Critical values improvement for the standard normal homogeneity test by combining Monte Carlo and regression approaches

The distribution of the test statistics of homogeneity tests is often unknown, requiring the estimation of the critical values through Monte Carlo simulations. The computation of the critical values at low \u3b1, especially when the distribution of the statistics changes with the series length (sample cardinality), requires a considerable number of simulations to achieve a reasonable precision of the estimates (i.e., 10^6 simulations or more for each series length). If, in addition, the test requires a noteworthy computational effort, the estimation of the critical values may need unacceptably long runtimes. To overcome the problem, the paper proposes a regression-based refinement of an initial Monte Carlo estimate of the critical values, also allowing an approximation of the achieved improvement. Moreover, the paper presents an application of the method to two tests: SNHT (standard normal homogeneity test, widely used in climatology), and SNH2T (a version of SNHT showing a squared numerical complexity). For both, the paper reports the critical values for \u3b1 ranging between 0.1 and 0.0001 (useful for the p-value estimation), and the series length ranging from 10 (widely adopted size in climatological change-point detection literature) to 70,000 elements (nearly the length of a daily data time series 200 years long), estimated with coefficients of variation within 0.22%. For SNHT, a comparison of our results with approximated, theoretically derived, critical values is also performed; we suggest adopting those values for the series exceeding 70,000 elements

Sustainable water use for rice agro-ecosystems in northern Italy

I n the Mediterranean basin, rice is cultivated over an area of 1,300,000 hectares. The most important rice-producing countries are Italy and Spain in Europe (72% of the EU production; 345,000 ha), and Egypt and Turkey among the extra-EU countries (almost totality of the production; 789,000 ha). Traditionally, rice is grown under continuous flooding; thus, it requires much more irrigation than non-ponded crops. The MEDWATERICE project (PRIMA-Section 2-2018; https://www.medwaterice.org/) aims at exploring sustainability of innovative rice irrigation management solutions, in order to reduce rice water consumption and environmental impacts, and to extend rice cultivation outside of traditional paddy areas to meet the escalating demand. Within the MEDWATERICE project, irrigation management options to address the main site-specific problems are being tested for each rice areas involved in the project (IT, ES, PT, EG, TR). Case studies are being conducted in pilot farms, with the involvement of Stake-Holder Panels (SHPs) in each country. Data collected at the farm level will be extrapolated to the irrigation district level, to support water management decisions and policies. Moreover, indicators for quantitative assessment of environmental, economic and social sustainability of the irrigation options will be defined. This work illustrates the first year of results for the Italian Case Study (Lomellina area, Pavia) at the pilot farm scale. This area is characterized by a growing water scarcity in drought years in many districts. Within the farm managed by the National Rice Research Center (CRR), in the agricultural season 2019 the experimentation was conducted in six plots of about 20 m x 80 m each, with two replicates for each of the following water regimes: i) water-seeded rice with continuous flooding (WFL), ii) dry-seeded rice with continuous flooding from the 3-4 leaf stage (DFL), and iii) water seeded-rice with alternate wetting and drying from fertilization at the tillering stage (AWD). One out of the two replicates of each treatment was instrumented with: water inflow and outflow meters, set of piezometers, set of tensiometers and water tubes for the irrigation management in the AWD plots. A soil survey was conducted before the agricultural season (EMI sensor and physico-chemical analysis of soil samples). Periodic measurements of crop biometric parameters (LAI, crop height, crop rooting depth) were performed. Moreover, nutrients (TN, NO3, PO4, K) and two widely used pesticides (Sirtaki \u2013 a.i. Clomazone; Tripion E \u2013 a.i. MCPA) were measured in irrigation water (inflow and outflow), groundwater, and porous cups installed at two soil depths (20 and 70 cm, above and below the plough pan). Finally, rice grain yields and quality (As and Cd in the grain) were determined. First results in terms of cumulative water balance components (rainfall, irrigation inflow and outflow, difference in soil and ponding water storage, evapotranspiration, net percolation), water application efficiency (evapotranspiration over net water input), and water productivity (grain production over net water input), will be presented and discussed. Results of a 1D Richard-equation-based numerical simulation model applied to generalize results obtained under the different irrigation regimes will be moreover illustrated

Laboratory determination of soil hydraulic conductivity for paddy soils: effects of different soil sample saturation methods

