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In flat plains groundwater affects agricultural production outcomes and risks. Agricultural land use decisions, however, may strongly impact groundwater levels available for production. This paper explores the scope for managing groundwater levels through land use decisions in a sub-basin of the Salado River in the Argentine Pampas, a very flat area that plays a key role in world agricultural production. A spatially distributed hydrological model implemented with MIKE SHE software was used to establish the impacts of different land uses on groundwater dynamics, and to assess the interdependencies among spatially close decision-makers sharing a water table (WT). Additionally, groundwater level changes in response to climate variability were quantified. We found land use has strong effects on WT levels both for oneself (e.g. pastures can lead to significant decreases (up to 4.5 m) in WT levels) and others, in the form of strong interdependencies that exist between farmers sharing a WT where land use decisions of one farmer effect groundwater level of neighbouring farms and vice versa. However, the effectiveness to control groundwater levels through land use decisions is subject to the rather unpredictable effects of rainfall variability. The results presented in this paper provide key insights in relation to physical and social aspects that should be considered for managing groundwater levels through land use decisions, in order to avoid negative and/or maximize positive effects on agricultural production. [ABSTRACT FROM PUBLISHER]

In this paper we review the published, scientific literature addressing the response of nutrients, sediment, pathogens, and cyanobacterial blooms to historical and potential future changes in air temperature and precipitation. The goal is to document how different attributes of water quality are sensitive to these drivers, to characterize future risk, to inform management responses, and to identify research needs to fill gaps in our understanding. Results suggest that anticipated future changes present a risk of water quality and ecosystem degradation in many United States locations. Understanding responses is, however, complicated by inherent high spatial and temporal variability, interactions with land use and water management, and dependence on uncertain changes in hydrology in response to future climate. Effects on pollutant loading in different watershed settings generally correlate with projected changes in precipitation and runoff. In all regions, increased heavy precipitation events are likely to drive more episodic pollutant loading to water bodies. The risk of algal blooms could increase due to an expanded seasonal window of warm water temperatures and the potential for episodic increases in nutrient loading. Increased air and water temperatures are also likely to affect the survival of waterborne pathogens. Responding to these challenges requires understanding of vulnerabilities, and management strategies to reduce risk. Research Impact Statement: Climate change will have direct and cascading effects on water quality that vary in different regional and watershed settings, and could present a risk to human health and the environment. [ABSTRACT FROM AUTHOR]

West Africa plays key roles in global climate and shows one of the strongest variations in hydro-climatic conditions. As it turns out, the region appears to be underrepresented in the existing compendium of Earth science and hydrology-focused journal papers when it comes to significant discussion on terrestrial hydrology and freshwater science. This prominent gap is largely precipitated by increasing number of constraints that include lack of considerable and robust investments in gauge measurements for meteorological and hydrological applications, poor funding of research institutions and other disincentives, among other factors. In this manuscript, the challenges and problems in large-scale terrestrial hydrology-focused investigation in West Africa are reviewed. Using a dossier of some recent contributions in the field of remote sensing hydrology, this review also highlights some of the progress in terrestrial hydrology and the opportunities that exist for hydro-geodetic research in West Africa that leverage on sustained investments in satellite geodetic missions. It is noted that West Africa is still a pristine environment for hydrology-focused research and can benefit from recent advancements in sophisticated space agency programs such as the Gravity Recovery and Climate Experiment, which undoubtedly has revolutionized terrestrial hydrology research around the world for nearly two decades. Given the poor density of gauge stations and limited ground observations, hydrological research in West Africa is expected to benefit more from independent space observations and multi-resolution data. This is because the lack of sufficient in-situ data for the parameterizations and adequate initialization of outputs from hydrological models and reanalysis data for hydrological applications results in poor representation of the West African land surface and hydrological state variables. To further improve our contemporary understanding of West Africa's terrestrial hydrology, the continued evaluation/validation of these observations and space-borne measurements is advocated. [ABSTRACT FROM AUTHOR]

