Precipitation as a proxy for climate variables: application for hydrological modelling

It has been proposed that linear regression curves can be used to estimate monthly climate variables from observed precipitation. This approach was explored by applying the MGB hydrological model to the Paraná Basin (Brazil). Linear regressions were obtained for 54 climate gauges, and most of them showed at least six months of significant correlation between monthly climate variables (sunlight hours and relative humidity) and precipitation. The regression equations were applied to 5201 raingauges to estimate monthly climate variables and evapotranspiration, and the results were compared with a scenario using long-term climate averages only. The main differences occurred in wetter periods, where negative correlations between monthly precipitation and evapotranspiration were obtained when using precipitation as a proxy. Long-term changes in the hydrological regime were assessed and showed that the effect of precipitation on relative humidity and sunlight hours seems to have a minor effect on the alterations observed in river discharge in the Paraná Basin.     

Forecasting annual precipitation to improve the operation of dams in the Comahue region,Argentina

ABSTRACT

This paper attempts to design statistical models to forecast annual precipitation in the Neuquen and Limay river basins in the Comahue region of Argentina. These forecasts are especially useful as they are used to better organize the operation of hydro-electric dams, the agriculture in irrigated valleys and the safety of the population. In this work, multiple linear regression statistical models are built to forecast mean annual rainfall over the two river basins. Since the maximum precipitation occurs in the winter (June–August), forecasting models have been developed for the beginning of March and for the beginning of June, just before the rainy season starts. The results show that the sea-surface temperatures of the Indian and Pacific oceans are good predictors for March models and explain 42.8% of the precipitation index variance. The efficiency of the models increases in June, adding more predictors related to the autumn circulation.     

Using InSAR to identify hydrological connectivity and barriers in a highly fragmented wetland

Hydrological connectivity is a critical determinant of wetland functions and health, especially in wetlands that have been heavily fragmented and regulated by human activities. However, investigating hydrological connectivity in these wetlands is challenging due to the costs of high-resolution and large-scale monitoring required in order to identify hydrological barriers within the wetlands. To overcome this challenge, we here propose an interferometric synthetic aperture radar (InSAR)-based methodology to map hydrologic connectivity and identify hydrological barriers in fragmented wetlands. This methodology was applied along 70 transects across the Baiyangdian, the largest freshwater wetland in northern China, using Sentinel 1A and 1B data, covering the period 2016–2019. We generated 58 interferograms providing information on relative water level changes across the transects that showed the high coherence needed for the assessment of hydrological connectivity. We mapped the permanent and conditional (temporary) barriers affecting connectivity. In total, 11% of all transects are permanently disconnected by hydrological barriers across all interferograms and 58% of the transects are conditionally disconnected. Areas covered by reed grasslands show the most undisturbed hydrological connectivity while some of these barriers are the result of ditches and channels within the wetland and low water levels during different periods of the year. This study highlights the potential of the application of Wetland InSAR to determine hydrological connectivity and location of hydrological barriers in highly fragmented wetlands, and facilitates the study of hydrological processes from large spatial scales and long-time scales using remote sensing technique.     

Tidal and annual variability of the population structure of Eurytemora affinis in the middle part of the Seine Estuary during 2005

Annual variability in abundance and population structure of the copepod Eurytemora affinis was studied in the maximum turbidity zone of the Seine Estuary in 2005. An Eulerian sampling strategy was applied monthly from March to July and from September to December. Chlorophyll a and suspended particulate matter (SPM) concentration, copepod abundance and stage distribution, and phytoplankton abundance were measured in sub-surface and near-bottom water during the ebb phase. Total E. affinis abundance was at a maximum in March and April (>200 × 10³ ind. m⁻³), and decreased from May to September (<25 × 10³ ind. m⁻³). This decrease corresponds to annual increases in temperature, salinity, chlorophyll a concentration and phytoplankton abundance, which was dominated by large diatoms, and decreases in SPM and river discharge. The phenology observed in 2005 was almost two months earlier compared to previous studies in the 1990s, when E. affinis reached maximum abundance in May and June. The low proportion of nauplii (<50%) in the population and high abundance of ovigerous females suggests that low recruitment is probably related to anomalously low temperatures in late winter (<5 °C). Whatever the horizontal position of the population in the estuary, adult and late copepodid stages are distributed in higher salinity than naupliar stages. Overall E. affinis population abundance was driven by parameters that characterize water masses at the tidal scale and by river discharge and chlorophyll a at the annual scale. By integrating the tidal effect, the high-frequency sampling protocol used appears to be optimal for investigating annual variability of planktonic communities in megatidal estuaries.     

Geostatistical Simulation of Acoustic Log Data for Seismic Depth Conversion

Seismic reflection methods measure the time a seismic wave takes to travel through the ground, from the user defined source to a series of signal monitoring sensors known as geophones. The measured times need to be depth converted to allow for integration with other geological data. In order to convert from time to depth, an estimate of the rock volume velocity field must be made. The velocity field estimate can be made by assignment of velocity estimates to a geological model independent of the seismic processing. This article presents the results of using the acoustic geophysical log data extrapolated via sequential Gaussian simulation to derive the velocity field. The uncertainties associated with the velocity estimates were significant and provided the means to assess confidence limits for the actual depth determination. The technique is assessed by application to a major coal deposit, approximately 2.1 m thick and 210 m deep. Considering only the uncertainty associated with estimating the velocity field, half of the confidence interval values showed approximately 1 m of uncertainty in depth. The application of sequential Gaussian simulation to model the 3D distribution of acoustic velocity can be extended to other geophysical log parameters or derived estimates.     

A physically-based method for removing pits in digital elevation models

Spurious pits in digital elevation models (DEMs) are traditionally removed by filling depressions, often creating flat regions that lead to inaccurate estimation of landscape flow directions. In this study, a physical approach based on a simple landscape evolution model is proposed for DEM pit removal. This method, an alternative to traditional geometrical procedures, enforces more realistic slopes and flow directions on topography. The procedure is compared with the method most commonly used in the literature and distributed with commercial GIS software where, generally, elevations of a depression are increased up to the lowest value among neighbouring cells. Several tests are performed and parameters sensitivity is carried out in order to demonstrate the performance of the proposed model as compared to traditional methods.     

Neural network based daily precipitation generator (NNGEN-P)

Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation.