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Reanalysis and earth observation data have enormous potential to support water resources management, particularly in river basins where data availability is poor or where available stations are unequally distributed. Despite this potential, these datasets continue to be underused around the world. In this article, we combine a recently established global water resources reanalysis that was developed within the EartH2Observe research project, and in-situ data in the Magdalena Cauca Macrobasin in Colombia. Through rigorous hydrological modelling we evaluate the contribution of this new information to the derivation of water availability indices used to support water policy and management. Results confirm the complementarity of the reanalysis, with water management indices derived from the reanalysis data showing good comparison to values obtained using in-situ data only. Indeed, the estimation of simple indices such as an Aridity Index and a Water Regulation Index show to be a suitable method for assessing and comparing the different reanalysis datasets. We also explore the value of the reanalysis and earth observations datasets in the study of climate and land use change scenarios in the basin. While considering the associated uncertainties, results show that on average rainfall is projected to increase in the basin and thus available water resources. Deforestation will increase the water balance due to lower evapotranspiration. However, reduced fog deposition in deforested cloud forest areas will lead to a decrease in the water balance. The promising results of the comparison have bolstered the confidence of the national hydro-meteorological agency to include the evaluated reanalysis datasets in the national water resources evaluation, complementing available in-situ datasets.

Funding Information: Field work scholarships, stakeholder workshops and travelling costs were supported by the CNRD Network Project ( www.cnrd.info ) and the Tropiseca project ( https://www.researchgate.net/project/TROPISECA-Multi-lateral-University-Cooperation-on-the-Management-of-Droughts-in-Tropical-Catchments ) funded by the German Federal Ministry of International Cooperation (BMZ)/ German Academic Exchange Service (DAAD). Publisher Copyright: © 2022 The Authors Droughts are causing severe damages to tropical countries worldwide. Although water abundant, their resilience to water shortages during dry periods is often low. As there is little knowledge about tropical drought characteristics, reliable methodologies to evaluate drought risk in data scarce tropical regions are needed. We combined drought hazard and vulnerability related data to assess drought risk in four rural tropical study regions, the Muriaé basin, Southeast Brazil, the Tempisque-Bebedero basin in Costa Rica, the upper part of the Magdalena basin, Colombia and the Srepok, shared by Cambodia and Vietnam. Drought hazard was analyzed using the variables daily river discharge, precipitation and vegetation condition. Drought vulnerability was assessed based on regionally available socioeconomic data. Besides illustrating the relative severity of each indicator value, we developed drought risk maps combining hazard and vulnerability for each grid-cell. While for the Muriaé, our results identified the downstream area as being exposed to severe drought risk, the Tempisque showed highest risk along the major streams and related irrigation systems. Risk hotspots in the Upper Magdalena were found in the central valley and the dryer Southeast and in the Srepok in the agricultural areas of Vietnam and downstream Cambodia. Local scientists and stakeholders have validated our results and we believe that our drought risk assessment methodology for data scarce and rural tropical regions offers a holistic, science based and innovative framework to generate relevant drought related information. Being applied to other tropical catchments, the approaches described in this article will enable the selection of data sets, indices and their classification - depending on basin size, spatial resolution and seasonality. At its current stage, the outcomes of this study provide relevant information for regional planners and water managers dealing with the control of future drought disasters in tropical regions.

Droughts are causing severe damages to water abundant tropical countries worldwide. Their resilience to water shortages tends to be low, often due to a lack of water infrastructure. Moreover, drought characteristics and risk in tropical catchments are poorly understood, which makes it difficult to select adequate adaptation measures. Thus, reliable methodologies to evaluate spatially distributed drought risk in data scarce tropical catchments are urgently needed. We combined drought hazard and vulnerability related information to assess drought risk in four rural tropical study regions, the Muriaé, subcatchment of the Paraíba do Sul in Southeast Brazil, the Tempisque-Bebedero in North Costa Rica, the upper part of the Magdalena basin, Colombia and the Srepok, a Mekong tributary shared by Cambodia and Vietnam. Drought hazard was defined based on three variables, daily river discharge and precipitation and vegetation condition. Conditions below defined thresholds were transformed into a cumulative drought index. To assess vulnerability, we reclassified and weighted globally and regionally available gridded socioeconomic data to represent the potential of a drought to cause damages in selected socioeconomic sectors of rural tropical regions. Besides illustrating the relative severity of each indicator value, we developed drought risk maps combining hazard and vulnerability severity for each grid cell. While for the Muriaé our results clearly identified the downstream area as being exposed to severe drought risk, the Tempisque showed highest risk along the major streams and related irrigation systems. Risk hotspots in the Upper Magdalena were found in the central valley and the dryer Southeast and in the Srepok in the agricultural areas of Vietnam and downstream in Cambodia. Plausibility of results was confirmed by local scientists and stakeholders, who evaluated the results for each indicator and risk hotspot. The presented risk assessment methodology for data scarce and rural tropical areas offers a holistic, science based and innovative solution to provide relevant drought related information. Being applied to individual catchments, the findings described in this article will enable the selection of data sets, indices and their classification - depending on basin size, spatial resolution and seasonality. At its current stage, the outcomes of this study provide relevant information for regional planners and water managers dealing with the control of future drought disasters in tropical regions.

