Search

As the negative impacts of hydrological extremes increase in large parts of the world, a better understanding of the drivers of change in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. To fill this gap, we present an IAHS Panta Rhei benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area (Kreibich et al. 2017, 2019). The contained 45 paired events occurred in 42 different study areas (in three study areas we have data on two paired events), which cover different socioeconomic and hydroclimatic contexts across all continents. The dataset is unique in covering floods and droughts, in the number of cases assessed and in the amount of qualitative and quantitative socio-hydrological data contained. References to the data sources are provided in 2023-001_Kreibich-et-al_Key_data_table.xlsx where possible. Based on templates, we collected detailed, review-style reports describing the event characteristics and processes in the case study areas, as well as various semi-quantitative data, categorised into management, hazard, exposure, vulnerability and impacts. Sources of the data were classified as follows: scientific study (peer-reviewed paper and PhD thesis), report (by governments, administrations, NGOs, research organisations, projects), own analysis by authors, based on a database (e.g. official statistics, monitoring data such as weather, discharge data, etc.), newspaper article, and expert judgement. The campaign to collect the information and data on paired events started at the EGU General Assembly in April 2019 in Vienna and was continued with talks promoting the paired event data collection at various conferences. Communication with the Panta Rhei community and other flood and drought experts identified through snowballing techniques was important. Thus, data on paired events were provided by professionals with excellent local knowledge of the events and risk management practices.

We thank Arnbjerg-Nielsen and co-authors for their constructive contribution. We endorse their key comments and suggestions on how to increase awareness of and action on water quality interactions in the water–energy–food (WEF) nexus. Here, we advance the discussion, commenting on the scope of water quality to embrace ecosystem as well as human needs, and the importance of transdisciplinarity and focusing at the city/aquifer/drainage basin scale in WEF nexus hotspots in ensuring that water quality is considered in WEF nexus approaches. We also identify how recent global events, the COVID-19 pandemic and the 26th Conference of the Parties to the UN Framework Convention on Climate Change (UNFCCC COP 26), may intensify the WEF nexus and its water quality interlinkages, highlighting the need to weave WEF considerations into addressing the United Nations Sustainable Development Goals and the climate and biodiversity emergencies.

A recent symposium and workshop in Khartoum, the capital of the Republic of Sudan, brought together broad expertise from three universities to address the current burden of communicable and non-communicable diseases facing the Sudanese healthcare system. These meetings identified common challenges that impact the burden of diseases in the country, most notably gaps in data and infrastructure which are essential to inform and deliver effective interventions. Non-communicable diseases, including obesity, type 2 diabetes, renal disease and cancer are increasing dramatically, contributing to multimorbidity. At the same time, progress against communicable diseases has been slow, and the burden of chronic and endemic infections remains considerable, with parasitic diseases (such as malaria, leishmaniasis and schistosomiasis) causing substantial morbidity and mortality. Antimicrobial resistance has become a major threat throughout the healthcare system, with an emerging impact on maternal, neonatal, and paediatric populations. Meanwhile, malnutrition, micronutrient deficiency, and poor perinatal outcomes remain common and contribute to a lifelong burden of disease. These challenges echo the UN sustainable development goals and concentrating on them in a unified strategy will be necessary to address the national burden of disease. At a time when the country is going through societal and political transition, we draw focus on the country and the need for resolution of its healthcare needs.

Cities are concentrations of demand to water and energy systems that rely on resources under increasing pressure from scarcity and climate change mitigation targets. They are linked in many ways across their different components, the collection of which is termed a nexus. In industrialised countries, the residential end-use component of the urban water-energy nexus has been identified as significant. However, the effect of the end-use water and energy interdependence on urban dynamics had not been studied. In this work, a novel system dynamics model is developed with an explicit representation of the water-energy interactions at the residential end use and their influence on the demand for resources. The model includes an endogenous carbon tax based climate change mitigation policy which aims to meet carbon targets by reducing consumer demand through price. It also encompasses water resources planning with respect to system capacity and supply augmentation. Using London as a case study, we show that the inclusion of end-use interactions has a major impact on the projections of water sector requirements. In particular, future water demand per capita is lower, and less supply augmentation is needed than would be planned for without considering the interactions. We find that deep decarbonisation of electricity is necessary to maintain an acceptable quality of life while remaining within water and greenhouse gas emissions constraints. The model results show a clear need for consideration of the end-use level water-energy interactions in policy analysis. The modelling tool provides a base for this that can be adapted to the context of any industrialised country.