The subject of the present paper is the flood situation in the German part of the Elbe river. For this, no (international) agreement could be reached till now. Problems occur due to missing, non-plausible or uncertain flow data and due to non-stationary hydrological conditions in the basin caused by climatological variability and human impact (diking and installation of reservoirs). Regarding this complex hydrological situation a systematic procedure consisting of existing hydrological tools was developed. In a harmonised and complementary structure this procedure mainly includes the consistency analysis of flow data, statistical analyses of the flow process, especially flood statistics and their regionalisation, stochastic simulations of the flow process in the basin and flow routing. Therewith a stepwise increasingly reliable, representative, detailed and funded knowledge of the flow process may be achieved. Till now complete longitudinal sections of statistical flood parameters along the German part of the watercourse were determined for a sufficiently long period (1964–1995) best reflecting the present situation. Moreover, approximative flood statistics could be derived for a longer period (1936–1995), which may be regarded as representative for the variable hydrological conditions during the 20th century.In addition to this fundamental study the tools were applied in order to contribute to a decision support with respect to the presently discussed measures of dike shifting. The analyses revealed that a significant impact of these measures—even the impact of all measures together—on flood peaks may be achieved only if control systems at the sites of the retention spaces are used.Finally, the paper considers options of a further utilisation of the hydrological analysis results by the definition of interfaces to other disciplines. [Copyright &y& Elsevier]

The objective of this paper is to analyse, for the Senegal River basin, the evolution of four classes of daily precipitation, the number of rainy days and the duration of the rainy season. The approach is to apply statistical tests of homogeneity to the series of annual rainfall over the period 1950–1998 to detect abrupt changes and to characterize the evolution of these six variables on each side of the change dates. The time series present an abrupt change (break) between 1966 and 1970. Deficits of rainy days in the period after the break compared to before vary between 6.2% at Kédougou and 38.6% at Saint Louis. The class of rain of more than 50 mm (P4) has the largest deficit, ranging from 14.8% at Mamou station to 66.6% at Nioro du Sahel.Editeur Z. W. Kundzewicz; Edithur Associé E. Gargouri [ABSTRACT FROM AUTHOR]

This paper quantifies the impacts of bushfire and climate variability on streamflow from three southeast Australian catchments where bushfires occurred in February 1983. Three hydrological models (AWRA-L, Xinanjiang and GR4J) were first calibrated against streamflow data from the pre-bushfire period and then used to simulate runoff for the post-bushfire period with the calibrated parameters. The difference in simulated streamflow between pre- and post-bushfire periods provides an estimate of the impact of climate variability on streamflow. The impact of bushfire on streamflow is quantified by removing the climate variability impact from the difference in mean annual observed streamflow between post- and pre-bushfire periods. For the first 15 years after the 1983 bushfires, the results from hydrological models for the three catchments indicate that there is a substantial increase in streamflow; this is attributed to initial decreases in evapotranspiration and soil infiltration rates resulting from the fires, followed by logging activity. After 15 years, streamflow dynamics are more heavily influenced by climate effects, although some impact from fire and logging regeneration may still occur. The results show that hydrological models provide reasonably consistent estimates of bushfire and climate impacts on streamflow for the three catchments. The models can be used to quantify relative contributions of forest disturbance (bushfire, logging and other forest management) and climate variability. The results presented can also help forest managers understand the relationship between bushfire and climate variability impacts on water yield in the context of climate variability. [ABSTRACT FROM AUTHOR]

In this paper, monthly, maximum seasonal, and maximum annual hydrometeorological (i.e., evaporation, lake water levels, and rainfall) data series from the Kariba catchment area of the Zambezi River basin, Zimbabwe, have been analyzed in order to determine appropriate probability distribution models of the underlying climatology from which the data were generated. In total, 16 probability distributions were considered and the Kolmogorov-Sminorv (KS), Anderson-Darling (AD), and chi-square (χ2) goodness-of-fit (GoF) tests were used to evaluate the best-fit probability distribution model for each hydrometeorological data series. A ranking metric that uses the test statistic from the three GoF tests was formulated and used to select the most appropriate probability distribution model capable of reproducing the statistics of the hydrometeorological data series. Results showed that, for each hydrometeorological data series, the best-fit probability distribution models were different for the different time scales, corroborating those reported in the literature. The evaporation data series was best fit by the Pearson system, the Lake Kariba water levels series was best fit by the Weibull family of probability distributions, and the rainfall series was best fit by the Weibull and the generalized Pareto probability distributions. This contribution has potential applications in such areas as simulation of precipitation concentration and distribution and water resources management, particularly in the Kariba catchment area and the larger Zambezi River basin, which is characterized by (i) nonuniform distribution of a network of hydrometeorological stations, (ii) significant data gaps in the existing observations, and (iii) apparent inherent impacts caused by climatic extreme events and their corresponding variability. [ABSTRACT FROM AUTHOR]

