained soil moisture values
corresponded the best to observed data. The results show that it is a useful tool to estimate soil water
volume stored in basins at a point scale, ensuring a correct application of this hydrological model.
To model soil moisture behaviour and the different hydric balance components, the TOPLATS model
(Famiglietti & Wood, 1994) was used. TOPLATS model simulates correctly the soil moisture behaviour.
The differences between observed and simulated values are the following: soil moisture does not surpass
5%; the infiltration fluctuates between 4% to 15%; in evapotraspiration depends on the season being
between 1 mm in winter to 2.7 mm in summer, soil temperature values difference fluctuates between
0.01ºC and 3.5ºC.The calibrated model predicts precisely the behaviour of different hydric balance
components. The measured water drainage amount is 11-41 % of total rain.
The observed and simulated soil water storage in the basin (ΔSW), has negative values during the driest
months. Soil moisture recovery during the rest of the months is only partial. In the medium part of the
basin, occupied by submediterranean ecosystems, soil moisture values are closer to drought conditions
during some months of the year. In the highest part of the basin (subalpine ecosystems) there are
intermediate soil moisture conditions in dry periods. Most part of water outputs are due to
evapotranspiration, interception, infiltration and drainage, in decreasing order of importance. Run-off
values are very low.

The level of spatial and vertical detail of important hydrologic processes within a watershed that needs to be represented by a conceptual rainfall-runoff (CRR) model in order to accurately simulate the streamflow is not well understood. The paucity of high-resolution hydrologic information in the past guided the direction of model development to more accurately represent processes directly related to the vertical movement of moisture within the watershed rather than the spatial variability of these processes. As a result, many of the CRR models currently available are so complex (vertically), that expert knowledge of the model and watershed system is required to successfully estimate values for model parameters using manual methods. Automatic parameter estimation procedures, developed to reduce the time and effort required with manual methods, do not provide parameter estimates and hydrograph simulations that are considered acceptable by the hydrologists responsible for operational forecasting. Newly available, high-resolution hydrologic information may provide insight to the spatial variability of important rainfall-runoff processes. However, effective and efficient methods to incorporate the data into the current modeling strategies need to be developed. This work describes a new hybrid multicriteria calibration approach that combines the strength of automatic and manual calibration methods. The new approach was used to investigate the benefits of different levels of spatial and vertical representation of important watershed hydrologic variables with conceptual rainfall runoff models.

A distributed hydrological rainfall-runoff model has been developed using a GIS integrated with a dynamic programming module (PCRaster). The model has been developed within the framework of the EU-project TWINBAS at IVL Swedish Environmental Research Institute, and is intended for use in WATSHMAN – a tool for watershed management developed at IVL. The model simulates runoff from a catchment based on daily mean values of temperature and precipitation. The GIS input data consist of maps with soil type, land-use, lakes, rivers and a digital elevation model. The model is a hybrid between a conceptual and a physical model. The snow routine uses the degree-day method, the evapotranspiration routine uses the Blainey-Criddle equation, the infiltration routine is based on Green-Ampt, groundwater is modelled assuming a linear reservoir and the flow routing is done with the kinematic wave equation combined with Manning’s equation.

The GIS and the hydrologic model are embedded in one another, allowing calculation of each parameter in each grid cell. The output from the model consists of raster maps for each time step for a pre-defined parameter, or a time series for a parameter at a specified grid cell. The flow network is generated from the digital elevation model and determines the water flow on the grid scale. The smallest possible grid size is thus obtained from the resolution of the digital elevation model. In this implementation the grid size was 50 m x 50 m. The raster structure of the model allows for easy use of data from climate models or remotely sensed data.

The model was evaluated using the River Kölstaån catchment, a part (110 km2) of the Lake Mälaren catchment, which has its outflow in central Stockholm, Sweden. The integration of the GIS and the hydrologic model worked well, giving significant advantages with respect to taking lakes and land-use into account. The evaluation data consisted of observed run-off for the period 1981 to 1991. The result from the calibration period shows a great variation in Reff (Nash & Sutcliffe) between the years, the three best years having Reff-values of 0.70 – 0.80. The Reff-value for the entire calibration period was 0.55 and 0.48 for the validation period, where again there was great variation between different years. The volume error was 0.1 % for the calibration period and -21 % for the validation period. The evapotranspiration was overestimated during the validation period, which is probably a result of excess rain during the calibration period. The results are promising and the model has many advantages – especially the integrated GIS-system – compared to the present WATSHMAN model. It could be further developed by introducing a second groundwater storage and refining the evapotranspiration and infiltration routine. Given the promising results, the model should be evaluated in other larger and hillier areas and preferably against more distributed data.

En helt distribuerad GIS-baserad hydrologisk modell för modellering i avrinningsområden på lokal/regional skala har byggts upp i PCRaster. Arbetet utfördes på IVL Svenska Miljöinstitutet AB inom ramen för EU-projektet TWINBAS, som har som mål att identifiera kunskapsluckor inför implementeringen av EU:s ramdirektiv för vatten. Modellen är tänkt att användas i WATSHMAN (Watershed Management System), IVLs verktyg för vattenplanering i avrinningsområden där bland annat källfördelningsberäkningar och åtgärdsanalyser ingår. Den uppbyggda modellen är en hybrid mellan en fysikalisk och en konceptuell hydrologisk modell och predikterar vattenföring på pixelnivå i avrinningsområden. Simuleringen drivs av dygnsmedelvärden för temperatur och nederbörd och modellen tar hänsyn till markanvändning, jordart, topografi och sjöar. De modellekvationer som används är grad-dagsmetoden för snö, Blainey-Criddle för evapotranspiration, Green-Ampt för infiltration, linjärt magasin för grundvatten och Mannings ekvation för flödesrouting.

Det geografiska informationssystemet och den hydrologiska modellen är helt integrerade, vilket gör att alla parametervärden beräknas för varje enskild pixel. Som utdata ger modellen en rasterkarta för varje tidssteg för en i förväg bestämd parameter, eller tidsserier över parametervärden i definierade punkter. Vattnet transporteras i ett utifrån höjdmodellen genererat flödesnätverk och vattnets flödesväg bestäms därmed på pixelnivå. Minsta möjliga pixelstorlek bestäms således utifrån höjdmodellens upplösning, och var vid denna tillämpning 50 m gånger 50 m. Modellens uppbyggnad med raster gör det enkelt att använda data från klimatmodeller eller fjärranalys.

Avrinningsområdet för Kölstaån, ett biflöde till Köpingsån i Mälardalen, har använts för att utvärdera modellen. Integreringen av GIS och hydrologisk modell fungerade mycket väl och gav stora fördelar t ex vad gäller att ta hänsyn till sjöar och markanvändning. Modellen kalibrerades med data från åren 1981 till 1986 och det erhållna volymfelet var då 0,1 % och Reff-värdet (Nash & Sutcliffe) 0,55. Stora variationer erhölls dock mellan åren; för de tre bästa åren låg Reff-värdet mellan 0,70 och 0,80. Ett mycket kraftigt nederbördstillfälle samt regleringar i huvudfåran av vattendraget ligger troligtvis bakom de mindre väl beskrivna åren. Även under valideringsperioden (1987 till 1991) fungerade modellen väl, så när som på att avdunstningen överskattades på vårarna (antagligen beroende av det stora regnet under kalibreringen), och Reff-värde och volymfel hamnade på 0,48 respektive -21 %, även här med stora variationer mellan åren. Resultaten är lovande och modellen har många fördelar jämfört med den nuvarande WATSHMAN-modellen. Den skulle kunna förbättras ytterligare genom att dela upp grundvattnet i två magasin samt förfina evapotranspirations- och infiltrationsrutinerna. Den höjdmodellsbaserade modellen bör utvärderas även i andra mer kuperade områden samt mot mer distibuerade data.

A physically-based, user friendly, hydrologic computer simulation model was developed for pinyon-juniper woodland watersheds. The data requirements are minimum, requiring vegetation conditions, basic soil survey information, and daily values for precipitation and temperature. The model predicts runoff from cleared and uncleared watersheds by simulating hydrologic processes on a daily basis. The model was tested with data from small pinyon-juniper watersheds in central Arizona. A crack-forming vertisol was the dominant soil type, and a special feature for addressing its effects on runoff was included. No significant difference between predicted and observed annual runoff was found at the ninety-five percent confidence level.

There are several sources of uncertainties in hydrologic modeling studies. Conventional deterministic modeling techniques typically ignore most of these uncertainties. However, there has been a growing need for better quantification of the accuracy and precision of hydrologic model predictions. Bayesian Recursive Estimation (BaRE) is an algorithm being developed towards considering these uncertainties for parameter estimation and prediction within an operational setting. This dissertation work evaluated and improved the current version of the algorithm. The methodology was improved using a progressive re-sampling of the Highest Probability Density (HPD) region of the parameter space, which concentrated the samples in the current HPD region while terminating computations in the nonproductive portions of the parameter space, rather than evaluating feasible parameter space based on the initial set of samples. The covariance structure of the well behaving parameter sets is used to generate new parameter sets, resulting in significant improvements compared to the original BaRE. Further, to reduce the "model/data overconfidence" problem, an entropy term and a data lack-of-confidence factor were introduced into the probability-updating rule. Comparison to batch calibration using the popular Shuffled Complex Evolution (SCE-UA) optimization method indicated that the improved recursive calibration technique is a powerful tool, especially useful where basins are recently gauged and hydrologic data are not well accumulated. The final method is also effective in tracing the temporal variations of parameters as a response to natural or human induced changes in the hydrologic system.

Models used as part of quantitative studies of vadose zone processes are becoming increasingly complex. However, even the most elaborate models can not capture the complex interactions between spatially distributed water, plant, and atmospheric components of the unsaturated flow system. These processes will always need to be approximated by relatively simple mathematical expressions with limited parameterization. Because of this, there is an ever increasing awareness among hydrologists of the need to describe and quantify these uncertainties to better understand the utility of model predictions and inform decisions concerning model development and data collection. Significant developments in the most recent generation of parameter estimation codes have facilitated the estimation of parameters and quantification of the associated uncertainty in the parameter estimates and model structure; however, these codes are computationally expensive. To facilitate the proposed analysis of more computationally efficient models are required.Computationally efficient models do not necessarily imply over simplified models In the appropriate context, simplifications are possible that reduce the complexity of the model but do not reduce the complexity of the system being represented by the model. I investigate a series of approaches to reduce the computational load of models, facilitating inverse analysis with readily available computing facilities.In light of the improvements to the methodology of parameter estimation, the success of the analysis still depends on the observed response to which the model is compared; the data and the information contained in the data. Given limited resources (both cost and technology) it is important to identify those data that will provide the greatest information about a system. To this end, the investigations presented here also investigate methods to identify informative data and to extract information from data effectively.

Thesis (Ph. D.)--Civil & Environmental Engineering, Georgia Institute of Technology, 2004.
Dr. Paul Work, Committee Member ; Dr. Philip Roberts, Committee Member ; Dr. Mustafa Aral, Committee Chair ; Dr. Terry Sturm, Committee Member ; Dr. Turgay Uzer, Committee Member. Vita. Includes bibliographical references (leaves 442-466).

The National Water Center (NWC) started using the National Water Model (NWM) in 2016. The NWM delivers state-of-the-science hydrologic forecasts in the nation. The NWM aims at operationally forecasting streamflow in more than 2,000,000 river reaches while currently river forecasts are issued for 4,000. The NWM is a specific configuration of the community WRF-Hydro Land Surface Model (LSM) which has recently been introduced to the hydrologic community. The WRF-Hydro model, itself, uses another newly-developed LSM called Noah-MP as the core hydrologic model. In WRF-Hydro, Noah-MP results (such as soil moisture and runoff) are passed to routing modules. Riverine water level and discharge, among other variables, are outputted by WRF-Hydro. The NWM, WRF-Hydro, and Noah-MP have recently been developed and more research for operational accuracy is required on these models. The overarching goal in this dissertation is improving the ability of these three models in simulating and forecasting hydrological variables such as streamflow and soil moisture. Therefore, data assimilation (DA) is implemented on these models throughout this dissertation. State-of-the art DA is a procedure to integrate observations obtained from in situ gages or remotely sensed products with model output in order to improve the model forecast. In the first chapter, remotely sensed satellite soil moisture data are assimilated into the Noah-MP model in order to improve the model simulations. The performances of two DA techniques are evaluated and compared in this chapter. To tackle the computational burden of DA, Massage Passing Interface protocols are used to augment the computational power. Successful implementation of this algorithm is demonstrated to simulate soil moisture during the Colorado flood of 2013. In the second chapter, the focus is on the WRF-Hydro model. Similarly, the ability of DA techniques in improving the performance of WRF-Hydro in simulating soil moisture and streamflow is investigated. The results of chapter 2 show that the assimilation of soil moisture can significantly improve the performance of WRF-Hydro. The improvement can reach 58% depending on the study location. Also, assimilation of USGS streamflow observations can improve the performance up to 25%. It was also observed that soil moisture assimilation does not affect streamflow. Similarly, streamflow assimilation does not improve soil moisture. Therefore, joint assimilation of soil moisture and streamflow using multivariate DA is suggested. Finally, in chapter 3, the uncertainties associated with flood forecasting are studied. Currently, the only uncertainty source that is taken into account is the meteorological forcings uncertainty. However, the results of the third chapter show that the initial condition uncertainty associated with the land state at the time of forecast is an important factor that has been overlooked in practice. The initial condition uncertainty is quantified using the DA. USGS streamflow observations are assimilated into the WRF-Hydro model for the past ten days before the forecasting date. The results show that short-range forecasts are significantly sensitive to the initial condition and its associated uncertainty. It is shown that quantification of this uncertainty can improve the forecasts by approximately 80%. The findings of this dissertation highlight the importance of DA to extract the information content from the observations and then incorporate this information into the land surface models. The findings could be beneficial for flood forecasting in research and operation.

The Distributed Hydrology-Soil-Vegetation Model (DHSVM) was applied to the Eightmile Creek watershed in western Montana. The purpose of this research was primarily to assess the applicability of the model as a cumulative effects assessment tool in the post-fire landscape of a forested watershed in this region. The model was first calibrated to the pre-fire watershed conditions using six years of historic streamflow data. DHSVM was able to accurately simulate the general shape of the measured hydrograph for each of the six simulated water years, and the normalized median absolute error statistics were below the target threshold of 50% for each year simulated. This relative success of the calibration efforts is particularly surprising when one considers the significant limitations presented by the lack of any sub-daily or high-elevation meteorological data for use in driving the calibration simulations. Because the accuracy of DHSVM simulations were greatly improved through rigorous calibration, this research demonstrates the need for model calibration to a watershed of interest, prior to hydrologic simulations of different landscape scenarios. Next, two different calibrated versions of DHSVM, including DHSVM version 2.0.1 and the DHSVM fire model, were each used to simulate runoff in the Eightmile Creek watershed following a near catchment-wide stand-replacing forest fire. Due to weather anomalies and limited, discontinuous streamflow data, no decisive conclusions could be made regarding the performance of either version of the model in the validation efforts. Results do suggest, though, that the DHSVM fire model has the potential to outperform the standard model version in fire-affected landscapes. Further research utilizing the DHSVM fire model with more substantial post-fire streamflow records for model validation is warranted.