Accelerated glaciers melt is expected to affect negatively the water resources of mountain regions and their adjacent lowlands, with tropical mountain regions being among the most vulnerable. In order to quantify those impacts, it is necessary to understand the changing dynamics of glacier melting, but also to map how glacier melt water contributes to current and future water use, which often occurs at considerable distance downstream of the glacier terminus. While the dynamics of tropical glacier melt are increasingly well understood and documented, major uncertainty remains on how tropical glacier meltwater contribution propagates through the hydrological system, and hence how it contributes to various types of human water use in downstream regions. Therefore, in this paper we present a detailed regional mapping of current water demand in regions downstream of the major tropical glaciers. We combine these maps with a regional water balance model to determine the dominant spatiotemporal patterns of glacier meltwater contribution to human water use at unprecedented scale and resolution. We find that the number of users relying continuously on water resources with a high (>25%) long-term average glacier melt contribution is low (391 000 domestic users, 398 km2 of irrigated land, and 11 MW of hydropower production). But this reliance increases sharply during drought conditions (up to 3.92 million domestic users, 2096 km2 of irrigated land, and 732 MW of hydropower production in the driest month of a drought year). A large share of domestic and agricultural users is located in rural regions where climate adaptation capacity tends to be low. Therefore, we suggest that adaptation strategies should focus on increasing the natural and artificial water storage and regulation capacity to bridge dry periods.

An initial ground validation of the Integrated Multisatellite Retrievals for GPM (IMERG) Day-1 product from March 2014 to August 2015 is presented for the tropical Andes. IMERG was evaluated along with the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) against 302 quality-controlled rain gauges across Ecuador and Peru. Detection, quantitative estimation statistics, and probability distribution functions are calculated at different spatial (0.1°, 0.25°) and temporal (1 h, 3 h, daily) scales. Precipitation products are analyzed for hydrometeorologically distinct subregions. Results show that IMERG has a superior detection and quantitative rainfall intensity estimation ability than TMPA, particularly in the high Andes. Despite slightly weaker agreement of mean rainfall fields, IMERG shows better characterization of gauge observations when separating rainfall detection and rainfall rate estimation. At corresponding space-time scales, IMERG shows better estimation of gauge rainfall probability distributions than TMPA. However, IMERG shows no improvement in both rainfall detection and rainfall rate estimation along the dry Peruvian coastline, where major random and systematic errors persist. Further research is required to identify which rainfall intensities are missed or falsely detected and how errors can be attributed to specific satellite sensor retrievals. The satellite-gauge difference was associated with the point-area difference in spatial support between gauges and satellite precipitation products, particularly in areas with low and irregular gauge network coverage. Future satellite-gauge evaluations need to identify such locations and investigate more closely interpixel point-area differences before attributing uncertainties to satellite products. Por pares

Despite growing interest in resilience, there is still significant scope for increasing its conceptual clarity and practical relevance in socio-hydrological contexts: specifically, questions of how socio-hydrological systems respond to and cope with perturbations and how these connect to resilience remain unanswered. In this opinion paper, we propose a novel conceptual framework for understanding and assessing resilience in coupled socio-hydrological contexts, and encourage debate on the inter-connections between socio-hydrology and resilience. Taking a systems perspective, we argue that resilience is a set of systematic properties with three dimensions: absorptive, adaptive, and transformative, and contend that socio-hydrological systems can be viewed as various forms of human–water couplings, reflecting different aspects of these interactions. We propose a framework consisting of two parts. The first part addresses the identity of socio-hydrological resilience, answering questions such as resilience of what in relation to what. We identify three existing framings of resilience for different types of human–water systems and subsystems, which have been used in different fields: (1) the water subsystem, highlighting hydrological resilience to anthropogenic hazards; (2) the human subsystem, foregrounding social resilience to hydrological hazards; and (3) the coupled human–water system, exhibiting socio-hydrological resilience. We argue that these three system types and resiliences afford new insights into the clarification and evaluation of different water management challenges. The first two types address hydrological and social states, while the third type emphasises the feedbacks and interactions between human and water components within complex systems subject to internal or external disturbances. In the second part, we focus on resilience management and develop the notion of the resilience canvas, a novel heuristic device to identify possible pathways and to facilitate the design of bespoke strategies for enhancing resilience in the socio-hydrological context. The resilience canvas is constructed by combining absorptive and adaptive capacities as two axes. At the corners of the resulting two-dimensional space are four quadrants which we conceptualise as representing resilient, vulnerable, susceptible, and resistant system states. To address projected change-induced uncertainties, we recommend that efforts now be focused on shifting socio-hydrological systems from resistant towards resilient status. In sum, the novel framework proposed here clarifies the ambiguity inherent in socio-hydrological resilience, and provides a viable basis for further theoretical and practical development.

