Modeling Watershed Response in Semiarid Regions With High-Resolution Synthetic Aperture Radars

In this paper, we propose a methodology devoted to exploit the outstanding characteristics of COSMO-SkyMed for monitoring water bodies in semiarid countries at a scale never experienced before. The proposed approach, based on appropriate registration, calibration, and processing of synthetic aperture radar (SAR) data, allows outperforming the previously available methods for monitoring small reservoirs, mainly carried out with optical data, and severely limited by the presence of cloud coverage, which is a frequent condition in wet season. A tool has been developed for computing the water volumes retained in small reservoirs based on SAR-derived digital elevation model. These data have been used to derive a relationship between storage volumes and surface areas that can be used when bathymetric information is unavailable. Due to the lack of direct measures of river's discharge, the time evolution of water volumes retained at reservoirs has been used to validate a simple rainfall-runoff hydrological model that can provide useful recommendation for the management of small reservoirs. Operational scenarios concerning the improvement in the efficiency of reservoirs management and the estimation of their impact on downstream area point out the applicative outcomes of the proposed method

Hydrological modeling in ungauged basins using SAR data

In this paper we propose a methodology devoted to exploit high resolution radars for monitoring water bodies in semi-arid countries. The proposed approach is based on appropriate registration, calibration and processing of SAR data, producing information ready to use by end-users. The obtained results were used to (i) estimate a relationship between surface and volume of water stored in reservoirs and (ii) validate a hydrological model that simulates the time evolution of water availability

Relating surface backscatter response from TRMM precipitation radar to soil moisture: Results over a semi-arid region

The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σº) of the surface. σº is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σº primarily depends on the soil water content. In this study we relate TRMMPR σº measurements to soil water content (m(s)) in the Lower Colorado River Basin (LCRB). σº dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σº that couples incidence angle, m(s), and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated m(s) is estimated using the Variable Infiltration Capacity (VIC) model and measured m(s) is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σº model is calibrated using VIC and WGEW m(s) data during 1998 and the calibrated model is used to derive m(s) during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with a correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σº derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σº dependence on soil water content in the arid regions

Multisensor Fusion Remote Sensing Technology For Assessing Multitemporal Responses In Ecohydrological Systems

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction

APPLICATION OF A DISTRIBUTED HYDROLOGIC MODEL FOR THE ANALYSIS OF LAND USE CHANGE IN KEDOUGOU, SENEGAL

West Africa has experienced extensive land use/land cover (LULC) change since the Sahel drought of the 1970s and 80s. Demographic changes, rapid urbanization, and other anthropogenic impacts on land cover in West Africa have potentially altered hydrological regimes. This work was undertaken while I served as a Peace Corps Volunteer in the Kedougou region of Senegal, an area that has experienced significant and rapid infrastructural improvements and urbanization since the early 2000s. The Gambia River flows near Kedougou, where flooding is an extensive problem, making access and livelihood practices difficult during the rainy season. Changes in climate and land use on hydrology have not been adequately assessed for this region, as well as many underdeveloped areas. A computational hydrological analysis is needed to understand recent and current conditions and forecast future impacts. The physically based and distributed Gridded Surface/Subsurface Hydrologic Analysis (GSSHA) model was employed to assess the effects of LULC change on inundation and local hydrology between 1992 and 2015. Urban area comprised 2.75 km2 of a 2,488-km2 watershed in 2015, growing from nothing in 1992. Forest cover between 1992 and 2015 increased 6%, whereas shrubland decreased by 4%. The 2015 land cover results showed a 20% increase in peak discharge during high intensity precipitation events (≥100 m). However, during lower intensity storms, urbanization did not significantly impact local hydrology or streamflow. In fact, a reverse trend was observed, in which peak flow was ~ 25 m3/s higher during the 1992 land cover scenario. Increased runoff may have resulted from the vegetation loss influenced by the drought from the 1980s. When compared to Sentinel-1 SAR imagery, GSSHA moderately overpredicted inundation extent

Earth Resources Laboratory research and technology

The accomplishments of the Earth Resources Laboratory's research and technology program are reported. Sensors and data systems, the AGRISTARS project, applied research and data analysis, joint research projects, test and evaluation studies, and space station support activities are addressed