Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin

Groundwater depletion has been one of the major challenges in recent years. Analysis of groundwater levels can be beneficial for groundwater management. The National Aeronautics and Space Administration’s twin satellite, Gravity Recovery and Climate Experiment (GRACE), serves in monitoring terrestrial water storage. Increasing freshwater demand amidst recent drought (2000–2014) posed a significant groundwater level decline within the Colorado River Basin (CRB). In the current study, a non-parametric technique was utilized to analyze historical groundwater variability. Additionally, a stochastic Autoregressive Integrated Moving Average (ARIMA) model was developed and tested to forecast the GRACE-derived groundwater anomalies within the CRB. The ARIMA model was trained with the GRACE data from January 2003 to December of 2013 and validated with GRACE data from January 2014 to December of 2016. Groundwater anomaly from January 2017 to December of 2019 was forecasted with the tested model. Autocorrelation and partial autocorrelation plots were drawn to identify and construct the seasonal ARIMA models. ARIMA order for each grid was evaluated based on Akaike’s and Bayesian information criterion. The error analysis showed the reasonable numerical accuracy of selected seasonal ARIMA models. The proposed models can be used to forecast groundwater variability for sustainable groundwater planning and management

Climate Resilience and Vulnerability of the Salt River Project Reservoir System, Present and Future

abstract: Water resource systems have provided vital support to transformative growth in the Southwest United States; and for more than a century the Salt River Project (SRP) has served as a model of success among multipurpose federal reclamation projects, currently delivering approximately 40% of water demand in the metropolitan Phoenix area. Drought concerns have sensitized water management to risks posed by natural variability and forthcoming climate change. Full simulations originating in climate modeling have been the conventional approach to impacts assessment. But, once debatable climate projections are applied to hydrologic models challenged to accurately represent the region’s arid hydrology, the range of possible scenarios enlarges as uncertainties propagate through sequential levels of modeling complexity. Numerous issues render future projections frustratingly uncertain, leading many researchers to conclude it will be some decades before hydroclimatic modeling can provide specific and useful information to water management. Alternatively, this research investigation inverts the standard approach to vulnerability assessment and begins with characterization of the threatened system, proceeding backwards to the uncertain climate future. Thorough statistical analysis of historical watershed climate and runoff enabled development of (a) a stochastic simulation methodology for net basin supply (NBS) that renders the entire range of droughts, and (b) hydrologic sensitivities to temperature and precipitation changes. An operations simulation model was developed for assessing the SRP reservoir system’s cumulative response to inflow variability and change. After analysis of the current system’s drought response, a set of climate change forecasts for the balance of this century were developed and translated through hydrologic sensitivities to drive alternative NBS time series assessed by reservoir operations modeling. Statistically significant changes in key metrics were found for climate change forecasts, but the risk of reservoir depletion was found to remain zero. System outcomes fall within ranges to which water management is capable of responding. Actions taken to address natural variability are likely to be the same considered for climate change adaptation. This research approach provides specific risk assessments per unambiguous methods grounded in observational evidence in contrast to the uncertain projections thus far prepared for the region.Dissertation/ThesisDoctoral Dissertation Geography 201

Assessing reservoir operations risk under climate change

Risk-based planning offers a robust way to identify strategies that permit adaptive water resources management under climate change. This paper presents a flexible methodology for conducting climate change risk assessments involving reservoir operations. Decision makers can apply this methodology to their systems by selecting future periods and risk metrics relevant to their planning questions and by collectively evaluating system impacts relative to an ensemble of climate projection scenarios (weighted or not). This paper shows multiple applications of this methodology in a case study involving California\u27s Central Valley Project and State Water Project systems. Multiple applications were conducted to show how choices made in conducting the risk assessment, choices known as analytical design decisions, can affect assessed risk. Specifically, risk was reanalyzed for every choice combination of two design decisions: (1) whether to assume climate change will influence flood-control constraints on water supply operations (and how), and (2) whether to weight climate change scenarios (and how). Results show that assessed risk would motivate different planning pathways depending on decision-maker attitudes toward risk (e.g., risk neutral versus risk averse). Results also show that assessed risk at a given risk attitude is sensitive to the analytical design choices listed above, with the choice of whether to adjust flood-control rules under climate change having considerably more influence than the choice on whether to weight climate scenarios

Real-Time Reservoir Operation For Drought Management Considering Ensemble Streamflow Predictions Derived From Operational Forecasts Of Precipitation In Japan

