Due to increasing pressures on water resources, there is a need to monitor regional water resource availability in a spatially and temporally explicit manner. However, for many parts of the world, there is insufficient data to quantify stream flow or ground water infiltration rates. We present the results of a pixel-based water balance formulation to partition rainfall into evapotranspiration, surface water runoff and potential ground water infiltration. The method leverages remote sensing derived estimates of precipitation, evapotranspiration, soil moisture, Leaf Area Index, and a single F coefficient to distinguish between runoff and storage changes. The study produced significant correlations between the remote sensing method and field based measurements of river flow in two Vietnamese river basins. For the Ca basin, we found R² values ranging from 0.88–0.97 and Nash–Sutcliffe efficiency (NSE) values varying between 0.44–0.88. The R² for the Red River varied between 0.87–0.93 and NSE values between 0.61 and 0.79. Based on these findings, we conclude that the method allows for a fast and cost-effective way to map water resource availability in basins with no gauges or monitoring infrastructure, without the need for application of sophisticated hydrological models or resource-intensive data.

European agriculture and water policies require accurate information on climate change impacts on available water resources. Water accounting, that is a standardized documentation of data on water resources, is a useful tool to provide this information. Pan-European data on climate impacts do not recognize local anthropogenic interventions in the water cycle. Most European river basins have a specific toolset that is understood and used by local experts and stakeholders. However, these local tools are not versatile. Thus, there is a need for a common approach that can be understood by multi-fold users to quantify impact indicators based on local data and that can be used to synthesize information at the European level. Then, policies can be designed with the confidence that underlying data are backed-up by local context and expert knowledge. This work presents a simplified water accounting framework that allows for a standardized examination of climate impacts on water resource availability and use across multiple basins. The framework is applied to five different river basins across Europe. Several indicators are extracted that explicitly describe green water fluxes versus blue water fluxes and impacts on agriculture. The examples show that a simplified water accounting framework can be used to synthesize basin-level information on climate change impacts which can support policymaking on climate adaptation, water resources and agriculture.

Hydrological ecosystem services (HESS) describe the benefits of water for multiple purposes with an emphasis on environmental values. The value of HESS is often not realized because primary benefits (e.g., food production, water withdrawals) get the most attention. Secondary benefits such as water storage, purification or midday temperature cooling are often overlooked. This results in an incorrect evaluation of beneficial water usage in urban and rural resettlements and misunderstandings when land use changes are introduced. The objective of this paper is to propose a standard list of 17 HESS indicators that are in line with the policy and philosophy of the Consultative Group of International Agricultural Research (CGIAR) and that are measurable with earth observation technologies in conjunction with GIS and hydrological models. The HESS17 framework considered indicators that can be directly related to water flows, water fluxes and water stocks; they have a natural characteristic with minimal anthropogenic influence and must be quantifiable by means of earth observation models in combination with GIS and hydrological models. The introduction of a HESS framework is less meaningful without proper quantification procedures in place. Because of the widely diverging management options, the role of water should be categorized as (i) consumptive use (i.e., evapotranspiration and dry matter production) and (ii) non-consumptive use (stream flow, recharge, water storage). Governments and responsible agencies for integrated water management should recognize the need to include HESS17 in water allocation policies, water foot-printing, water accounting, transboundary water management, food security purposes and spatial land-use planning processes. The proposed HESS17 framework and associated methods can be used to evaluate land, soil and water conservation programs. This paper presents a framework that is non-exhaustive but can be realistically computed and applicable across spatial scales.

Droughts have directly affected at least 1.5 billion people in the last century, generating economic losses up to $124 billion. They are a recurrent, creeping meteorological hazard that may endanger the water and food security of large regions. The frequency and severity of droughts are expected to increase with climate change, especially in Africa, Central America, and also in Europe where annual losses may multiply by 7 and represent up to 2 times the size of the European economy in the medium-long term.Drought Early Warning Systems (DEWS) are key pillars of a risk-based, proactive management strategy. The increasing number of sources of EO data has remarkedly improved the monitoring capabilities of DEWS. Despite there are good examples of global and regional drought monitoring systems, these tools still lack of seasonal forecasting capabilities able to provide enough accurate and specific predictions of drought impacts at the subregion level (e.g. basin, district). These deficiencies constitute a challenge for the scientific community and provide an opportunity to improve the current services.To address this gap in the DEWS landscape, the InfoSequia DEWS is developed to integrate the strengths of spatial, satellite-derived data with machine learning techniques for seasonal forecasting. InfoSequia consists of two modules:InfoSequia-MONITOR provides more than 50 drought predictors including meteorological (SPI, SPEI), vegetative (VCI / TCI / VHI), and hydrological (water level in reservoirs, groundwater storage status) drought indices, as well as atmospheric oscillation indices, all of them retrieved from satellite (e.g. MODIS, Sentinel-2, Sentinel-3, GRACE), hybrid (eg CHIRPS), or reanalysis and modeling (ERA5-Land) products. All indices are obtained from dekad values ​​ which are timescale aggregated at 1, 3, 6 and 12 months. The spatial resolution of the indices ranges from 5km (SPI, SPIE) to 250m (VH indices). Acknowledging the limitations of physically-based modelling on the seasonal time scale, the InfoSequia-4CAST module rests on the Fast and Frugal Tree (FFT) algorithm, a machine learning technique in which binary decision trees are trained and generated at the subregional level with the historical and spatially-aggregated predictors of drought. Final outputs are delivered in the form of monthly warnings of risk of failure up to 6 month lead times. All InfoSequia algorithms run on a cloud platform, with cloud geoprocessing functionalities.With support of the European Space Agency (ESA), InfoSequia is being developed and piloted to provide operational seasonal forecasts of: a) crop yield failures at the district level in Mozambique, and b) water supply failures in the Segura river basin in SE Spain.Seasonal outlooks of drought impact support improvement of the water and food security of a region by allowing the early exploitation of groundwater reserves or unconventional water resources (desalination, reuse), the optimal water allocation of limited resources among users, or the implementation of ex-ante cash transfers or food vouchers. This research introduces the general workflow which underpins InfoSequia, how limitations due to technical barriers and data gaps are addressed, and the key performance indicators generated for both pilot cases.