Irrigation-Induced Environmental Changes around the Aral Sea: An Integrated View from Multiple Satellite Observations

The Aral Sea basin (ASB) is one of the most environmentally vulnerable regions to climate change and human activities. During the past 60 years, irrigation has greatly changed the water distribution and caused severe environmental issues in the ASB. Using remote sensing data, this study investigated the environmental changes induced by irrigation activities in this region. The results show that, in the past decade, land water storage has significantly increased in the irrigated upstream regions (13 km 3 year -1 ) but decreased in the downstream regions (-27 km 3 year -1 ) of the Amu Darya River basin, causing a water storage decrease in the whole basin (-20 km 3 year -1 ). As a result, the water surface area of the Aral Sea has decreased from 32,000 in 2000 to 10,000 km2 in 2015. The shrinking Aral Sea exposed a large portion of the lake bottom to the air, increasing (decreasing) the daytime (nighttime) temperatures by about 1 °C year -1 (0.5 °C year -1 ). Moreover, there were other potential environmental changes, including drier soil, less vegetation, decreasing cloud and precipitation, and more severe and frequent dust storms. Possible biases in the remote sensing data due to the neglect of the shrinking water surface area of the Aral Sea were identified. These findings highlight the severe environmental threats caused by irrigation in Central Asia and call attention to sustainable water use in such dryland regions. Keywords: environmental issues; the shrinking Aral Sea; irrigation; desertification; dust storm; remote sensing; NDVI; GRACE; MODI

RAPID applied to the SIM-France model

International audienceSIM-France is a large connected atmosphere/land surface/river/groundwater modelling system that simulates the water cycle throughout metropolitan France. The work presented in this study investigates the replacement of the river routing scheme in SIM-France by a river network model called RAPID to enhance the capacity to relate simulated flows to river gauges and to take advantage of the automated parameter estimation procedure of RAPID. RAPID was run with SIM-France over a 10-year period and results compared with those of the previous river routing scheme. We found that while the formulation of RAPID enhanced the functionality of SIM-France, the flow simulations are comparable in accuracy to those previously obtained by SIM-France. Sub-basin optimization of RAPID parameters was found to increase model efficiency. A single criterion for quantifying the quality of river flow simulations using several river gauges globally in a river network is developed that normalizes the square error of modelled flow to allow equal treatment of all gauging stations regardless of the magnitude of flow. The use of this criterion as the cost function for parameter estimation in RAPID allows better results than by increasing the degree of spatial variability in optimization of model parameters. Likewise, increased spatial variability of RAPID parameters through accounting for topography is shown to enhance model performance

Irrigation-Induced Environmental Changes around the Aral Sea: An Integrated View from Multiple Satellite Observations

The Aral Sea basin (ASB) is one of the most environmentally vulnerable regions to climate change and human activities. During the past 60 years, irrigation has greatly changed the water distribution and caused severe environmental issues in the ASB. Using remote sensing data, this study investigated the environmental changes induced by irrigation activities in this region. The results show that, in the past decade, land water storage has significantly increased in the irrigated upstream regions (13 km3 year−1) but decreased in the downstream regions (−27 km3 year−1) of the Amu Darya River basin, causing a water storage decrease in the whole basin (−20 km3 year−1). As a result, the water surface area of the Aral Sea has decreased from 32,000 in 2000 to 10,000 km2 in 2015. The shrinking Aral Sea exposed a large portion of the lake bottom to the air, increasing (decreasing) the daytime (nighttime) temperatures by about 1 °C year−1 (0.5 °C year−1). Moreover, there were other potential environmental changes, including drier soil, less vegetation, decreasing cloud and precipitation, and more severe and frequent dust storms. Possible biases in the remote sensing data due to the neglect of the shrinking water surface area of the Aral Sea were identified. These findings highlight the severe environmental threats caused by irrigation in Central Asia and call attention to sustainable water use in such dryland regions

River network routing on the NHDPlus dataset

International audienceThe mapped rivers and streams of the contiguous United States are available in a geographic information system (GIS) dataset called National Hydrography Dataset Plus (NHDPlus). This hydrographic dataset has about 3 million river and water body reaches along with information on how they are connected into net- works. The U.S. Geological Survey (USGS) National Water Information System (NWIS) provides stream- flow observations at about 20 thousand gauges located on theNHDPlus river network.Ariver networkmodel called Routing Application for Parallel Computation of Discharge (RAPID) is developed for the NHDPlus river network whose lateral inflow to the river network is calculated by a land surface model. A matrix-based version of the Muskingum method is developed herein, which RAPID uses to calculate flow and volume of water in all reaches of a river network with many thousands of reaches, including at ungauged locations. Gauges situated across river basins (not only at basin outlets) are used to automatically optimize the Muskingum parameters and to assess river flow computations, hence allowing the diagnosis of runoff com- putations provided by land surfacemodels.RAPIDis applied to theGuadalupe and SanAntonioRiver basins in Texas, where flow wave celerities are estimated at multiple locations using 15-min data and can be reproduced reasonably with RAPID. This river model can be adapted for parallel computing and although the matrix method initially adds a large overhead, river flow results can be obtained faster than with the traditionalMuskingummethod when using a few processing cores, as demonstrated in a synthetic study using the upper Mississippi River basin

Quantifying Parameter Sensitivity, Interaction and Transferability in Hydrologically Enhanced Versions of Noah-LSM over Transition Zones

We use sensitivity analysis to identify the parameters that are most responsible for shaping land surface model (LSM) simulations and to understand the complex interactions in three versions of the Noah LSM: the standard version (STD), a version enhanced with a simple groundwater module (GW), and version augmented by a dynamic phenology module (DV). We use warm season, high-frequency, near-surface states and turbulent fluxes collected over nine sites in the US Southern Great Plains. We quantify changes in the pattern of sensitive parameters, the amount and nature of the interaction between parameters, and the covariance structure of the distribution of behavioral parameter sets. Using Sobol s total and first-order sensitivity indexes, we show that very few parameters directly control the variance of the model output. Significant parameter interaction occurs so that not only the optimal parameter values differ between models, but the relationships between parameters change. GW decreases parameter interaction and appears to improve model realism, especially at wetter sites. DV increases parameter interaction and decreases identifiability, implying it is overparameterized and/or underconstrained. A case study at a wet site shows GW has two functional modes: one that mimics STD and a second in which GW improves model function by decoupling direct evaporation and baseflow. Unsupervised classification of the posterior distributions of behavioral parameter sets cannot group similar sites based solely on soil or vegetation type, helping to explain why transferability between sites and models is not straightforward. This evidence suggests a priori assignment of parameters should also consider climatic differences