Ensemble hydrological predictions are considered to be useful for robust reservoir operation as they include multiple possible hydrological scenarios in the future as well as dispersion of the predictions, from which the degree of prediction uncertainty can be estimated. Although operational ensemble hydrological predictions have been available in many regions, they have not yet been widely used in the actual reservoir management due to the difficulty in the handling such complex information. In order to facilitate effective utilization of ensemble hydrological predictions in the actual reservoir management, a real-time reservoir operation method for drought management is developed considering operational ensemble forecasts of precipitation in Japan. One-week and one-month ensemble predictions of precipitation (EPPs) with respectively 51 and 50 ensemble members provided by Japan Meteorological Agency are employed here. Firstly, an ensemble prediction of daily precipitation in the target basin is estimated for the coming one month from EPPs by using artificial neural networks (ANNs), which are developed so as to model the statistical relationships between predicted values and observed basin precipitations. An ensemble streamflow prediction is then estimated by use of Hydrological River Basin Environment Assessment Model (Hydro-BEAM), a distributed rainfall-runoff model. Reservoir operation for drought management is then optimized considering the prediction for the coming one month by using sampling stochastic dynamic programming, which can consider both the stochastic and time-series natures of the ensemble prediction, so as to minimize drought damage caused by deficit in release waters. Water release is conducted according to the optimized operation strategy, updating EPPs and ensemble streamflow prediction. The proposed method is applied to water supply operation of Sameura Reservoir in the Yoshino River basin in Japan, demonstrating effectiveness of considering operational EPPs as well as the effects of uncertainty contained in the predictions on performances of the reservoir operation

Does the Danube exist? Versions of reality given by various regional climate models and climatological datasets

We present an intercomparison and verification analysis of several regional climate models (RCMs) nested into the same run of the same Atmospheric Global Circulation Model (AGCM) regarding their representation of the statistical properties of the hydrological balance of the Danube river basin for 1961-1990. We also consider the datasets produced by the driving AGCM, from the ECMWF and NCEP-NCAR reanalyses. The hydrological balance is computed by integrating the precipitation and evaporation fields over the area of interest. Large discrepancies exist among RCMs for the monthly climatology as well as for the mean and variability of the annual balances, and only few datasets are consistent with the observed discharge values of the Danube at its Delta, even if the driving AGCM provides itself an excellent estimate. Since the considered approach relies on the mass conservation principle and bypasses the details of the air-land interface modeling, we propose that the atmospheric components of RCMs still face difficulties in representing the water balance even on a relatively large scale. Their reliability on smaller river basins may be even more problematic. Moreover, since for some models the hydrological balance estimates obtained with the runoff fields do not agree with those obtained via precipitation and evaporation, some deficiencies of the land models are also apparent. NCEP-NCAR and ERA-40 reanalyses result to be largely inadequate for representing the hydrology of the Danube river basin, both for the reconstruction of the long-term averages and of the seasonal cycle, and cannot in any sense be used as verification. We suggest that these results should be carefully considered in the perspective of auditing climate models and assessing their ability to simulate future climate changes.Comment: 25 pages 8 figures, 5 table

Disaggregation of global ensemble rainfall forecasts for improved stormwater management