Data assimilation has been widely tested for flood forecasting, although its use in operational systems is mainly limited to a simple statistical error correction. This can be due to the complexity involved in making more advanced formal assumptions about the nature of the model and measurement errors. Recent advances in the definition of rating curve uncertainty allow estimating a flow measurement error that includes both aleatory and epistemic uncertainties more explicitly and rigorously than in the current practice. The aim of this study is to understand the effect such a more rigorous definition of the flow measurement error has on real-time data assimilation and forecasting. This study, therefore, develops a comprehensive probabilistic framework that considers the uncertainty in model forcing data, model structure, and flow observations. Three common data assimilation techniques are evaluated: (1) Autoregressive error correction, (2) Ensemble Kalman Filter, and (3) Regularized Particle Filter, and applied to two locations in the flood-prone Oria catchment in the Basque Country, northern Spain. The results show that, although there is a better match between the uncertain forecasted and uncertain true flows, there is a low sensitivity for the threshold exceedances used to issue flood warnings. This suggests that a standard flow measurement error model, with a spread set to a fixed flow fraction, represents a reasonable trade-off between complexity and realism. Standard models are therefore recommended for operational flood forecasting for sites with well-defined stage-discharge curves that are based on a large range of flow observations.

Quantifying the impact of land use and cover (LUC) change on catchment hydrological response is essential for land-use planning and management. Yet hydrologists are often not able to present consistent and reliable evidence to support such decision-making. The issue tends to be twofold: a scarcity of relevant observations, and the difficulty of regionalizing any existing observations. This study explores the potential of a paired catchment monitoring network to provide statistically robust, regionalized predictions of LUC change impact in an environment of high hydrological variability. We test the importance of LUC variables to explain hydrological responses and to improve regionalized predictions using 24 catchments distributed along the Tropical Andes. For this, we calculate first 50 physical catchment properties, and then select a subset based on correlation analysis. The reduced set is subsequently used to regionalize a selection of hydrological indices using multiple linear regression. Contrary to earlier studies, we find that incorporating LUC variables in the regional model structures increases significantly regression performance and predictive capacity for 66% of the indices. For the runoff ratio, baseflow index, and slope of the flow duration curve, the mean absolute error reduces by 53% and the variance of the residuals by 79%, on average. We attribute the explanatory capacity of LUC in the regional model to the pairwise monitoring setup, which increases the contrast of the land-use signal in the data set. As such, it may be a useful strategy to optimize data collection to support watershed management practices and improve decision-making in data-scarce regions.

Changes in land use and land cover are major drivers of hydrological alteration in the tropical Andes. However, quantifying their impacts is fraught with difficulties because of the extreme diversity in meteorological boundary conditions, which contrasts strongly with the lack of knowledge about local hydrological processes. Although local studies have reduced data scarcity in certain regions, the complexity of the tropical Andes poses a big challenge to regional hydrological prediction. This study analyses data generated from a participatory monitoring network of 25 headwater catchments covering three of the major Andean biomes (páramo, jalca and puna) and links their hydrological responses to main types of human interventions (cultivation, afforestation and grazing). A paired catchment setup was implemented to evaluate the impacts of change using a ‘trading space‐for‐time’ approach. Catchments were selected based on regional representativeness and contrasting land use types. Precipitation and discharge have been monitored and analysed at high temporal resolution for a time period between 1 and 5 years. The observed catchment responses clearly reflect the extraordinarily wide spectrum of hydrological processes of the tropical Andes. They range from perennially humid páramos in Ecuador and northern Peru with extremely large specific discharge and baseflows, to highly seasonal, flashy catchments in the drier punas of southern Peru and Bolivia. The impacts of land use are similarly diverse and their magnitudes are a function of catchment properties, original and replacement vegetation and management type. Cultivation and afforestation consistently affect the entire range of discharges, particularly low flows. The impacts of grazing are more variable but have the largest effect on the catchment hydrological regulation. Overall, anthropogenic interventions result in increased streamflow variability and significant reductions in catchment regulation capacity and water yield, irrespective of the hydrological properties of the original biome. Por pares