Saturated soil hydraulic conductivity (Ks) is a key factor in predicting vertical percolation fluxes, especially in paddy areas, where the peculiar agricultural practices adopted (especially flooding) lead to the formation of a dense and low permeable layer below the ploughed horizon. The core method, reproducing the Darcy\u2019s experiment over large undisturbed soil samples, is considered the reference method for Ks determination. To prepare soil samples for the analysis, two different soil saturation procedures can be adopted: vessel (AtmSat) and under-vacuum saturation (VacSat). A comparison between Ks values obtained by the core method after applying the two procedures is still missing in the literature, and is presented in this work. Five soil profiles were opened in three paddy fields located close to Pavia (Northern Italy) in the context of the WATPAD project (Fondazione Cariplo, grant n 2014-1260). In particular, five couples (replicates) of large undisturbed soil samples (H 15.0 cm, \uf8 14.6 cm) were collected from the less conductive layer (LCL) of each profile. Ks was determined by the core method after the saturation of soil samples with the two procedures (AtmSat and VacSat). To assess the reliability of the resulting Ks values, vertical percolation fluxes estimated by the Darcy\u2019s law (applied by considering lab-measured Ks) for the three paddy fields were compared to the same fluxes obtained as residual terms in the water balance equation applied to the fields. The main outcomes of the study, probably justified by the peculiar characteristics of the analysed soils (low-permeability layers of paddy soils), are the following: (1) the duration of flux experiments to reach the steady-state flux at which the convergence Ks value is obtained was generally very long (up to 25 days in the case of VacSat); (2) also the time needed to reach an evident trend in the measured flux was often long (even more than 20 hours); (3) AtmSat was found to provide reasonable results only for samples with a higher sand content, while, in case of low Ks, the underestimation was found to be up to 10 fold (probably because of air entrapment); (4) when vacuum was applied slowly, VacSat provided accurate estimations of Ks at the steady-state, while a fast vacuum application may produce a relevant hydraulic gradient within the core that can lead to damaging the sample; (5) when applying VacSat, the initial estimation of Ks was misleading (often more than 10 times higher than the convergence value), which can be explained by changes in the electrical diffuse layer (EDL) due to interactions between pore water and within-aggregate water, and/or to the release of biological gasses due to vacuum conditions; (6) in case of VacSat, pouring water under vacuum (instead of before the vacuum application) increased the time needed to reach the steady-state flux, but allowed a smoother convergence to the final Ks value and an earlier evidence of the trend. Due to the low number of samples analysed, outcomes need to be further investigated by considering a larger number of samples and other soil type

A comprehensive modelling approach to assess water use efficiencies of different irrigation management options in rice irrigation districts of northern Italy

European rice production is concentrated in limited areas of a small number of countries. Italy is the largest European producer with over half of the total production grown on an area of 220,000 hectares, predominantly located in northern Italy. The traditional irrigation management (wet seeding and continuous flooding until few weeks before harvest-WFL) requires copious volumes of water. In order to propose effective 'water-saving' irrigation alternatives, there is the need to collect site-specific observational data and, at the same time, to develop agro-hydrological models to upscale field/farm experimental data to a spatial scale of interest to support water management decisions and policies. The semi-distributed modelling system developed in this work, composed of three sub-models (agricultural area, groundwater zone, and channel network), allows us to describe water fluxes dynamics in rice areas at the irrigation district scale. Once calibrated for a 1000 ha district located in northern Italy using meteorological, hydrological and land-use data of a recent four-year period (2013-2016), the model was used to provide indications on the effects of different irrigation management options on district irrigation requirements, groundwater levels and irrigation/drainage network efficiency. Four scenarios considering a complete conversion of rice irrigation management over the district were implemented: WFL; DFL-dry seeding and delayed flooding; WDA-alternate wetting and drying; WFL-W-WFL followed by post-harvest winter flooding from 15 November to 15 January. Average results for the period 2013-2016 showed that DFL and WDA would lead to a reduction in summer irrigation needs compared to WFL, but also to a postponement of the peak irrigation month to June, already characterized by a strong water demand from other crops. Finally, summer irrigation consumption for WFL-W would correspond toWFL, suggesting that the considered winter flooding period ended too early to influence summer crop water needs

Water balance implications of switching from continuous submergence to flush irrigation in a rice-growing district

Studies conducted at the field scale report significant reductions in the irrigation requirements of ricewhen continuous submergence (CS) is replaced by less water-demanding regimes such as flush-irrigation(FI, i.e. intermittent irrigations of rice growing in non-submerged soils). However, the effects of theirextensive application in paddy areas with shallow groundwater is much less studied. We present a sce-nario analysis investigating the impacts on irrigation requirements induced by a shift from CS to FI inan irrigation district of Northern Italy where rice is the main crop, followed by maize and poplar. Thearea is characterised by a shallow water Table whose depth fluctuates between two meters (in winter)and less than 1 m (in summer). We applied a three-stage procedure, where we first analysed presentstate conditions using the SWAP (Soil, Water, Atmosphere, Plant) model to simulate irrigation deliver-ies and percolation fluxes. Then, we calibrated an empirical relationship between estimated percolationfluxes and measured depths to groundwater. Finally, we applied this relationship, in combination withthe SWAP model, to predict the variation of district irrigation requirements due to a widespread shiftfrom CS to FI. Results show that neglecting the feedback between groundwater recharge due to irrigationand groundwater depth led to overestimating the reduction of irrigation requirements of rice, whichdecreased from around 80% when no feedback was considered to around 60% when it was accountedfor. Moreover, increased groundwater depths resulted in higher irrigation requirements for maize withan estimated growth of more than 50% due to the need of shortening the irrigation turn. These resultsdemonstrate the importance of considering the impacts on the hydrological processes at larger scaleswhen planning the conversion of CS into more efficient field irrigation methods

