Application of Different Evapotranspiration Models to Calculate Total Agricultural Water Demand in a Tropical Region

Today, water in the Long Xuyen Quadrangle-An Giang (LXQAG)(Mekong River delta, Vietnam) is becoming scarce in some seasonsand some districts in the region, especially when the scenariosof climate change will affect water resources in the future.Therefore, it is necessary to make decisions about water conservationand distribution to ensure compatibility with the socialobjectives such as economic efficiency, sustainability and fairness.The mathematical models used for water distribution andbalance calculations are the prominent themes nowadays. To performthis task, it needs to calculate the water needs for all economicsectors. In this article we are particularly concerned aboutwater demand calculation methods for crops and aquaculture.Because these are the two main commodities accounting for thehighest water usage in the region. Water demand for crops is calculatedthrough potential evaporation using the methods of Hargreaves& Samani; Priestley and Taylor and Penman-Monteithto check if the first two simpler methods with less data demandcould be used to estimate evapotranspiration. The results showthat the simpler methods were significantly different and thereforewater demand calculations must be based on the Penman-Monteith method for the water demand of crops and the methodsof Penman to calculate expansion evaporation for aquaculture.The result shows that the total water demand in 2015 is 6,428million m3/year. It is estimated that in 2020, agricultural waterdemand will rise by 71% compared to 2015 to 22,531 millionm3/year. The main reason for this rise is that the local managersexpect the catfish farming area to increase by 80%, if peopleapply the “VietGAP standards”

Áramlási holtterek eloszlása és ökológiai jelentősége a Tisza magyar szakaszán = Distribution and ecological significance of aggregated dead zones along the Hungarian section of River Tise

A Tisza magyar szakaszán a fitoplankton biomasszáját és összetételét alapvetően a Szamos fitoplanktonja határozza meg. A Tisza heterogén, ám szokatlanul mély medrében az áramlási holtterek hatása jelentéktelen. A tiszalöki és a kiskörei duzzasztás miatt lelassuló vízből a kovaalgák kiülepednek, az óriási kiterjedésű holttérként funkcionáló Tisza-tóból bemosódó Cryptophyta és zöldalgák ezt a biomassza veszteséget nem tudják pótolni. Az áramlási holtterek akkor tudják jelentősen befolyásolni a folyó fitoplanktonját, ha az aljzatra ülepedő algák számára elegendő fény áll rendelkezésre a fotoszintézishez. Ilyen viszonyok jellemzőek a Szamos romániai szakaszára - és feltevésünk szerint számos olyan nagy folyóra - melynek kisvízi medrét nem szabályozták. A sekély áramlási holtterekben a kiülepedés - felkeveredés - kiülepedés spirálja révén sokszorosára növekszik az algák tartózkodási ideje a vízhez képest. Ez a mechanizmus a nagyobb méretű, gyorsabban ülepedő fajokat részesíti előnyben. A hazai vízfolyásokon általánosságban is igazoltuk, hogy a fitoplankton bioamasszáját nem a tápanyag ellátottság, hanem a tartózkodási idő határozza meg. Ezért a tápanyag kibocsátás csökkentése önmagában nem hatékony beavatkozás a vízfolyások ökológiai állapotának javítására. A vízhálózat fitoplankton szempontból vett topológiai viszonyai azonban lehetnek olyanok, hogy a tápanyagterhelésen keresztül csökkenthessük a hálózat algatermő képességét. | The import of algae from the Szamos River is the major determinant of the biomass and composition of phytoplankton along the Hungarian Tisza River (from rkm 686 to rkm 177). The impact of aggregated dead zones is negligible in the highly heterogeneous but exceptionally deep channel of the latter. As a consequence of diminutive flow velocities upstream of the two dams (rkm 523 and 402), the dominant diatoms sediment rapidly. The mass export of crypto- and chlorophytes from the shallow floodplain complex created by Dam 2 (maximum area 104 km2) cannot compensate for the biomass loss. To significantly influence the dynamics of riverine phytoplankton, the bottom of dead zones must be illuminated and sustain photosynthesis by sedimented algae. Such shallows are characteristic of the Romanian Szamos and presumably of several large rivers that escaped regulation of their low water channel. Repeated sedimentations and resuspensions ensure manifold longer residence time for phytoplankton relative to water. The mechanism selectively favors larger cells that sediment faster. Statistical analysis of long-term water quality data from Hungarian running waters revealed that algal biomass was independent of nutrient availability and was related to water residence time. Consequently, emission control is an inefficient measure to improve the trophic status of streams and rivers, unless the topology of the whole fluvial network is considered with respect to phytoplankton growth

Global patterns of light saturation and photo-inhibition of lake primary production

The importance of light availability on lake gross primary production (GPP) depends on the physical setting and meteorological and chemical conditions characterizing the lake. While it has long been recognized that complex interactions between external and in-lake conditions affect whole lake GPP, few studies have evaluated the importance of different drivers of daily and seasonal variability in lake GPP for a diverse set of lake types. Here we use a unique dataset covering a wide spectrum of lakes in terms of size, nutrient loading and phytoplankton density combined with high-frequency meteorology, dissolved oxygen (DO), and light data to determine the GPP of lakes and investigate the degree of light limitation and photo-inhibition of GPP over multiple time scales. We used a Bayesian modeling approach to model diel changes in DO as a non- linear function of light and temperature, making it possible to determine the parameters describing the light dependency of hourly GPP rates under in situ conditions. 52% of the 1641 analyzed days had moderate to strongly light saturated GPP during midday summer conditions, and photo- inhibition occurred on at least 48% of the days. GPP became increasingly light limited with increasing nutrient and phytoplankton concentrations, conditions which also reduced the likelihood of photo-inhibition. The highest summer rates of GPP were found in the light limited, non-photo-inhibited, nutrient and phytoplankton rich lakes although annual rates were much lower her. This shows the strong interacting effects of light and nutrients for the magnitude and temporal variability in GPP of lake ecosystems

Coupling high frequency dissolved oxygen and chlorophyll fluorescence data for a robust estimation of lake metabolism parameters

Gross primary production (GPP) and community respiration (R) are increasingly calculated from high-frequency measurements of dissolved O2 (DO) by fitting dynamic metabolic models to the observed DO time series. Since different combinations of metabolic components result in the same DO time series, theoretical problems burden this inverse modeling approach. Bayesian parameter inference could improve identification of processes by including independent knowledge in the estimation procedure. This, however, requires model development, because parameters of existing metabolic models are too abstract to achieve a significant improvement. As algal biomass is a key determinant of GPP and R, and high-frequency data on phytoplankton biomass are increasingly available, coupling DO and biomass time series within a Bayesian framework has a high potential to support identification of individual metabolic components. We demonstrate this in three lakes where both high frequency DO and chlorophyll fluorescence data were available. Phytoplankton data were digested via a sequential Bayesian learning procedure coupled with an error model that accounted for systematic errors caused by structural deficiencies of the metabolic model. This method provided ecologically coherent and therefore presumably robust estimates for biomass-specific metabolic rates. This can contribute to a better understanding of metabolic responses to natural and anthropogenic changes

An integrated approach to characterize genetic interaction networks in yeast metabolism

Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between similar to 185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments