The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

On behalf of Mr. Koichiro Matsuura, Director-General of UNESCO, it is my great pleasure to welcome you all to this international scientific conference. Drylands are often considered fragile ecosystems, yet they have a remarkable resilience to stress. They are home to unique and well-adapted plant and animal species that we need to conserve. Some of the world’s greatest cultures and belief systems have originated in drylands. On the other hand, desertification and land degradation in drylands often result in poverty and cause environmental refugees to abandon their homes. These problems can only be addressed in a holistic manner, based on sound scientific research and findings. Solutions to the problems of dryland degradation need to be communicated as widely as possible through education at all levels. These are many reasons why UNESCO – within its mandate of science, education, culture and communication – took the intiative to organize this conference. And we are glad that so many partners have responded to our call. UNESCO considers this conference as its main contribution to the observance of the International Year of Deserts and Desertification in 2006. We have deliberately chosen the title ‘The Future of Drylands’ as we feel it is time to redefine our priorities for science, education and governance in the drylands based on 50 years of scientific research in arid and semi-arid zones. In fact UNESCO has one of the longest traditions, within the UN system, of addressing dryland problems from an interdisciplinary, scientific point of view. In 1955, the ‘International Arid Land Meetings’ were held in Socorro, New Mexico (USA). They were organized by the American Association for the Advancement of Science (AAAS), sponsored by UNESCO and supported by the Rockefeller Foundation. One important output of the International Arid Land Meetings was a book entitled The Future of Drylands, edited by Gilbert F. White and published in

United Nations Educational, Scientific and Cultural Organization (UNESCO)

The Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium 2012 held in Delft (23–25 October 2012), set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with increasing realism and predictive power. This paper reviews the work that has been done under the six science themes of the PUB Decade and outlines the challenges ahead for the hydrological sciences community.Editor D. KoutsoyiannisCitation Hrachowitz, M., Savenije, H.H.G., Bloschl, G., McDonnell, J.J., Sivapalan, M., Pomeroy, J.W., Arheimer, B., Blume, T., Clark, M.P., Ehret, U., Fenicia, F., Freer, J.E., Gelfan, A., Gupta, H.V., Hughes, D.A., Hut, R.W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P.A., Uhlenbrook, S., Wagener, T., Winsemius, H.C., Woods, R.A., Zehe, E., and Cudennec, C., 2013. A d...

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A decade of Predictions in Ungauged Basins (PUB)—a review

Despite significant recent advancements, global hydrological models and their input databases still show limited capabilities in supporting many spatially detailed research questions and integrated assessments, such as required in freshwater ecology or applied water resources management. In order to address these challenges, the scientific community needs to create improved large-scale datasets and more flexible data structures that enable the integration of information across and within spatial scales; develop new and advanced models that support the assessment of longitudinal and lateral hydrological connectivity; and provide an accessible modeling environment for researchers, decision makers, and practitioners. As a contribution, we here present a new modeling framework that integrates hydrographic baseline data at a global scale (enhanced HydroSHEDS layers and coupled datasets) with new modeling tools, specifically a river network routing model (HydroROUT) that is currently under development. The resulting ‘hydro-spatial fabric’ is designed to provide an avenue for advanced hydro-ecological applications at large scales in a consistent and highly versatile way. Preliminary results from case studies to assess human impacts on water quality and the effects of dams on river fragmentation and downstream flow regulation illustrate the potential of this combined data-and-modeling framework to conduct novel research in the fields of aquatic ecology, biogeochemistry, geo-statistical modeling, or pollution and health risk assessments. The global scale outcomes are at a previously unachieved spatial resolution of 500 m and can thus support local planning and decision making in many of the world's large river basins. Copyright © 2013 John Wiley & Sons, Ltd.

Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems

. Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments ( http://www.gloh2o.org .

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MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Traditionally, the modeling of large watersheds for the estimation of runoff and discharges in rivers is done to obtain the input information for the modeling of lakes, lagoons and estuaries. This approach requires the preparation of data and compatibility of two models, being costly when used for hydrological studies or operationalized in water level prediction systems. Recent studies have demonstrated that it is possible to use the structure of a single hydrological model to represent these variables in lagoon environments. However hydrological models of basins generally do not include the consideration of wind shear stress effect, which makes the representation incomplete. In the present work this subject is investigated, where it was aimed to answer the following question: is it possible to include the wind effect in a hydrological model to improve the representation of lagoon environments water levels? The case study used was the Laguna dos Patos basin (RS, Brazil) and the model used was the MGB-IPH Inertial.

