The impact of lake and reservoir parameterization on global streamflow simulation.

Lakes and reservoirs affect the timing and magnitude of streamflow, and are therefore essential hydrological model components, especially in the context of global flood forecasting. However, the parameterization of lake and reservoir routines on a global scale is subject to considerable uncertainty due to lack of information on lake hydrographic characteristics and reservoir operating rules. In this study we estimated the effect of lakes and reservoirs on global daily streamflow simulations of a spatially-distributed LISFLOOD hydrological model. We applied state-of-the-art global sensitivity and uncertainty analyses for selected catchments to examine the effect of uncertain lake and reservoir parameterization on model performance. Streamflow observations from 390 catchments around the globe and multiple performance measures were used to assess model performance. Results indicate a considerable geographical variability in the lake and reservoir effects on the streamflow simulation. Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics improved for 65% and 38% of catchments respectively, with median skill score values of 0.16 and 0.2 while scores deteriorated for 28% and 52% of the catchments, with median values -0.09 and -0.16, respectively. The effect of reservoirs on extreme high flows was substantial and widespread in the global domain, while the effect of lakes was spatially limited to a few catchments. As indicated by global sensitivity analysis, parameter uncertainty substantially affected uncertainty of model performance. Reservoir parameters often contributed to this uncertainty, although the effect varied widely among catchments. The effect of reservoir parameters on model performance diminished with distance downstream of reservoirs in favor of other parameters, notably groundwater-related parameters and channel Manning's roughness coefficient. This study underscores the importance of accounting for lakes and, especially, reservoirs and using appropriate parameterization in large-scale hydrological simulations.

Integrating remotely sensed surface water extent into continental scale hydrology.

In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° × 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 in situ river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE', NSE, PBIAS%, R2, RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that remotely sensed surface water extent holds potential for improving rainfall-runoff streamflow simulations, potentially leading to a better forecast of the peak flow.

The timing of unprecedented hydrological drought under climate change.

Droughts that exceed the magnitudes of historical variation ranges could occur increasingly frequently under future climate conditions. However, the time of the emergence of unprecedented drought conditions under climate change has rarely been examined. Here, using multimodel hydrological simulations, we investigate the changes in the frequency of hydrological drought (defined as abnormally low river discharge) under high and low greenhouse gas concentration scenarios and existing water resource management measures and estimate the time of the first emergence of unprecedented regional drought conditions centered on the low-flow season. The times are detected for several subcontinental-scale regions, and three regions, namely, Southwestern South America, Mediterranean Europe, and Northern Africa, exhibit particularly robust results under the high-emission scenario. These three regions are expected to confront unprecedented conditions within the next 30 years with a high likelihood regardless of the emission scenarios. In addition, the results obtained herein demonstrate the benefits of the lower-emission pathway in reducing the likelihood of emergence. The Paris Agreement goals are shown to be effective in reducing the likelihood to the unlikely level in most regions. However, appropriate and prior adaptation measures are considered indispensable when facing unprecedented drought conditions. The results of this study underscore the importance of improving drought preparedness within the considered time horizons.

Modelling rotavirus concentrations in rivers: Assessing Uganda's present and future microbial water quality.

Faecal pathogens can be introduced into surface water through open defecation, illegal disposal and inadequate treatment of faecal sludge and wastewater. Despite sanitation improvements, poor countries are progressing slowly towards the United Nation's Sustainable Development Goal 6 by 2030. Sanitation-associated pathogenic contamination of surface waters impacted by future population growth, urbanization and climate change receive limited attention. Therefore, a model simulating human rotavirus river inputs and concentrations was developed combining population density, sanitation coverage, rotavirus incidence, wastewater treatment and environmental survival data, and applied to Uganda. Complementary surface runoff and river discharge data were used to produce spatially explicit rotavirus outputs for the year 2015 and for two scenarios in 2050. Urban open defecation contributed 87%, sewers 9% and illegal faecal sludge disposal 3% to the annual 15.6 log10 rotavirus river inputs in 2015. Monthly concentrations fell between -3.7 (Q5) and 2.6 (Q95) log10 particles per litre, with 1.0 and 2.0 median and mean log10 particles per litre, respectively. Spatially explicit outputs on 0.0833 × 0.0833° grids revealed hotspots as densely populated urban areas. Future population growth, urbanization and poor sanitation were stronger drivers of rotavirus concentrations in rivers than climate change. The model and scenario analysis can be applied to other locations.

South-to-North Water Diversion stabilizing Beijing's groundwater levels.

Groundwater (GW) overexploitation is a critical issue in North China with large GW level declines resulting in urban water scarcity, unsustainable agricultural production, and adverse ecological impacts. One approach to addressing GW depletion was to transport water from the humid south. However, impacts of water diversion on GW remained largely unknown. Here, we show impacts of the central South-to-North Water Diversion on GW storage recovery in Beijing within the context of climate variability and other policies. Water diverted to Beijing reduces cumulative GW depletion by ~3.6 km3, accounting for 40% of total GW storage recovery during 2006-2018. Increased precipitation contributes similar volumes to GW storage recovery of ~2.7 km3 (30%) along with policies on reduced irrigation (~2.8 km3, 30%). This recovery is projected to continue in the coming decade. Engineering approaches, such as water diversions, will increasingly be required to move towards sustainable water management.

Excess nutrient loads to Lake Taihu: Opportunities for nutrient reduction.