This research assesses possible methods for extending the Streamflow Prediction Tool from a streamflow forecasting model to a flood extent forecasting model. This new flood extent forecasting model would allow valuable and easy to understand information be disseminated in a timely manner for flood preparation and flood response. The Height Above Nearest Drainage (HAND) method and AutoRoute method were considered for flood extent models but the HAND was the better option for its simple and quick computation as well as its viability on a global scale. Due to the importance of Digital Elevation Models (DEMs) in these flood extent models, an analysis was performed on the sensitivity and response of different DEMs with the HAND method. The HAND method with the differing DEMs was also analyzed using the Streamflow Prediction Tool for model boundary conditions against Sentinel-1 SAR generated flood extent images from August 24, 2017. The MERIT DEM performed the best in this analysis and is recommended for future research in creating a global forecasting flood extent model. The HAND method covered about 25% of the generated flood extent images and more complex flood extent models may need to be considered in areas where HAND underperforms. Finally, a proof of concept flood extent model was created and deployed as a web application for easy accessibility and distribution of flood information. Additional research to consider is flood impact based on affected population or an economic analysis, as well as optimizing model parameters for increased accuracy and performance. Additional research is also needed for HAND DEM analysis in other parts of the world.

The successful application of a CRR model depends on how well we handle each phase of model calibration. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not impossible, to obtain a unique set of optimal parameters for a CRR model. Unless the best set of parameters associated with a given calibration data set can be found, it is impossible to determine how sensitive the parameter estimates (and hence the model forecasts) are to factors such as input and output data error, model error, quantity and quality of data, objective function used, and so on. In this dissertation, results that clearly establish the nature of the problem of multiple optima in CRR models are presented. Based on these results it is shown why currently used optimization procedures have little chance of successfully finding the optimal parameter sets. This understanding is then used to develop a new global optimization procedure, the Shuffled Complex Evolution (SCE) method, which can efficiently and effectively identify the optimal values for the model parameters. The efficiency and effectiveness of the SCE method is first demonstrated on some theoretical test functions. It is then used to calibrate a research version of the SMA-NWSRFS model--the SIXPAR model. The SCE method is compared to other available methods used in practice on the theoretical test functions and the SIXPAR model. Finally, the SCE method is applied to the full scale SMA-NWSRFS model using both synthetic data and real data. The test results clearly indicate that the SCE method is superior to other methods tested in this research.

Drought is the most costly hazard among all natural disasters. Despite the significant improvements in drought modeling over the last decade, accurate provisions of drought conditions in a timely manner is still one of the major research challenges. In order to improve the current drought monitoring and forecasting skills, this study presents a hybrid system with a combination of remotely sensed data assimilation based on particle filtering and a probabilistic drought forecasting model. Besides the proposed drought monitoring system through land data assimilation, another novel aspect of this dissertation is to seek the use of data assimilation to quantify land initial condition uncertainty rather than relying entirely on the hydrologic model or the land surface model to generate a single deterministic initial condition. Monthly to seasonal drought forecasting products are generated using the updated initial conditions. The computational complexity of the distributed data assimilation system required a modular parallel particle filtering framework which was developed and allowed for a large ensemble size in particle filtering implementation. The application of the proposed system is demonstrated with two case studies at the regional (Columbia River Basin) and the Conterminous United States. Results from both synthetic and real case studies suggest that the land data assimilation system significantly improves drought monitoring and forecasting skills. These results also show how sensitive the seasonal drought forecasting skill is to the initial conditions, which can lead to better facilitation of the state/federal drought preparation and response actions.

Thesis (Ph.D.)--University of Adelaide, Dept. of Civil and Environmental Engineering, 2002.
"May 2002" Includes list of papers published during this study. Errata slip inserted inside back cover of v. 1. Includes bibliographical references (leaves 331-357).

At the large watershed scale, we emphasize the effects of flow through a river network on streamflow under dry conditions. An immediate consequence of assuming dry conditions is that evapotranspiration causes flow in the river network to exhibit oscillations. When all links in the river network combine their flow patterns, the oscillations interact in ways that change the timing and amplitude of the streamflow waves at the watershed outlet. The geometric shape of the river network is particularly important, so we develop an analytic solution for streamflow which emphasizes that importance. Doing hydrology backward is a strategy recently developed by several researchers to deal with uncertainty in measurements of forcing terms applied to hydrologic models. The strategy has also been applied to resolve the assumption of homogeneity on realistic catchments that exhibit many heterogeneous properties. In this work, we demonstrate hydrology in the backward direction applied to two examples: using streamflow at the catchment scale to determine runoff at the hillslope scale and using the hillslope runoff to infer the applied evapotranspiration forcing under the assumption of dry conditions. In order to work across scales, we utilize the analytic solution for streamflow at the outlet of a river network. At the hillslope scale, we develop a soil model to create fluxes consistent with observed soil processes.

