Probabilistic Forecasting of Drought Events Using Markov Chain- and Bayesian Network-Based Models A Case Study of an Andean Regulated River Basin

[EN] The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC) and Bayesian network- (BN) based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertakenAvilés-Añazco, A.; Celleri, R.; Solera Solera, A.; Paredes Arquiola, J. (2016). Probabilistic Forecasting of Drought Events Using Markov Chain- and Bayesian Network-Based Models A Case Study of an Andean Regulated River Basin. Water. 8(2). doi:10.3390/w8020037S8

Rainfall variability and rainfall-runoff dynamics in the Paute river basin - Southern Ecuadorian Andes

Mondiaal is minstens de helft van de beschikbare oppervlaktewaterbronnen afkomstig van berggebieden. Zij spelen dus een essentiële rol in de globale watercyclus en zijn de voornaamste bron van drinkwatervoorziening, electriciteitsproductie via waterkracht en irrigatie. Ondanks dit belang, is de hydrologie van berggebieden slechts in beperkte mate gekend; ontwikkeling van duurzaam waterbeheer voor de gemeenschappen in deze gebieden verloopt bijgevolg zeer moeilijk. Het doel van voorliggend onderzoek bestond erin om een beter inzicht te bekomen in de neerslagpatronen, in de neerslagvariatie in zowel ruimte als tijd en in de hydrologische processen van een typisch bekken in het tropische Andesgebergte. Het Paute-rivierbekken (5070 km2) is gelokaliseerd in Zuid-Ecuador en werd geselecteerd als gevalstudie omwille van de relatief hoge dichtheid aan neerslag- en debietmeetstations. De analyse toonde grote neerslagvariabiliteit over het relatief beperkte studiegebied. Neerslaghoeveelheden verschillen sterk tussen de valleigebieden in het Andesgebergte en de brongebieden (het zogenaamde “páramo”-ecosysteem), waarbij de valleigebieden beduidend langere en meer frequente droge perioden kennen. Anderzijds is de neerslag in het “páramo”-gebied nagenoeg gelijkmatig verdeeld over het jaar. Dit geeft het gebied een unieke waterbergingscapaciteit; het vervult de rol van autentiek waterreservoir voor de Andesgemeenschappen. De voorliggende studie heeft vier typen neerslagregimes en bijhorende deelgebieden geïdentificeerd. Zij hebben een beter inzicht gegeven in de bronnen van runoffproductie en de bekkenhydrologie van het gebied in het algemeen. Voor de modellering van het neerslagafstromingsproces werd de VHM-modelleringsmethode toegepast en getest. Het is een data-gebaseerde methode waarbij stapsgewijs bijkomende en complementaire informatie wordt afgeleid uit de gegevensset van neerslag- en potentiële evapotranspiratieinvoer en rivierdebietmetingen afwaarts van deelbekkens, en gebruikt voor identificatie van de meest optimale spaarse conceptuele deelgebiedsgemiddelde modelstructuur. De methode werd getest voor zes deelbekkens met oppervlakten variërend van 145 tot 1260 km2 en met contrasterende fysische gebiedseigenschappen. Resultaten hebben aangetoond dat de eenvoudigere modelstructuren nauwkeurigere resultaten opleveren in vergelijking met de meer complexe structuren. De geïdentificeerde modellen geven een nauwkeurige beschrijving van de macroscopisch hydrologisch invoer-uitvoerverbanden. De nauwkeurigheid is vooral afhankelijk van de neerslaginvoer die de belangrijkste bron van onzekerheid bleek in de modellering. Gegeven de ruimtelijke schaal van het bekken, de beperkte tijds- en ruimteresolutie van de neerslaggegevens, en de sterke variabiliteit in de fysische gebiedseigenschappen, modelresultaten zijn zeer beloftevol. De studie heeft verder aangetoond dat onnauwkeurige neerslaginvoergegevens tot sterk onzekere parametercalibraties kunnen leiden, en dat overeenkomstig de installatie van bijkomende pluviografen in het hooggebergte sterk aanbevolen moeten worden. Door middel van modelstructuuridentificaties en -calibraties bij verschillende tijdschalen heeft de studie aangetoond waarom de VHM-methode tot robuuste modelparameters leidt. Analyse van deze resultaten heeft verder inzicht gegeven in de hydrologische processen op rivierbekkenschaal zoals de tijdsvariatie in bodemvochtgehaltee. Ten slotte is aangetoond dat transfer van modelparameters tussen naburige parameters toelaat om voldoende betrouwbare modelresultaten te bekomen voor deelbekkens met voldoende neerslaggegevens. Deze benadering bleek superieur in vergelijking met de gangbare aanpak waarbij debieten vanuit rivierbekkens wordt neergeschaald naar de kleinere deelbekkens op basis van oppervlakteverhoudingen.1 Introduction 1 1.1. Importance of mountain hydrology 1 1.2. Objectives of the doctoral research 3 1.3. Overview of the study area: The Paute River Basin 6 1.4. Overview of data availability 9 1.5. Outline of the doctoral thesis 10 2 Description of the hydrology of meso-scale basins 13 2.1. Introduction 13 2.2. Analysis of rainfall series 14 2.2.2. Completion of missing gaps 16 2.2.3. Interpolation of precipitation 18 2.3. Hydrology of meso-scale basins 21 2.3.1. Methods 22 2.3.2. Results 22 2.4. Conclusions 25 3 Analysis of space-time rainfall variability 29 3.1. Introduction 29 3.2. Data and study area 32 3.3. Methods 33 3.3.1. Relation between elevation and rainfall 33 3.3.2. Rainfall regimes 33 3.3.3. Seasonality 34 3.3.4. Trends 35 3.4. Results 35 3.4.1. Relation between elevation and rainfall 35 3.4.2. Rainfall regimes 36 3.4.3. Seasonality 40 3.4.4. Trends 44 3.5. Conclusions 48 4 Modelling the hydrology of meso-scale Andean basins using a data-mining approach 51 4.1. Introduction 51 4.2. Materials and Methods 56 4.2.1. The data-mining approach and lumped-conceptual model 56 4.2.2. Model calibration, validation and performance evaluation 59 4.2.3. Step-wise methodology 61 4.2.4. Sensitivity analysis 64 4.2.5. Study basins and data available 66 4.3. Results and discussion 67 4.3.1. Application of modelling approach 67 4.3.2. Sensitivity analysis for model parameters 74 4.3.3. Sensitivity analyses for the base flow separation process and the criteria to select independent events 78 4.4. Conclusions 83 5 Impact of limited hydro-meteorological input data on the VHM approach and hydrological model parameters 87 5.1. Introduction 87 5.2. Materials and Methods 89 5.2.1. Study basins and data available 89 5.2.2. Methodology 91 5.2.2.1. Impact of sub-daily input data 91 5.2.2.2. Impact of rain gauge density 92 5.3. Results and discussion 93 5.3.1. Impact of daily versus sub-daily discharge data 93 5.3.2. Impact of rain gauge density 98 5.4. Conclusions 102 6 Transference of model parameters between basins 105 6.1. Introduction 105 6.2. Materials and Methods 107 6.2.1. Study basins and data available 107 6.2.2. Methodology 108 6.3. Results and discussion 109 6.3.1. Calibration of model parameters from observations at different time-scales of aggregation 109 6.3.2. Transference of model parameters between similar basins 113 6.4. Conclusions 117 7 Conclusions 121 7.1. Recapitulation 121 7.2. Further research 124 References 125 Curriculum Vitae 141 Appendix A. Map of study area Appendix B. The VHM approach Appendix C. The WETSPRO model Appendix D. Summary of modelling resultsstatus: publishe

