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Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety ofpredictions from dynamical, physics-based models - such as numerical weather prediction, climate, land, hydrology, and Earth system models - into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporatinginitial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes., Hydrology and Earth System Sciences, 27 (9), ISSN:1027-5606, ISSN:1607-7938

Sub-Saharan Africa is affected by a high level of temporal and spatial climate variability, with large impacts on water resources, human lives, and economies, notably through hydrological extremes such as floods. Nevertheless, the key climatic factors driving interannual variability in flood frequency remain poorly documented and understood. To address this research gap, we first compile information on large-scale climate drivers that may potential affect sub-Saharan African hydroclimate (e.g., El Niño–Southern Oscillation, Atlantic Multidecadal Variability). Then, using a new 65-year long daily streamflow dataset of over 600 stations in sub-Saharan Africa, a bootstrapped stepwise regression and relative importance analysis is applied to quantify the relative contribution of different ocean basins to interannual variability in flood frequency between 1950 and 2014. Results show that interannual variations in the frequency of flood events are significantly linked to different modes of climate variability in the Pacific, Indian, and Atlantic Oceans. These modes of climate variability together explain around 60% of observed interannual variation in seasonal flood frequency. The relative influence of each ocean basin, however, differs from one region to another. The Indian and Pacific Oceans, for instance, have significant influences on interannual variations in the frequency of floods between December and May across much of southern and eastern Africa. In western Africa, the Mediterranean and Atlantic Oceans appear to have a dominant influence between September and November. In central Africa, the relative influence of different oceans basins is seasonally variable. Using the best combination of Sea-Surface Temperature predictors, we then examine projected future trends using a large ensemble of climate models from the CMIP6 experiments.

Almost 2 billion people depend on freshwater provided by the Asian water towers, yet long-term runoff estimation is challenging in this high-mountain region with a harsh environment and scarce observations. Most hydrologic models rely on observed runoff for calibration, and have limited applicability in the poorly gauged Asian water towers. To overcome such limitations, here we propose a novel data-driven model, SM2R (Soil Moisture to Runoff), to simulate monthly runoff based on soil moisture dynamics using reanalysis forcing data. The SM2R model was applied and examined in 20 drainage basins across seven Asian water towers during the past four decades of 1981–2020. Without invoking any observations for calibration, the overall good performance of SM2R-derived runoff (correlation coefficient ≥0.74 and normalized root mean square error ≤0.22 compared to observed runoff at 20 gauges) suggests considerable potential for runoff simulation in poorly gauged basins. Even though the SM2R model is forced by ERA5-Land (ERA5L) reanalysis data, it largely outperforms the ERA5L-estimated runoff across the seven Asian water towers, particularly in basins with widely distributed glaciers and frozen soil. The SM2R approach is highly promising for constraining hydrologic variables from soil moisture information. Our results provide valuable insights for not only long-term runoff estimation over key Asian basins, but also understanding hydrologic processes across poorly gauged regions globally.

Accurate long-term flood predictions are increasingly needed for flood risk management in a changing climate, but are hindered by the underestimation of climate variability by climate models. Here, we drive a statistical flood model with a large ensemble of dynamical CMIP5-6 predictions of precipitation and temperature. Predictions of UK winter flooding (95th streamflow percentile) have low skill when using the raw 676-member ensemble averaged over lead times of 2–5 years from the initialization date. Sub-selecting 20 ensemble members that adequately represent the multiyear temporal variability in the North Atlantic Oscillation (NAO) significantly improves the flood predictions. Applying this method we show positive skill in 46% of stations compared to 26% using the raw ensemble, primarily in regions most strongly influenced by the NAO. Our findings reveal the potential of decadal predictions to inform flood risk management at long lead times.