This dissertation presents a new multiple objective optimization algorithm that is capable of solving for the entire Pareto set in one single optimization run. The multi-objective complex evolution (MOCOM-UA) procedure is based on the following three concepts: (1) population, (2) rank-based selection, and (3) competitive evolution. In the MOCOM-UA algorithm, a population of candidate solutions is evolved in the feasible space to search for the Pareto set. Ranking of the population is accomplished through Pareto Ranking, where all points are successively placed on different Pareto fronts. Competitive evolution consists of selecting subsets of points (including all worst points in the population) based on their ranks and moving the worst points toward the Pareto set using the newly developed multi-objective simplex (MOSIM) procedure. Test analysis on the MOCOM-UA algorithm is accomplished on mathematical problems of increasing complexity and based on a bi-criterion measure of performance. The two performance criteria used are (1) efficiency, as measured by the ability of the algorithm to converge quickly and (2) effectiveness, as measured by the ability of the algorithm to locate the Pareto set. Comparison of the MOCOM-UA algorithm against three multi-objective genetic algorithms (MOGAs) favors the former. In a realistic application, the MOCOM-UA algorithm is used to calibrate the Soil Moisture Accounting model of the National Weather Service River Forecasting Systems (NWSRFS-SMA). Multi-objective calibration of this model is accomplished using two bi-criterion objective functions, namely the Daily Root Mean Square-Heteroscedastic Maximum Likelihood Estimator (DRMS, HMLE) and rising limb-falling limb (RISE, FALL) objective functions. These two multi-objective calibrations provide some interesting insights into the influence of different objectives in the location of final parameter values as well as limitations in the structure of the NWSRFS-SMA model.

The HYDROPAC model was developed to improve the technology transfer from the science of hydrology to environmental planning disciplines by initiating advanced spatial analysis techniques for predicting rainfall-runoff relationships. This model integrates the Soil Conservation Service (SCS) equations for calculating runoff and a Geographic Information System (Map Analysis Package) in a framework that allows the simulation of runoff processes over a digital elevation model. The simulations are done in discrete time steps allowing the generation of a hydrograph at any desired point in the watershed and the overland flow patterns are displayed in maps. This framework addresses some of the current limitations of hydrologic model for stormwater management planning in terms of capabilities for analysis and communication of results. This manuscript describes the methods used to develop the framework of the HYDROPAC model and its usefulness for analyzing potential runoff problems during the planning process.

Groundwater models are often developed as tools for environmental decision-making. However, sparse data availability can limit a model's utility by confounding attempts to select a single structural representation of a system or to find a unique and optimal set of model parameters. As a result, estimates of prediction uncertainty and the value of further data collection may be important results of a modeling effort. The Discrimination/Inference to Reduce Expected Cost Technique (DIRECT) is a new method for developing an ensemble of models that collectively define prediction uncertainty in a manner that supports risk-based decision making and monitoring network design optimization. We apply aspects of DIRECT to a modeling investigation of an aquifer system in Central Utah where a major Coalbed Methane gas field is located and a new approach for stimulating gas production is being explored. In the first stage of this study we develop an ensemble of regional MODFLOW models and calculate their relative likelihood using a set of observation data. These regional results and likelihoods are then transferred to a regional MT3D residence time model and to a local advective transport model to provide further information for the well design. A cost function is applied to the transport results to assess the relative expected costs of several proposed well field designs. The set of hydrologic results and associated likelihoods from the ensemble are combined into cost curves that allow for the selection of designs that minimize expected costs. These curves were found to be a useful tool for visualizing the ways that design decisions and hydrologic results interact to generate costs. Furthermore, these curves reveal ways in which uncertainty can add to the cost of implementing a design. A final analysis explored the cost of having uncertain model results by applying and manipulating synthetic likelihood distributions to the transport results. These results suggest the value that may be added by reducing uncertainty through data collection. Overall, the application of DIRECT was found to provide a rich set of information that is not available when ensemble methods and cost consideration are omitted from a modeling study.

Green Stormwater Infrastructure (GSI) has become a popular method for flood mitigation as it can prevent runoff from entering streams during heavy precipitation. In this study, a recently developed neighborhood in Gresham, Oregon hosts a comparison of various GSI projects on runoff dynamics. The study site includes dispersed GSI (rain gardens, retention chambers, green streets) and centralized GSI (bioswales, detention ponds, detention pipes). For the 2017-2018 water year, hourly rainfall and observed discharge data is used to calibrate the EPA's Stormwater Management Model to simulate rainfall-runoff dynamics, achieving a Nash-Sutcliffe efficiency of 0.75 and Probability Bias statistic of 3.3%. A synthetic scenario analysis quantifies the impact of the study site GSI and compares dispersed and centralized arrangements. Each test was performed under four precipitation scenarios (of differing intensity and duration) for four metrics: runoff ratio, peak discharge, lag time, and flashiness. Design structure has significant impacts, reducing runoff ratio 10 to 20%, reducing peak discharge 26 to 68%, and reducing flashiness index 56 to 70%. There was a reverse impact on lag time, increasing it to 50 to 80%. Distributed GSI outperform centralized structures for all metrics, reducing runoff ratio 22 to 32%, reducing peak discharge 67 to 69%, increasing lag time 133 to 500%, and reducing flashiness index between 32 and 62%. This research serves as a basis for researchers and stormwater managers to understand potential impact of GSI on reducing runoff and downstream flooding in small urban watersheds with frequent rain.

Hydrological models are widely used tools in water resources estimation, but they are simple representations of reality and are frequently based on inadequate input data and uncertainties in parameter values. Data observation networks are expensive to establish and maintain and often beyond the resources of most developing countries. Consequently, measurements are difficult to obtain and observation networks in many countries are shrinking, hence obtaining representative observations in space and time remains a challenge. This study presents some guidelines on the identification, quantification and reduction of sources of uncertainty in water resources estimation in southern Africa, a data scarce region. The analyses are based on example sub-basins drawn from South Africa and the application of the Pitman hydrological model. While it has always been recognised that estimates of water resources availability for the region are subject to possible errors, the quantification of these uncertainties has never been explicitly incorporated into the methods used in the region. The motivation for this study was therefore to contribute to the future development of a revised framework for water resources estimation that does include uncertainty. The focus was on uncertainties associated with climate input data, parameter estimation (and recognizing the uncertainty due model structure deficiencies) methods and water use data. In addition to variance based measures of uncertainty, this study also used a reservoir yield based statistic to evaluate model output uncertainty, which represents an integrated measure of flow regime variations and one that can be more easily understood by water resources managers. Through a sensitivity analysis approach, the results of the individual contribution of each source of uncertainty suggest regional differences and that clear statements about which source of uncertainty is likely to dominate are not generally possible. Parameter sensitivity analysis was used in identifying parameters which are important withinspecific sub-basins and therefore those to focus on in uncertainty analysis. The study used a simple framework for evaluating the combined contribution of uncertainty sources to model outputs that is consistent with the model limitations and data available, and that allows direct quantitative comparison between model outputs obtained by using different sources of information and methods within Spatial and Time Series Information Modelling (SPATSIM) software. The results from combining the sources of uncertainties showed that parameter uncertainty dominates the contribution to model output uncertainty. However, in some parts of the country especially those with complex topography, which tend to experience high rainfall spatial variability, rainfall uncertainty is equally dominant, while the contributions of evaporation and water use data uncertainty are relatively small. While the results of this study are encouraging, the weaknesses of the methods used to quantify uncertainty (especially subjectivity involved in evaluating parameter uncertainty) should not be neglected and require further evaluations. An effort to reduce data and parameter uncertainty shows that this can only be achieved if data access at appropriate scale and quality improves. Perhaps the focus should be on maintaining existing networks and concentrating research efforts on making the most out of the emerging data products derived from remote sensing platforms. While this study presents some initial guidelines for evaluating uncertainty in South Africa, there is need to overcome several constraints which are related to data availability and accuracy, the models used and the capacity or willingness to adopt new methods that incorporate uncertainty. The study has provided a starting point for the development of new approaches to modelling water resources in the region that include uncertain estimates.

The basic concepts of hydrologic forecasting using the Streamflow Synthesis And Reservoir Regulation Model of the U.S. Army Corps of Engineers, auto-regressive-moving-average time series models (including Greens' functions, inverse functions, auto covariance Functions, and model estimation algorithm), and the Kalman filter (including state space modeling, system uncertainty, and filter algorithm), were explored. A computational experiment was conducted in which the Kalman filter was applied to update Mehama local basin model (Mehama is a 227 sq. miles watershed located on the North Santiam River near Salem, Oregon.), a typical SSARR basin model, to streamflow measurements as they became available in simulated real time. Among the candidate AR and ARMA models, an ARMA(l,l) time series model was selected as the best-fit model to represent the residual of the basin model. It was used to augment the streamflow forecasts created by the local basin model in simulated real time. Despite the limitations imposed by the quality of the moisture input forecast and the design and calibration of the basin model, the experiment shows that the new stochastic methods are effective in significantly improving the flood forecast accuracy of the SSARR model.

