Understanding Drought Dynamics during Dry Season in Eastern Northeast Brazil

Eastern Northeast Brazil (ENEB) generally experiences a high variability in precipitation in the dry season, with amplitudes that can overcome 500mm. The understanding of this variability can help in mitigating the socio-economic issues related to the planning and management of water resources this region, which is highly vulnerable to drought. This work aims to assess spatio-temporal variability of precipitation during the dry season and investigate the relationships between climate phenomena and drought events in the ENEB, using univariate (Spearman correlation) and multivariate statistical techniques, such as Principal Component Analysis, Cluster Analysis, and Maximum Covariance Analysis. The results indicate that the variability of precipitation in the dry season can be explained mainly (62%) by local physical conditions and climate conditions have a secondary contribution. Further analysis of the larger anomalous events suggests that the state of Atlantic and Pacific oceans can govern the occurrence of those events, and the conditions of Atlantic Ocean can be considered a potential modulator of anomalous phenomena of precipitation in ENEB

Variabilidade e mudança climática no Brasil e America do Sul

Neste capítulo é apresentado um estudo de séries temporais de precipitação e de vazões/cotas de rios em diversas regiões do Brasil, com o objetivo de observar a existência de variações sistemáticas nesses parâmetros que indicariam mudanças climáticas. Séries históricas de dados desde o início do século foram analisadas e o teste de Mann-Kendall (Lettenmaier et al. 1991, 1994, Marengo 1995) foi utilizado para determinar a presença de tendências, suas direções e se são estatisticamente significantes ou não.Pages: on lin

Climate warming effects on hydropower demand and pricing in California

High-elevation hydropower units in California might be sensitive to climate warming since they have been designed to take advantage of snowmelt and have low built-in storage capacities. Snowmelt is expected to shift to earlier in the year and the system might not be able to store sufficient water for release in high-electricity-demanding periods. Previous studies have tried to explore the climate warming effects on California's high-elevation hydropower system by focusing on the supply side only (exploring the effects of hydrological changes on generation and revenues). This study extends the previous work by also considering climate warming effects on hydropower demand and pricing. A long-term price forecasting tool is developed using Artificial Neural Network (ANN) models. California's Energy-Based Hydropower Optimization Model (EBHOM) is then applied to estimate the adaptability of California's high-elevation hydropower system to climate warming considering simultaneous changes in supply, demand and pricing. The model is run for dry and wet warming scenarios, representing a range of hydrological changes under climate change

Climate Change Effects on the High-Elevation Hydropower System with Consideration of Warming Impacts on Electricity Demand and Pricing

While only about 30 percent of California’s usable water storage capacity lies at higher elevations, high‐elevation hydropower units generate, on average, 74 percent of California’s in‐ state hydroelectricity. In general, high‐elevation plants have small man‐made reservoirs and rely mainly on snowpack. Their low built‐in storage capacity is a concern with regard to climate warming. Snowmelt is expected to shift to earlier in the year, and the system may not be able to store sufficient water for release in high‐demand periods. Previous studies have explored the climate warming effects on California’s high‐elevation hydropower system by focusing on the supply side (exploring the effects of hydrological changes on generation and revenues) but they have ignored the warming effects on hydropower demand and pricing. This study extends the previous work by simultaneous consideration of climate change effects on high‐elevation hydropower supply and demand in California. Artificial Neural Network models were developed as long‐term price estimation tools, to investigate the impact of climate warming on energy prices. California’s Energy‐Based Hydropower Optimization Model (EBHOM) was then applied, to estimate the adaptability of California’s high‐elevation hydropower system to climate warming, considering the warming effects on hydropower supply and demand. The model was run for dry and wet warming scenarios, representing a range of hydrological changes under climate change. The model’s results relative to energy generation, energy spills, reservoir energy storage, and average shadow prices of energy generation and storage capacity expansion are examined and discussed. The modeling results are compared with previous studies to emphasize the need to consider climate change effects on hydroelectricity demand and pricing when exploring the effects of climate change on California’s hydropower system

The spatio-temporal influence of atmospheric teleconnection patterns on hydrology in Sweden

Study region: Sixty-four river gauging stations distributed over Sweden. Study focus: To investigate the influence of climate teleconnection patterns (TP) on streamflow in Sweden. Streamflow data is regionalized and the average hydrographs of each homogeneous region is divided into hydrological seasons. Thereafter the impact of different TPs on the streamflow, per homogeneous region and per hydrological season is analyzed. New hydrological insights for the region: Five homogeneous regions are identified; three located in the north, where snow dominates the hydrological processes, and two located in the south, where rain dominates hydrological processes. The northern hydrographs are separated into three hydrological periods: low streamflow when snow is accumulated, high streamflow during the melting of the snowpack and a transition period in between. The southern hydrographs are characterized by streamflow above the yearly average during the winter and below during the summer. Hydrological periods in different homogeneous regions are influenced by diverse combinations of TPs. Arctic Oscillation, North Atlantic Oscillation and Scandinavian Pattern influence the streamflow in most of the regions during most hydrological periods. The further south and east the region is located, the more TPs influence the streamflow. The resulting streamflow variability is related to the interplay between different TPs both before and during each hydrological period. This interplay may enhance or decrease the individual influence of each TP on streamflow

Precipitation variability and its relation to climate anomalies in the Bolivian Altiplano

Precipitation variability over the Bolivian Altiplano is strongly affected by local climate and temporal variation of large-scale atmospheric flow. Precipitation is the main water source for drinking water and agricultural production. For this reason, a better understanding of precipitation variability and its relation with climate phenomena can provide important information for forecasting of droughts and floods, disaster risk reduction, and improvement of water management. We present results of an analysis of the austral summer precipitation variability at six locations in the Bolivian Altiplano and connections to climate variability. For this purpose, the variability of the summer precipitation was related to El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Antarctic Meridional Mode (AMM), and Atlantic Multidecadal Oscillation (AMO). A statistically significant correlation between climate indices and precipitation was found in various spectral frequencies and power. The variability of the summer precipitation was associated with the climate indices using a band-pass filter, representing the signal at a particular period of time. For the ENSO, band-pass filtering was applied for Niño3.4 and Niño3 at band ~2–7 years, for NAO band ~5–8 years, and for AMM band ~10–13 years. The variability of summer precipitation was related to all studied climate modes by negative relationships. The physical explanation for this is first the dry air transported from the Pacific Ocean to the Altiplano during El Niño events. Second, NAO and ENSO are dynamically linked through teleconnections. Third, the intertropical convergence zone (ITCZ) shifts are northwards during the warm phases of AMM. These physical mechanisms lead to a reduced austral summer precipitation associated with positive phases of the ENSO, NAO, and AMM. The results can be used to better forecast precipitation in the Bolivian Altiplano and provide support for the development of policies to improve climate resilience and risk management of water supply

Is correlation dimension a reliable indicator of low-dimensional chaos in short hydrological time series?

The reliability of the correlation dimension estimation in short hydrological time series is investigated using an inverse approach. According to this approach, first predictions are made using the phase-space reconstruction technique and the artificial neural networks. The correlation dimension is estimated next independently and is compared with the prediction results. A short hydrological series, monthly runoff series of 48 years (with a total of only 576 values) observed at the Coaracy Nunes/Araguari River watershed in northern Brazil, is studied. The correlation dimension results are in reasonably good agreement with the optimal embedding dimension obtained from the phase-space method and the optimal number of inputs from the neural networks. No underestimation of the correlation dimension is observed due to the small data size, rather there seems to be a slight overestimation due to the presence of noise in the data. The results indicate that the accuracy of the correlation dimension may not be judged on the basis of the length of the time series but on whether the time series is long enough to reasonably represent the dynamical changes in the system. Such an observation suggests that the correlation dimension could indeed be a reliable indicator of low-dimensional chaos even in short hydrological time series, which is certainly encouraging news for hydrologists who often have to deal with short time series

On the Suitability of Non-Parametric Tests for Detection of Trends in Brazilian Rivers

The non-parametric test of Mann-Kendall is used to determine whether there is, or not, an statistically significant trend in the series and as an indication of the géneral direction of the change. The Mann-Kendall statistic demands statistical independence of the series. Since the year-to-year correlation of streamflow in a given year with the same streamflow of the previous or subsequent year is usually quite low, the test has been widely applied in climatic and hydrologic data. However, it has been found that for regions with large basin-memory such as the Amazon basin or Pantanal, or under intense use of water for irrigation or electricity generation such as the São Francisco River basin, this test may be misleading due to a large serial autocorrelation of river data.Pages: 1492-149