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Due to population growth, urbanization, industrial and agricultural development, water demands have increased especially during the recent decades. On the other hand, shortage of the available water resources is a considerable challenge for water allocation and conflict resolution particularly in the semi-arid regions. The Laja Lake is a natural reservoir located in the Bío-Bío Region of Chile. It provides water for different stakeholders including energy and agriculture. This lake releases water to the downstream river by natural seepage through the volcanic barrier and by a controlled outlet via a transmission line. Two hydropower plants have been constructed on the mentioned river and transmission line and four other hydropower plants have been built in a series downstream. However, agricultural stakeholders have rights to supply their irrigation water demands from the lake. Accordingly, the control of the upstream hydropower plant effects all other water users. In this research, optimal trade-offs between energy generation by the hydropower plants (transmission line and river) and supply of agricultural irrigation water have been determined by applying NSGA-II (non-dominant sorting genetic algorithm). A water balance simulation model has been coupled with NSGA-II to be applied for monthly time series of water availability and demand. Using TOPSIS (technique for order preference by similarity to ideal solution), a multi-criteria analysis method, the non-dominated solutions from ten runs have been reduced to a few solutions. They were used to perform a trade-off analysis among stakeholders to achieve an acceptable optimal operation. The results have been compared to those of the SOP (standard operating policy) and the actual reservoir operation. The application of trade-off analysis based on simulation-optimization results allowed finding a better compromise between different stakeholder utilities. Furthermore, the system’s analytic tool can be applied for different hydro-meteorological inputs and thus be used for predictive development of operation policies under changed climate.Keywords: multi-objective optimization, conflict resolution, water-energy trade-offs

Supplementary Methods from Mathematical and Experimental Approaches to Identify and Predict the Effects of Chemotherapy on Neuroglial Precursors

Small reservoirs play an important role in providing water to rural communities. Increased construction of small reservoirs to mitigate the effects of droughts leads to a High-density Reservoirs Network (HdRN) of small reservoirs, which can potentially modify the streamflows both in dry and wet periods. However, there is a lack of understanding of the interannual behavior of flow retention and the impact of future increases in the number of small reservoirs, mainly for HdRN in dryland catchments. This research aims to determine the possible impact of the increase in the number of small reservoirs on dry hydrological networks, evaluating the annual flows generated at the outlet of a dryland watershed for scenarios with different densities of small reservoirs (number of reservoirs per area). The study area was the Conceição river catchment (3347 km2) in the semiarid of Brazil. The hydrological model of the study area was developed in SWAT. The model obtained appropriate results for daily streamflows, with values of 0.63, 0.81, and 0.53% for NSE, KGE, and PBIAS, respectively. The current density of small reservoirs in the region was estimated at 0.068 reservoirs per square kilometer (res/km2). Eight expansion scenarios were defined for densities between 0.1 res/km2 and 3.0 res/km2. The results showed that the influence of the HdRN on runoff reduction mostly occurs for a probability of exceedance between 1% and 10% of month flows and is very small for months with very high peaks of flow. The reduction in the outlet flow due to the increase in the number of small reservoirs was stronger during dry years (up to 30%) than during wet years (up to 8%), and it tended to increase in years with a consecutive lack of rain (from about 7% in the first year to about 20% in the last year and in the worst scenario), which may intensify the period of extended droughts. This research provides insights about the impact of the increase in the number of small reservoirs on the interannual variability of flow retention, and the understanding of the influence of small reservoirs on runoff reduction may help water resources agencies better prepare for hydrologic extremes (droughts and floods).