Three hydrologic simulation models of different resolutions were evaluated to determine model response to predicting runoff under changing vegetation cover. Two empirically-based regression models (Baker-Kovner Streamflow Regression Model and ECOSIM) and one multiple component water balance model (Yield) were modified, using FORTRAN 77 and calibrated on a southwestern ponderosa pine ecosystem. Statistical analysis indicate no significant difference between the Baker-Kovner and Yield models, while ECOSIM consistently under predicts by as much as 50 percent from the observed runoff. This is mainly attributed to a sensitivity to the insolation factor. Yield is the best predictor for moderate and high flows, to within 10 and 20 percent respectively. Of the four watershed treatments, the light overstory thinning on Watershed 8 yielded the best response for all three models. This is in contrast to the strip-cut treatment on Watershed 14 which consistently over-predicted, in large part due an inaccurate estimation of snowpack evaporation on the exposed, south-facing strip-cuts. Runoff responses are highly influenced by the precipitation regime, soil and topographic characteristics of a watershed as well as by a reduction in evapotranspiration losses from changes in vegetation cover.

Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach.

The capability of radar measured rainfall to enhance the simulation of storm hydrographs was assessed. Six rainfall events which occurred in 1986 and 1987 over an 8.13 km$ sp2$ agricultural watershed in south-western Quebec were used in model simulations. Radar measured rainfall rates were calibrated using measurements from a single tipping-bucket raingauge located at the study site.
A deterministic, event-based model, HYMO, was used to simulate streamflow using radar and gauge measured rainfall. The model utilized two rainfall abstraction techniques, i.e. the SCS Curve Number method and the Green-Ampt infiltration equation. Simulated streamflow hydrographs were compared with observed storm flows.
For short duration, high intensity, simple rainfall events, there were minor improvements in hydrograph simulations when calibrated radar measured rainfalls were input to the model, compared to tipping-bucket raingauge measurements. Complex, low intensity storms were poorly simulated by the model using either rainfall data source. Neither rainfall abstraction method proved consistently superior.

Irrigation practices in the Rincon Valley and Mesilla Basin of the Lower Rio Grande have evolved over the last century into a complex setting of transboundary conjunctive use. Three major water users have surface and groundwater appropriation rights regulated by compact, treaty, and operating rules and agreements. The analysis of complex relationships between supply/demand components and the effects of surface-water and groundwater use requires an integrated hydrologic model to track all of the use and movement of water. Models previously developed for the region relied on a priori estimates of net irrigation flux or externally-calculated landscape water budgets. This study instead utilizes a MODFLOW model with the Farm Process (MF-FMP), which directly couples the surface-water and groundwater regimes through simulation of landscape processes. This allows the assessment of stream-aquifer interactions in the context of fulfilling irrigation demands with variable supplies of surface water allotments and supplemental groundwater pumping. MF-FMP also simulates direct uptake of groundwater by crops, an important utility for modeling a region with significant acreage dedicated to pecan orchards, a phreatophytic crop. The abilities and limitations of this new model are explored through scenario simulations meant to estimate streamflow depletions caused by historic pumping levels.

This research presents an integration of a stochastic space-time rainfall model and distributed hydrologic simulation with GIS. The integrated simulation system consists of three subsystems: a stochastic space-time rainfall model, a geographical information system (GIS), and a distributed physically-based hydrologic model. The developed stochastic space-time rainfall model is capable of estimating the storm movement and simulating a random rainfall field over a study area, based on the measurement from three raingauges. An optimization-based lag-k correlation method was developed to estimate the storm movement, and a stochastic model was developed to simulate the rainfall field. A GIS tool, ARC/INFO, was integrated into this simulation system. GIS has been applied to automatically extract the spatially distributed parameters for hydrologic modeling. Digital elevation modeling techniques were used to process a high resolution digital map. A distributed physically-based hydrologic model, operated in HEC-1, simulated the stochastic, distributed, interrelated hydrological processes. The Green-Ampt equation is used for modeling the infiltration process, kinematic wave approximation for infiltration-excess overland flow, and the diffusion wave model for the unsteady channel flow. Two small nested experimental watersheds in southern Arizona were chosen as the study area where three raingauges are located. Using five recorded storm events, a series of simulations were performed under a variety of conditions. The simulation results show the model performs very well, by comparing the simulated runoff peak flow and runoff depth with the measured ones, and evaluated by the model efficiency. Both model structure and model parameter uncertainties were investigated in the sensitivity analysis. The statistical tests for the simulation results show that it is important to model stochastic rainfall with storm movement, which caused a significant change in runoff peak flow and runoff depth from that where the input is only one gage data. The sensitivity of runoff to roughness factor N and hydraulic conductivity Ks were intensively investigated. The research demonstrated this integrated system presents an improved simulation environment for the distributed hydrology.