Probabilistic forecasting of drought events using Markov chain- and Bayesian network-based models: A case study of an Andean regulated river basin

The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC) and Bayesian network- (BN) based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertaken

The Spatio-Temporal Cloud Frequency Distribution in the Galapagos Archipelago as Seen from MODIS Cloud Mask Data

Clouds play an important role in the climate system; nonetheless, the relationship between climate change in general and regional cloud occurrence is not yet well understood. This particularly holds for remote areas such as the iconic Galapagos archipelago in Ecuador. As a first step towards a better understanding, we analyzed the spatio-temporal patterns of cloud cover over Galapagos. We found that cloud frequency and distribution exhibit large inter- and intra-annual variability due to the changing influence of climatic drivers (trade winds, sea surface temperature, El Niño/La Niña events) and spatial variations due to terrain characteristics and location within the archipelago. The highest cloud frequencies occur in mid-elevations on the slopes exposed to the southerly trade winds (south-east slopes). Towards the highlands (>900 m a.s.l), cloud frequency decreases, with a sharp leap towards high-level crater areas mainly on Isabela Island that frequently immerse into the trade inversion layer. With respect to the diurnal cycle, we found a lower cloud frequency over the islands in the evening than in the morning. Seasonally, cloud frequency is higher during the hot season (January–May) than in the cool season (June–December). However, spatial differences in cloudiness were more pronounced during the cool season months. We further analyzed two periods beyond average atmospheric forcing. During El Niño 2015, the cloud frequency was higher than usual, and differences between altitudes and aspects were less pronounced. La Niña 2007 led to negative anomalies in cloud frequency over the islands, with intensified differences between altitude and aspect. © 2023 by the authors

data descriptor: high-resolution hydrometeorological data from a network of headwater catchments in the tropical andes

