Accelerated melting of glaciers is expected to have a negative effect on 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 glacial melting, but also to map how glacial meltwater contributes to current and future water use, which often occurs at considerable distance downstream of the terminus of the glacier. While the dynamics of tropical glacial melt are increasingly well understood and documented, major uncertainty remains on how the contribution of tropical glacial meltwater 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 the contribution of glacial meltwater to human water use at an unprecedented scale and resolution. We find that the number of users relying continuously on water resources with a high (>25%) long-term average contribution from glacial melt is low (391 000 domestic users, 398 km 2 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 km 2 of irrigated land, and 732 MW of hydropower production in the driest month of a drought year). A large proportion of domestic and agricultural users are 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.

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

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.

The green revolution represents one of the greatest environmental changes in India over the last century. The Upper Ganges (UG) basin is experiencing rapid rates of change of land cover and irrigation practices. In this study, we investigated the historical rate of change and created future scenario projections by means of 30 m-resolution multitemporal Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus data of the UG basin. Post-classification change analysis methods were applied to Landsat images in order to detect and quantify land-cover changes in the UG basin. Subsequently, Markov chain analysis was applied to project future scenarios of land-cover change. Fifteen different scenarios were generated based on historic land-cover change. These scenarios diverged in terms of future projections, highlighting the dynamic nature of the changes. This study has shown that between the years 1984 and 2010 the main land-cover change trends are conversion from shrubs to forest (+4.7%), urbanization (+5.8%), agricultural expansion (+1.3%), and loss of barren land (-9.5%). The land-cover change patterns in the UG basin were mapped and quantified, showing the capability of Landsat data in providing accurate land-cover maps. These results, in combination with those derived from the Markov model, provide the necessary evidence base to support regional land-use planning and develop future-proof water resource management strategies.

Abstract. 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 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.

The decreasing trend in rainfall in the last few decades over the Indo-Gangetic Plains of northern India as observed in groundbased 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.