É possível incluir o efeito do vento em um modelo hidrológico para melhorar a representação de ambientes lagunares

Short and medium term hydrological forecasts are usually associated with large uncertainty, especially concerning the use of information from numerical weather forecasts. One way to consider this uncertainty in the forecasting and decision-making process is the use of probabilistic predictions, obtained as ensemble forecasts. Usually at these kinds of forecasts several possible future scenarios are generated in order to quantify the uncertainties in the process. Recent studies have shown promising benefits on using ensemble forecasts rather than deterministic ones. This paper proposes a complementary approach related to the topic. First a brief introduction to streamflow forecasts current use in Brazil is made, and discussed how ensemble forecasts could be taken in this context. Then we present results of a game held during the XX Brazilian Symposium on Water Resources in 2013 in the city of Bento Goncalves (RS), which had the public participation at the event playing the whole of decision makers that uses forecast information, using and not using information from probabilistic forecasts. The game results showed, in general, that probabilistic forecasts led in most cases to better and more consistent decisions among multiple persons. We believe that this argument helps to motivate the development of more research and more operational ensemble forecasting systems in the Brazilian scenario

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Sobre o uso de previsões hidrológicas probabilísticas para tomada de decisão

Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interconnected Energy System (SIN) is an example of a unique and complex system of coordinated operation composed by more than 160 large reservoirs. We proposed and evaluated an integrated approach to simulate daily outflows from most of the SIN reservoirs (138) using an Artificial Neural Network (ANN) model, distinguishing run-of-the-river and storage reservoirs and testing cases whether outflow and level data were available as input. Also, we investigated the influence of the proposed input features (14) on the simulated outflow, related to reservoir water balance, seasonality, and demand. As a result, we verified that the outputs of the ANN model were mainly influenced by local water balance variables, such as the reservoir inflow of the present day and outflow of the day before. However, other features such as the water level of 4 large reservoirs that represent different regions of the country, which infers about hydropower demand through water availability, seemed to influence to some extent reservoirs outflow estimates. This result indicates advantages in using an integrated approach rather than looking at each reservoir individually. In terms of data availability, it was tested scenarios with (WITH_Qout) and without (NO_Qout and SIM_Qout) observed outflow and water level as input features to the ANN model. The NO_Qout model is trained without outflow and water level while the SIM_Qout model is trained with all input features, but it is fed with simulated outflows and water levels rather than observations. These 3 ANN models were compared with two simple benchmarks: outflow is equal to the outflow of the day before (STEADY) and the outflow is equal to the inflow of the same day (INFLOW). For run-of-the-river reservoirs, an ANN model is not necessary as outflow is virtually equal to inflow. For storage reservoirs, the ANN estimates reached median Nash-Sutcliffe efficiencies (NSE) of 0.91, 0.77 and 0.68 for WITH_, NO_ and SIM_Qout respectively, compared to a median NSE of 0.81 and 0.29 for the STEADY and INFLOW benchmarks respectively. In conclusion, the ANN models presented satisfactory performances: when outflow observations are available, WITH_Qout model outperforms STEADY; otherwise, NO_Qout and SIM_Qout models outperform INFLOW.

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Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system

Sudden floods, known in much of Brazil as a cabeça d’água, are relatively frequent and bring threats to human life in mountain rivers. In two previous papers we described this type of flood and the processes that lead to its occurrence. In the present paper (the third and last in the sequence) we present a synthesis, proposing a perceptual model of cabeça d’água formation. According to this model, a cabeça d’água is born as a relatively normal flash-flood, but then turns into a sudden or instant flood while it propagates downstream along a river where certain conditions are met. One of the conditions necessary for the development of the cabeça d’água is the occurrence of rain restricted to the basin headwaters. Due to the absence of rainfall on the downstream parts of the basin, this type of flood is highly lethal, even though they are not the ones with the highest maximum flows in a given location. Palavras-Chave – Modelo perceptual, choque cinemático, bacia montanhosa.

Walter Collischonn

Papers

É possível incluir o efeito do vento em um modelo hidrológico para melhorar a representação de ambientes lagunares

Sobre o uso de previsões hidrológicas probabilísticas para tomada de decisão

Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system

A hidrologia da cabeça d'água (3) : síntese

Global determination of rating curves in the Amazon basin from satellite altimetry