Hydrological information on water availability and demand is vital for sound water allocation decisions in irrigation districts, particularly in times of water scarcity. However, water allocation decisions are often taken based on uncertain hydrological information, which may lead to sub-optimal decisions and agricultural production loss. This study aims to assess the availability of hydrological information in large irrigated areas (>250 km2) and evaluate water allocation decisions being taken. An index tool that measures the level of availability of hydrological information in irrigation districts that is used in planning and operation was developed. The index is calculated based on a compound that considers the period of record, temporal and spatial resolution of the data. Contingency tables that compare the observed discharge in water extraction sites, supply in the main canals, and irrigation demand estimates, were generated allowing the rate of occurrence of sub-optimal water allocation decisions to be determined. Through this method, excellent index results were found for an irrigation district in Australia (Murrumbidgee district), while irrigation districts in Colombia (Coello district) and Costa Rica (DRAT district) showed fair to poor information availability, which correspond to a higher rate of occurrence of sub-optimal water allocation decisions. The results imply that the use of additional hydrological information is beneficial in reducing the rate of occurrence of sub-optimal water allocation decisions, ultimately contributing to higher crop yields.

Wetlands are often vital physical and social components of a country’s natural capital, as well as providers of ecosystem services to local and national communities. We performed a network analysis to prioritize Sustainable Development Goal (SDG) targets for sustainable development in iconic wetlands and wetlandscapes around the world. The analysis was based on the information and perceptions on 45 wetlandscapes worldwide by 49 wetland researchers of the Global Wetland Ecohydrological Network (GWEN). We identified three 2030 Agenda targets of high priority across the wetlandscapes needed to achieve sustainable development: Target 6.3—“Improve water quality”, 2.4—“Sustainable food production”, and 12.2—“Sustainable management of resources”. Moreover, we found specific feedback mechanisms and synergies between SDG targets in the context of wetlands. The most consistent reinforcing interactions were the influence of Target 12.2 on 8.4—“Efficient resource consumption”, and that of Target 6.3 on 12.2. The wetlandscapes could be differentiated in four bundles of distinctive priority SDG-targets: “Basic human needs”, “Sustainable tourism”, “Environmental impact in urban wetlands”, and “Improving and conserving environment”. In general, we find that the SDG groups, targets, and interactions stress that maintaining good water quality and a “wise use” of wetlandscapes are vital to attaining sustainable development within these sensitive ecosystems.

Accurate estimates of precipitation are needed for many applications in hydrology as rainfall is one of the most influential variables of the water cycle. The common sources of information used to estimate rainfall fields are in situ rain gauges, remote sensing information and outputs from climate models. However, each of the above- mentioned sources has its own limitations, which can be reduced by blending information from these sources, in a product that takes advantage of the strengths of each dataset. In this research we study the double smoothing merging algorithm, creating a rainfall distributed product that combines remote sensed and reanalysis data, and information from a rain gauge network. The main objective of the study is to investigate the implications of varying the rain gauge density and configuration, on the merging parameters and global performance of the blended product. The results of a daily 3-year period experiment show that, although the errors in cross validation (CV) and against an independent dataset (IV) are in general low, the performance of the blended product and also the sensitivity of the parameters are highly influenced by the rain gauge configuration and density. The bandwidth merging parameters increase as the network density is artificially reduced.