Les bassins de rétention sans plan d'eau permanent (bassins "secs") sont largement répandus pour diminuer les aspects négatifs du ruissellement urbain sur le milieu récepteur. À l'heure actuelle, de tels bassins sont conçus avec un contrôle statique, ce qui signifie que leur fonctionnement est seulement basé sur une limitation de leur débit maximal de sortie. Le Contrôle en Temps Réel (CTR) du degré d'ouverture de leur vanne de sortie permettrait d'améliorer leurs performances.
 Le travail présenté ici a notamment permis le développement de scénarios de CTR d'un bassin de rétention sec situé à l'exutoire d'une petite zone urbaine (3.5 km2) sur le territoire de la Ville de Québec, au Canada. Le ruissellement et sa concentration en Matières En Suspension (MES) ont été simulés par le modèle SWMM5, dans lequel la formulation du lessivage de surface a été améliorée dans le cadre de ce travail. Les stratégies de gestion en temps réel proposées utilisent comme information les données du réseau pluviométrique, la mesure de la hauteur d'eau dans le bassin de rétention et, dans certains des scénarios, des prévisions météorologiques. 
 Les prévisions de pluie peuvent en effet s'avérer intéressantes pour une large gamme d'utilisateurs, comme ceux impliqués dans la prévention des crues, et la gestion de l'eau de manière générale, puisqu'elles permettent une certaine anticipation du comportement du système. Les prévisions de pluie d'ensemble fournissent de plus une description explicite et dynamique de l'incertitude liée à la prévision. Cependant, de telles prévisions sont à l'heure actuelle disponibles à des échelles trop grandes pour être directement compatibles avec des modèles hydrologiques mis en œuvre sur de petits bassins versants. 
 Cette thèse de doctorat s'est donc de plus penchée sur la désagrégation spatiale du système de prévision d'ensemble Canadien, afin de rendre les prévisions d'ensemble de pluie plus appropriées à l'échelle du petit bassin urbain pour lequel des règles de CTR du bassin de rétention ont été élaborées. Pour cela, diverses variantes de la méthode statistique de désagrégation spatiale proposée par Perica et Foufoula-Georgiou (1996b) ont été comparées, pour faire passer les prévisions d'ensemble globales de pluie (émises par Environnement Canada) d'une résolution de 100 km par 70 km à celle de 6 km par 4 km. Cette technique permet d'augmenter la variance des hauteurs de pluie prévues à l'intérieur d'un pixel original lors de la désagrégation, tout en préservant la valeur moyenne initialement prévue pour la pluie sur ce pixel. Ces prévisions d'ensemble de pluie ont été émises par le système de prévision d'ensemble global Canadien, dans sa forme opérationnelle en 2009. La méthode statistique de Skaugen (2002) a également été appliquée à ces prévisions, et a mené à la production de prévisions d'ensemble ayant une résolution de 10 km par 7 km. Pour comparaison, des méthodes plus simples comme celle de l'interpolation bilinéaire, ont aussi été appliquées. Cette dernière permet le raffinement des prévisions globales de pluie sans augmenter la variance des hauteurs de pluie lors du processus de raffinement spatial.
 Les produits météorologiques désagrégés ont été évalués d'un point de vue météorologique et hydrologique, en utilisant différents scores et diagrammes. Pour l'évaluation météorologique, neuf jours présentant d'importants évènements de précipitation ont été utilisés pour comparer les hauteurs de pluie prévues à celles observées par le réseau de pluviomètres de la ville de Québec.
 Des prévisions hydrologiques d'ensemble avec un pas de temps compris entre 3 et 24 heures ont été mises en œuvre sur une période de 3 mois, avec les prévisions d'ensemble originales et celles issues de la désagrégation. Cette chaîne de prévision hydro-météorologique opérationnelle a été élaborée en utilisant les modèles GR4J et SWMM5. Ces modèles ont été mis en œuvre sur 4 bassins situés dans la région de Québec, avec une taille comprise entre 5 et 350 km2. L'évaluation hydrologique s'est basée sur la comparaison des débits prévus avec ceux observés.
 Les résultats obtenus avec la méthode de Skaugen (2002) ne se sont pas révélés aussi intéressants que ceux basés sur la technique de Perica et Foufoula-Georgiou (1996b). Avec cette dernière, les conclusions principales de ce travail de thèse sont: 1) la qualité globale des prévisions est préservée lors du processus de raffinement, et 2) les produits désagrégés par cette méthode qui permet d'augmenter la variance des hauteurs de pluie présentent une qualité similaire à celle des produits désagrégés par la méthode de l'interpolation bilinéaire. En revanche, la variance et la dispersion des différents membres des prévisions d'ensemble se sont avérées largement améliorées pour les produits désagrégés par la méthode de Perica et Foufoula-Georgiou (1996b), ce qui représente un avantage considérable comparativement à la méthode de l'interpolation bilinéaire.
 Ces résultats ont été confirmés du point de vue hydrologique. Par conséquent, il est avancé à l'issue de ces travaux de doctorat que la méthode de désagrégation statistique de Perica and Foufoula-Georgiou (1996b) représente une manière intéressante pour réduire le problème d'incompatibilité existant entre les résolutions des modèles météorologiques globaux et le haut degré de précision parfois requis dans la représentation spatiale des champs de précipitation par les modèles hydrologiques semi-distribués et par ceux montés sur de petits bassins versants. 
 Les stratégies de CTR mises en place pour le bassin de rétention sec étudié ici ont permis une amélioration significative de ses performances - l'efficacité d'enlèvement des MES est passée de 46 à 90% - tout en restant sécuritaire (du point de vue du risque de débordement) et en prenant en compte une contrainte liée au risque de prolifération de moustiques. Cependant, les prévisions de pluie désagrégées ne se sont pas révélées supérieures aux prévisions originales du modèle d'ensemble global Canadien, dans ce contexte spécifique de gestion en temps réel. Les différentes prévisions considérées ont en effet mené à des résultats similaires pour les performances de ce bassin de rétention soumis à des règles de CTR.
