Mesoscale mapping of sediment source hotspots for dam sediment management in data-sparse semi-arid catchments

Land degradation and water availability in semi-arid regions are interdependent challenges for management that are influenced by climatic and anthropogenic changes. Erosion and high sediment loads in rivers cause reservoir siltation and decrease storage capacity, which pose risk on water security for citizens, agriculture, and industry. In regions where resources for management are limited, identifying spatial-temporal variability of sediment sources is crucial to decrease siltation. Despite widespread availability of rigorous methods, approaches simplifying spatial and temporal variability of erosion are often inappropriately applied to very data sparse semi-arid regions. In this work, we review existing approaches for mapping erosional hotspots, and provide an example of spatial-temporal mapping approach in two case study regions. The barriers limiting data availability and their effects on erosion mapping methods, their validation, and resulting prioritization of leverage management areas are discussed.BMBF, 02WGR1421A-I, GROW - Verbundprojekt SaWaM: Saisonales Wasserressourcen-Management in Trockenregionen: Praxistransfer regionalisierter globaler Informationen, Teilprojekt 1DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Smart models to improve agrometeorological estimations and predictions

La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional. Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural. Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave. Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país. Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés.The world population, which is constantly growing, is estimated to reach 9.7 billion people in 2050. This increase, combined with the rise in living standards and the climate emergency situation (increase in temperature, intensification of the water cycle, etc.), presents us with the enormous challenge of managing increasingly scarce resources in a sustainable way. The agricultural sector must face important challenges such as improving natural resource management, reducing environmental degradation, and ensuring food and nutritional security. All of this is conditioned by water scarcity and aridity, limiting factors in crop production. To guarantee sustainable agricultural production under these conditions, it is necessary to based all the decision made on knowledge, innovation, and the digitization of agriculture to ensure the resilience of agroecosystems, especially in arid, semi-arid, and sub-humid dry environments where water deficit is structural. Therefore, this work focuses on improving the precision of current agrometeorological models by applying artificial intelligence techniques. These models can provide accurate estimates and predictions of key variables such as precipitation, solar radiation, and reference evapotranspiration. This way, it is possible to promote more sustainable agricultural strategies by reducing water and energy consumption, for example. In addition, the number of measurements required as input parameters for these models has been reduced, making them more accessible and applicable in rural areas and developing countries that cannot afford the high cost of installing, calibrating, and maintaining complete automatic weather stations. This approach can help provide valuable information to technicians, farmers, managers, and policy makers in key wáter and agricultural planning areas. This doctoral thesis has developed and validated new methodologies based on artificial intelligence that have been used to improve the precision of crucial variables in the agrometeorological field: precipitation, solar radiation, and reference evapotranspiration. Specifically, prediction systems and gap-filling models for precipitation at different scales have been modeled using neural networks. Models for estimating solar radiation using only thermal parameters have also been developed and validated in areas with similar climatic characteristics to the training location, without the need to be geographically in the same region or country. Similarly, models for estimating and predicting reference evapotranspiration at the local and regional level have been developed using only temperature data for the entire process: regionalization, training, and validation. Finally, an internationally open-source Python library (AgroML) has been created to facilitate the development and application of artificial intelligence models, not only focused on the agrometeorological sector but also on any supervised model that improves decision-making in other areas of interest

Spatio‑temporal calibration of Hargreaves-Samani model in the Northern Region of Nigeria

One of the significant components of the hydrological cycle is evapotranspiration. Monthly meteorological parameters of 35 years from 19 meteorological stations across the Northern Region of Nigeria (NRN) were obtained and utilized for the calibration of Hargreaves–Samani (HS) model by comparing between potential evapotranspiration (ETo) values estimated from the original HS and the Penman–Monteith (FAO-56 PM) models. The calibrated HS equation was assessed using trend patterns and some statistical indices. The average value of root mean square error (RMSE) and the mean absolute error (MAE) decreased by 37.1 and 40%, respectively, after the calibration of the model. Also, the correlation coefficients (R) of stations that had values > 0.8 increased from 6 to 11 and the minimum R value increased by 12% above that of the original HS equation. The trend and spatial map of the statistical tests conducted also indicate better performance in most climatic regions after calibration. The precision of the HS equation improved significantly after calibration for semi-arid, humid, and sub-humid regions. However, few stations in the semi-arid, humid, and sub-humid regions did not show drastic improvement due to the peculiarity of the location and high variations in the wind speed and relative humidity parameters

Groundwater quality for irrigation in an arid region-application of fuzzy logic techniques