Intensive agriculture and rapid urbanization have increased nutrient inputs to Lake Taihu in recent decades. This resulted in eutrophication. We aim to better understand the sources of river export of total dissolved nitrogen (TDN) and phosphorus (TDP) to Lake Taihu in relation to critical nutrient loads. We implemented the MARINA-Lake (Model to Assess River Inputs of Nutrients to seAs) model for Lake Taihu. The MARINA-Lake model quantifies river export of dissolved inorganic and organic N and P to the lake by source from sub-basins. Results from the PCLake model are used to identify to what extent river export of nutrients exceeds critical loads. We calculate that rivers exported 61 kton of TDN and 2 kton of TDP to Lake Taihu in 2012. More than half of these nutrients were from human activities (e.g., agriculture, urbanization) in Sub-basins I (north) and IV (south). Most of the nutrients were in dissolved inorganic forms. Diffuse sources contributed 90% to river export of TDN with a relatively large share of synthetic fertilizers. Point sources contributed 52% to river export of TDP with a relatively large share of sewage systems. The relative shares of diffuse and point sources varied greatly among nutrient forms and sub-basins. To meet critical loads, river export of TDN and TDP needs to be reduced by 46-92%, depending on the desired level of chlorophyll-a. There are different opportunities to meet the critical loads. Reducing N inputs from synthetic fertilizers and P from sewage systems may be sufficient to meet the least strict critical loads. A combination of reductions in diffuse and point sources is needed to meet the most strict critical loads. Combining improved nutrient use efficiencies and best available technologies in wastewater treatment may be an effective opportunity. Our study can support the formulation of effective solutions for lake restoration.

Solar and wind energy enhances drought resilience and groundwater sustainability.

Water scarcity brings tremendous challenges to achieving sustainable development of water resources, food, and energy security, as these sectors are often in competition, especially during drought. Overcoming these challenges requires balancing trade-offs between sectors and improving resilience to drought impacts. An under-appreciated factor in managing the water-food-energy (WFE) nexus is the increased value of solar and wind energy (SWE). Here we develop a trade-off frontier framework to quantify the water sustainability value of SWE through a case study in California. We identify development pathways that optimize the economic value of water in competition for energy and food production while ensuring sustainable use of groundwater. Our results indicate that in the long term, SWE penetration creates beneficial feedback for the WFE nexus: SWE enhances drought resilience and benefits groundwater sustainability, and in turn, maintaining groundwater at a sustainable level increases the added value of SWE to energy and food production.

Increasing nitrogen export to sea: A scenario analysis for the Indus River.

The Indus River Basin faces severe water quality degradation because of nutrient enrichment from human activities. Excessive nutrients in tributaries are transported to the river mouth, causing coastal eutrophication. This situation may worsen in the future because of population growth, economic development, and climate change. This study aims at a better understanding of the magnitude and sources of current (2010) and future (2050) river export of total dissolved nitrogen (TDN) by the Indus River at the sub-basin scale. To do this, we implemented the MARINA 1.0 model (Model to Assess River Inputs of Nutrients to seAs). The model inputs for human activities (e.g., agriculture, land use) were mainly from the GLOBIOM (Global Biosphere Management Model) and EPIC (Environmental Policy Integrated Model) models. Model inputs for hydrology were from the Community WATer Model (CWATM). For 2050, three scenarios combining Shared Socio-economic Pathways (SSPs 1, 2 and 3) and Representative Concentration Pathways (RCPs 2.6 and 6.0) were selected. A novelty of this study is the sub-basin analysis of future N export by the Indus River for SSPs and RCPs. Result shows that river export of TDN by the Indus River will increase by a factor of 1.6-2 between 2010 and 2050 under the three scenarios. >90% of the dissolved N exported by the Indus River is from midstream sub-basins. Human waste is expected to be the major source, and contributes by 66-70% to river export of TDN in 2050 depending on the scenarios. Another important source is agriculture, which contributes by 21-29% to dissolved inorganic N export in 2050. Thus a combined reduction in both diffuse and point sources in the midstream sub-basins can be effective to reduce coastal water pollution by nutrients at the river mouth of Indus.

Managing the effects of multiple stressors on aquatic ecosystems under water scarcity. The GLOBAQUA project.

Water scarcity is a serious environmental problem in many European regions, and will likely increase in the near future as a consequence of increased abstraction and climate change. Water scarcity exacerbates the effects of multiple stressors, and thus results in decreased water quality. It impacts river ecosystems, threatens the services they provide, and it will force managers and policy-makers to change their current practices. The EU-FP7 project GLOBAQUA aims at identifying the prevalence, interaction and linkages between stressors, and to assess their effects on the chemical and ecological status of freshwater ecosystems in order to improve water management practice and policies. GLOBAQUA assembles a multidisciplinary team of 21 European plus 2 non-European scientific institutions, as well as water authorities and river basin managers. The project includes experts in hydrology, chemistry, biology, geomorphology, modelling, socio-economics, governance science, knowledge brokerage, and policy advocacy. GLOBAQUA studies six river basins (Ebro, Adige, Sava, Evrotas, Anglian and Souss Massa) affected by water scarcity, and aims to answer the following questions: how does water scarcity interact with other existing stressors in the study river basins? How will these interactions change according to the different scenarios of future global change? Which will be the foreseeable consequences for river ecosystems? How will these in turn affect the services the ecosystems provide? How should management and policies be adapted to minimise the ecological, economic and societal consequences? These questions will be approached by combining data-mining, field- and laboratory-based research, and modelling. Here, we outline the general structure of the project and the activities to be conducted within the fourteen work-packages of GLOBAQUA.