Accurate knowledge of water content in seasonal snow can be helpful for water resource management. In this study, a distributed temperature index snow model based on temperature and precipitation as forcing data, is used to estimate snow storage in the Jollie catchment approximately 20km east of the main divide of the central Southern Alps, New Zealand. The main objective is to apply a frequently used assimilation method, the ensemble Kalman square root filter, to assimilate remotely sensed snow covered area into the model and evaluate the impacts of this approach on simulations of snow water equivalent. A 250m resolution remotely sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS), specifically tuned to the study location was used. Temperature and precipitation were given on a 0.055 latitude/longitude grid. Precipitation was perturbed as input into the model, generating 100 ensemble members, which represented model error. Only observations of snow covered area that had less that 25% cloud cover classification were used in the assimilation precess. The error in the snow covered area observations was assumed to be 0.1 and grow linearly with cloud cover fraction up to 1 for a totally cloud covered pixel. As the model was not calibrated, two withholding experiments were conducted, in which observations withheld from the assimilation process were compared to the results. Two model states were updated in the assimilation, the total snow accumulation state variable and the total snow melt state variable. The results of this study indicate that the model underestimates snow storage at the end of winter and/or does not detect snow fall events during the ablation period. The assimilation method only affected simulated snow covered area and snow storage during the ablation period. That corresponded to higher correlation between modelled snow cover area and the updated state variables. Withholding experiments show good agreement between observations and simulated snow covered area. This study successfully applied the ensemble Kalman square root filter and showed its applicability for New Zealand conditions.

The coupled transport of water and heat, involving freezing and thawing, in the road structure and its immediate environment is important to consider for optimal design and maintenance of roads and when assessing solute transport, of e.g. de-icing salt, from roads. The objective of this study was to develop mechanistic models, and measurement techniques, suitable to describe and understand water flow and heat flux in road structures exposed to a cold climate.

Freezing and thawing was accounted for by implementing new routines in two numerical models (HYDRUS1D/2D). The sensitivity of the model output to changes in parameter values and operational hydrological data was investigated by uncertainty and sensitivity analyses. The effect of rainfall event characteristics and asphalt fractures on the subsurface flow pattern was investigated by scenario modelling. The performance of water content reflectometers (WCR), measuring water content, was evaluated using measurements in two road structure materials. A numerical model was used to simulate WCR sensor response. The freezing/thawing routines were stable and provided results in agreement with laboratory measurements. Frost depth, thawing period, and freezing-induced water redistribution in a model road was greatly affected by groundwater level and type of subgrade. The simulated subsurface flow patterns corresponded well with published field observations. A new method was successful in enabling the application of time domain reflectometer (TDR) calibration equations to WCR output. The observed distortion in sampling volume for one of the road materials could be explained by the WCR sensor numerical model. Soil physical, hydrological, and hydraulic modules proved successful in simulating the coupled transport of water and heat in and on the road structure. It was demonstrated in this thesis that numerical models can improve the interpretation and explanation of measurements. The HYDRUS model was an accurate and pedagogical tool, clearly useful in road design and management.

A key element of hydrologic routing models is the storage-discharge relationship assumed to follow a certain mathematical form, usually a linear or a power function, the parameters of which are calibrated based on existing inflow-outflow data. While this assumption simplifies the model calibration process, it also constrains the models to operate by this function throughout their flow range. In view of the complex and nonlinear river flow behavior, this approximation undoubtedly introduces errors. This research presents a new hydrologic river routing approach that is not limited by the above assumption. River reaches are modeled as cascades of interacting conceptual reservoirs, with storage-discharge functions identified by the data. A novel parameter estimation approach has been developed to identify these functions and all other model parameters based on control theory concepts. After calibration, these functions indeed exhibit different mathematical forms at different regions of their active variation range. The new approach is applied and successfully demonstrated in real world reservoir and river routing applications from the Nile River Basin. A Bayesian forecasting scheme was also developed that uses the new approach to generate flow forecasts with explicit uncertainty characterization.

As land-surface modeling moves from the off-line mode to the coupled mode, it is also highly desirable to extend the off-line calibration of land-surface models to coupled applications. Using the NCAR SCCM as an example, this study proposed and implemented some effective schemes for the application of automatic parameter estimation procedures in a locally coupled environment, where other relevant issues such as parameterization tests, sensitivity analyses, and off-line calibrations were also involved. A parameterization deficiency having serious negative impacts on the performance of the NCAR SCCM was identified and rectified in this work, which led to significantly improved model performances and formed the basis for the subsequent sensitivity analysis and calibration experiments. To facilitate the calibration studies, an independent sensitivity analysis was conducted to identify some sensitive model parameters, followed by a multi-objective sensitivity analysis using the MOGSA algorithm to obtain better understanding of the model. Some off-line calibrations using the NCAR LSM were also conducted for comparison purposes. In the locally coupled environment, both land-surface and atmospheric variables/parameters were involved in the calibration processes of 14 different predesigned calibration cases. In brief, the results show that atmospheric parameters are of critical importance for the calibration of a coupled land surface-atmosphere model, and atmospheric forcing variables generally contain more useful information for calibration than land-surface fluxes/variables. In the coupled environment, step-wise calibration schemes, with land-surface and atmospheric parameters optimized successively in the off-line and coupled modes, respectively, appear to be superior to the single-step calibration schemes which optimize land-surface and atmospheric parameters simultaneously in the coupled environment, in that the former can provide better converged optimal solutions with less uncertainties. In addition, the results also show that better optimization effects can be achieved in the partially decoupled environment by replacing the model-generated precipitation and net radiation with the corresponding observations to drive the land-surface part of the model, indicating the dominant importance of precipitation and radiation in a coupled land surface-atmosphere model.