ALBERT

Description: To achieve good water conservancy under the well-off society before 2020, the future water conservancy planning is undergoing in Yunnan Province. In this study, by analysing the research results of domestic relevant water evaluation index systems and combining this with the water conservancy construction key of Yunnan Province, an unique evaluation index system was proposed to evaluate the well-off water conservancy level of Yunnan Province. It is composed of three levels which are the target layer, criterion layer and index layer. And the criterion layer includes six systems, namely flood control and drought relief mitigation, reasonable allocation of water resources, highly effective water utilization, water source protection and river health security, water management and securing of water development. The analytic hierarchy process (AHP) was used to determine the weight of each index. According to the present situation of water development and the related water conservancy planning in Yunnan Province, the target value of each index and evaluation standards are put forward for Yunnan Province in 2020. The results show that the evaluation results are consistent with the actual condition of water development in Yunnan Province and can be used to examine the effects of well-off water conservancy planning.

Description: Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

Description: The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA) and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE) data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF) to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS) snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC) of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions), the stand-alone ISURF and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile). The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic) forecasting method in terms of providing improved single-valued (e.g., ensemble mean) streamflow predictions as well as meaningful ensemble predictions.

Description: The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA) and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE) data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF) to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS) snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC) of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions), the stand-alone ISURF, and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile). The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic) forecasting method in terms of providing improved single-valued (e.g., ensemble mean) streamflow predictions as well as meaningful ensemble predictions.

Description: Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented into operational forecast systems to improve the skill of forecasts to better inform real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, The Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical considerations in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modelling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

Description: In recent years, droughts have frequently hit China's terrestrial ecosystems. How these droughts affected carbon sequestration by China's terrestrial ecosystems is still unclear. In this study, the process-based Boreal Ecosystem Productivity Simulator (BEPS) model, driven by remotely sensed vegetation parameters, was employed to assess the effects of droughts on net ecosystem productivity (NEP) of terrestrial ecosystems in China for the period from 2000 to 2011. Different categories of droughts, as indicated by a standard precipitation index (SPI), extensively hit terrestrial ecosystems in China, particularly in 2001, 2006, 2009 and 2011. The national total NEP exhibited a slight decline of −11.3 Tg C yr−2 during the study period, mainly due to large reductions of NEP in typical drought-hit years 2001, 2006, 2009 and 2011, ranging from 61.1 Tg C yr−1 to 168.8 Tg C yr−1. National and regional total NEP anomalies were correlated with corresponding annual mean SPI, especially in Northwest China, North China, Central China, and Southwest China. In drought years, the reductions of NEP might be caused by a larger decrease in gross primary productivity (GPP) than in respiration (RE) (2001 and 2011), a decrease in GPP and an increase in RE (2009), or a larger increase in RE than in GPP (2006). Droughts had lagged effects of up to 3–6 months on NEP due to different reactions of GPP and RE to droughts. In east humid and warm parts of China, droughts have predominant and short-term lagged influences on NEP. In western cold and arid regions, the effects of droughts on NEP were relatively weaker and might last for a longer period of time.

Description: Rivers have been under sampled to establish them as sinks or sources of the atmospheric carbon oxide (CO2). Such poor coverage is well known for tropical and sub-tropical, particularly monsoon driven rivers. An unprecedented high-temporal-resolution (daily) sampling during July 2008–August 2009 were conducted from the Longchuanjiang River of the upper Yangtze basin, a subtropical monsoon river in China to reveal the daily-to-seasonal dynamics of the partial pressure of CO2 (pCO2) and CO2 degassing flux from the river. The pCO2 levels were supersaturated in CO2 with respect to atmospheric equilibrium (380 μatm) during the entire survey period with obvious daily and seasonal variations, ranging from 450 to 63 000 μatm with an average of 3900 μatm. pCO2 values in the surface water in the wet season were relatively low, except flooding period in November, due to a dilution effect by heavy rainfall. However, both daily and monthly minimal and maximal pCO2 also occurred in this period. In contrast, the pCO2 levels in the dry season were much higher, mainly resulted from lower pH by anthropogenic activities. Net CO2 flux and pCO2 were strongly correlated with pH, but weakly with water temperature, dissolved inorganic carbon and water discharge, and uncorrelated with particulate nutrients and biogenic elements. The estimated water-to-air CO2 degassing flux in the Longchuanjiang River was about 110 mol m−2 yr−1, with the upper limit of 460 mol m−2 yr−1. Our study also indicated that among the total organic carbon remobilized through soil erosion, around 17% (11 400 t C yr−1) of was emitted to the atmosphere, 52% (35 000 t C yr−1) deposited in the river-reservoirs system and 31% (21 000 t C yr−1) exported further downstream. High spatial and temporal resolution of estimates of CO2 emission from the world large rivers is required due to that catchment characteristics and anthropogenic activities are extremely heterogeneous in space and time.

Description: In recent years, China's terrestrial ecosystems have experienced frequent droughts. How these droughts have affected carbon sequestration by the terrestrial ecosystems is still unclear. In this study, the process-based Boreal Ecosystem Productivity Simulator (BEPS) model, driven by remotely sensed vegetation parameters, was employed to assess the effects of droughts on net ecosystem productivity (NEP) of terrestrial ecosystems in China from 2000 to 2011. Droughts of differing severity, as indicated by a standard precipitation index (SPI), hit terrestrial ecosystems in China extensively in 2001, 2006, 2009, and 2011. The national total annual NEP exhibited the slight decline of −11.3 Tg C yr−2 during the aforementioned years of extensive droughts. The NEP reduction ranged from 61.1 Tg C yr−1 to 168.8 Tg C yr−1. National and regional total NEP anomalies were correlated with the annual mean SPI, especially in Northwest China, North China, Central China, and Southwest China. The reductions in annual NEP in 2001 and 2011 might have been caused by a larger decrease in annual gross primary productivity (GPP) than in annual ecosystem respiration (ER). The reductions experienced in 2009 might be due to a decrease in annual GPP and an increase in annual ER, while reductions in 2006 could stem from a larger increase in ER than in GPP. The effects of droughts on NEP lagged up to 3–6 months, due to different responses of GPP and ER. In eastern China, where is humid and warm, droughts have predominant and short-term lagged influences on NEP. In western regions, cold and arid, the drought effects on NEP were relatively weaker but prone to lasting longer.

Description: The forthcoming three-satellite mission Swarm will allow us to investigate plasma processes and phenomena in the upper ionosphere from an in-situ multi-spacecraft perspective. Since with less than four points in space the spatiotemporal ambiguity cannot be resolved fully, analysis tools for estimating spatial gradients, wave vectors, or boundary parameters need to utilise additional information such as geometrical or dynamical constraints. This report deals with gradient estimation where the planar component is constructed using instantaneous three-point observations or, for quasi-static structures, by means of measurements along the orbits of two close spacecraft. A new least squares (LS) gradient estimator for the latter case is compared with existing finite difference (FD) schemes and also with a three-point LS technique. All available techniques are presented in a common framework to facilitate error analyses and consistency checks, and to show how arbitrary combinations of planar gradient estimators and constraints can be formed. The accuracy of LS and FD planar gradient estimators is assessed in terms of prescribed and adjustable discretization parameters to optimise their performance along the satellite orbits. Furthermore, we discuss the implications of imperfect constraint equations for error propagation, and address the effects of sub-scale structures. The two-spacecraft LS scheme is demonstrated using Cluster FGM measurements at a planar and essentially force-free plasma boundary in the solar wind where all three different types of constraints to construct out-of-plane derivatives can be applied.