ZusammenfassungNeuroendokrine Neoplasien (NEN) umfassen eine seltene Tumorentität mit heterogener Biologie, Prognose und therapeutischen Optionen. In Zusammenhang mit der kürzlichen Publikation der ersten deutschen Leitlinie zur Diagnostik und Therapie von NEN erfolgte die Analyse der Kohorte des Deutschen NET-Registers der Deutschen Gesellschaft für Endokrinologie (DGE). Hierzu wurden 2686 Fälle extrahiert und ihre Patientencharakteristika wie Alter, Geschlecht, Primärtumorlokalisation, Grading und Staging dargestellt sowie das Gesamtüberleben berechnet. Zusätzlich wurden die systemischen Behandlungsstrategien in den beiden größten Untergruppen, NEN des Dünndarms und Pankreas, im Stadium der Metastasierung analysiert.Die Verteilung der Primärtumoren, die histopathologische Charakterisierung, das Tumorstadium sowie das Gesamtüberleben waren vergleichbar mit den Ergebnissen internationaler Registerstudien. Somatostatinanaloga (SSA) und die Peptid-Rezeptor-Radionuklid-Therapie (PRRT) waren die häufigsten systemischen Therapieverfahren bei Dünndarm-NEN. Hingegen wurde eine Chemotherapie – in Übereinstimmung mit der neuen Leitlinie – vor allem bei pankreatischen NEN eingesetzt und kam in der Erstlinie in ähnlicher Frequenz wie die SSA-Therapie bzw. in der Zweitlinie ähnlich häufig wie eine PRRT zum Einsatz. Prognostisch relevante Parameter waren die WHO-Klassifikation 2010 und das TNM-Staging.Die aktuelle Analyse des deutschen NET-Registers charakterisiert damit eine multizentrische, interdisziplinäre, deutschlandweite Kohorte von NEN-Patienten und beschreibt die angewendeten systemischen Therapieverfahren, das Gesamtüberleben und die prognostische Bedeutung der WHO-Klassifikation 2010 sowie des TNM-Stadiums.

Irrigation water is one of the most substantial water uses worldwide. Thus, global simulation studies about water availability and demand typically include irrigation. Nowadays, regional scale is of major interest for water resources management but irrigation lacks attention in many catchment modelling studies. This study evaluated the performance of the agro-hydrological model SWAT (Soil and Water Assessment Tool) for simulating streamflow, evapotranspiration and irrigation in four catchments of different agro-climatic zones at meso-scale (Baitarani/India: Subtropical monsoon; Ilmenau/Germany: Humid; Itata/Chile: Mediterranean; Thubon/Vietnam: Tropical). The models were calibrated well with Kling-Gupta Efficiency (KGE) varying from 0.74-0.89 and percentage bias (PBIAS) from 5.66-6.43%. The simulated irrigation is higher when irrigation is triggered by soil-water deficit compared to plant-water stress. The simulated irrigation scheduling scenarios showed that a significant amount of water can be saved by applying deficit irrigation (25-48%) with a small reduction in annual average crop yield (0-3.3%) in all climatic zones. Many catchments with a high share of irrigated agriculture are located in developing countries with a low availability of input data. For that reason, the application of uncorrected and bias-corrected National Centers for Environmental Prediction (NCEP) and ERA-interim (ERA) reanalysis data was evaluated for all model scenarios. The simulated streamflow under bias-corrected climate variables is close to the observed streamflow with ERA performing better than NCEP. However, the deviation in simulated irrigation between observed and reanalysis climate varies from -25.5-45.3%, whereas the relative irrigation water savings by deficit irrigation could be shown by all climate input data. The overall variability in simulated irrigation requirement depends mainly on the climate input data. Studies about irrigation requirement in data scarce areas must address this in particular when using reanalysis data.

Among natural hazards, droughts are known to be very complex and disastrous owing to their creeping nature and widespread impacts. Specifically, the occurrence of agricultural droughts poses a threat to the productivity and socio-economic development of countries such as India. In this study, we propose a novel framework for agricultural drought monitoring integrating the different indicators of vegetation health, crop water stress and soil moisture, that are derived from remote sensing satellite data. The drought monitoring is performed over Odisha, India, for the period 2000-2019. Soil moisture and land surface temperature datasets from GLDAS Noah Land Surface Model and surface reflectance data from MODIS (MOD09GA) are used in this study. We compared the utility of popular indices: (i) soil moisture condition index, soil moisture deficit index and soil wetness deficit index to represent the soil moisture level; (ii) temperature condition index, vegetation condition index and normalised difference water index to indicate vegetation health; (iii) short wave infrared water stress to represent crop water stress condition. Correlation analyses between these indices and the seasonal crop yields are performed, and suitable indicators are chosen. The popular entropy weight method is then used to integrate the indices and develop the proposed composite drought index. The index is then used for monitoring the agricultural drought condition over the study area in drought periods. The proposed framework for week- to month-scale monitoring have potential applications in identification of agricultural drought hotspots, analysis of trends in drought severity, and drought early warning for agricultural water management.