Exploring scale-effects on water balance components and water use efficiency of toposequence rice fields in Northern Italy

Water use efficiencies (WUEs) between 20% and 60% are commonly reported for single rice paddies. When larger spatial domains are considered, higher WUE than minimum values observed for individual fields are expected due to water reuse. This study investigates scale-effects on water balances and WUEs of four adjacent rice fields located in Northern Italy and characterized by different elevations (A 45 B + C > D). Water balance terms for the paddies were quantified during the agricultural season 2015 through the integrated use of observational data and modelling procedures. Following a Darcy-based approach, percolation was distinguished from net seepage. Results showed net irrigation of about 2,700 and 2,050 mm for fields A and B, and around 640 and nearly 0 mm for C and D. WUE of A, B, C and D amounted, respectively, to 21, 28, 66 and >100%. Values for C and D were due to less permeable soils, to seepage fluxes providing extra water inputs and to the shallow groundwater level. When the group of paddies ACD was considered (B was not included since it was separated by a deep channel), net irrigation and WUE were found to reach 1,550 mm and 39%, confirming the important role of water reuses in paddy agro-ecosystems

A procedure for the detection of undocumented multiple abrupt changes in the mean value of daily temperature time series of a regional network

This paper presents the new procedure MAC-D for the automated detection of undocumented Multiple Abrupt Changes in the mean value of Daily temperature series, recorded in a network of meteorological stations. MAC-D can be applied to series containing seasonality, multiple change points, outliers, and with a noise component that can be autocorrelated and non-normally distributed. The main novelties of the procedure are (1) the pretreatment of the observed series, to derive a series of daily values that complies with the theoretical requirements of the change point detection tests and in (2) the combined use of the reference series and pairwise comparison approaches. MAC-D consists of three phases in sequence. In phase 1, the seasonal and climatic fluctuations are estimated and removed, using the reference series approach. Phase 2 combines a linear filtering with a change point detection test in an iterative algorithm, which runs until full compliance between the characteristics of the filtered series and the test requirements is achieved. Phase 3 is aimed at removing the false change points, due to error propagation in the reference series analysis, by double checking the detected change points with the pairwise comparison approach. Monte Carlo estimations of the actual significance and overall performance of the procedure for different series features and test resolutions are provided. Results demonstrate that MAC-D performs very well with daily series having a wide range of different characteristics

A composite statistical method for the detection of multiple undocumented abrupt changes in the mean value within a time series

The time series of measurements of hydro-meteorological variables often suffer from imperfections such as missing data, outliers and discontinuities in the mean values. The discontinuity in the mean can be the effect of: instrumental offsets and of their corrections, of changes in the monitoring station or in the surrounding environment. If the discontinuities can be identified with a reasonable precision, a correction of the erroneous data can be made. Several authors have put their great effort into developing techniques to identify non-climatic inhomogeneities; the resulting statistical methods are especially effective when the series contains a single change point, while their performances decline when the series contains multiple change points or inhomogeneous segments (a portion of the series bounded by two complementary shifts). These limitations also affect the standard normal homogeneity test (SNHT), one of the most effective and widely applied tests. We present a composite method of homogeneity testing, standard normal homogenization composite method (SNHCM), including the SNHT as one component, which improves the SNHT performances with multiple change points and inhomogeneous segments. A number of comparisons among the new method, the SNHT and a powerful optimal segmentation method (OSM-CM), are illustrated in the paper. SNHCM demonstrates their performances in change-point detection similar to, or better than, the SNHT and very close to the OSM-CM. The SNHCM is effective in recognizing complex patterns of discontinuities, especially inhomogeneous segments, which represent a severe problem for SNHT; on the contrary, SNHT performs slightly better only when the series contains a single change point, but the difference between the two methods is negligible. Compared to the OSM-CM, SNHCM provides very similar performances, with some favourable features deriving from the fact that it is computationally lighter, simpler to implement, can easily handle very long series and is based on statistical hypothesis tests with a well-defined and adjustable significance level