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology and Earth system models – into a final prediction product. They are recognised as a promising way of enhancing prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at sub-seasonal to decadal scales, a better appreciation of the strengths of machine learning, plus expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally-expensive offline land model, can minimize the effect of biases that exist within dynamical outputs without explicit bias correction and downscaling, benefit from the strengths of machine learning models, and can learn from large datasets, while combining different sources of predictability with varying time-horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities. These include obtaining physically-explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating modelled initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

Universal access to safe drinking water is essential to population health and well-being, as recognized in the Sustainable Development Goals (SDG). To develop targeted policies which improve urban access to improved water and ensure equity, there is the need to understand the spatial heterogeneity in drinking water sources and the factors underlying these patterns. Using the Shannon Entropy Index and the Index of Concentration at the Extremes at the enumeration area level, we analyzed census data to examine the spatial heterogeneity in drinking water sources and neighborhood income in the Greater Accra Metropolitan Area (GAMA), the largest urban agglomeration in Ghana. GAMA has been a laboratory for studying urban growth, economic security, and other concomitant socio-environmental and demographic issues in the recent past. The current study adds to this literature by telling a different story about the spatial heterogeneity of GAMA’s water landscape at the enumeration area level. The findings of the study reveal considerable geographical heterogeneity and inequality in drinking water sources not evidenced in previous studies. We conclude that heterogeneity is neither good nor bad in GAMA judging by the dominance of both piped water sources and sachet water (machine-sealed 500-ml plastic bag of drinking water). The lessons from this study can be used to inform the planning of appropriate localized solutions targeted at providing piped water sources in neighborhoods lacking these services and to monitor progress in achieving universal access to improved drinking water as recognized in the SDG 6 and improving population health and well-being.

The ability to predict the frequency and magnitude of flooding at lead times of 1 to 10 years is of great interest to governments and institutions responsible for flood risk management. However, at these lead times there is significant uncertainty about dynamical changes in atmospheric circulation. The current generation of models underestimate the predictable signal of the North Atlantic Oscillation (NAO), the principal mode of variability in North Atlantic atmospheric circulation, leading to low confidence in predictions of regional precipitation and flooding. Recent work has shown that by post-processing a sufficiently large model ensemble, decadal variations in North Atlantic winter climate can become highly predictable (Smith et al., 2020). Here, we investigate whether this NAO-matching technique can be used to improve the skill of flood forecasts at decadal lead times in the United Kingdom. We use a large ensemble of decadal hindcasts consisting of 169 members drawn from CMIP phases 5 and 6, and observed flood records for the period 1960-2015. Following Smith et al. (2020), we adjust the variance of the raw ensemble mean NAO to match that of the observed predictable signal, then select the ensemble members showing the lowest absolute difference with the variance-adjusted ensemble mean. Working only with the selected members (n=20), we supply the ensemble mean precipitation and temperature to a distributional regression model to predict the occurrence and magnitude of winter floods at lead times of 1 to 10 years. We compare these predictions with those from an equivalent model which uses predictors drawn from the full ensemble (n=169) to assess the improvement in predictive skill. Our preliminary results suggest that NAO-matching shows promise at improving decadal flood predictions in northern Europe.ReferenceSmith, D.M., Scaife, A.A., Eade, R. et al. North Atlantic climate far more predictable than models imply. Nature 583, 796–800 (2020). https://doi.org/10.1038/s41586-020-2525-0.

Drought early warning systems (DEWSs) aim to spatially monitor and forecast risk of water shortage to inform early, risk-mitigating interventions. However, due to the scarcity of in situ monitoring in groundwater-dependent arid zones, spatial drought exposure is inferred using maps of satellite-based indicators such as rainfall anomalies, soil moisture, and vegetation indices. On the local scale, these coarse-resolution proxy indicators provide a poor inference of groundwater availability. The improving affordability and technical capability of modern sensors significantly increases the feasibility of taking direct groundwater level measurements in data-scarce, arid regions on a larger scale. Here, we assess the potential of in situ monitoring to provide a localized index of hydrological drought in Somaliland. We find that calibrating a lumped groundwater model with a short time series of groundwater level observations substantially improves the quantification of local water availability when compared to satellite-based indices. By varying the calibration length, we find that a 5-week period capturing both wet and dry season conditions provides most of the calibration capacity. This suggests that short monitoring campaigns are suitable for improving estimations of local water availabilities during drought. Short calibration periods have practical advantages, as the relocation of sensors enables rapid characterization of a large number of wells. These well simulations can supplement continuous in situ monitoring of strategic point sources to setup large-scale monitoring systems with contextualized and localized information on water availability. This information can be used as early warning evidence for the financing and targeting of early actions to mitigate impacts of hydrological drought.

The exposure of urban populations to flooding is highly heterogeneous, with the negative impacts of flooding experienced disproportionately by the poor. In developing countries experiencing rapid urbanization and population growth a key distinction in the urban landscape is between planned development and unplanned, informal development, which often occurs on marginal, flood-prone land. Flood risk management in the context of informality is challenging, and may exacerbate existing social inequalities and entrench poverty. Here, we adapt an existing socio-hydrological model of human-flood interactions to account for a stratified urban society consisting of planned and informal settlements. In the first instance, we use the model to construct four system archetypes based on idealized scenarios of risk reduction and disaster recovery. We then perform a sensitivity analysis to examine the relative importance of the differential values of vulnerability, risk-aversion, and flood awareness in determining the relationship between flood risk management and social inequality. The model results suggest that reducing the vulnerability of informal communities to flooding plays an important role in reducing social inequality and enabling sustainable economic growth, even when the exposure to the flood hazard remains high. Conversely, our model shows that increasing risk aversion may accelerate the decline of informal communities by suppressing economic growth. On this basis, we argue for urban flood risk management which is rooted in pro-poor urban governance and planning agendas which recognize the legitimacy and permanence of informal communities in cities.

Water logging is one of the most detrimental phenomena continuing to burden Dhaka dwellers. This study aims to spatio-temporarily identify the water logging hazard zones within Dhaka Metropolitan area and assess the extent of their water logging susceptibility based on informal settlements, built-up areas, and demographical characteristics. The study utilizes integrated geographic information system (GIS)-remote sensing (RS) methods, using the Normalized Difference Vegetation Water and Moisture Index, distance buffer zone from drainage streams, and built-up distributions to identify waterlogged zones with a temporal extent, incorporating social and infrastructural attributes to evaluate water logging effects. These indicators were integrated into an overlay GIS method to measure the vulnerability level across Dhaka city areas. The findings reveal that south and south-western parts of Dhaka were more susceptible to water logging hazards. Almost 35% of Dhaka belongs to the high/very highly vulnerable zone. Greater number of slum households were found within high to very high water logging vulnerable zones and approximately 70% of them are poorly structured. The built-up areas were observed to be increased toward the northern part of Dhaka and were exposed to severe water logging issues. The overall findings reveal the spatio-temporal distribution of the water logging vulnerabilities across the city as well as its impact on the social indicators. An integrated approach is necessary for future development plans to mitigate the risk of water logging.

The consumption of packaged water in Ghana has grown significantly in recent years. By 2017, “sachet water”—machine-sealed 500ml plastic bags of drinking water—was consumed by 33% of Ghanaian households. Reliance on sachet water has previously been associated with the urban poor, yet recent evidence suggests a customer base which crosses socioeconomic lines. Here, we conduct a repeated cross-sectional analysis of three nationally representative datasets to examine the changing demography of sachet water consumers between 2010 and 2017. Our results show that over the course of the study period sachet water has become a ubiquitous source of drinking water in Ghana, with relatively wealthy households notably increasing their consumption. In 2017, the majority of sachet water drinking households had access to another improved water source. The current rate and form of urbanisation, inadequate water governance, and an emphasis on cost recovery pose significant challenges for the expansion of the piped water supply network, leading us to conclude that sachet water will likely continue to be a prominent source of drinking water in Ghana for the foreseeable future. The main challenge for policymakers is to ensure that the growing sachet water market enhances rather than undermines Ghana’s efforts towards achieving universal and equitable access to clean drinking water and sanitation.

Mountainous regions are a hotspot for water scarcity and anthropogenic pressure on water resources. Substantial uncertainty surrounds projections of future climate and water availability. Furthermore, quantitative and distributed data on water demand are generally scarce, dispersed, and highly heterogeneous. This forms a major bottleneck to studying water resources issues and developing strategies to improve water resource management. Here we present a methodology to produce and evaluate high-resolution gridded maps of anthropogenic surface water demand with application to the Andean region. These data are disaggregated according to the major types of water demand: domestic users, irrigated area, and hydropower. This dataset was built by homogenizing, integrating, and interpolating data obtained from various national institutions in charge of water resource management as well as relevant global datasets. The maps can be used to research anthropogenic impacts on water resources, and to guide regional decision-making in regions such as the Andes.