The calibration of hydrological models using bio-inspired meta-heuristic optimization techniques has been extensively tested to find the optimal parameters for hydrological models. Shuffled frog-leaping algorithm (SFLA) is a population-based cooperative search technique containing virtual interactive frogs distributed into multiple memeplexes. The frogs search locally in each memeplex and are periodically shuffled into new memeplexes to ensure global exploration. Though it is developed for discrete optimization, it can be used to solve multi-objective combinatorial optimization problems as well.In this study, a hydrological catchment model, Hydrological Predictions for the Environment (HYPE) is calibrated for streamflow and nitrate concentration in the catchment using SFLA. HYPE is a semi-distributed watershed model that simulates runoff and other hydrological processes based on physical as well as conceptual laws. SFLA with 200 runtimes and 5 memeplexes containing 10 frogs each is used to calibrate 22 model parameters. It is compared with manual calibration and Differential Evolution Markov Chain (DEMC) method from the HYPE-tool. The preliminary results of the statistical performance measures for streamflow calibration show that SFLA has the fastest convergence speed and higher stability when compared with the DEMC method. NSE of 0.68 and PBIAS of 7.72 are recorded for the best run of SFLA during the calibration of streamflow. In comparison, the HYPE-tool DEMC produced the best NSE of 0.45 and a PBIAS of -3.37 while the manual calibration resulted in NSE of 0.64 and PBIAS of 2.01.

The world's water resources are continuously facing challenges in fulfilling the needs of increasing agricultural water demand with finite or diminishing resources. Therefore, it is important to quantify the amount of irrigation water required to attain sustainable yield at a local, regional, and global level, especially in arid and semi-arid regions. This is mostly quantified by using agro-hydrological or agricultural models. The advances in simulation models and several options incorporated in them allow catchment/site-specific application of irrigation water to depict the field management practices undertaken by farmers. The objective of the present study is to provide a review of the simulation of irrigation water demand at catchment scale by agro-hydrological and agricultural models. This study discusses the different types of models, their dimensions, and the hydrological and agricultural process models incorporated into them. Additionally, this review provides an overview of how irrigation can be scheduled, how water is applied, and from which sources irrigation water can be extracted by the considered models, taking horizontal hydrological connectivity into consideration. Adding to the model review, seven different fields of innovative case studies are covered. Many agricultural models have been applied in a regional context without simulating horizontal hydrological fluxes, but only a few hydrological catchment models provide full support of both irrigation and plant growth simulation, which are important for the simulation of future crop yield under different climatic and agricultural management scenarios.

An innovative socio-hydrological modeling framework for the development of environmental policies tailored to farmers' attitudes and economic interests is proposed with the inclusion of optimal environmental criteria. It focuses on modeling the complex relation of farmers, the environment, and the agricultural practices recommended by policy developers. An on-site survey of farmers is used to develop a behavior model based on a modified Theory of Planned Behavior (TPB). An agent-based model (ABM) is coupled with an agro-hydrological model for vegetative filter strips (VFS) to create dynamics of the social and environmental system.The farmers from the Larqui river basin, Chile took part in the survey to understand their standpoint on the use of VFS to reduce soil loss in their agricultural fields and protect water bodies. The farmers were categorized into perceptive, proactive, bounded rational and interactive agents based on their answers to the survey. This categorization along with decision rules, and utility functions of agricultural activities including the VFS implementation and management are used to develop an ABM. Partial least square structural equation modeling (PLS-SEM) is used to analyze the modified TPB of farmers. Behavioral morality, behavioral willingness, knowledge of farmers showed a significant effect on modeling the intention and behavior of farmers to have VFS in their land. Subjective norm was the only construct that was not significant. The results of the ABM validate with the survey of the farmers. It shows that the decision on the width of VFS is not solely dependent on the utility generated and the reduction in soil losses but also on the behavior of farmers. This behavioral socio-hydrological modeling framework is capable of supporting policy-makers in developing tailored environmental policies that might improve the acceptance of sustainable agricultural practices by farmers.