This article presents a hydrometeorological dataset from a network of paired instrumented catchments, obtained by participatory monitoring through a partnership of academic and non-governmental institutions. The network consists of 28 headwater catchments (<20 km^2) covering three major biomes in 9 locations of the tropical Andes. The data consist of precipitation event records at 0.254 mm resolution or finer, water level and streamflow time series at 5 min intervals, data aggregations at hourly and daily scale, a set of hydrological indices derived from the daily time series, and catchment physiographic descriptors. The catchment network is designed to characterise the impacts of land-use and watershed interventions on the catchment hydrological response, with each catchment representing a typical land use and land cover practice within its location. As such, it aims to support evidence-based decision making on land management, in particular evaluating the effectiveness of catchment interventions, for which hydrometeorological data scarcity is a major bottleneck. The data will also be useful for broader research on Andean ecosystems, and their hydrology and meteorology. © The Author(s) 2018.This article presents a hydrometeorological dataset from a network of paired instrumented catchments, obtained by participatory monitoring through a partnership of academic and non-governmental institutions. The network consists of 28 headwater catchments (<20 km^2) covering three major biomes in 9 locations of the tropical Andes. The data consist of precipitation event records at 0.254 mm resolution or finer, water level and streamflow time series at 5 min intervals, data aggregations at hourly and daily scale, a set of hydrological indices derived from the daily time series, and catchment physiographic descriptors. The catchment network is designed to characterise the impacts of land-use and watershed interventions on the catchment hydrological response, with each catchment representing a typical land use and land cover practice within its location. As such, it aims to support evidence-based decision making on land management, in particular evaluating the effectiveness of catchment interventions, for which hydrometeorological data scarcity is a major bottleneck. The data will also be useful for broader research on Andean ecosystems, and their hydrology and meteorology. © The Author(s) 2018

Evaluation of Markov Chain Based Drought Forecasts in an Andean Regulated River Basin Using the Skill Scores RPS and GMSS

On behalf of the decision-makers of Andean regulated river basins a drought index was developed to predict the occurrence and extent of drought events. Two stochastic models, the Markov Chain First Order (MCFO) and the Markov Chain Second Order (MCSO) model, predicting the frequency of monthly droughts were applied and the performance checked using two skill scores, respectively the ranked probability score (RPS) and the Gandin-Murphy skill score (GMSS). Data of the Chulco River basin (3200–4300 m.a.s.l.), situated in the Ecuadorian southern Andes, were employed to test the performance of both models. Results indicate that events with greater drought severity were more accurately predicted. The study also revealed the importance of verifying the quality of the forecasts and to have an assessment of the likely performance of the forecasting models before adopting any model and accepting the resulting information for decision-making

Impact of rain gauges distribution on the runoff simulation of a small mountain catchment in southern Ecuador

In places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km 2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges. View Full-TextIn places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km 2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges. View Full-Tex

Impacts of land use on the hydrological response of tropical Andean catchments

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. Copyright © 2016 The Authors. Hydrological Processes. Published by John Wiley & Sons Ltd

Flash-Flood Forecasting in an Andean Mountain Catchment - Development of a Step-Wise Methodology Based on the Random Forest Algorithm

© 2018 by the authors. Flash-flood forecasting has emerged worldwide due to the catastrophic socio-economic impacts this hazard might cause and the expected increase of its frequency in the future. In mountain catchments, precipitation-runoff forecasts are limited by the intrinsic complexity of the processes involved, particularly its high rainfall variability. While process-based models are hard to implement, there is a potential to use the random forest algorithm due to its simplicity, robustness and capacity to deal with complex data structures. Here a step-wise methodology is proposed to derive parsimonious models accounting for both hydrological functioning of the catchment (e.g., input data, representation of antecedent moisture conditions) and random forest procedures (e.g., sensitivity analyses, dimension reduction, optimal input composition). The methodology was applied to develop short-term prediction models of varying time duration (4, 8, 12, 18 and 24 h) for a catchment representative of the Ecuadorian Andes. Results show that the derived parsimonious models can reach validation efficiencies (Nash-Sutcliffe coefficient) from 0.761 (4-h) to 0.384 (24-h) for optimal inputs composed only by features accounting for 80% of the model's outcome variance. Improvement in the prediction of extreme peak flows was demonstrated (extreme value analysis) by including precipitation information in contrast to the use of pure autoregressive models.status: publishe