Satellite precipitation products are becoming increasingly useful to complement rain gauge networks in regions where these are too sparse to capture spatial precipitation patterns, such as in the Tropical Andes. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (TPR) was active for 17 years (1998–2014) and has generated one of the longest single-sensor, high-resolution, and high accuracy rainfall records. In this study, high-resolution (5 km) gridded mean monthly climatological precipitation is derived from the raw orbital TPR data (TRMM 2A25) and merged with 723 rain gauges using multiple satellite-gauge (S-G) merging approaches. The resulting precipitation products are evaluated by cross validation and catchment water balances (runoff ratios) for 50 catchments across the Tropical Andes. Results show that the TPR captures major synoptic and seasonal precipitation patterns and also accurately defines orographic gradients but underestimates absolute monthly rainfall rates. The S-G merged products presented in this study constitute an improved source of climatological rainfall data, outperforming the gridded TPR product as well as a rain gauge-only product based on ordinary Kriging. Among the S-G merging methods, performance of inverse distance interpolation of satellite-gauge residuals was similar to that of geostatistical methods, which were more sensitive to gauge network density. High uncertainty and low performance of the merged precipitation products predominantly affected regions with low and intermittent precipitation regimes (e.g., Peruvian Pacific coast) and is likely linked to the low TPR sampling frequency. All S-G merged products presented in this study are available in the public domain. Por pares

The impact of a changing climate on the Amazon basin is a subject of intensive research because of its rich biodiversity and the significant role of rainforests in carbon cycling. Climate change has also a direct hydrological impact, and increasing efforts have focused on understanding the hydrological dynamics at continental and subregional scales, such as the Western Amazon. New projections from the Coupled Model Inter-comparison Project Phase 5 ensemble indicate consistent climatic warming and increasing seasonality of precipitation in the Peruvian Amazon basin. Here we use a distributed land surface model to quantify the potential impact of this change in the climate on the hydrological regime of the upper Amazon river. Using extreme value analysis, historical and future projections of the annual minimum, mean, and maximum river flows are produced for a range of return periods between 1 and 100 yr.Weshow that the RCP 4.5 and 8.5 scenarios of climate change project an increased severity of the wet season flood pulse (7.5% and 12% increases respectively for the 100 yr return floods). These findings agree with previously projected increases in high extremes under the Special Report on Emissions Scenarios climate projections, and are important to highlight due to the potential consequences on reproductive processes of in-stream species, swamp forest ecology, and socio-economy in the floodplain, amidst a growing literature that more strongly emphasises future droughts and their impact on the viability of the rainforest system over greater Amazonia. This paper became research highlight 'Amazon flooding’ in Nature Climate Change, 6, 232; doi:10.1038/nclimate2949 ispartof: Environmental Research Letters vol:11 issue:1 pages:1-9 status: published

The Tropical Rainfall Measuring Mission 3B42 precipitation estimates are widely used in tropical regions for hydrometeorological research. Recently, version 7 of the product was released. Major revisions to the algorithm involve the radar reflectivity-rainfall rate relationship, surface clutter detection over high terrain, a new reference database for the passive microwave algorithm, and a higher-quality gauge analysis product for monthly bias correction. To assess the impacts of the improved algorithm, the authors compare the version 7 and the older version 6 products with data from 263 rain gauges in and around the northern Peruvian Andes. The region covers humid tropical rain forest, tropical mountains, and arid-to-humid coastal plains. The authors find that the version 7 product has a significantly lower bias and an improved representation of the rainfall distribution. They further evaluated the performance of the version 6 and 7 products as forcing data for hydrological modeling by comparing the simulated and observed daily streamflow in nine nested Amazon River basins. The authors find that the improvement in the precipitation estimation algorithm translates to an increase in the model Nash-Sutcliffe efficiency and a reduction in the relative bias between the observed and simulated flows by 30%-95%. Por pares

The EVO Pilot (EVOp) was an ambitious two year project to test the value of new cloud technologies for connecting and integrating fragmented data, models, and tools to deliver new holistic approaches to environmental challenges. The need for such an approach has become increasingly clear as we seek to improve food, water and energy security. These all require a new way of working which spansdisciplines and organisations, and that breaks down science-culture boundaries. If successful, the project would demonstrate the vision and opportunities for further funding, attract academic, policy, industry, and global partners, and create a step-change in the way that NERC science is delivered and exploited.