Hydrological ensembles have gained importance for prediction and forecasting in water cycle variables. In spite of this, the relevance of the individual models in the ensemble is not usually established, in terms of the ensemble structure (i.e. their members) and the performance this structure exhibits through different climatic conditions (intrannual variability, for example). This analysis accounts for the uncertainty in the structure of the models and their responses (e.g. outputs), in comparison to the observed data. In this regard, the research here described attempts to determine the incidence of the ensemble members built for each month of the year, in the prediction of daily flows, through the use of the Bayesian Model Averaging (BMA) method. Moreover, using BMA calibrated parameters as inputs, an uncertainty analysis is carried out for the calibration period, and in monthly average terms, obtaining finer uncertainty bounds. This analysis was implemented in the Sumapaz River basin, part of the Magdalena Cauca Macro- Basin (MCMB) in Colombia. Results showed differences in ensemble structures and performance according to its original performance criteria, and better results when using a monthly BMA for the uncertainty analysis.

The Magdalena-Cauca macro-basin (MCMB) in Colombia, by its tropical location, annually experiences the effects of movement of the Intertropical Convergence Zone, and it is highly affected by interannual macro-climatic phenomena, such as El Niño– Southern Oscillation (ENSO). With the aim of increasing the use of global reanalysis and remote sensing data for supporting water management decisions at the watershed scale and within the framework of the eartH2Observe research project, the aridity index (AI) was calculated with three different data sources. Precipitation products and AI results were compared with their corresponding in-situ national official data. The comparison shows high correlations between the AI derived from observed data and AI obtained from the reanalysis, with Pearson correlation coefficients above 0.8 for two of the products investigated. This shows the importance of using global reanalysis data in water availability studies on a regional scale for the MCMB and the potential of this information in others macrobasins in Colombia including the Orinoquia and Amazon regions, where in-situ data is scarce.

To support policy formulation for rehabilitation of the natural environment in the Western Mancha region in Spain, a planning support system was developed and applied. The system is based on a framework developed for planning and decision making, and includes three main components, namely, a water balance model of the groundwater basin, a planning model and an evaluation model. The water balance model, which makes use of GIS and remote sensing, simulates the average yearly recharge of the aquifer system in relation to the land use changes for average meteorological conditions, to help understand the current situation; the planning model, which makes use of mixed integer programming, simulates the reaction of farmers towards the changes in the present subsidy schemes and helps formulate a proper policy instruments; and finally the evaluation model, which makes use of multicriteria decision analysis to support the evaluation of developed policies and selection of attractive scenarios based on the identified criteria and the preferences/opinion of various decision makers.

Se presenta un estudio para determinar los parámetros estadísticos y las características de los residuos en las series de precipitación máxima en 24 horas con un nivel de agregación temporal mensual, para estaciones localizadas sobre la cuenca del río Cauca, en los departamentos de Caldas, Quindío, Risaralda y la parte norte del departamento del Valle del Cauca. Las series de tiempo se ajustan a modelos de predicción ARIMA cuyos resultados, basados en los criterios de menor error y pruebas estadísticas, conducen a obtener un residuo de tipo ruido blanco. Para validar el modelo de predicción, en las estaciones que cuentan con registros posteriores al periodo concurrente seleccionado, se realizan pruebas al residuo para verificar que corresponden a variables de tipo normal independiente. Finalmente, se hace una correlación cruzada entre registros de estaciones vecinas, o que se ajusten a un mismo modelo ARIMA con el fin de identificar la existencia de posibles relaciones estadísticas a escala regional.

In order to support policy formulation for rehabilitation of the natural environment in the Western Mancha region of Spain, a planning support system was developed and applied. The system is based on a framework developed for planning and decision-making and includes three main components, namely, a water balance model of the groundwater basin, a planning model and an evaluation model. The water balance model, which makes use of GIS and remote sensing, simulates the average yearly recharge of the aquifer system in relation to the land use changes for average meteorological conditions, to help understand the current situation. The planning model which makes use of mixed integer programming, simulates the reaction offarmers towards the changes in the present subsidy schemes and helps formulate proper policy instruments. Finally, the evaluation model makes use of multicriteria decision analysis to support the evaluation of developed policies and the selection of attractive scenarios based on the identified criteria and the preferences/opinions of various decision-makers.