Dry detention ponds are commonly implemented to mitigate the impacts of urban runoff on receiving water bodies. They currently rely on static control through a fixed limitation of their maximum outflow rate. Real-Time Control (RTC) allows optimizing their performance by manipulation of an outlet valve.
 This thesis developed several enhanced RTC scenarios of a dry detention pond located at the outlet of a small (3.5 km2) urban catchment near Québec City, Canada. The catchment's runoff quantity and Total Suspended Solids' (TSS) concentration were simulated by the SWMM5 model with an improved wash-off formulation. The control procedures rely on rain gauge data, on measures of the pond's water height, and, in some of the RTC scenarios, on rainfall forecasts.
 Rainfall forecasts are indeed valuable to a wide variety of end users in the field of flood risk assessment and water management, as they allow some anticipation of the behaviour of the system under consideration. Ensemble rainfall forecasts thus provide an explicit and dynamic assessment of the uncertainty in the forecast. However, for hydrological forecasting, their low resolution currently limits their use to large watersheds. 
 Therefore, this thesis explores rendering the Canadian Ensemble Prediction System's (EPS's) rainfall forecasts more appropriate for hydrological modeling of such a small urban catchment as the one studied here. To bridge this spatial gap, various implementations of the spatial statistical downscaling method proposed by Perica and Foufoula-Georgiou (1996b) were compared, bringing Environment Canada's (EC's) global Ensemble Rainfall Forecasts (ERFs) from a 100-km by 70-km resolution down to 6-km by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. These ERFs were issued by the Canadian Global Ensemble Prediction System (GEPS) in its 2009 operational version. The statistical downscaling method of Skaugen (2002) was also applied to these ERFs, producing rainfall fields with a resolution of 10 km by 7 km. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance.
 The downscaled meteorological products were evaluated, using different scores and diagrams, from both a meteorological and a hydrological view points. The rainfall forecasts were compared against nine days (presenting strong precipitation events) of observed values taken from Québec City's rain gauge database. 
 Ensemble Hydrologic Forecasts (EHFs) with a time step of 3 and 24 hours were performed over a 3-month period for the original and disaggregated rainfall forecasts. This hydro-meteorological operational forecasting chain was conducted using hydrological models GR4J, a modified version of GR4J, and SWMM5. These models were implemented on four catchments ranging between 5 and 350 km2, and located in the Québec City region. The hydrological evaluation was based on the comparison of forecasted flows to the observed ones.
 Results obtained with the method of Skaugen (2002) were not as interesting as those based on the technique of Perica and Foufoula-Georgiou (1996b). This is due to the fact that with the method of Skaugen (2002), the final rainfall field corresponds to the average of ten downscaled fields, what tends to dampen the variance added through the disaggregation process. For the technique of Perica and Foufoula-Georgiou (1996b), the most important conclusions are: 1) the overall quality of the forecasts is preserved during the disaggregation procedure and 2) the disaggregated products using the variance-enhancing method are of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members are, of course, much improved for the variance-enhanced products, compared to the bi-linear interpolation, which is a decisive advantage. 
 These results were confirmed by the hydrological evaluation. The disaggregation technique of Perica and Foufoula-Georgiou (1996b) hence represents an interesting way of bridging the gap between the resolution of meteorological models and the high degree of spatial precision sometimes required (in the precipitation representation) by semi-distributed hydrological models and by models built on small watersheds.
 RTC strategies of the studied dry pond allowed for a substantial improvement of the performance compared to those with its current static control– the TSS removal efficiency increased from 46 to about 90% - while remaining safe and taking a mosquito-breeding risk constraint into account. However, the downscaled rainfall forecasts were not superior to the original ones (issued by the Canadian GEPS) in this context, as they led to the same performance for the RTC scenarios relying on rainfall forecasts.&#10

A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes

Runoff Forecast for the Flood Season Based on Physical Factors and Their Effect Process and Its Application in the Second Songhua River Basin, China

The Second Songhua River Basin is located at the northern edge of the East Asian monsoonregion in China. The river basin has a large interannual rainfall-runoff variation often associated withfrequent droughts and floods. Therefore, the mid-long-term runoff prediction is of great significance.According to a review of the national and international literature, there are few studies on sunspots inthe prediction of medium- and long-term runoff. In this study, sunspots are selected as the influencingfactors of runoff based on the mechanism of astronomical factors; sensitivity analysis was used toidentify the time delay of sunspots’ influence on runoff and determine the prediction factor (relativenumber of sunspots in January and March). The BP (backpropagation) network is used to identifythe correlation between prediction factors and prediction items (monthly average inflow rate of theFengman Reservoir and the Baishan Reservoir in the flood season), and then the prediction model isconstructed. According to the test results of historical data and the actual forecast results, the forecastis working well, and the accuracy of qualitative forecasting is high