The mismatch between natural water availability and demand in dryland regions is overcome by reservoirs of different sizes with the purpose of storing water. The increase in population in dryland regions and the consequent growth in water demand expanded the construction of small reservoirs, generating in these regions a dense network of reservoirs, which increases the complexity of modeling these hydrological systems. For dryland watersheds modeling with daily time-step, the horizontal connectivity of the reservoir network needs careful representation in order to achieve acceptable model performance, including cumulative effects of reservoirs. However, the horizontal connectivity of reservoir networks is often less investigated in large-scale catchment models. This work presents an innovative way of implementing the dense-reservoir network into the widely used eco-hydrological model Soil and Water Assessment Tool (SWAT), with detailed representation of large and small reservoirs, and an extensive analysis about the cumulative impact of small reservoirs on the horizontal hydrological connectivity for large-scale dryland catchments. A two-fold cross-validation was used against streamflow at a catchment outlet and against in-catchment reservoir water levels. The model daily performance was acceptable despite the input data uncertainty, with good reliability for peak flow in wet years, for nonflow periods and for the rising limb of the hydrograph. The efforts in the parameterization of reservoirs and aggregation of ponds allowed a better analysis of the hydrological processes and their impacts in the catchment. The results showed that small reservoirs decreased the streamflow, but had a low impact on catchment retention and water losses, with 2% of water retention in wet years. However, the water retention reached 9% in dry years, which may worsen periods of water scarcity in the large reservoirs. The spatial representation of small reservoirs for a high-density network in the SWAT model and the results of the cumulative impact of small reservoirs may be relevant for a better understanding of hydrology in dryland catchments, and can be applied to catchments in similar climatic and socio-economic environments.

The quantification of soil moisture under different soils and crops at regional scale is a challenging task. Hence, such studies are limited by the availability of ground based measurements. The current study evaluates the spatial and temporal patterns of daily soil moisture simulated by the Soil and Water Assessment Tool (SWAT) for the upper 30 cm of the soil profile with indirect soil moisture estimates from Landsat for 2016. The Thermal Vegetation Difference Index ( TVDI ), was calculated based on the Normalized Difference Vegetation Index ( NDVI ) and the brightness temperature ( BT ) using Landsat images, from which regression models were trained by using field measurements from Time Domain Reflectometer (TDR) to calculate soil moisture. Two agricultural catchments namely, Gerdau and Wipperau in Germany were satisfactorily calibrated using SWAT for daily streamflow (1975–2000) with NSE (Nash-Sutcliffe-Efficiency) >0.55 and PBIAS (Percent bias) p-factor = 0.57–0.83; r-factor = 0.52 − 1.3). Spatial and temporal patterns of soil moisture from Landsat and SWAT were evaluated by using boxplots and absolute soil moisture difference maps. Results revealed that the overall spatial and temporal patterns of boxplots matched better for the dry period (correlation, r ≥ 0.90) compared to the wet period (r ≥ 0.57). The mean absolute difference between soil moisture from Landsat and SWAT ranged between 0.9–10% for most soils. In addition to it, the soil map was refined to match soil moisture patterns shown in Landsat images for one sandy soil, which further improved the mean absolute difference (1.06–6%). The current study provides an approach to use remotely sensed soil moisture for verifying hydrological modeling results and for optimizing the parameterization of soils, which may bridge the gap between global, regional and field studies in agricultural water management.