Groundwater is the main source to answer the irrigation supply in several arid and semi-arid areas. In the present work, groundwater quality for irrigation purposes in the arid region of Menzel Habib (Tunisia) for thirty-six groundwater samples is assessed considering the application of different conventional water quality indicators, particularly, electrical conductivity (EC), sodium absorption ratio (SAR), soluble sodium percentage (SSP), magnesium adsorption ratio (MAR), Kelly ratio (KR), and permeability index (PI). The results obtained indicate a variability for EC: 3.06 to 14.98 mS.cm-1; SAR: 4.08 to 19.30; SSP: 35.78 to 71.53%; MAR: 34.19 to 56.01; PI: 38.47 to 72.74; and KR: 0.56 to 2.47. These results suggest that groundwater from Menzel Habib aquifer system is classified between excellent to unsuitable according to the applied water quality indices. Furthermore, the groundwater samples are also plotted in the Richards diagram classification system, based on the relation between SAR and EC, suggesting that almost groundwater samples present a harmful quality. Moreover, fuzzy logic model has been proposed and created to assess groundwater quality for irrigation. The membership functions are constructed for six significant parameters such as EC, SAR, SSP, MAR, KR, and PI and the rules are, then, fired to get a simple Fuzzy Irrigation Water Quality Index (FIWQI). The obtained groundwater quality results suggest that 3% of the samples from Menzel Habib region are considered as "good" for irrigation, 3% are classified as "good to permissible", 33% with a "permissible" quality, 36% "permissible to unsuitable", while 25% of groundwater present an "unsuitable" quality. Thus, the use of fuzzy logic techniques has more reliable and robust results by overcoming the uncertainties in the decision-making attributed to the conventional methods by the creation of new classes (excellent to good, good to permissible, and permissible to unsuitable) in addition to the classes proposed by Richards diagram classification (excellent, good, permissible, and unsuitable) to assess the groundwater quality suitability for irrigation purposes.This research was developed under the FCT–Fundação para a Ciência e a Tecnologia, I.P. program, through the project’s reference UIDB/04683/2020 and UIDP/04683/2020

AgroML: An Open-Source Repository to Forecast Reference Evapotranspiration in Different Geo-Climatic Conditions Using Machine Learning and Transformer-Based Models

Accurately forecasting reference evapotranspiration (ET0) values is crucial to improve crop irrigation scheduling, allowing anticipated planning decisions and optimized water resource management and agricultural production. In this work, a recent state-of-the-art architecture has been adapted and deployed for multivariate input time series forecasting (transformers) using past values of ET0 and temperature-based parameters (28 input configurations) to forecast daily ET0 up to a week (1 to 7 days). Additionally, it has been compared to standard machine learning models such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), extreme learning machine (ELM), convolutional neural network (CNN), long short-term memory (LSTM), and two baselines (historical monthly mean value and a moving average of the previous seven days) in five locations with different geo-climatic characteristics in the Andalusian region, Southern Spain. In general, machine learning models significantly outperformed the baselines. Furthermore, the accuracy dramatically dropped when forecasting ET0 for any horizon longer than three days. SVM, ELM, and RF using configurations I, III, IV, and IX outperformed, on average, the rest of the configurations in most cases. The best NSE values ranged from 0.934 in Córdoba to 0.869 in Tabernas, using SVM. The best RMSE, on average, ranged from 0.704 mm/day for Málaga to 0.883 mm/day for Conil using RF. In terms of MBE, most models and cases performed very accurately, with a total average performance of 0.011 mm/day. We found a relationship in performance regarding the aridity index and the distance to the sea. The higher the aridity index at inland locations, the better results were obtained in forecasts. On the other hand, for coastal sites, the higher the aridity index, the higher the error. Due to the good performance and the availability as an open-source repository of these models, they can be used to accurately forecast ET0 in different geo-climatic conditions, helping to increase efficiency in tasks of great agronomic importance, especially in areas with low rainfall or where water resources are limiting for the development of crops

Estimation of daily reference evapotranspiration from NASA POWER reanalysis products in a hot summer Mediterranean climate

This study aims at assessing the accuracy of estimating daily reference evapotranspiration (ETo) computed with NASA POWER reanalysis products. Daily ETo estimated from local observations of weather variables in 14 weather stations distributed across Alentejo Region, Southern Portugal were compared with ETo derived from NASA POWER weather data, using raw and biascorrected datasets. Three different methods were used to compute ETo: (a) FAO Penman-Monteith (PM); (b) Hargreaves-Samani (HS); and (c) MaxTET. Results show that, when using raw NASA POWER datasets, a good accuracy between the observed ETo and reanalysis ETo was observed in most locations (R2 > 0.70). PM shows a tendency to over-estimating ETo with an RMSE as high as 1.41 mm d-1, while using a temperature-based ET estimation method, an RMSE lower than 0.92 mm d-1 is obtained. If a local bias correction is adopted, the temperature-based methods show a small over or underestimation of ETo (–0.40 mm d-1 MBE < 0.40 mm d-1). As for PM, ETo is still underestimated for 13 locations (MBE < 0 mm d-1) but with an RMSE never higher than 0.77 mm d-1. When NASA POWER raw data is used to estimate ETo, HS_Rs proved the most accurate method, providing the lowest RMSE for half the locations. However, if a data regional bias correction is used, PM leads to the most accurate ETo estimation for half the locations; also, when a local bias correction is performed, PM proved the be the most accurate ETo estimation method for most locations. Nonetheless, MaxTET proved to be an accurate method; its simplicity may prove to be successful not only when only maximum temperature data is available but also due to the low data required for ETo estimationinfo:eu-repo/semantics/publishedVersio