Dealing with the massive increase in global data availability of all sorts is increasingly being known as big data science. Indeed, largely leveraged by the internet, a new resource of data sets emerges that are so large and heterogeneous that they become awkward to work with. New algorithms, methods and models are needed to filter such data to find trends, test hypotheses, make predictions and quantify uncertainties. As a considerable share of the data relate to environmental processes (e.g., satellite images, distributed sensor networks), this evolution provides exciting challenges for environmental sciences, and hydrology in particular. Web-enabled models are a promising approach to process large and distributed data sets, and to provide tailored products for a variety of end-users. It will also allow hydrological models to be used as building blocks in larger earth system simulation systems. However, in order to do so we need to reconsider the ways that hydrological models are built, results are made available, and uncertainties are quantified. We present the results of an experimental proof of concept of a hydrological modelling web-service to process heterogeneous hydrological data sets. The hydrological model itself consists of a set of conceptual model routines implemented with on a common platform. This framework is linked to global and local data sets through web standards provided by the Open Geospatial Consortium, as well as to a web interface that enables an end-user to request stream flow simulations from a self-defined location. In essence, the proof-of-concept can be seen as an implementation of the Models of Everywhere concept introduced by Beven in 2007. Although the setup is operational and effectively simulates stream flow, we identify several bottlenecks for optimal hydrological simulation in a web-context. The major challenges we identify are related to (1) model selection; (2) uncertainty quantification, and (3) user interaction and scenario analysis. Model selection is inherent to hydrological modelling, because of the large spatial and temporal variability of processes, which inhibits the use of one optimal model structure. However, in a web context it becomes paramount that such selection is automatic, yet objective and transparent. Similarly, uncertainty quantification is a mainstream practice in hydrological modelling, but in a web-context uncertainty analysis face unprecedented challenges in terms of tracking uncertainties throughout a possibly geographically distributed workflow, as well as dealing with an extreme heterogeneity of data availability. Lastly, the ability of end-users to interact directly with hydrological models poses specific challenges in terms of mapping user scenarios (e.g., a scenario of land-use change) into the model parameter space for prediction and uncertainty quantification. The setup has been used in several scientific experiments, including the large-scale UK consortium project on an Environmental Virtual Observatory pilot.

Multi-objective criteria have long been used to infer hydrological simulations and fit the natural world. On the other hand, modelling frameworks are also becoming more and more popular as identification of the processes occurring in a catchment is still a very uncertain matter. In theory, multi-objective criteria and multi-model frameworks should be used in combination so that the ’representation’ of the catchment is fitted to the observations, not only the simulated results. In practise those approaches are highly computationally demanding. The modeller is often obliged to find a compromise reducing either the number of objective functions or model structures taken into consideration. This compromise is becoming obsolete using parallel computing. In the present study we investigate the extend to which model selection algorithms and regionalisation techniques can be improved by such facilities and highlight the challenges that still need to be addressed. The model simulations are obtained using an ensemble of conceptual lumped models (FUSE by Clark et al. 2008), but techniques and suggestions are of general use and applicable to any modelling frameworks. In particular we developed a novel model selection algorithm tuned to drastically reduce the subjectivity in the analysis. The procedure was automated and coupled with redundancy reduction techniques such as PCA and Cluster Analysis. Results show that the actual model ’representation’ has the shape of a set of complementing model structures. It is also possible to capture intra-annum dynamics of the response as the algorithm recognises subtle variations in the selected model structures in different seasons. Similar variations can be found analysing different catchments. This suggests the same methodology would be suitable for analysing spatial patterns in the distribution of suitable model structures and maybe long term dynamics in relation with expedited climate modifications. Although the mentioned methodology has proven to be successful with regards to the case study, some limitations are worth noting. If this is going to be applied to the more general case of ’models of everywhere’, for instance, there could be dominant processes not described in the FUSE framework. Further studies could therefore extend the current framework to include routines able to simulate missing processes.