Understanding water user behavior and its potential outcomes is important for the development of suitable water resource management options. Computational models are commonly used to assist water resource management decision making; however, while natural processes are increasingly well modeled, the inclusion of human behavior has lagged behind. Improved representation of irrigation water user behavior within models can provide more accurate and relevant information for irrigation management in the agricultural sector. This paper outlines a model that conceptualizes and proceduralizes observed farmer irrigation practices, highlighting impacts and interactions between the environment and behavior. It is developed using a bottom‐up approach, informed through field experience and farmer interaction in the state of Uttar Pradesh, northern India. Observed processes and dynamics were translated into parsimonious algorithms, which represent field conditions and provide a tool for policy analysis and water management. The modeling framework is applied to four districts in Uttar Pradesh and used to evaluate the potential impact of changes in climate and irrigation behavior on water resources and farmer livelihood. Results suggest changes in water user behavior could have a greater impact on water resources, crop yields, and farmer income than changes in future climate. In addition, increased abstraction may be sustainable but its viability varies across the study region. By simulating the feedbacks and interactions between the behavior of water users, irrigation officials and agricultural practices, this work highlights the importance of directly including water user behavior in policy making and operational tools to achieve water and livelihood security.

Ensemble techniques, common in many disciplines, have yet to be fully exploited with spatially explicit projections from land-change models. We trial a land-change model ensemble to assess the impact of policies designed to conserve tropical rainforest at the municipality scale in Brazil, noting the achievements made and challenges ahead. Four spatial model frameworks that were calibrated with the same predictor variables produced 21 counterfactual simulations of the actual landscape. Individual projections with a uniform calibration period gave estimates that between 29 and 68% of the simulated deforestation was saved, but lacked an uncertainty estimate, whilst batch projections from two different model frameworks provided a more dependable mean estimate that 38 and 49% deforestation was prevented with an uncertainty range of 1900 and 1000 km2. The consensus ensembles used agreement between the projections and found that the seven examples with a uniform calibration period produced an error margin of ±435.94 km2 and a prevented forest loss estimate of 50%. Using all 21 projections with diverse calibration periods improved these errors to ±179.26 km2 with a 53% estimate of prevented forest loss. Whilst we achieved a method of combining projections of different frameworks to reduce uncertainty of individual modelling frameworks, demonstrating a control model and accounting for non-linear conditions are challenges that will provide better confidence in this method as an operational tool. Such retrospective evidence could be used to make timely rewards for efforts of governments and municipalities to support tropical forest conservation and help mitigate deforestation.

In several developing cities flooding has become an annual occurrence which threatens lives, livelihoods and homes. However, the exposure of urban populations to climate risks such as flooding is highly heterogeneous, with the effects of climate variability felt disproportionately by the poor. Rapid urbanisation and population growth, combined with systemic factors such as complex land tenure arrangements, inadequate transportation and weak governance, has led to the proliferation of unplanned urban settlements which lack basic infrastructure and are frequently situated on marginal, flood-prone land. While the immediate effects of floods can cause physical, economic and social devestation, floods also have multiple long-lasting effects which may increase vulnerability and exacerbate inequalities. Here, we adapt an existing socio-hydrological model of human-flood interactions (Di Baldassarre et al. 2013; Viglione et al. 2014) to explicitly account for a stratified society consisting of the “haves”, who reside in planned settlements with some degree of flood protection, and the “have-nots”, who live in unplanned, informal settlements which are more vulnerable to flooding. Initially, we conduct a sensitivity analysis to identify the parameters in the original model which may be influenced by social inequality. We then introduce a new state variable to represent the transfer of wealth from the “haves” to the “have-nots”, and use this setup to investigate the effects of wealth redistribution on the co-evolution of the coupled system.References:Baldassarre, G. D. et al. (2013). Socio-hydrology: conceptualising human-flood interactions. Hydrology and Earth System Sciences, 17(8), 3295-3303. Viglione, A. et al. (2014). Insights from socio-hydrology modelling on dealing with flood risk–roles of collective memory, risk-taking attitude and trust. Journal of Hydrology, 518, 71-82.