Reference evapotranspiration estimate with limited weather data across a range of Mediterranean climates

The standard FAO Penman–Monteith (PM-ETo) method for computing the reference evapotranspiration (ETo), in addition to air temperature, needs data on solar radiation or sunshine duration, relative humidity and wind speed which are often lacking and/or do not respect appropriate quality requirements. Hence, in many cases, ETo has to be estimated with limited weather data using maximum and minimum temperature only. Essentially, two procedures are used when no more than temperature data are available: (i) the well-known Hargreaves–Samani equation (HS), or (ii) the PM-ETo method with weather parameters estimated from the limited available data, called PM temperature (PMT) method. The application of these temperature-based approaches often led to contradictory results for various climates and world regions. The data used in the analysis refer to 577 weather stations available through the CLIMWAT database. The results, confirmed by various statistical indicators, emphasized that: (a) in hyper-arid and arid zones, the performance of HS and PMT methods are similar, with root mean square errors (RMSEs) around 0.60–0.65 mm d 1; (b) in semi-arid to humid climates, the PMT method produced better results than HS, with RMSE smaller than 0.52 mm d 1; (c) the performance of PMT method could be improved when adopting the corrections for aridity/humidity in the estimation of the dew point temperature from minimum temperature data. The spatial elaboration of results indicated high variability of ETo estimates by different methods. Thus, a site-specific analysis using daily datasets of sufficient quality is needed for the validation and calibration of temperature methods for ETo estimate. Maps presenting indicative results on under/over estimation of ETo by both temperature methods may be useful for their more accurate application over different Mediterranean climate

DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR SUSTAINABLE WATER PLANNING IN ABU DHABI, UAE

One of the main challenges for water managers is to foresee the future accurately; then, design appropriate policies and infrastructure plans accordingly. The use of decision support systems in the field of water resource management and planning is now widely implemented, but its use in sustainable water planning of a nation or state in arid and semi-arid areas, such as Middle East countries, remains limited. The main objective of this dissertation is to present a graphical software tool that can assist water planners and decision-makers in long-term water management and planning. Sustainable planning for Abu Dhabi’s future water supply is a very challenging task that requires consideration of various drivers and decision criteria. To produce realistic future scenarios for the EAD, sound knowledge of the supply-side elements and demand-side elements; for existing and future usages are required. Therefore, Abu Dhabi Dynamic Water Budget Model (ADWBM) was developed to help water policymakers of Abu Dhabi to assess various components of the Abu Dhabi water budget. The model, which can produce future scenarios of the water budget, was calibrated and validated using historical data. Additionally, the sensitivity of the model outputs to changes in the inputs was determined by conducting a sensitivity analysis. A second tool named Abu Dhabi Capacity Planning Model (ADWPM) is developed to manage the supply of water which is designed to form part of an integrated plan of water resources and the capacity planning of infrastructures. This is a multi-period optimization model based on mixed integer linear programming (MILP) and incorporated several parameters including various types of economic and environmental costs, capacity expansion options of treatment plants and water transmission systems, and environmental aspects (such as carbon footprint and brine discharge). The AWCPM was programmed in General Algebraic Modeling System (GAMS) and solved using the Cplex solver. This provides an ability for water resource managers to identify the optimal combination of sources to meet both the present and future demands of Abu Dhabi. Finally, a decision support system for water resource managers is then provided by coupling key components of these models (ADWBM and ADWCPM) and is named “Sustainable Water Budgeter for Abu Dhabi” (SuWaB-AD). This has a graphical interface such that various scenarios can be explored and consequences of decisions can be made. The use of SuWaB-AD is demonstrated through the case study of Abu Dhabi could help decision makers in promoting sustainable plans. The results and applications show that SuWaB-AD approach can be adapted to support long-term water decision-making. The proposed tools would be helpful to water administrators, water professionals, and other water management authorities for sustainable water planning worldwide

Evaluating the Strategy of Ensemble Empirical and Tree-Based Methods in Estimating Reference Evapotranspiration

In the present research, three data-driven models including M5P, REP tree, and random forest were used to estimate daily reference evapotranspiration. The abilities of these three models to estimate reference evapotranspiration were studied in single and combined modes. To this end, the daily meteorological data of five synoptic stations in Kerman province in the period from 2000 to 2020 were used. A combination of meteorological variables, using sensitivity analysis versus the reference evapotranspiration values ​​obtained from FAO-Penman-Monteith, was considered as input for each of the mentioned models. Finally, the accuracy of the mentioned models and empirical methods in estimating the evapotranspiration of the reference plant were compared using statistical indicators, and the superior model was selected. The results of validation data showed that the M5P model in the form of individually (RMSE = 0.083 and NS = 0.998 in Bam station) and the weighted averaging in the form of the ensemble (RMSE = 0.155 and NS = 0.994 in Bam and Sirjan stations) in all stations had better results for estimating evapotranspiration rates than other methods. In general, tree models, especially M5P, had better results in estimating daily evapotranspiration than empirical models