Summary Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distributed) meteorological inputs instead of spatially uniform (lumped) meteorological inputs. Both lumped and distributed versions of the EXP-HYDRO model are implemented at 41 meso-scale (500–5000 km 2 ) catchments in the Pacific Northwest region of USA. We use two complementary metrics of long-term spatial climate variability, moisture homogeneity index ( I M ) and temperature variability index ( I TV ), to analyze the performance improvement with distributed model. Results show that the distributed model performs better than the lumped model in 38 out of 41 catchments, and noticeably better (>10% improvement) in 13 catchments. Furthermore, spatial variability of moisture distribution alone is insufficient to explain the observed patterns of model performance improvement. For catchments with low moisture homogeneity ( I M < 80%), I M is a better predictor of model performance improvement than I TV ; whereas for catchments with high moisture homogeneity ( I M > 80%), I TV is a better predictor of performance improvement than I M . Based on the results, we conclude that: (1) catchments that have low homogeneity of moisture distribution are the obvious candidates for using spatially distributed meteorological inputs, and (2) catchments with a homogeneous moisture distribution benefit from spatially distributed meteorological inputs if they also have high spatial variability of precipitation phase (rain vs. snow). [ABSTRACT FROM AUTHOR]

Summary: The Mekong River Basin in South East Asia has experienced in recent decades large inter-annual variations between large floods and severe droughts. The general perception is that climate variability has increased in the region. In this paper our aim was to examine the role of El Niño-Southern Oscillation (ENSO) on these recent climate variations. To achieve our aim, we analysed the influence of ENSO on precipitation (over period 1981–2005) and discharge (1910–2008) by using spatial GIS analyses and statistical methods, such as linear correlations, spectral analysis and stochastic regression models. We found that the Mekong’s hydrology was significantly influenced by ENSO, particularly in the decay year of ENSO events. The influence was strongest in the southern parts of the basin. The precipitation and discharge decreased (increased) during El Niño (La Niña) and the annual flood period was shorter (longer) during El Niño (La Niña). We found also decadal variations in the ENSO-discharge relationship. Our findings indicate high potential for developing prediction methods for ENSO induced hydrological anomalies. These findings may significantly improve the possibility of preparing for extreme hydrological events, either drought or exceptional floods, and thus potentially improve water resources management in the basin. [ABSTRACT FROM AUTHOR]

Summary: Miyun Reservoir is the main raw water source for Beijing’s domestic water supply. Flow discharge to Miyun Reservoir decreased drastically over a 50-year period, from 1956 to 2005, and had seriously affected Beijing’s water supply. Climate variability and human activity had been identified as the two main reasons for the decrease in flow. Here, climate variability refers to changes in precipitation and temperature. Impact of human activity includes direct withdrawal of water (referred to as “direct abstraction” in this paper) from the river (primary) or groundwater and indirect impact due to man-made changes in land use and vegetation in the upstream of the reservoir. According to the historical record, “direct abstraction” from the upstream of the reservoir has increased significantly since 1984. The study period was split into two sub-periods, from 1956 to 1983 and from 1984 to 2005. Based on the historical record, annual runoff in the upstream catchment of Miyun Reservoir (i.e., inflow into the reservoir) had decreased from 90.3mm to 41.8mm for the two sub-periods, a decrease of 48.5mm. Over the same period, average annual “direct abstraction” increased from 2.2mm to 13.4mm, an increase of 11.2mm. The latter accounted for 23% (11.2mm/48.5mm) of the decrease in inflow into the reservoir. This study utilised: (1) a distributed hydrological model (geomorphology-based hydrological model, or GBHM) and (2) a climate elasticity model to conduct a quantitative assessment of the impact of climate variability and the indirect impact of human activity on the inflow into the reservoir. Simulation results of GBHM and the climate elasticity model showed that climate impact was accountable for about 55% and 51% of the decrease in reservoir inflow, respectively. The indirect impact of human activity (mainly man-made land use and vegetation changes) accounted for 18% of the decrease in reservoir inflow. [Copyright &y& Elsevier]

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility. [ABSTRACT FROM AUTHOR]