The effects of anthropogenic water use play a significant role in determining the hydrological cycle of north India. This paper explores anthropogenic impacts within the regions’ hydrological regime by explicitly including observed human water use behaviour, irrigation infrastructure and the natural environment in the CHANSE (Coupled Human And Natural Systems Environment) socio‐hydrological modelling framework. The model is constrained by observed qualitative and quantitative information collected in the study area, along with climate and socio‐economic variables from additional sources. Four separate scenarios, including business as usual (BAU, representing observed irrigation practices), groundwater irrigation only (where the influence of the canal network is removed), canal irrigation only (where all irrigation water is supplied by diverted surface water) and rainfed only (where all human interventions are removed) are used. Under BAU conditions the modelling framework closely matched observed groundwater levels. Following the removal of the canal network, which forces farmers to rely completely on groundwater for irrigation, water levels decrease, while under a canal only scenario flooding occurs. Under the rainfed only scenario, groundwater levels similar to current business as usual conditions are observed, despite much larger volumes of recharge and discharge entering and leaving the system under BAU practices. While groundwater abstraction alone may lead to aquifer depletion, the conjunctive use of surface and groundwater resources, which includes unintended contributions of canal leakage, create conditions similar to those where no human interventions are present. Here, the importance of suitable water management practices, in maintaining sustainable water resources, are shown. This may include augmenting groundwater resources through managed aquifer recharge and reducing the impacts on aquifer resources through occasional canal water use where possible. The importance of optimal water management practices that highlight trade‐offs between environmental impact and human wellbeing are shown, providing useful information for policy makers, water managers and users.

The decreasing trend in rainfall in the last few decades over the Indo-Gangetic Plains of northern India as observed in ground-based observations puts increasing stress on groundwater because irrigation uses up to 70 of freshwater resources. In this work, we have analyzed the effects of extensive irrigation over the Gangetic Plains on the seasonal mean and intra-seasonal variability of the Indian summer monsoon, using a general circulation model and a very high-resolution soil moisture dataset created using extensive field observations in a state-of-the-art hydrological model. We find that the winter-time (November�March) irrigation has a positive feedback on the Indian summer monsoon through large scale circulation changes. These changes are analogous to a positive North Atlantic Oscillation (NAO) phase during winter months. The effects of the positive NAO phase persist from winter to spring through widespread changes in surface conditions over western and central Asia, which makes the pre-monsoon conditions suitable for a subsequent good monsoon over India. Winter-time irrigation also resulted in a reduction of low frequency intra-seasonal variability over the Indian region during the monsoon season. However, when irrigation is practiced throughout the year, a decrease in June�September precipitation over the Gangetic Plains, significant at 95 level, is noted as compared to the no-irrigation scenario. This decrease is attributed to the increase in local soil moisture due to irrigation, which results in a southward shift of the moisture convergence zone during the active phase of monsoon, decreasing its mean and intraseasonal variability. Interestingly, these changes show a remarkable similarity to the long-term trend in observed rainfall spatial pattern and low-frequency variability. Our results suggest that with a decline in the mean summer precipitation and stressed groundwater resources in the Gangetic Plains, the water crisis could exacerbate, with irrigation having a weakening effect on the regional monsoon.

In recent decades India has undergone substantial land use/land cover change as a result of population growth and economic development. Historical land use/land cover maps are necessary to quantify the impact of change at global and regional scales, improve predictions about the quantity and location of future change and support planning decisions. Here, a regional land use change model driven by district-level inventory data is used to generate an annual time series of high-resolution gridded land use/land cover maps for the Indian subcontinent between 1960–2010. The allocation procedure is based on statistical analysis of the relationship between contemporary land use/land cover and various spatially explicit covariates. A comparison of the simulated map for 1985 against remotely-sensed land use/land cover maps for 1985 and 2005 reveals considerable discrepancy between the simulated and remote sensing maps, much of which arises due to differences in the amount of land use/land cover change between the inventory data and the remote sensing maps.

We present the lulcc software package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of alternative models; and (3) additional software is required because existing applications frequently perform only the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a data set included with the package. It is envisaged that lulcc will enable future model development and comparison within an open environment.

Land use change has important consequences for biodiversity and the sustainability of ecosystem services, as well as for global environmental change. Spatially explicit land use change models improve our understanding of the processes driving change and make predictions about the quantity and location of future and past change. Here we present the lulccR package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of different models; (3) different aspects of the modelling procedure must be performed in different environments because existing applications usually only perform the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the widely used CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a dataset included with the package. It is envisaged that lulccR will enable future model development and comparison within an open environment.